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.  

As enterprises struggle to manage their AI strategies, the US AI regulatory environment is sending a wide range of contradictory signals. OpenAI’s Wednesday announcement that it will now release GPT-5.6 Sol, along with Terra and Luna, on Thursday highlights the confusion.

Initially, the US government said that it was asking OpenAI to limit access to its top models, including the three releasing Thursday, to a short list of companies. OpenAI seemingly agreed and held back their general availability.

But on Wednesday, OpenAI reversed its position, with a statement on X saying simply: “GPT-5.6 Sol, along with Terra and Luna, will launch publicly this Thursday. We’re expanding preview access globally now.” No details were released about the extent of the expansion.

Then the White House issued a statement, a copy of which it emailed to InfoWorld, saying that the US government “did not give OpenAI a ‘green light,’ approval or clearance to release its models. No such permission is required or granted. The Administration does not provide approvals for private companies to release AI models – decisions on timing and scope of releases rest entirely with the companies.”

The statement then quoted from the June 2 White House executive order that said, “nothing in this section shall be construed to authorize the creation of a mandatory governmental licensing, preclearance, or permitting requirement for the development, publication, release, or distribution of new AI models, including frontier models.” It also said, “any testing or meetings with government experts is voluntary. Participation is not required to release a model.”

Yet last month, the US Commerce Department weighed in on how Anthropic’s models can be distributed. 

The ‘worst of both worlds’

Lewis Carhart, CEO of software development firm Comp AI, said the statement is frustrating for IT executives on multiple fronts. 

“Think about what that [White House statement] means. OpenAI stationed engineers in Washington for weeks, submitted to government testing, staggered its launch at the government’s request. And the official position is that none of that was required,” Carhart said. “We now have a de facto licensing regime that legally doesn’t exist. There’s no statute, no appeal process, no published criteria. Just [the US Department of] Commerce deciding model by model what ships and when.”

That’s the worst of both worlds, he noted: “All the friction of regulation with none of the predictability. Compliance people have a name for this – it’s an audit with no framework. And the precedent is now locked in for both frontier labs: if you build at the frontier, your launch calendar runs through Washington whether the law says so or not.”

Carhart argued that this regulatory reality should be of extreme concern to enterprise IT executives, given it indicates that model availability is now “a regulatory variable” not driven by the vendor roadmap.

“Anthropic’s most advanced models disappeared from the market for three weeks in June. It was not because of an outage, not because of a pricing change. It was because of an export control directive,” he pointed out. “If your AI architecture assumes the model you deployed today is available tomorrow, that assumption is now demonstrably false. Multi-model resilience just went from nice to have to a board-level risk item.”

It also offers an opportunity, given that a model that cleared government security testing is a model that auditors and boards sign off on faster. “Government review is quietly becoming a procurement asset,” he observed. “The CIOs who win here are the ones who treat ‘regulatory posture of the model itself’ as a line item in vendor risk assessments – most risk teams are still only looking at the provider’s SOC 2 attestation.”

Jason Andersen, principal analyst at Moor Insights & Strategy, agreed with Carhart and described the overall back-and-forth as “a bit of pageantry. OpenAI needs its model to look as powerful, potentially dangerous, as Anthropic’s so it can be a contender to be at the absolute frontier. It also helps to burnish OpenAI’s PR efforts to look more responsible than it has in the past.”

Furthermore, he added, “tech CEOs are acutely aware that flattery towards this administration could keep regulators or the threat of regulation at bay.”

Criteria not clear

Brian Jackson, a principal research director at Info-Tech Research Group, echoed the political concerns. 

“What’s still not clear is the actual criteria being used to deem the models safe for release. The government has said that it’s concerned about cybersecurity risk as well as the risk of AI being used to develop biological weapons,” he said. “But so long as the actual release criteria lack transparency, there will be some perception that the evaluation could be politically motivated.”

Jackson said that one, presumably unintended, result of the US government’s efforts to control AI rollouts is that it is making companies look far more seriously at using non-US vendors for AI strategies. 

“Organizations are looking for alternatives to the private US-based cloud-delivered frontier models. That’s so they can maintain control over their AI supply chain,” he said. “Chinese open source models are one option that’s available, but there are other options too, from Canada and Europe. Companies can either set up AI access through other APIs not connected to US-based AI providers, or download open-source models to run locally.”

He noted that the added US regulatory risk means that some organizations will avoid becoming entrenched within OpenAI’s and Anthropic’s interfaces, where there’s no option to swap out the LLMs for an alternative.

Impacts enterprise AI strategy

Rock Lambros, director of AI standards and governance at AI agent vendor Zenity, shared the frustration that little to no actionable compliance data is being released. 

“Nobody outside a closed room can tell you what standard [the model] passed because it was never written down. For two weeks, [US government officials] kept a model out of defenders’ hands that’s better at guarding your network than breaking into anyone else’s,” Lambros said. “Call that a security review if it helps you sleep better. But it reads to me like a bouncer working a velvet rope nobody hired him to run, waving people through today because he’s in a better mood than he was a couple of weeks ago.”

This unpredictability is likely to have impacts on AI strategy far beyond traditional compliance concerns, Lambros said.

“We’ve built way too much operational reliance on these models to hang it on a review with no rulebook,” he said, pointing out that hospitals, pipelines, banks and water utilities are relying on frontier AI whose availability “can swing from ‘on’ to ‘off’ to ‘on’ with no notice, no appeal, and no published standard behind any of it.”

“You can’t run critical infrastructure on a tool that runs fine Friday and is offline by Monday because an approval process nobody can see reached a verdict nobody can predict,” Lambros said. “That is a supply chain risk with a government hand on the switch, and almost nobody has priced it into a continuity plan.”

To protect themselves, companies need to adjust their expectations. “Treat model availability like any single point of failure you don’t own by standing up a fallback you’ve tested, getting a continuity clause in your contract, and drilling for the blackout, because ‘the government backed off this time’ is not a plan,” he advised.

As enterprises struggle to manage their AI strategies, the US AI regulatory environment is sending a wide range of contradictory signals. OpenAI’s Wednesday announcement that it will now release GPT-5.6 Sol, along with Terra and Luna, on Thursday highlights the confusion.

Initially, the US government said that it was asking OpenAI to limit access to its top models, including the three releasing Thursday, to a short list of companies. OpenAI seemingly agreed and held back their general availability.

But on Wednesday, OpenAI reversed its position, with a statement on X saying simply: “GPT-5.6 Sol, along with Terra and Luna, will launch publicly this Thursday. We’re expanding preview access globally now.” No details were released about the extent of the expansion.

Then the White House issued a statement, a copy of which it emailed to InfoWorld, saying that the US government “did not give OpenAI a ‘green light,’ approval or clearance to release its models. No such permission is required or granted. The Administration does not provide approvals for private companies to release AI models – decisions on timing and scope of releases rest entirely with the companies.”

The statement then quoted from the June 2 White House executive order that said, “nothing in this section shall be construed to authorize the creation of a mandatory governmental licensing, preclearance, or permitting requirement for the development, publication, release, or distribution of new AI models, including frontier models.” It also said, “any testing or meetings with government experts is voluntary. Participation is not required to release a model.”

Yet last month, the US Commerce Department weighed in on how Anthropic’s models can be distributed. 

The ‘worst of both worlds’

Lewis Carhart, CEO of software development firm Comp AI, said the statement is frustrating for IT executives on multiple fronts. 

“Think about what that [White House statement] means. OpenAI stationed engineers in Washington for weeks, submitted to government testing, staggered its launch at the government’s request. And the official position is that none of that was required,” Carhart said. “We now have a de facto licensing regime that legally doesn’t exist. There’s no statute, no appeal process, no published criteria. Just [the US Department of] Commerce deciding model by model what ships and when.”

That’s the worst of both worlds, he noted: “All the friction of regulation with none of the predictability. Compliance people have a name for this – it’s an audit with no framework. And the precedent is now locked in for both frontier labs: if you build at the frontier, your launch calendar runs through Washington whether the law says so or not.”

Carhart argued that this regulatory reality should be of extreme concern to enterprise IT executives, given it indicates that model availability is now “a regulatory variable” not driven by the vendor roadmap.

“Anthropic’s most advanced models disappeared from the market for three weeks in June. It was not because of an outage, not because of a pricing change. It was because of an export control directive,” he pointed out. “If your AI architecture assumes the model you deployed today is available tomorrow, that assumption is now demonstrably false. Multi-model resilience just went from nice to have to a board-level risk item.”

It also offers an opportunity, given that a model that cleared government security testing is a model that auditors and boards sign off on faster. “Government review is quietly becoming a procurement asset,” he observed. “The CIOs who win here are the ones who treat ‘regulatory posture of the model itself’ as a line item in vendor risk assessments – most risk teams are still only looking at the provider’s SOC 2 attestation.”

Jason Andersen, principal analyst at Moor Insights & Strategy, agreed with Carhart and described the overall back-and-forth as “a bit of pageantry. OpenAI needs its model to look as powerful, potentially dangerous, as Anthropic’s so it can be a contender to be at the absolute frontier. It also helps to burnish OpenAI’s PR efforts to look more responsible than it has in the past.”

Furthermore, he added, “tech CEOs are acutely aware that flattery towards this administration could keep regulators or the threat of regulation at bay.”

Criteria not clear

Brian Jackson, a principal research director at Info-Tech Research Group, echoed the political concerns. 

“What’s still not clear is the actual criteria being used to deem the models safe for release. The government has said that it’s concerned about cybersecurity risk as well as the risk of AI being used to develop biological weapons,” he said. “But so long as the actual release criteria lack transparency, there will be some perception that the evaluation could be politically motivated.”

Jackson said that one, presumably unintended, result of the US government’s efforts to control AI rollouts is that it is making companies look far more seriously at using non-US vendors for AI strategies. 

“Organizations are looking for alternatives to the private US-based cloud-delivered frontier models. That’s so they can maintain control over their AI supply chain,” he said. “Chinese open source models are one option that’s available, but there are other options too, from Canada and Europe. Companies can either set up AI access through other APIs not connected to US-based AI providers, or download open-source models to run locally.”

He noted that the added US regulatory risk means that some organizations will avoid becoming entrenched within OpenAI’s and Anthropic’s interfaces, where there’s no option to swap out the LLMs for an alternative.

Impacts enterprise AI strategy

Rock Lambros, director of AI standards and governance at AI agent vendor Zenity, shared the frustration that little to no actionable compliance data is being released. 

“Nobody outside a closed room can tell you what standard [the model] passed because it was never written down. For two weeks, [US government officials] kept a model out of defenders’ hands that’s better at guarding your network than breaking into anyone else’s,” Lambros said. “Call that a security review if it helps you sleep better. But it reads to me like a bouncer working a velvet rope nobody hired him to run, waving people through today because he’s in a better mood than he was a couple of weeks ago.”

This unpredictability is likely to have impacts on AI strategy far beyond traditional compliance concerns, Lambros said.

“We’ve built way too much operational reliance on these models to hang it on a review with no rulebook,” he said, pointing out that hospitals, pipelines, banks and water utilities are relying on frontier AI whose availability “can swing from ‘on’ to ‘off’ to ‘on’ with no notice, no appeal, and no published standard behind any of it.”

“You can’t run critical infrastructure on a tool that runs fine Friday and is offline by Monday because an approval process nobody can see reached a verdict nobody can predict,” Lambros said. “That is a supply chain risk with a government hand on the switch, and almost nobody has priced it into a continuity plan.”

To protect themselves, companies need to adjust their expectations. “Treat model availability like any single point of failure you don’t own by standing up a fallback you’ve tested, getting a continuity clause in your contract, and drilling for the blackout, because ‘the government backed off this time’ is not a plan,” he advised.

This article originally appeared on InfoWorld.

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Revised transaction structure enables immediate closing, accelerating the Company’s strategic pivot toward AI data centers, enterprise GPU compute, and digital power infrastructure.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) (“EVTV” or the “Company”) today announced the successful completion of its merger with Azio AI Corporation (“Azio AI”) on July 2, 2026, paving the way for the Company to transform to an AI Datacenter Provider and meeting the growing market demand for artificial intelligence (“AI”) infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; a market that the International Data Corporation (IDC) projects will reach $487 billion in global spending in 2026 and exceed $1 trillion by 2029.[1] The transaction marks a defining milestone in the Company’s strategic transformation and establishes the foundation for its next phase of commercial execution and long-term growth.

The parties amended the proposed transaction structure to expedite the closing timeline, allowing the combined company to begin operating as a fully integrated public company significantly sooner than originally anticipated. The accelerated closing enables management to immediately focus on commercialization across its expanding AI Datacenter strategy.

With the merger complete and the combined company operating as one organization, management is now fully focused on commercial execution, infrastructure deployment, strategic growth initiatives, and creating long-term shareholder value.

Over the past several months, the Company advanced development activities at its South Texas site and deployed six megawatts of off-grid power for its modular data centers. The Company further secured rights to a 548-acre site with the capacity to scale up to 500 MW, supporting the future development of AI hyperscale data centers.

Management believes these achievements demonstrate that the combined company is entering its next phase with meaningful operational momentum already in place rather than beginning from a standing start. Infrastructure deployment is underway, customer commitments have already been established, commercial execution is actively progressing, and the Company’s corporate structure is now aligned with an operating platform built to support long-term expansion.

The completion of the merger comes at a time when investment in AI infrastructure continues to accelerate globally as enterprises increasingly require access to high-performance computing resources, GPU infrastructure, and scalable digital power solutions. Management believes the combined company is well positioned to capitalize on these long-term industry trends through a diversified infrastructure strategy designed to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations.

Following the closing of the transaction, the Company intends to continue expanding its AI Infrastructure strategy through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships designed to maximize utilization of its power resources while creating multiple long-term revenue opportunities.

In connection with the closing of the merger, Phillip Oldridge has stepped down as Chief Executive Officer. Jason Maddox vacates the President position and is now the Chief Financial Officer. The Company’s Board of Directors appointed Simon Yu as President and Chris Young as Chief Executive Officer, effective immediately.

Mr. Yu is a serial entrepreneur and public markets operator with almost a decade of experience taking companies public, executing capital raises, and scaling businesses. He has previously served in founder, C-suite, and board roles at three publicly traded companies, two of which reached market capitalizations in excess of $1 billion. Mr. Yu has led legal, accounting, and advisory teams through Regulation A+ Tier 2 offerings, PCAOB audits, and public company reporting, alongside leading M&A transactions. As an active early-stage venture investor, he has evaluated investment opportunities across artificial intelligence, SaaS, and B2B technology.

Mr. Young brings extensive experience in launching and leading public companies and investing in and advising emerging technology companies, with a particular focus on artificial intelligence, software innovation, and strategic growth initiatives. Prior to joining EVTV, he served as Chief Executive Officer of Clubhouse Media Group, a publicly traded social media company and an Entrepreneur in Residence at Amplify, where he worked alongside founders and venture-backed technology companies to accelerate commercialization and support the development of high-growth technology businesses.

“Today’s announcement represents far more than the completion of a merger—it marks the beginning of our next chapter,” said Chris Young, Chief Executive Officer of EVTV. “Over the past several months, our teams have been building the operational foundation of this business while simultaneously working toward completing this transaction. With the merger now finalized, we move forward as one company with one leadership team and one strategy, focused on executing against the opportunities in front of us. We believe demand for AI infrastructure, enterprise compute, and digital infrastructure will continue expanding for years to come. Our objective is to build a scalable platform capable of serving that demand while creating long-term value for our shareholders.”

Jason Maddox, Chief Financial Officer of EVTV, added, “Completing this transaction under the amended merger structure allows us to immediately focus on execution. We have already established meaningful operational momentum, and we believe operating as a unified public company enhances our ability to deploy infrastructure, serve customers, pursue strategic growth opportunities, and continue building long-term shareholder value.”

The transaction establishes a unified operating platform designed to support the Company’s long-term growth strategy through continued investment in AI infrastructure, enterprise computing, digital power assets, and digital infrastructure development. Management believes the completion of the merger provides the operational and organizational foundation necessary to pursue the next phase of commercialization while expanding its presence across some of the fastest-growing sectors of the global technology market.

Transaction and Operational Highlights

• Successfully completed the merger with Azio AI pursuant to an amended and restated merger agreement.
• Approximately six megawatts of off-grid digital infrastructure deployed at the Company’s South Texas development site.
• Development footprint exceeding 548 acres with the potential to support up to 500 MW of AI infrastructure capacity.
• Combined company positioned to accelerate commercialization across AI infrastructure, enterprise GPU compute, digital power solutions, and digital asset mining operations.
• Merger consideration consisted of 2,655,157 shares of common stock and 973,450 shares of non-voting convertible preferred stock in exchange for 100% of outstanding capital stock of Azio AI, of which 194,807 shares of common stock were reserved for convertible notes of Azio AI assumed by the Company upon closing.
• Each share of preferred stock convertible into 100 shares of Company common stock subject to stockholder approval.
• Chris Young appointed Chief Executive Officer and Chairman of the Board.
• Simon Yu appointed President.
• Jason Maddox appointed Chief Financial Officer.
• Phillip Oldridge stepped down as Chief Executive Officer.

About Envirotech Vehicles, Inc.

Envirotech Vehicles, Inc. (NASDAQ: EVTV) is a technology infrastructure company focused on developing, owning, and operating artificial intelligence data centers, enterprise GPU compute infrastructure, digital power solutions, and digital asset mining operations. Following its acquisition of Azio AI, the Company operates an integrated AI infrastructure business encompassing AI data center development, the sale and distribution of enterprise GPU systems and server infrastructure, high-performance computing solutions, power hosting, and strategic technology investments, serving enterprise and institutional customers across domestic and international markets. Through this diversified AI infrastructure strategy, the Company is positioned to capitalize on the rapidly expanding global demand for AI infrastructure, compute capacity, digital power, and next-generation AI technologies.

