AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

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In public-sector and regulated-cloud work, I learned that sovereignty rarely starts as a national strategy. It starts as an auditor’s question: Who can prove where the data went, which system made the decision and what changes when the vendor or infrastructure does? That question is now moving into AI, and most sovereign-AI debates answer the wrong version of it.

They ask whether a country can build its own model on domestic data and hardware. For the United States and China, which together hold more than 90% of global AI data-center capacity, per a January 2026 Tony Blair Institute analysis, that question is worth asking. However, for almost every other government, it is the wrong place to start. The operative question is narrower: Once AI is embedded in public services, who controls the stack?

The 5 layers of public-sector control

For a CIO, sovereign AI means enforceable control across the AI lifecycle; model ownership is a separate question. Control has five layers:

• Data control: Where sensitive public data sits, and whether it can train a vendor’s model.
• Model control: Which models clear which workloads, and under what validation.
• Infrastructure control: Whether critical workloads run in approved environments.
• Operational control: Whether AI-assisted actions are logged, monitored and reversible.
• Vendor control: Whether the agency keeps portability, audit rights and a real exit.

Those five layers are the control plane for public-service AI. Floyd Dcosta recently made the enterprise case in “AI without sovereignty is just outsourced intelligence”: capability is what a tool can do; authority over how and when it does it is something a buyer can quietly lose. For public services, losing that authority plays out in the public eye.

Public-sector AI risk differs from enterprise risk. A retailer’s bad recommendation costs a sale; a government’s AI touches benefits, tax enforcement, policing and emergency response, raising the bar to due process, records retention and continuity of operations. A government that cannot reconstruct an AI-assisted decision lacks operational sovereignty, even in a domestic data center.

Evaluating risk: Concentration, jurisdiction and shadow AI

Foreign dependency is a real risk, but the exposure that matters is a sudden cutoff: A model you cannot audit, switch or exit, shut off by someone else’s order. A vendor’s nationality is a poor guide to that risk; control is.  Two markers matter. The first is concentration. In July 2024, a single faulty CrowdStrike update crashed about 8.5 million Windows machines, disrupting airlines, hospitals, banks and governments worldwide. No attacker was involved; one homogeneous dependency failed everywhere at once. The lesson points away from vendor nationality and toward uniformity as the fault line, making portability and provider diversity resilience controls.

The second is jurisdiction. In June 2025, Microsoft’s legal director for France told a Senate inquiry, under oath, that it could not guarantee that French public-sector data, even in French data centers, would be protected against US demands under the 2018 CLOUD Act. No such request had been made, and EU data has stayed in the EU since January 2025; senators called the assurance purely declarative. For the most sensitive data, residency does not equal control; the parent’s jurisdiction can matter as much as the server’s. Three US hyperscalers hold about 70% of the European cloud market, while European providers’ share fell from 29% in 2017 to roughly 15%. Concentration plus jurisdiction is the exposure a CIO must price. I have watched teams treat vendor selection as the moment risk was solved; it rarely was.

The wrong response is self-isolation. Most countries will never build frontier models, advanced chips, hyperscale clouds and talent pipelines at once; the Tony Blair Institute calls full self-sufficiency “too expensive, too slow and, for most countries, simply impossible.” The better test is workload sensitivity. Low-risk uses, such as drafting, translation and summarization, can run on commercial platforms with controls; high-risk uses, such as benefits eligibility, fraud investigation and healthcare triage, demand stricter control over data, model behavior and auditability.

Mandating domestic-only provision before a competitive option exists inverts sovereignty. Europe 2031, a five-year scenario from June 2026 by European technologists and policy researchers, illustrates the failure mode: A 2027 “buy European” mandate lands as offensive cyber capability spreads, and agencies that switched to weaker providers are locked out and paying ransoms. The scenario is fiction; the mechanism is not. Leverage comes from being indispensable, not half-hearted self-sufficiency. The closer-to-home effect is shadow AI: Mandate an inferior sanctioned tool and staff bypass it, the way shadow IT grows up around tools people find too slow. A rule that pushes sensitive work into ungoverned shadow AI reduces control instead of adding it.

Regulation and data-residency rules belong in any serious strategy, but carry failure modes. Blanket localization raises hosting costs and slows adoption without guaranteeing control, and a “sovereign cloud” on a foreign parent’s stack can amount to sovereignty theater. The more useful pattern tiers requirements by sensitivity. India’s BHASHINI shows the application layer done well: A public platform serving 100 million-plus inferences a month across 22-plus languages on a vendor- and cloud-agnostic design that keeps data and switching rights public. Sovereignty resides in the portability, not in a national model.

Building an operational sovereignty strategy

Public trust is the constraint sovereignty rhetoric tends to skip. The OECD’s 2025 review of government AI warns that opaque systems make AI-assisted decisions hard to explain and can give public servants false confidence in tools that fail quietly. State-controlled AI is the same problem from the other side: A government that deploys models against its own citizens without audit or record has gained control and lost accountability. An agency that can log, explain and reverse an AI-assisted action can defend it to citizens, courts, auditors and elected officials. If it cannot, it has bought access and called it sovereignty.

None of this is new. AI sovereignty repeats earlier fights over cloud, telecom, semiconductors and cybersecurity. Europe’s flagship cloud project, GAIA-X, became a cautionary tale; the Dutch technologist Bert Hubert called it an “expensive distraction” that produced no European cloud, the familiar result of ambition without absorptive capacity. Cloud taught governments that outsourcing infrastructure does not outsource accountability; telecom, that vendor dependency becomes strategic exposure; chips, that supply chains matter before a crisis; cybersecurity, that trust must be verified continuously. AI inherits all four at once.

Over the next five to ten years, some countries will build national platforms, more will build trusted cloud and trusted model regimes, and most will run hybrids that pair domestic data control with global model access. Trade policy will harden those choices: Export controls on compute and data-localization rules will pull the vendor market into blocs that track alliances more than open markets. For a CIO, that turns a vendor and hosting decision into a five-year bet on whose rules and supply chains will still hold. The ones that succeed will treat sovereignty as an operating requirement, backed by leverage, not a slogan. Start with the control plane before the model: Most agencies will never own the model, and the controls are what decide whether the AI they do run stays accountable. Even when procurement policy is dictated from above, these questions remain within the CIO’s authority:

1. Can we classify AI workloads by public-service risk?
2. Can we prove where sensitive data goes across training, retrieval, inference, logging and retention?
3. Can we restrict which models are approved for which data classes and functions?
4. Can we reconstruct an AI-assisted action in enough detail to explain it?
5. Can we change providers without losing continuity or institutional knowledge?
6. Can we explain the system to citizens, regulators, auditors and elected officials?

A “no” to any of these does not mean the agency lacks AI. It means the agency has access it does not yet control. Public institutions can use global innovation without surrendering public authority, but only once they know what to hold, what to rent and where dependency turns into risk.

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The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

• AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
• Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
• These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

• Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
• Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
• Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
• Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

• Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
• Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
• Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
• AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

• Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
• Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
• Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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Cisco and AMD have expanded their partnership with a new package of hardware and security software that’s designed to help enterprise customers protect, deploy, and manage distributed AI resources.

During AMD’s Advancing AI event this week, Cisco’s president and chief product officer Jeetu Patel took to the stage during AMD CEO Lisa Su’s keynote to talk about how AI inference will be widely distributed and will require an architectural stack of software and tools that Cisco and AMD are partnering to develop.

The joint architecture combines AMD’s compact, high-performance Ryzen AI Halo hardware and a variety of Cisco networking, observability, governance, and security technologies. “AMD provides the deskside/local AI platform. At the foundation is AMD Ryzen AI Halo hardware, an isolated agent sandbox and the services needed for local-first inferencing, including model routing and token limits via AMD’s Semantic Router and local inference on Lemonade,” wrote Cisco’s Yash Sheth, senior director, engineering and research, in a blog post about the new package.

Ryzen AI Halo (pictured below) is designed to support local AI inference on an AI PC using its CPU, GPU, and XDNA neural processing unit (NPU), according to AMD. A resilient AI platform should continue delivering useful AI services even when connectivity is limited, models need to change, or workloads shift, AMD stated.

Cisco then wraps that platform in a secure harness that includes its Splunk Agent Observability plus Splunk Infrastructure Monitoring to provide full-stack observability, tracking agent behavior, tokenomics and compute operation, according to Sheth.

Cisco also brings its AI Defense for model and agent security; DefenseClaw for security policy enforcement, so guardrails are enforced directly on-device, within the agent harness; and Cisco Cloud Control offering a single pane of glass for unified policy and control, Sheth stated.

“To make deskside and local AI computing work at enterprise scale, every AI node must be treated as a secure, managed node in the enterprise network,” Sheth wrote.

“The need for token efficiency and data sovereignty is driving a new class of computing, deskside computing, with users and teams putting AI agents right by their sides,” Sheth wrote. “Inference is moving to a hybrid architecture with thousands of ambient deskside agents in an enterprise helping employees have 24×7 productivity. That’s an extraordinary opportunity. It’s also a brand-new operating challenge.”

As agentic AI moves from experimentation to real enterprise workflows, organizations need more than powerful endpoints. AI agents can run continuously and act on enterprise data, but create new requirements for network infrastructure, tokenomics, agent behavior, and security, according to a statement from AMD.

“Running more AI locally can help improve responsiveness, keep sensitive data closer to users, and reduce dependence on cloud-only approaches, but enterprises also need a way to monitor and manage these systems at scale. AMD and Cisco are addressing that gap by collaborating to pair high-performance local AI compute with the observability, governance, and control infrastructure needed for enterprises to deploy it responsibly,” AMD stated.

“By combining AMD Ryzen AI Halo systems and our broader local AI software capabilities with Cisco’s enterprise networking, observability and security technologies, we are helping customers deploy AI in a way that is performant, secure, observable and manageable at scale,” said Jack Huynh, senior vice president and general manager, computing and graphics group with AMD, in a statement.

A few other interesting statistics and trends cited in AMD CEO Su’s keynote include:

• AI adoption is accelerating across all industries, with agentic AI driving a surge in compute demand and shifting workloads from training to inference, which accounts for 60% of global AI compute capacity in 2026.
• AI is moving beyond the cloud, with edge and personal devices becoming critical for real-time, distributed intelligence.
• The AI accelerator market is projected to reach $1.4 trillion by 2030, nearly tripling previous forecasts, with GPUs expected to dominate but CPUs gaining new growth vectors due to agentic AI.
• Server CPU market is forecasted to grow over 50% to $200 billion by 2030, fueled by rapid agentic AI adoption and the need for massive CPU infrastructure.

Earlier this month, Nokia provided an AI-RAN platform update that brings an AI-native and programmable compute which is projected to double spectral efficiency by 2028. This increases speed, but more importantly, it can allow mobile operators to create some actual monetization beyond connectivity.

With this release, Nokia is introducing what it calls the industry’s first commercial AI-RAN platform, built on its AI‑native anyRAN software and Nvidia’s Aerial AI-RAN stack running on merchant GPU-based accelerated computing. The company is already seeing more than 20% gains in spectral efficiency from AI-driven radio algorithms, with a roadmap to reach 50% by 2027 and more than 100% by 2028, effectively doubling capacity on existing spectrum in dense cells.

Legacy RAN infrastructure enables connectivity but not much beyond that. The AI-RAN makes the network intelligent and extends AI into the physical world, enabling telcos to get more from their infrastructure investments, including providing a path to 6G. The partnership with Nvidia brings CUDA and AI into mobile environments.

For Network World readers, the headline isn’t just that Nokia got to market first with AI‑RAN—it’s that the company is using AI and GPUs to break the historical coupling between radio performance and custom silicon refresh cycles, and to turn the RAN into an application platform.

What AI-RAN actually is

At a technical level, Nokia’s AI‑RAN is a software‑defined baseband architecture that runs Layer 1/Layer 2 RAN functions and AI models on accelerated compute, primarily GPUs, instead of being locked into fixed‑function ASICs. Udayan Mukherjee, Nokia’s CTO for RAN and core, summarized the vision in the analyst briefing: “AI‑RAN is essentially a platform that turns the radio network into a true AI‑native programmable platform… one software detached from the hardware, defining flexible hardware deployment configurations, including part of the AI grid.”

Several pillars stand out:

• AI‑native design: Algorithms move from traditional linear models to increasingly nonlinear techniques (e.g., advanced channel estimation, deep receivers/transmitters, RKHS-based methods), which demand tensor-heavy compute best delivered by GPUs.
• Software-defined RAN: The same anyRAN software stack runs across different hardware configurations—plug‑in cards, standalone AI‑RAN nodes, and COTS/cloud RAN—so innovation comes via software releases rather than baseband card swaps.
• Programmable “D‑apps” layer: Nokia is pushing a new real‑time E3 interface from Layer 1/2 into an application layer for distributed apps (D‑apps) that can tap IQ samples, channel estimation and scheduling data for use cases such as sensing and location services.
• Crucially, this isn’t meant to replace all custom silicon overnight. Mukherjee was explicit: “We are not dropping the purpose‑built product… but we want to also get to merchant silicon, because that’s the future as we want to develop bigger models and AI elements and value‑added services on top of it.” The result is a hybrid era where AI‑accelerated platforms coexist with existing basebands but begin to shoulder the most compute‑intensive workloads.

Why AI-RAN matters for operators

Nokia and its early operator partners are trying to solve three perennial problems: finite spectrum, changing traffic patterns, and the drag of hardware refresh cycles.

First, spectrum constraints. Aji Ed, Nokia’s head of AI‑RAN and cloud RAN, called spectrum “the first constraint everybody has,” noting that operators have paid “huge amount of money” for bands and now need to “get up to the 2x spectrum” in terms of usable capacity. By running more complex AI models for multi‑user MIMO pairing, channel estimation, carrier aggregation and deep receiver/transmitter functions on GPUs, Nokia believes it can unlock those gains where traditional platforms simply run out of compute headroom.

Second, traffic is shifting. Generative AI and distributed inference workloads are driving more uplink-heavy, latency‑sensitive patterns that current RANs weren’t designed for. AI‑RAN’s ability to adapt scheduling, beamforming and resource allocation dynamically via AI models deployed at the baseband is meant to keep up with this shift.

Third, innovation cadence. In Ed’s words, “hardware upgrades can’t keep up with the innovation… we can’t really have a silicon refresh cycle linked with every three‑year cycle.” Nokia’s subscription‑based software model is designed to deliver new AI algorithms, spectral‑efficiency improvements and network optimization features continuously, without requiring “forklift” hardware replacements.

For operators, the message is attractive: comparable TCO and power to existing basebands, “no hardware premium” for GPU adoption, but higher capacity and a path to new services. Nokia told analysts it has reached performance, price and energy efficiency parity between its custom GridShark silicon and GPU-based systems, while moving the baseband roadmap to merchant silicon.

Nokia’s differentiation strategy

Every major RAN vendor is talking about AI‑enhanced radio, but Nokia is drawing a line between incremental gains and what it claims is a platform shift. When asked why its 2x spectral efficiency ambition is so much higher than the ~20% numbers competitors discuss, Ed pointed to the underlying architecture: “We are able to bring much more complex algorithms into this compute infrastructure… all of these require much higher compute, which is exactly what is coming from the accelerated computing.”

Several differentiators emerge:

• Aggressive spectral roadmap: Nokia is targeting 1.5x by 2027 and 2x by 2028, across TDD massive MIMO and FDD scenarios, with a feature roadmap built jointly with Nvidia and other partners.
• Single code base, three deployment paths: The same anyRAN software stack runs on (1) a GPU‑powered AirScale capacity plug‑in card, (2) a high‑capacity standalone AI‑RAN node, and (3) GPU‑based COTS/cloud RAN servers. This lets operators modernize “at their own pace” and mix brownfield evolution with greenfield AI-native deployments.
• Open ecosystem with D‑apps: Nokia is leaning into ORAN compliance (front‑haul, O1/O2) and actively championing the E3 interface and D‑apps concept within ORAN and AI‑RAN alliances, with Bell Labs and at least two external partners already building sensing and location applications on the platform.
• Software subscription tied to value: The commercial model builds on existing software subscriptions but ties pricing more explicitly to delivered value, such as spectral efficiency improvements and new AI services, rather than pure license metrics.

Mukherjee emphasized the openness angle in the briefing: “We see a lot of third‑party applications, whether it’s improving spectral efficiency or location service or sensing, can be developed on this platform… any AI‑powered services from us in Nokia or from ecosystems can be actually developed on top of it.” For operators burned by closed optimization stacks, that’s a notable pivot.

How AI-RAN unlocks new revenue

Most operators will sign off on AI‑RAN if the capacity and TCO story holds, but the more strategic question is monetization beyond connectivity. Nokia’s spokespeople spent considerable time on this in the analyst call, pointing to several classes of services that are difficult or impossible to deliver without AI running in the RAN itself.

Examples include:

• Integrated sensing: Turning the RAN into a distributed sensor grid that can support applications such as 3D mapping, gesture recognition and environmental monitoring, using the same RF infrastructure. Mukherjee noted, “We have at least two to three partners developing sensing applications on top of it… as well as two other companies developing location services.”
• Physical AI and location services: For factories, logistics hubs and smart cities, AI‑RAN can provide high‑precision positioning and real‑time telemetry for robots, drones and autonomous systems by fusing radio data and AI models at the edge.
• Distributed AI infrastructure: Operators exploring “AI‑native cities” can use AI‑RAN nodes and COTS GPU servers as a distributed inference fabric for applications that need tight latency to endpoints—think AR/VR offload, real‑time video analytics or interactive generative AI experiences.
• Premium connectivity tiers: With fine‑grained, AI‑driven control over uplink/downlink scheduling and QoS, operators can create differentiated SLAs for enterprise slices, mission‑critical IoT and AI workloads, charging for guaranteed performance rather than best‑effort connectivity.

Ed framed the opportunity as a continuum: Superior connectivity from 2x spectral efficiency creates “space for new AI workloads and other use cases,” while the D‑apps ecosystem and subscription model provide a mechanism to package and sell those capabilities. In practice, that could look like:

• Industrial sensing-as-a-service, where Nokia and partners supply D‑apps for integrated sensing and positioning, and operators monetize them per site or per device.
• Network‑exposed APIs for inference, location and RF sensing, integrated into operators’ broader network API portfolios as they pursue “network-as-a-platform” strategies.
• Sector‑specific AI‑native services, such as stadium analytics, transportation corridor monitoring, or drone traffic management, built by ISVs on top of Nokia’s exposed E3 data.

For operators that already use Nokia’s MantaRay and SMO stacks for cross‑network optimization, AI‑RAN essentially becomes the local real‑time execution environment, while R‑apps/X‑apps continue to orchestrate macro-level behaviors. Mukherjee described this layered architecture as “DU and CU on the platform running D‑apps using E3, interfacing to X‑apps and R‑apps through E2SM and connecting to the overall management system/SMO for lifecycle management.”

Adoption path and reality check

Nokia is not promising instant transformation. AI‑RAN pilots are slated for late 2026, with commercial availability on card‑based systems in 2027 and AirScale-based systems around 2028, all driven from a single software stack that supports 4G, 5G and is upgradable to 6G. The company already has trials and collaborations underway with T‑Mobile US, SoftBank, Indosat Ooredoo Hutchison, BT, Elisa, Vodafone, Orange, NTT Docomo, Deutsche Telekom and others.

There are still open questions around 3GPP vs ORAN standardization of E3, the maturity of the D‑apps ecosystem, and how operators will digest yet another subscription layer tied to radio software. But Nokia’s move puts a stake in the ground: in the AI era, the RAN is not just a throughput engine; it’s a programmable AI computer that can be monetized.

For Network World readers evaluating vendor roadmaps, this launch suggests a clear directional change. If Nokia hits its targets, AI‑RAN could mark the point where baseband becomes less about hardware SKUs and more about an AI platform strategy—one where spectral efficiency and new services are rolled out at “software speed,” as Ed described it, rather than at the pace of the next card generation.

AMD executives took to the stage at its Advancing AI 2026 event in San Francisco today to detail the company’s next generation of AI infrastructure solutions, from Instinct MI455X AI accelerator GPUs and 6th Gen EPYC “Venice” CPUs, to Pensando networking, ROCm.AI software and its Helios rack-scale platform that ties it all together.

AMD has been working towards rack-scale AI system solutions for years. Its ZT Systems acquisition last year added valuable engineering talent and intellectual property that is now finally bearing the real fruits. Its Helios AI platform is a major platform evolution for AMD, with shipments scheduled to begin in the second half of this year (which is here and now).

The announcements at Advancing AI show how the company has engineered its AI platform solutions for large reasoning models, sustained inference and agentic workflows. These workloads pressure memory capacity, data movement, networking and CPU orchestration. AMD’s approach is to keep as much data close to the compute engines as possible and move it more efficiently throughout the system, but there’s deeper nuance here that’s obvious versus AMD’s chief rival, NVIDIA.  

AMD’s MI455X targets the AI memory wall

The Instinct MI455X GPU is the compute engine that fuels the Helios rack, and the first GPU based on AMD’s new CDNA 5 architecture. Built with a modular mix of 2nm and 3nm chiplets, it carries 432GB of HBM4 and 23.3TB/s of peak memory bandwidth.

Compared to AMD’s current MI355X, the MI455X offers 1.5 times the memory capacity, up to 2.9 times the peak memory bandwidth and up to four times the peak matrix performance with MXFP4 and MXFP8 data types, which are lower-precision numerical formats designed to accelerate AI processing while reducing memory demands. With MXFP6 (6-bit floating point), performance is rated at up to twice that of MI355X.

AMD also shared some actual, measured internal results using production silicon. The company claims MI455X delivers 3.8 times higher FP8 decode performance, 3.5 times more measured FP4 compute performance and between 2.5 and 3.5 times more networking bandwidth than MI355X, depending on the transfer path tested. Those figures provide more context than just numerical specifications, though they remain AMD-provided comparisons that will need independent validation.

AMD

The architectural choices behind the numbers are important. Reasoning models and long context windows require sizeable KV caches for maintaining AI attention states, while mixture-of-experts models frequently move large amounts of data across accelerators. MI455X should let more model data, activation states and cache remain local. New dedicated IP in hardware can transfer data while the GPU continues processing, and expanded cache and multicast capabilities are designed to reduce redundant data movement to further improve efficiency.

The aforementioned lower-precision formats can also raise throughput and reduce memory use, but model developers still have to determine where they can be applied without unacceptable accuracy loss.

AMD’s Helios rack takes aim at Vera Rubin

Dave Altavilla

Helios is AMD’s primary rack-scale competitor to NVIDIA’s Vera Rubin platform. Each liquid-cooled rack combines 72 MI455X GPUs, 18 single-socket Venice host CPUs and Pensando networking technologies.

In its most complete, premium configuration, AMD rates Helios for 2.9 exaflops of low-precision AI compute, with 31TB of aggregate HBM4 capacity, 1.7PB/s of memory bandwidth, 260TB/s of bidirectional scale-up bandwidth and 43TB/s of scale-out bandwidth.

These are formidable figures, but they are technical specifications rather than actual application benchmarks. The more consequential development is AMD’s move from collections of eight-GPU servers to a 72-GPU shared-memory domain. Models too large for one node can operate across the rack without treating every exchange as a scale-out networking transaction, which benefits large-model inference as well as training.

AMD uses UALink over Ethernet, or UALoE, for an open standard scale-up fabric. Each MI455X provides 3.6TB/s of bidirectional scale-up bandwidth, while the complete rack delivers all-to-all connectivity through a single switch layer. AMD also claims six times more scale-out bandwidth per GPU than MI355X when MI455X is configured with three Pensando Vulcano 800 AI NICs.

While open standards give cloud providers more control over suppliers and system design, AMD and its partners now have to prove those components can deliver the predictable performance, reliability and deployment experience customers expect from a tightly controlled, more vertically integrated platform.

Finally, AMD designed Helios with automatic rerouting around failed links, virtual rack partitions, tray-level serviceability and rack-wide power, cooling and health monitoring. Major hyperscalers and potentially large-scale enterprise customers will likely key in on these capabilities, which can affect the availability, total cost and consistency of the AI services they consume.

Kind of like cowbell, AMD Venice gives agentic AI more CPU

AMD

AMD’s agentic CPU messaging regarding its upcoming Venice-based EPYC processors is mostly marketing speak, but the underlying requirement is very real. An AI agent can invoke retrieval, databases, security checks, code execution and other tools before a GPU generates a response. Running many agents concurrently increases the amount of conventional compute requirements surrounding the accelerators.

Venice scales to 256 Zen 6 cores with support for 512 threads, 16 memory channels, up to 1GB of L3 cache per socket, along with PCIe 6.0 and CXL 3.1 connectivity. AMD is also offering several Venice configurations for other applications, including general-purpose servers, high-frequency workloads, GPU hosts and high-density CPU sandbox systems used to execute agent tools.

Treating the CPU solely as a GPU host understates its role. Gateways, tokenization, vector search, databases and short-lived code execution stress different mixes of per-core performance, thread count, memory bandwidth and I/O. Specifically, AMD’s internal testing shows Venice significantly outperforming its current EPYC 9965 Turin CPU across five parts of the agentic AI pipeline, including gateway processing, context assembly, vector search, enterprise applications and short-lived tool execution. Individual gains vary by workload, but AMD details the overall generational improvement at up to a 1.7 times lift. As with the MI455X figures though, these comparisons come from AMD and will require independent validation.

Pensando networking and ROCm software advance

Keeping GPUs fed with data and coordinating traffic across racks directly affects utilization and operating costs. In fact, GPU utilization is a pretty sad state of affairs currently for some of the major frontier model providers.

As such, Pensando networking has become central to AMD’s roadmap. Helios can connect each MI455X to as many as three 800Gbps Vulcano AI NICs, while Salina DPUs handle front-end networking and infrastructure services.

On the software side, which is an equally critical component, AMD also introduced ROCm.AI, an AI-assisted development layer due to arrive in August. It includes reusable skills for coding agents, simplified management and Hyperloom, which can profile workloads, tune serving configurations, modify kernels and validate results.

These tools address two persistent AMD challenges: developer efficiency and ease of use, and software tuning. Automated optimization still has to produce repeatable gains without creating hard-to-maintain code, however. And while ROCm has progressed significantly over the last few years, NVIDIA’s CUDA retains an advantage in maturity, tooling and developer familiarity.

Customer commitments underscore rack-scale confidence

AMD now has commitments that give its MI450 generation and Helios considerably more weight. Meta and OpenAI have announced multi-generation agreements composed of up to 6GW of AMD compute capacity, with initial 1GW deployments planned for the second half of 2026.

Oracle plans a 50,000-GPU public cloud cluster beginning in the third quarter, while Microsoft will deploy Helios for Azure AI inference. Finally, just before the AMD event, Anthropic announced a strategic partnership for up to 2 Gigawatts of AMD-fueled AI compute, with its first gigawatt expected online in the first half of 2027.

Commitments of this scale reflect confidence in more than just MI455X performance. These customers are evaluating the complete architecture, including Venice CPUs, Pensando networking, ROCm software, rack integration, serviceability and AMD’s ability to deliver and execute across multiple product generations.

There is some financial alignment behind the agreements as well. AMD issued OpenAI performance-based warrants and committed to investing up to $5 billion in Anthropic. That context matters when evaluating these deals as market validation, but these planned deployments are substantial nonetheless and put Helios on a much stronger foundation as it begins shipping.

AMD expands its robotics and embedded foundation

AMD also expanded its physical AI portfolio, building on credible traction from its Xilinx-derived Kria adaptive system-on-modules and embedded technologies that are already powering robotics, machine vision and industrial automation applications.

