Was ist Claude Mythos?

Claude Mythos ist ein KI-Modell, das von Anthropic entwickelt wurde und für Anwendungen in den Bereichen Cybersicherheit und Gesundheitswesen optimiert ist. Ursprünglich wurde Mythos 5 im April einer kleinen Gruppe geprüfter Technologiepartner zugänglich gemacht – im Vorfeld eines geplanten, breiter angelegten Rollouts.

Zu diesem Zweck rief das KI-Unternehmen das Projekt „Glasswing“ ins Leben, ein Konsortium, das Infrastrukturanbietern, Open-Source-Entwicklern und großen Technologieunternehmen einen begrenzten, kontrollierten Zugriff zu Mythos gewährt. Ziel der Initiative ist es, Verteidigern zu ermöglichen, Schwachstellen schneller aufzuspüren und zu beheben, als Angreifer sie identifizieren können. Das erscheint auch dringend notwendig, setzen diese doch zunehmend selbst auf KI-gestützte Werkzeuge.

Über welche Fähigkeiten verfügt Claude Mythos?

Die ersten 50 Partner von Project Glasswing konnten mithilfe von Mythos mehr als 10.000 Schwachstellen mit hohem oder kritischem Schweregrad in allen gängigen Betriebssystemen und Webbrowsern aufspüren.

So identifizierte das Modell Sicherheitslücken, die selbst den fähigsten Sicherheitsforschern jahrelang entgangen waren – etwa einen 27 Jahre alten Fehler in OpenBSD. Zudem hat Mythos bewiesen, dass es mehrere Schwachstellen miteinander verknüpfen kann.

Wie schränkt Anthropic den Zugang zu Claude Mythos ein?

Anthropic teilte bei der Vorstellung von Claude Mythos mit, es schränke die Verfügbarkeit des Spitzen-KI-Modells bewusst ein, da dessen Fähigkeiten von Angreifern leicht missbraucht werden könnten.

Im Juni wurde die Technologie dann für weitere 150 Organisationen freigegeben. Alle Mythos-Partner müssen hierbei aber zustimmen, dass ihre Daten 30 Tage lang zu Sicherheitsüberwachungszwecken gespeichert werden.

Am 15. Juni verhängte die Trump-Regierung allerdings Exportbeschränkungen für Claude Fable 5 und Claude Mythos 5. Diese sollten ausländischen Staatsangehörigen sowohl innerhalb als auch außerhalb der USA den Zugang verwehren. Am 30. Juni wurden die Beschränkungen jedoch bereits wieder aufgehoben.

Was ist Claude Fable?

Für einen breiteren Einsatzbereich bietet Anthropic Claude Fable 5 an. Das Modell basiert auf derselben technischen Grundlage wie Mythos. Es verfügt jedoch über strenge Sicherheitsmechanismen, die den Betrieb in als „riskant“ eingestuften Bereichen der Cybersicherheit einschränken. Alle als problematisch deklarierten Anfragen werden stattdessen automatisch an das ältere und weniger leistungsfähige Large Language Model (LLM) Opus 4.8 weitergeleitet.

Wie nutzen Security-Partner von Anthropic den Zugriff auf Mythos?

Cisco, einer der Projekt-Glasswing-Partner, hat seine „Foundry Security Spec“ als Open-Source-Lösung veröffentlicht. Hierbei handelt es sich um ein modellunabhängiges Framework für Sicherheitstests, das es anderen Anbietern und Sicherheitsexperten in Unternehmen ermöglichen soll, ähnliche Arbeitsabläufe zu entwickeln, ohne bei Null anfangen zu müssen.

Vor kurzem betonten Vertreter von Cisco auf einer Online-Veranstaltung, dass Verteidiger KI nutzen können, um Sicherheitsprobleme wesentlich schneller und in größerem Umfang zu identifizieren, zu bestätigen und zu beheben. Ältere Modelle, die nach dem Prinzip „eine Schwachstelle finden und patchen“ funktionieren, seien nicht mehr zeitgemäß. Dies liege daran, dass Angreifer KI einsetzen, um den Weg von der Entdeckung einer Schwachstelle bis zu deren Ausnutzung zu beschleunigen. Cisco wiederum setzt KI intern bereits in der Defensive ein, um 1,8 Milliarden Zeilen Code im gesamten Produktportfolio zu scannen.

Gleichzeitig betonen die Experten, dass kleinere Unternehmen keinen Zugriff auf eingeschränkte KI-Modelle benötigen, um ihre Sicherheit zu verbessern. In solchen Betrieben lasse sich stattdessen mehr erreichen, indem grundlegende Sicherheitsmaßnahmen optimiert werden. Hierzu zählen laut Cisco unter anderem Authentifizierung, Netzwerk-Segmentierung, Zero Trust und die Behebung aktiv ausgenutzter Schwachstellen.

Bieten andere KI-Anbieter etwas Vergleichbares zu Claude Mythos an?

Mythos ist das prominenteste Beispiel für Frontier-KI-Modelle. Mit ihnen kann die Suche nach Zero-Day-Lücken in einem Tempo und Ausmaß automatisiert werden, die weit über die Fähigkeiten menschlicher Teams hinausgeht.

Allerdings arbeiten auch etliche andere Anbieter an hochleistungsfähigen, auf Sicherheit ausgerichteten „Frontier“-KI-Modellen. Zudem gibt es andere leistungsstarke Open-Source-Modelle, die sich problemlos für die Cybersicherheitsforschung nutzen lassen. Claude Mythos ist also bei weitem nicht die einzige verfügbare Option.

So werden beispielsweise die Modelle GPT-5.4-Cyber sowie GPT-5.5 von OpenAI genutzt, um Schwachstellen zu erkennen und zu analysieren. Auch in der Malware-Analyse und der Bedrohungsmodellierung kommen sie zum Einsatz. Zugang zu diesen Technologien können Sicherheitsanbieter, Unternehmen und Forscher über das Programm „Trusted Access for Cyber“ (TAC) von OpenAI erhalten.

Zusätzlich hat das chinesische Cybersicherheitsunternehmen 360 Security Technology mit Tulongfeng ein System entwickelt, das als Gegenstück zu Anthropics „Mythos“ beschrieben wird.

Privat lassen sich leistungsstarke offene Modelle – darunter DeepSeek V3.2 von DeepSeek und Llama 4 von Meta – auf GPU-Infrastrukturen betreiben und in der Cybersicherheitsforschung einsetzen. Fugu des japanischen Anbieters Sakana AI ist eine weitere Option in dieser Kategorie.

Was kritisieren Cybersicherheitsexperten an Claude Mythos?

Kritiker aus dem Cybersecurity-Bereich räumen ein, dass Claude Mythos zweifellos hochentwickelt sei. Die Marketing-Behauptung, wonach es zuverlässig produktive IT-Systeme lahmlegen könne, übersteige jedoch die tatsächlichen Fähigkeiten.

Darüber hinaus beklagen sich Sicherheitsexperten – etwa in Podcasts – über die übertrieben restriktiven Sicherheitsmechanismen von Claude Fable: Bereits Anfragen, einen sicherheitsrelevanten Blogbeitrag zusammenzufassen oder sogar das Wort „Exploit“ zu buchstabieren, würden auf das deutlich schwächere Modell Opus 4.8 zurückgestuft. Dadurch würden selbst alltägliche Aufgaben in der Informationssicherheit unnötig erschwert.