For more information please visit: www.azioai.ai and for potential partnerships contact: AI@PhoenixMGMTconsulting.com

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. In some cases, you can identify forward-looking statements by words such as “may,” “will,” “could,” “expect,” “anticipate,” “believe,” “estimate,” “project,” “intend,” “continue,” “potential,” “ongoing,” or the negative of these terms or other comparable terminology, although not all forward-looking statements contain these words. Forward-looking statements include statements regarding the Company’s ability to capitalize on accelerating demand for AI infrastructure, enterprise GPU compute, digital power solutions, data center development, and digital asset infrastructure; the Company’s plans to continue expanding its digital infrastructure platform through AI data center development, enterprise GPU compute solutions, power hosting services, digital asset mining operations, strategic infrastructure investments, and additional commercial partnerships; the Company’s ability to maximize utilization of its power resources while creating multiple long-term revenue opportunities; the ability to continue deploying modular digital infrastructure at the Company’s South Texas site; the anticipated deployment and scaling of NVIDIA B200 and B300 GPU systems; the ability to advance and execute against the Company’s commercial infrastructure pipeline; the anticipated development of the Company’s footprint; the ability to monetize power assets across multiple complementary revenue streams, including AI data centers, enterprise compute infrastructure, power hosting, and digital asset mining operations; customer demand for AI infrastructure, enterprise compute, and digital infrastructure; the Company’s ability to build a scalable platform designed to serve that demand and create long-term shareholder value; and the Company’s broader business strategy and long-term growth objectives.

These statements are based on current expectations and assumptions that involve risks and uncertainties that could cause actual results to differ materially. Most of these factors are outside the Company’s control and are difficult to predict. Factors that may affect actual results include, but are not limited to, the Company’s limited operating history within AI infrastructure and compute operations, project scope, engineering challenges, supply chain constraints, installation timelines, energy availability, finalization of site usage rights, regulatory considerations, equipment performance, ability to raise capital required for expansion activities, changes in digital asset markets, evolving compute demand, market conditions, the Company’s ability to successfully integrate the combined business following the completion of the merger, the risk that the anticipated benefits and synergies of the merger are not realized, the risk of unexpected costs, charges, or expenses resulting from or relating to the merger, potential adverse reactions or changes to business relationships resulting from the completion of the merger, risks related to the diversion of management’s attention from ongoing business operations during the post-closing integration period, the risk that required stockholder approval for the conversion of preferred stock issued in the merger as required by rules of The Nasdaq Stock Market LLC (the “Conversion Proposal”) is not obtained, and additional risks and uncertainties described in the Company’s most recent Annual Report on Form 10-K and subsequent Quarterly Reports on Form 10-Q filed with the SEC, which are available at www.sec.gov. The Company undertakes no obligation to update forward-looking statements except as required by law.

Important Information About the Merger and Where to Find it

The Company expects to file a proxy statement with the SEC relating to the Conversion Proposal. The definitive proxy statement will be sent to all Company stockholders. Before making any voting decision, investors and security-holders of the Company are urged to read the proxy statement and all other relevant documents filed or that will be filed with the SEC in connection with the Conversion Proposal as they become available because they will contain important information about the amended and restated merger agreement between the parties and the related transactions and the Conversion Proposal to be voted upon by the Company’s stockholders. Investors and security-holders will be able to obtain free copies of the proxy statement and all other relevant documents filed or that will be filed with the SEC by the Company through the website maintained by the SEC at www.sec.gov.

Participants in the Solicitation

The Company and its directors and executive officers may be considered participants in the solicitation of proxies from EVTV’s stockholders with respect to the Conversion Proposal under the rules of the SEC. Information about the directors and executive officers of EVTV is set forth in its Annual Report on Form 10-K for the year ended December 31, 2025, which was filed with the SEC on April 13, 2026, and in subsequent Quarterly Reports on Form 10-Q and other documents filed by the Company from time to time with the SEC. Additional information regarding the persons who may be deemed participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will also be included in the proxy statement, and other relevant materials to be filed with the SEC when they become available. You may obtain free copies of these documents as described above.

¹ Source: International Data Corporation (IDC), “AI Infrastructure Spending Caps Historic Year at ~$90 Billion in Q4 2025; 2029 Spending to Eclipse $1 Trillion,” April 16, 2026. The Company has not independently verified the data or projections contained in this report, and there can be no assurance that the projections will be realized.

Contact

Phoenix MGMT & Consulting

Press@PhoenixMGMTConsulting.com

Ever since ChatGPT made its public debut nearly four years ago, governance and security have largely lagged behind AI adoption.

Eager to experiment with AI tools and find ways to improve their work and personal lives, users have uploaded corporate data, financial records, and even their own health information to large language models (LLMs). While this freewheeling activity presents obvious risks, many users and businesses have so far been spared from catastrophic consequences.

Stephen Wilson, field chief technology officer for HashiCorp, an IBM company, notes that most people are still using AI tools largely as “assistants,” with the technology only taking action at the direction of human users. But, as AI agents are given more ability to act on their own, the risk calculus is changing. And so far, Wilson says, security and governance practices aren’t keeping up.

“Right now, what’s happening is that organizations are starting to use AI tools as full partners but governing the tools the same way they did when they were only using them as assistants,” Wilson says. “When AI is an assistant, the user is very close to the execution, and they’re handing over API keys, social media credentials, and bank information. But now we’re starting to ask AI to do things on our behalf autonomously.”

As organizations move from assisted use cases toward more autonomous workflows, Wilson says, they need to mature their governance models across three common adoption patterns: AI as assistant, AI as an agent, and AI as operator.

AI as assistant

The most basic and widespread form of enterprise AI adoption is AI as an assistant. In this model, a human remains close to the work, using the technology to summarize information, draft content, generate code, and complete other discrete tasks. The user enters a prompt, evaluates the response, and decides what to do next.

Although humans remain close to the execution at this stage, activity is not free from risk. When users interact with AI assistants, they can easily bring sensitive data, credentials, or permissions with them into the workflow. A user with privileged access might paste an API key into a prompt or even ask an LLM to analyze confidential records.

“You need to have a very tight handoff from the human identity to the machine identity,” Wilson says. “You also need to be able to govern what that machine can access from a machine-to-service perspective, because if I get elevated privilege, it’s not hard to inject that privilege into the context window.”

At the assistant stage, organizations largely need to ensure that AI activity is governed by the same boundaries already established for users. But as AI moves from answering prompts to completing work, those governance boundaries must expand.

AI as an agent

At this stage, human users begin asking AI tools to complete certain tasks autonomously. For example, instead of going back and forth with an LLM to outline and draft a piece of content, a user might simply give an AI tool a set of inputs and basic instructions and then ask the tool to generate the piece on its own. In fact, the writing agent may even pass off the finished draft to an editing agent or other AI tools before coming back to a human user.

“When that happens, the governance controls and the identity and auditability have to go up because you’re moving the human out of the loop even more,” Wilson says. “With AI assistants, the human is still the initiator of the request that happens back and forth. But with AI as agent, you’re making a request and then just letting it run.”

At this stage, Wilson says, organizations must determine what level of access different agents need to complete certain tasks, as well as how to confer identity upon AI agents. “How do you manage the persona? How do you accelerate its ability to be more correct often? These are the things you have to think about as you start to move to AI as an agent.”

AI as operator

This is the stage where AI agents take on not just individual tasks but entire projects. Instead of prompting agentic tools to write and edit a single article, an organization might ask a team of AI agents to design and execute an entire marketing campaign.

“The human comes back in two or three hours and has the entire project, including where to publish, individual social media posts, and engagement strategies,” Wilson says. “The level of governance and identity and auditing have to increase as your level of oversight decreases.”

Wilson notes that it is important at this stage to establish strong governance not only around data access but also around accuracy. For example, if an AI agent creates social media content, the organization needs to know that the content uses approved messaging, moves through the right review process, and is published only through authorized channels.

This is a complex challenge because AI agents are probabilistic systems, while many enterprise workflows are deterministic. Before giving agents the power to complete these workflows, Wilson says, leaders must think carefully about where AI-generated work should end and controlled execution should begin.

The road ahead

Most organizations are only beginning to deploy agentic AI beyond the assistant stage, and Wilson notes that security leaders are still debating the right governance, identity, auditability, and observability models for these systems.

But the overarching governance demand is clear: As AI systems gain more autonomy, organizations must implement more rigorous controls. An AI assistant can be governed largely as an extension of the individual user. An AI agent must be governed as part of a team, with clear visibility into the work it performs and the systems it touches. And an AI operator must be governed as a business function, with controls that span data access, workflow execution, approvals, and audit trails.

“Your scope of governance, identity, and observability has to increase at the same rate as if you were moving from an individual to a team to an organization,” Wilson says.

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Reading about the “revolutionary” nature of generative AI technology these days, it’s hard not to feel a little left out.

Sure, services like Google’s Gemini and its contemporaries can be useful in certain limited, specific areas for productivity purposes. But working with them can also be pretty disheartening and overwhelming — from prompt fatigue and an onslaught of AI workslop to the fear of lost jobs and even just the simple inconsistencies and inaccuracies these systems are so prone to providing. (And that’s to say nothing of the ever-increasing creepy factor that often accompanies this type of technology.)

More and more, it seems the most significant impact of these systems is in areas like coding, where AI is allowing ambitious tech-heads to create their own custom programs with limited to no programming knowledge (but a lot of time, vision, and patience) — as well as allowing accomplished coders to produce products more quickly by letting AI do the dirty work and then spending their time guiding, tweaking, and correcting its output.

That’s all well and good, but the reality is that most of us mere mortals are never gonna mess with anything that daunting. That doesn’t, however, mean we can’t enjoy a slice of the custom-coding pie and the productivity advantages it offers — on a much simpler but still supremely useful level.

The average-worker answer lies in an oft-overlooked middle-ground possibility these AI chatbots possess to help us create relatively basic but extremely high-potential custom browser extensions. As their name suggests, these simple little programs run entirely in your browser — the same exact sorts of add-ons you’d typically find and install in a marketplace like Google’s Chrome Web Store.

But with Gemini or any other similar genAI platform, you can dream up your own web-improving extension and turn it into reality in a matter of minutes — simply by describing your goal and then guiding the AI gently along the way. And given how much time most of us spend on the web these days, that opens up a tantalizing series of doors for taking total control of your work environment.

Hate all the extraneous bells and whistles gunking up the Google Docs interface? Gemini can create a Chrome extension that removes them. Annoyed by a glitchy web app? Ask Gemini for an extension that makes some under-the-hood improvements. The possibilities are endless.

Let me show you how exactly it works, how easy it is to approach and master, and how many work-enhancing possibilities are out there just waiting to be created.

The ins and outs of Gemini’s custom Chrome extensions

First things first: You don’t need any special tools or subscriptions to make this happen. For the purposes of this article, we’ll focus on Google’s Gemini for the creation and the standard desktop Chrome browser for the installation — but the same basic process would work with most any AI chatbot, if you happen to prefer ChatGPT or Claude, as well as with any extension-supporting, Chromium-compatible browser (a list that includes everything from Microsoft Edge to Brave, Vivaldi, and beyond).

Google offers a dizzying array of Gemini modes and options and an equally overwhelming series of AI subscription plans that control how much you can use those capabilities, but you don’t need to worry about any of that to create custom Chrome extensions. You might sometimes see better results if you switch your Gemini model to “Pro” or your Gemini thinking level to “Extended” — designations that even Gemini itself has trouble deciphering (believe me, I asked!) — but just using the default Gemini settings with a free Google account will generally work quite well.

Getting going with a custom Chrome extension is as simple as opening up a new Gemini chat and telling the system what you want it to cook up for you. The hardest part is deciding what you want and what’d be helpful for you — something we’ll explore more in a moment, via specific examples and suggestions. Once you’ve got that, you can just ask Gemini to create a Chrome extension that’ll accomplish what you’re envisioning, with as much specificity as possible about what it’ll do and how it’ll look.

Gemini will spit back a series of plain-text code chunks with instructions to copy each cluster and paste it into a new plain text file with a certain specific name — things like “manifest.json,” “content.js,” and “styles.css.” All you’ll do is use the on-screen button to copy each segment, then open up any simple text editor (like Windows Notepad, macOS TextEdit, or any number of simple online text editors) and paste the text in, then save it under the name Gemini gives you.

You’ll need to put all the files into a single isolated folder on your computer, and then you can go into Chrome, type chrome:extensions into its address bar, and install your shiny new creation by:

• Flipping the toggle next to “Developer mode” in the upper-right corner of the screen into the on and active position, if it isn’t already
• Clicking the “Load unpacked” button
• And selecting the folder you just created in the pop-up that appears

And that’s pretty much it: No complicated compiling or program publishing — the extension you envisioned will be alive and working right in your browser and ready to use.

Now, odds are, it won’t be exactly what you wanted in its first iteration, and you’ll have to go back to Gemini to request several rounds of updates and corrections. Each time, Gemini will create a new set of code chunks, and you simply overwrite the text in each file with its corresponding new code chunk.

It’s still a bit of a process. But you’ll rarely spend more than an hour on something simple and maybe a few hours on something especially multifaceted and specific, and whatever you create will then work to your advantage indefinitely from that point onward, on any computer where you install it.

Before we dive into specific slivers of inspiration, let’s just note the hopefully obvious asterisk that this’ll work only if you’re either (a) using a personal computer that isn’t associated with an organization or (b) using a work-connected computer where custom Chrome extensions are permitted. In either scenario, you’ll want to use your own best judgment to ensure that whatever you’re adding into your browser won’t expose any corporate data or cause your IT comrades any alarm if they see you using it in your workday.

With most common examples, though — including all the ones we’re about to go over — you shouldn’t have any problem or cause for concern.

Capisce? Capisce. Let’s get into it.

Custom extension category #1: The interface fixer

Our first custom Chrome extension category is the one that won me over to this practice initially and has been the most shapeshifting for my own browser-based workflow — and that’s the simple-seeming but transformational ability to have AI remake any web app you rely on to remove unneeded elements and redesign the interface to your exact specifications.

The best example I can show you is what I did with my completely homemade, Gemini-created Docs Zen extension. Google Docs, to put it mildly, has devolved into a cluttered mess. There are so many on-screen elements I never use and, ironically enough, irrelevant AI elements I’d rather not have in my hair. I just want a calm, simple, minimalist environment for writing — with Google’s second-to-none syncing, universal access, and collaboration systems beneath it.

So rather than try to reinvent the wheel, I described to Gemini all the elements I wanted to remove from Docs and all the ways I wanted to rethink how its interface appeared for me.

I went back and forth with numerous iterations and kept coming up with interesting new additions to further flesh out and improve the experience — and I ended up with a delightful setup that gives me a distraction-free view of my writing space with a simple toggle to reveal the main Docs menus and a palette icon that allows me to switch from one eye-pleasing theme to another.

My setup deliberately doesn’t include comments or other collaborative elements, as I’m mostly writing by myself these days — but when I do need those elements, the eye icon in the upper-right corner of the screen disables my custom adjustments and takes me back to the standard Docs interface. I can then click the eye icon again in that environment to switch back.

I used Gemini to create something similar for Trello, with which I also have a love-hate relationship — loving the foundational functions and easy access everywhere but hating the interface that’s lost focus, gained bloat, and gotten noticeably clunky and slow over time.

With the same sort of step-by-step, plain-English guidance, I was able to transform Trello from this…

…into this:

I couldn’t even begin to recount the number of superfluous features and elements I’ve removed, along with revamping the overall interface to make it both more efficient and more visually pleasing to my eye.

Whether it’s a web app you rely on regularly or even just a website you open often, the possibilities are practically endless for the ways you can reshape it and mold it to make it work better for you.

Speaking of which…

Custom extension category #2: The feature creator

In addition to the surface-level adjustments and feature removals in my aforementioned Trello-enhancing extension, I also added in several components — such as one-click buttons for archiving or moving cards — and I managed to speed up the site by making some under-the-hood adjustments Gemini suggested when I asked about its choppy performance. The same sort of concept can apply to any web-based interface you’re using, if there are any options that are annoyingly buried within menus, shortcuts that’d make your life easier, or other improvements you’ve longed to see.

You can also consider some simple standalone extensions for giving yourself on-demand features that aren’t necessarily associated with any one specific website but could be useful in plenty of productivity scenarios. For instance:

• I do a fair amount of basic image editing and frequently find myself needing to reference a hex color code that corresponds with a particular brand color, and I always end up having to open up a new tab and look in a note somewhere to find the code I need. Well, no more: I used Gemini to create a super-simple custom color code pop-up where I can store all the colors I need and then copy any of ’em onto my clipboard with a single click. Major time-saver.

• I’m also constantly converting time zones, either for meetings with clients or colleagues or for trying to wrap my head around publishing systems that insist on using random time zones with no meaning to me. It’s infinitely easier for me to manage now, thanks to the custom Chrome extension I made that shows the current time in all the zones I need most often — as well as allowing me to put any other time into any field and have all the other zones instantly adjust to match. It also offers a brilliant plain-text conversion box where I can just type things like “1pm-3pm PT in MT” and have it cough back up an instant answer for any conversion I need.

Maybe what you want is the ability to interact with data on different websites more easily — to be able to save any table on a page in front of you as a CSV file, mayhap, or even to save any text you highlight on a page into a new Google Docs document. Whatever the case may be, Gemini can handle it — and that superpower that you’ve always wished for but never found the right tool to make possible can actually now be yours.