The new Ryzen AI Embedded X100 combines up to 16 Zen 5 CPU cores, integrated Radeon graphics, a second-generation NPU and as much as 128GB of unified LPDDR5X memory shared across its compute engines. To me this looks a lot like a repackaging and optimization of the company’s Strix Halo platform, but with specific optimizations for the embedded space. Regardless, AMD is pairing X100 with the Kria AI Robotics Developer Platform, which includes a System Module or SOM, and a new Robotics Partner Network spanning hardware, software and platform providers.

Samples began shipping in June, with full production expected in the fourth quarter. This broader objective is to give developers a path across AMD x86 CPUs, GPUs, NPUs and FPGAs for real-time autonomous systems, rather than requiring them to assemble those hardware engines and software components independently.

Execution for AMD is now the test

AMD has assembled a credible platform for the burgeoning agentic AI market that’s blowing up currently with no signs of stopping. MI455X addresses memory and data movement, Venice handles dense agentic CPU workloads, Pensando networking connects global system resources, and ROCm.AI addresses software complexity. Finally, Helios assembles these components into a true competitive threat for NVIDIA’s latest Vera Rubin platform.

AMD’s open architecture may appeal to customers seeking supplier choice, but openness must also translate into reliable deployments, competitive total cost and software that does not require a significant rip-up. NVIDIA enters this cycle with a stronger ecosystem and far more rack-scale deployment experience. The true test will be how easily and reliably customers can integrate, operate and maintain these AMD solutions at scale.

As it stands, AMD now has major customers and a clearly defined architecture with systems engineering expertise behind it. Delivering Helios on schedule and showing that its performance claims translate into a real production workload throughput advantage and total cost of ownership gains will determine how much the competitive gap narrows. And of course, this is in a market that is clamoring for ever-more compute resources with a seemingly insatiable demand for AI services and capacity. That’s an environment for big iron success. Now AMD just has to deliver optimized, turnkey AI platforms. This is far easier said than done, but time will soon tell as deployments take shape this year.

This article is published as part of the Foundry Expert Contributor Network.
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Google’s AI offerings span its internal and cloud offerings. Its data centers are processing seven times more AI tokens compared to last year. To keep up, Google is upgrading its data-center hardware and software technologies at a faster clip. It plans to raise $80 billion to build new data centers. (See related story: Google transforms its data center architecture for agent era)

Network World spoke with Mark Lohmeyer, vice president and general manager of AI and computing at Google, about how the company’s infrastructure is keeping pace with AI demand.

Network World: What is the primary shift in infrastructure needs?

Mark Lohmeyer: We’ve seen the rise of agents and agentic use cases. Years ago, it was the chat phase: Ask a question, get an answer. Now we’re in the agentic era, where you express your intent, agents spin off multiple sub-agents, working in parallel, preserving state. This is a radical shift in what infrastructure needs to do; make them fast, cost effective, secure, reliable. We’re delivering infrastructure optimized for the age of agents.

NW: What’s the goal of the infrastructure buildout, and what should customers expect regarding costs?

ML: Ultimately, it’s about enabling customers with leading-edge capabilities and models at scale cost-effectively. With agents, inference transactions increase by 50x, 100x versus non-agentic workloads. We’re driving the cost per transaction down exponentially. In our latest platforms, we reduce the cost by almost 2x for the same work. Customers serve twice the number of users at the same cost, directly driving profitability.

NW: How are you addressing energy efficiency?

ML: Energy is a critical resource, and Google has optimized for years. We design data centers and compute [to drive] high PUE (power usage effectiveness). We introduced liquid cooling over five years ago, and these latest systems are all liquid cooled. For agentic workloads, CPUs come to the forefront… orchestrating agents, calling tools, doing evaluation loops in reinforcement learning. Our latest Axion-based CPU platform called N4A has energy efficiency and is significantly better than the prior generation and x86 comparables.

NW: How do you think about token efficiency as you build-out systems?

ML: Performance and efficiency gains are powered by co-design of the model and infrastructure. Gemini is trained on TPUs, primarily served on TPUs with high frontier model capability, in a token and cost-efficient way. This stems from co-design across the full stack.

NW: How do you project what infrastructure will be needed years in advance?

ML: Hardware cycles deliver a new next generation roughly every year, but design cycles are two years or more in advance. We work with DeepMind doing core research, to application teams taking models into production, to billions of users, to our team building infrastructure. We work upstream with DeepMind and application teams to understand what’s coming. Agents weren’t being broadly spoken of externally, but internally we had those insights around what they would need. That shows up in hardware design. We hit the timing right — these platforms are built for agents.

NW: What’s the eighth generation TPU platform?

ML: We deliver new platforms every year, and ones launched years ago are close to 100% utilized because demand for AI-optimized compute is high. The eighth-generation TPU platform is the first delivering two complete systems, from the chip all the way up to the network and storage and software, that are optimized.

TPU-8t is optimized for training, and TPU-8i is optimized for inference. For TPU-8i, we increased SRAM on the chip to 384MB — three times the prior generation — and increased the HBM by 50%.

NW: How are you approaching GPU and TPU compatibility?

ML: People in a single cluster do not commingle GPUs and TPUs. We offer both options based on specific workload needs. We’ve been investing on the TPU side in using software frameworks customers are comfortable with on GPUs and enabling those on TPUs. For example, PyTorch and vLLM. Customers could have a pool of GPUs and TPUs, running vLLM on top of that. Start with a workload on TPUs, but if the TPU pool is fully utilized, spill to GPUs or vice versa. This works because it’s all leveraging the same compatible software layer on top.

NW: How has the orchestration platform changed for agents?

ML: Kubernetes is becoming the orchestration platform of choice for AI. Google is transforming GKE [Google Kubernetes Engine] into an agent-native orchestration solution. When expressing intent to an agent and it spins up multiple sub-agents, compute needs to spin up rapidly — TPUs or GPUs — without long delays, then run and spin back down. We’re optimizing at every layer of the GKE stack: significantly improving node startup time and how rapidly we start and stop containers. Lovable demonstrates this with GKE, spinning up hundreds of sandboxes for live coding sessions on their platform in parallel, paying for infrastructure when needed.

NW: What is the role of the network and storage infrastructure?

ML: The network is critical for AI. This requires creating large-scale clusters of GPUs or TPUs and enabling them to talk to each other in a high-performance way. We created the Virgo network — a collapsed network architecture, non-blocking within a data center, where multiple pods or NVLink72 domains connect together.

In TPU8T, we can connect over a million TPUs together leveraging Virgo, creating large-scale, high-performance, reliable clusters that shrink innovation cycles. Storage is equally critical. In large-scale clusters, something is always failing. The ability to take snapshots and go back to a checkpoint is important.

We’ve introduced Managed Lustre 10T, with 10 terabytes per second of bandwidth, 18 petabytes of storage in single clusters. This is 10 times faster than last year and 20 times faster than competition. We have Rapid Bucket, low-latency storage backed by Google storage systems. Both are impactful in large-scale training environments.

NW: How does KV cache strategy differ between training and inference?

ML: For TPU-8i, we increased SRAM on the chip to 384 megabytes — three times the prior generation — and increased the HBM by 50%. Storing KV cache directly in chip memory allows responding to inference requests much more rapidly and cost-effectively than going to an external system. For inference workloads, storing as much KV cache as possible on-chip is critical.

We’re introducing a dedicated KV cache storage subsystem that works across GPUs and TPUs. As KV caches get larger, being able to fall back to this dedicated subsystem becomes critical. Loading model weights rapidly is important in dynamic inference environments where accelerators switch between models hour by hour.

Google’s data center team is racing to turn its infrastructure into a well-oiled machine for AI and the onslaught of agents. At this year’s Google I/O, CEO Sundar Pichai shared startling numbers: Google’s data centers processed about 3.2 quadrillion tokens a month, roughly seven times more than the 480 trillion processed in May 2025.

“Multiple agents work together, and now you’ve got millions, billions of users around the world potentially spinning off agents to help them do things,” said Mark Lohmeyer, vice president and general manager for AI and computing infrastructure at Google.

Google’s new data-center blueprint includes updated hardware, software, and orchestration layers to keep always-running agents operational.

In the LLM era, users sent prompts and received responses, and Google’s infrastructure was designed for latency and throughput. But agents could increase inference transactions by up to 100 times non-agentic workloads, Lohmeyer said. Google’s redesigned AI data-center stack has the elasticity for agents to be widely distributed, run for long periods, and make decisions independently.

“We’re delivering new platforms every year, each one optimized for what we think the world is going to need for the age of agents going forward,” Lohmeyer said.

Efficient data flow is key so agents can act, reason, and decide faster.

Google adjusted the Google Kubernetes Engine into an agent-native environment, where agents could be quickly spun up in sandboxes and containers. “From an infrastructure perspective, you need to spin up a bunch of TPUs or GPUs very rapidly. Then you need to be able to run them and spin them back down,” Lohmeyer said.

Google also made drastic improvements to its silicon to support its middleware changes. It recently introduced new AI chips, with the TPU-8t for training, and TPU-8i for inference. The 8t chip has three times more computing power than the previous-generation Ironwood chip. The 8i chip has 384 megabytes of SRAM and 288GB of HBM3e memory, which is 50% more than the previous-generation chip.

The platform is optimized for KV cache (key-value cache), which stores important contextual information needed by agents to make decisions, which reduces the round trips to other memory and storage systems. “Being able to store more of the KV cache directly on the chip allows you to respond much more rapidly and cost-effectively,” Lohmeyer said.

A new CPU called Axion N4A is more power efficient at agentic workloads such as orchestration and tool calling, Lohmeyer said.

Google also made many network and storage improvements to cut training and inference time. A new technology called TPUDirect can move data from storage directly into the memory of the TPU quickly by bypassing any orchestration overhead, Lohmeyer said.

A networking technology called Virgo can coordinate 1 million TPUs across a widely distributed network. It can also link up GPUs such as Nvidia’s latest CPU-GPU package called Vera Rubin. “In the case of Vera Rubin, we’ll be able to connect up to 960,000 GPUs leveraging Virgo,” Lohmeyer said.

A new technology called Pathways is a distributed training framework that efficiently scales machine learning across millions of TPUs and GPUs. Pathways solves bottleneck issues typically associated with JAX, and both help coordinate across wide networks.

“The software to orchestrate these large-scale distributed training jobs is also just as important as the hardware that it runs on top of,” Lohmeyer said.

Weighing Google’s AI data-center stack

Google is the only provider with its own data centers, software, hardware and models, said Jack Gold, principal analyst at J. Gold Associates. Google can optimize each on a regular cadence, which “many data centers can’t easily afford given the high cost of new chips,” Gold said.

Google’s stack may not be best for every data center need compared to Nvidia’s general-purpose GPUs, CPUs, and networking. AWS and Microsoft are also creating their chips.

“There is no real risk of Nvidia being replaced by Google in a big way. But with an ever-expanding market, there is plenty of room for all players,” Gold said.

But Logan Wolfe, partner at Kyndryl’s global AI strategy and sovereign transformation, advised enterprises to adopt a multi-cloud strategy to reduce risk from system failures, however superior an infrastructure may be. “I think that kind of hybrid and liquid infrastructure, we’re definitely getting there,” Wolfe said.

The cost per token varies depending on the provider of inference, whether that’s Microsoft, Google, OpenAI or Anthropic. That will matter as AI moves from experimentation to a powerful tool that drives business changes.

“Ultimately it really comes down to how much money are we spending on AI to move a certain business outcome,” Wolfe said.

The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

• AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
• Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
• These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

• Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
• Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
• Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
• Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

• Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
• Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
• Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
• AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

• Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
• Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
• Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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Leadership wants to scale AI. Budgets are tripling. Adoption is up.

Then the CFO asks the question every board now asks: which of these initiatives is actually profitable?

Most organizations cannot answer that question, not because they lack visibility into cost, but because the cost data they have was never designed to produce that answer.

Applying lessons learned from managing cloud spend won’t be a fix for the AI and ROI quandary. True, cloud taught a generation of CFOs that billing without business context is noise. So to get cloud ROI, they stitched two data sources together: cost data plus business data. AWS reveals which account, which region, which tag, which resource. Merge in customer and product mappings on top and the ROI of the cloud spend comes into focus.

But AI is harder. It requires three data sources: cost, business, and telemetry—the automatic collection of data from disparate sources that helps to clarify the whole picture of what happened and why. An executive or engineering lead can have AI invoices and customer revenue. But they have no way to connect them to business value. The token count on the OpenAI invoice does not specify which customer triggered which call, which feature it served, or whether the prompt produced a business outcome. That data does not exist in the provider’s billing.

AI providers won’t fix this problem

The situation is not likely to change anytime soon because AI providers are not in the business of attributing an enterprise’s costs to that enterprise’s customers. Instead, AI providers are in the business of selling tokens. The granularity they expose is the granularity their billing systems require, not the granularity a CFO requires.

Not convinced? Compare what AWS gives you to what an AI provider gives you.

AWS billing exposes resource IDs, account hierarchies, region, SKU, tag metadata, usage by the minute. Every dollar can be attributed to a workload, a team, a customer segment if it was tagged correctly. The data is rich enough that mature FinOps teams built unit economics on top of it years ago.

An AI provider invoice gives you tokens consumed by model, with optional grouping by API key. That is the resolution. No request-level attribution. No customer ID. No feature mapping. No prompt outcome. No retry identification. Multi-step agent workflows collapse into a token count. Imagine a large bank receives a multi-million dollar AI invoice each month. But it has no visibility into what parts of the business were responsible for what parts of the cost so cannot allocate them.

If an enterprise wants to know what AI cost drove which customer or feature, it has to capture that data itself, inside an application, before the call leaves it. 

Three required sources

Building AI ROI measurement requires three data sources, stitched together in a single model.

1. Cost data, normalized across providers. Every AI provider delivers cost differently. OpenAI invoices in one taxonomy, Anthropic in another, fine-tuning vendors and inference platforms each in their own. Cloud GPU costs sit in AWS or Azure billing. Vector database costs land in Pinecone or Snowflake invoices. None interoperate by default. Normalization is necessary but not sufficient. It will put all your AI costs in one schema. It does not tell you what they produced.
2. Application-layer telemetry. This is the source most organizations are missing, and the one that makes AI ROI structurally different from cloud ROI. It requires instrumenting AI calls inside your application across six categories: request-level tracing tied to a customer or session ID; feature attribution tied to the product surface that triggered the call; agent-step capture for multi-step workflows; retry and fallback identification so recovery costs don’t get attributed to primary calls; model selection logging that records which model was chosen and why; and outcome capture that ties each call to whether it produced business value. None of this data exists in the provider’s billing. All of it has to be captured at the moment the call is made and stored in a system that can be stitched to the cost data.
3. Business data. Revenue, customer segments, product hierarchies, and feature usage. The same business data already feeding your CRM and analytics stack, mapped to the customers and features the telemetry layer attributes calls to.

Stitched together, the three sources produce the unit economics every AI investment decision now requires: cost per customer interaction, margin per feature, profitability per agent workflow, ROI per model choice. None of these can be calculated from billing data alone. None can be calculated from telemetry alone. They require all three sources, modeled together in a way that maps cost to outcome.

Why agentic AI makes this urgent

Single-call inference is the easy case. One request, one cost, one customer, one outcome.

Agentic workflows are different. An agent decomposes a task into multiple steps. Each step calls a model. Some steps fall back to a different model when the first fails. Some steps retry on a poor result. Some steps invoke external tools that themselves cost money. A single user request can produce dozens of inference calls across multiple providers, with the cost compounding in ways the provider invoice cannot disaggregate.

If telemetry does not capture agent-step granularity, no one will know which steps are profitable. Aggregate costs will show up three weeks later in the invoice. By then, the workflow has been running at scale, customers are onboarded, and unprofitable paths have been retried thousands of times.

When agents make the calls, the volume of cost-generating events without business context attached grows by an order of magnitude. The window for instrumenting this before it becomes unmanageable is closing.

What changes when the three sources come together

Once the three sources are stitched together, the AI investment conversation changes.

Five different ways to build the same AI capability stop looking equivalent. They converge on adoption metrics and diverge by 10x on cost. The team picks the approach that delivers a similar business outcome at one-fifth the cost, because the team can finally see the difference. Product teams design features with margin awareness from the architecture phase, not from the post-launch budget review. Engineering teams choose model architectures with cost-per-outcome data alongside latency and quality. Leadership evaluates AI initiatives the way they evaluate any other capital allocation: on unit economics, not on the engagement chart. Aggregated invoices track the cost per customer interaction. Engagement metrics reveal margin per feature. Gut-instinct model selection is checked against real cost-per-outcome model selection results. 

Within seconds, everyone can see which AI features are profitable, which should scale, and which should be killed. This is the insight everyone is looking for and companies that achieve it will optimize the benefits of AI.

The build trap

AI costs are compounding now. The board is not waiting 18 months for an internal project to reach production.

The temptation to build it anyway has never been sharper. AI coding tools have changed what a small engineering team can ship in a quarter. The instrumentation layer looks tractable. The cost normalization looks like a weekend project. The semantic model feels like something a senior engineer could draft over a sprint.

It is a trap. Three reasons.

Volume is the first. A production AI footprint generates millions of telemetry events per hour, and that volume scales with agentic adoption. Real-time ingestion, correlation, and attribution at that scale is not the same problem as vibe coding a prototype in an afternoon. It is a permanent operational system that has to be right every minute of every day.

The vendor landscape is the second. Cost data arrives in delayed billing windows from providers with non-interoperable schemas. Schemas change without notice. New AI providers enter the landscape monthly, each with its own taxonomy and metering. The system is not built once. It is maintained against a moving target that moves faster than most internal release cycles.

The third is what the first two add up to: this is business-critical infrastructure. The CFO and the board are going to make capital allocation decisions on the data this system produces. When schema drift goes unnoticed for two weeks, when an agent telemetry stream stops correlating to a vendor that quietly changed its billing API, the cost of being wrong is not a sprint of cleanup. It is a quarter of misallocated capital.

The build-vs.-buy question for engineering leaders has changed. It’s not “can we build this?” The honest answer is yes. The real question is whether the marginal hour of your strongest engineers is best spent stitching cost data to telemetry to business outcomes, or building the AI products that produce the revenue the cost data is measuring.

The capability is reproducible in weeks. The choice is whether to spend the next 18 months building it, or the next 18 months acting on it.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

In public-sector and regulated-cloud work, I learned that sovereignty rarely starts as a national strategy. It starts as an auditor’s question: Who can prove where the data went, which system made the decision and what changes when the vendor or infrastructure does? That question is now moving into AI, and most sovereign-AI debates answer the wrong version of it.

They ask whether a country can build its own model on domestic data and hardware. For the United States and China, which together hold more than 90% of global AI data-center capacity, per a January 2026 Tony Blair Institute analysis, that question is worth asking. However, for almost every other government, it is the wrong place to start. The operative question is narrower: Once AI is embedded in public services, who controls the stack?

The 5 layers of public-sector control

For a CIO, sovereign AI means enforceable control across the AI lifecycle; model ownership is a separate question. Control has five layers:

• Data control: Where sensitive public data sits, and whether it can train a vendor’s model.
• Model control: Which models clear which workloads, and under what validation.
• Infrastructure control: Whether critical workloads run in approved environments.
• Operational control: Whether AI-assisted actions are logged, monitored and reversible.
• Vendor control: Whether the agency keeps portability, audit rights and a real exit.

Those five layers are the control plane for public-service AI. Floyd Dcosta recently made the enterprise case in “AI without sovereignty is just outsourced intelligence”: capability is what a tool can do; authority over how and when it does it is something a buyer can quietly lose. For public services, losing that authority plays out in the public eye.

Public-sector AI risk differs from enterprise risk. A retailer’s bad recommendation costs a sale; a government’s AI touches benefits, tax enforcement, policing and emergency response, raising the bar to due process, records retention and continuity of operations. A government that cannot reconstruct an AI-assisted decision lacks operational sovereignty, even in a domestic data center.

Evaluating risk: Concentration, jurisdiction and shadow AI

Foreign dependency is a real risk, but the exposure that matters is a sudden cutoff: A model you cannot audit, switch or exit, shut off by someone else’s order. A vendor’s nationality is a poor guide to that risk; control is.  Two markers matter. The first is concentration. In July 2024, a single faulty CrowdStrike update crashed about 8.5 million Windows machines, disrupting airlines, hospitals, banks and governments worldwide. No attacker was involved; one homogeneous dependency failed everywhere at once. The lesson points away from vendor nationality and toward uniformity as the fault line, making portability and provider diversity resilience controls.

The second is jurisdiction. In June 2025, Microsoft’s legal director for France told a Senate inquiry, under oath, that it could not guarantee that French public-sector data, even in French data centers, would be protected against US demands under the 2018 CLOUD Act. No such request had been made, and EU data has stayed in the EU since January 2025; senators called the assurance purely declarative. For the most sensitive data, residency does not equal control; the parent’s jurisdiction can matter as much as the server’s. Three US hyperscalers hold about 70% of the European cloud market, while European providers’ share fell from 29% in 2017 to roughly 15%. Concentration plus jurisdiction is the exposure a CIO must price. I have watched teams treat vendor selection as the moment risk was solved; it rarely was.

The wrong response is self-isolation. Most countries will never build frontier models, advanced chips, hyperscale clouds and talent pipelines at once; the Tony Blair Institute calls full self-sufficiency “too expensive, too slow and, for most countries, simply impossible.” The better test is workload sensitivity. Low-risk uses, such as drafting, translation and summarization, can run on commercial platforms with controls; high-risk uses, such as benefits eligibility, fraud investigation and healthcare triage, demand stricter control over data, model behavior and auditability.

Mandating domestic-only provision before a competitive option exists inverts sovereignty. Europe 2031, a five-year scenario from June 2026 by European technologists and policy researchers, illustrates the failure mode: A 2027 “buy European” mandate lands as offensive cyber capability spreads, and agencies that switched to weaker providers are locked out and paying ransoms. The scenario is fiction; the mechanism is not. Leverage comes from being indispensable, not half-hearted self-sufficiency. The closer-to-home effect is shadow AI: Mandate an inferior sanctioned tool and staff bypass it, the way shadow IT grows up around tools people find too slow. A rule that pushes sensitive work into ungoverned shadow AI reduces control instead of adding it.

Regulation and data-residency rules belong in any serious strategy, but carry failure modes. Blanket localization raises hosting costs and slows adoption without guaranteeing control, and a “sovereign cloud” on a foreign parent’s stack can amount to sovereignty theater. The more useful pattern tiers requirements by sensitivity. India’s BHASHINI shows the application layer done well: A public platform serving 100 million-plus inferences a month across 22-plus languages on a vendor- and cloud-agnostic design that keeps data and switching rights public. Sovereignty resides in the portability, not in a national model.

Building an operational sovereignty strategy

Public trust is the constraint sovereignty rhetoric tends to skip. The OECD’s 2025 review of government AI warns that opaque systems make AI-assisted decisions hard to explain and can give public servants false confidence in tools that fail quietly. State-controlled AI is the same problem from the other side: A government that deploys models against its own citizens without audit or record has gained control and lost accountability. An agency that can log, explain and reverse an AI-assisted action can defend it to citizens, courts, auditors and elected officials. If it cannot, it has bought access and called it sovereignty.

None of this is new. AI sovereignty repeats earlier fights over cloud, telecom, semiconductors and cybersecurity. Europe’s flagship cloud project, GAIA-X, became a cautionary tale; the Dutch technologist Bert Hubert called it an “expensive distraction” that produced no European cloud, the familiar result of ambition without absorptive capacity. Cloud taught governments that outsourcing infrastructure does not outsource accountability; telecom, that vendor dependency becomes strategic exposure; chips, that supply chains matter before a crisis; cybersecurity, that trust must be verified continuously. AI inherits all four at once.

Over the next five to ten years, some countries will build national platforms, more will build trusted cloud and trusted model regimes, and most will run hybrids that pair domestic data control with global model access. Trade policy will harden those choices: Export controls on compute and data-localization rules will pull the vendor market into blocs that track alliances more than open markets. For a CIO, that turns a vendor and hosting decision into a five-year bet on whose rules and supply chains will still hold. The ones that succeed will treat sovereignty as an operating requirement, backed by leverage, not a slogan. Start with the control plane before the model: Most agencies will never own the model, and the controls are what decide whether the AI they do run stays accountable. Even when procurement policy is dictated from above, these questions remain within the CIO’s authority:

1. Can we classify AI workloads by public-service risk?
2. Can we prove where sensitive data goes across training, retrieval, inference, logging and retention?
3. Can we restrict which models are approved for which data classes and functions?
4. Can we reconstruct an AI-assisted action in enough detail to explain it?
5. Can we change providers without losing continuity or institutional knowledge?
6. Can we explain the system to citizens, regulators, auditors and elected officials?

A “no” to any of these does not mean the agency lacks AI. It means the agency has access it does not yet control. Public institutions can use global innovation without surrendering public authority, but only once they know what to hold, what to rent and where dependency turns into risk.

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Cisco has revealed a family of open-weight AI models called Antares that, it said, can help security teams isolate potentially vulnerable parts of a software repository before deeper investigation begins.

Rather than detecting a specific CVE or generating a patch, these models search a codebase using only a Common Weakness Enumeration (CWE) description and return the files most likely to contain that class of vulnerability.

“Its purpose is to reduce a large codebase to a focused set of files that a security professional or a downstream security workflow should investigate,” Cisco’s AI researcher Supriti Vijay said via email. “The goal is not to replace a security engineer’s judgement or send them on a wild-goose chase, but to reduce fatigue and workload by helping them triage an issue earlier and focus their investigation on the most relevant parts of the codebase.”

The Antares family consists of models with 350 million, 1 billion, and 3 billion parameters trained specifically for repository-scale vulnerability localization.

The company said its largest model approaches the performance of GPT-5.5 on its internal vulnerability localization (Vloc) benchmark while remaining small enough for low-cost local deployment.

A search assistant, not a vulnerability detector

Cisco is careful to define what Antares is, and what it is not.

“Antares outputs a ranked list of source files likely to contain a relevant vulnerability, along with the terminal exploration trace that led to that result,” Cisco Foundation AI Chief Scientist Amin Karbasi wrote in a blog post, adding that the models are not meant to replace the broader application security toolchain: Human analysts or downstream security tools will still be needed to confirm exploitability, identify vulnerable lines of code, assess severity and generate fixes.

Antares differs from conventional static analysis platforms such as Semgrep or CodeQL, which primarily rely on predefined rules or queries. Cisco instead describes Antares as an evidence-driven exploration agent that adapts its search as it traverses the repository.

Cisco’s argument is that large repositories often contain thousands of files, making manual reviews exhaustive and unrealistic. By reducing the search space to a manageable shortlist, the company hopes to reduce investigation fatigue without replacing human judgement.

Claims of specialization over scale

Cisco is also making a statement about how cybersecurity models should evolve.

Instead of pursuing larger foundational models, Cisco argued that specialized, task-trained models can outperform much larger open-weight alternatives for vulnerability localization. In its evaluation Antares-3B, the largest model intended for single-GPU deployments, produced results comparable to GPT-5.5 while outperforming several substantially larger open models by Google, OpenAI and Meta.

The family also includes Antares-350M for resource-constrained environments and Antares-1B for laptops and workstations, which Cisco has made available as open-weight models on Hugging Face.

The command line interface (CLI) on the models supports targeted CWE investigations, repository-wide scans, SARIF output and local inference, which Cisco said enables organizations to keep proprietary code inside their own trust boundary.

However, because Antares identifies candidate files rather than confirmed vulnerabilities, organizations will still need to understand how often such repository-wide searches should be run, how much they improve existing triage workflows, and whether the reduction in investigation effort ultimately translates into measurable security or cost benefits.

Cisco has revealed a family of open-weight AI models called Antares that, it said, can help security teams isolate potentially vulnerable parts of a software repository before deeper investigation begins.

Rather than detecting a specific CVE or generating a patch, these models search a codebase using only a Common Weakness Enumeration (CWE) description and return the files most likely to contain that class of vulnerability.