Andere Experten warnen davor, dass Frontier-KI-Modelle anfällig für False Positives seien. Eher grundsätzlich ist die Kritik, dass das schnellere Aufspüren von mehr Schwachstellen das eigentliche Problem nicht löst, nämlich zuverlässig Sicherheitslücken zu beheben oder nicht-technische Angriffsvektoren wie Social Engineering zu verhindern.

Wie sollten CISOs auf Mythos reagieren?

Die Nachrichtendienste der „Five Eyes“ (USA, Großbritannien, Kanada, Australien und Neuseeland)  warnen davor, dass hochmoderne KI-Modelle wie Claude Mythos „sowohl offensive als auch defensive Cyber-Fähigkeiten grundlegend verändern werden“ – und zwar in einem Zeitraum von Monaten statt Jahren.

„Während KI uns dabei helfen wird, die Cyberabwehr im Laufe der Zeit zu verbessern, erhöht sie zugleich Geschwindigkeit, Ausmaß und Raffinesse von Cyberbedrohungen“, heißt es in der Erklärung der Gruppe. Unternehmen sollen daher KI nutzen, um ihre Abwehrmechanismen im Rahmen umfassenderer Strategien zur Stärkung der Cybersicherheits-Resilienz zu verbessern.

Die meisten Unternehmen seien jedoch bei weitem noch nicht darauf vorbereitet, was dies für ihre Bedrohungsmodelle bedeutet, warnt ein Experte. „Wir verfügen heute über KI-Systeme, die realistische Angriffswege über Software, Anbieter und kritische Infrastrukturen hinweg schneller abbilden können, als menschliche Angreifer sie erfassen können“, erläutert Joe Hubback, Partner und CISO beim Beratungsunternehmen Elixirr sowie ehemaliger McKinsey-Partner. Dadurch, dass „Fähigkeiten der ‚Mythos-Klasse‘ vor der breiten kommerziellen Einführung stünden, handle es sich nicht mehr um „ein Nischenproblem der Forschung“, ergänzt er. Vielmehr sei es jetzt ein Faktor, den jedes Unternehmen in sein Bedrohungsmodell einbeziehen müsse, so der Experte.

Auch ein Bericht der Cloud Security Alliance warnt davor, dass KI die Zeitspanne zwischen der Entdeckung einer Schwachstelle und deren Ausnutzung drastisch verkürzt habe. Damit seien herkömmliche Sicherheitsmodelle, die auf „Patchen und Reagieren“ basieren, überholt. Unternehmen sollten sich vielmehr auf anhaltende Wellen von Schwachstellen einstellen, die durch „Project Glasswing“ und andere Quellen mittels KI aufgedeckt werden.

„Die bei Mythos beobachteten Fähigkeiten werden schon bald breiter verfügbar sein. Dadurch wird sich die Anzahl sowie Häufigkeit komplexer, neuartiger Angriffe, denen sich Unternehmen gegenübersehen, drastisch erhöhen“, heißt es in der Warnung. Sicherheitsverantwortliche müssten daher ihre Verteidigungsstrategien auf einen „Mythos-ready“-Ansatz umstellen, der auf kontinuierlichem Schwachstellenmanagement, schnellerer Priorisierung und verbesserter Reaktion auf Sicherheitsvorfälle basiert. (tf)

Dieser Artikel basiert auf einem Beitrag von CSO.

See also:

• What Anthropic Glasswing reveals about the future of vulnerability discovery
• Anthropic’s Mythos signals a structural cybersecurity shift
• Beware the ‘son of Mythos,’ security experts warn
• Mythos is a signal, not a siren: What frontier AI should change for CISOs

Microsoft has released TypeScript 7.0, a native port of the company’s strongly typed version of the JavaScript language based on the Go programming language.

With this new codebase, TypeScript 7.0 brings native code speed, shared memory multithreading, and a number of new optimizations that typically yield speedups between 8x and 12x on full builds, Microsoft said in a July 8 announcement. This port of TypeScript to native code was done as faithfully as possible, writing new code while maintaining the structure and logic of the original codebase to keep results consistent and compatible between the two compilers, according to Microsoft. 

TypeScript 7.0 is available via npm:

npm install -D typescript

To help with the TypeScript 6.0 to TypeScript 7.0 transition process, Microsoft has published a new compatibility package, @typescript/typescript6. This package provides an executable named tsc6, so that if needed, a developer can install TypeScript 7.0 (which ships its own tsc binary) side-by-side with TypeScript 6.0 without naming conflicts. The new package also re-exports the TypeScript 6.0 API, so that a developer can use tsc for TypeScript 7, while other tooling can continue to rely on TypeScript 6.0.

TypeScript 7.0 is a major milestone in the TypeScript project, Microsoft said. This port has been the primary focus for Microsoft’s team for more than a year. With TypeScript 7.0 now out, the team will return to new feature work, ergonomic improvements, more performance wins, and implementing a new API for the broader ecosystem. Microsoft expects a fairly similar timeline to releases prior to TypeScript 7.0, with new versions published every three to four months.

TypeScript 7.0 was announced as a release candidate on June 18.

Two vulnerabilities found in Anthropic’s Claude for Chrome extension remain exploitable months after they were reported to the company, a research by Manifold Security noted.

According to the researchers, the flaws can allow a malicious browser extension to trigger Claude into performing privileged actions, including reading Gmail messages, Google Docs content, and Calendar entries on behalf of a user.

In a new blog post, the researchers said the issues are reproducible in Claude for Chrome version 1.0.80, released on July 7, and remain unresolved eight releases after they were first reported in May.

“Anthropic shipped v1.0.73 through v1.0.80 in the weeks following our report,” the researchers added. “The internal tracking issue covering this class of vulnerability was marked ‘resolved’ in the weeks following our report.”

However, the content script and side-panel handlers in the latest version remain “byte-identical” to v1.0.72 the researchers originally tested. Researchers drew parallels with the earlier ClaudeBleed disclosure, where the issue was found exploitable despite Anthropic announcing a fix.

Anthropic did not immediately respond to CSO’s request for comment.

Synthetic clicks trick trusted AI

The first vulnerability stems from how Claude for Chrome handles user interaction before executing privileged actions. According to Manifold, the extension’s click handler does not verify whether an approval click originated from a real user through the browser’s “event.isTrusted” property.

Meaning, another browser extension capable of injecting scripts into Claude.ai can generate synthetic clicks that Claude accepts as legitimate.

In default configurations, the attack can automatically trigger one of Claude’s predefined browser tasks before presenting an approval dialog. However, if users have enabled the extension’s “Act without asking” mode, Claude may execute those actions silently, allowing an attacker to retrieve Gmail content, Google Docs data, or Calendar information, the researcher noted.

Rather than exploiting a flaw in Chrome itself, the attack abuses the trust placed in Claude as an authorized browser agent. Manifold demonstrated an exploit in six lines of JavaScript.

The researchers said the flaw can simply be fixed with one added line, “if (!n.isTrusted) return;” at the top of the click handler.

A problematic privilege mode

The second finding focuses on how Claude’s side panel initializes its permission model.

Manifold found that loading the panel with “?skipPermissions=true” parameter makes the extension enter a privileged mode meant to bypass repeated confirmation prompts. While the researchers did not identify a direct external path to control this parameter today, they argued that the design creates a latent security risk because privileged behavior depends on a URL value rather than user-controlled inputs.