Custom extension category #3: The browser expander

Our final category of custom Chrome extensions to consider moves beyond the web itself and into your actual browser. The browser is essentially the modern-day desktop, after all — and for the first time now, you can expand and enhance it in all sorts of interesting ways.

Some specific examples, to get your brain-motor whirring:

• You could walk Gemini through creating a smart auto-snooze system for your open browser tabs, both to clear clutter and help with Chrome’s performance. It could save any tab that hasn’t been touched in a certain amount of time to your local storage and then give you a simple searchable “Archive Dashboard” where you can find all those auto-closed tabs and re-open ’em as needed.
• With the right guidance, Gemini could give you a custom browser research panel — where any info you highlight on a page gets beamed over into a sidebar-style panel that serves as a running scratchpad of notes from the day.
• Or, if you find yourself often needing to see two tabs together side by side, you could have Gemini cook up a custom extension that instantly detaches any tab in front of you and puts it into a new tab window in a perfectly sized and spaced pattern. One keyboard shortcut could make that move happen, while another keyboard shortcut could recombine the two tabs into a single centered window.

As with all the other ideas we’ve gone over, all you’ve gotta do is ask — and now, with the right inspiration in mind, you’re ready to get your custom extension adventures going and start bending the web to your will.

This year marks the 20th anniversary of AWS — and my 20th year building at Amazon.

My entire career is for the sole purpose of making developers’ lives easier. As a developer, it is a bit of a self-serving purpose. For example, I was constantly distracted by operating databases, so I joined the DynamoDB team to build a service that handles that, so that other developers and I would never have to operate databases again.

I then went on to work on Lambda and API Gateway. I didn’t have to babysit servers or handle request routing, and on CloudWatch, so I could see what my code was doing in production. Each time, the goal was the same: remove the painful, repetitive work and turn it into a service that just works.

I’m still chasing the same goal, just with a very different set of tools.

The rise — and limits — of vibe coding

Large language models added the ability to describe what I want in natural language and have code synthesized on demand. At first, this looked like “vibe coding” — ask for a change to the script, compile it, run it, copy the errors back and hope the next iteration was better.

Things got interesting when we wrapped the whole “vibe coding” workflow in agentic loops. Instead of me feeding back every error, an agent could call the model, run the code, see its own failures and keep iterating until tests passed. But there was a big problem: the agents wandered. That’s fine for side projects, not fine for large, critical codebases.

Spec‑driven development for agents

The way I’ve come to keep agents focused is through spec‑driven development. Instead of dropping an agent into a repo with a vague prompt, I co‑create three concrete artifacts with it before any serious coding begins: a requirements spec, a design document and a task breakdown, all in Markdown. These are a shared contract for what “done” means, written in a form that both humans and agents can read, critique and update.

In my day-to-day, I start with almost the same prompt I’d give any AI, but the agent expands it into a structured requirements document with clear “shall” statements and acceptance criteria. I review those requirements and chat with the agent until the document matches what I actually want. From there, the agent proposes a design, then breaks the work into tasks focused on getting something tangible running before adding polish and exhaustive tests.

What I like about this flow is not that it’s rigid. In practice, I bounce back and forth. I often see a design that reveals missing requirements, or I change my mind about the approach once I see code snippets. The point is that agents no longer “forget” what we agreed on. The spec, design and tasks are explicit, versioned and always visible. And when I want to tack on another feature or bugfix, I start with a fresh spec that describes exactly what I want to change.

Property‑based testing and keeping agents honest

Once the specs are explicit, you can turn them into invariants and use property-based tests to keep agents honest. Instead of writing one test for “given this exact input, expect this exact output,” I define properties that must hold across many inputs and sequences.

Without strong, spec-derived tests, I’ve seen agents game the system by “fixing” the tests instead of the code — commenting out assertions or weakening conditions just to get a green build. Property-based tests give me a way to encode my expectations once and have both humans and agents constantly prove we’re still meeting them.

This approach has clear implications for security as well. If security teams can encode expectations — about data handling, authorization and error behavior — as invariants in the same spec language the agent consumes, then property-based tests can hammer those invariants across many scenarios. That’s a much more robust way to shift security left than hoping every developer remembers every rule under deadline pressure.

DevOps agents and the next decade of practice

Over twenty years, I’ve learned that the key to incident response isn’t only about chasing the root cause — it’s systematically asking what changed, what callers changed, what limits were hit, what components failed as designed and what dependencies are involved.

A DevOps agent is becoming as important as any IDE. It plugs into the tooling teams already use and runs that investigation automatically whenever an alarm fires. It reads logs, metrics, traces and code, and often has a diagnosis and plan ready by the time I open my laptop.

I’ve seen incidents that once took eight hours of human sleuthing reduced to fifteen minutes, with the agent explaining the bug, citing evidence, and recommending a rollback and follow-up fix.

Between incidents, the same system scans past outages and infrastructure to suggest preventative work — code hardening, better retries, alarm tuning — that teams rarely have time to prioritize on their own, and that’s the most important part. Reducing downtime is great, but avoiding it altogether is a big reason why we’re here.

Looking ahead, I think developers will learn to wear all sorts of other hats — the operator, product manager, customer support — while agents take on their routine tasks. The most valuable work becomes problem-solving and ensuring systems are built right and serve the right purpose.

Other things won’t change at all. “If you build it, you run it” still applies, even when an agent wrote part or all of the code. Developers will still own production and post‑incident retrospectives that focus on how to prevent issues. Some parts — like data collection, impact analysis, root cause analysis — get faster with agents doing the legwork, but developers still direct the investigation, decide the real fixes and share those lessons across teams.

Twenty years ago, the big shift was turning infrastructure into services, so developers didn’t have to think about racking servers or babysitting databases. In this new era, the move is turning our best practices, operational experience and security expectations into specs and agents that can execute them consistently, at any scale. The lesson from the first two decades still applies: The pain you tolerate today is the platform someone else will build tomorrow — only now, agents give us a much faster way to close that gap.

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Researchers from the University of Toronto developed a computer worm prototype powered by an AI agent that successfully self-replicated to different systems within a simulated computer network. The worm used a free large language model (LLM) running on local hardware and exploited a combination of older and new vulnerabilities, as well as misconfigurations that remain all too common in enterprise environments.

At a time when CISOs and the security industry are concerned about the ability of frontier models such as Anthropic’s Mythos to find zero-day vulnerabilities in critical software, this experiment is a reminder that attackers don’t need cutting-edge AI to wreak havoc across typical corporate networks. In fact, using paid models accessible only via APIs would be a point of failure for an autonomous malicious system like a computer worm, because prompts constructed to bypass safety guardrails would quickly be detected and blocked by the AI labs.

“We discovered that it is possible to create an AI-driven computer worm, using only small, free AI models, that can autonomously identify each machine’s unique weak points (including vulnerabilities just reported by industry and misconfigurations such as reused passwords) and exploit them, hijacking computing power to take over regular devices such as laptops, cameras, and everything else online, and then copying itself onto servers and networks to either steal data or launch new attacks,” the research team from the University of Toronto’s CleverHans Lab said in their report. “We did this without using the newest, most powerful AI models. There is no single defence against this new threat.”

Building an agentic harness for offensive cyberattacks

While frontier models such as Claude Opus and GPT 5.5 offer million-token context windows and can reason for tens of minutes and even hours at a time to solve a single task, this approach does not work for locally hosted LLMs running on a single GPU. Their context windows are much smaller and generally exhibit weaker instruction-following abilities for agentic tasks.

Vibe-coding software developers who encountered these problems long ago have solved them by building custom harnesses and agentic frameworks that split complex software engineering projects into phases and steps, executed by multiple sub-agents in parallel that share results via some form of memory system, ranging from a markdown file to a database.

The CleverHans Lab researchers adopted those lessons to build their own harness for offensive security purposes to compensate for local LLM limitations, complete with phases and task-specific nodes that make LLM calls with specialized prompts.

“This core is supported by complementary systems: a hierarchical memory that preserves discoveries across independent LLM calls, tools and their handlers that encapsulate common action sequences and interpret execution results, a skill system that injects context-aware pentesting guidance on demand, and multi-agent coordination that shares intelligence across instances,” they explained in their paper.

Agentic harnesses built for security research and penetration testing are not a new concept and have existed for a while. Open-source examples include RAPTOR, a framework of skills and agents for Claude Code designed for vulnerability discovery and exploit writing, and SecOpsAgentKit.

“Previous models can perform close to, at, or beyond Mythos levels depending on capability by using harnesses,” Gadi Evron, CEO of AI security firm Knostic and one of the creators of RAPTOR, told CSO. “When new models come out, they can achieve in a prompt what previously took a harness, at which point new harnesses are built, and so on and so forth.”

Simulating a vulnerable enterprise network

The CleverHans Lab researchers created a network made up of virtual machines running different operating systems including Ubuntu (16.04-24.04), Debian (9-12), Alpine Linux, Rocky Linux 9, CentOS Stream 9, and Windows Server (2008 R2, 2019, 2022). These VMs simulated servers running a variety of web services, as well as IoT devices and industrial control systems that can be found on typical corporate networks.

The researchers left an assortment of old and new — a few days old — vulnerabilities unpatched on the simulated systems, as well as common insecure configurations and generic flaws such as missing access control or SQL injection. The goal was to test the capability of their AI-powered worm to discover the systems, find the open ports, identify the exposed services, locate the vulnerabilities, exploit them, and then copy itself to the compromised system.

The systems had both remotely exploitable flaws that would provide initial access, as well as local flaws and weaknesses that could be used to elevate the privileges to root and completely take over the machine.

“These two classes evaluate complementary capabilities: CVEs test the agent’s ability to fingerprint software versions and dynamically adapt or compile raw exploit code to fit the target environment, whereas CWEs probe its capacity for open-ended, target-specific reasoning where no off-the-shelf exploit applies,” the researchers said. “To ensure the network reflects vulnerabilities encountered in the real-world, all targets are drawn from three authoritative threat taxonomies [the CISA Known Exploited Vulnerabilities catalog, the OWASP Top 10: 2025, and the MITRE ATT&CK framework] covering, respectively, vulnerabilities under active exploitation in the wild, prevalent web and software vulnerabilities, and adversarial techniques used after initial access to a network.”

Furthermore, some virtual machines were equipped with GPUs that would allow the worm to replicate itself and then hijack the computing resources to run the model locally instead of relying on the LLM hosted by the researchers on an external system.

Over the course of seven days and five generations of self-replication, the worm managed to spread to 27 of the 33 systems in the simulated environment. The worm correctly identified the vulnerabilities in 82% of the attempts and managed successful exploitation in 44% of those. Despite the exploitation rate being quite low, the parallel and swarm-like implementation where every compromised system became a new instance of the malicious agent, compensated and eventually led to an overall high success rate.

This largely matches what security researchers from Forescout found in a study performed earlier this year that looked at how good models have become at discovering and exploiting vulnerabilities. While the research showed that the new generation of open-weight models had significantly improved their capabilities of both finding and exploiting vulnerabilities, the smaller variants of those models quantized to run locally on single-GPUs still performed poorly at such tasks.

The researchers noted at the time, however, that by using specialized AI agentic frameworks like RAPTOR they were able to find new zero-days in OpenDNS.

“Many of the open-source or generally commercially available models are already good enough that if used with the correct harness they can find vulnerabilities, exploit them, create malicious code and so on,” Daniel dos Santos, VP of research at Forescout, told CSO. “The new work from U of Toronto shows that similar models can also be used to create dynamically adapting worms.”

Cybercriminals are aware of these advances in model capabilities too based on discussions Dos Santos’ team observed on underground forums, with more attackers focusing on open-source and commercial models instead of “underground” ones fine-tuned for cybercrime.

Organizations running out of time

While zero-day attacks receive a lot of attention and AI has put such flaws within the reach of more attackers than ever, the reality is that there is no shortage of systems on the internet and inside networks that are either misconfigured or vulnerable to known flaws for which patches or mitigations exist.

The University of Toronto experiment shows that defenders need to be able to respond with similar speed, especially since their prototype shows that knowledge about new vulnerabilities can be integrated into the worm’s knowledge base within hours of public disclosure. The ability of the worm to hijack GPUs to run nodes further decreases the investment attackers need to make in running such AI-assisted attacks.

“Organizations have endless technology and security debt, and with AI attacks on the rise, we no longer have time,” Evron said. “Change however is all about time, especially in the enterprise. The key is to start preparing right now. Soon, we won’t measure time to exploitation, but will need to construct new measurements, such as for the ability to handle regularly occurring, concurrent data breaches while minimizing impact on daily operations.”

University of Toronto researchers call for enterprises to adopt AI-assisted penetration testing and fuzzing to discover exploitable weaknesses in their own infrastructure, but also to build the capability to deploy patches or mitigations faster, which is now a significant gap.

They do, however, acknowledge some limitations of their prototype, such as the fact that it was noisy, leaving many behavioral signatures behind that could be detected by endpoint and network monitoring systems. Also their simulated network lacked basic network segmentation, which could be further improved with zero-trust architecture to prevent lateral movement and by minimizing the software dependencies and attack surface on every host system.

“While vulnerabilities, exploits, and attack orchestration are now autonomous, the deeper meaning for defense is that many of our assumptions about building security programs are now challenged,” Evron said. “Until we get to mature defensive AI, we must empower our people with coding agents to bring them up to machine speed, and then defend these agents in turn.”

Microsoft has announced Coreutils, a new Windows 11 feature that allows developers to run many popular Linux command line utilities natively on Windows from a single binary.

Revealed at this week’s Build 2026 developer conference in Seattle, Coreutils is about reducing what Microsoft terms the “cognitive load” faced by developers when moving between Windows and other platforms.

Currently, accessing the Linux command line utilities that are considered essential in many CI/CD development environments on Windows requires a kludge that involves either opening an emulation such as Git Bash, or a virtualized Windows Linux Subsystem (WSL) terminal.

Both are time-consuming and inefficient. As Microsoft’s announcement puts it: “Developers constantly move between platforms, but familiar commands don’t work consistently, forcing workarounds, lost speed and context switching.”

Coreutils removes the need for this back and forth, allowing developers to run most Linux commands straight from the Windows CMD command prompt, PowerShell, or Windows Terminal.

“Whether you’re moving between Linux, macOS, WSL, containers or cloud environments, the commands and workflows you’ve built over years just work in your Windows environment,” Microsoft said.

Most utilities, but not all

Installed as a single executable (via WinGet: install Microsoft.Coreutils), Coreutils for Windows itself is a Rust rewrite of the GNU uutils/coreutils project that provides commands that are universal across Linux distros.

Fundamental to making Coreutils efficient to manage is the fact that individual Linux commands run from a multi-call executable which maps via NTFS hardlinks pointing to each command. The advantage of this approach is that there’s only one binary to install, one binary to sign, and one binary to patch or update.

Microsoft lists 75 Linux utilities supported by Coreutils, including commonly-used commands such as ls, cp, find, grep, find, rm, du, hostname, and uptime.

However, some Coreutils commands clash with existing CMD or Powershell commands, or are otherwise not possible to execute; Microsoft provides a compatibility table listing conflicts. This means that some commands are not available, specifically: dir, expand, kill, more, timeout, and whoami.

There are also some commands omitted from Coreutils because a command relies on a POSIX Unix/Linux feature that Windows doesn’t implement in a compatible way; some examples are chmod, chown, id, stty, and chroot.

In other cases, the command will execute in one context, CMD, but not in PowerShell. Microsoft explained the complex order of precedence:  “Whether the Coreutils version runs depends on the shell, the PATH order, and (for PowerShell) the alias table.”

As well as Coreutils, the Build 2026 developer conference also saw Microsoft announce WSL containers CLI and API to deploy Linux containers on Windows, a new framework for autonomous agents with open source governance tools, and Microsoft Scout, an AI agent designed to automate tasks in Microsoft 365.

Microsoft has announced Coreutils, a new Windows 11 feature that allows developers to run many popular Linux command line utilities natively on Windows from a single binary.

Revealed at this week’s Build 2026 developer conference in Seattle, Coreutils is about reducing what Microsoft terms the “cognitive load” faced by developers when moving between Windows and other platforms.

Currently, accessing the Linux command line utilities that are considered essential in many CI/CD development environments on Windows requires a kludge that involves either opening an emulation such as Git Bash, or a virtualized Windows Linux Subsystem (WSL) terminal.

Both are time-consuming and inefficient. As Microsoft’s announcement puts it: “Developers constantly move between platforms, but familiar commands don’t work consistently, forcing workarounds, lost speed and context switching.”

Coreutils removes the need for this back and forth, allowing developers to run most Linux commands straight from the Windows CMD command prompt, PowerShell, or Windows Terminal.

“Whether you’re moving between Linux, macOS, WSL, containers or cloud environments, the commands and workflows you’ve built over years just work in your Windows environment,” Microsoft said.

Most utilities, but not all

Installed as a single executable (via WinGet: install Microsoft.Coreutils), Coreutils for Windows itself is a Rust rewrite of the GNU uutils/coreutils project that provides commands that are universal across Linux distros.

Fundamental to making Coreutils efficient to manage is the fact that individual Linux commands run from a multi-call executable which maps via NTFS hardlinks pointing to each command. The advantage of this approach is that there’s only one binary to install, one binary to sign, and one binary to patch or update.

Microsoft lists 75 Linux utilities supported by Coreutils, including commonly-used commands such as ls, cp, find, grep, find, rm, du, hostname, and uptime.