“Its purpose is to reduce a large codebase to a focused set of files that a security professional or a downstream security workflow should investigate,” Cisco’s AI researcher Supriti Vijay said via email. “The goal is not to replace a security engineer’s judgement or send them on a wild-goose chase, but to reduce fatigue and workload by helping them triage an issue earlier and focus their investigation on the most relevant parts of the codebase.”

The Antares family consists of models with 350 million, 1 billion, and 3 billion parameters trained specifically for repository-scale vulnerability localization.

The company said its largest model approaches the performance of GPT-5.5 on its internal vulnerability localization (Vloc) benchmark while remaining small enough for low-cost local deployment.

A search assistant, not a vulnerability detector

Cisco is careful to define what Antares is, and what it is not.

“Antares outputs a ranked list of source files likely to contain a relevant vulnerability, along with the terminal exploration trace that led to that result,” Cisco Foundation AI Chief Scientist Amin Karbasi wrote in a blog post, adding that the models are not meant to replace the broader application security toolchain: Human analysts or downstream security tools will still be needed to confirm exploitability, identify vulnerable lines of code, assess severity and generate fixes.

Antares differs from conventional static analysis platforms such as Semgrep or CodeQL, which primarily rely on predefined rules or queries. Cisco instead describes Antares as an evidence-driven exploration agent that adapts its search as it traverses the repository.

Cisco’s argument is that large repositories often contain thousands of files, making manual reviews exhaustive and unrealistic. By reducing the search space to a manageable shortlist, the company hopes to reduce investigation fatigue without replacing human judgement.

Claims of specialization over scale

Cisco is also making a statement about how cybersecurity models should evolve.

Instead of pursuing larger foundational models, Cisco argued that specialized, task-trained models can outperform much larger open-weight alternatives for vulnerability localization. In its evaluation Antares-3B, the largest model intended for single-GPU deployments, produced results comparable to GPT-5.5 while outperforming several substantially larger open models by Google, OpenAI and Meta.

The family also includes Antares-350M for resource-constrained environments and Antares-1B for laptops and workstations, which Cisco has made available as open-weight models on Hugging Face.

The command line interface (CLI) on the models supports targeted CWE investigations, repository-wide scans, SARIF output and local inference, which Cisco said enables organizations to keep proprietary code inside their own trust boundary.

However, because Antares identifies candidate files rather than confirmed vulnerabilities, organizations will still need to understand how often such repository-wide searches should be run, how much they improve existing triage workflows, and whether the reduction in investigation effort ultimately translates into measurable security or cost benefits.

This article first appeared on InfoWorld.

You are one of the six billion people (75% of the world population) online today, and every click you make is routed through the data center. Data centers, whether knowingly or unknowingly, play a very critical role in your daily online activities. With an increasing population, increasing usage of online presence, and now omniscient AI, the demand for data centers has increased manyfold, and the trend seems similar to the year 2000, when telephone towers were built to accommodate increased digital presence.

To win the AI race, Hyperscalers (Google, Meta, Amazon, Microsoft, Alibaba, Oracle, IBM, Tencent) are spending huge amounts of money on data center development. In the USA, the hyperscalers are planning to spend $650 billion in 2026, which is around 70% higher than 2025 spending, according to Yahoo Finance.

As per McKinsey research, by 2030, companies will invest around $7 trillion in Capex on data center infrastructure globally. More than $4 trillion will go towards computing hardware investment. More than 40% of this spending will be invested in the United States.

Demand growth in data centers

McKinsey analysis shows that global demand for data center capacity can more than triple by 2030, with a compound annual growth rate (CAGR) of around 22 per cent. In the USA, data center demand could grow by 20-25 per cent at the same time.  

The data center industry is currently undergoing a violent transition. We are moving away from the era of “bespoke projects” — where every facility was a unique architectural feat — into an era of industrialized infrastructure. With global capital expenditure in the sector projected to hit $3 trillion by 2028, the “bottleneck” has shifted. It is no longer about securing the capital; it is about the physics of the supply chain.

During my tenure at Vantage, managing the intersection of data center construction management (DCCM) and infrastructure management (DCIM), I saw firsthand that the most successful players aren’t those with the deepest pockets, but those with the most integrated data threads. If your construction data in Procore doesn’t talk to your financial reality in Yardi, or your operational capacity in DCIM, you aren’t building a data center — you’re managing a $500 million blind spot.

The death of “sticks and bricks”

Traditionally, data center construction was treated as civil engineering. But for the modern CIO, a data center is a complex product assembly.

The challenges are systemic. We are facing 50-to-80-week lead times for critical “long-pole” items: extra-high-voltage transformers, switchgear, and the liquid cooling manifolds required for the next generation of AI chips. In this environment, the traditional reactive supply chain model is a liability.

To survive the $3 trillion inflow, we must adopt a hybrid-agile SCOR (supply chain operations reference) model. This means applying continuous flow logic to standardized components (like modular power skids) while maintaining agile responsiveness for the volatile IT layer.

The digital bridge: Construction management software  to ERP

The most significant opportunity for CIOs lies in financial-operational integration. In many organizations, there is a data chasm between the construction site and the corporate office. Construction teams live in the construction management software tracking tasks, trades, RFIs and payment submittals. Finance teams operate corporate offices with project management tools (worth remembering that email is a key tool besides spreadsheets and phone calls) tracking capex schedule, commissioning timeline, capital drawdowns and asset lifecycle management.

These systems are siloed; the CIO loses visibility into the total cost to serve. By integrating construction management into the financial system, we create real-time financial visibility of the build. We can see exactly how a three-week delay in a chiller delivery impacts the internal rate of return (IRR) of the entire asset. This isn’t just accounting; it’s strategic telemetry.

From BIM to DCIM: The lifecycle thread

The second bridge is the handoff from construction (BIM) to operations (DCIM). Historically, this handoff was a nightmare of PDFs and Excel sheets. By the time the operations team took the keys, the “as-built” design information was already out of date.

The opportunity today is to maintain a continuous data thread. The sensor data and asset tags established during the “make” phase in our SCOR model should flow directly into the DCIM. This allows us to perform virtual commissioning. Before a single server is racked, we should already have a digital replica of the airflow, power distribution, and cooling capacity.

The scientific inference: AI in the supply chain

As someone who has led data and AI initiatives, I’ve seen the hype. But in the supply chain, the application of AI must be pragmatic, not generative. We don’t need AI to write poems; we need it for predictive procurement. Most organizations manage their procurement in ERP or a mix of a few tools to manage the source-to-settle business flow. Adopting a system workflow improves data collection and the state of the procurement cycle, which in turn provides AI with the context to draw inferences for possible delays and anomalies in original specifications and change orders.

By applying machine learning to global logistics data, we can move from just-in-time to just-in-case modeling. AI can analyze geopolitical risks, shipping lane congestion, and raw material pricing to tell a CIO: “Order your switchgear 14 months early, or your Q3 2027 ‘Power On’ date is at risk.”

Bringing it all together: AI in the supply chain and finance

Why it matters: Approximately 70% of the capex is on this workflow and making timely decisions that directly impact the ready-for-service dates. The current challenge of reactionary adjustment in design to procurement to local fit-out is a significant drain on capex efficiency and cost of capital. Because single-project delivery delays have become so volatile, a massive structural shift is occurring in how digital infrastructure is funded. Single-project debt (special purpose vehicles or SPVs) is facing severe friction. To insulate themselves from RFS shocks, the largest institutional players are moving toward permanent platform capital — aggregating exposure across dozens of global assets simultaneously.

Navigating these complex multi-billion-dollar engineering projects distributed over a large geography is simply unmanageable without rethinking and re-engineering existing tools and processes.

The roadmap for the modern CIO

To lead this transformation, CIOs must move beyond the IT shop mentality and become master orchestrators of the supply chain. Here is the 1500-word reality condensed into three mandates:

1. Standardize the product: Stop designing bespoke facilities. Move toward DFMA (design for manufacturing and assembly). If 70% of your data center can be built in a factory and shipped as modules, you bypass the unpredictability of on-site labor.
2. Integrate the financial stack: If your construction management software and your ERP aren’t sharing a heartbeat, your data is lying to you. Force the integration between Procore and Yardi.
3. Own the long poles: Don’t leave the procurement of transformers and cooling units to general contractors. Use your balance sheet to secure these items years in advance. In 2026, inventory is the new currency.

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In the client-server era, the penalty for inefficient programming, such as unoptimized database calls, was largely confined to application responsiveness. Today, in the AI era, code, architectural, and platform inefficiencies are no longer just a performance issue, they’re a financial and environmental liability. Left unchecked, poor code cascades into soaring token costs and spikes data center power consumption, directly undermining both cloud budgets and corporate sustainability goals.

AI’s impact on sustainability

By 2029, IDC projects that the number of actively deployed AI agents will exceed 1 billion worldwide, which is 40 times more than in 2025. And these agents will perform 217 billion actions per day.

To deliver on this demand, AI data centers are being built out at an unprecedented rate, with Gartner forecasting that global spending on data centers over the next three years will increase 31.7% to surpass $650 billion, driven primarily by hyperscaler cloud providers building out AI foundations, and optimizing servers for heavy AI workloads.

All this presents a significant strain on the energy grid as well as environmental sustainability, including:

• The power double-down: The International Energy Agency (IEA) projects that global data center electricity consumption will more than double from about 415 to 945 TWh by 2030, primarily fueled by energy-intensive accelerated computing for AI.
• The inference premium: AI workloads are vastly more demanding than standard web activities. A gen AI query consumes roughly 10 times the electricity of a conventional keyword search, or roughly 2.9 watt-hours as opposed to 0.3 watt-hours.
• Water consumption: Cooling these dense clusters is highly resource intensive. Global AI-related water demand is expected to reach 4.2 to 6.6 billion cubic meters by 2027.

The good news, however, is it’s not all out of the control of end user organizations and CIOs. Just as in the client-server era, through careful planning and execution, CIOs have the potential to significantly improve the performance, costs, and sustainability impacts of their AI application portfolio.

Here are four recommendations to maximize value as you look across your AI applications and infrastructure estate.

Revisit business objectives in light of AI

AI applications and platforms bring several new headaches for CIOs and CFOs in terms of FinOps. The variable nature of AI vendor billing due to variable monthly token costs is just one well-known example. To avoid unpleasant surprises, be sure to carefully review vendor contracts to decipher pricing models. Look for what’s included in seat-based license fees and what’s added as variable charges for agentic AI usage.

In addition, explore new metrics and KPIs such as intelligence per watt to help make sense of your return on AI. Just as miles per gallon helps us evaluate new car purchases, IPW can help to measure the computational efficiency of a system. It quantifies how much intelligence — typically measured in AI inferences, tokens processed, or model training iterations — a processor can deliver for every watt of electrical power it consumes.

According to Max Romanenko, chief engineering officer at relational database platform EDB, cost per query tells you almost nothing in an agentic world where autonomous systems are spinning up databases, pipelines, and queries around the clock. “The metric that matters is intelligence per watt, how much useful AI you get for every unit of energy you spend,” he says. “It isn’t just an environmental number, it’s also a performance indicator.”

With the measurements in place, you can then start to manage and optimize each layer in the AI stack from the infrastructure, or hyperscaler, layer to your own data and application layers.

It’s important to bear in mind that high token usage isn’t necessarily a bad thing. It depends on the net value delivered by each AI application and use case. Managing and optimizing the AI stack is important, but you’ll also want to measure the business value being delivered by each of these applications so you can measure your return.

Take a sovereign AI approach when evaluating hyperscalers

As you work with hyperscalers like Amazon, Google and Microsoft, it’s important to understand how they charge and how much, but also their environmental footprints. For example, by reading their sustainability reports, you can find out their annual water consumption across their global data centers and compare them with other providers.

In 2025, Amazon’s global data center operations used 0.12 liters of water per kilowatt-hour, which amounts to 2.5 billion gallons, or 5% of the annual water consumed by the metro Seattle area. The company has been able to operate more than seven times better than the industry average and have improved their water efficiency by 52% since 2021.

As demand for cloud computing and AI grows, water efficiency is another important metric for CIOs to monitor within hyperscaler ESG reports. While not at the same level of regulation as scope 2 and 3 greenhouse gas (GHG) emissions reporting, enterprises need to pay increasing attention to water use efficiency (WUE) with water scarcity becoming a growing risk for hyperscalers.

The key requisite at the infrastructure layer, though, is to ensure sovereign AI. This doesn’t mean you need to own everything, but you need control over your AI-driven operations when conditions change. With 71% of global executives stating that switching their primary AI vendor or model would be difficult if required today, it’s important to understand AI dependencies and be able to avoid vendor lock-in. 

Control efficiency at the data layer

The AI energy conversation has fixated on models and GPUs, but every agent, model, and inference call runs on the data layer beneath them, and that’s the one place a CIO can actually move the numbers.

“You can’t control consumption at the model layer,” says Romanenko. “Agents consume what they consume. But you can control efficiency at the data layer, and for most enterprises that’s the only real lever they have. Optimize search, retrieval, and vector indexing where the work actually happens and you cut compute, cost, and carbon at the same time. Ignore it, and it’s like running the heat with every window open.”

Ann Dunkin, distinguished professor of the practice at Georgia Tech, adds that CIOs who bring models in house and run them in their own infrastructure, or in the cloud infrastructure of their choosing, can have more control over the sustainability of inference, as well as of their costs and how their data is used.

Fine tune the application layer

When balancing a mix of commercial AI packages and custom-built code, costs can quickly spiral due to inefficient design and orchestration, redundant APIs, and unoptimized model routing.

With inference calls costing approximately 10 times that of conventional web queries, for custom AI applications, it’s important to design them to only use probabilistic code where necessary. Since many custom applications utilize a combination of both probabilistic and deterministic code, this is exactly where software developers need to make smart choices in their designs.

Other techniques to fine tune the application layer include semantic caching, intelligent model routing, and internal AI capability registries. “CIOs can implement intelligent routing solutions to select the most cost-efficient model for every prompt,” says Dunkin. “The most flexible routing solutions can drop into a user’s existing environment and orchestrate the actions of the company’s existing models.”

For CIOs looking to maximize the business value of every AI application in their portfolio, these new considerations, including new metrics, tools and approaches from the infrastructure layer all the way up to the application layer, should be an essential part of the equation.

A client called me last week with a problem I have been hearing about more often lately. They had made significant reserved instance commitments with a major cloud provider, overbuying for what they thought would be heavy AI training workloads. Now they were sitting on thousands of dollars in idle capacity every month. Their plan was simple: resell it to someone else. Except they couldn’t.

I have been doing cloud consulting for a long time, and this situation once had a straightforward solution. You went to the marketplace, listed your unused reservations, and found a buyer. The process was a bit clunky, but it worked. These days, the answer is far more complicated, and my client learned this the hard way.

AI has made this problem increasingly common. Companies initially committed to compute capacity based on ambitious training plans. Prototype projects were expected to scale, and inference workloads were projected to grow substantially. Then reality hit. Some projects did not materialize. Some models trained faster than expected. Some inference patterns were lighter than anticipated.

Many organizations now hold reserved capacity they can’t use, discard, or share without a complex, increasingly restricted process. This reality is something every company with significant cloud spend needs to clearly understand.

The history of cloud resale

There was once a functioning resale market for cloud reserved instances. AWS, for example, maintained a Reserved Instances Marketplace where companies that had purchased reserved capacity could sell those reservations to other AWS customers. This was a legitimate, AWS-sanctioned process. Companies would register as sellers, list their unused reservations with pricing and terms, and if a buyer appeared, the marketplace would facilitate the transaction.

The resale market was useful for companies that had overestimated their needs or whose business changes reduced their cloud consumption. Instead of simply absorbing the cost of unused commitments, they could recoup some of that investment by selling to other organizations with unmet demand. It created a secondary market that added liquidity to what was otherwise a rigid financial arrangement.

My client had some experience with this resale market a few years ago and assumed they could use it again. They were unpleasantly surprised to learn that the rules had changed.

AWS changes the rules

In January 2024, AWS implemented a significant policy change that effectively shut down the resale of EC2 Reserved Instances on its platform. AWS stopped allowing companies to resell their unused reserved capacity through the Reserved Instance Marketplace or any other official channel. If you have a reserved instance commitment with AWS, you are essentially stuck with it unless you can use it yourself or modify your reservation.

This change had a real impact on companies that had relied on resale as part of their cloud financial management strategy. It reduced flexibility and increased the risk of long-term reserved commitments. When I explained this AWS policy change to my client’s representatives, I could hear the frustration in their voices. They had made their commitment in good faith, carefully modeled their expected AI workloads, and now faced the reality that there was no easy exit.

The reasoning behind this change is not entirely clear, but AWS likely viewed capacity resales as something that complicated their billing and commitment models without providing enough benefit to the overall ecosystem. Regardless of the company’s reasons, the primary resale path for the largest cloud provider has been effectively closed.

What options still exist?

What can companies do now when they find themselves with reserved capacity they no longer need? The first possibility is to work directly with the cloud provider to modify or exchange the reservation if it is convertible. Some reservation types allow modifications, such as changing the instance type, region, or tenancy. This will not eliminate the commitment, but it may help companies better align their reservations with actual workload needs.

The second option is to use third-party brokers and marketplaces that operate independently of the cloud providers. Although AWS has shut down its official resale channel, brokers and marketplaces still facilitate resale arrangements for other cloud providers and for some AWS scenarios. These arrangements can be more complex and carry more risk, but they remain a possibility for companies determined to move unused capacity.

The third alternative is to optimize usage. Companies can invest in better utilization monitoring, workload placement, and automation to ensure that reserved capacity is used as efficiently as possible. This does not recover the money already spent, but it reduces future waste.

My client explored all three alternatives and found that each had significant limitations. Modifications were possible, but only within a narrow range. Third-party brokers were interested, but the process was opaque and uncertain. Optimization helped, but it could not eliminate the fundamental overcommitment they had already made.

The broader implications

Cloud commitments are more rigid than many enterprises initially realize because they lack a liquid market and because providers control modifications, transfers, or cancellations. Right now, I see this pattern most often in the AI space. Companies commit to massive amounts of compute for training and inference based on projections that rarely reflect the actual workloads. Then they are surprised to find themselves locked into payments. The AI boom has led to significant overcommitment because enterprises remain unaware that the resale mechanisms that once existed have been largely shut down.

This is why cloud financial management has become such an important discipline. Companies need to be far more thoughtful about how they commit to cloud resources, how they model their future consumption, and how they build flexibility into their cloud strategies. The days of assuming you can always resell your way out of an overcommitment are effectively over, at least with AWS.

For Azure and Google Cloud, the resale landscape is slightly different, but the same general principles apply. These providers have their own capacity transfer policies and, like AWS, those policies can change at any time. Companies should understand their options before making large, committed purchases and build contingency plans in case their actual usage diverges from their projections—or if resale policies change.

The bottom line is that reselling unused reserved cloud instances is far more complicated than it sounds. The market is not as open as it once was, the options are limited, and the providers themselves hold most of the cards. My client got burned, and I doubt they will be the only one. Companies that want to optimize their cloud spending should focus on accurate forecasting, thoughtful commitment sizing, and ongoing optimization rather than relying on resale as a safety valve. That approach worked at one point, but those days are largely gone.

Microsoft and Mistral are betting that the future of enterprise AI is in sovereign infrastructure and model choice, rather than with one locked-in system. 

The companies have announced a “significant expansion” of their strategic partnership, which includes a multibillion dollar commitment from Microsoft. Mistral will add to its GPU infrastructure in Europe and extend access to its frontier multilingual models, while Microsoft will expand its sovereign cloud capabilities. The companies will also align on a joint go-to-market plan and will pursue enterprise opportunities together across Europe and globally, as well as funding proofs of concept (PoCs), offering Azure credits, and leading workshops to drive AI innovation with customers.

The partnership between the tech giant and the three-year-old French startup might seem an odd combination at first glance, analysts note, as both develop enterprise AI models and offer access as-a-service. But it reflects changing AI market dynamics.

“It’s possible to be both a competitor and a partner at the same time,” noted technology analyst Carmi Levy. Large cloud providers are becoming AI marketplaces in their own right, he pointed out, and are drifting away from exclusively promoting their own models. Building Mistral support into their infrastructure avoids platform lock-in and removes a “key objection for customers looking for options.”

“As much as Microsoft would want everybody standardizing on Copilot and Phi, it recognizes the simple fact that customers increasingly want to choose their own models,” said Levy.

Expands model access, sovereign cloud capabilities

As part of the agreement, Mistral will expand its Europe-based capacity with thousands of Nvidia Vera Rubin GPUs.

Mistral CEO and co-founder Arthur Mensch described a “slight gap” in compute capacity in Europe, noting that this expansion will provide more compute capability and support Microsoft’s cloud and AI services, providing a “shared platform for training, inference and large-scale deployment.” The companies call it a critical step to allow Microsoft customers to benefit from Mistral’s “scientific and compute innovations.”

In addition, Mistral Medium 3.5 and OCR 4 models are now available in Microsoft Foundry, and Mistral Medium 3.5 can be used in Microsoft Copilot Studio.

The partnership also extends Microsoft’s sovereign cloud infrastructure as well as combining Mistral’s frontier models with Microsoft’s security, compliance, and cloud-to-edge platform. This gives enterprises, particularly those in regulated markets, the ability to deploy AI where they see fit, while maintaining control over their data and workloads, according to the companies.

Further, customers will be able to build AI using the same models, tools, APIs, and workflows they’re used to, across Microsoft Foundry, Foundry Local, and Azure Local, and opt for fully Azure-hosted cloud environments; cloud-connected, controlled Azure Local environments that only use cloud-based Azure when necessary; and fully-disconnected environments that can operate independently for more sensitive scenarios.

“Europe should have access to the world’s most capable AI without compromising control over their data, operations or digital future,” said Brad Smith, vice chair and president, Microsoft, noting that with this partnership, the company is honoring its European digital commitments and giving customers a foundation for AI so they can “operate on their own terms.” Customers with “heightened sovereignty needs” will be able to exercise more control with “resilience and assurance” and continued access to Mistral’s open-weight models.

Enterprise credibility

Gartner distinguished VP analyst Arun Chandrasekaran noted that there’s no doubt that this agreement strengthens Microsoft’s sovereignty messaging and its position in regulated industries, and the tech giant benefits by expanding its AI portfolio with a “credible European frontier model provider”

He pointed to key differences from the initial partnership struck by the two companies in 2024; whereas originally Microsoft was hosting Mistral’s models, it is now consuming capacity built by Mistral in Europe.

Ultimately, the deal emphasizes European data centers, customer-controlled deployments, Azure Local, and fully-disconnected environments, addressing many of the concerns that surrounded the original Azure cloud only relationship, Chandrasekaran explained.

For Mistral, the partnership provides “enterprise credibility, and repeatable infrastructure revenue” that can fund continued AI platform development, he said. The combination of Microsoft’s enterprise AI platform with Mistral’s models and European AI infrastructure will give joint customers more deployment flexibility and expand options around data residency, sovereign AI deployments, and disconnected/on-premises environments.

“It also gives customers more model choice, reducing dependence on a single AI provider,” said Chandrasekaran.

A complementary partnership

Mistral continues to innovate with its frontier AI models and its chat and coding agent, Vibe (formerly Le Chat), yet it doesn’t attract as much attention as Claude or ChatGPT.

One of the company’s key differentiators is its targeted business model. Levy pointed out that not every workload requires “full-flight GPT.” For customers trying to rein in costs and limit exposure with on-premises deployments, Mistral’s “more focused capabilities can represent a cost-effective alternative.”

Chandrasekaran pointed to Mistral’s combination of high-performance open-weight models, strong multilingual capabilities, and a “focus on efficient inference that lowers deployment costs.”

Unlike many frontier AI companies, it offers customers greater flexibility to self-host and customize models; this makes it particularly attractive for enterprises and governments with sovereignty or regulatory requirements, he said. Its European roots also position it as the leading alternative for organizations seeking cutting-edge AI outside the US and Chinese ecosystems.

Bill Wong, research fellow at Info-Tech Research Group, also pointed to Mistral’s high-quality models and “adeptness as a sovereign AI leader.” There is growing demand for AI companies that comply with regional laws and data residency, and Mistral is established as “one of the most prominent European players.”

“Such a strategic position makes it a great partner for Microsoft to further expand its AI offerings beyond just being a single-model provider,” he said. Customers get freedom of choice while complying with data sovereignty and regulatory limitations without having to execute a separate AI deployment, while Mistral, for its part, can go beyond Europe and gain more visibility with international businesses.

Mistral brings both “technological and political advantages,” Levy noted. The startup’s European roots give Microsoft more credibility “at a fraught time for geopolitical relationships.” Customers in Europe and beyond are concerned about US exposure, and Mistral can provide a safer choice.

Meanwhile, Microsoft can deploy European-developed AI models running on European infrastructure, thus maximizing regulatory compliance while offering next-level enterprise marketing scale that Mistral “simply couldn’t achieve on its own,” said Levy. Mistral-based workloads deployed on Azure will also benefit from Microsoft’s “comprehensive security certifications, governance frameworks, and monitoring.”

Bottom line: Both companies can maximize their unique roadmaps through the partnership, he said. “As the rules of the AI economy continue to evolve, expect more eyebrow-raising deals like this to be signed.”

AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models.

The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post.

That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said.

“Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added.

That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain, principal analyst at Pareekh Consulting.

Richer billing data to boost enterprise AI cost governance

Beyond reducing engineering overhead, the update could also help enterprises improve AI cost governance.

Before this update FinOps teams struggled to identify which model Bedrock related to because usage type fields were inconsistent, and there was no unified product family name that captured all Bedrock costs in one place, Chopra said.

“Now those attributes — model provider, model name, inference type, inference mode, pricing unit — are standardized and available by default. That’s the plumbing work no one talks about, but it’s what makes downstream reporting actually reliable,” Chopra added.

This, said Jain, makes it easier to build dashboards showing cost by model, provider, token type or inference mode while also identifying expensive workloads, unusual token growth and opportunities to move to cheaper models or batch processing.

It’s a timely update, especially in light of last week’s AWS billing issue that caused some customers to see incorrect cost estimates of services consumed in the AWS Management Console, said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“Anything that gives customers clearer, more granular and more trustworthy billing data is welcome when confidence in the numbers has just been shaken. It does not fix what went wrong, but better visibility into where spend is going is exactly what teams want more of after an episode like that,” Bandta added.

This article first appeared on InfoWorld.

AWS has updated AWS Data Exports, its service for generating and managing cost and usage Reports (CURs), to include standardized Amazon Bedrock product metadata, making it easier for enterprise engineering teams to analyze AI usage and spending as they scale AI deployments spanning multiple foundation models.

The update extends billing exports with normalized fields for model provider, model name, inference type, inference mode, billing unit and Bedrock product family, and will enable enterprises to identify which models generated costs and compare spending across providers without relying on custom parsing or normalization of billing records, AWS wrote in a blog post.

That reduced reliance on custom parsing will reduce the engineering effort required to analyze billing data, analysts said.

“Before the update, a data engineer would typically need to maintain a model ID registry, write regex against usage type strings, or join AWS CloudTrail with CUR to figure out which provider generated which cost,” said Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

The new standardized fields “can be the difference between a billing pipeline that needs constant babysitting and one that doesn’t,” Chopra added.

That’s because custom parsing logic is more prone to break down or require maintenance when AWS adds new models or updates pricing in Bedrock, said Pareekh Jain, principal analyst at Pareekh Consulting.

Richer billing data to boost enterprise AI cost governance

Beyond reducing engineering overhead, the update could also help enterprises improve AI cost governance.

Before this update FinOps teams struggled to identify which model Bedrock related to because usage type fields were inconsistent, and there was no unified product family name that captured all Bedrock costs in one place, Chopra said.