Any future vulnerability capable of influencing that parameter could inherit elevated privileges without requiring additional security checks, the researchers noted.

To address both findings, Manifold recommends validating genuine user interactions before executing privileged actions, avoiding URL-driven privilege transitions, and strengthening the authentication of internal extension workflows.

This is Part 2 of a series on tech worker unionization. See Part 1: “A brewing battle: More IT workers want unions. The industry doesn’t.”

The best time for tech workers to unionize was 20 years ago, when they had plenty of leverage. The second-best time is now, when they don’t.

Mass layoffs, AI-driven displacement, corporate surveillance, workplace disillusionment have created conditions that have made organizing compelling for tech professionals. But the federal labor board that has historically protected workers’ right to organize has been weakened, and the companies that once feared it are openly defying it.

Here’s how organizers and labor experts describe the pros and cons to organizing — and how you can get started.

What unions can — and can’t — do for you

The single biggest benefit of a union contract for most tech workers isn’t pay — it’s protection against arbitrary termination, especially in the wake of recent mass layoffs in tech. In the United States, nonunion “at-will” workers can be fired at any time without a stated reason, while unionized workers negotiate protections written into their contracts.

“That fear of the company letting you go for anything at any time…with a union they just can’t do that,” says Zak Thompson, a senior software engineer at Kickstarter and union steward at Kickstarter United. Now that Kickstarter employees are unionized, people are less worried that saying something negative will result in termination.

“I’ve been shocked at the willingness of my co-workers to speak up against what they see as poor or controversial business decisions,” Thompson says.

Beyond job security, unions can deliver concrete material gains. Kickstarter United was formed in 2020, although getting there wasn’t easy: two employees were fired during the organizing campaign — which itself became a galvanizing event. And while the union hasn’t been able to prevent layoffs, it did negotiate better terms: four months of severance pay and four to six months of continued health insurance, versus the two to three weeks per year of work that management had initially proposed.

Other benefits include a four-day work week; AI protections; a minimum pay floor; and standards for raises, promotions, and time off for the company’s 59 employees.

Unions can give tech workers a voice in decisions that affect their daily work — including how AI tools are deployed. “Nobody I’ve spoken to is against new technology or getting trained in it,” says Max Belasco, a business systems analyst at the University of California Los Angeles School of Law and co-chair of the UCLA chapter of the University Professional and Technical Employees/Communications Workers of America (UPTE-CWA) Local 9119.

“But when new technology is being implemented, we want to know: what’s the five-year vision, the 10-year vision? Are we implementing this in a way that betters staffing, increases efficiency, or eases the lives of people already working? Or are we trying to take away jobs, automate people out of their pension or paycheck?” Belasco says.

The challenges are real. Tech professionals are less inclined to leave their jobs in the current market because wages haven’t been increasing as fast as they once were, and it can take longer to land another job.

“Tech moved from a very tight labor market in 2022 (1.85% unemployment rate) to a noticeably weaker one in 2024–2026 (3.49%),” although that’s still better than the national unemployment rate of 4.36% through May of this year, says Liya Palagashvili, senior research fellow and director of the Labor Policy Project at the Mercatus Center at George Mason University.

Flexibility is a concern. The more substantive challenge, raised by economists including Palagashvili, is that traditional union contracts impose uniform terms across an entire bargaining unit, limiting the flexibility that many tech workers — and their employers —currently enjoy. Tech firms need to move fast, adjusting teams, products, and roles on the fly.

“Collective bargaining agreements can make those adjustments much more difficult, whether by making them slower, costlier, or inconsistent with the contract,” she says.

Workers skeptical of unions in a survey of 1,900 tech professionals conducted by the career site Blind cited specific concerns: that unions are “not meritocratic,” “prevent innovation,” and “hold back earnings of top performers.”

Thompson from Kickstarter United pushes back: “We have nothing in our contract about ‘you can’t bend down and pick up a piece of trash because that’s someone else’s job.’ The company is free to give bonuses and individual raises as much as they like. This is all just up to the people who are bargaining the contract from the union side.”

Organizing carries potentially serious personal risks. During negotiations for a second three-year contract in 2025, Kickstarter United went on strike for 42 days. A few months later, the company announced layoffs.

“They let go strong union leaders, including a person who had bargained our last contract,” Thompson says. The union appealed, and the issue is now going to arbitration.

If you form a union, don’t expect much support from the National Labor Relations Board, the agency that certifies US labor unions and protects workers’ right to organize, in terms of prosecuting complaints of unfair labor practices, Thompson warns. “We’re in a political moment in this country with a pretty weakened NLRB. You have to be ready to organize and withhold worker power without any guarantee of safety.”

Organizers are up against an enormous union avoidance industry. Organizers can expect fierce pushback as soon as the business discovers that organizing is underway.

“There’s a multi-billion-dollar industry in union avoidance,” says Alan McAvinney, a Google software engineer and organizing chair, Alphabet Workers Union-CWA, a 1,400-member minority union of Alphabet employees. (Google is a subsidiary of Alphabet.)

US employers spend roughly $1.7 billion a year on union avoidance consultants and law firms, according to a May 2026 report by the Economic Policy Institute and LaborLab.

Expect hardball tactics. Management may play hardball during the time between when organizers announce their intention to unionize and the actual vote. For example, management can threaten to fire foreign-born workers in the US on H-1B visas if they support the union. Those workers would then have just 60 days to find a new sponsoring employer or lose their H-1B status, according to a recent Tech Workers Coalition blog post.

And at venture capital-backed startups, investment agreements sometimes require management to attest there is no union activity — meaning a public organizing drive can trigger funding withdrawal. Or, if a unionized company is acquired, the new management can dissolve the union overnight by reclassifying unionized workers as new hires.

With these sobering facts in mind, here is how organizers who have done it describe the process of creating a union.

Step 1: Start a conversation with your co-workers

At the University of California, a two-tier system had evolved where some tech workers were unionized and some weren’t, Belasco says. Management created new titles that fell outside the union even though they had similar job descriptions and responsibilities to those in the union. Those nonunion employees received lower pay and benefits than their unionized peers, which created resentment and instability.

Belasco and other organizers wanted to eliminate that division by bringing everyone under the same contract. But when they began their unionization drive, “the biggest barrier we faced wasn’t management opposition — it was that people felt this was just the best-case scenario realistically available: ‘We have this job at the university, we have concerns about automation and layoffs, but what can we really do about it?'” he says.

The antidote to that fatalism, organizers say, is simple: “Just start talking to your immediate co-workers. Are they experiencing the same challenges you are experiencing?” says McAvinney. “There’s no need to start talking about a union at this point.”

Just get a consensus and start building a group of like-minded individuals, Thompson advises. “Always start with one-on-one conversations, and that’s what you should do the whole time. That’s the key to organizing,” he says.

Tech workers often think they’re a special case, says Thompson, and therefore that unionization isn’t a good fit. “You’re not special. You are a company of workers, you are organizing, and there is a playbook for that. Trust the process, because it tends to work pretty well,” he says.

Step 2: Who’s on board, and who’s not? Map your workplace, but keep it quiet

Once there’s a consensus, continue to grow your network. Keep a list of everyone you’ve spoken with and note their disposition: “Is this person union-friendly or anti-union? Would they be a strong organizer?” Thompson says.