However, some Coreutils commands clash with existing CMD or Powershell commands, or are otherwise not possible to execute; Microsoft provides a compatibility table listing conflicts. This means that some commands are not available, specifically: dir, expand, kill, more, timeout, and whoami.

There are also some commands omitted from Coreutils because a command relies on a POSIX Unix/Linux feature that Windows doesn’t implement in a compatible way; some examples are chmod, chown, id, stty, and chroot.

In other cases, the command will execute in one context, CMD, but not in PowerShell. Microsoft explained the complex order of precedence:  “Whether the Coreutils version runs depends on the shell, the PATH order, and (for PowerShell) the alias table.”

As well as Coreutils, the Build 2026 developer conference also saw Microsoft announce WSL containers CLI and API to deploy Linux containers on Windows, a new framework for autonomous agents with open source governance tools, and Microsoft Scout, an AI agent designed to automate tasks in Microsoft 365.

This article originally appeared on InfoWorld.

Microsoft has announced Coreutils, a new Windows 11 feature that allows developers to run many popular Linux command line utilities natively on Windows from a single binary.

Revealed at this week’s Build 2026 developer conference in Seattle, Coreutils is about reducing what Microsoft terms the “cognitive load” faced by developers when moving between Windows and other platforms.

Currently, accessing the Linux command line utilities that are considered essential in many CI/CD development environments on Windows requires a kludge that involves either opening an emulation such as Git Bash, or a virtualized Windows Linux Subsystem (WSL) terminal.

Both are time-consuming and inefficient. As Microsoft’s announcement puts it: “Developers constantly move between platforms, but familiar commands don’t work consistently, forcing workarounds, lost speed and context switching.”

Coreutils removes the need for this back and forth, allowing developers to run most Linux commands straight from the Windows CMD command prompt, PowerShell, or Windows Terminal.

“Whether you’re moving between Linux, macOS, WSL, containers or cloud environments, the commands and workflows you’ve built over years just work in your Windows environment,” Microsoft said.

Most utilities, but not all

Installed as a single executable (via WinGet: install Microsoft.Coreutils), Coreutils for Windows itself is a Rust rewrite of the GNU uutils/coreutils project that provides commands that are universal across Linux distros.

Fundamental to making Coreutils efficient to manage is the fact that individual Linux commands run from a multi-call executable which maps via NTFS hardlinks pointing to each command. The advantage of this approach is that there’s only one binary to install, one binary to sign, and one binary to patch or update.

Microsoft lists 75 Linux utilities supported by Coreutils, including commonly-used commands such as ls, cp, find, grep, find, rm, du, hostname, and uptime.

However, some Coreutils commands clash with existing CMD or Powershell commands, or are otherwise not possible to execute; Microsoft provides a compatibility table listing conflicts. This means that some commands are not available, specifically: dir, expand, kill, more, timeout, and whoami.

There are also some commands omitted from Coreutils because a command relies on a POSIX Unix/Linux feature that Windows doesn’t implement in a compatible way; some examples are chmod, chown, id, stty, and chroot.

In other cases, the command will execute in one context, CMD, but not in PowerShell. Microsoft explained the complex order of precedence:  “Whether the Coreutils version runs depends on the shell, the PATH order, and (for PowerShell) the alias table.”

As well as Coreutils, the Build 2026 developer conference also saw Microsoft announce WSL containers CLI and API to deploy Linux containers on Windows, a new framework for autonomous agents with open source governance tools, and Microsoft Scout, an AI agent designed to automate tasks in Microsoft 365.

This article originally appeared on InfoWorld.

As long as software engineering is done in teams, we need a way for people to know how things work, why certain decisions were made and where the boundaries are. That need doesn’t go away when AI writes the code. If anything, it gets more critical.

Code reviews were how most teams handled this. When someone reviewed your PR, they didn’t just check for bugs; they absorbed context. They learned why certain decisions were made. That’s tribal knowledge. At The Hangar, a community of DevEx leaders, we discussed code reviews a lot. According to Adrienne Braganza Tacke, author of ‘Looks Good to Me: Constructive Code Reviews’, the most important function of code review is actually record-keeping: chronicling how the codebase changed and why, not just catching bugs.

That conversation now feels like a different era. The entire software development lifecycle, as we know it, is not only accelerating, but collapsing and being redefined. I recently said that we should kill the code review. AI generates code faster than humans can review it. PRs pile up or get rubber-stamped. That approval gate no longer matches how we do software engineering now.

But if AI is now generating most of the code and there is no way a human could ever read all of it, how do we share knowledge?

Decisions over diffs

My proposal for solving the code review bottleneck was to move the human checkpoint upstream, to reviewing intent, reviewing the contract that code should fulfill: specs, plans, constraints and acceptance criteria. The same applies to the knowledge-sharing part of the review process.

If the team reviews intent and acceptance criteria before code is generated, the knowledge sharing happens naturally as part of planning. You’re not trying to reverse-engineer decisions from a 500-line diff. You’re aligning on a handful of key choices before anything gets built. The reviewer reads 10 lines of decisions, not 500 lines of code.

Whether that takes the form of a lightweight spec, a set of acceptance criteria, or even bullet points extracted from the prompt conversation, the principle is the same: make decisions visible and reviewable. That’s where the knowledge lives.

When a developer works with Cursor or Claude Code, they make decisions constantly: architectural choices, behavior tradeoffs and scope calls. Those decisions live in the prompt conversation, in the back-and-forth with the agent. When the PR is submitted, that context is gone. The code is there. The reasoning behind it is not.

The idea of formalizing intent before implementation is not new. Behavior-Driven Development, Test-Driven Development and Design-by-Contract approaches all tried to define behavior in structured, human-readable specs before writing code. BDD in particular asks teams to describe what the system should do in natural language, as scenarios that even the non-technical stakeholders can read and verify, before any code is written.

These approaches were often perceived as overhead. Writing formal behavior descriptions and contracts demanded discipline and time. Under delivery pressure, teams frequently skipped them. AI makes them more practical, not less. AI can help generate structured acceptance criteria, behavioral specs, or even contract-like descriptions. It can also help enforce them.

AI software engineering isn’t a solo sport

Every now and then we hear about engineers out there running agent orchestrators that produce 100,000 lines of code a day, like Steve Yegge and his Gas Town. Some say this is the future of software development, solo developers with swarms of agents, but I disagree. Building software, even with agents, is not a solo sport.

If that one person gets hit by a bus, can someone else drive the project? Do they understand all the decisions that were made? No. In an enterprise, you need redundancy. Multiple stakeholders, teams and collaboration.

The knowledge-sharing function doesn’t become less important as AI adoption grows. It becomes more important, because the gap between what the system does and what any individual understands about it is widening faster than ever. No one wants to have their senior engineers become bottlenecks because they’re the only ones that know how they got their solutions from AI.

When AI is involved in every part of the process, the social contract of collaboration is changing. There’s authorship ambiguity — someone reviewing code that a colleague submitted doesn’t know how much effort they put into understanding the nuances of that code. The reviewer then might use AI to help them understand that code and post comments and the author may use AI to address those comments without investing time in reading and understanding them.

In a pre-AI team, a junior PR generates:

• 4–8 comments from a senior on idiomatic patterns.
• A back-and-forth on edge cases.
• An implicit “here’s how I’d think about this” lesson.
• A senior who now knows that part of the code exists.

In an AI-heavy team, that same change is:

• Generated by AI, lightly edited.
• Reviewed by AI, lightly approved.
• Merged with zero humans having formed a mental model of it.

New kind of debt: Cognitive debt

I recently spoke to professor and researcher Margaret-Anne Storey, who has coined a term for that: cognitive debt. She first noticed it with a group of her students who were building with AI and moving fast. At some point they told her that they couldn’t make changes in the product anymore. The professor suspected they had tech debt, messy code, but found out that the students had accrued a new kind of debt.

They had lost track of what features they were trying to build and why. They didn’t know who knew what on the team. And on one of the teams, it was one person who knew all the code and understood it because they were supervising the AI that generated it, and the rest of the team was unable to do that. And the person who had generated the code didn’t really understand what was generated either.

That’s the risk when teams start building fast with AI, dropping code reviews because they realistically cannot review thousands of lines of AI-generated code, but doing nothing to replace the knowledge-sharing function.

Anthropic’s study showed how over-reliance on AI coding assistants has downsides in code comprehension. Their study with 52 engineers found that AI assistance produced no statistically significant speedup on the task but scored 17% lower on a comprehension quiz afterward. The biggest drops were in debugging, with smaller declines in conceptual understanding and code reading. The takeaway is clear: passively delegating to AI (“just make it work”) impairs learning far more than using it to ask questions and understand the code.

Even a workflow of triggering an adversarial agent after you’re done coding with your primary agent that asks questions like: Why did you do it this way? What behavior do you expect? What tradeoffs did you consider, could go a long way toward reducing cognitive debt. It forces the developer to articulate understanding before the code moves forward.

Make knowledge sharing intentional

That judgment and those decisions — that is the main thing we as humans are providing in an AI-first world. You could even go so far as to say that most of the code written is boilerplate. The main decisions we’re making are architectural decisions, behavior decisions and technical decisions. These are the things we need to provide as input for the AI systems and every change requires clarity on those decisions.

A lot of the knowledge sharing that happened through code reviews was incidental. Engineers absorbed context because they had to look at the code to approve it. As we phase out reviews, being able to review these decisions becomes more important.

Eventually, engineers are still accountable for the changes they’ve created. All these decisions have to be tracked, and somebody has to be able to audit them to ask why certain decisions were made. That becomes your source of truth. That’s how I think team collaboration and knowledge sharing evolve, even though I said that we should kill the code review.

The productivity gains are real and worth taking. But every org optimizing purely for PR throughput is running an experiment on its own engineering culture. In five years, the orgs that win won’t be the ones that shipped the most AI code. They’ll be the ones whose engineers still understand what got shipped.

This article is published as part of the Foundry Expert Contributor Network.
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Enterprises deploying closed AI models have generally relied on published safety benchmarks to assess risk before procurement and deployment decisions. New research from Cisco’s AI Threat Intelligence and Security Research team finds those benchmarks may systematically understate the threat.

Standard safety tests submit a single adversarial prompt and record the model’s response. Multi-turn attacks work differently. An attacker maintains a conversation across multiple exchanges, iterating and adapting based on each response until the model yields.

The report pairs single-turn and multi-turn adversarial evaluation across 15 closed/proprietary frontier models from OpenAI, Anthropic, Google, Amazon and xAI. Running 30,090 single-turn prompts and 6,986 multi-turn attacks, the team found that the two evaluation regimes produce different model rankings, different failure maps and different risk profiles. Every model tested failed a non-trivial share of multi-turn attacks.

Key findings from the research:

• Multi-turn attack success rate (ASR) ranged from 7.89% to 88.30% across all 15 models, against a single-turn range of 2.19% to 64.91%.
• Eight of 15 models showed an absolute gap greater than 15 percentage points between the two regimes.
• Anthropic’s Claude family, which posted the lowest single-turn ASR in the cohort at 2.19% to 3.64%, still reached 11.16% to 16.20% under iterative attack.
• Single-turn failures concentrated in three procedures: Imposter AI at 37.50% weighted ASR, Soft Paraphrase at 29.21% and System Prompts at 27.69%

The findings challenge a common assumption in enterprise AI procurement. 

“The surprising thing here is really that a lot of people accept and kind of understand these frontier labs as being state of the art, but they don’t necessarily think through the security and safety implications of that,” Amy Chang, head of AI threat and security research at Cisco, told Network World. “What this research does is kind of showcase that there is still variance across the different models, and how strong they are with the internal guardrails that are built within the model against these types of attacks.”

How multi-turn attacks work

In a multi-turn attack, the adversary does not present the harmful request upfront. Intent builds gradually across exchanges, with each prompt appearing benign in isolation while steering toward a harmful outcome. The model processes each turn without recognizing the pattern forming across the conversation.

The research tested five attack strategy families:

• Crescendo escalation. The attacker escalates the ask incrementally, each prompt appearing harmless until the full picture emerges. “It seems like, oh, benign prompt, benign prompt, benign prompt, but as it builds, you start to put the pieces together,” Chang said.
• Refusal reframe. When the model declines a request, the attacker reframes their identity or purpose to push past it. “You reframe the refusal and be like, no, no, you don’t understand, I’m not a bad person, this is what I need it for,” she said.
• Role-play and persona adoption. The attacker assumes a character or persona, shifting the conversational framing so the model perceives a different obligation to comply. The report identifies this as the highest-weighted strategy family in the cohort at 29.89% weighted ASR.
• Contextual ambiguity and misdirection. The attacker uses vague or misleading framing to obscure the true nature of the request, steering the conversation without stating harmful intent directly.
• Information decomposition and reassembly. The attacker breaks a harmful request into component parts distributed across multiple turns, each appearing innocuous in isolation. The model responds to each piece without recognizing the assembled outcome.

What multi-turn failures say about AI safety

Every model in the cohort failed a meaningful share of multi-turn attacks. The root cause is structural. Chang said the vulnerability is a fundamental characteristic of how generative AI models work. They are probabilistic systems trained to predict the next likeliest token, and that mechanism produces unintended outputs that pre-deployment testing cannot fully eliminate. For closed models, where training data is not publicly disclosed, the problem is compounded because defenders cannot fully audit what the model has learned.

The pattern is not limited to closed models. Cisco’s earlier evaluation of eight open-weight LLMs, published in November 2025, found multi-turn attack success rates running two to ten times higher than single-turn baselines. The report concludes that multi-turn vulnerability is a structural property of the current AI frontier regardless of whether model weights are public or proprietary, and regardless of whether a lab publicly emphasizes safety or capability.

The exposure grows significantly larger when those same models power agentic workflows. “These models are the ones that power agents, and agents have broader access, broader ability to conduct actions on behalf of the human,” Chang said.

The network layer as a defense point

For network security professionals, the instinct is to apply a familiar paradigm: Proxy LLM traffic at the network layer, inspect inputs and outputs, and enforce policy the same way a WAF or IPS handles web traffic. Chang said that instinct is right in part, but LLM security introduces a dimension that signature-based controls cannot address. The difference is intent. 

“There’s also an intent component there as well, where traditional network security approaches kind of fall short,” Chang said. 

A WAF operates on known patterns, payload signatures, protocol violations, known attack strings. Natural language does not reduce to those primitives. An agent responding to an instruction to delete a home directory cannot determine from the request alone whether the person asking is authorized or is attempting to manipulate the agent into a destructive action. 

Network-layer inspection remains a valid baseline for deployments that generate network traffic. “I would say that that is one component of a core principle that should be applied in terms of making sure that at least as traffic gets passed through the network layer, whether they’re inputs or outputs, should have some sort of either guardrail or sanitation check to ensure that the prompts that are coming back and forth are safe,” she said.

Evaluation practices for enterprise teams

For security teams reading the report, Chang’s guidance centers on three actions.

• Use the report and the LLM Security Leaderboard to inform model selection. Cisco’s leaderboard publishes adversarial evaluation signals against leading models on a rolling basis and gives security teams a more current picture than static model cards or published benchmarks.
• Do not take vendor safety claims at face value. Published single-turn benchmarks can misrank models by a wide margin. Multi-turn exposure is invisible to any single-turn evaluation, and procurement decisions made on that basis carry unquantified risk.
• Layer additional defenses on top of the model. No base model in the cohort is safe under iterative attack. Runtime guardrails, application-layer controls, and pre-deployment testing are necessary regardless of which model an organization selects.

“Out of the box, without any additional protections, these models, whether they’re closed or open, are insufficient on their own to kind of be used in a way that [has] potential ramifications,” Chang said.

Vibe coding is expanding beyond the realm of software development teams into a variety of business units at a range of enterprises, and technology leaders are not only supporting these efforts but in some cases leading the charge.

This democratization of software development, buoyed by vibe coding tools, including chatbots and agents that generate code via prompts, has made vibe coding a thing in departments such as human resources and marketing.

With guidance from IT, such efforts can cut down development backlogs, move solution-building closer to business problems, and open opportunities not previously pursued. But vibe coding can be edgy business. IT leaders interested in unleashing the power of vibe coding in the hands of business users must establish the governance and guardrails necessary to ensure secure results.

Here is a look at how several IT leaders are deploying vibe coding beyond IT and the challenges they’ve faced.

Feeling the vibe

At financial services technology provider EnFi, everyone in the organization, including the C-suite, is actively encouraged to use Claude Code to build their own sub-agents and then get the rest of their teams to use their creations.

“The results have surprised us,” says Scott Weller, CTO. “What started as an engineering productivity initiative has become a company-wide capability, where anyone from the CEO to a customer success manager can turn an idea into a working prototype in hours, not weeks.”

Vibe coding using the tool quickly expanded when non-engineering employees realized they could participate directly in building applications, Weller says. “Product leadership, customer success, and executive stakeholders now routinely initiate development work through the same system,” he says. “Every branch, regardless of who initiates [coding projects], passes through the same automated quality gates, architectural checks, and human code review before anything reaches production.”

When an AI agent writes code, “it follows the same rules as a senior engineer,” Weller says. “AI-powered code review catches architectural violations, security issues, and pattern inconsistencies before a human reviewer sees it. This means the output from a product manager’s Slack request meets the same quality bar as an engineer’s pull request.”

Enabling everyone in the company to come up with an idea and see it running by simply asking a bot to build it “has fundamentally changed who participates in building the product,” Weller says.

Making AI-assisted coding broadly available has shortened the time to completing some projects from weeks to hours, Weller notes. “It has dramatically increased the number of experiments we can run, on [user experience] improvements, feature explorations, and workflow alternatives,” he says.