“Now those attributes — model provider, model name, inference type, inference mode, pricing unit — are standardized and available by default. That’s the plumbing work no one talks about, but it’s what makes downstream reporting actually reliable,” Chopra added.

This, said Jain, makes it easier to build dashboards showing cost by model, provider, token type or inference mode while also identifying expensive workloads, unusual token growth and opportunities to move to cheaper models or batch processing.

It’s a timely update, especially in light of last week’s AWS billing issue that caused some customers to see incorrect cost estimates of services consumed in the AWS Management Console, said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“Anything that gives customers clearer, more granular and more trustworthy billing data is welcome when confidence in the numbers has just been shaken. It does not fix what went wrong, but better visibility into where spend is going is exactly what teams want more of after an episode like that,” Bandta added.

What if the next breakthrough in Enterprise AI doesn’t come from computer science alone?

What if it comes from applying principles that physicists have understood for more than a century?

According to Gartner, rising token-driven AI spend is straining budgets and challenging cost justification. As organizations race to deploy generative AI and agentic systems, token consumption dominates nearly every executive discussion: How many tokens did we use? How much did inference cost? Can we reduce our AI bill?

These are important operational questions. But they are not the strategic questions.

I believe the economics of enterprise AI can be viewed through the lens of three well-established principles from thermodynamics: the conservation of energy, entropy, and exergy.

While these principles describe physical systems — not AI —they offer a useful way to think about how organizations should measure AI success.

Principle 1: Value is created through transformation

The 1^st Law of Thermodynamics tells us that energy cannot be created or destroyed. It can only be transformed.

Enterprise AI presents a similar management lesson: Tokens are not valuable because they are consumed; they become valuable only when they are transformed into business outcomes: A faster loan application decision. A better customer experience. Faster and more accurate software. Reduced fraud. Higher employee productivity. A new product. A strategic insight.

The executive question therefore is not, “How many tokens did we consume?” It is: “How much business value did those tokens create?”

This leads to a new executive metric: return on tokens (ROT).

Just as organizations measure return on investment, they should begin measuring the business value generated for every million AI tokens consumed.

The organizations that win will not necessarily consume fewer tokens. They will generate more value from every token they use.

Principle 2: Every transformation creates waste

The 2nd Law of Thermodynamics teaches us that every energy transformation introduces inefficiencies.

Some energy inevitably becomes less useful for doing work.

The same pattern appears in enterprise AI: Not every token contributes equally to business outcomes.

Some are spent on:

• Repeated prompts
• Oversized context windows
• Redundant reasoning
• Hallucinations requiring correction
• Multiple agents performing the same work
• Expensive models solving simple problems

Those tokens are not “lost.” They simply produce very little business value.

I think of this as token entropy. Every enterprise deploying AI will experience it. The goal is not to eliminate token entropy completely — that would be unrealistic. The goal is to continuously identify it, measure it and reduce it. Because every unnecessary token represents an opportunity to improve both cost and business performance.

Principle 3: Useful work matters more than energy consumed

Thermodynamics introduces another important idea: Exergy.

Unlike energy, exergy measures how much energy can actually be converted into useful work. Two systems may consume the same amount of energy while producing dramatically different results.

The same is true for enterprise AI.

Imagine two companies each consuming one billion tokens. One produces meeting summaries. The other transforms claims operations, accelerates software delivery, detects fraud, improves customer retention, and creates new revenue opportunities. Both consumed the same number of tokens. Only one extracted significantly more business value.

Borrowing this concept as a management analogy, I call this token exergy.

Token exergy represents an organization’s ability to convert AI intelligence into meaningful business outcomes:

• High token exergy means AI is solving important business problems.
• Low token exergy means AI is generating activity without creating proportional enterprise value.

The distinction matters, because activity is not the same as impact.

A new responsibility for CIOs

For years, CIOs have monitored infrastructure: Cloud costs, storage, network utilization, GPU consumption.

These metrics remain important, but they tell only part of the story.

Token usage needs to be measured, planned, optimized and governed with the same discipline as any other cloud resource. This means that the next generation of CIO dashboards should answer different questions:

• What is our return on tokens?
• Where is token entropy reducing our effectiveness?
• How much token exergy are we generating?
• Which AI initiatives produce the greatest business value?
• Which use cases create the strongest competitive advantage?

These are no longer technology metrics. They are business metrics.

The next generation of CIOs will not simply deploy AI. They will manage an economy of intelligence.

Their role will resemble that of a portfolio manager — allocating AI capacity where it creates the greatest enterprise value, reducing waste and continuously improving the productivity of every autonomous workflow.

That responsibility cannot be fulfilled by dashboards alone.

It requires an intelligent layer capable of observing, learning and optimizing the entire AI  ecosystem. Three-layer enterprise agentic architecture Will enable this.

The next competitive advantage

Every major technology revolution eventually shifts from measuring inputs to measuring outcomes:

• Factories stopped measuring coal consumption and began measuring productivity.
• Cloud computing evolved beyond server utilization to business agility.
• Digital businesses measured customer acquisition costs and lifetime value.

Enterprise AI is approaching the same inflection point. Organizations that focus only on token costs will optimize for efficiency. Organizations that measure return on tokens, minimize token entropy and maximize token exergy will optimize for business transformation.

That is a fundamentally different objective. And I believe it will separate AI leaders from AI followers.

Because in the end, the future of enterprise AI will not be determined by how many tokens an organization consumes. It will be determined by how effectively those tokens are transformed into lasting business value. The AI adoption spending spree is over. Time to focus on value.

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AMD has expanded its AI infrastructure portfolio with the launch of Helios, an open, rackscale AI infrastructure designed for frontier AI and sovereign computing. Helios is built around AMD’s next-generation Instinct GPUs, EPYC Venice processors, Pensando networking and the ROCm software stack.

“Helios is AMD’s first complete AI rack system with GPUs, CPUs, and networking built together, instead of selling separate chips. It is well suited for training large AI models, memory heavy models, long context processing and high volume inference, and AMD’s biggest shot yet at challenging Nvidia’s dominance,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting.

AMD has also secured an early hyperscale deployment for Helios with Microsoft agreeing to deploy it to power its frontier model AI inference, its AI customers, and support Azure AI services.

The architecture behind Helios

The launch of Helios marks AMD’s latest attempt to strengthen its position in a market where Nvidia continues to dominate AI infrastructure. Unlike previous AMD AI offerings centred on individual accelerators, Helios is designed as a complete rack-scale system integrating compute, networking and software.

According to Jain, Helios goes up against Nvidia’s Vera Rubin rack. “Nvidia is faster on raw inference speed and has a faster internal connection between chips whereas AMD wins on memory size and offers better value for the price and power used. It’s standout feature is memory, where each rack packs about 50% more total memory than Nvidia’s competing system, which helps run very large AI models. It also uses open, industry-standard connections instead of Nvidia’s private technology, giving buyers more flexibility,” he said.

The AMD Helios rackscale design includes 72 AMD Instinct MI455X GPUs with AMD EPYC Venice CPUs and AMD Pensando Vulcano networking using UALink, optimized for compute, data movement, and system efficiency. The platform also supports both OCP and MX data types, delivering up to 2.9 EFLOPS of FP4 and 1.4 EFLOPS of FP8 compute for AI training and inference.

It also integrates 31TB of HBM4 memory with 19.6TB/s of memory bandwidth, while a liquid-cooling design uses quick-disconnect connections to efficiently dissipate heat. It is designed on open standards including OCP Open Rack Wide (ORW), Ultra Accelerator Link (UALink), and Ultra Ethernet Consortium (UEC) and can be scaled efficiently across datacenters while optimizing power, cooling, and serviceability for modern AI infrastructure, said the company.

On the security front, Helios incorporates a hardware root of trust and continuous attestation at every layer. It supports hardware-enforced isolation, encrypted memory and interconnects to help protect AI models, data and workloads in multi-tenant environments.

The software challenge

While the launch of Helios might help AMD close the hardware gap with Nvidia’s rack-scale systems, it will be the software compatibility that will be the real driver of enterprise adoption.

For this, AMD is expanding its ROCm AI software platform too, which supports frameworks including PyTorch, TensorFlow, and JAX, for enabling high-throughput inference and efficient distributed training while preserving familiar developer workflows.

Jain stated While hardware parity or superiority in memory bandwidth is achievable, software maturity remains the key differentiator for Nvidia. The Nvidia’s CUDA software has a 15-20 year head start, and almost every AI tool, tutorial, and codebase defaults to it.

He added software has been AMD’s weak spot. AMD has improved  ROCm a lot but it still lags behind on the newest, most specialized optimizations, and setup is more complicated. For everyday AI work, ROCm is usable but for cutting-edge performance, CUDA still leads.

Evaluating the trade-offs

For CIOs evaluating AI infrastructure, Helios launch brings in another option to a market that has largely revolved around Nvidia’s dominance. But when considering Helios, CIOs will have to evaluate factors such as performance, software readiness, deployment models, procurement timelines and total cost of ownership before committing to a platform.

While AMD has not publicly announced a specific price tag for the Helios, Jain believes it to be noticeably cheaper to buy and run with lower chip prices and lower power use per GPU.

“It gives companies a real second option besides Nvidia, easing supply shortages and giving leverage in negotiations. The catch is software, where teams need to check whether their AI tools run well on AMD’s stack, since some advanced tools are still CUDA only,” Jain said.

For CIOs planning to deploy both, Jain warns the two systems can’t be plugged together into one combined machine as they use different, incompatible connection technology. But companies can and do run both side by side in the same data center, just as separate systems handling different jobs.

Enterprise AI is entering a new phase. While the first wave of generative AI focused on human productivity and content creation, the next wave — agentic AI — will fundamentally change how organizations operate. Agentic AI systems are capable of reasoning, planning, making decisions and executing actions across applications, workflows and business processes with minimal human intervention.

As organizations move toward agentic frameworks that can independently resolve customer issues, optimize supply chains, manage infrastructure, coordinate workflows and even operate IT environments, one reality becomes clear: The network becomes the nervous system of the autonomous enterprise.

The infrastructure requirements of agentic AI differ dramatically from those of traditional applications. These systems are highly distributed, continuously exchanging information, interacting with APIs, accessing multiple data sources and making decisions in real time. The performance, security, visibility and adaptability of the network will directly determine the effectiveness of AI agents. Organizations that view AI readiness solely as a compute or data challenge risk overlooking one of the most critical enablers of future success — the network itself.

From AI-ready networks to autonomous networks

The long-term destination is the autonomous network: A network capable of self-monitoring, self-optimizing, self-healing and self-securing through the use of AI and automation. However, autonomous networking will not emerge overnight. The investments enterprises make today to support agentic AI are the same foundational building blocks required for tomorrow’s autonomous operations.

In many ways, agentic AI serves as both the driver and beneficiary of network transformation. AI agents require networks that can dynamically adapt to changing demands, while autonomous networks will increasingly rely on AI agents to manage and optimize themselves. The result is a reinforcing cycle where AI and networking evolve together.

The core characteristics of the network of the future

One of the most critical requirements for AI-ready networks is real-time observability and telemetry. Agentic AI thrives on context, and AI agents must continuously gather information from users, applications, devices, clouds, security systems and operational platforms. Future-ready networks must provide end-to-end visibility across campus, branch, cloud and data center environments. High-fidelity telemetry streams, real-time performance monitoring, application-aware analytics, AI-aware analytics and unified operational visibility are essential. Without comprehensive visibility, AI agents operate with incomplete information, limiting their effectiveness and increasing operational risk.

Another cornerstone is intent-based automation. Traditional networks are configured manually, often requiring administrators to define thousands of individual settings. In contrast, autonomous networks operate according to business intent. Enterprises increasingly need to define desired outcomes — such as maintaining application performance, optimizing user experience or automatically isolating compromised devices — rather than micromanaging configurations. The network continuously adjusts itself to achieve those objectives, providing the foundation upon which AI agents can make decisions safely and consistently.

Agentic AI also introduces entirely new traffic patterns that require AI-optimized connectivity. Large language models, retrieval systems, vector databases, cloud AI services, edge inference platforms and multi-agent orchestration frameworks create significant east-west and cloud-bound traffic. Future networks must provide low-latency connectivity, high-capacity fabrics, dynamic traffic engineering, edge-to-cloud optimization and policies that identify and prioritize AI workloads. The organizations that can move data efficiently will gain a competitive advantage in AI execution speed and responsiveness.

Security is another non-negotiable element. Agentic AI expands the enterprise attack surface because AI agents increasingly access sensitive systems, interact with APIs, consume proprietary data and execute actions across business environments. Future-ready networks must embed zero trust security into their architecture, with continuous identity verification, fine-grained access controls, microsegmentation, policy-driven authorization and continuous risk assessment. Security can no longer be bolted onto the network; it must be integral to its design and AI agents need to adhere to their own identity rules.

Finally, distributed intelligence across edge and cloud environments is essential. Many AI use cases require decisions to occur close to the source of data. Manufacturing systems, healthcare environments, retail operations, transportation networks and smart facilities often cannot tolerate the latency associated with centralized processing. Future networks must support edge AI deployment, distributed processing architectures, local inference, hybrid cloud operations and intelligent workload placement. The ability to move intelligence closer to users, devices and operational environments will become increasingly important as agentic AI expands across the enterprise.

Human expertise remains essential

Despite rapid advances in AI, the future will not eliminate the need for human expertise. In fact, it may increase its importance. One of the most significant misconceptions surrounding AI is that automation eliminates the need for skilled professionals. The reality is that autonomous systems require expert oversight, governance, validation and continuous optimization.

As AI systems become more capable, enterprises will need professionals who understand network architecture, security policy, AI governance, operational risk management, data quality, regulatory compliance and human-in-the-loop decision frameworks. The challenge is compounded by the unprecedented pace of AI innovation. New models, architectures, orchestration frameworks, security concerns and governance requirements emerge almost monthly. Most enterprise IT teams cannot be expected to independently evaluate every development while simultaneously modernizing infrastructure and maintaining day-to-day operations.

Organizations need access to experts who continuously track technology evolution, understand emerging best practices and can help translate innovation into practical deployment strategies. These experts provide not only implementation support but also ongoing operational guidance, helping enterprises maintain appropriate human oversight as AI capabilities expand. The future is not fully autonomous decision-making without people; it is intelligent automation operating under expert human governance.

5 actions enterprises should take now

Organizations should be preparing for the autonomous future right now. The following investments deliver immediate value while laying the groundwork for long-term AI transformation:

1. Modernize network observability. Establish comprehensive visibility across users, applications, devices, clouds and infrastructure. Rich telemetry and operational data will become the fuel that powers both Agentic AI and autonomous network operations.
2. Build an automation-first operating model. Identify repetitive operational processes and begin automating them. Automation maturity is a prerequisite for autonomous networking and creates the operational foundation AI agents will eventually leverage.
3. Adopt zero-trust principles across the enterprise. Implement identity-centric security controls, segmentation and continuous policy enforcement. As AI agents gain access to enterprise systems, security architectures must evolve to leverage the same identity controls.
4. Design for edge-to-cloud AI workloads. Evaluate network architectures for latency, bandwidth and resiliency requirements associated with distributed AI. Future AI deployments will span data centers, public clouds, branch locations and edge environments.
5. Invest in skills and strategic partnerships. Develop internal expertise while leveraging partners that possess deep networking, automation, security and AI knowledge. Human expertise remains one of the most important success factors in building AI-ready and autonomous infrastructures.

The road ahead

Agentic AI is poised to transform enterprise operations in much the same way cloud computing transformed infrastructure and the internet transformed business itself. But AI agents cannot operate effectively without a modern network foundation. The enterprises that succeed will recognize that AI readiness extends beyond models and data. It requires networks that are observable, automated, secure, intelligent and increasingly autonomous. The investments made today in AI-ready networking are not merely infrastructure upgrades — they are strategic building blocks toward the autonomous enterprise of the future, where AI agents and autonomous networks work together under human guidance to deliver unprecedented levels of agility, efficiency, and innovation.

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Thinking Machines Lab, the San Francisco startup founded by former OpenAI CTO Mira Murati, has released Inkling, its first general-purpose AI model. The launch adds another US-developed entrant to an open-weight market where Chinese developers produce several leading coding and reasoning models.

Inkling uses a mixture-of-experts architecture with 975 billion total parameters, of which 41 billion are active during processing. It supports a context window of up to 1 million tokens and was pretrained on 45 trillion tokens spanning text, images, audio, and video. Thinking Machines said it also trained the model for coding, tool use, and multimodal tasks.

The release follows the October 2025 launch of Tinker, Thinking Machines’ first product and an API-based platform for customizing AI models. Developers can fine-tune Inkling through the platform.

In a June 2026 assessment, AI model routing platform OpenRouter highlighted DeepSeek V4 Flash, GLM 5.2, MiniMax M3, and Nvidia Nemotron 3 Ultra as four notable open-weight models. Nemotron was the only US-developed model in the group.

Performance and developer access

Thinking Machines Lab’s benchmark table shows mixed results. Inkling scored 77.6% on SWE-Bench Verified, behind DeepSeek V4 Pro and GLM 5.2 but ahead of Nvidia Nemotron 3 Ultra. It also recorded 74.1% on MCP Atlas, 77.1% on BrowseComp with context management, and 79.8% on IFBench.

Thinking Machines said Inkling’s result used a bash-only harness, while the comparison figures were reported by the competing models’ developers.

The model includes a reasoning-effort setting that developers can adjust from 0.2 to 0.99. Thinking Machines said the setting allows users to balance performance against the number of generated tokens. In the company’s testing, Inkling matched Nemotron 3 Ultra’s Terminal Bench 2.1 score while generating about one-third as many tokens.

Developers can fine-tune Inkling through Tinker using context lengths of 64,000 or 256,000 tokens and test it through the Inkling Playground. The model is available through APIs from Together AI, Fireworks, Modal, Databricks, and Baseten. It is also supported by inference software, including SGLang, vLLM, TokenSpeed, llama.cpp, and Hugging Face Transformers.

Inkling’s full weights are available on Hugging Face as the original checkpoint and as a quantized NVFP4 checkpoint. Thinking Machines also previewed Inkling-Small, which has 276 billion total parameters and 12 billion active parameters. The company said it would release the smaller model’s full weights after completing testing.

Enterprise impact

Inkling’s differentiation lies in its open weights, multimodal capabilities, controllable reasoning, and integration with Tinker, rather than benchmark leadership, according to Biswajeet Mahapatra, principal analyst at Forrester.

“Enterprises are most likely to benefit in workloads where domain adaptation matters more than generic model performance, including knowledge-intensive copilots, multimodal customer service, document understanding, operational workflow automation, and agentic tasks that require organization-specific data, policies, and processes,” Mahapatra said.  

Inkling’s US origin could also influence adoption among Western enterprises, according to Pareekh Jain, CEO of Pareekh Consulting. He said many Western organizations face regulatory or procurement barriers when considering Chinese-developed AI models.

“Inkling gives those organizations a US-developed open-weight option that they can deploy on their own infrastructure,” Jain said.

However, the benefits will need to be weighed against the cost of deploying the full model.

Running Inkling on private infrastructure requires a GPU cluster with at least 2 TB of aggregated VRAM for the BF16 checkpoint, according to the model card. Thinking Machines lists configurations of eight Nvidia B300 GPUs or 16 H200 GPUs. A quantized NVFP4 checkpoint lowers the requirement to at least 600 GB and can run on four B300 GPUs or eight H200 GPUs.

“Because Inkling is a massive model with 975 billion total parameters, running the full model still requires significant GPU infrastructure, making closed-model APIs more economical for many organizations,” Jain said.

Jain said Inkling-Small may be a more feasible option for many enterprises because it could reduce infrastructure costs and latency while retaining useful performance across key workloads.

Safety and governance

Thinking Machines said it trained Inkling for calibration, instruction following, and resistance to censorship. The company said the model showed “strong patterns of censorship non-compliance” when evaluated by Cognition on its Propaganda and Censorship Eval.

Inkling scored 98.6% on StrongREJECT, which Thinking Machines described as a test of whether models refuse unambiguous harmful requests.

The model’s safety behavior should be retested after an enterprise customizes it, according to Jain. “Model fine-tuning can weaken safety filters, so companies should retest safety after customizing the model rather than assuming it stays safe,” Jain said.

He added that self-hosted and modified versions could diverge from Thinking Machines’ official model over time without receiving automatic updates.

“CIOs need to ensure every AI agent action is logged, auditable, and governed by human approval for high-risk tasks,” Jain said.

The article originally appeared on InfoWorld.

Thinking Machines Lab, the San Francisco startup founded by former OpenAI CTO Mira Murati, has released Inkling, its first general-purpose AI model. The launch adds another US-developed entrant to an open-weight market where Chinese developers produce several leading coding and reasoning models.

Inkling uses a mixture-of-experts architecture with 975 billion total parameters, of which 41 billion are active during processing. It supports a context window of up to 1 million tokens and was pretrained on 45 trillion tokens spanning text, images, audio, and video. Thinking Machines said it also trained the model for coding, tool use, and multimodal tasks.

The release follows the October 2025 launch of Tinker, Thinking Machines’ first product and an API-based platform for customizing AI models. Developers can fine-tune Inkling through the platform.

In a June 2026 assessment, AI model routing platform OpenRouter highlighted DeepSeek V4 Flash, GLM 5.2, MiniMax M3, and Nvidia Nemotron 3 Ultra as four notable open-weight models. Nemotron was the only US-developed model in the group.

Performance and developer access

Thinking Machines Lab’s benchmark table shows mixed results. Inkling scored 77.6% on SWE-Bench Verified, behind DeepSeek V4 Pro and GLM 5.2 but ahead of Nvidia Nemotron 3 Ultra. It also recorded 74.1% on MCP Atlas, 77.1% on BrowseComp with context management, and 79.8% on IFBench.

Thinking Machines said Inkling’s result used a bash-only harness, while the comparison figures were reported by the competing models’ developers.

The model includes a reasoning-effort setting that developers can adjust from 0.2 to 0.99. Thinking Machines said the setting allows users to balance performance against the number of generated tokens. In the company’s testing, Inkling matched Nemotron 3 Ultra’s Terminal Bench 2.1 score while generating about one-third as many tokens.

Developers can fine-tune Inkling through Tinker using context lengths of 64,000 or 256,000 tokens and test it through the Inkling Playground. The model is available through APIs from Together AI, Fireworks, Modal, Databricks, and Baseten. It is also supported by inference software, including SGLang, vLLM, TokenSpeed, llama.cpp, and Hugging Face Transformers.

Inkling’s full weights are available on Hugging Face as the original checkpoint and as a quantized NVFP4 checkpoint. Thinking Machines also previewed Inkling-Small, which has 276 billion total parameters and 12 billion active parameters. The company said it would release the smaller model’s full weights after completing testing.

Enterprise impact

Inkling’s differentiation lies in its open weights, multimodal capabilities, controllable reasoning, and integration with Tinker, rather than benchmark leadership, according to Biswajeet Mahapatra, principal analyst at Forrester.

“Enterprises are most likely to benefit in workloads where domain adaptation matters more than generic model performance, including knowledge-intensive copilots, multimodal customer service, document understanding, operational workflow automation, and agentic tasks that require organization-specific data, policies, and processes,” Mahapatra said.  

Inkling’s US origin could also influence adoption among Western enterprises, according to Pareekh Jain, CEO of Pareekh Consulting. He said many Western organizations face regulatory or procurement barriers when considering Chinese-developed AI models.

“Inkling gives those organizations a US-developed open-weight option that they can deploy on their own infrastructure,” Jain said.

However, the benefits will need to be weighed against the cost of deploying the full model.

Running Inkling on private infrastructure requires a GPU cluster with at least 2 TB of aggregated VRAM for the BF16 checkpoint, according to the model card. Thinking Machines lists configurations of eight Nvidia B300 GPUs or 16 H200 GPUs. A quantized NVFP4 checkpoint lowers the requirement to at least 600 GB and can run on four B300 GPUs or eight H200 GPUs.

“Because Inkling is a massive model with 975 billion total parameters, running the full model still requires significant GPU infrastructure, making closed-model APIs more economical for many organizations,” Jain said.

Jain said Inkling-Small may be a more feasible option for many enterprises because it could reduce infrastructure costs and latency while retaining useful performance across key workloads.

Safety and governance

Thinking Machines said it trained Inkling for calibration, instruction following, and resistance to censorship. The company said the model showed “strong patterns of censorship non-compliance” when evaluated by Cognition on its Propaganda and Censorship Eval.

Inkling scored 98.6% on StrongREJECT, which Thinking Machines described as a test of whether models refuse unambiguous harmful requests.

The model’s safety behavior should be retested after an enterprise customizes it, according to Jain. “Model fine-tuning can weaken safety filters, so companies should retest safety after customizing the model rather than assuming it stays safe,” Jain said.

He added that self-hosted and modified versions could diverge from Thinking Machines’ official model over time without receiving automatic updates.

“CIOs need to ensure every AI agent action is logged, auditable, and governed by human approval for high-risk tasks,” Jain said.

For a decade, Kubernetes was the right answer. It organized containers, scaled services horizontally and gave platform teams a shared vocabulary for running software in production. It abstracted away enough of the underlying complexity that engineers could stop thinking about servers and start thinking about services. Most cloud-native infrastructure today is built on top of it, directly or in spirit, and EKS made that model the default for the majority of enterprise teams running workloads on AWS.

The workload that defined that era was the stateless HTTP request, fast in, fast out, disposable. A user action triggers a request, the request hits a service, the service returns a response and the container is done. Kubernetes was optimized for that pattern down to the scheduler internals: Bin-pack containers onto nodes, autoscale on CPU and memory, evict and reschedule when something goes wrong. The whole system is tuned around the assumption that individual units of work are short, stateless and interchangeable.

That assumption no longer holds for the workloads that matter most right now.

The agent workload is structurally different

Agents are long-running, stateful processes. They reason across time, call external tools, spawn subprocesses, write and execute code, and make decisions that depend on what happened five steps earlier in the same task. A single-agent workflow might run for minutes or hours, touching a dozen external systems and generating intermediate outputs that subsequent steps depend on. The compute layer for that kind of work needs to do things the old model was never asked to do. That is the new pattern: Execution infrastructure designed around agent semantics rather than request semantics.

The Kubernetes community itself has acknowledged this mismatch. In March 2026, Kubernetes SIG Apps published an introduction to Agent Sandbox, a new CRD-based abstraction designed specifically for singleton, stateful agent workloads. The framing is direct: The ecosystem is moving from short-lived, isolated tasks to deploying multiple, coordinated AI agents that run continuously, and mapping those workloads to traditional Kubernetes primitives requires an entirely new abstraction. The fact that the Kubernetes maintainers built a dedicated primitive for this, rather than recommending teams compose one from existing resources, is itself the clearest signal that agent execution does not fit the old model.

What agent execution actually requires

Concretely, it requires four things. First, isolated execution environments that provision in milliseconds, not minutes, so each agent task gets its own sandbox for code execution and tool calls without blocking the reasoning loop. The difference between a two-second environment and a two-minute environment is not a performance optimization; it determines whether the architecture is viable at all. Second, durable state management across the full task lifecycle, so an agent can pause, hand off or resume without re-initializing from scratch and burning tokens to reconstruct context it already built. Third, coordination primitives for multi-agent work: The ability to spawn subagents, pass structured outputs between them and track task dependencies across a graph of concurrent processes. Production agent systems are rarely single agents; they are pipelines of specialized agents with handoffs that need to be reliable and inspectable. Fourth, credentials and secrets management that travel with the execution context, so agents can authenticate to external services securely without exposing credentials in the task definition, logs or the environment variables of a shared container.