Maintaining secrecy early on is essential, because anti-union tactics will start immediately, and that can stop union organizing before it can gain momentum.

“Generally, employers do not want to share power with their workforce,” McAvinney says. Employers will deploy every means at their disposal to stop organizing efforts and peel away potential yes votes.

“If you look at historical examples, having 70% approval before the employer finds out about you results in a high percentage of wins when you actually cast the vote. Historically, that’s an effective buffer,” he says.

There’s a real threat of firing and layoffs. The traditional tech worker belief that job mobility makes collective action unnecessary is now being tested by a tighter job market, McAvinney says, noting that workers who many believe were fired for speaking out back in 2019 were a galvanizing factor in his union’s formation.

“You generally don’t want to be in a situation where the employer feels comfortable firing everyone. Part of that is thinking from a cynical standpoint about what the consequences would be to the employer if they did fire everyone,” he says.

One-on-one conversations that include personally asking co-workers to keep conversations confidential are key to keeping things quiet, Belasco says. When 2,100 UC tech workers voted to unionize in May, 96% voted in favor. To stay out of earshot of managers, avoid employee surveillance tools, and sidestep conference calls that could be recorded, organizers met with workers in their homes.

“That tactic is probably what made the difference between winning the election and getting the majority we got,” he says.

Step 3: Find the right union affiliation or go it alone

“Running a campaign against major employers requires the resources and expertise of the larger labor movement, even if workers publicly present as independent,” says Kate Bronfenbrenner, director of labor education research and senior lecturer emeritus at Cornell University’s School of Industrial and Labor Relations.

Options include the Communications Workers of America (CWA), Service Employees International Union (SEIU), and the Office and Professional Employees International Union (OPEIU), among others. Another resource, the Tech Workers Coalition (TWC), provides training on organizing tactics, AI-in-workplace issues, and contract negotiation, and can match workers to the right unions for their needs.

The Alphabet Workers Union decided early on to affiliate with CWA. “They gave us a bunch of support early on in our campaign with no strings attached,” McAvinney says.

Kickstarter is organized through OPEIU, Thompson says. “They’ll usually have resources and staff that can help you through the next steps: collecting signatures in support of a union, bringing that to management, holding a vote — the more formalized things that interact with US labor law. They’ll also help with organizing along the way,” he says.

For workers at institutions where a union already exists, there may be a faster path. Organizers at UCLA did what’s called a “unit modification,” aligning with UPTE. By organizing under UPTE, the workers didn’t have to negotiate a new contract from scratch — they joined an already-negotiated contract covering existing UPTE tech members, which put them in “a much stronger position” than starting fresh, Belasco says.

Step 4: Choose your union model: majority vs. pre-majority or minority

Assess what’s practical for your organizing effort. In a majority union, more than 50% of all workers in a defined bargaining unit must vote to join the union through an NLRB-supervised election in the private sector, or a Public Employment Relations Board (PERB)-supervised election for public sector workers.

The NLRB must certify the union, which then operates under its legal protections. This means, for example, that the employer must bargain, negotiated contracts are enforceable, violations must go to the NLRB or arbitration, and workers can’t be dismissed without just cause.

A pre-majority or minority union is a minority labor organization operating without NLRB protections or collective bargaining agreements. “Pre-majority means that workers are able to demonstrate majority support — through signed cards, petitions, a walkout, or everyone wearing solidarity T-shirts — without going through a formal election,” Bronfenbrenner says.

The Alphabet Workers Union-CWA (AWU-CWA) formed as a pre-majority union because achieving majority status across a globally distributed workforce of over 100,000 was not a realistic near-term goal. “An underground model where you try to reach 70% support across a workforce of over 100,000 people isn’t realistic,” McAvinney says.

A pre-majority union can still make a difference, he says. For example, the Alphabet Workers Union-CWA convinced management to offer voluntary exit packages — buyouts — prior to announcing layoffs.

For smaller organizations, a majority union may be the more practical option — it’s more attainable, McAvinney says. “I don’t think [the pre-majority union model] is the correct thing to do in all situations. I certainly would not recommend it to a 200-person shop.”

Kickstarter, which had fewer than 100 employees, was able to form a majority union, with 55% voting to organize.

Ultimately, says McAvinney, “there’s no inflection point where you go from being able to win nothing to winning everything, even with a contract and a supermajority. But the more people you have who are willing and able to fight for what they want, the more you’ll be able to get.”

Step 5: Who should — and should not — be in your union?

Belasco’s situation at UCLA illustrates a broader strategic choice that every organizing campaign must make. He had been in a union position in educational technology when he was told his role would be reclassified as a non-union position.

“I was given a choice: apply to the new non-union position to continue doing the work I’d trained for, or stay in my union position doing service desk work I wasn’t used to,” he says. “Essentially, it was a choice between job security and career progression.”

Belasco joined a “wall-to-wall” union, which represents a broad range of university professional and technical employees across the UC system rather than a single job category, such as engineers or tech professionals.

Kickstarter United is another example of a wall-to-wall union. “It’s not just the engineers who are unionized, but also customer support, designers — everyone,” Thompson says.

Wall-to-wall unions are more powerful, but they’re also more difficult to achieve. Under US labor law, “professionals have to vote separately on whether they want to be combined with other workers,” says Bronfenbrenner. “You can never have a wall-to-wall unit without giving professionals the chance to decide whether they want to be separate.”

The law’s “professional employees” category includes roles like software engineers and developers but not necessarily others. For example, customer support specialists and QA analysts would fall into the “non-professional workers” category.

“For decades, the pattern was either to organize everybody except the engineers, or manage to organize the engineers and fail to bring in everybody else — neither of which builds real worker power,” says Simone Robutti, an organizer with Tech Workers Coalition Global, an international branch of TWC based in Berlin.

Step 6: You won the vote. Get ready for what comes next

Winning a union vote means having a seat at the table, says Thompson. “Once the workers have come together and agreed they want that seat, you bring that to management, and they have a chance to voluntarily recognize a union,” he says.

But in most cases employers contest the results, which must be certified by the NLRB or PERB. That process, in which the employer uses various tactics to challenge the legitimacy of the outcome, can take weeks or months.

Unfortunately, the legal framework that is supposed to protect workers during this process has been significantly weakened in the last few years. In a potentially more ominous development, SpaceX, Amazon, Trader Joe’s, Starbucks, and the University of Southern California have in separate legal actions challenged the constitutionality of the NLRB, arguing that the agency’s structure violates the separation of powers. The Fifth Circuit Court of Appeals upheld injunctions against the NLRB in SpaceX’s case in August 2025 — a serious challenge to the agency’s authority.

In the meantime, some companies may disregard negotiated contracts, which can lead to lengthy legal appeals or extended arbitration.

“The NLRB can still force an election, but it can’t force a contract, and companies are saying they simply won’t comply,” Bronfenbrenner says. This is where the expertise and resources of affiliation with a major union can help, she adds.

As a result, contract negotiations can take far longer than workers might expect. At Kickstarter, for example, two years and four months elapsed from the time of the union vote to the first contract, and that was at a 59-person company with a relatively cooperative employer. At larger companies with more aggressive legal teams, the timeline will be longer.

Forming a union is hard work, Robutti says. “It’s not a service you pay for and they protect you. It doesn’t happen spontaneously, and it doesn’t happen magically. It’s the choice to take responsibility for improving your workplace.”