The most significant benefit is that new development ideas are no longer bottlenecked by lack of engineering capacity. “When anyone in the company can describe what they want and see it built, the rate of experimentation goes up dramatically,” Weller says.

The momentum of building solutions to business problems

Skillsoft, a provider of technology training services and products, is also pushing vibe coding outside its software development teams.

“While it initially emerged within IT teams, we’ve been intentional about encouraging this mindset beyond traditional software development,” says Orla Daly, CIO. “We see real value in enabling teams across the organization to explore, experiment, and problem‑solve using AI in ways that are directly connected to their day‑to‑day work.”

Within Skillsoft’s infrastructure and operations organization non-development individuals are building new products and capabilities or leveraging the principles of vibe coding to troubleshoot production issues, Daly says.

“We’ve seen this show up through cross‑functional experimentation such as prototyping customer intelligence solutions for our go-to-market teams and rapid development of a customer portal,” Daly says. “When people are learning and applying AI to solve a real business problem, it creates purpose and momentum.”

With vibe coding, teams are not just learning new concepts, “they’re developing judgment, curiosity, and a better understanding of how AI can elevate their role and the business more broadly,” Daly says. “The benefits show up in very tangible ways. Teams learn faster because they’re applying AI directly to their work. Engagement increases when people feel trusted to explore and contribute ideas. And as an organization, we gain better visibility into where skills already exist and how they’re evolving.”

More broadly, vibe coding supports adaptability, Daly says. “It helps people move from seeing AI as something abstract or intimidating to something they can work with thoughtfully, using judgment and collaboration rather than relying on rigid processes,” she says. “The solutions created as a result of vibe coding have filled capability gaps and delivered solutions to production in shorter timeframes, producing real value.”

Emphasizing experimentation

At Corevist, an ecommerce platform provider, broad use of vibe coding didn’t start as a formal initiative. “It showed up in different parts of the business, mostly in sales and customer-facing teams, because people were trying to move faster and make ideas easier to communicate,” says Terry Stahler, CIO and chief customer officer.

It also didn’t spread purely on its own. “Without leadership pushing for experimentation, it likely would have moved at a much slower, consensus-driven pace,” Stahler says. “Instead, there was clear encouragement to try things, along with budget behind it.”

Today, Corevist uses vibe coding mainly as a prototyping tool. “It helps people get to something concrete faster, which makes conversations a lot clearer,” Stahler says. “That has been the biggest benefit. We are not using it as a shortcut to production software. Anything that is going to live beyond a prototype goes through our normal engineering and security process.”

Sales was the first place where Stahler saw use cases emerge. One was live prototyping during the sales process. “In a normal B2B conversation, a prospect explains what they need, the account team takes notes, and then everyone goes back and tries to interpret it later,” he says. “What changed here was the ability to turn an idea into something visible much faster, sometimes even while the conversation was still happening.”

That gave the prospect something concrete to react to and made it easier to tell whether a sales rep was actually understanding the request correctly. “It cut down on ambiguity early, which is valuable in any complex sales cycle,” Stahler says.

Marketing has also used vibe coding, for an update of its website. “The value there has been speed in the early creative and planning stages,” Stahler says. “They can generate rough versions of pages and flows much faster than they could through written direction or static mockups alone. That has made it easier to align on direction and has reduced some of the usual back-and-forth that comes with a website rebuild.”

Supplementing the engineering queue

Business growth platform provider ZenBusiness encourages vibe coding “across the board,” says Alex Victoria, CTO. “The way we think about it is pretty simple: If AI tools can help you go from idea to something working without waiting in an engineering queue, we want you to do that,” he says.

Vibe coding began within the product and engineering teams but has spread to other departments.

“We’ve seen people on the data side building their own query tools, product managers creating interactive prototypes with no design background, and teams across the company using AI to handle tasks they used to outsource to specialists,” Victoria says. “The culture we’ve tried to build is one where experimentation isn’t reserved for engineers. If you’re willing to learn and use the tools we have, anyone can code to improve their workflows on their own.”

Although Victoria has been using agentic coding tools for years, he says the “real unlock” of benefits came in 2025, with the release of Claude Code.

“I’ve seen a huge impact since then, and also with Cursor and Codex,” he says. “When someone can build their own tool or prototype in an afternoon instead of waiting weeks in an engineering queue, it changes what’s worth doing.”

The bigger benefit is how it’s changed the relationship between roles, Victoria says. “People who know how to build software can now produce far more value than ever before,” he says. “There’s still a gap between a vibe-coded idea and a running product. If you know how to ship a running product, you’re very valuable in this world.”

Facing down challenges

Embracing more widespread use of vibe coding in the enterprise comes with its own set of challenges.

One of these is maintaining quality when everyone can build. “When you open development to non-engineers, the risk is code that works but doesn’t follow project conventions, creating technical debt faster than manual development,” Weller says.

EnFi has addressed this by investing heavily in the rules and skills layer of coding. More than 40 custom skills impact AI output to match pre-determined architectural patterns.

“The agent doesn’t just write code; it writes code that passes the same review criteria we apply to human engineers,” Weller says. “Every branch, whether initiated by the CEO or a junior engineer, goes through the same automated quality gates and human code review.”

For business software provider Agiloft, “the biggest challenge isn’t technical, it’s organizational,” says Noe Ramos, vice president of AI operations. Agiloft is building an AI-native development capacity across every business function, embedding it into processes such as finance, human resources, and professional services.

“Most companies, including ours, are still learning where work actually happens versus where they think it happens,” Ramos says. “Before you can extend AI into a business function, you have to understand the real workflow, not the documented one. That discovery work is underestimated almost everywhere.”

The company also had to work through “the natural friction of trust and adoption,” Ramos says. “The human variable, though critically important to AI, is always the rate-limiting factor in this case, not the technology.”

On the governance side, Agiloft has had to address issues such as access controls, identity management, and data handling. “We built a formal approval process and a structured use-case lifecycle to manage this, so AI doesn’t get introduced into business teams in ways that create technical debt or compliance exposure,” Ramos says.

The primary challenge for healthcare technology provider iCore was creating confidence among non-technical users rather than technical barriers, says Thiago Soares, COO. The company has actively supported AI-assisted development outside its engineering team, including operations and client success functions that are building automated reporting tools, internal workflow templates, and client onboarding checklists that previously required developer time to produce.

“Staff unfamiliar with AI-assisted development needed structured onboarding before adoption felt natural,” Soares says. “We addressed that through peer-led sessions, where early adopters demonstrated practical use cases relevant to each team’s specific workflows.

Uneven confidence levels can slow progress, especially when people worry about getting it wrong, Daly says. “Creating a safe space to learn is important,” she says. “Creating small teams to work together with peer-to-peer support and encouraging shared learning has also been helpful to support progress through practical application, which is where we see people learn best.”

Governance is another key consideration, particularly in regulated industries with strict data handling protocols, such as healthcare. “Successful organizations set out explicit boundaries within which machine-generated code can be developed outside of formal development pathways,” Soares says.

IT leadership at iCore has built the guardrails needed to make expansion of vibe coding safe, “defining data access boundaries and compliance checkpoints that align with our commitment to trust and security,” Soares says. “That governance foundation is what allows cloud-driven innovation to move forward without creating the regulatory exposure healthcare environments cannot afford.”

The future of coding

Organizations that are moving vibe coding beyond software development teams are aiming to expand these efforts further.

At iCore, expansion of vibe coding into marketing and human resources functions is already under way, Soares says, focused on content workflows and documentation automation. These are “areas where vibe coding delivers efficiency without touching sensitive clinical or compliance infrastructure,” he says.

Agiloft plans to expand vibe coding further and make it into a standard practice “carefully and iteratively,” Ramos says, moving from isolated use cases toward a cohesive AI operating model with a shared infrastructure and AI-literate teams across functions, rather than just AI-enabled tools scattered across departments.

“That said, expansion for us means scaling what works, not scaling the toolset,” Ramos says. “We track every AI initiative through a structured lifecycle, from intake through decommission, precisely to avoid accumulating a stack of underutilized capabilities. Every function will eventually have embedded AI, but the goal is for those capabilities to be connected, governed, and [properly] used, not just deployed.”

EnFi is expanding from engineering-adjacent roles, such as product development and customer success, to other functions. “The same pattern — describe what you want, see it built, review and decide — applies to internal tooling, reporting, operational workflows, and documentation,” Weller says.

Organizations that master AI-assisted development internally “will be the ones capable of deploying AI-assisted workflows to their customers with the quality, governance, and reliability that regulated industries demand,” Weller says. “The internal practice is the proof point.”

With Google’s annual I/O gala in full force this week, Gemini and AI are taking center stage and being presented as the future of practically everything.

Here in the land of Android, though, Gemini’s been quietly competing for attention with another relatively youthful on-demand assistant — and that’s a far less in-your-face feature called Circle to Search.

Circle to Search is essentially an instant portal to the even less widely known Android Google Lens setup, which has been serving up genuinely practical real-world advantages for Android device-owners in the know for years now — since way back before the word “Gemini” had any Googley meaning.

And whether you also adore Gemini or find it to be more hype than help, it’s well worth your while to dig into Circle to Search — or maybe just revisit its potential, if you’d perhaps explored it briefly early on and then forgotten about it — to see what it can do for you.

Here, specifically, are 10 simple but supremely useful ways Circle to Search can make your day-to-day life easier without allowing any Gemini AI avalanches to overtake you.

[Psst: Want even more practical Android knowledge? Check out my free Android Intelligence newsletter for three new things to try every Friday and my Android Notification Power-Pack today!]

Circle to Search 101

Real quick, first, a fast primer on where Circle to Search lives and how you can access it:

At this point, Circle to Search is available on a bunch of Android devices beyond just the latest high-end flagships. But it isn’t available everywhere. And there’s no clear, up-to-date list of exactly which devices have it and which still don’t.

To see if it’s present on your current phone, try going into your system settings and searching for the word circle. If you see “Circle to Search” show up as an option, tap it and then make sure the toggle next to the “Circle to Search” line is in in the on and active position.

Then, to summon Circle to Search, press and hold the bottom-center area of your screen — either the thin navigation bar line, if you’re using the current Android navigation gestures, or the Home button, if you’re still stickin’ with the old legacy three-button nav approach — and you should see an overlay appear on top of whatever else you were viewing with a Google logo at its top and a search bar at its bottom.

From there, you can use your favorite fingie to circle any image, text, or broad area on your screen to highlight it. You can also tap any area to select it (and then have the opportunity to refine your selection) or scribble over any area to mark it, too.

And whatever you select will become the subject of a search for additional info.

If you don’t seem to have Circle to Search available on your device, download the Google Lens Android app — then try taking a screenshot of anything in front of you and sharing it directly into the Lens app. It won’t feel quite as interactive or instantaneous as what you’d get with Circle to Search present, but you’ll be able to accomplish most of the same feats we’re about to go over in that environment, with just a couple of extra steps needed to get there.

Capisce? Capisce. Now, let’s get to the good stuff.

Circle to Search superpower #1: Instant searching

As I often say, it’s the simplest stuff that frequently proves to be the most useful. For all the complex feats Gemini may be able to perform (at least in theory), the action I actually find myself relying on more than anything is the refreshingly routine ability of Circle to Search to look up any word or phrase on my screen, anytime, and give me more information about it — without interrupting anything I’m doing or forcing me to switch apps.

That might mean coughing up a quick definition, at the simplest possible level. Or it might mean dousing me with details about a person, place, or product I’ve seen within an email, a web page, a document, you name it.

Whatever the case may be, all I’ve gotta do is summon Circle to Search from wherever I happen to be on my device at that moment, tap my finger onto the term in question, and boom: I’ve got the info I need right in front of me — no complicated commands, frustrating back-and-forth dialogue, or effort-wasting app switching required.

Easy peasy, no? And there’s lots more where that came from.

Circle to Search superpower #2: Fast text actions

In addition to surfacing basic info, Circle to Search can help you take a variety of actions on text you highlight with just one more tap and no awkward multistep pasting or other clunky mechanics.

The next time you see a phone number you want to call, text, or save to your contacts; an email address you want to save or send a message to; a physical address you want to look up or navigate to; or a URL you want to open when it isn’t set to be a tappable link on its own, call up Circle to Search and tap the text in question.

So long as the item is the only text selected, Circle to Search should recognize its format and offer up the logical associated action for you to caress next.

Speaking of which…

Circle to Search superpower #3: Quick copy

Back to the idea of simplicity, one of the ways I find Circle to Search to be most useful is in its ability to let me copy text from anything, anytime — even when it isn’t text you could typically copy.

From phrases in my Android settings to words appearing within images, Circle to Search converts everything it sees into standard copy-ready dialog, and it takes just one tap on anything to highlight it in that environment and then beam it to your Android system clipboard from there.

And, of course, with the right sort of setup — like a recently released third-party service that works wonders in this area — it takes shockingly little effort to send something from there onward toward your computer’s clipboard for desktop-level use as well.

I can’t tell you how often this comes in handy.

Circle to Search superpower #4: Image identifying

Text aside, Circle to Search integrates the long-Lens-offered ability to identify any image in front of ye and then allow you to interact with it in all sorts of interesting ways.

This can range from telling you the name of a person, place, or product to giving you specific identifying info for a plant, flower, tree, animal, or even type of screw or computer component.

Just tap or circle any image on your screen — whether it’s in a web page, an email, a document, or anywhere else imaginable — and you’ll see the results right away.

And from there…

Circle to Search superpower #5: Deeper context

Once you’ve gotten an initial result from Circle to Search — with an image, with text, or with most anything you’ve highlighted and selected — you can tap the microphone icon at the bottom of the Circle to Search popup and ask additional questions.

Depending on what you’re seeing and what you want to know, the possibilities are practically endless:

• Can you use this word in a sentence?
• Where can I find this?
• How much does this cost?

You get the idea. And while we’re thinking about products…

Circle to Search superpower #6: Intelligent comparisons

The next time you see something that strikes your interest anywhere in your Android adventures — be it a new phone within an image somewhere, some software or service mentioned in an email, or whatever else the case may — fire up Circle to Search, select the thing you’re ogling, and then use the Circle to Search search prompt or microphone icon to ask for comparisons:

• How does this phone compare to the Pixel 9?
• Does this cost more or less than a MacBook Pro?
• Is this app basically like Notion?

Once you’ve selected something, all you’ve gotta do is ask.

Circle to Search superpower #7: Split smarts

Speaking of comparisons, here’s a really cool Circle to Search trick few mere mortals realize is possible:

You can start up a split-screen of any two apps together, side by side, then activate Circle to Search and use it to analyze things across the two processes.

Let’s all summon our strongest inner Keanus and say it together now: Whoaaaa…..

And — oh, yes — there’s more yet.

Circle to Search superpower #8: Your translation station

When the need to translate anything between languages arises, skip your usual multistep process and just summon Circle to Search instead. Tap the translate icon — the “A” inside a circle, at the right end of the bottom-of-screen search bar — and you can then select any two languages and have everything on your screen translated on the fly.

If you tap the icon that appears next to the “A” — the one showing a hand alongside an upward-pointing arrow — you can keep the instant translation mode active as you scroll around and even move between apps.

That, suffice it to say, is insanely powerful.

Circle to Search superpower #9: Zoom without borders

Back to simplicity again, one surprising way Circle to Search can be helpful is by unlocking the ability to zoom into anything, anytime — even when it’s part of an area that you can’t ordinarily enlarge.

Press and hold that bottom-center area of your device’s display, then just pinch two fingers apart or together. You’ll be able to zoom in, no matter where you are or what you’re viewing.

And finally…

Circle to Search superpower #10: Song Search, Circle-style

All right, so this last Circle to Search superpower isn’t exactly productivity-related. But it is useful, in the right sort of scenario. (And sometimes, you need to satisfy a non-work-related itch before you can get back to Getting Stuff Done™!)

When you’re hearing a song and scratching your head as to what it’s called or who sings it, Circle to Search can actually activate Android’s excellent Song Search system and show you that answer.

Just activate Circle to Search, no matter what else you’re doing, and tap the music note icon in that search bar at the bottom of the screen. (For fair warning, the correct answer is always Men at Work.)

Good to know, no? And, just like everything else on this page, all this sorcery is never more than a tap away — without the need for any manner of Gemini-scented AI chicanery.

All you’ve gotta do is remember.

Remember to sign up for my free Android Intelligence newsletter, if you haven’t already, to get three new things to try in your inbox every Friday.

Artificial intelligence (AI) is no longer the playground of hobbyists and programmers. From automating customer‑service transactions to optimizing supply‑chain decisions, AI is rapidly becoming the central nervous system of today’s enterprises. McKinsey surveys have found that nearly nine in ten organizations are now using AI regularly in at least one business function, compared with 78% the previous year.

But adoption rates are far lower when it comes to scaling AI programs throughout the enterprise. Only about one‑third of companies have advanced past the pilot stage. Two‑thirds of organizations use AI technologies in multiple functions and 64% believe AI has had a positive impact on innovation. Just 39% say they’ve seen a significant impact on the bottom line.

This research shows that AI has gone mainstream, but its benefits are still concentrated in the hands of a relative few. This reality makes even more compelling the case for establishing a formal center of excellence (CoE).

Per Microsoft’s cloud‑adoption framework, a CoE is a centralized team responsible for standardizing best practices across the organization. An AI CoE functions as an internal team of experts that helps business units realize valuable and successful AI outcomes while avoiding pockets of AI solutions built without governance or standards. A well‑managed AI CoE builds consensus for standards and pilots and provides business and technical guidance to help convert AI excitement into measurable business value.