The mismatch shows up fast in production

Kubernetes and EKS expose the mismatch quickly in practice. Pod eviction terminates an agent mid-task with no clean recovery path. Autoscaling reads CPU utilization as the load signal, but an agent holding a long inference connection looks idle to the scheduler even when it is doing the most consequential work in the pipeline. Provisioning a new environment takes 45 seconds to two minutes on a well-tuned cluster; agent workloads need that in under two seconds or the reasoning loop stalls and the user experience degrades visibly. These are not edge cases or misconfigurations. They are the normal operating conditions for production agent workloads running on infrastructure that was not designed for them.

The utilization data makes the broader cost picture even starker. The 2026 State of Kubernetes Optimization Report from CAST AI, drawn from analysis of over 23,000 production clusters across AWS, Azure and GCP, found average CPU utilization at 8 percent, down from 10 percent the year prior. Memory utilization fell from 23 to 20 percent. CPU overprovisioning jumped from 40 to 69 percent year over year. These numbers reflect clusters running traditional workloads, and the pattern is worsening, not improving, as environments scale. Agent workloads compound this problem further. An agent holding an open inference connection or waiting on a tool call registers as idle to a scheduler that reads CPU and memory as the only meaningful load signals. The infrastructure responds to the wrong metric, overprovisioning capacity for demand it cannot measure, while the actual bottleneck, environment provisioning latency and state continuity, goes unaddressed.

Security is not the same problem it was before

Agent workloads change the threat model at the infrastructure level. A compromised stateless service exposes a narrow surface defined by its API contracts. A compromised agent exposes every system it can reach, every credential it holds and every action it is authorized to take on behalf of the user. Agents generate and execute their own code, make non-deterministic tool-call decisions and accumulate context across long-running sessions. Standard container namespacing does not contain that kind of risk. Kernel-level isolation, default-deny network egress, scoped credentials per session and agent-aware observability are not optional hardening steps. They are baseline requirements for running agents in production.

What teams that ship agents have already figured out

Some of the clearest evidence for this shift comes not from infrastructure vendors but from product engineering teams running agents at scale on their own code. In late 2025, Ramp’s engineering team published a detailed account of building Inspect, their internal background coding agent. Each Inspect session runs in a sandboxed VM with a full-stack development environment and deep integrations across their observability, CI, and deployment tooling. The architecture requirements map almost exactly to the four primitives above. Filesystem snapshots keep sessions starting in seconds rather than minutes. Sessions are isolated and stateful. The agent can run tests, review telemetry, query feature flags and visually verify frontend changes in a real browser. And the whole system supports unlimited concurrency, so engineers can spin up ten parallel sessions exploring different approaches to the same problem without contention.

The results speak for themselves. Within months of launch, roughly 30 percent of all pull requests merged to Ramp’s frontend and backend repositories were written by Inspect. That level of adoption was not mandated. It happened because the execution environment was fast enough, capable enough and well-integrated enough that the agent was strictly better than a local workflow for a meaningful share of tasks. The key insight from the Ramp case is not about the model. It is about the execution layer. As their team put it, session speed should only be limited by model-provider time-to-first-token; everything else, like cloning and installing, needs to be done before the session starts. That is a statement about infrastructure, not intelligence.

The ecosystem is catching up, but defaults are sticky

None of that is a criticism of the tools. Kubernetes solved exactly the problem it was designed for, and it solved it well. The issue is that infrastructure defaults are sticky. Teams inherit them, build on top of them and optimize within their constraints long after the underlying workload has changed. The Kubernetes community’s own response, the Agent Sandbox project under SIG Apps, validates the thesis that a new abstraction is necessary. The new primitives the community is building include warm pools for near-zero cold starts, lifecycle management for suspending and resuming idle agents without losing state, and pluggable kernel isolation for secure execution of untrusted code. These are not incremental improvements to existing resources. They are net-new abstractions that acknowledge the old model does not stretch to fit.

But adoption of purpose-built agent infrastructure remains early. Enterprises building agent pipelines today are largely running a request-oriented orchestration model against an execution-oriented workload, and the mismatch shows up in task failure rates, runaway costs and debugging cycles that have no good tooling because the observability layer was also designed for stateless services.

The structural advantage is available now

The infrastructure to close that gap exists now. The prerequisite is recognizing that agent execution is a first-class compute pattern with its own primitives and its own requirements, not a variant of the stateless service model that defined the last decade. Teams that make that shift early will have a meaningful structural advantage. The ones that do not will spend the next two years wondering why their agent systems are unreliable at a scale that should be tractable.

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Gen AI spending is moving beyond the familiar software model of seats, licenses, and pilots. As AI shifts from copilots to embedded workflows and autonomous agents, one user request can trigger multiple model calls, retrieval steps, retries, orchestration layers, and infrastructure events. A tool that looks affordable in pilot may behave very differently once connected to production systems or allowed to act with less human supervision.

According to Michael Corrigan, CIO of World Insurance Associates, AI introduces a fundamentally different cost model — one that’s usage driven, non-linear, and tightly coupled to business activity. “Success requires shifting from traditional IT budgeting to FinOps-style discipline where consumption, value, and governance are actively managed in real time,” he says.

Here are five ways CIOs can build that discipline before AI costs spiral.

Forecast AI by workflow, not by user

At World, a top 25 insurance broker with about 3,000 employees across roughly 300 locations, AI use falls into three broad categories, Corrigan says. One is broad tools, such as copilots. Another is embedded AI inside SaaS platforms. And the third is bespoke AI built around specific workflows and manual processes.

“The bespoke is the area that’s growing the most right now,” he says. “And that’s where the model, from a cost perspective, has really been shifting from a license seat cost to a token consumption or token burn cost, or even a hybrid.”

Seat-based pricing is relatively easy to forecast whereas consumption-based AI isn’t. Costs may depend on prompt complexity, output length, model choice, workflow design, and whether the system calls a model once or many times in the background.

World tries to manage that uncertainty by defining the business problem, success criteria, and expected operational improvement upfront. Pilots help estimate consumption before scaling, but Corrigan says they don’t remove the ambiguity.

“We’ll try our best in the pilot to understand what the consumption rate is, what the token burn rate is,” he says. But once a consumption-based workflow goes into production, he adds, an estimate is put into place. That estimate is informed, but still rough.

Elmer Morales, founder and CEO of koder.com, an agentic AI coding startup, says CIOs should think less about headcount and more about workflow mechanics. Agentic AI costs are driven by the number of decisions an agent makes, how often it retrieves external data, how much context it carries, and how many systems it touches.

“CIOs should start by mapping workflows, not necessarily users,” he says. “The relevant variable isn’t going to be the headcount but how many decisions an agent makes per task.”

Model the failure path, not just the happy path

Pilots can mislead because they often test the cleanest version of an AI workflow. Morales says many enterprises model agentic AI costs around the happy path: the user gives a clear prompt, the system understands the request, the agent completes the task, and the process ends. Production is messier.

“They generally don’t model for situations where the agent is going to need to go back and check its work and redo things,” Morales says. “A lot of times, agents are wrong, either because they hallucinate or they understood the problem incorrectly.”

In an agentic workflow, the system may check its work, call another tool, retrieve more data, or redo a step. While that may improve quality, it also adds cost.

The difference between copilots and agents is central. A copilot interaction is often one prompt and one response. An agentic workflow may involve agents moving through a decision tree, executing tasks in sequence or in parallel, and calling sub-agents or external systems along the way. “By the time it’s achieved the original goal, the agent might have made 50 or 100 model calls, compared with a single call for a traditional copilot prompt,” Morales says.

That’s why CIOs should require teams to model the failure path before production, like how many retries are allowed, how much context is resent, which tools can be called, when a human should intervene, and what happens when the agent can’t complete the task.

Build cost controls into the architecture

Traditional FinOps practices still matter, but AI requires more than retrospective dashboards and chargebacks.

According to Pavan Madduri, senior cloud platform engineer at industrial supply company Graigner, looking backward at usage data, as traditional FinOps often does, can be too late. Costs are shaped by prompt design, model selection, agent behavior, orchestration choices, and runtime loops.

“Dashboards or chargebacks, those are historical accounting,” he says. “The money’s already gone.” For AI, he argues, cost controls need to be embedded into the architecture. That includes hard token caps, retry-depth limits, maximum runtime limits, workload prioritization, background-job throttling, and cluster-level controls that prevent runaway consumption.

“The real FinOps means you need to have the cost constraints embedded into your architecture framework,” Madduri says.

Those controls also extend to infrastructure. Expensive GPUs may sit warm between jobs because systems need capacity available when inference demand arrives. Teams may pass huge schemas, databases, or thousands of lines of code into frontier models when a smaller or more focused prompt would do.

Enterprises should also adopt event-driven autoscaling, Madduri says. “Use tools like KEDA to scale GPU nodes down to zero the moment inference demand drops, so teams only pay for the windows when the silicon is actively crunching tokens.”

Corrigan says World uses rate limits, spend limits, alerts, and approval gateways for consumption-based tools. When users approach token consumption limits, automated alerts allow IT and the business to review whether the continued spend is justified.

“If it’s not meeting the success criteria we expected, you have to have the control in place to say we’re going to move on or kill that process,” Corrigan says.

Route work to the right model

CIOs can also reduce AI bill shock by avoiding a default assumption that every task requires the most powerful model available. While some tasks need advanced reasoning, many others don’t. A simple support ticket, log-parsing task, or structured database transaction may be handled by a smaller or cheaper model. A complex architecture decision, legal analysis, or multi-step reasoning task may justify a more powerful one.

“Choosing the right model for the right prompt and right question — that’s where you leverage the maximum from that model, and you can decrease the costing,” Madduri says. “If you default every single call to a frontier model, that’s architectural laziness.”

Morales makes a similar point. Not every step in an agentic workflow requires a top-of-the-line model. Model routing, he says, is the discipline of determining the best model for the task, and providing the relevant context when the model needs it.

According to Jim Olsen, CTO of enterprise software company ModelOp, CIOs should use the least expensive model that can accomplish the business goal. Using the biggest model for everything is easier, but expensive. “It’s like hiring the most expensive engineer to change a few colors in a website’s CSS, or visual styling,” he says. “You wouldn’t do that. You use the appropriate tools for the task.”

Tie consumption to business value

For Olsen, the deeper enterprise problem is AI value shock, not just bill shock. Spending $200,000 in a quarter on AI is justified if it produces $2 million in business value. The problem is spending heavily on use cases that don’t generate a meaningful return.

“Are you actually getting that return on investment, or are you just blowing tokens for something that’s not delivering the value to your business?” Olsen asks. Tracking token usage by user or department may show who consumed AI, but not whether the consumption mattered.

For most enterprise AI systems, Olsen says costs should be tied back to business use cases. A model may be used for HR document search, customer support, code review, problem resolution, or other functions. Each use case may draw on the same underlying models or agents, but the business value can be very different.

That’s why he argues that companies need an AI inventory, a record of which business workflows use which models, agents, providers, workflows, and systems. Without that inventory, enterprises can’t connect consumption to value.

Corrigan takes a similar approach from a governance perspective. At World, new AI ideas go through an intake process. Business users propose improvements, and IT, finance, operations, sales, and business stakeholders evaluate, prioritize, and monitor them from pilot through production.

That may be where the next stage of AI FinOps is heading, toward a clearer understanding of which AI consumption deserves to scale, not just to lower bills. So the question, as Olsen puts it, isn’t whether someone used a million tokens. It’s what are they using them for.

If you just read the headlines, it would seem as if AI is now writing all of the world’s code and powering every application businesses run on.

That’s far from true. Just 4 of 33 AI pilots reach production, according to IDC Research — leaving legacy applications still fueling the wheels of commerce. This “silent majority” represents trillions of dollars spent each year on building, maintaining, testing, validating and monitoring legacy applications.

These applications won’t be replaced overnight. Companies and organizations depend on their predictability. The 60-plus-year-old COBOL programming language remains the backbone of banking software for good reason: it is extraordinarily efficient at processing massive transaction volumes with precision. Furthermore, do you want your bank revolutionizing how they manage your money? Probably not.

So, while AI investment continues to build inside the software development lifecycle (SDLC), it isn’t instantly rendering older software obsolete. What it will do is steadily enable easier tweaking, updating and testing of legacy applications — and in some cases, full migrations to modern platforms. And really, this isn’t a new phenomenon. Businesses have always looked to wring more efficiency and profit from existing products through intelligent prioritization.

The argument then is that CIOs and CTOs can take a proactive look at their legacy application portfolios to determine which ones, if any, should migrate sooner. Five considerations can help guide that decision.

Before replacing legacy apps with AI, ask these 5 important questions

1. Does the legacy application still work?

Is its utility still there? Customers often appreciate the consistency of legacy applications. They’re reliable, predictable and well understood. Don’t fix what isn’t broken. Another way to think about this is the degree to which the technical approach of your legacy application is still viable. It’s pretty much a guarantee nowadays in software that an application built one way, with some set of technologies, would be built a totally different way just two to three years later. There is no avoiding that, but what you want to avoid is investing further into a technical approach powering a legacy application that has been completely replaced with new software or a technical approach, especially if it is 10x better across the vectors of software development (latency, cost, accuracy).

2. Does it still make financial sense?

Running a system over a long period amortizes costs significantly. Even as growth rates slow or plateau, it can still be less expensive to let legacy applications run than to overhaul them. Another way to think about this is: how viable is my customer base in the near-term and the long-term? If you anticipate modest—or even flat—earnings growth for your product, then that’s an indicator that it’s possibly worth optimizing your development processes with AI. Where it’s probably not worth investing is when you have no confidence in your future earnings, whether that’s due to the customer base shrinking or commoditization or something else.

3. Can you integrate AI into existing workflows?

A significant portion of upcoming software development lifecycle work will focus on refactoring applications to be more AI-native. Some legacy applications may be strong candidates for a full AI rebuild, while others are better positioned for an AI add-on. Gartner research from 2025 found that only 28% of AI use cases in infrastructure and operations fully succeeded.

Among those that did, success was attributed primarily to integrating AI into existing workflows and systems. “As AI becomes part of day‑to‑day operations, it boosts adoption and creates visible impact within the organization,” Gartner states.

It’s important to keep in mind the distinction between using AI to optimize an existing process or workflow within your application, versus powering a workflow or feature with AI. The former approach is more palatable for legacy applications because it generally doesn’t change the cost profile of running that application. In the latter case, if you’re introducing an AI-powered module into the application, you’re generally going to incur inference costs at runtime, and they are an order of magnitude more expensive for today’s frontier models than base compute.

4. Do you have documented processes for maintaining legacy applications?

If so, you’ll more quickly identify where AI can optimize. The more coherent, organized and detailed processes are, the faster AI can find its footing and drive tangible efficiency gains. If documentation is lacking, start there. Keep detailed instructions and workflows for how you do things. Consistency matters. Don’t do things by heart. Don’t approach tasks casually, and don’t do things differently each time. The more uniform your process, the more easily you can insert AI into discrete steps and achieve efficiencies without disrupting the broader software development lifecycle. The organization in the most precarious position is the one managing legacy applications with no documented process for doing so.

5. Can you prioritize?

Making a change to a piece of legacy software might involve 20 or more steps. Only one or two of those steps may be clear candidates for AI-driven optimization. Identifying and prioritizing those opportunities will help you realize early wins and build the case for broader return on investment. Also, not all candidates for optimization make sense in light of broader financial and operational constraints. As always, prioritize ruthlessly in favor of ROI—bang for your buck. If your team has been struggling to operate a particular part of your system due to a lack of expertise or time, you might consider using AI to buttress the maintenance of that component. Having AI own that part of the workflow might unlock big time savings—or it might erode crucial domain knowledge that your team used to possess through repetition. There is no one-size-fits-all; think through the second-order effects.

Adding AI in testing in the SDLC

Beyond coding and application development, AI is opening new possibilities in how we test software. As leaders examine processes and look for places to insert AI, testing is often a natural entry point. There has been substantial innovation here, including new autonomous AI-driven testing solutions, those that have been enhanced with AI, and hybrid approaches that blend both. Each organization will be at a different place in its AI journey. Testing solutions exist to meet everyone where they are. Also, the state of applications will help determine which approach fits best—and when it fits as you evolve applications.

Of course, there is some substance to the AI hype around how much code AI will write and how many applications it is already creating faster than ever. But one school of thought is that AI’s biggest economic impact will be in the creation of massive new markets and industries rather than in the complete displacement of existing industries. Regardless of how far AI takes us through the universe, it’ll take some time and it’ll be bankrolled by the trillions of dollars of existing products and industries that we depend on every day.

That’s all good news for legacy players, but no one can afford to stay still. AI capabilities are advancing rapidly. Make it a habit to revisit legacy applications and workflows regularly. The right moment to introduce AI will keep shifting, and staying ahead of it is a competitive advantage.

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Generative AI is a kind of artificial intelligence that creates new content, including text, images, audio, and video, based on patterns it has learned from existing data.

Today’s generative models are typically built on foundation-model architectures such as large-language models (LLMs) and multimodal systems, enabling them to carry on conversations, answer questions, write stories, generate code, and produce images or videos from brief prompts.

Generative AI is different from discriminative AI, which draws distinctions between different kinds of input. Where discriminative AI answers questions like “Is this image of a rabbit or a lion?”, generative AI instead responds to prompts such as “Describe to me how a rabbit and lion look different from one another” or “Draw me a picture of a lion and a rabbit sitting next to each other” — and in both cases produces text or imagery that, while grounded in the AI’s training data, isn’t just a copy of something that already existed.

Just a few years ago, generative AI was once a novelty focused on chatbots and artistic image generation. Today, it has become a core enterprise technology, and powers everything from content creation and software development to customer support and analytics workflows. But with that power comes a new set of challenges — from model alignment and hallucination to governance and data-integration hurdles.

In this article, we’ll look at how generative AI works, explore how it has evolved into the foundation-model era, examine how to implement it effectively, and offer best practices for getting value out of it, today and in the future.

How does generative AI work?

For decades, early artificial-intelligence efforts often focused on rule-based systems or narrowly trained models that were built for one task at a time. While these efforts produced useful systems that could reason and solve human tasks, they were generally a far cry from sci-fi visions of thinking machines. Programs that could talk to people never seemed to get very far past the level of ELIZA, a “computer therapist” created at MIT in the mid 1960s; even Siri and Alexa after much fanfare were revealed to be fairly limited.

The big structural shift that gave birth to modern generative AI came with the concept of a transformer, first introduced in “Attention Is All You Need,” a 2017 paper from Google researchers.

Using a transformer architecture as a basis, you can build a system that derives meaning from analyzing long sequences of input tokens (words, sub-words, bytes) to understand how different tokens might be related to one another, then determines how likely any given token is to come next in a sequence, given the others. In AI lingo, we call these systems models. Because a model analyzes very large datasets and parameter counts, it can pick up on statistical patterns and knowledge implicitly embedded in the data.

This is all easier said than done. The process of adjusting a model’s internal parameters so it gets better at predicting the next token in sequences is called training. During training, the model repeatedly guesses the next token in a given sequence, compares its prediction to the actual one, measures the error, and updates its parameters to reduce that error across billions of examples. Over time, that process teaches the model the statistical relationships that will allow it to generate coherent language (or code, or images) later.

What is a foundation model?

You’ll often hear the word large used for transformer-based models of these types, like the LLMs we mentioned earlier. Large in this context refers to the large number of internal numerical values that the model adjusts during training to represent what it has learned, along with breadth and diversity of data used to train the model and the underlying compute resources powering this whole process.

This is in contrast with the narrow models of the earlier era of AI/ML, which werebuilt for one purpose and trained on a limited dataset. For instance, a spam filter may be very good at what it does, but it’s only trained on email data and all it can do is classify emails. Large models, by contrast, serve as what’s known as foundation models. They’re trained broadly on diverse data (text, code, images, or multimodal data) and then adapted or specialized for many downstream tasks.

These foundation models are the basis for most of the popular generative AI tools and services on the market today. They can be specialized in several ways:

• Fine-tuning: Giving a foundation model further training on a smaller, task-specific dataset
• Retrieval-augmented generation (RAG): Giving the model the ability to pull in external knowledge when asked a question
•  Prompt engineering: Tailoring a query so the model gives the sort of answers you’re looking for.

How do AI systems write computer code?

One of the surprising discoveries of the gen AI era was that in recent years was that foundation models trained on natural-language text can also, when fine-tuned with code examples, also write computer code — often better than many purpose-built systems. Still, it makes sense, when you think about it — after all, high-level computer languages are designed by humans and ultimately based on human language.

This 2023 InfoWorld article highlights how models like PaLM, LLaMA and other transformer-based systems fine-tuned on code repositories propelled this shift, but since AI giants like OpenAI have moved into this space. This all matters because code generation (or code-assisted productivity) has become a key enterprise use case of generative AI — perhaps the key use, given the industry’s enthusiastic adoption of it.

What are AI agents?

So far, we’ve been talking about chatbots, writing assistants, image-generation tools. They respond to prompts, output text or images, and then stop. A new category of tool called agentic AI goes further: it plans, executes, and in many cases learns as it works.

Because large models already understand language, code, and even structured data to some extent, they can be repurposed to generate not only descriptive text but operational instructions. For example: an agent might parse the intent “generate a sales-report”, then format internal calls like getData(salesDB, region=NA, period=lastQuarter), and then call an API, all by generating text that’s interpreted as instructions. The MCP framework standardizes the “language” of those instructions and the plug-points into tools and data so that the model doesn’t need bespoke integrations for each new workflow.

These kinds of autonomous agents have several enterprise use cases:

• Software automation: Agents that generate code, call unit tests, deploy builds, monitor logs and even roll back changes autonomously.
• Customer support: Instead of simply drafting responses, agents interact with CRM APIs, update ticket statuses, escalate issues, and trigger follow-up workflows.
• IT operations/AIOps: Agents monitor infrastructure, identify anomalies, open/close tickets, or auto-remediate based on defined rules and context from logs.
• Security: Agents may detect threats, initiate alerts, isolate compromised systems, or even attempt to manage threat containment — though this raises new risks.

How can you implement generative AI in the enterprise?

We’ve now touched on what generative AI can do. But how can you make it work reliably in your business. The difference between a pilot and full-scale deployment often comes down to systems, structure and governance as much as to models themselves. InfoWorld’s Matt Asay offers a deep dive into enterprise gen AI essentials, but here are some important points to keep in mind:

Choosing between API, open-source or custom fine-tuned models. One of the first major decisions for any enterprise project is: do you use a model via an API (e.g., from a vendor like OpenAI or Anthropic), deploy an open-source model internally, or build/fine-tune a custom model yourself? Each has trade-offs.

APIs offer speed and minimal setup, but may expose data, limit customization or accrue high cost — and will leave you at the mercy of your vendor. Open source allows internal control and may ease fine-tuning, but requires infrastructure, expertise, and support. Custom fine-tuning gives you the tightest alignment to your use-case, but lengthens time to value and increases risk.

Governance, data privacy and compliance. Deploying generative AI in an enterprise setting raises new governance, privacy and regulatory issues. For example: Who owns the data that’s ingested? How is proprietary data protected if you call a third-party API? What traceability exists for model outputs—a huge question for regulated industries? One useful framework is covered in “A GRC framework for securing generative AI” Data governance must adapt for the new era,  and new frameworks are evolving to help.

Human-in-the-loop review. Even the best models make mistakes and cannot simply be put on autopilot. You need a human-in-the-loop (HITL) process: real people need to review outputs, validate for bias, approve high-stakes content, and tune prompts or models based on feedback. Incorporating HITL checkpoints helps mitigate risk and improve overall quality.

Integration with existing systems and RAG pipelines. Retrieval-augmented generation, which we touched on earlier, connects foundation models into business workflows, systems, and enterprise data stores. RAG can bind LLMs to your organization’s internal knowledge bases, thereby reducing hallucinations (which we’ll discuss in a moment) and increasing the relevance of gen AI output.

What causes AI hallucinations?

Probably the biggest limitation of generative AI is what those in the industry call hallucinations, which is a perhaps misleading term for output that is, by the standards of humans who use it, false or incorrect.  

Every generative AI system, no matter how advanced, is built around prediction. Remember, a model doesn’t truly know facts—it looks at a series of tokens, then calculates, based on analysis of its underlying training data, what token is most likely to come next. This is what makes the output fluent and human-like, but if its prediction is wrong, that will be perceived as a hallucination.

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Because the model doesn’t distinguish between something that’s known to be true and something likely to follow on from the input text it’s been given, hallucinations are a direct side effect of the statistical process that powers generative AI. And don’t forget that we’re often pushing AI models to come up with answers to questions that we, who also have access to that data, can’t answer ourselves.

In text models, hallucinations might mean inventing quotes, fabricating references, or misrepresenting a technical process. In code or data analysis, it can produce syntactically correct but logically wrong results. Even RAG pipelines, which provide real data context to models, only reduce hallucination—they don’t eliminate it. Enterprises using generative AI need review layers, validation pipelines, and human oversight to prevent these failures from spreading into production systems.

What are some other problems with generative AI?

Generative AI has proven to be such a disruptive technology that’s stoking near-apocalyptic fears that it will result in a superintelligence that will enslave or destroy humanity. Meanwhile, in the present day, increasingly troubling reports of so-called AI psychosis are emerging, where people have mental health episodes triggered by the uncanny and sometimes sycophantic ways chatbots affirm whatever you talk to them about and try to keep the conversation going.

Compared to such existential questions, the following business-related problems may seem petty. But they’re real issues for enterprises considering investing in AI tools.

• Data leakage and regulatory risk. When a model is fine-tuned or prompted with sensitive information, that data may be memorized and unintentionally reproduced. Using third-party APIs without strict controls can expose proprietary or personally identifiable information (PII). Regulatory frameworks like GDPR and HIPAA require explicit governance around where training data resides and how inference results are stored.
• Prompt injection occurs when an attacker manipulates a model’s instructions—embedding hidden directives or malicious payloads in user input or external content the model reads. This can override safety rules, expose internal data, or execute unintended actions in agentic systems. Guardrails that sanitize inputs, restrict tool-calling permissions, and validate outputs are becoming essential.
• Copyright and content ownership. Many foundation models are trained on data scraped from the public internet, creating disputes over copyright and data provenance. Enterprises using generated output commercially need to confirm usage rights and review indemnity terms from vendors.
• Unrealistic productivity expectations. Finally, organizations sometimes expect generative AI to deliver instant productivity gains. The reality, it turns out, is more mixed. Enterprise adoption requires infrastructure, governance, retraining, and cultural change. The models accelerate work once properly integrated, but they don’t automatically replace human judgment or oversight.

The current generation of enterprise AI systems includes several layers of defense against these risks:

• Guardrails that constrain model behavior and filter unsafe outputs.
• Model validation frameworks that measure factual accuracy and consistency before deployment.
• Policy layers that enforce compliance rules, redact sensitive data, and log model actions.

These safeguards reduce—but don’t remove—the inherent uncertainty that defines generative AI.

GenAI: essential for the enterprise

Generative AI has evolved from a novelty into a core layer of enterprise technology. Foundation models and agentic systems now power automation, analytics, and creative workflows — but they remain fundamentally probabilistic tools. Their strength lies in scale and adaptability, not perfect understanding.

For organizations, success depends less on chasing model breakthroughs than on integrating these systems responsibly: building guardrails, maintaining oversight, and aligning them with real business needs. Used wisely, generative AI can amplify human capability rather than replace it.

What is cloud native? Cloud native defined

The term “cloud-native computing” encompasses the modern approach to building and running software applications that exploit the flexibility, scalability, and resilience of cloud computing. The phrase is a catch-all that encompasses not just the specific architecture choices and environments used to build applications for the public cloud, but also the software engineering techniques and philosophies used by cloud developers.