I attended several major technology conferences in 2025 where the first AI agents embedded in enterprise SaaS platforms were announced. Some of these agents showed promise and a glimpse into the future of work, while others looked like natural language extensions of a platform’s existing functionality.  

At the end of 2025, Anthropic and OpenAI launched new AI models and code-generating capabilities. More developers tried vibe coding, and some platforms launched spec-driven development capabilities. By February 2026, even The New York Times reported that the AI disruption had arrived, noting that code generators were building “apps that may be flawed, but credible.”

Wall Street investors took notice of the code-generation improvements and other disruptive factors, driving a selloff in SaaS stocks, now referred to as the “SaaSpocalypse.” Part of their concern stemmed from the belief that CIOs would use AI to write software that would replace SaaS solutions.

AI innovations from SaaS and solution providers

But I thought differently and wrote a response in my article asking whether AI is the end of SaaS as we know it. CIOs might use AI to accelerate application modernization, but I doubt they would replace their ERP, CRM, and even smaller SaaS point solutions by building them.

Instead, I believed it would be SaaS companies that would take the most advantage of AI code-generation capabilities.

This hypothesis drove me to attend nine conferences this spring to see how SaaS companies were launching AI agents and defining a new future of work. I wrote eight articles on what CIOs need to know about data management, agile organizations, marketing, ERPs, critical process management, and other evolutions to plan for in the AI era.

Now, looking across all nine conferences, I can draw some conclusions about how AI agents are shaping the future of work. Here are my learnings and what CIOs need to consider when evaluating and deploying AI agents in the workplace.

Agentic, human-in-the-middle, or augmenting human?

SaaS companies have very distinct perspectives on the future of work, including the extent to which humans will play which roles and whether and how quickly we’ll see agentic, fully automated work.

For example, Atlassian proclaimed, “step into the future of human-AI collaboration,” while SAP unveiled “the autonomous enterprise.” Snowflake aimed to “make AI real for business,” while Appian targeted “serious AI built on process.”

These vendors’ marketers had to decide whether to lead with AI, people, or business in their messaging, but so must CIOs as they contemplate their AI strategies and how to get employees to fully adopt AI agents.

Some CIOs see a fully automated agentic AI as the future, with human-in-the-middle as a transitional phase as departments build trust in AI agents’ decision-making and automation capabilities.

Other CIOs see AI more as a tool that delivers productivity improvements by augmenting human decision-making capabilities. Many of these CIOs see human augmentation as essential to supporting critical thinking, innovation, and creativity.

Deloitte’s State of AI Report, published in January, provides a benchmark. It states that 36% of IT leaders expect at least 10% of their jobs to be fully automated in the next year, and 82% expect to reach that benchmark in three years.

Many organizations will have a mix of AI agents, choosing automation where reliability at scale is possible, but opting for human augmentation in operationally critical or customer-facing domains. But how CIOs position AI agents is not only an operational strategy; it’s also a cultural statement that shapes employees’ embrace of AI and whether detractors vocalize job-loss fears.

In the short term, it will also weigh in on which AI agents to use from different partners and which areas to build in-house.

Many options to test and deploy AI agents

Many solution providers are demonstrating significantly more AI agents this year. For example, SAP went from 40 Joule Agents in 2025 to over 200 in 2026. Three technology capabilities are fueling this significant growth:

• Adobe, Appian, Boomi, Cisco, Domo, Salesforce, SAP, Snowflake, and others offer data fabrics and data-pipeline capabilities to connect data sources outside the primary workflows supported by their platforms. Appian, Pega, Quickbase, and SAP also centralize business process automation, an important starting point for developing AI agents.  
• MCP servers enable integration and communication between AI agents and are used to facilitate multistep agentic workflows. Virtually all the companies announcing major investments in AI agents are also announcing MCP integration capabilities and related partnerships.
• Solution providers are not just using AI code-generating capabilities; many are launching their own AI agent development tools. The first beneficiaries of these development tools are the solution providers themselves and their integration partners, who use them to accelerate the development of AI agents and make them available to customers.

The result is that CIOs will have many options about which agents to test, but will have to dedicate analysts to understand the capability, cost, and compliance trade-offs. Additionally, expect AI agent capabilities to evolve significantly over the next few years, so CIOs should continuously revisit their decisions regarding deployed AI agents, focusing on performance, benefits, and ROI.

CIOs should also watch for signs of shadow AI and employee confusion about which AI agents to experiment with on different platforms. The AI strategy should include a transparent, defined process for selecting, reviewing, evaluating, procuring, deploying, driving adoption, monitoring, and collecting end-user feedback around AI agents.

AI development capabilities for engineers and citizen builders

The apparent ease-of-use of AI code generators may lead some engineering teams to build AI agents rather than buy them from SaaS providers. But CIOs should quickly realize that coding is just one step in developing AI agents, and that aggressively pursuing a build strategy can lead to AI debt and increased AI costs.

DevOps teams can code AI agents using tools such as Claude, Codex, Lovable, and Replit — a do-it-yourself approach. Some SaaS companies are providing an alternative, with AI agent development tools that leverage the data, infrastructure, and governance baked into their platforms. Many of these development tools offer flexibility, allowing developer teams to select AI models and development environments.

Examples of new and enhanced AI development tools I saw at conferences this quarter include:

• Appian Composer and Agent Studio
• Atlassian Rovo Dev
• Boomi Companion
• Cisco Cloud Control Studio
• Domo App Catalyst
• Pega Infinity Studio
• Quickbase Pave
• Snowflake CoCo
• SAP Joule Studio.

I also reviewed Nutanix Agentic AI, a platform-as-a-service for accelerating the deployment of agentic AI workloads, and Adobe Firefly AI Assistant for creatives.

These development tools can target different audiences. Some look like low-code development tools targeted at software developers, whereas others are no-code and enable citizen developers, i.e., businesspeople, to develop applications and agents. Additionally, some of these tools support spec-driven development and generate artifacts such as product requirement documents (PRDs), data models, and testing capabilities.

Before commissioning AI development for apps and agents, CIOs should sponsor proofs of technical, data, modeling, security, and governance capabilities.

The context layer powering AI agents

Between AI agents and the enterprise’s intelligence, including structured data sources, defined business processes, and agent interactions (both human-to-agent and agent-to-agent), lies an evolving “context layer.”

This layer refers to the enterprise knowledge that AI agents draw on when evaluating signals and recommending or taking actions. Context may include a knowledge graph, a semantic layer, cleansed document repositories, and other knowledge bases.

The context layer, skills, tools, out-of-the-box agents, and governance capabilities are some areas to review where solution providers differentiate. Some examples: 

• Many support the Open Semantic Interchange, and some brand their context layers, such as the Atlassian Teamwork Graph, Boomi Knowledge Hub, and the SAP Knowledge Graph.
• Some are branding their guardrails, such as Adobe’s AI Brand Intelligence, Appian’s Private AI, and Quickbase AI Control Center.
• To manage AI agents at scale, some are extending the notion of data catalogs and other governance tools to the AI domain with products such as Boomi Connect, SAP AI Agent Hub, and Snowflake Horizon Catalog.

CIOs should recognize that while solution providers will compete on capabilities, the real “secret sauce” of the context layer lies in the company’s trusted data, well-defined business processes, and employee adoption of AI agents.