An artificial intelligence Center of Excellence is a strategic capability, not an ivory tower nor a vanity project for the few AI early adopters. An AI CoE can dramatically improve the quality and safety of your organization’s AI adoption efforts. In this article, we will discuss why every organization needs an AI hub like a CoE. We’ll explore how to staff and structure your center and provide recommendations to help it adapt over time.

Why create an AI CoE?

An AI CoE “acts as the central nervous system” for your company’s AI strategy. Without a centralized AI organization:

• Siloed initiatives. Separate business units kick off AI pilots that aren’t centrally coordinated, leading to duplicated effort and fragmented results.
• Inconsistent governance. Data governance, model security and compliance efforts differ from project to project putting your organization at risk of regulatory infractions or biased models.
• Lack of standards. Groups spend time reinventing the wheel instead of building on reusable assets and shared toolkits.
• Difficulty scaling. Smaller pilots become roadmaps unto themselves because there’s no standardized process for prioritizing and scaling AI solutions.

An AI CoE helps solve these issues by setting forth an AI strategy, aligning AI efforts with your business objectives and offering governance, policy and expertise. Additionally, an AI CoE promotes responsible and ethical AI principles through fairness, privacy and transparency policies. According to IDC research, CoEs enable cross‑functional collaboration between data scientists, domain experts and chief finance officers to help translate AI prototypes into repeatable solutions that align AI spending with business strategy. IDC analysts also find that effective CoEs can provide talent development, knowledge sharing, partnerships and an innovation mindset.

The business case only strengthens when looking at the economics of generative AI specifically. IDC’s 2024 “Business Opportunity of AI” study, sponsored by Microsoft, revealed generative‑AI adoption rose from 55% in 2023 to 75% in 2024. Companies that deployed generative AI were also found to experience significant returns. Organizations see an average ROI of $3.7 realized for every dollar spent on generative AI. For high performers, ROI reached $10.3 for every dollar spent. The majority of companies spend less than eight months deploying generative AI solutions, with ROI seen after just thirteen months. The research highlights how well‑managed AI initiatives can produce exceptional returns and why centralized governance and expertise are critical.

In addition to mitigating risks, a properly established CoE also creates opportunities. According to IDC, successful COEs can provide workforce enablement, knowledge dissemination, strategic alliances, certification and training and a culture that fosters innovation and creativity. Leaders at Hitachi Vantara call their COE “enabling AI to go from theoretical exploration to practical implementation that optimizes processes, supercharges efficiency and unlocks data‑driven insights.”

When it comes to AI in financial services, BizTech Magazine found that while 81% of executives say they are currently using AI and will invest more money in it, only 25% of them have completely deployed monitoring and management tools even though 87% said they already have governance in place. This is a clear example of how businesses are adopting new technologies but lack operational maturity and how a centralized CoE can help bridge that final mile and make governance frameworks a reality.

Business impacts achieved through CoEs are faster AI adoption, more efficient resource utilization, better decision‑making, lower risk, stronger strategic alignment and better collaboration. Boards and investors are demanding it; according to IDC analysts, organizations with mature AI governance have seen increased returns on invested capital (ROIC) and view CoEs as critical source of competitive differentiation.

How to build and structure the CoE

An AI CoE should have an executive sponsor who can offer budget, authority and credibility to ensure standards are upheld. Create a steering committee of business and IT leaders and schedule frequent reviews to monitor progress. Then, identify a leader for your CoE who is devoted to its success and has deep AI skills, as well as reach across the enterprise. Hire a diverse team of business leaders, data scientists, machine‑learning engineers, governance and security professionals. This variety of backgrounds will help ensure AI initiatives meet technical and business needs, as well as regulatory and ethical standards.

Define the operating model and responsibilities

IDC analysts note that an important objective of a CoE should be to close the supply‑and‑demand gap for AI skills. This can be accomplished by creating a centralized team of employees from different business units or geographies who pool their collective knowledge and then disburse back out into the business. Hardy recommends that this cross‑functional team include not only deep technical experts such as data scientists, AI engineers and machine‑learning specialists but also business leaders as well as IT and cybersecurity professionals. These members can help ensure AI initiatives are applied to business problems and integrated into production environments securely. Team members may include data scientists, software engineers, business analysts, subject‑matter experts and project managers. Common skills include domain knowledge, programming and data skills, problem solving, communications and a team mindset. Skills required of the Center of Excellence leader include a strong knowledge of AI, along with a visionary but execution focused approach to work. Additional leadership traits include practicing radical candor with your teams and colleagues while staying agile and flexible in your decisions due to the rapidly changing nature of AI.

Skills gaps are another common challenge to AI scaling. According to Microsoft’s 2024 IDC-commissioned survey, 30 % of respondents stated their organizations don’t have specialized AI skills and another 26 % said their organization has too few employees with the skills necessary to learn and work with AI. By tapping into talent from across the business, the CoE can centralize hard-to-find expertise. It can also administer training initiatives to fill these gaps. The CoE should partner with HR leaders to create learning journeys, certification initiatives and mentorship programs to ensure the talent pool continues to grow with advancing AI technology.

Determine where the CoE sits in your company’s hierarchy. A centralized hub makes sense early in your AI journey to centralize knowledge and ensure consistent practices. But as your organization adopts AI, the CoE can evolve into a decentralized, enablement model that provides guardrails and allows product teams to own their own AI applications. Clearly establish your CoE’s primary functions. These may include:

• AI strategy and use‑case identification. Partnering with business leaders to identify and prioritize AI opportunities that will provide the most value to the organization.
• Skills development. Determining your current level of AI skills and implementing learning and hands‑on experimentation programs.
• Pilot projects. Leading targeted pilots to prove out AI methodologies and provide proof of business value.
• Standards and governance. Establishing governance frameworks and security standards, monitoring usage to ensure AIs are being used ethically and performing routine data security and compliance audits.
• Intake and prioritization. Establishing a formal process to accept requests and evaluate them based on potential business value, feasibility and resource demands.
• Reusable assets. Creating checklists, templates and code libraries to speed up future initiatives.
• Metrics and reporting. Measuring adoption, compliance and business value and using that information to foster continual improvement.

Integrate ethics and responsible AI

AI solutions should be ethical, which means they should be helpful and unbiased. The CoE should develop policies around responsible AI use, so models are transparent, unbiased and reflect company values. Teams should conduct audits of training data and model outputs to identify and reduce bias and the potential for inadvertent harm. AI governance should include privacy and data‑security principles.

Avoid bureaucratic bottlenecks

One pitfall of CoEs is that they tend to turn into gatekeepers and slow down innovation. Instead, Microsoft recommends shifting the CoE from being a gatekeeper to playing an advisory role once your AI adoption becomes more established. Build AI delivery into platform teams and allow product teams to execute against AI solutions under guardrails. The CoE can concentrate on things like setting standards, sharing knowledge and mentorship while teams on the frontline own the execution.

How to ensure continuous improvement

AI is still an emerging technology and science, which means that a one‑time standing CoE will become irrelevant almost immediately. It should evolve constantly, leveraging three primary levers:

• Feedback and learning loops. Establish processes to capture input from users and stakeholders in production and pilot environments. This feedback should be used to update models, training datasets, documentation and governance processes.
• Investment in skills and culture. Embed AI literacy into your culture by providing regular training and building communities of practice. Forums like these allow employees to share failures and best practices. Focus change‑management efforts on employees’ misperceptions about how AI will replace their jobs. Communicate how AI tools will make their jobs easier instead.
• Metrics‑driven evolution. Define a measurement framework that encompasses adoption, compliance and ROI metrics. Use these measurements to surface bottlenecks and opportunities for improvement. If your metrics indicate your central governance is slowing adoption, consider a more federated approach.

Researchers from IDC stress that “unlocking the power of frontier AI … requires building a culture of continuous learning.” COEs should implement processes like internal training initiatives, communities of practice and sandbox spaces for experimentation so that knowledge can continue flowing to employees as fast-changing AI capabilities develop. Benchmarking your progress is also key, say the analysts in their suggestions for how to measure COE success with AI. IDC recommends defining clear goals, building KPIs into projects, tracking completed initiatives, gathering feedback on customer satisfaction and looking at indicators for revenue growth and innovation.

Conclusion: A strategic capability, not a side project

An AI Center of Excellence won’t solve every challenge. It needs ongoing senior leadership sponsorship, cross‑functional teamwork and an openness to shift your operating model as your organization matures in its AI journey. However, when done right, it provides a framework for enterprise‑level AI enablement. It ensures AI initiatives are aligned to business priorities, sets standards and governance, develops your workforce and speeds up the responsible delivery of AI solutions.

AI Centers of Excellence are already helping organizations drive value across industries by scaling AI adoption, strengthening governance, accelerating experimentation and moving AI use cases from ideation to production.

• Financial services. Major banks are forming federated AI CoEs made up of divisional CoEs within business units like retail banking, wealth management and asset management. These cross‑functional teams customize AI for their functions, whether that’s portfolio optimization or customer support automation, backed by a centralized GenAI layer that provides unified governance, tools and evaluation frameworks. The federated approach limits redundancy, fosters collaboration and scales AI more broadly.
• Professional services and technology. With Microsoft’s guidance, NTT DATA developed an agentic AI CoE. The center offers a centralized environment for customers to design, deploy and operate AI agents spanning different cloud environments. Highlights include unified governance that’s aligned with its cloud center of excellence (CCoE) architecture, shared infrastructure to build agent‑based applications and coordination with Microsoft subject matter experts. The AI CoE serves as an engine for delivery, helping accelerate the path from experimentation to production at scale with security built in.
• Consulting firms. Capgemini applies the principles of an AI CoE to its suite of offerings to ensure consistent AI governance, reuse assets and tools and link AI projects to quantifiable business outcomes. Standardizing the how behind project execution allows Capgemini to decrease variation across customer projects and empower organizations to operate at speed without losing sight of enterprise needs.
• Enterprise experimentation. EY created an AI CoE focused on providing a secure sandbox environment. Teams can experiment with AI use cases, validate their feasibility and associated risk and fast‑track promising use cases to production. Centralized visibility and governance allow for consistent security and compliance standards, shortening the time between ideation and execution and preventing siloed adoption.

Taken together, these industry examples highlight how a CoE is less of a technology endeavor and more focused on creating institutional trust and capacity. Want more proof points on how COEs make an impact? Consider how at ECS, a provider of cloud, cybersecurity and artificial intelligence (AI) services, the data and AI COE unify more than 200 data professionals across the business, shares their collective expertise across town halls and events and manages strategic partnerships. The COE enables proposals, solutions and fosters a culture of creativity and innovation.

Over at Hitachi Vantara, the AI COE is tasked with transforming ideas into production‑ready prototypes and is already being recognized for improving efficiency across operations and creating new revenue streams from advanced machine‑learning models. The board wants ROI and organizations with mature AI governance and COEs can increase returns on invested capital and create new sources of revenue. These are just a few examples of how a well architected CoE turns strategy into tangible business value.

The race for AI supremacy is picking up speed. Organizations that invest time and resources into building an effective AI Center of Excellence will be best positioned to turn innovation into competitive advantage. The CoE is how your biggest ideas go from concept to business solution–helping leaders do their best work.

This article is published as part of the Foundry Expert Contributor Network.
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Boris Cherny, creator of Anthropic’s Claude Code, says he hasn’t written a line of code by hand in months. He shipped 22 pull requests one day, 27 the next, all AI-generated. Company-wide, Anthropic reports that 70 to 90% of its code is now written by AI. CEO Dario Amodei has predicted that AI could handle “most, maybe all” of what software engineers do within months.

And yet Anthropic typically has dozens of software engineering openings, one reportedly carrying $570K in total compensation. As one observer noted, the company is simultaneously predicting the end of the profession and paying top dollar to hire into it.

Meanwhile, during his GTC 2026 keynote, NVIDIA CEO Jensen Huang said that 100% of NVIDIA now uses AI coding tools, including Claude Code, Codex and Cursor, often all three. Then, in a conversation on the All-In Podcast during GTC week, Huang sharpened the point: A $500,000 engineer who doesn’t consume at least $250,000 in AI tokens annually is like “one of our chip designers who says, guess what, I’m just going to use paper and pencil.”

This isn’t cognitive dissonance. It’s a signal. And CIOs who look past the headlines will find a pattern that explains not just where AI coding is going, but where all of enterprise AI is headed.

Tellers, not toll booth workers

The instinct is to see this as an extinction event. AI writes all the code; engineers become toll booth workers, replaced entirely by automation with no complementary role left behind. But the data tells a different story, one I explored in a recent CIO.com article on AGI skepticism.

When ATMs rolled out, bank teller employment didn’t collapse. It doubled, from 268,000 in 1970 to 608,000 in 2006. The machines eliminated the routine transaction. But cheaper branch operations meant banks opened more locations, which created demand for tellers who could handle complex financial conversations. Economists call this Jevons Paradox: When technology makes something more efficient, demand expands rather than contracts.

Software engineers are bank tellers, not toll booth workers. AI agents are eliminating routine implementation: The boilerplate, the CRUD endpoints, the standard test scaffolding. But that efficiency is expanding the total surface area of what “engineering” means. Anthropic isn’t paying $570K for someone to type code. They’re paying for the judgment to orchestrate AI agents that type code: Deciding what to build, evaluating whether the output is correct, governing what gets deployed and maintaining systems that are increasingly written by machines.

Cherny confirmed this shift directly. His team now hires generalists over specialists, because traditional programming specialties are less relevant when AI handles implementation details. The skill premium has moved from writing code to supervising it, from production to orchestration.

The reason AI coding agents work

Here’s the question CIOs should be asking: Why are AI agents succeeding in software development faster than in any other enterprise function?

It’s not because coding models are better than models for customer service, legal review or financial analysis. The underlying LLMs are the same. The difference is that software development already had the infrastructure that every other enterprise function lacks.

Developers didn’t build this infrastructure for AI. They built it for themselves, over decades. But it maps almost perfectly to the six infrastructure gaps that are currently blocking AI agents from moving beyond employee-facing pilots into customer-facing production.

6 gaps the SDLC already solved

1. Governance: Right data, right users, right permissions

In software development, governance is built into the workflow. Branch protection, code review policies and role-based access controls create a clear chain of permission from draft to deploy, whether the author is human or agent.

Most enterprise functions have nothing equivalent. When an AI agent drafts a customer response, accesses a patient record or modifies a financial model, the governance layer (who approved this action, what data was it allowed to see, which policies constrain its output) is either ad hoc or absent. Microsoft’s 2026 Cyber Pulse survey found that while 80% of Fortune 500 companies have deployed AI agents, only 47% have agent-specific security policies in place.

2. Observability: Trace and audit the decision trail

Every line of AI-generated code has a paper trail. Git blame shows who (or what) wrote it. CI/CD pipelines log every build, test and deployment. When something breaks in production, engineers can trace the failure from alert to commit to the specific agent session that produced the change.

Outside of engineering, AI agent decisions are largely opaque. A customer-facing agent that denies a claim or escalates a complaint leaves no audit trail. Without observability, enterprises can’t debug bad outcomes, satisfy regulators or build the trust necessary to expand agent autonomy.

3. Evaluation: Measure correctness at scale

Unit tests, integration tests, type checking, linting and automated QA give software engineering something no other enterprise function has: Continuous, objective measurement of whether AI-generated output is correct. That provides a foundation for proving an agent gets it right.

This is the gap other enterprise functions feel most acutely. DigitalOcean’s 2026 survey of 1,100 technology leaders found that 41% cite reliability as their number one barrier to scaling AI agents. Reliability is an evaluation problem: Without automated, continuous measurement of agent output quality, organizations can’t trust agents enough to put them in front of customers.

4. Memory: Persistent context beyond the context window

Developers take persistent context for granted. Version control, documentation and architectural decision records provide context that survives across sessions, teams and years. An AI coding agent can read the commit history, understand why a design choice was made in 2019, and factor it into today’s implementation.

Most enterprise AI agents operate in a memoryless state. Each customer interaction starts from scratch. Each agent session has no awareness of prior decisions, escalations or context beyond what fits in the context window. This is why employee-facing agents (IT help desks, NOC ticketing) succeed where customer-facing agents stall: Internal users tolerate repeating context. Customers do not.

5. Cost controls: Manage LLM spend across providers

Jensen Huang’s $250K-per-engineer token budget isn’t an abstraction. It’s a real cost management challenge that engineering teams are already navigating. Smart teams route differently depending on the task: Use a lightweight model for boilerplate generation, a reasoning model for architectural decisions and a code-specific model for refactoring. They set token budgets per agent session. They measure cost-per-PR and cost-per-feature, not just cost-per-token.

Enterprises deploying AI agents in other functions rarely have this granularity. When Goldman Sachs stated AI near-zero GDP impact in 2025, the missing variable was cost discipline at the workflow level. Without the ability to route, throttle and measure LLM spend per agent task, scaling agents means scaling costs linearly, which eventually kills ROI.

6. Deployment flexibility: Any cloud, on-prem, no lock-in

In software development, the runtime has always been portable. Code that runs on AWS today can run on Azure tomorrow, or on bare metal in your own data center. Containerization, Kubernetes and infrastructure-as-code tools like Terraform mean that engineering teams can change their minds about where workloads run without rewriting the application. Software has had this mindset for decades.

We’re early enough in this agentic development game that it’s tempting to take short cuts. Organizations that build on a single hyperscaler’s agent framework find themselves locked into that provider’s model ecosystem, observability tooling and pricing structure. As agentic AI matures, deployment flexibility (the ability to run agents on any cloud, on-prem or across hybrid environments without vendor lock-in) will separate organizations that scale from those that stall.