The Cloud Native Computing Foundation (CNCF) is an open source organization that hosts many important cloud-related projects and helps set the tone for the world of cloud development. The CNCF offers its own definition of cloud native:

Cloud native practices empower organizations to develop, build, and deploy workloads in computing environments (public, private, hybrid cloud) to meet their organizational needs at scale in a programmatic and repeatable manner. It is characterized by loosely coupled systems that interoperate in a manner that is secure, resilient, manageable, sustainable, and observable.

Cloud native technologies and architectures typically consist of some combination of containers, service meshes, multi-tenancy, microservices, immutable infrastructure, serverless, and declarative APIs — this list is not exhaustive.

This definition is a good start, but as cloud infrastructure becomes ubiquitous, the cloud native world is beginning to spread behind the core of this definition. We’ll explore that evolution as well, and look into the near future of cloud-native computing.

Cloud native architectural principles

Let’s start by exploring the pillars of cloud-native architecture. Many of these technologies and techniques were considered innovative and even revolutionary when they hit the market over the past few decades, but now have become widely accepted across the software development landscape.

Microservices. One of the huge cultural shifts that made cloud-native computing possible was the move from huge, monolithic applications to microservices: small, loosely coupled, and independently deployable components that work together to form a cloud-native application. These microservices can be scaled across cloud environments, though (as we’ll see in a moment) this makes systems more complex.

Containers and orchestration. In could-native architectures, individual microservices are executed inside containers — lightweight, portable virtual execution environments that can run on a variety of servers and cloud platforms. Containers insulate the developers from having to worry about the underlying machines on which their code will execute. That is, all they have to do is write to the container environment. 

Getting the containers to run properly and communicate with one another is where the complexity of cloud native computing starts to emerge. Initially, containers were created and managed by relatively simple platforms, the most common of which was Docker. But as cloud-native applications got more complex, container orchestration platforms that augmented Docker’s functionality emerged, such as Kubernetes, which allows you to deploy and manage multi-container applications at scale. Kubernetes is critical to cloud native computing as we know it — it’s worth noting that the CNCF was set up as a spinoff of the Linux Foundation on the same day that Kubernetes 1.0 was announced — and adhering to Kubernetes best practices is an important key to cloud native success. 

Open standards and APIs. The fact that containers and cloud platforms are largely defined by open standards and open source technologies is the secret sauce that makes all this modularity and orchestration possible, and standardized and documented APIs offer the means of communication between distributed components of a larger application. In theory, anyway, this standardization means that every component should be able to communicate with other components of an application without knowing about their inner workings, or about the inner workings of the various platform layers on which everything operates.

DevOps, agile methodologies, and infrastructure as code. Because cloud-native applications exist as a series of small, discrete units of functionality, cloud-native teams can build and update them using agile philosophies like DevOps, which promotes rapid, iterative CI/CD development. This enables teams to deliver business value more quickly and more reliably.

The virtualized nature of cloud environments also make them great candidates for infrastructure as code (IaC), a practice in which teams use tools like Terraform, Pulumi, and AWS CloudFormation, to manage infrastructure declaratively and version those declarations just like application code. IaC boosts automation, repeatability, and resilience across environments—all big advantages in the cloud world. IaC also goes hand-in-hand with the concept of immutable infrastructure—the idea that, once deployed, infastructure-level entities like virtual machines, containers, or network appliances don’t change, which makes them easier to manage and secure. IaC stores declarative configuration code in version control, which creates an audit log of any changes.

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How the cloud-native stack is expanding

As cloud-native development becomes the norm, the cloud-native ecosystem is expanding; the CNCF maintains a graphical representation of what it calls the  cloud native landscape that hammers home to expansive and bewildering variety of products, services, and open source projects that contribute to (and seek to profit from) to cloud-native computing. And there are a number of areas where new and developing tools are complicating the picture sketched out by the pillars we discussed above.   

An expanding Kubernetes ecosystem. Kubernetes is complex, and teams now rely on an entire ecosystem of projects to get the most out of it: Helm for packaging, ArgoCD for GitOps-style deployments, and Kustomize for configuration management. And just as Kubernetes augmented Docker for enterprise-scale deployments. Kubernetes itself has been augmented and expanded by service mesh offerings like Istio and Linkerd, which offer fine-grained traffic control and improved security

Observability needs. The complex and distributed world of cloud-native computing requires in-depth observability to ensure that developers and admins have a handle on what’s happening with their applications. Cloud-native observability uses distributed tracing and aggregated logs to provide deep insight into performance and reliability. Tools like Prometheus, Grafana, Jaeger, and OpenTelemetry support comprehensive, real-time observability across the stack.

Serverless computing.  Serverless computing, particularly in its function-as-a-service guise, offers to strip needed compute resources down to their bare minimum, with functions running on service provider clouds using exactly as much as they need and no more. Because these services can be exposed as endpoints via APIs, they are increasingly integrated into distributed applications, operating side-by-side with functionality provided by containerized microservices. Watch out, though: the big FaaS providers (Amazon, Microsoft, and Google) would love to lock you in to their ecosystems.  

FinOps. Cloud computing was initially billed as a way to cut costs — no need to pay for an in-house data center that you barely use — but in practice it replaces capex with opex, and sometimes you can run up truly shocking cloud service bills if you aren’t careful. Serverless computing is one way to cut down on those costs, but financial operations, or FinOps, is a more systematic discipline that aims to aligns engineering, finance, and product to optimize cloud spending. FinOps best practices make use of those observability tools to best determine what departments and applications are eating up resources.

How cloud-native architecture is adapting to AI workloads

Enterprises deploy larger AI models and make use of more and more real-time inference services. That’s putting demands on cloud-native systems and forcing them to adapt to remain scalable and reliable.

For instance, organizations are re-engineering cloud environments around GPU-accelerated clusters, low-latency networking, and predictable orchestration. These needs align with established cloud-native patterns: containers package AI services consistently, while Kubernetes provides resilient scheduling and horizontal scale for inference workloads that can spike without warning.

Kubernetes itself is changing to better support AI inference, adding hardware-aware scheduling for GPUs, model-specific autoscaling behavior, and deeper observability into inference pipelines. These enhancements make Kubernetes a more natural platform for serving generative AI workloads.

AI’s resource demands are amplifying traditional cloud-native challenges. Observability becomes more complex as inference paths span GPUs, CPUs, vector databases, and distributed storage. FinOps teams contend with cost volatility from training and inference bursts. And security teams must track new risks around model provenance, data access, and supply-chain integrity.

Application frameworks for building distributed cloud-native apps

Microsoft’s Aspire is one of the most visible examples of a shift towards application frameworks to simplify how teams build distributed systems. Opinionated frameworks like Aspire provide structure, observability, and integration out of the box so developer don’t need to stitch together containers, microservices, and orchestration tooling by hand.

Aspire in particular is a prescriptive framework for cloud-native applications, bundling containerized services, environment configuration, health checks, and observability into a unified development model. Aspire provides defaults for service-to-service communication, configuration, and deployment, along with a built-in dashboard for visibility across distributed components.

While Aspire was originally aligned with Microsoft’s .NET platform,Redmond now sees it as having a  polyglot future. This positions Aspire as part of a broader trend: frameworks that help teams build cloud-native, service-oriented systems without being locked into a single language ecosystem. Several other frameworks are gaining traction: Dapr provides a portable runtime that abstracts many of the plumbing tasks in cloud-native distributed applications, and Orleans offers an actor-model-based framework for large-scale systems in the .NET world, and Akka gives JVM teams a mature, reactive toolkit for elastic, resilient services.

Frameworks and tools in the expanding cloud-native ecosystem

While frameworks like Aspire simplify how developers compose and structure distributed applications, most cloud-native systems still depend on a broader ecosystem of platforms and operational tooling. This deeper layer is where much of the complexity—and innovation—of cloud-native computing lives, particularly as Kubernetes continues to serve as the industry’s control plane for modern infrastructure.

Kubernetes provides the core abstractions for deploying and orchestrating containerized workloads at scale. Managed distributions such as Google Kubernetes Engine (GKE), Amazon EKS, Azure AKS, and Red Hat OpenShift build on these primitives with security, lifecycle automation, and enterprise support. Platform vendors are increasingly automating cluster operations—upgrades, scaling, remediation—to reduce the operational burden on engineering teams.

Surrounding Kubernetes is a rapidly expanding ecosystem of complementary frameworks and tools. Service meshes like Istio and Linkerd provide fine-grained traffic management, policy enforcement, and mTLS-based security across microservices. GitOps platforms such as Argo CD and Flux bring declarative, version-controlled deployments to cloud-native environments. Meanwhile, projects like Crossplane turn Kubernetes into a universal control plane for cloud infrastructure, letting teams provision databases, queues, and storage through familiar Kubernetes APIs. These tools illustrate how cloud-native development now spans multiple layers: developer-focused application frameworks like Aspire at the top, and a powerful, evolving Kubernetes ecosystem underneath that keeps modern distributed applications running.

Advantages and challenges for cloud-native development

Cloud native has become so ubiquitous that its advantages are almost taken for granted at this point, but it’s worth reflecting on the beneficial shift the cloud native paradigm represents. Huge, monolithic codebases that saw updates rolled out once every couple of years have been replaced by microservice-based applications that can be improved continuously. Cloud-based deployments, when managed correctly, make better use of compute resources and allow companies to offer their products as SaaS or PaaS services. 

But cloud-native deployments come with a number of challenges, too:

• Complexity and operational overhead: You’ll have noticed by now that many of the cloud-native tools we’ve discussed, like service meshes and observability tools, are needed to deal with the complexity of cloud-native applications and environments. Individual microservices are deceptively simple, but coordinating them all in a distributed environment is a big lift.
• Security: More services executing on more machines, communicating by open APIs, all adds up to a bigger attack surface for hackers. Containers and APIs each have their own special security needs, and a policy engine can be an important tool for imposing a security baseline on a sprawling cloud-native app. DevSecOps, which adds security to DevOps, has become an important cloud-native development practice to try to close these gaps.
• Vendor lock-in: This may come as a surprise, since cloud-native is based on open standards and open source. But there are differences in how the big cloud and serverless providers works, and once you’ve written code with one provider in mind, it can be hard to migrate elsewhere.
• A persistent skills gap: Cloud-native computing and development may have years under its belt at this point, but the number of developers who are truly skilled in this arena is a smaller portion of the workforce than you’d think. Companies face difficult choices in bridging this skills gap, whether that’s bidding up salaries, working to upskill current workers, or allowing remote work so they can cast a wide net. 

Cloud native in the real world

Cloud native computing is often associated with giants like Netflix, Spotify, Uber, and AirBNB, where many of its technologies were pioneered in the early ’10s. But the CNCF’s Case Studies page provides an in-depth look at how cloud native technologies are helping companies. Examples include the following:

• A UK-based payment technology company that can switch between data centers and clouds with zero downtime
• A software company whose product collects and analyzes data from IoT devices — and can scale up as the number of gadgets grows
• A Czech web service company that managed to improve performance while reducing costs by migrating to the cloud

Cloud-native infrastructure’s capability to quickly scale up to large workloads also make it an attractive platform for developing AI/ML applications: another one of those CNCF case studies looks at how IBM uses Kubernetes to train its Watsonx assistant. The big three providers are putting a lot of effort into pitching their platforms as the place for you to develop your own generative AI tools, with offerings like Azure AI Foundry,Google Firebase Studio, and Amazon Bedrock. It seems clear that cloud native technology is ready for what comes next.

Learn more about related cloud-native technologies:

• Platform-as-a-service (PaaS) explained
• What is cloud computing
• Multicloud explained
• Agile methodology explained
• Agile development best practices
• Devops explained
• Devops best practices
• Microservices explained
• Microservices tutorial
• Docker and Linux containers explained
• Kubernetes tutorial
• CI/CD (continuous integration and continuous delivery) explained
• CI/CD best practices

Cloud computing continues to be the platform of choice for large applications and a driver of innovation in enterprise technology. Gartner forecasts public cloud spending alone to the public cloud services market alone will reach $1.42 trillion in current U.S. dollars, driven by AI workloads and enterprise modernization.

Driving this growth are the rise of AI and machine learning on the cloud, adoption of edge computing, the maturation of serverless computing, the emergence of multicloud strategies, improved security and privacy, and more sustainable cloud practices.

What is cloud computing?

While often used broadly, the term cloud computing is defined as an abstraction of compute, storage, and network infrastructure assembled as a platform on which applications and systems are deployed quickly and scaled on the fly.

Most cloud customers consume public cloud computing services over the internet, which are hosted in large, remote data centers maintained by cloud providers. The most common type of cloud computing, SaaS (software as service), delivers prebuilt applications to the browsers of customers who pay per seat or by usage, exemplified by such popular apps as Salesforce, Google Docs, or Microsoft Teams.

5 top trends in cloud computing

1. Agentic cloud ecosystems: The shift from AI as a tool to AI as an autonomous operator within cloud environments.
2. Sovereign and localized clouds: Meeting strict national data residency and digital sovereignty laws.
3. Specialized AI hardware access: Navigating the GPU capacity crunch through reserved instances and boutique AI clouds.
4. Integrated greenOps: Merging cost optimization with mandatory carbon-footprint reporting.
5. Industry-specific walled gardens: The maturation of vertical clouds into highly regulated, precompliant environments for finance and healthcare.

Next in line is IaaS (infrastructure as a service), which offers vast, virtualized compute, storage, and network infrastructure upon which customers build their own applications, often with the aid of providers’ API-accessible services.

When people refer to the “the cloud” today, they most often mean the big IaaS providers: AWS (Amazon Web Services), Google Cloud Platform, or Microsoft Azure. All three have become ecosystems of services that go way beyond infrastructure and include developer tools, serverless computing, machine learning services and APIs, data warehouses, and thousands of other services. With both SaaS and IaaS, a key benefit is agility. Customers gain new capabilities almost instantly without the capital investment in hardware or software on-premises — and they can instantly scale the cloud resources they consume up or down as needed.

According to Foundry’s Cloud Computing Study, 2025, enterprises are moving to the cloud to improve security and/or governance, increase scalability​, accelerate adoption of artificial intelligence and machine learning and other new technologies, replace on-premises legacy technology, ​improve employee productivity, and ensure disaster recovery and business continuity.

Hyperscalers now dominate cloud services

The largest cloud service providers are often described as hyperscalers, due to their capability to provide large-scale data centers across the globe. Hyperscalers typically offer a wide range of cloud services, including IaaS, PaaS, SaaS, and more.

As mentioned above, notable hyperscalers include Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure. They offer the following capabilities.

• Scalability: Hyperscalers can handle massive workloads and scale resources up or down quickly.
• Cost-effectiveness: Hyperscalers often offer competitive pricing and economies of scale.
• Global reach: Hyperscalers operate data centers around the world, providing low-latency access to customers in different regions.
• Innovation: Hyperscalers are at the forefront of cloud innovation, offering new services and features.

Challenges of working with hyperscalers

• Vendor lock-in: Relying heavily on a single hyperscaler can create vendor lock-in, making it difficult to switch to another provider and charging large egress fees if you do move.
• Complexity: Hyperscalers offer a vast array of services, which can be overwhelming for some customers.
• Security concerns: Because hyperscalers handle sensitive data, security is a major concern.

AI, Agents, and the Sovereign Cloud

The AI-enabled enterprise has moved beyond simple chatbots. The focus has shifted to agentic workflows — autonomous systems that reside in the cloud and possess the authority to execute business processes, manage cloud spend, and self-patch security vulnerabilities without human intervention.

The shift to agentic infrastructure

Cloud providers are no longer just selling compute. They are selling inference-as-a-service. Modern cloud budgets are now dominated by the high cost of specialized GPU clusters (such as Nvidia’s Blackwell architecture). This has led to the rise of boutique AI clouds that compete with hyperscalers by offering bare-metal access to the latest silicon specifically for model training and fine-tuning.

Data sovereignty and private AI

A major shift in late 2025 is the move away from public AI models for sensitive data. Organizations are increasingly using retrieval-augmented generation (RAG) within walled garden environments. This ensures that a company’s proprietary data never leaves their specific cloud instance to train a provider’s base model.

Furthermore, sovereign AI has become a requirement for global operations. Governments now demand that the AI models processing their citizens’ data be hosted on infrastructure that is owned, operated, and governed within their own borders.

The challenges of ghost AI

Just as shadow IT plagued the 2010s, ghost AI—unauthorized AI agents running on corporate cloud accounts — has become a primary security risk. Managing these autonomous entities requires a new layer of AI governance, where the cloud provider automatically audits the intent and permissions of every running agent to prevent runaway costs or data leaks.

Cloud computing definitions

In 2011, NIST posted a PDF that divided cloud computing into three “service models” — SaaS, IaaS, and PaaS (platform as a service) — the latter being a controlled environment within which customers develop and run applications. These three categories have largely stood the test of time, although most PaaS solutions now are made available as services within IaaS ecosystems rather than as dedicated PaaS clouds.

Two evolutionary trends stand out since NIST’s threefold definition. One is the long and growing list of subcategories within SaaS, IaaS, and PaaS, some of which blur the lines between categories. The other is the explosion of API-accessible services available in the cloud, particularly within IaaS ecosystems. The cloud has become a crucible of innovation where many emerging technologies appear first as services, a big attraction for business customers who understand the potential competitive advantages of early adoption.

SaaS (software as a service) definition

This type of cloud computing delivers applications over the internet, typically with a browser-based user interface. Today, most software companies offer their wares via SaaS — if not exclusively, then at least as an option.

The most popular SaaS applications for business are Google’s G Suite and Microsoft’s Office 365. Most enterprise applications, including giant ERP suites from Oracle and SAP, come in both SaaS and on-premises versions. SaaS applications typically offer extensive configuration options as well as development environments that enable customers to code their own modifications and additions. They also enable data integration with on-prem applications.

IaaS (infrastructure as a service) definition

At a basic level, IaaS cloud providers offer virtualized compute, storage, and networking over the internet on a pay-per-use basis. Think of it as a data center maintained by someone else, remotely, but with a software layer that virtualizes all those resources and automates customers’ ability to allocate them with little trouble.

But that’s just the basics. The full array of services offered by the major public IaaS providers is staggering: highly scalable databases, virtual private networks, big data analytics, AI and machine learning services, application platforms, developer tools, devops tools, and so on. Amazon Web Services was the first IaaS provider and remains the leader, followed by Microsoft Azure, Google Cloud Platform, IBM Cloud, and Oracle Cloud.

PaaS (platform as a service) definition

PaaS provides sets of services and workflows that specifically target developers, who can use shared tools, processes, and APIs to accelerate the development, testing, and deployment of applications. Salesforce’s Heroku and Salesforce Platform (formerly Force.com) are popular public cloud PaaS offerings; Cloud Foundry and Red Hat’s OpenShift can be deployed on premises or accessed through the major public clouds. For enterprises, PaaS can ensure that developers have ready access to resources, follow certain processes, and use only a specific array of services, while operators maintain the underlying infrastructure.

FaaS (function as a service) definition

FaaS, the original and most basic version of serverless computing, adds another layer of abstraction to PaaS, so that developers are insulated from everything in the stack below their code. Instead of futzing with virtual servers, containers, and application runtimes, developers upload narrowly functional blocks of code, and set them to be triggered by a certain event (such as a form submission or uploaded file). All of the major clouds offer FaaS on top of IaaS: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM Cloud Functions. A special benefit of FaaS applications is that they consume no IaaS resources until an event occurs, reducing pay-per-use fees.

Private cloud definition

A private cloud downsizes the technologies used to run IaaS public clouds into software that can be deployed and operated in a customer’s data center. As with a public cloud, internal customers can provision their own virtual resources to build, test, and run applications, with metering to charge back departments for resource consumption. For administrators, the private cloud amounts to the ultimate in data center automation, minimizing manual provisioning and management.

VMware remains a force in the private cloud software market, but the acquisition by Broadcom has created confusion and raised concerns among some customers about potential changes in pricing, licensing, and support. This could lead some organizations to explore alternative solutions.

OpenStack continues to be a popular open-source choice for building private clouds. It offers a flexible and customizable platform that can be tailored to specific needs. However, OpenStack can be complex to deploy and manage, and it may require significant expertise to maintain.

Kubernetes, a container orchestration platform that has gained significant traction in recent years, is often used in conjunction with other technologies like OpenStack to build cloud-native applications. Red Hat OpenShift is a comprehensive cloud platform based on Kubernetes that provides a managed experience for deploying and managing container-based, applications.

Many cloud providers offer their own cloud-native platforms and tools, such as AWS Outposts, Azure Stack, and Google Cloud Anthos.

Common factors to consider when evaluating private cloud platforms include the following:

1. Pricing: The initial cost of deployment and ongoing maintenance costs.
2. Complexity: The level of technical expertise needed to manage the platform.
3. Flexibility: The ability to customize the platform to meet specific needs.
4. Vendor lock-in: The degree to which the organization is tied to a particular vendor.
5. Security: The security features and capabilities of the platform.
6. Scalability: The capability to expand the platform to meet future needs.

Hybrid cloud definition

A hybrid cloud is the integration of a private cloud with a public cloud. At its most developed, the hybrid cloud involves creating parallel environments in which applications can move easily between private and public clouds. In other instances, databases may stay in the customer data center and integrate with public cloud applications — or virtualized data center workloads may be replicated to the cloud during times of peak demand. The types of integrations between private and public clouds vary widely, but they must be extensive to earn a hybrid cloud designation.

Public APIs (application programming interfaces) definition

Just as SaaS delivers applications to users over the internet, public APIs offer developers application functionality that can be accessed programmatically. For example, in building web applications, developers often tap into the Google Maps API to provide driving directions; to integrate with social media, developers may call upon APIs maintained by Twitter, Facebook, or LinkedIn. Twilio has built a successful business delivering telephony and messaging services via public APIs. Ultimately, any business can provision its own public APIs to enable customers to consume data or access application functionality.

iPaaS (integration platform as a service) definition

Data integration is a key issue for any sizeable company, but particularly for those that adopt SaaS at scale. iPaaS providers typically offer prebuilt connectors for sharing data among popular SaaS applications and on-premises enterprise applications, though providers may focus more or less on business-to-business and e-commerce integrations, cloud integrations, or traditional SOA-style integrations. iPaaS offerings in the cloud from such providers as Dell Boomi, Informatica, MuleSoft, and SnapLogic also let users implement data mapping, transformations, and workflows as part of the integration-building process.

IDaaS (identity as a service) definition

The most difficult security issue related to cloud computing is managing user identity and its associated rights and permissions across data centers and pubic cloud sites. IDaaS providers maintain cloud-based user profiles that authenticate users and enable access to resources or applications based on security policies, user groups, and individual privileges. The ability to integrate with various directory services (Active Directory, LDAP, etc.) and provide single sign-on across business-oriented SaaS applications is essential.

Leaders in IDaaS include Microsoft, IBM, Google, Oracle, Okta, Capgemini, Okta, Junio Corporation, OneLogin, and JumpCloud.  

Collaboration platforms

Collaboration solutions such as Slack and Microsoft Teams have become vital messaging platforms that enable groups to communicate and work together effectively. Basically, these solutions are relatively simple SaaS applications that support chat-style messaging along with file sharing and audio or video communication. Most offer APIs to facilitate integrations with other systems and enable third-party developers to create and share add-ins that augment functionality.

Vertical clouds

Key providers in such industries as financial services, healthcare, retail, life sciences, and manufacturing provide PaaS clouds to enable customers to build vertical applications that tap into industry-specific, API-accessible services. Vertical clouds can dramatically reduce the time to market for vertical applications and accelerate domain-specific B2B integrations. Most vertical clouds are built with the intent of nurturing partner ecosystems.

Other cloud computing considerations

The most widely accepted definition of cloud computing means that you run your workloads on someone else’s servers, but this is not the same as outsourcing. Virtual cloud resources and even SaaS applications must be configured and maintained by the customer. Consider these factors when planning a cloud initiative.

Cloud computing security considerations

Objections to the public cloud generally begin with cloud security, although the major public clouds have proven themselves much less susceptible to attack than the average enterprise data center.

Of greater concern is the integration of security policy and identity management between customers and public cloud providers. In addition, government regulation may forbid customers from allowing sensitive data off-premises. Other concerns include the risk of outages and the long-term operational costs of public cloud services.

Multicloud management considerations

To enhance their operational efficiency, reduce costs, and improve security, many companies are increasingly turning to multicloud strategies. By distributing workloads across multiple cloud providers, organizations can avoid vendor lock-in, optimize costs, and leverage the best-of-breed services offered by different providers.

This multicloud approach also improves performance and reliability by minimizing downtime and optimizing latency. Additionally, multicloud strategies strengthen security by diversifying the attack surface and facilitating compliance with industry regulations. Finally, by replicating critical workloads across multiple regions and providers, companies can establish robust disaster recovery and business continuity plans, ensuring minimal disruption in the event of catastrophic failures.

The bar to qualify as a multicloud adopter is low: A customer just needs to use more than one public cloud service. However, depending on the number and variety of cloud services involved, managing multiple clouds can become complex from both a cost optimization and a technology perspective.

In some cases, customers subscribe to multiple cloud services simply to avoid dependence on a single provider. A more sophisticated approach is to select public clouds based on the unique services they offer and, in some cases, integrate them. For example, developers might want to use Google’s Vertex AI Studio on Google Cloud Platform to build AI-driven applications, but prefer Jenkins hosted on the CloudBees platform for continuous integration.

To control costs and reduce management overhead, some customers opt for cloud management platforms (CMPs) and/or cloud service brokers (CSBs), which let you manage multiple clouds as if they were one cloud. The problem is that these solutions tend to limit customers to such common-denominator services as storage and compute, ignoring the panoply of services that make each cloud unique.

Edge computing considerations

You often see edge computing incorrectly described as an alternative to cloud computing. Edge computing is about moving compute to local devices in a highly distributed system, typically as a layer around a cloud computing core. There is typically a cloud involved to orchestrate all of the devices and take in their data, then analyze it or otherwise act on it. 

To the cloud and back – why repatriation is real

While public cloud offers scalability and flexibility, some enterprises are opting to return to on-premises infrastructure due to rising costs, data security concerns, performance issues, vendor lock-in, and regulatory compliance challenges. While the public cloud offers scalability and flexibility, on-premises infrastructure provides greater control, customization, and potential cost savings in certain scenarios leading some technology decision-makers to consider repatriation. However, a hybrid cloud approach, combining public and private cloud, often offers the best balance of benefits.

More specific reasons to repatriate including the following:

• Unanticipated costs, such as data transfer fees, storage charges, and egress fees, can quickly escalate, especially for large-scale cloud deployments.  
• Inaccurate resource provisioning or underutilization can lead to higher-than-expected costs.
• Stricter data privacy regulations require organizations to store and process data within specific geographic boundaries.  
• For highly sensitive data, companies may prefer to maintain greater control over security measures and access permissions. 
• On-premises infrastructure can offer lower latency, particularly for applications requiring real-time processing or high-performance computing.  
• Overreliance on a single cloud provider can limit flexibility and increase costs. Repatriation allows organizations to diversify their infrastructure and reduce vendor dependency.  
• Industries with stringent compliance requirements may find it easier to meet standards with on-premises infrastructure.  
• On-premises environments offer greater control over hardware, software, and network configurations, allowing for customized solutions.  

Benefits of cloud computing

The cloud’s main appeal is to reduce the time to market of applications that need to scale dynamically. Increasingly, however, developers are drawn to the cloud by the abundance of advanced new services that can be incorporated into applications, from machine learning to internet of things (IoT) connectivity.

Although businesses sometimes migrate legacy applications to the cloud to reduce data center resource requirements, the real benefits accrue to new applications that take advantage of cloud services and “cloud native” attributes. The latter include microservices architecture, Linux containers to enhance application portability, and container management solutions such as Kubernetes that orchestrate container-based services. Cloud-native approaches and solutions can be part of either public or private clouds and help enable highly efficient devops workflows.