Conversational user experiences and coworkers

AI agents use the context layer, but also tap into skills, which encode the procedures they can follow, and tools, which prescribe the actions they can take. Before AI agents are ready to pilot, their governance, including permissions, approval gates, and other guardrails, must be defined. Other capabilities to look for when defining AI agents include orchestration, testing evals, and observability.

In 2025, many solution providers bolted on AI agents to their existing user experiences. This year, many solution providers showcased new conversational user experiences that employees can use instead of traditional ones built with forms, flows, reports, and static dashboards. Conversational user experiences are where AI agents and people come together, whether it’s human-in-the-middle or human augmentation.

Solution providers also grouped their AI agents into assistants or coworkers. For example, Adobe CX Coworker illustrates human augmentation, helping marketers manage campaigns with prompts and monitor their performance. SAP launched Joule Assistants across several business functions, including finance, human capital, supply chain, and customer experience. Other assistants, such as Appian AI Copilot, Atlassian Rovo, Cisco AI Assistant, Nutanix NIVA, and Snowflake CoWork, offer AI-first user experiences to assist different end-user types.

CIOs should demo these AI-first user experiences to glimpse the future of work.

Developers are already getting used to these experiences through code generators and vibe coding tools. Now, similar capabilities are being tailored across all business functions. CIOs should ramp up their change management programs to accelerate the adoption of these AI capabilities.

Solution providers are showcasing AI capabilities that can help CIOs reshape their businesses. But in Q2, there were only a few examples of how AI can help CIOs drive growth, evolve business models, or embed AI into customer-facing products. I expect to see a wave of further AI innovations that will go beyond productivity improvements and efficiencies and help CIOs pursue growth-driving digital transformation strategies.  

Sacolick travelled to conferences mentioned in this article as a guest of Adobe, Appian, Atlassian, Domo, Nutanix, SAP, and Snowflake. In addition, he was hired by Quickbase to speak at its conference.

If you’re a software developer or architect, you might wonder if programming certifications are still worth the effort, especially in the era of rapid AI-driven evolution. The short answer is, it depends.

“Certifications are shifting from a checkbox to a compass. They’re less about proving you memorized syntax and more about proving you can architect systems, instruct AI coding assistants, and solve problems end-to-end,” says Faizel Khan, lead AI engineer at Landing Point, an executive search and recruiting firm.

“In the AI era, fewer students will get trained on the job, which means they have to train themselves,” Khan says. “Certifications—especially architectural ones like AWS, Kubernetes, Terraform—are still the clearest path to do that.”

Pros and cons of programming certifications

It’s not all black and white when it comes to deciding whether to pursue programming certifications. The effort involves both pros and cons.

“In terms of pros, certifications concretely demonstrate that you have a skillset at a documented level,” says Chris Riccio, vice president of engineering at Uplevel, an engineering optimization system provider. “They also show that you’ve put in the time and effort to learn, study, and prepare.”

Programming certifications are “a useful way to validate foundational skills and show that someone understands core concepts,” says Greg Fuller, vice president of Skillsoft’s training provider, Codecademy. “They’re especially helpful for people entering the field or shifting from adjacent roles.”

Certifications offer a structured path to demonstrate proficiency, and they can confirm your ability to build and deploy in various environments, Fuller says.

These types of certifications often demonstrate baseline proficiency and continuous learning, says Reshmi Ramachandran, head of partnerships and GTM strategy for Cprime, a consultancy. “These are often key indications of proficiency for companies looking to filter large candidate pools,” she says.

Certifications really do two things, Khan adds. “First, they force you to learn by doing,” he says. “If you’re taking AWS Solutions Architect or Terraform, you don’t pass by guessing—you plan, build, and test systems. That practice matters. Second, they act as a public signal. Think of it like a micro-degree. You’re not just saying, ‘I know cloud.’ You’re showing you’ve crossed a bar that thousands of other engineers recognize.”

But there are cons, too. “In tech, employers don’t just want credentials, they want proof you can deliver,” says Kevin Miller, CTO at IFS, a maker of factory automation software. “Programming certifications can be a valuable indicator of your baseline knowledge and competencies, especially if you’re early in your career or pivoting into tech, but their importance is dwindling.”

AI tools that can generate, debug, and optimize code are already performing tasks once done by entry-level developers, “which means fewer traditional programming roles are available,” Miller says. “As a result, the job market is becoming more competitive, and certifications aren’t seen as the noteworthy achievement they once were.”

What’s more, not all certifications carry the same weight, Riccio says. “Some may reflect only familiarity rather than true expertise,” he says. “Certifications also often measure ‘book knowledge’ rather than practical experience, and they don’t always map clearly to the requirements of a specific role.”

Programming certifications “can be a helpful signal, especially for confirming baseline knowledge in areas like cloud, security, or devops, but they’re not the full picture,” says Morgan Watts, vice president of IT at 8×8, a contact center platform developer.

“I’m more interested in a candidate’s attitude and aptitude: what problems they’ve solved, what they’ve built, and how they’ve approached challenges,” Watts says. “Certifications can show commitment and discipline, and they’re especially useful in highly specialized roles. But I’m cautious when someone presents a laundry list of certifications with little evidence of real-world application.”

A certification without experience doesn’t carry much weight, Watts says, and over-certification can sometimes signal the wrong focus. “Ultimately, it’s the ability to apply knowledge, collaborate, and adapt that sets great developers apart,” he says.

Finally, certifications can age fast, Khan says. “Tech stacks evolve and a badge from two years ago may already feel dusty,” he says. “And some certifications are paper-thin—multiple-choice exams that don’t prove you can debug production at 2 a.m. So, the risk is you collect badges but still can’t ship.”

Which certifications will get you noticed?

Despite the drawbacks, certifications are still very much in demand, and some carry more weight than others.

The most in-demand certifications are typically platform-based—Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and others, Riccio says. “Many of these platforms provide managed services that integrate with existing systems or serve as the glue between them,” he says. “Today’s engineering teams aren’t just building standalone systems in isolation; they’re using other systems to store data, orchestrate business workflows, and connect applications.”

A certification that demonstrates the ability to build solutions on these platforms can put a development professional ahead of the competition, Riccio says.

“The certifications I see in highest demand tend to reflect the evolving tech landscape,” Watts says. “Cloud certifications from AWS, Azure, and GCP are incredibly valuable, especially as distributed systems become the norm.”

Also in demand are certifications for devops and CI/CD tools including Kubernetes, Docker, and Jenkins, Watts says, “because deployment automation and reliability are critical at scale. Also, with AI reshaping development, we’re seeing growing interest in certifications around machine learning, data science, and AI model integration. These certifications stand out because they align directly with the skills that teams need to move faster and more intelligently.”

On the AI front, certifications in TensorFlow and other machine learning platforms are gaining traction as organizations look to embed AI across the development process, Watts says. “These are the certifications that align closely with where modern engineering is headed—scalable, secure, and AI-enabled,” he says.

And then there are cybersecurity credentials that continue to be in high demand. Security certifications, such as CompTIA Security+ or Certified Ethical Hacker, “have become essential as every company faces increasing cyber threats and compliance requirements,” Miller says.

“Core programming certifications are still a bit niche, but the adjacent skills, like those that help developers deploy, secure, and scale their code, are driving demand,” Fuller says. “Companies want developers who understand the full lifecycle, not just how to write code.”

Also see: The best Java certifications for software developers.