Sometimes you’ll want agents to run close to your data. Other times, you’ll want agents close to the users. And you’ll want your developers to be able to move back and forth between different agent code bases without having to learn a different framework between them.

What CIOs should watch at Build and I/O

Google I/O and Microsoft Build will dominate May with dueling AI coding announcements. The temptation will be to compare model benchmarks. That’s the wrong lens. The models are converging. The real competition is one layer down, in the infrastructure that makes AI agents viable outside of software development.

CIOs watching these conferences should evaluate each announcement against the six gaps: Is Microsoft closing the governance gap with Azure AI Foundry? Is Google advancing observability through Vertex AI? Which platform is making it easier to evaluate agent output at scale, maintain persistent memory across sessions, control costs at the workflow level and deploy without lock-in?

The company that wins the AI coding war will be the one that builds the infrastructure layer that transfers to every other enterprise function. That’s the real stakes of May’s developer conferences, and it’s the real reason CIOs should be paying attention.

The canary’s message

Software engineers are the first knowledge workers to live inside a fully agentic workflow. They’re the canary in the coal mine for every other enterprise function. And right now, the canary is singing, not dying.

The lesson isn’t that AI coding agents have made engineers obsolete. It’s that AI coding agents work because engineers already built the infrastructure that makes agents trustworthy. Governance, observability, evaluation, memory, cost controls and deployment flexibility: These aren’t nice-to-haves. They’re the reason Anthropic can ship 27 AI-generated pull requests in a day and sleep at night.

Every other enterprise function will need to build its own version of that infrastructure before AI agents can move from employee-facing pilots to customer-facing production. The models aren’t the bottleneck. The scaffolding around them is.

Anthropic paying $570K for a software engineer whose job might not exist in a year isn’t a contradiction. It’s Jevons Paradox. And it’s the most expensive leading indicator in enterprise AI.

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Who doesn’t want an AI to pump out more code in minutes than a human might write in a month? Who doesn’t like magic? That’s what the hype around vibe coding has asked of developers and business users alike since its inception.

But now the tools might have matured enough to deliver.

Yes, cautious leaders are right in wondering, “What’s the catch? Is this a trap?” After all, the AIs learned to code from examining code created by humans, and humans fail. So it should be no surprise that some vibe coders and industry experts are reporting vulnerabilities such as undocumented endpoints and sensitive data leakage.

Still, many are diving right into the vibe-code deep end and are reporting positive results. The new tools, they say, are amazing. Vibe coders can build a prototype in minutes and a minimum viable product in a few iterations. In goes a few handwavy sentences, and out comes something that used to take weeks to produce — not to mention all the red tape in tapping development time, if you’re a business user. Sure there can be errors and omissions, but are they any worse than what a team of humans might inadvertently include or overlook?

The reality is that vibe coding is legit enough that enterprises need to start experimenting. The platforms offer numerous differences. Some are better suited to helping professional developers who often need to work with large code bases. Others want to help not-so-professional programmers who know what they want but aren’t ready to write it all by themselves. Maybe they don’t know a particular programming language or maybe they don’t know much about programming at all. Still others are aimed at complete novices who can barely turn on a computer.

And it’s not just the level of experience that distinguishes them. Some tackle smaller wishes —the kind of tools that professional developers can use as a “force multiplier.” Others create entire applications, from database to front-end, and they’re best for people who want to create a prototype. The architecture may not be ready to scale for a large user base, but they’re ideal for presenting to the boss or some investors. They can also do a pretty good job with a smaller collection of users.

Are any good enough? Can we work around their flaws? The only way to find out is to fire them up and look at the results.

Here is a list of 19 vibe coding tools worth checking out, in alphabetical order. They all promise to provide some amount of app-dev magic.

Base44/Wix

The process at Base44 (now owned by Wix) begins with a Builder Chat in which the discussion focuses on the data architecture. From there, your words guide a tool that merges React and Tailwind code for the front-end with a Deno backend. To speed things up, templates can jumpstart common use cases — ecommerce, content management, productivity, etc. Many parts of the UI can be adjusted with a drag-and-drop visual editor that can be much faster than trying to describe all your changes with words.

Betty Blocks

The developers of the Betty Blocks no-code system say they’re targeting “citizen developers” —non-programmers who know what their corner of the enterprise needs. The platform takes a description and then produces React code that can be exported to a code repository for future development or be compiled down to WASM level deployment. They also offer a “low-code” approach that gives a visual interface for further tweaking and refinement.

Blink

The code generation agent from Blink produces TypeScript React applications starting with two modes: agent mode, for building; and chat mode, for discussing and planning. Developers start with chatting and then toggle back and forth. Blink will host any application with its internal CDN or allow you to export it to your own servers or cloud.

Bolt

The no-code chat service from Bolt is designed to provide a single visual interface to various backend coding AIs. It’s possible to work with different coding agents, including some of the best-known ones, such as Anthropic’s Claude and Google’s Gemini. The design layer is broken out making it possible to create a standard design that is then adopted by any app that’s produced by the AI. An open-source version released under the MIT license is also available.

Bubble

The no-code tool from Bubble includes several features that let human users do more than just chat. A full visual editor lets developers adjust the interface directly. A workflow view outlines much of what’s going on underneath, again making it possible for the user to do more than guess. The goal is to make humans more of a partner.

Claude Code

Anthropic’s main LLM, Claude, is skilled in answering many programming needs, including either creating new applications or fixing old ones. The backend connects to many traditional IDEs, such as VSCode, but many users connect with Claude through a terminal window or even a Slack channel. One common use is to search through a large code base for the right place to begin fixing an issue. Many praise how it adapts to your local coding standards.

Continue

The open-source agent from Continue is best for professional developers who need a bit of extra help vibe coding. The tool will watch a code base for triggers, such as new pull releases, and then invoke AI agents to handle many of the chores. The goal isn’t to do everything, just the most boring things, so humans can be creative. The tool integrates with many IDEs and AI APIs.

Create

The tool from Create.xyz is called Anything because they want users to be able to create any possible React/Tailwind app from a simple text prompt. The results are crafted from many stylized components for tasks such as database access that run well in either the browser or a mobile platform. Developers can then dive into the code and add any human touches.

Cursor

Many old-school developers love Cursor because it’s designed to be the assistant they’ve never had. It writes new code, audits old code, and tracks issues evolving over channels like Slack. The tool can juggle multiple files and analyze entire codebases before proposing a plan of action and then executing it. It’s not exactly fair to call it “no-code” because it’s designed to work in a traditional development environment, but many users aren’t writing much code anymore because Cursor does so much.

Emergent

The web application from Emergent is a front-end for a team of AI agents that will turn your text description into a frontend (React), backend (Node.js), databases (MongoDB), and collection of APIs with full integrations (Stripe, etc.).The goal is not just to perform hand-holding, but to hide all the complexity of development behind a big facade. Non-developers can build everything they want while developers can knock off prototypes — all deployed in close to production-ready form.

Kilo Code

The open-source coding agent from Kilo has a number of features that will appeal to coders maintaining and extending larger code bases. Orchestrator Mode, for instance, helps create work plans while Code Review double checks for errors. A Memory Bank stores high-level details about the project’s architecture. Connections to more than 500 models avoids lock in and lets you choose the right model that’s delivering just what you want.

Lindy

The tool from Lindy focuses on creating “agents” that tend to be bits of code that sit in the background and respond to triggers like a Slack message or a new commit to a repository. There are hundreds of different web applications that can generate these events, including all major clouds and office organization sites such as Jira or Zoho. Many of the standard applications can be built more quickly by leveraging pre-defined templates. Some of the most common use cases include support agents.

Lovable

The no-code interface from Lovable chats with you for a bit and then builds the app and deployment in Lovable’s cloud. It handles the UI (React plus Tailwind), business logic, and database (mainly Supabase). The result is one of the fastest ways to spin up an enterprise-ready prototype with a fairly polished interface and many of the security and access control features required for bigger environments.

Replit

The no-code solution from Replit delivers code in up to 30 programming languages, supporting all the major languages and many of the minor ones. The main interface is a no-code chatbot, but after that it dumps the code in a repository where it can be further refined using traditional methods. The database layer is broken out and treated separately, which allows for a more traditional approach by enabling options such as a separate database for production and testing. There are enterprise features that allow a team to collaborate as they chat together to improve the app.

Softgen

The tool for creating full Next.js web apps from Softgen works with several of the major models, such as Claude 4.5 or Gemini, to turn a basic text description into a full minimum viable product. A pay-as-you-go option allows you to pay for only the tokens your application requires.

Solid

Creating basic applications isn’t too hard anymore. Solid emphasizes creating apps that offer “enterprise-grade” deployments with top-notch security models and distributed deployment. The documentation emphasizes working iteratively with the AI and playing to its strengths, which is crafting a React/Tailwind front end with a wide variety of backends that generally mean TypeScript code running on Node.js with PostgreSQL.

Tempo Labs

The visual editor from Tempo Labs aims to allow human users to create a React app ten times faster. Simple visual tasks can be performed without relying on the AI. The tool emphasizes design and maintains a library of standard elements for each project. Any React code base can be imported and extended with predeveloped components and templates. It’s an editor that lets you vibe.

Vercel

The v0 system from Vercel offers a large collection of templates as a foundation for any app designer. The main interface is still a chat box that accepts any designs, but the templates act as both inspiration and a shared language for writing the specifications. There are also design templates that make it simpler to harmonize several different applications by allowing you to define a look once and then reuse it easily. The templates are also focused on mobile browsers to make it simpler to create mobile-ready sites.

Windsurf

Teams working with big code bases can use Windsurf, an IDE with embedded AI that’s designed to handle longer, multi-step plans for fixing bugs or adding features to a code base. It’s vibe coding, but focused on assisting traditional techniques. The tab key in the Windsurf IDE is quite powerful. As you hit the tab key, it moves from suggested fix to suggested fix waiting for you to signal your approval by hitting it again. The IDE aims to produce multistep plans that it calls “cascades.”

I keep hearing the same push: start using AI in your workflows. But that’s easier said than done! In enterprise environments, AI isn’t just a quick productivity fix—it also raises harder questions around data control, governance, and risk.

According to IDC, 42% of organizations cite concerns about GenAI jeopardizing control of data and intellectual property as a major barrier to adoption. [1]

I think that’s the real issue for CIOs now—not whether to invest in AI, but how to use it in ways that improve productivity without creating new security, compliance, and management concerns.

That’s where Galaxy AI for business comes in.

Increase productivity throughout the workday

A lot of AI value depends on whether it actually saves time during the workday. I’ve noticed that inefficiencies tend to show up in the same parts of the workflow: piecing together fragmented information (bonus points if it’s across different platforms!), figuring out what actually mattered in a meeting, losing time to research and source-checking, and identifying action items for your team.

None of it sounds particularly time-consuming on its own, but together, it can quietly add up to a meaningful amount of lost productivity.

Galaxy AI tackles those routine pain points in a few key ways:

• Start with better context: Now Brief [2] brings timely information together at a glance, helping employees get ready for the day faster.
• Capture meeting takeaways faster: Note Assist [3] and Transcript Assist [4] organize notes, generate summaries, support translations, and turn recordings into more usable outputs.
• Find information faster: Browsing Assist and Circle to Search [5] make research, source-checking, summarizing, and translation easier without interrupting workflows.
• Turn insights into action: Google Gemini [6] supports deeper research, live on-screen assistance, and seamless actions across apps.

TLDR: Galaxy AI helps reduce routine bottlenecks, making day-to-day work more organized, efficient, and easier to move through.

Simplify communication across the business

Communication has a way of getting messy in ordinary, unremarkable ways. A call moves too quickly, and someone misses a key detail. A message needs to be translated, then rewritten so it still sounds natural. The one that gets me the most is reworking the same email draft over and over because the tone is somehow still off.

If this sounds familiar, that’s because it is—and when it keeps happening across calls, chats, and teams, it eats up more of the workday than it should.

This is where Galaxy AI can cut down on that unnecessary back-and-forth:

• Break through language barriers: Call Assist and Interpreter support real-time translation for calls and face-to-face conversations in supported languages.[7]
• Capture key points faster: Call Transcript turns recorded calls into searchable text and concise summaries.
• Keep multilingual messaging moving: Chat Translate makes it easier to send messages across languages.
• Draft more polished business writing: Style and Tone and Composer help refine grammar, adjust tone, and create more professional emails and messages.

TLDR: Galaxy AI reduces communication delays by helping teams communicate across languages and spend less time revisiting calls or reworking messages, enabling them to stay clear, responsive, and aligned.

Keep AI on enterprise terms

Finally, we need to address the elephant in the room for AI adoption. Once employees start using AI to summarize meeting notes, process internal information, or speed up every day work, enterprises need to know where that data goes (like whether it’s retained or used for model training) and how much control IT actually has.

To address those concerns, Galaxy AI is built around a few important safeguards:

• Start from a secure device foundation: Samsung Galaxy devices are protected by Samsung Knox, with multi-layered protection across hardware and software from the moment the device powers on.
• Give sensitive workflows a more secure path: Compatible Galaxy AI features can use on-device processing to keep key tasks and data on the device, while Knox Service Plugin lets admins disable cloud processing for compatible features. For cloud-based Galaxy AI features, data isn’t retained by Samsung or Google or used for model training.
• Keep business AI use tied to the company: Samsung Account for Business lets employees use Galaxy AI through company-managed accounts, so corporate data stays with the organization rather than the individual. That means AI use for work doesn’t walk out the door with a personal account when an employee leaves.

TLDR: Galaxy AI helps reduce one of the biggest barriers to enterprise AI adoption by giving organizations clearer guardrails around data handling, cloud exposure, and IT oversight.

Take the next step with Galaxy AI for business

By now, we know that AI can save time. The harder question is whether those benefits can be delivered without losing control over security, access, and device management.

Galaxy AI for business starts to answer that by combining productivity features with stronger security and more deliberate IT oversight.

Ready to adopt mobile AI into your enterprise workflows with more security and control? Explore Galaxy AI for business [8] to see how it can support your organization.

[1] Source: IDC, Are You Ready for AI Everywhere?, October 2023. Based on IDC GenAI Awareness, Readiness and Commitment Survey.

[2] The information displayed in Now Brief may vary depending on apps used. Some apps may require internet connection and user consent for data access. Personal data intelligence must be enabled.

[3] Note Assist features are available only within Samsung Notes. A Samsung account login and internet connection are required. Text input may be limited to 200-4,000 characters.

[4] Transcript Assist is only available only with recordings using Samsung Voice Recorder and audio files under three hours. Summarizing features are available for text in Samsung Notes only, with input limits of 200-4,000 characters. Translation features require language presets, a Samsung account login, and an internet connection. Results and quality may vary depending on input and recording conditions.

[5] Circle to Search works with compatible apps and requires an internet connection. Results may vary depending on visual matches.

[6] Google Gemini is a trademark of Google LLC. Gemini Live features require an internet connection and a Google account login. Feature availability may vary depending on subscription, region, and device. Accuracy of results isn’t guaranteed, and use is limited to users 18 years and older.

[7] Call Assist and Interpreter features availability and supported languages may vary by region. A Samsung account may be required, and some language may need to be downloaded. For details on supported languages by feature, visit the Samsung Galaxy AI page.

[8] A Samsung account login may be required to use certain AI features. Samsung doesn’t make any processes, assurances, or guarantees as to the accuracy, completeness, or reliability of the output provided by AI features. Availability of Galaxy AI features may vary by device model. Galaxy AI features will be provided for free until the end of 2025 on supported Samsung Galaxy devices. Different terms may apply for AI features by third parties. Galaxy AI features may be limited for minors in certain regions with age restrictions on AI usage.

If your organization uses Microsoft 365 (a.k.a. Office), chances are you’ve encountered Teams, at least for video meetings. But it’s capable of a lot more, providing an effective way for groups of people to collaborate on work and advance business objectives.

Teams is, at its core, group chat software with videoconferencing capabilities and some interesting features around working with documents and spreadsheets, especially those stored in SharePoint and OneDrive for Business. In other words, it’s a collaboration hub. Teams can be bundled with Microsoft 365 plans, and it’s also available as a standalone subscription for both enterprise and small business customers.

Once you get to know it, Teams is a genuinely helpful tool for teams in companies that use Microsoft 365, since it brings together a bunch of suite components and surfaces them in one convenient place.

It’s available as a web app, desktop application (Windows, macOS, Linux), and mobile app (iOS, Android). Microsoft regularly releases updates to the Teams apps, usually with minor user interface refreshments, but sometimes they’re major updates with significant performance improvements, UI changes, and/or new features.

This guide covers the essentials for getting started with Teams and working efficiently within the platform. We also cover extras such as using Microsoft Loop components in Teams and practical use cases for Microsoft’s ubiquitous generative AI tool, Copilot. In this story we’ll demonstrate in the Mac app, but you’ll find that Teams works similarly in any environment.

In this article:

• Getting around Teams
• Joining and creating teams
• Understanding channels
• Chat: Direct messages and group conversations
• Video and audio calls
• Working with files and OneDrive
• Microsoft Loop within Teams
• Copilot Chat in Teams
• Tips for using Teams effectively

Getting around Teams

The navigation bar on the left side of the Teams window is your primary way to move through the app.

Understanding these core sections will help you navigate Teams effectively. Note that, depending on your organization’s configuration, not all of these options may be available to you:

• Activity: Shows notifications about mentions, replies, and other updates relevant to you.
• Chat: Your hub for one-on-one conversations, group chats, and (with recent updates) access to your teams and channels. The unified interface now combines what were previously separate Chat and Teams/Channels sections.
• Calendar: View and schedule meetings, linked to your Outlook calendar.
• Calls: Make and receive voice calls, access voicemail, and view call history, if your organization has a Teams Phone license assigned to your account. (This is not terribly common.)
• Files: Quick access to recent files, OneDrive files, and downloads.
• Apps: Browse and add applications that integrate with Teams.