Cloud computing, be it public or private or hybrid or multicloud, has become the platform of choice for large applications, particularly customer-facing ones that need to change frequently or scale dynamically. More significantly, the major public clouds now lead the way in enterprise technology development, debuting new advances before they appear anywhere else. Workload by workload, enterprises are opting for the cloud, where an endless parade of exciting new technologies invite innovative use.

SaaS has its roots in the ASP (application service provider) trend of the early 2000s, when providers would run applications for business customers in the provider’s data center, with dedicated instances for each customer. The ASP model was a spectacular failure because it quickly became impossible for providers to maintain so many separate instances, particularly as customers demanded customizations and updates.

Salesforce is widely considered the first company to launch a highly successful SaaS application using multitenancy — a defining characteristic of the SaaS model. Rather than each Salesforce customer getting its own application instance, customers who subscribe to the company’s salesforce automation software share a single, large, dynamically scaled instance of an application (like tenants sharing an apartment building), while storing their data in separate, secure repositories on the SaaS provider’s servers. Fixes can be rolled out behind the scenes with zero downtime and customers can receive UX or functionality improvements as they become available.

Meta has unveiled Muse Spark 1.1, saying the frontier AI model rivals leading LLMs on coding, computer use, and agentic AI benchmarks while undercutting OpenAI and Anthropic on API pricing, potentially lowering the cost of deploying AI agents in enterprises.

Meta unveiled Muse Spark 1.1 on Thursday, pairing frontier-model performance with aggressive pricing in a move that analysts say could pressure rivals such as OpenAI and Anthropic and reshape enterprise AI procurement decisions.

Meta is betting that lower inference costs can help it gain ground in the enterprise AI market with the launch of Muse Spark 1.1, a frontier model that rivals top competitors on key benchmarks while costing a fraction as much to deploy.

The latest model, which was teased last week, matched or was competitive with leading models, such as Claude Opus 4.8, Gemini 3.1 Pro, and GPT 5.5, across several agentic AI, coding, and computer-use benchmarks, including SWE-bench Verified, Terminal-bench, BrowseComp, SpreadsheetBench, and OSWorld, Meta wrote in a blog post.

Muse Spark 1.1, which is currently in public preview and available via the Meta Model API, will cost $1.25 per million input tokens and $4.25 per million output tokens, the company noted.

By comparison, OpenAI charges $5 per million input tokens and $30 per million output tokens for GPT-5.5, while Anthropic charges $5 and $25, respectively, for Claude Opus 4.8. Google’s Gemini 3.1 Pro, on the other hand, is priced at $2 per million input tokens and $12 per million output tokens.

Lower prices may open doors, not close deals

That sheer difference in API pricing, according to Pareekh Jain, principal analyst at Pareekh Consulting, is enough to attract CIOs’ attention, at least for pilots, at a time when enterprises are trying to scale agentic deployments: “Pricing matters because inference costs increase rapidly when thousands of agents are working continuously.”

“Output tokens are often the largest model expense in coding, customer service, and process automation agents. Muse Spark’s output price is about 86% below GPT-5.5 and more than 90% below Claude Opus 4.8,” Jain said.

However, Muskan Bandta, cloud associate at FinOps services providing firm ZopDev, pointed out that the price is not a guarantee of adoption, despite the fact that most enterprises are likely to deploy the Muse Spark 1.1 for new projects.

“Cost becomes the primary differentiator only once the model is judged good enough. Developers don’t pick the cheapest model; they pick the cheapest model that clears their quality bar. So, price is the reason people show up, capability is the reason they stay,” Bandta said.

Similarly, CIOs are also likely to put more emphasis on the model’s security, data protection, uptime, audit trails, regional availability, support, and predictable behavior, rather than just the price, Jain said.

That distinction, according to Bandta, reflects a familiar pattern in enterprise technology buying: “This is the same lesson we saw in the cloud, where the cheapest provider on paper rarely won the biggest enterprise share. Price is one input in the total cost of ownership that includes risk, control, and switching cost, not the whole decision.”

Even so, the lower pricing could still shift the balance of power in enterprise procurement, Jain said: “This could help CIOs negotiate larger volume discounts, committed-use agreements, and better pricing from OpenAI, Anthropic, and cloud providers. It also strengthens the case for multi-model procurement rather than depending on one vendor.”

“Companies that do not even adopt Muse Spark can also use its pricing as evidence that frontier-level inference is becoming cheaper,” Jain added.

Meta’s pricing could reshape competition between rivals

Analysts pointed out that Meta’s new model could intensify competition in the frontier model market by forcing rivals to compete on inference economics and model sizes.

“It’s a real shot across the bow, and I’d expect OpenAI and Anthropic to respond on two fronts. Some of it will be price, cheaper tiers, and better cached and batch rates, because Meta has just reset what the market thinks a frontier token should cost,” Bandta said.

“But the incumbents won’t win the race with lower-priced offerings and more flexible pricing models. I expect them to lean harder into the things price can’t buy, governance, security, reliability, and enterprise support, to justify premium pricing,” Bandta added, likening the shift to an “early innings” of a price war that the industry saw with the expansion of cloud.

“The cloud infrastructure price war showed that while prices fell over time, vendors ultimately differentiated themselves through platform capabilities rather than cost alone,” Bandta further added.

In contrast, Amit Jena, head of AI at IT consulting firm Kanerika, pointed out that a cloud-infrastructure-style pricing war was unlikely: “Frontier models are capital-intensive; margins are already thin. Vendors can’t sustain aggressive repricing without sacrificing quality.”

Rather, Jena sees Meta increasing prices soon after launch: “History suggests what happens next — aggressive entry pricing, then repricing once market share solidifies. See Meta’s advertising platform and cloud pricing evolution across the industry. If that pattern repeats, pricing could rise 30–50% in 18–24 months.”

For now, Meta is offering developers $20 in free API credits to experiment with Muse Spark 1.1.

The article originally appeared on InfoWorld.

Meta has unveiled Muse Spark 1.1, saying the frontier AI model rivals leading LLMs on coding, computer use, and agentic AI benchmarks while undercutting OpenAI and Anthropic on API pricing, potentially lowering the cost of deploying AI agents in enterprises.

Meta unveiled Muse Spark 1.1 on Thursday, pairing frontier-model performance with aggressive pricing in a move that analysts say could pressure rivals such as OpenAI and Anthropic and reshape enterprise AI procurement decisions.

Meta is betting that lower inference costs can help it gain ground in the enterprise AI market with the launch of Muse Spark 1.1, a frontier model that rivals top competitors on key benchmarks while costing a fraction as much to deploy.

The latest model, which was teased last week, matched or was competitive with leading models, such as Claude Opus 4.8, Gemini 3.1 Pro, and GPT 5.5, across several agentic AI, coding, and computer-use benchmarks, including SWE-bench Verified, Terminal-bench, BrowseComp, SpreadsheetBench, and OSWorld, Meta wrote in a blog post.

Muse Spark 1.1, which is currently in public preview and available via the Meta Model API, will cost $1.25 per million input tokens and $4.25 per million output tokens, the company noted.

By comparison, OpenAI charges $5 per million input tokens and $30 per million output tokens for GPT-5.5, while Anthropic charges $5 and $25, respectively, for Claude Opus 4.8. Google’s Gemini 3.1 Pro, on the other hand, is priced at $2 per million input tokens and $12 per million output tokens.

Lower prices may open doors, not close deals

That sheer difference in API pricing, according to Pareekh Jain, principal analyst at Pareekh Consulting, is enough to attract CIOs’ attention, at least for pilots, at a time when enterprises are trying to scale agentic deployments: “Pricing matters because inference costs increase rapidly when thousands of agents are working continuously.”

“Output tokens are often the largest model expense in coding, customer service, and process automation agents. Muse Spark’s output price is about 86% below GPT-5.5 and more than 90% below Claude Opus 4.8,” Jain said.

However, Muskan Bandta, cloud associate at FinOps services providing firm ZopDev, pointed out that the price is not a guarantee of adoption, despite the fact that most enterprises are likely to deploy the Muse Spark 1.1 for new projects.

“Cost becomes the primary differentiator only once the model is judged good enough. Developers don’t pick the cheapest model; they pick the cheapest model that clears their quality bar. So, price is the reason people show up, capability is the reason they stay,” Bandta said.

Similarly, CIOs are also likely to put more emphasis on the model’s security, data protection, uptime, audit trails, regional availability, support, and predictable behavior, rather than just the price, Jain said.

That distinction, according to Bandta, reflects a familiar pattern in enterprise technology buying: “This is the same lesson we saw in the cloud, where the cheapest provider on paper rarely won the biggest enterprise share. Price is one input in the total cost of ownership that includes risk, control, and switching cost, not the whole decision.”

Even so, the lower pricing could still shift the balance of power in enterprise procurement, Jain said: “This could help CIOs negotiate larger volume discounts, committed-use agreements, and better pricing from OpenAI, Anthropic, and cloud providers. It also strengthens the case for multi-model procurement rather than depending on one vendor.”

“Companies that do not even adopt Muse Spark can also use its pricing as evidence that frontier-level inference is becoming cheaper,” Jain added.

Meta’s pricing could reshape competition between rivals

Analysts pointed out that Meta’s new model could intensify competition in the frontier model market by forcing rivals to compete on inference economics and model sizes.

“It’s a real shot across the bow, and I’d expect OpenAI and Anthropic to respond on two fronts. Some of it will be price, cheaper tiers, and better cached and batch rates, because Meta has just reset what the market thinks a frontier token should cost,” Bandta said.

“But the incumbents won’t win the race with lower-priced offerings and more flexible pricing models. I expect them to lean harder into the things price can’t buy, governance, security, reliability, and enterprise support, to justify premium pricing,” Bandta added, likening the shift to an “early innings” of a price war that the industry saw with the expansion of cloud.

“The cloud infrastructure price war showed that while prices fell over time, vendors ultimately differentiated themselves through platform capabilities rather than cost alone,” Bandta further added.

In contrast, Amit Jena, head of AI at IT consulting firm Kanerika, pointed out that a cloud-infrastructure-style pricing war was unlikely: “Frontier models are capital-intensive; margins are already thin. Vendors can’t sustain aggressive repricing without sacrificing quality.”

Rather, Jena sees Meta increasing prices soon after launch: “History suggests what happens next — aggressive entry pricing, then repricing once market share solidifies. See Meta’s advertising platform and cloud pricing evolution across the industry. If that pattern repeats, pricing could rise 30–50% in 18–24 months.”

For now, Meta is offering developers $20 in free API credits to experiment with Muse Spark 1.1.

OpenAI is sharpening its enterprise AI strategy with the launch of ChatGPT Work, a new agentic platform designed to automate workplace tasks, alongside the broader rollout of its GPT-5.6 models, which the company says deliver stronger performance at lower operating costs.

According to the company, ChatGPT Work can operate across applications and files, execute long-running tasks, coordinate multiple tools, and produce business documents, presentations, spreadsheets, and websites, allowing employees to delegate more complex workflows rather than interact through individual prompts.

GPT- 5.6 models, generally available weeks after a limited preview following US government restrictions on their broader rollout due to concerns about advanced cybersecurity and biology capabilities, can deliver stronger performance across coding, enterprise knowledge work, cybersecurity, and scientific research while lowering inference costs and token consumption, OpenAI said.

The launch marks a shift in OpenAI’s enterprise strategy. Rather than emphasizing benchmark leadership alone, the company is pitching GPT-5.6 around performance per dollar, arguing that enterprises deploying AI at scale increasingly care as much about operating costs as raw model capability.

“We trained GPT-5.6 to get more useful work from every token,” OpenAI said in a statement. “The result is stronger performance per dollar: more successful work for the same spend, or comparable results at a lower total cost.”

The models are now generally available through ChatGPT, Codex, and the OpenAI API. OpenAI has priced Sol at $5 per million input tokens and $30 per million output tokens, while Terra and Luna provide progressively lower-cost options for organizations scaling AI deployments, the statement added.

Enterprise AI shifts from experimentation to economics

ChatGPT Work combines GPT-5.6 with enterprise integrations and agentic capabilities that allow users to perform multi-step tasks across connected business applications instead of interacting with AI through isolated prompts. OpenAI said the platform is designed to help organizations automate knowledge work while maintaining enterprise-grade governance and security.

The launch comes as enterprises move beyond AI experimentation and begin deploying models across production workloads, making inference costs a growing concern for CIOs.

“The AI wave has brought productivity gains, but rising token consumption has also created bill shocks for enterprises,” said Neil Shah, vice president for research and partner at Counterpoint Research. “This is forcing organizations to adopt different models for different workloads, making performance per dollar the key metric.”

Faisal Kawoosa, co-founder and chief analyst at Techarc, said enterprises are now evaluating AI investments more pragmatically.

“The exploratory stage of AI is over,” he said. “Organizations can derive value from AI today, but performance per dollar will determine whether it becomes part of everyday business operations or remains an ad hoc tool.”

Tiered models for different workloads

GPT-5.6 Sol is OpenAI’s flagship model for complex reasoning, Terra targets mainstream enterprise applications, and Luna is designed for lower-cost, high-volume deployments.

According to OpenAI, GPT-5.6 Sol scored 53.6 on Agents’ Last Exam, a benchmark for long-running professional workflows, outperforming competing frontier models while requiring significantly lower compute costs.

The company also introduced two new reasoning modes. The max mode allocates additional compute for complex problems, while ultra coordinates four AI agents in parallel to accelerate demanding workflows.

“Ultra goes further by coordinating four agents in parallel by default, trading higher token use for stronger results and faster time-to-result on demanding tasks,” the statement added.

Shah said the architecture reflects how enterprises are increasingly orchestrating multiple AI models.

“GPT-5.6 gives enterprise architects flexibility to route workloads from Luna to Sol depending on whether they require automation, logic, or complex reasoning,” he said.

Kawoosa added that the tiered approach aligns with how enterprise software has traditionally been consumed.

“It gives enterprises of different sizes the flexibility to optimize technology consumption according to their requirements,” he said.

Coding, productivity, and security gains

OpenAI said GPT-5.6 Sol achieved a score of 80 on the Artificial Analysis Coding Agent Index while consuming fewer than half the output tokens of competing models. It also reported state-of-the-art results on Terminal-Bench 2.1 and DeepSWE, benchmarks that measure real-world software engineering tasks.

The company said the models also improve enterprise productivity through stronger document generation capabilities and integrations with Microsoft 365, Google Drive, Slack, and Notion.

On cybersecurity, GPT-5.6 Sol scored 73.5% on ExploitBench, up from 47.9% for GPT-5.5, and nearly doubled its predecessor’s performance on ExploitGym.

“GPT-5.6 supports important defensive tasks such as secure code review, patching, threat modeling, and blue teaming,” OpenAI said.

Security remains an enterprise focus

OpenAI said GPT-5.6 incorporates its “most robust safeguards to date,” combining model-level protections with real-time monitoring and extensive safety testing, including approximately 700,000 GPU hours of automated red-team evaluations.

Shah said layered guardrails and monitoring could become an important differentiator for enterprise deployments.

Kawoosa, however, said CIOs will continue demanding greater transparency before fully trusting frontier AI systems.

“Competition among LLM providers will continue, with vendors constantly testing and challenging each other’s guardrails,” he said.

The article originally appeared on InfoWorld.

OpenAI is sharpening its enterprise AI strategy with the launch of ChatGPT Work, a new agentic platform designed to automate workplace tasks, alongside the broader rollout of its GPT-5.6 models, which the company says deliver stronger performance at lower operating costs.

According to the company, ChatGPT Work can operate across applications and files, execute long-running tasks, coordinate multiple tools, and produce business documents, presentations, spreadsheets, and websites, allowing employees to delegate more complex workflows rather than interact through individual prompts.

GPT- 5.6 models, generally available weeks after a limited preview following US government restrictions on their broader rollout due to concerns about advanced cybersecurity and biology capabilities, can deliver stronger performance across coding, enterprise knowledge work, cybersecurity, and scientific research while lowering inference costs and token consumption, OpenAI said.

The launch marks a shift in OpenAI’s enterprise strategy. Rather than emphasizing benchmark leadership alone, the company is pitching GPT-5.6 around performance per dollar, arguing that enterprises deploying AI at scale increasingly care as much about operating costs as raw model capability.

“We trained GPT-5.6 to get more useful work from every token,” OpenAI said in a statement. “The result is stronger performance per dollar: more successful work for the same spend, or comparable results at a lower total cost.”

The models are now generally available through ChatGPT, Codex, and the OpenAI API. OpenAI has priced Sol at $5 per million input tokens and $30 per million output tokens, while Terra and Luna provide progressively lower-cost options for organizations scaling AI deployments, the statement added.

Enterprise AI shifts from experimentation to economics

ChatGPT Work combines GPT-5.6 with enterprise integrations and agentic capabilities that allow users to perform multi-step tasks across connected business applications instead of interacting with AI through isolated prompts. OpenAI said the platform is designed to help organizations automate knowledge work while maintaining enterprise-grade governance and security.

The launch comes as enterprises move beyond AI experimentation and begin deploying models across production workloads, making inference costs a growing concern for CIOs.

“The AI wave has brought productivity gains, but rising token consumption has also created bill shocks for enterprises,” said Neil Shah, vice president for research and partner at Counterpoint Research. “This is forcing organizations to adopt different models for different workloads, making performance per dollar the key metric.”

Faisal Kawoosa, co-founder and chief analyst at Techarc, said enterprises are now evaluating AI investments more pragmatically.

“The exploratory stage of AI is over,” he said. “Organizations can derive value from AI today, but performance per dollar will determine whether it becomes part of everyday business operations or remains an ad hoc tool.”

Tiered models for different workloads

GPT-5.6 Sol is OpenAI’s flagship model for complex reasoning, Terra targets mainstream enterprise applications, and Luna is designed for lower-cost, high-volume deployments.

According to OpenAI, GPT-5.6 Sol scored 53.6 on Agents’ Last Exam, a benchmark for long-running professional workflows, outperforming competing frontier models while requiring significantly lower compute costs.

The company also introduced two new reasoning modes. The max mode allocates additional compute for complex problems, while ultra coordinates four AI agents in parallel to accelerate demanding workflows.

“Ultra goes further by coordinating four agents in parallel by default, trading higher token use for stronger results and faster time-to-result on demanding tasks,” the statement added.

Shah said the architecture reflects how enterprises are increasingly orchestrating multiple AI models.

“GPT-5.6 gives enterprise architects flexibility to route workloads from Luna to Sol depending on whether they require automation, logic, or complex reasoning,” he said.

Kawoosa added that the tiered approach aligns with how enterprise software has traditionally been consumed.

“It gives enterprises of different sizes the flexibility to optimize technology consumption according to their requirements,” he said.

Coding, productivity, and security gains

OpenAI said GPT-5.6 Sol achieved a score of 80 on the Artificial Analysis Coding Agent Index while consuming fewer than half the output tokens of competing models. It also reported state-of-the-art results on Terminal-Bench 2.1 and DeepSWE, benchmarks that measure real-world software engineering tasks.

The company said the models also improve enterprise productivity through stronger document generation capabilities and integrations with Microsoft 365, Google Drive, Slack, and Notion.

On cybersecurity, GPT-5.6 Sol scored 73.5% on ExploitBench, up from 47.9% for GPT-5.5, and nearly doubled its predecessor’s performance on ExploitGym.

“GPT-5.6 supports important defensive tasks such as secure code review, patching, threat modeling, and blue teaming,” OpenAI said.

Security remains an enterprise focus

OpenAI said GPT-5.6 incorporates its “most robust safeguards to date,” combining model-level protections with real-time monitoring and extensive safety testing, including approximately 700,000 GPU hours of automated red-team evaluations.

Shah said layered guardrails and monitoring could become an important differentiator for enterprise deployments.

Kawoosa, however, said CIOs will continue demanding greater transparency before fully trusting frontier AI systems.

“Competition among LLM providers will continue, with vendors constantly testing and challenging each other’s guardrails,” he said.

A researcher has an idea worth testing before lunch. The model is ready. The data is sitting right there. But the data is sensitive — regulated, proprietary; the kind that legal has been very clear cannot leave the building. So it can’t go to the cloud cluster. And even if it could, the GPU queue is hours deep, the meter is running, and by the time the run finishes and the bill lands, the spark of the idea has cooled into a ticket in a backlog. 

This is the unglamorous reality behind a lot of enterprise AI. Not a shortage of talent or ambition, but friction — the quiet tax paid every time a brilliant question has totravel a long way to find the computer that can answer it. We’ve spent the better part of a decade assuming that distance didn’t matter, that everything important would happen in some vast facility hundreds of miles away. For a whole class of work, that assumption is now the thing holding teams back. 

The last mile of AI 

Cloud and hyperscale data centers did something extraordinary: they made a near-infinite compute available to anyone with a credit card. That scale is genuinely irreplaceable for training frontier models. But scale solved the wrong problem for a surprising number of teams. 

Because a lot of real AI work isn’t a once-a-quarter mega run, it’s iteration — fine-tuning, experimenting, debugging, testing an agent’s behavior, running a model against data that’s too sensitive or too large to keep shipping back and forth. That work rewards immediacy and control, not raw scale. And on those two axes, the cloud-only model starts to strain in three ways. 

Governance is the first. The most valuable enterprise data is often the data that’s hardest to move — patient records, financial details, proprietary source code, designs under NDA. Sending it to a shared, off-premises environment can mean a compliance review, a risk sign-off, or simply a “no.” When the data can’t travel, neither can the AI work that depends on it — unless the compute comes to the data instead. 

Velocity is the second. AI progress is a function of how many experiments a team can run per week. Every cloud queue, every cold start, every round trip between a workstation and a remote cluster adds latency not just to a job but to learning. The teams that win aren’t the ones with the biggest single run; they’re the ones who can iterate fastest, privately, without asking permission. 

And then there’s the missing middle. Until recently, professionals had two options, and a chasm between them. On one side, a traditional workstation — convenient and local, but utterly unable to hold a trillion-parameter model in memory. On the other, a data center you don’t own, don’t control, and have to wait in line for. There was nothing in between: no way to put genuine, data-center-class AI power directly under the desk of the person doing the work. 

That gap is exactly where the next wave of productivity is hiding. 

When the supercomputer comes back to the desk 

Computing has always swung between the central and the personal. The mainframe gave way to the PC. Now, after a decade of centralizing intelligence in the cloud, the pendulum is swinging again — and the supercomputer is coming back to the desk, this time built specifically for AI. 

The implications for IT leaders are strategic, not just technical. A local, private AI supercomputer means sensitive workloads stay under the organization’s own governance. It means a predictable cost instead of a variable cloud meter. It means teams iterate at the speed of their own curiosity. And it means the data center is still there when a workload genuinely needs to scale — connected, not replaced. The goal isn’t to abandon the cloud. It’s to close the last mile. 

The deskside AI supercomputer: ASUS ExpertCenter Pro ET900N G3 

This is the gap the ASUS ExpertCenter Pro ET900N G3 is engineered to close. Built on NVIDIA DGX Station architecture and powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip, it brings data-center-class AI to a system that fits on a standard desk — a deskside AI supercomputer purpose-built for the way AI teams actually work. 

What that delivers, mapped to the friction it removes: 

• Run the big models locally. With 748GB of coherent unified memory and up to 20 PFLOPS of AI performance, the ET900N G3 can develop and run trillion-parameter models and autonomous AI agents right at the deskside — far beyond the reach of a conventional workstation, and without a trip to a shared cluster. 

• Keep sensitive work private. Because the compute lives where the team and the data do, sensitive and regulated workloads can stay on-premises under the organization’s own governance. Full compatibility with NVIDIA AI Enterprise and NVIDIA NemoClaw enables enterprises to build and run always-on AI assistants and agents within a secure, local environment. 

• Iterate without waiting. A 72-core NVIDIA Grace CPU paired with an NVIDIA Blackwell Ultra GPU over high-bandwidth NVLink-C2C interconnect puts supercomputer-class iteration at a developer’s fingertips — no queue, no cold start, no round-trip. 

• Scale out when you need to. An integrated NVIDIA ConnectX-8 SuperNIC provides up to 800 Gbps of networking, so the deskside system bridges cleanly to data center infrastructure when a workload outgrows the desk. 

• Run it around the clock. Data-center-grade thermal design built for sustained 24/7 operation means the system maintains peak performance through long training and inference runs rather than throttling when the work gets serious. 

And it runs the NVIDIA AI software stack out of the box, giving development teams a turnkey environment for training, fine-tuning, inference, and agentic AI from day one. 

The question worth asking now 

For years, the strategic question in AI infrastructure was how big a cluster can we reach. For a growing share of the work that actually moves a business forward, the better question is: how close can we put the power in the hands of the people doing the work? 

The idea was never the bottleneck. The distance was. Closing it is the next advantage. 

Discover how the ASUS ExpertCenter Pro ET900N G3 brings data-center-class AI to the deskside. Visit us here to learn more.  

Building an agent in an afternoon is now within reach of almost anyone in the enterprise with a credit card. The tools are accessible, the deployments are easy. The hard part is delivering the intended results.

Gartner predicts that more than 40% of agentic AI projects will be canceled by 2027, and the EU AI Act Article 14 requirements for human oversight for high-risk AI systems take effect on August 2, 2026. The deciding factor for whether agentic AI reaches production isn’t the model, the framework, or the use case. It’s the infrastructure beneath the agent: the part the people building agents have never had to think about.

Organizations are racing to deploy agentic AI to stay competitive, which means pressure-testing is often overlooked. Every agent project should be scrutinized by three executives asking three different sets of questions. The CISO asks whether we are exposed. The CFO asks whether we are overspending. The chief AI officer asks whether we are getting value.

As a product leader focused on AI governance, I see this pattern across customer environments. Three architecture layers answer those three questions: identity, observability, and cost optimization. I’ll walk through each of the layers and provide a four-question diagnostic for the next production push.

Why AI pilots stall

An agent is not a faster chatbot. It chains dozens of steps, calls external tools, retains state across sessions, and triggers real-world actions. Most inherit the credentials of whoever deployed them. They operate at machine speed without context for the consequences of each step.

The mismatch is not a competence gap on the human side. It is a time-horizon gap. An engineer reasons about a database change over hours. An agent triggers a hundred of them before anyone reviews the first. Traditional audit logging captures request and response. That does not catch this pattern.

When something breaks, the cost is rarely the incident. It is the months of stalled deployment that follow. The risk committee freezes pilots. The productivity gains the program was supposed to deliver never materialize. Finance still gets the API bill. Three architecture layers decide whether a deployment survives that pattern. Each one is the answer to a question the people building agents never had to ask.

Layer 1: Identity for non-human actors

Start with identity. The default failure looks routine: a product manager with broad API access spawns an agent that inherits the full scope of those credentials and runs at machine speed across systems no one inventoried.

The scale is bigger than most teams realize. Industry IAM research puts non-human identities at more than 100 to 1 versus human accounts, with some 2026 surveys putting the ratio as high as 144 to 1. A May 2026 Identity Gap Report found two-thirds are unseen and unmanaged.

Agents are moving from human identities with their “owners”’ permissions to first-class principals. They are purpose-bound, cryptographically attested, and scoped to one task at a time. Google’s Agent Identity, built on SPIFFE, is one early example. The production pattern has three properties. Credentials are issued per agent task. Token lifetime is measured in minutes to hours, not weeks. Scope is narrowed to the specific tools and data classes the task requires, and the credential revokes automatically on task completion.

If a single static credential is good for a week and 50 different tasks, you are not running agentic AI. You are running a service account with extra steps.

Layer 2: Observability that serves all three executives

Identity controls what an agent can do. Observability shows what it’s actually doing. One instrumentation layer, three views.