Certifications in the hiring process

Experts are clear that programming certifications alone will not get you the job. But they do play a role in the hiring process.

“The information technology world is characterized by rapid and continuous evolution, including the skills and knowledge required to work in the field,” says Diane Rafferty, managing director of the National Technology Group at Atrium, a global talent solutions and extended workforce management firm.

“Certifications not only prove that you have the skills and knowledge needed, but they also show employers that you’re invested in your education and career growth,” Rafferty says. “They can give you a competitive edge when looking for a job, as many companies now require candidates to have them.”

Certifications are one part of the hiring equation, “but never the only part,” Watts says. “They help validate that a candidate has taken the time to build foundational knowledge, and that’s a good sign. But I put more weight on how a person thinks, solves problems, and contributes to the team. I look for people who are curious and proactive, who are learning because they want to, not just because a course told them to.”

Certifications can also play a valuable role in retention, Watts says. “I encourage team members to pursue growth, and when they invest in their own development, the whole organization benefits,” he says. “But again, it’s that balance of knowledge, attitude, and applied experience that really moves the needle.”

Certifications “may allow you to breeze through the initial résumé screening process, potentially getting you to the next stage faster,” Riccio says. “At a minimum, they will set your profile apart from the rest of the pack. They also demonstrate that you’ve reached a baseline level of expertise, allowing hiring managers to quickly evaluate whether you have the skills for the role.”

Employers today “care far less about whether someone has passed an exam and far more about whether they can apply knowledge effectively in real-world situations, leverage AI tools, and solve complex problems,” Miller says. “A certification might get someone an interview, but being able to demonstrate problem-solving skills, teamwork, and adaptability will really make them stand out.”

Popular programming certifications

The following certifications consistently rose to the top in my conversations with tech leaders and hiring managers.

AWS Certified Developer—Associate

Showcases skills and knowledge in developing, optimizing, packaging, and deploying applications, using CI/CD workflows, and identifying and resolving application issues, according to AWS. This certification is said to be a good starting point on the AWS certification journey for professionals in IT or cloud developer job roles.

Azure Developer Associate

This certificate from Microsoft is intended for developers participating in all phases of cloud development, including design, deployment, maintenance, and monitoring. The course teaches developers how to create end-to-end solutions in Microsoft Azure, using the Microsoft Learn Sandbox environment to access Azure resources and services.

Certified Kubernetes Application Developer (CKAD)

This certification was created by the Linux Foundation and Cloud Native Computing Foundation. It demonstrates that candidates can design, build, and deploy cloud-native applications for Kubernetes.

Certified Secure Software Lifecycle Professional (CSSLP)

This certification, from ISC2, focuses on secure software development practices. It recognizes leading application security skills and demonstrates advanced technical skills and knowledge needed for authentication, authorization, and auditing throughout the software development lifecycle.

Databricks Certified Machine Learning Professional

Professionals learn about the latest data and AI techniques and how they can use the Databricks Data Intelligence Platform to build a variety of solutions across data engineering, data warehousing, data science, and AI.

Professional Cloud Architect

This certification from Google assesses the ability to design and plan a cloud solution architecture, manage and provision the cloud solution infrastructure, design for security and compliance, analyze and optimize technical and business processes manage implementations of cloud architecture, and ensure solution and operations reliability.

Terraform Associate

This certification from HashiCorp is for cloud engineers specializing in operations, IT, or development who know the basic concepts and skills associated with Terraform. It validates foundational skills in using Terraform for infrastructure as code development.

For several decades now, the most innovative software has always emerged from the world of open source software. It’s no different with machine learning and large language models. If anything, the open source ecosystem has grown richer and more complex, because now there are open source models to complement the open source code.

For this article, we’ve pulled together some of the most intriguing and useful projects for AI and machine learning. Many of these are foundation projects, nurturing their own niche ecology of open source plugins and extensions. Once you’ve started with the basic project, you can keep adding more parts.

Most of these projects offer demonstration code, so you can start up a running version that already tackles a basic task. Additionally, the companies that build and maintain these projects often sell a service alongside them. In some cases, they’ll deploy the code for you and save you the hassle of keeping it running. In others, they’ll sell custom add-ons and modifications. The code itself is still open, so there’s no vendor lock in. The services simply make it easier to adopt the code by paying someone to help.

Here are 16 open source projects that developers can use to unlock the potential in machine learning and large language models of any size—from small to large, and even extra large.

Agent Skills

AI coding agents are often used to tackle standard tasks like writing React components or reviewing parts of the user interface. If you are writing a coding agent, it makes sense to use vetted solutions that are focused on the task at hand. Agent Skills are pre-coded tools that your AI can deploy as needed. The result is a focused set of vetted operations capable of producing refined, useful code that stays within standard guidelines. License: MIT.

Awesome LLM Apps

If you are looking for good examples of agentic coding, see the Awesome LLM Apps collection. Currently, the project hosts several dozen applications that leverage some combination of RAG databases and LLMs. Some are simple, like a meme generator, while others handle deeper research like the Journalist agent. The most complex examples deploy multi-agent teams to converge upon an answer. Every application comes with working examples for experimentation, so you can learn from what’s been successful in the past. Altogether, the apps in this collection are great inspiration for your own projects. License: Apache 2.0.

Bifrost

If your application requires access to an LLM service, and you don’t have a particular one in mind, check out Bifrost. A fast, unified gateway to more than 15 LLM providers, this OpenAI-compatible API quickly abstracts away the differences between models, including all the major ones. It includes essential features like governance, caching, budget management, load balancing, and it has guardrails to catch problems before they are sent out to service providers, who will just bill you for the time. With dozens of great LLM providers constantly announcing new and better models, why limit yourself? License: Apache 2.0.

Claude Code

If the popularity of AI coding assistants tells us anything, it’s that all developers—and not just the ones building AI apps—appreciate a little help writing and reviewing their code. Claude Code is that pair programmer. Trained on all the major programming languages, Claude Code can help you write code that is better, faster, and cleaner. It digests a codebase and then starts doing your bidding, while also making useful suggestions. Natural language commands plus some vague hand waving are all the Anthropic LLM needs to refactor, document, or even add new features to your existing code. License: Anthropic’s Commercial TOS.

Clawdbot

Many of the tools in this list help developers create code for other people. Clawdbot is the AI assistant for you, the person writing the code. It integrates with your desktop to control built-in tools like the camera and large applications like the browser. A multi-channel inbox accepts your commands through more than a dozen different communication channels including WhatsApp, Telegram, Slack, and Discord. A cron job adds timing. It’s the ultimate assistant for you, the ruler of your data. If AI exists to make our lives easier, why not start by organizing the applications on your desktop? License: MIT.

Dify

For projects that require more than just one call to an LLM, Dify could be the solution you’ve been looking for. Essentially a development environment for building complex agentic workflows, Dify stitches together LLMs, RAG databases, and other sources. It then monitors how they perform under different prompts and parameters and puts it all together in a handy dashboard, so you can iterate on the results. Developing agentic AI requires rapid experimentation, and Dify provides the environment for those experiments. License: Modified version of Apache 2.0 to exclude some commercial uses.

Eigent

The best way to explore the power and limitations of an agentic workflow is to deploy it yourself on your own machine, where it can solve your own problems. Eigent delivers a workforce of specialized agents for handling tasks like writing code, searching the web, and creating documents. You just wave your hands and issue instructions, and Eigent’s LLMs do their best to follow through. Many startups brag about eating their own dogfood. Eigent puts that concept on a platter, making it easy for AI developers to experience directly the abilities and failings of the LLMs they’re building. License: Apache 2.0.