The navigation bar is customizable — you can reorder items by dragging them or pin frequently used apps for quick access. You can also add things to the navigation bar by clicking the three dots. A few minutes spent rearranging and adding/removing can make your regular daily Teams use much more snappy, without a lot of friction.

Access your chats, teams, and channels in one place

One of the most significant recent changes to the Teams app is the unified chat and channel interface. In Teams, chats are private conversations (one-on-one or with a small group) that you start on the fly, whereas teams are set up for specific groups of people who need to collaborate on an ongoing basis. A team can be a small group you’re working with on a project, your whole department, or in some cases even the entire company. Each team has one or more channels where members collaborate (more on this later in the story).

Until recently, Chats and Teams/Channels were entirely separate sections in Teams, but an interface update has put all conversations in the same place. Here’s how to navigate:

1. Click Chat in the left navigation bar.
2. Next to the “Chat” header at the top of the left sidebar, click the three-dot menu icon.
3. Select Your teams and channels.
4. From here you can browse and access your individual teams and channels.

This change initially confused many users, but the unified approach aims to reduce switching between chats and channel conversations.

Use search as a navigation shortcut

The search bar at the top of Teams is one of its most powerful features for finding messages, files, people, and more. You can search for messages in chats and channels, files and documents stored in your OneDrive for Business or any linked SharePoint libraries, interactions with people in your organization, and “@mentions” of yourself.

Here are some tips to make search more useful:

•  Use / commands in the search box for quick actions (e.g., /call to start a call, /files to find files). If you use Cmd-space in macOS to launch Spotlight, this is similar, but for Teams.
• Filter results by category using the tab buttons that appear across the top (e.g., Messages, People, Files).
• Use keywords like from: to search for messages from specific people.
• If you perform the same searches over and over again, you will find that recent searches appear as suggestions when you click the search box, so just grab the one you want in a single click.

Joining and creating teams

Your organization probably already has several teams that appear in your Teams client. To join an existing team, click Chat in the left navigation, and then under “Teams and channels,” click See all your teams. You’ll see teams you’ve already joined as well as public teams available in your organization for immediate membership.

Private teams are invite only; to join one, you respond to an invitation that comes from a current manager of the team.

You can also create teams ad-hoc (unless your administrator has disabled this option) to facilitate working together. When you create a team, Microsoft 365 automatically sets up a bunch of supporting apparatuses:

• A Microsoft 365 Group
• A SharePoint site for file storage
• A shared OneNote notebook
• Microsoft Planner integration
• An Exchange mailbox for the group

To create a new team:

1. Click Chat in the left navigation.
2. Under “Teams and channels,” click See all your teams.
3. Click Create team in the top right.
4. By default, you’re building a new team from scratch. You can click the More create team options link to pick an existing team as a template on which Teams can model the new team you are creating.
5. Name your team and add a description.
6. Select Private (invite-only) or Public (anyone in the organization can join).
7. Add a first team channel. If you don’t know what you want, just click the dots on the side of the field and it’ll set the name to General.
8. Click Create.

9. On the next screen that pops up, add members by typing their names or email addresses.

If Microsoft 365 is configured appropriately in your company, you can even invite guests from outside your organization, such as vendors and contractors, simply by typing their email addresses into the team-picking screen. Their “guest” status will be clearly denoted in all of their actions. (If you don’t have permission from your administrator to do this, Teams will report back that you are not authorized.)

To manage your team at any time, click the three-dot icon next to its name in the sidebar on the left. You’ll see a pop-up menu where you can add or remove members, create channels for the team (more on that below), change the team name or description, and more.

Understanding channels

Channels are where you converse and collaborate within a team. The General channel is meant to be a catch-all place where you go to start conversations when you first begin using the Teams product; usually more specific topic-related channels will spring from there.

You can create multiple channels for any given team — the Widget Launch team might want to have sales, production, and marketing channels, for example. To add a channel, click the three-dot icon next to the team name in the navigator bar on the left. You’ll see a pop-up menu, and then click Add channel. On the screen that appears, type in a name and a description for the channel, pick a privacy option if you want to limit the people who can access the channel, and click the Create button. All the channels for a team appear underneath the name of the team in the left pane.

Each channel has different pages that let you bring documents, apps, and websites directly into your channels for quick access. From the channel name, click the down arrow icon underneath, and you can choose from a new page, an existing page, other embedded Teams apps, conversations, files in the related SharePoint library, notes in a OneDrive shared notebook, or other items.

Common uses include pinning a specific Excel spreadsheet that your team updates frequently, a OneNote notebook for meeting notes, or a Power BI dashboard for tracking metrics.

The Posts tab in a channel kind of works like Facebook or LinkedIn in that you can comment to your teammates in an ongoing conversation. Channel managers can choose to have a traditional channel, where you just have posts, or a channel organized by threads where you can follow conversations and related activities. (See more on the new threads layout from Microsoft.)

Composing messages is straightforward: Just click the Start a new conversation text box, or click Reply below an existing conversation and start typing.

You can call teammates’ attention to certain parts of the conversation by tagging them with an @ sign when typing, like this: @Adele Can you share the latest workback grid? Users who have been tagged will see, in their own copies of the Teams clients, those tagged parts of the conversation highlighted in bright red so they can easily see and respond to messages. You can use emoticons, emojis, and GIFs as well — that’s what I mean by thinking of this area like Facebook.

Other useful features:

• Formatting: Use the toolbar to add bold, italics, highlights, bullet points, numbered lists, and more.
• Attachments: Click the paperclip icon to attach files.
• Emojis and GIFs: Click the emoji or GIF icons beneath the compose box. Everyone loves a funny meme at work. OK, perhaps not everyone, but sometimes they’re a needed relief.
• Important/Urgent: Mark messages with priority flags using the ! icon.

Chat: Direct messages and group conversations

While channels are great for team-wide or topic-specific discussions, chat provides space for smaller conversations.

To start a one-on-one or group chat:

1. Click Chat in the left navigation.
2. Click the New chat button (pencil icon).
3. Type the name(s) of the people you want to include.
4. Type your message and press Enter.

Chats persist, so you can return to them anytime. They won’t vanish when you close Teams or log off for the day.

If you have an ongoing conversation and find you might want to add someone to a conversation, you can easily start a group chat: head up to the top right of your chat window, hit the three dots, click Participants, and from the pop-up submenu select Start a group chat.

You might be wondering when it’s appropriate to use a chat as opposed to convening everyone involved in a Teams channel. I suggest using channels for topics that need transparency and team-wide visibility. In contrast, use chat for quick questions, sensitive discussions, or conversations involving people outside your main team.

Video and audio calls

Teams offers robust calling and meeting capabilities, from quick calls to scheduled meetings with dozens of participants.

To make an immediate call, click Calls in the left navigation. On the Calls screen, start typing the name of the person you want to call, then select their name from the list that appears. Then click the Call button.

To schedule a call or meeting in advance, click Calendar in the left nav and click the New button in the top right corner. A “New event” pane pops up, where you can add a title, invite attendees, type a message, and add other information if desired. Alternatively, you can set up the meeting from Outlook. Either way, “Teams meeting” is selected by default as soon as you add attendees. You can also book a conference room or other shared resource here, too.

If you’ve been invited to a meeting or call, you’ll typically receive an email or other notification message with a meeting link or Join button. Click the button or link to join the call. For scheduled meetings, you can also click Calendar in the left navigation bar, click on the meeting, and click Join. If you have been sent a meeting invitation with an ID number, you can use the Join with an ID button at the top of the Calendar screen. Or if someone is just calling you ad-hoc, then you can choose to either accept the call without video, accept the call with video, or decline it on the pop-up that appears while the call is “ringing.”

Note: The first time you start or join a call in Teams, you’ll be prompted with a screen to help you set up your audio and video settings for that call. Also, on some systems, particularly Macs, you may be prompted to give Teams permission to record your screen, camera, or microphone. On Windows, your organization may have already given permission to Teams through its systems management software, and you may not be prompted. But if Teams is asking, click Allow to let Teams get the necessary access to each device.

Once you have accepted a call, you can control it using the buttons on the top of the call window, including a dial pad (for voice calls that need key presses); hold; transfer; chat; turn the camera and mic on or off; share content like an app, window, or screen; and then of course hang up the call.

If you have a webcam connected, Teams will grab that camera feed automatically. You can adjust cameras and backgrounds using the Camera button on the top bar. Blurring your messy room or switching to a solid color can often help reduce distractions in meetings. If you want to start the meeting with your camera off, just click the Camera button.

Using the Mic button and dropdown, you can select your audio devices, including your speaker volume. In most cases, Teams selects this correctly, but if you want to switch to a Bluetooth or USB headset, for instance, choose the right device from the Speaker and Microphone section of the right pane. (For headsets, you’ll most likely choose the same device for speaker and microphone.) If you want to start the meeting with your mic off, just click the button.

Under the More control, you can opt to record and transcribe a call, insert video effects, change audio, ask for translation options, and other settings.

To share other content with participants, click Share. You can then elect to share your screen, collaborate on a shared digital whiteboard, present a PowerPoint slide deck, or share individual files to your meeting participants without broadcasting them on the screen.

Working with files and OneDrive

Teams integrates deeply with SharePoint and OneDrive, making file collaboration seamless throughout the platform. When you share files in a channel, those files are automatically stored in the team’s SharePoint site. Similarly, files that are shared in a chat are stored in your OneDrive for Business account.

The Files menu in a Teams channel offers several useful features for document management. It displays all documents that have been shared in that particular channel, allowing you to create new folders to organize your content, upload files from various sources, and open relevant libraries in SharePoint when you need to do so.

To upload files to Teams, you can navigate to the Files menu within a channel (remember, that’s the dropdown menu under the channel name). You can upload a file with the three-dot menu or create a new file of a typical type, like a Word document or Excel spreadsheet, using the + New menu. Alternatively, you can drag and drop files directly into the Files tab or even into a conversation thread for quick sharing.

When you need to work with files in Teams, you can edit them directly within the application. Office files can be edited right in Teams by simply clicking on a file to open it. If you prefer the full desktop experience, you can choose to open files in Word, Excel, or PowerPoint by selecting the Open in Desktop App option. Teams also supports co-authoring, which means multiple people can edit the same document simultaneously. Additionally, you can access the version history to review previous versions of documents through the three-dot menu.

Teams recently updated its interface to bring OneDrive functionality directly into the Teams environment. This integration allows you to access your personal OneDrive files without having to leave the Teams application, streamlining your workflow.

To access OneDrive within Teams, you can click on the OneDrive option in the left navigation panel. This integration makes it significantly easier to share personal files in Teams conversations without the inconvenience of switching between different applications.

Microsoft Loop within Teams

Microsoft Loop components are collaborative blocks of content that can be shared and edited across Microsoft 365 apps, including Teams. These components function as living, shared content blocks that operate similarly to a task list, table, or paragraph, but with a key difference: they stay in sync across applications. When you create a Loop component in Teams and share it in Outlook, any edits made in either location will update everywhere the component appears.

There are several common types of Loop components available for different collaboration needs, such as:

• Lists allow you to create shared to-do lists with assignments and due dates, making task management more efficient.
• Tables serve as collaborative data grids where team members can input and organize information together.
• Paragraphs function as shared notes and content that multiple people can edit simultaneously, enabling real-time collaborative writing and brainstorming.

To create a Loop component within your workflow, you begin by typing /loop directly into the compose box of a chat or conversation to access the component creation menu. Next, you choose the specific type of component you want to create based on your needs, whether that’s a list, table, paragraph, or another option. After selecting your component type, you add your content and click Send to share it with your team members.

Once the component has been shared, team members can click on the component to edit it collaboratively, with all changes syncing in real time across all instances of that component throughout Microsoft 365.

For a deep dive on Loop components, see “How to use Loop components in Microsoft 365 apps.” (There’s also a standalone Loop web app — see “Microsoft Loop cheat sheet” for details.)

Copilot Chat in Teams

Copilot Chat offers several powerful generative AI capabilities to enhance your productivity within Teams using Microsoft’s favorite new child, Copilot.

It can summarize recent chats and channel conversations, helping you quickly catch up on discussions you may have missed. The tool can also answer questions about your Teams activity, providing insights into your communication patterns and interactions. Additionally, Copilot Chat can help draft messages for you, saving time when composing communications. Beyond these specific functions, it provides general information and assistance across a wide range of topics and tasks.

To access Copilot Chat within Teams, you need to look for the Copilot icon, which can be found either in the left navigation panel or at the top of the Teams interface. Once you locate the icon, click it to open a chat window with Copilot. From there, you can ask questions or request assistance with whatever you need help with, and Copilot will respond to your queries.

There are many practical ways to use Copilot Chat in your daily work.

• For example, you might ask it to “Summarize the Project Alpha channel from the last week” to get a quick overview of all the important discussions and decisions made in that channel.
• You could inquire “What did Sarah say about the budget?” to find specific information from past conversations without scrolling through lengthy message threads.
• Another useful application is asking “Help me write a message asking the team for status updates” when you need assistance crafting clear and effective communications to your colleagues.

This basic Copilot functionality is distinct from the more advanced Microsoft 365 Copilot integration which happens only with an additive license beyond the standard Microsoft 365 subscription. For organizations with Microsoft 365 Copilot licenses, Teams offers more advanced AI capabilities:

• Meeting-related features: Real-time meeting summaries and transcription, automated action items and follow-ups, and chat-like answers to questions about what was discussed during the meeting (even if you joined late).
• Chat and channel features: Comprehensive conversation summaries across multiple channels, suggested replies and message composition, and key highlights extraction from long conversation threads.
• Productivity features: Meeting preparation briefs based on related emails and documents, intelligent recaps for meetings you missed, and follow-up task identification and tracking.

You might not have this level of license, as they’re relatively expensive — $30 per user per month at the enterprise level or $21 per user per month for companies with fewer than 300 employees. That’s on top of what your organization is already paying for Microsoft 365. Microsoft recently reported 15 million paid M365 Copilot seats — just 3.3% of the Microsoft 365 user base.

Tips for using Teams effectively

Here are some battle-tested tips for getting the most out of Teams and, perhaps more importantly, not letting Teams eat your workday and become the place where knowledge goes to die.

Tamp down notifications and activity settings

Managing notifications prevents Teams from becoming overwhelming. If you go with the defaults, Teams will buzz you — and your phone, too, if you have the companion app installed and set up — every time you get a call, message, or chat with an @mention to you. In a busy organization, this can drive you up the wall or result in your entire day being swallowed by Teams notifications.

To customize notifications:

1. Click your three-dot menu in the upper right and select Settings.
2. Go down to Notifications and Activity on the left side.

Adjust everything to your preference. Teams is a very chatty app, so you may want to play around with these settings over time to find your particular sweet spot.

Add apps to Teams

In addition to its built-in integrations with Microsoft 365 tools like OneDrive, Loop, and Copilot, Teams lets you bring a variety of Microsoft and third-party apps right into a channel or group chat. You can add apps for brainstorming, project management, polling, training, data visualization, sales, customer support, and countless other uses, allowing you to work in them without having to leave the Teams environment.

See “18 Microsoft Teams apps for content collaboration and management” for complete instructions on finding and installing apps, but in a nutshell, you click the Apps button in the left navigation bar, browse or search the available apps, and then install the one you want. (Note that your organization may restrict which apps you can install in Teams.)

5 more quick tips

• Keep channels focused: Create channels for specific topics, projects, or workstreams rather than having everything in General.
• Use @mentions strategically: Mention individuals when you need their input, @channel when everyone in the channel should see it, and @team sparingly for truly urgent, team-wide announcements.
• Pin important messages: Hover over any message and click the three dots (…) to pin it to the top of the channel for easy reference.
• Save messages: Click the bookmark icon on messages you want to find later, then access them through the Saved item on the left navigation bar, toward the top.
• Use status messages: Update your status with custom messages like “In a meeting until 3pm” or “Working from home today” to keep teammates informed.
• Use keyboard shortcuts: Press Ctrl + / (Windows) or Cmd + / (Mac) to see a list of keyboard shortcuts that can speed up your work.

What to avoid

Teams is definitely a big step up over endless email chains, but that doesn’t mean it’s suitable for everything. Here are two things to avoid:

Trying to replace all emails with Teams conversations and links. Sometimes we humans have a tendency to gravitate to whatever new features and tools there are, proclaiming them the “killer” of whatever came before and trying to force old square pegs into shiny new round holes. Teams is no different.

As an instant messaging platform, Teams is ideal for back-and-forth quick hits. If your message is longer than a paragraph, chances are, it should go back to email. Longer conversations, project planning, longer term development, all of those types of deep thinking and analysis are best suited for email. In email, you can sort, filter, set up rules, and do other automated things to manage how you see and find information. It’s not impossible to use Teams for longer conversations, but it’s suboptimal.

Trying to send emails to external folks. Unfortunately, there is no way for Teams to send email out to the internet, so unless you want to invite external users as guests into your team (assuming you have permission to do that), you will need to handle some subjects that involve people outside of your organization via old-fashioned email messages. That, of course, limits the utility of using Teams in projects or environments with a lot of collaboration with external users when they’re not a part of your Teams environment.

Your organization may restrict how external users can interact with your Microsoft 365 setup, usually via data loss prevention policies and prohibitions on folks outside your organization accessing Teams chats and channels. Ask your IT department if you have questions.

This article was originally published in March 2018 and most recently updated in April 2026.