First, the security view. Traditional logging captures request and response, which assumes one human action per logged event. An agent’s unit of work is a chain. Pick a tool, call it, read the result, decide the next step. Twenty steps, some of them writing to production. Instrument every step as a durable audit object, independently queryable. Understand which tool was invoked, what data was accessed, what policy applied, and what the agent reasoned to justify the next step. That’s what Article 14 oversight requires for production.

Second, the business-outcomes view. Audit objects answer the CISO. The chief AI officer asks a different question. Is the agent accomplishing what we deployed it for, or burning compute on a tangent? An agent can run 200 tool calls, generate clean audit logs, and produce nothing. It might be looping on a sub-goal that drifted three steps back. Observe each step against the declared business purpose: on-task ratio, sub-goal coherence, progress markers. Project management telemetry for a non-human worker.

Third, the cost view. The same per-step instrumentation produces cost telemetry: token count per step, model per call, context size per turn, downstream tool-call costs. Without that attribution, the next section’s optimizations are blind.

A busy agent and a productive agent look identical in the security log. They look identical on the bill too. The difference shows up only when all three views run from the same instrumentation.

Layer 3: Cost optimization

Cost is where the architecture pays back. Gartner’s March 2026 analysis put agentic workloads at five to 30 times the token cost per task of a standard chatbot. The FinOps Foundation’s 2026 State of FinOps report found that 73% of organizations exceeded their original AI budget projections. Three failure modes drive that overrun.

First, using the wrong model. Agents default to the most capable one available. They call a frontier model for tasks a smaller one could handle with identical quality: summarizing a transcript, formatting JSON, classifying a ticket. The RouteLLM paper at ICLR 2025 demonstrated that intelligent routing cuts total LLM inference cost 40% to 80% with no measurable quality loss on routine work. Move model selection from a per-developer choice to a per-policy layer.

Second, running in loops. Agents can spend without limit if no one is watching. A widely-cited 2026 incident saw a LangChain multi-agent system run an infinite loop for 11 days and burn $47,000 in API charges. Per-session token ceilings, loop-detection circuit breakers that flag tool calls highly similar to prior calls, and hard daily caps stop this before it generates the bill. In our deployments, a three-tier cost structure catches the bulk of runaway patterns: a $50 daily soft alert, a $100 daily hard cutoff forcing routing to cheaper models, and a $1,000 monthly ceiling requiring manager approval.

Third, re-paying for the same context on every step. Every step re-sends the accumulated system prompt and conversation history. By step 20 the agent has paid for that context 20 times. Vantage’s 2026 analysis of agentic coding sessions found re-sent context accounts for roughly 62% of the average agent’s bill, the biggest single optimization target in agentic workloads. Three patterns help: anchored summarization at phase boundaries, sliding context windows, and provider-native prompt caching at the gateway. Most agents skip caching entirely, though Anthropic prices cached input at roughly 10% of base, Gemini at 10% to 25%, and OpenAI at 50%.

Governing agent cost means seeing every call, every model, every token attributed to the agent and the business purpose. Then act on it. Token counts without business attribution tell you how many gallons of gas you burned, not where you drove.

The deployment velocity payoff

The three layers serve the three executive questions. Identity gates what the agent can do. Observability shows what it is doing. Cost optimization controls what it spends.

The honest counterargument is that governance always slows deployment. That is true when governance is bolted on as approval gates layered over an agent that wasn’t built with observability or per-task identity. It is false when governance is built into the architecture from day one. Teams that experience governance as a brake installed the brake without the steering wheel.

Governance built right still costs something. Per-task credentials add work on every tool call. Observability infrastructure adds compute. The question is whether that cost beats the alternative.

The layers compound. Identity without observability is theoretical. Observability without cost control is descriptive. Without identity at the bottom, cost control becomes caps without context, forever reactive. All three together produce a governance review that runs in weeks, not quarters, because the data each executive needs already exists. In our experience, organizations with that infrastructure can deploy six workflows to production in the time competitors complete one governance review. The real ROI of agentic AI is not how much faster a single workflow runs. In practice, it’s how many workflows your team can defensibly put into production in a year.

Before the next pilot

Here are four questions to run against any agent your team is about to push to production:

1. Identity. For each agent in production, can you point to the per-task credentials it uses today, and the maximum scope of any single token?
2. Observability. For any agent session, can you produce three views from the same instrumentation: the audit object per step, the on-task ratio versus tangents, and the per-step cost broken down by model and context size?
3. Cost optimization. Does your platform automatically route by model, cap runaway loops, and avoid re-sending the same context every step?
4. Velocity. How long does it take a new agent workflow to move from approved pilot to production in your environment today?

If the answer is months, the architecture above is the gap. Gartner’s 40% stat is about your next pilot.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

The first time I used Kubernetes in an enterprise setting, I understood the hype. It gives every team the same way to package, deploy and run their apps. No more custom scripts or unique deployment hacks, just one control plane to rule them all. And really, that’s why it’s so popular with big companies: Kubernetes is an open-source system for automating deployment, scaling and management of containerized applications. It says so right on the box, and that’s what people want. But here’s the truth: Kubernetes doesn’t erase operational headaches. It just moves them around.

When your Kubernetes install is small, it feels like rocket fuel for engineers. At enterprise scale, though, suddenly it’s about governance, not just engineering. The game is no longer “Can we get this container running?” It’s “How do hundreds of engineers roll out their stuff safely, consistently, securely and without breaking the bank or burning out the platform team?”

This is where the fun really starts.

YAML isn’t the enemy

Folks new to Kubernetes obsesses over manifests, Helm charts, namespaces, ingress rules, deployments, all that stuff. But they’re not the hardest part once you start scaling. The real beast is standardization.

Every big company I’ve seen ends up with teams going their own way. One group writes beautiful deployment templates. Someone else copies and pastes from a two-year-old manifest. Some folks set resource requirements properly. Others skip them entirely. One team sticks to a strong naming convention, and someone else throws together random namespaces and service accounts that make sense only to them. Individually, this more or less works. At scale, when the whole platform has to operate like one system, it’s a mess.

That’s why I’ll say it: you don’t just need a Kubernetes cluster. You need a paved road. This would involve ensuring that there are approved templates, good deployment patterns, observability, security controls as defaults, good issue escalation processes and accountability.

There is no need for developers to be Kubernetes experts just to release their services. The best enterprise Kubernetes setups work like real products. They let application teams self-serve but never let anyone veer off road without good reason.

RBAC: necessary, but never enough

Security is paramount. Kubernetes supports role-based access control (RBAC), so on paper you can control who does what. In practice, in a big company, RBAC gets confusing fast.

The issue isn’t that engineers ignore security. It’s that permissions grow over time. You need a quick fix during an incident, so you give a service account more access. Maybe a team needs cluster-wide rights for a migration. That “just for now” permission sticks around because no one cleans it up. Month by month, the gap widens between what a workload should do and what it’s actually allowed to do. The only thing that works long-term: treat RBAC as a living thing, not a one-time checklist. Review it. Test it. Stick to least privilege. Service accounts get only what they need. Cluster-admin rights? Rare. Expiring exceptions. Set permissions as code so changes aren’t invisible.

Same story with workload security. Kubernetes brings you Pod Security Standards. There is baseline, restricted and privileged profiles, so everyone speaks the same language. But simply setting a standard isn’t enough. We’d also need things like admission controls, image scanning, runtime monitoring and audit trails.

Honestly, the NSA/CISA Kubernetes Hardening Guidance is still gold. Scan containers and pods. Run workloads as locked down as possible. Use strong authentication. Separate networks. Set up solid logging. These ideas sound obvious until you see what happens when your organization scales without good ops.

Network policies: where “it should work” meets reality

Kubernetes networking can trip up even the best teams. Engineers often think different namespaces mean automatic isolation between apps. Not true.

Kubernetes network policies decide which pods can talk to which, but the policies only matter if your networking plugin actually enforces them. I’ve seen a lot of teams write network controls that look great in YAML but don’t work, because the underlying network just ignores them. Security validation beats documentation every time. If two namespaces shouldn’t talk, test it. If a workload only needs access to a specific backend, check it. If only specific ingress is allowed, make sure nothing else gets through.

At scale, your Kubernetes security has to prove itself. “We have a policy” means nothing unless the platform can show the policy actually works.

Resource management becomes all about money

One of the biggest challenge is resource allocation. Kubernetes lets you set CPU and memory limits, and sure, there are official docs. But getting these numbers right is tough.

Set them too low, and your workload might get throttled or evicted under load. Set them too high, and you’re paying for unused infrastructure. That barely registers on a small cluster, but when you’re running thousands of pods? That’s cloud bills gone wild.

This is where Kubernetes ops and FinOps meet. Platform teams have to know who’s burning through which resources, what’s over-provisioned or flying blind, and where the real money goes. ResourceQuota helps keep things in check, but quotas alone don’t hold people accountable.

The culture shift is moving from “the cluster has spare capacity” to “every service has an owner, a cost profile and a plan for staying lean.” Teams should understand their infrastructure bill. Platform teams need dashboards that point out waste. Engineering leaders need to care about efficiency, not just hear from finance when things go off the rails.

Autoscaling isn’t a magic trick

The Horizontal Pod Autoscaler is handy. It adjusts your workloads automatically to match demand. But don’t overestimate it. Most real-world services don’t scale simply by CPU or memory. Sometimes a service hits latency limits before CPU usage spikes. Workers chewing through queues? You care more about backlog size. Machine learning? Maybe it’s all about GPU use or loading time. Customer-facing apps? You want to be scaled up before traffic hits, not scramble after users start complaining.

So autoscaling isn’t just a box you check. It’s a feedback loop, and it only works if you use the right signals. Sometimes CPU is enough. Sometimes you need to scale on queue length, request rate, latency or something totally custom.

Then there’s node autoscaling to provision infrastructure in response to demand. On paper, it just works. In real life, it runs into startup delays, availability zones, quotas, cloud provider quirks and pod disruption budgets. Scale pods faster than nodes? Users still see delays.

Test autoscaling like you test your app. Load-test it, break it, see what happens after an incident. Otherwise, you’ll find the limits when it hurts most.

Observability doesn’t matter unless it answers questions

Kubernetes has mountains of data. Things like  logs, metrics, traces, events, audits, deployment history, container restarts, control plane noise, you name it. The real challenge isn’t collecting info, but actually it’s making sense of it. The CNCF and others have best practices for logging and telemetry, like centralizing logs and not leaking secrets. Those matter, but at the end of the day, engineers need answers, not just data. When something breaks, no one’s asking, “Is Kubernetes alive?” They want to know what changed. Did something roll out? Did a pod crash? Did autoscaling fire too late? Was a node unhealthy, a secret rotated, a network policy too tight, a downstream DB choking?

Observability should line up with real operational questions and not just ticking boxes for logs, or metrics. Dashboards need to match service ownership. Alerts need to mean something to end users. Telemetry should connect to deployments and incidents. Measure how quickly engineers spot the root cause, not just that you have the data somewhere.

CNCF talks about newer models of unified telemetry and proactive troubleshooting for a reason. All the dashboards in the world don’t help when your team has to play detective during an outage.

Upgrades: Don’t wing it

Kubernetes upgrades catch people out. The CNCF Maturity Model says: Kubernetes drops three big releases a year, so maintenance is part of life—not a once-in-a-blue-moon project.

Upgrading at enterprise scale can involve everything: workloads, admission controllers, CI/CD, service mesh, ingress, storage drivers, monitoring, security, custom controllers. Version skew policies keep you between the lines, but that’s just the beginning. The real question is: can you test your whole stack?

Good upgrade programs need a repeatable process, staging environments that actually look like production, and clear communication so teams know what to expect. The worst upgrade process is the one that relies on heroes to pull it off at the last second. A strong platform turns upgrades into routine.

Reliability: Kubernetes helps, but it doesn’t guarantee it

Yes, Kubernetes restarts crashed containers, reschedules pods and does rolling deployments. But it doesn’t make a bad app reliable.

A poorly coded app will fail on Kubernetes just like anywhere else. Bad readiness or liveness probes? Your app gets traffic too soon. No graceful shutdown? Requests drop during deploy. Forgot pod disruption budgets? The app goes down during node maintenance. A flaky dependency? It will cascade through your services even if all your pods look healthy.

The mature approach is setting service-level objectives and making reliability a product of both platform and engineering. Cluster health isn’t user experience. That green status page can hide a lot of pain.

The platform team is a product team

Here’s the biggest lesson I’ve picked up is that running Kubernetes at enterprise scale isn’t really about the tech. One cluster? Maybe one expert can handle that. But for a full enterprise platform, you need a product mindset. The platform team serves customers such as engineers, security, compliance, finance and business. Everyone wants something a bit different.

Developers want speed and reliability. Security wants oversight. Finance wants transparency. Compliance wants proof. Ops wants predictability. The business wants all of those.

The platform team has to pull those threads together with APIs, docs, dashboards, paved roads, support and feedback. That also means saying “no” to the unique snowflake patterns that create chaos later. Kubernetes is powerful. But it doesn’t replace organizational discipline. That’s still on the shoulders of engineering leaders. The real challenge at enterprise scale isn’t memorizing every API object. It’s building a system where any team can ship safely without needing to be Kubernetes experts themselves.

When you reach that point, Kubernetes stops being just a cluster. It becomes your platform.

This article is published as part of the Foundry Expert Contributor Network.
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The strangest thing about cloud strategy is how confident it looks in PowerPoint and how nervous it feels in real life.

I’ve sat in rooms where the cloud slide looked clean enough to frame. Public cloud here. Private cloud there. Hybrid for the awkward middle child. Multi-cloud for resilience, bargaining power and the faint hope that no single vendor would ever own our sleep.

Then AI arrived.

At first, it looked like another conversation about workload. Bigger compute. More storage. Faster experiments. Some awkward cost questions. Nothing we couldn’t absorb with a thicker roadmap.

Then the bills landed. The data moved in odd ways. Teams built things before governance could find its shoes. Vendors became more central than anyone had admitted.

The old cloud strategy didn’t collapse. It blushed. AI exposed the assumptions beneath it.

Now, quantum changes something deeper. It asks whether the decisions behind the workload can survive time, secrecy, suppliers, weak evidence and uncertainty.

That’s a much less comfortable meeting.

Cloud strategy was built for workloads we thought we understood

For years, cloud strategy was a sensible debate about location, cost, control and speed. Public cloud for scale. Private cloud for sensitive workloads. Hybrid cloud for compromise. Multi-cloud for resilience, negotiation or, if we’re being honest, organizational politics with a nice diagram.

The logic was sound. Move faster. Cut heavy infrastructure spend. Improve recovery. Give developers what they need before they grow old waiting for a server. It worked because the work behaved in familiar ways. Systems had owners. Costs had patterns. Data had borders, or at least we pretended it did.

The question was simple: Where should this workload live? That question still matters. But it no longer carries enough weight.

AI changed that. AI changed the pattern, not just the platform AI didn’t politely join the cloud strategy. It wandered through the house, opened every cupboard and asked why the plumbing sounded tired.

The first shock was demand.

Traditional systems consume resources in ways you can usually model. AI workloads behave differently. Training, testing, inference and data processing can spike, pause, restart and spread before anyone has agreed on who owns the meter.

Cloud cost control used to ask a billing question, “How much will we use?” AI asks an operating question: “Who is allowed to create demand, at what scale, for what purpose and with whose approval?”

The second shock was data.

AI does more than store data. It chews it, reshapes it, remembers parts of it, produces new versions of it and leaves traces in places people forget to check. Prompts, logs, embeddings, model outputs, copied files and forgotten notebooks can become quiet risk pockets.

A cloud strategy that only asks where data sits misses how data behaves.

The third shock was supplier dependency.

Many firms thought they had a cloud strategy. AI revealed they had a supplier dependency strategy wearing a cloud badge. GPUs, model platforms, managed services, specialist APIs and third-party tools became central to delivery.

AI compressed the distance between idea and exposure. A team could test, connect and release faster than governance could form a working group. I say that with affection. I’ve seen working groups age in dog years.

Cloud strategy had become a test of decision speed, risk appetite, financial discipline and data control. It now goes beyond architecture.

Then quantum changed the clock.

Quantum changes the time horizon

Quantum risk often gets dumped into the cryptography drawer. That is understandable. It is also dangerous.

The leadership issue adds time to the future of quantum computers.

Some data stolen today may still matter years from now. Some secrets age badly. Trade secrets, legal records, health data, source code, identity data and sensitive contracts don’t all expire at the same speed. Some decay like fruit. Some sit like plutonium.

That is why “harvest now, decrypt later” matters. An attacker may collect encrypted data today and wait for better tools tomorrow. You don’t need to panic. You do need to ask which data has a long secrecy life.

If your most sensitive long-lived data spans cloud platforms, SaaS services, backups, archives, collaboration tools and supplier systems, where exactly is your quantum exposure? Which encryption protects it? Who manages the keys? Which supplier has a plan? Which one has a brochure?

A brochure is a scented candle for anxious executives.

Migration also takes time. Cryptography hides everywhere. In applications. In identity systems. In network devices. In APIs. In firmware. In backup tools. In old systems, nobody wants to touch.

Quantum readiness goes beyond a weekend patch. It is discovery, classification, design, testing, contracts, funding, sequencing and proof.

The risky sentence is, “We’ll revisit this when things become clearer.”

By then, the cheap decisions may have left the building.

The real issue is decision infrastructure

AI exposed assumptions about speed, cost, data and suppliers. Quantum exposes timing, ownership, evidence and memory. Together, they point to a quieter weakness: decision infrastructure.

By decision infrastructure, I mean the system by which leaders frame risk, assign ownership, make trade-offs, record choices, track evidence and revisit assumptions when facts change. That sounds dull. Good. Dull is where serious governance lives. The glamorous stuff gets applause. The dull stuff prevents regret.

Many organizations saw the risk and still failed because too many people saw different pieces of it, and nobody owned the decision. The cloud team sees architecture. Security sees exposure. Legal sees liability. Procurement sees contract gaps. Finance sees cost drift.

The board sees amber. Amber is often where hard decisions go to nap.

This is why AI and quantum belong in the same leadership conversation. AI asks whether your cloud strategy can keep pace. Quantum asks whether it can cope with time. Both punish vague ownership.

Who owns long-term cryptographic exposure? Who can force a supplier conversation? Who accepts residual risk if migration cannot happen fast enough? Who records why a decision was made and when it must be reviewed?

Suppose those questions feel awkward, good. Awkward questions earn their rent.

The questions leaders should ask now

The board needs better questions.

Start with exposure. What protects your most sensitive systems and data? Where do you rely on supplier-managed encryption? Which systems are old, critical, poorly documented and painful to change?

Exposure is a map of assets, data, dependencies and time.

Then ask about ownership. Who owns quantum readiness across cloud, cyber, legal, procurement, privacy, resilience and the business? Who can make trade-off decisions when risk reduction competes with cost and delivery? Which risks are stuck because everyone is involved and nobody is accountable?

Awareness without ownership is just anxiety with better stationery.

Then ask about evidence. Can you show progress by system, supplier, business service and data class? Would your evidence survive a board review, a regulator’s questioning or a post-incident investigation?

Evidence built under pressure is expensive. It is also sweaty. Build the proof trail before the room gets hot.

Finally, ask about timing. Which choices must be made now because migration will take years? What event would trigger faster action? When will the board revisit the risk?

Which delay would you regret if the timeline moves faster than expected?

That last question matters. Regret is often the most honest risk metric in the room.

What a quantum-aware cloud strategy looks like

A quantum-aware cloud strategy is not a glossy side document owned by three cryptographers and a nervous intern.

It is a cloud strategy with better questions built into it:

1. Build cryptographic visibility. Start with the services that matter most. Find the encryption, certificates, protocols, keys, libraries and suppliers that protect them. Perfection can wait. Blindness cannot.
2. Classify data by secrecy life. Not just sensitivity. Time. How long must this information stay protected? A short-lived report and a long-life trade secret do not belong in the same queue.
3. Press suppliers for evidence. Ask what they are doing, what you must do and how they will prove progress. Confidence is lovely. Evidence pays the rent.
4. Rank migration by risk. Start where business value, long-life data, weak visibility and migration pain meet. Treating everything as equal is how serious work becomes theatre.
5. Change board reporting. Don’t report quantum as a foggy science project. Report decisions required, risks accepted, blockers, supplier gaps and review dates. Boards govern choices. Give them choices.
6. Build a review rhythm. Standards, tools, suppliers, threats and regulations will continue to evolve. A stale roadmap is just a risk register wearing a lab coat.

No panic. Panic burns energy and produces bad slides. The aim is readiness with owners, evidence and judgment.

The cloud question grew up

Cloud strategy began as an architecture question.

AI turned it into an operating question. Quantum turns it into a leadership question.

That is the shift.

To handle this well, organizations will need to build decision muscle early. They will know what matters, who owns it, what evidence exists, which suppliers are ready and when the next decision must be made.

But beneath cloud, AI and quantum sits the discipline leaders often avoid until pressure arrives, wearing a suit: decision quality.

AI changed the cloud bill. Quantum changes the clock.

And the clock is where risk hides.

This article is published as part of the Foundry Expert Contributor Network.
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The world is advancing toward a future in which over 10 billion humans coexist with trillions of autonomous agents in a superintelligent universe. However, the cloud-hosted systems that currently dominate AI’s operational models lack the architectural elasticity to support this growing demand for compute resources. Championed by managed GPU Clusters and centralized data centers, cloud AI relies on infrastructure that expands the operational scope of AI, requiring users to literally “rent” intelligence or the tools to develop it.

Tether argues that this system has a weak advantage, similar to scaling a database by simply buying a bigger server. AI scaling, in contrast, amplifies intelligence and availability. Cloud-hosted AI falters in both cases. Infinite scalability and universality in AI are therefore driven by the ability to deploy intelligent systems in any environment using readily available toolsets.

Tether’s AI research and development team believes that this “ability” is inherent in edge-optimized, local AI: self-hosted infrastructure for AI inference, training, and development on user-grade devices. Essentially, local AI converts intelligence into a portable capital asset readily available to the user, rather than a rented utility, as with cloud-hosted AI.

Operational constraints for AI developers and businesses.

Over 4,200 (43% of the world’s data centers) are located in the United States; eight times more than second-placed UK and third-placed Germany, which hosts more than twice as many data centers as all of Africa. This availability bias cuts across several other core infrastructure for AI development and routine use, impacting cost-effectiveness, performance, and data sovereignty.

Furthermore, data centers and other AI infrastructure, swamped with resource demands from unicorns and AI startups, continually adjust rental fees to offset operating costs and generate revenue. By extension, the high capital expenditure (CapEx) required for AI infrastructure is forcing companies to restructure their balance sheets. Major AI companies are expected to spend $500 billion on capital costs this year, a 30% increase from 2025 records. This is projected to reach $1.3 trillion by 2030, growing at 25% per annum.

Beyond availability and cost-efficacy, reliance on third-party infrastructure introduces additional operational factors and points of failure. Unplanned outages and abrupt changes in usage terms could significantly affect developers and end users.

In contrast, local AI operates autonomously, runs on infrastructure available to everyone, and works on any system. Plainly, they are agnostic, run on-premise, and eliminate barriers to availability.

Reputation capital of centralized AI

In an ideal scenario, anyone should be able to confidently use AI tools without worrying about what happens to their data post-execution. However, third-party access to user data opens channels for data mismanagement, this time, more advanced due to the quality of data unintentionally supplied by users during model training, fine-tuning, and inference.

34% of cybersecurity leaders identified “data leaks through generative AI” as their top concern for 2026, surpassing hacker capabilities for the first time. Elsewhere, the majority of the 670 data breach incidents reported in the first quarter of 2026 are either directly AI-driven or involve centralized data management infrastructure.

“Third-party” in this context includes AI companies and as-a-service infrastructure providers contracted to serve as a liaison between AI tools and users. Tens of the former are already headed to court in major class-action lawsuits over the handling of user data.

Local AI positions users as the only control point. User data is stored on-device and managed by software that has zero contact with external systems.

Tether AI’s research and development focuses on advancing machine intelligence as a readily available utility worldwide through technologies that let anyone build or use AI tools anywhere.

Universality for superintelligence and agnostic, on-premise AI.

Tether believes that efficient, self-hosted AI can transform machine intelligence into a new element of the periodic table that powers new possibilities in dynamic systems. This (self-hosted AI) will set in a new paradigm in which superintelligence is a foundational element owned by the user. However, effective agnostic, on-premise AI can only be achieved through creative engineering. This includes modifications from the model level to the complete architecture that accommodate design differences. Tether is leading innovations in pursuit of this. It is also contributing to open-source efforts to localize AI and abstract its complexities. This expands opportunities for even more advancements in local and edge-first AI.

The idea is to decouple AI from the current siloed, controlled, and fragile model. Tether is re-engineering Artificial Intelligence and modifying existing technologies to achieve infinite, scalable intelligence.

The first task here is to build a base infrastructure that operates as a self-governed unit. To this end, Tether developed the Pear runtime and co-founded Holepunch. Pear Runtime and Holepunch employ decentralized resource networks, databases, and communication protocols to achieve a serverless P2P backend for edge applications.

Next, Tether addresses the heavy computational overhead of AI models by developing resource-efficient models and infrastructure that run locally on user-grade devices and across heterogeneous environments. It launched QVAC (QuantumVerse Automatic Computer), an AI research team, and a development framework for local-first and edge-first AI research and development.

This unit has led the development of:

• A fine-tuning framework for Bitnet’s 13-billion-parameter LLM on regular devices, bypassing the GPU limitations of the ternary quantized model
• QVAC Fabric LLM: A high-throughput local-first AI framework that transforms regular devices into sovereign compute machines for AI development, model training, and inference.
• An Edge-first Parameter-efficient fine-tuning framework for training the QVAC Fabric LLM locally on everyday devices and heterogeneous GPUs

Building on the QVAC framework, Tether’s AI coverage has expanded to include tools for deploying intelligence across diverse systems, from personal computers to interfaces for controlling smart homes and appliances. This includes runtime environments, training data, fine-tuning frameworks, and edge-optimized AI applications.

Tether has built a suite of tools that put local AI into practice across every layer of the stack, including:

• QVAC Genesis I & II: Synthetic datasets for training AI models on STEM disciplines.
• WDK: AI-ready wallet development framework that enables autonomous agents to build local and edge-first cryptocurrency wallets.
• QVAC MedPsy: A range of (1.7B and 4B parameter) local-first medical AI models that run on heterogeneous everyday devices and produce more accurate and precise results than some larger medical AI models.
• QVAC Workbench: A unique general-purpose AI tool for researching, coding, and execution on edge devices.

Developers equipped with these can implement local AI integrations across diverse systems through a serverless backend, models that can run on resource-limited systems, and UI modules that simplify usage. This is further reinforced by the QVAC SDK, which consolidates all of Tether’s AI-related achievements to date. QVAC SDK is a toolkit of prebuilt modules for components of the QVAC AI infrastructures. It provides usage guides, integration contexts, and functional samples. This enables developers to build intelligent on-premises applications for any system without requiring permissions.

Committing to a human-centric future for AI

Artificial intelligence is arguably the most human-targeted internet-based technology in history. In an AI-dominated future, the current user base will be only a fraction of the demand scale. However, Institutional capital expenditure capacity isn’t unlimited, and the bloat in rental costs has no ceiling.

Tether’s local-first approach to AI acknowledges the relevance of machine intelligence to humans and its deep connection to everyday life. In response, it is dedicated to developing a universally accessible AI that is modular enough to be embedded in the fabric of any device, system, or environment. This ranges from industrial servers to the smallest chip in a light bulb.

In all of these cases, the ‘users’ own their AI, can build on their own terms, without permission or external constraints, choose their own biases, and control how their data is used. Practically, this is the only way to ensure that superintelligence is successfully delivered to the billions of humans it is meant for.

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