Headroom

Programmers often think like packrats. If the data is good, why not pack in some more? This is a challenge for code that uses an LLM because these services charge by the token, and they also have a limited context window. Headroom tackles this issue with agile compression algorithms that trim away the excess, especially the extra labels and punctuation found in common formats like JSON. A big part of designing working AI applications is cost engineering, and saving tokens means saving money. License: Apache 2.0.

Hugging Face Transformers

When it comes to starting up a brand-new machine learning project, Hugging Face Transformers is one of the best foundations available. Transformers offers a standard format for defining how the model interacts with the world, which makes it easy to drop a new model into your working infrastructure for training or deployment. This means your model will interact nicely with all the already available tools and infrastructure, whether for text, vision, audio, video, or all of the above. Fitting into a standard paradigm makes it much easier to leverage your existing tools while focusing on the cutting edge of your research. License: Apache 2.0.

LangChain

For agentic AI solutions that require endless iteration, LangChain is a way to organize the effort. It harnesses the work of a large collection of models and makes it easier for humans to inspect and curate the answers. When the task requires deeper thinking and planning, LangChain makes it easy to work with agents that can leverage multiple models to converge upon a solution. LangChain’s architecture includes a framework (LangGraph) for organizing easily customizable workflows with long-term memory, and a tool (LangSmith) for evaluating and improving performance. Its Deep Agents library provides teams of sub-agents, which organize problems into subsets then plan and work toward solutions. It is a proven, flexible test bed for agentic experimentation and production deployment. License: MIT.

LlamaIndex

Many of the early applications for LLMs are sorting through large collections of semi-structured data and providing users with useful answers to their questions. One of the fastest ways to customize a standard LLM with private data is to use LlamaIndex to ingest and index the data. This off-the-shelf tool provides data connectors that you can use to unpack and organize a large collection of documents, tables, and other data, often with just a few lines of code. The layers underneath can be tweaked or extended as the job requires, and LlamaIndex works with many of the data formats common in enterprises. License: MIT.

Ollama

For anyone experimenting with LLMs on their laptop, Ollama is one of the simplest ways to download one or more of them and get started. Once it’s installed, your command line becomes a small version of the classic ChatGPT interface, but with the ability to pull a huge collection of models from a growing library of open source options. Just enter: ollama run and the model is ready to go. Some developers are using it as a back-end server for LLM results. The tool provides a stable, trustworthy interface to LLMs, something that once required quite a bit of engineering and fussing. The server simplifies all this work so you can tackle higher level chores with many of the most popular open source LLMs at your fingertips. License: MIT.

OpenWebUI

One of the fastest ways to put up a website with a chat interface and a dedicated RAG database is to spin up an instance of OpenWebUI. This project knits together a feature-rich front end with an open back end, so that starting up a customizable chat interface only requires pulling a few Docker containers. The project, though, is just a beginning, because it offers the opportunity to add plugins and extensions to enhance the data at each stage. Practically every part of the chain from prompt to answer can be tweaked, replaced, or improved. While some teams might be happy to set it up and be done, the advantages come from adding your own code. The project isn’t just open source itself, but a constellation of hundreds of little bits of contributed code and ancillary projects that can be very helpful. Being able to customize the pipeline and leverage the MCP protocol supports the delivery of precision solutions. License: Modified BSD designed to restrict removing OpenWebUI branding without an enterprise license.

Sim

The drag-and-drop canvas for Sim is meant to make it easier to experiment with agentic workflows. The tool handles the details of interacting with the various LLMs and vector databases; you just decide how to fit them together. Interfaces like Sim make the agentic experience accessible to everyone on your team, even those who don’t know how to write code. License: Apache 2.0.

Sloth

One of the most straightforward ways to leverage the power of foundational LLMs is to start with an open source model and fine-tune it with your own data. Unsloth does this, often faster than other solutions do. Most major open source models can be transformed with reinforcement learning. Unsloth is designed to work with most of the standard precisions and some of the largest context windows. The best answers won’t always come directly from RAG databases. Sometimes, adjusting the models is the best solution. License: Apache 2.0.

vLLM

One of the best ways to turn an LLM into a useful service for the rest of your code is to start it up with vLLM. The tool loads many of the available open source models from repositories like Hugging Face and then orchestrates the data flows so they keep running. That means batching the incoming prompts and managing the pipelines so the model will be a continual source of fast answers. It supports not just the CUDA architecture but also AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, Arm CPUs, and TPUs. It’s one thing to experiment with lots of models on a laptop. It’s something else entirely to deploy the model in a production environment. vLLM handles many of the endless chores that deliver better performance. License: Apache-2.0.

A new era of customer engagement is taking shape. AI agents are quickly becoming integral to the way businesses serve, support, and sell to customers — able to respond, reason, and take action in ways that go far beyond scripted automation.

Many customer journeys, however, are still built on systems that don’t talk to each other. Customer data lives in one place, channel history in another, and AI agents often operate with only part of the picture. Customers feel the pain when they switch between channels like voice and messaging, get transferred, and have to repeat themselves yet again. It doesn’t matter that they’ve been loyal to a brand for years, every interaction feels like a cold start. That is the conversation gap.

It’s clear that AI isn’t the problem, infrastructure is. Closing the gap requires new building blocks that focus on continuity, so context can carry forward across systems, channels, human agents, and AI agents.

To bridge the gap, at SIGNAL 2026, we are introducing a new conversation layer for the Twilio Platform.

Twilio Conversation Orchestrator, Twilio Conversation Memory, and Twilio Conversation Intelligence are now generally available. Together, they help businesses coordinate interactions, preserve context, and connect human and AI agents so every conversation is more continuous and useful.

In addition to the new Conversations layer, we’re also announcing platform updates that make it easier to build, manage, and scale customer engagement on Twilio — from a reimagined Twilio Console to expanded channels and new voice AI capabilities.

New building blocks for connected conversations

The conversation gap does more than create inconsistent customer experiences. It hurts conversion and retention, increases operational costs, adds integration complexity, and makes agents less productive. The new platform capabilities we’re introducing are designed to fix that by coordinating interactions, maintaining context, and surfacing signals as conversations happen.

Conversation Orchestrator

Conversation Orchestrator helps businesses coordinate interactions across Twilio channels without complex custom logic. Teams can configure it in Console or configure their implementation with the API. It connects interactions into a single thread and manages handoffs between human agents and automated systems.

Conversation Memory

Conversation Memory creates a living, identity-resolved profile by connecting customer data with conversation history and customer traits. That means each interaction starts with the right context. It’s built specifically for LLMs to reduce latency and token usage by surfacing the most relevant details when they matter.

A new Enterprise Knowledge API (now generally available) also allows teams to deliver more relevant experiences and ground interactions in trusted business knowledge such as FAQs, policies, and product documentation.

Conversation Intelligence

Conversation Intelligence provides real-time understanding of live interactions. Using prebuilt and custom LLM-based operators, it can detect changes in sentiment, flag potential escalations, and trigger action during a conversation, not only after it ends.

That gives teams the ability to respond sooner, support agents more effectively, and improve customer outcomes while the conversation is still in progress.

Together, these products help businesses create customer experiences that feel more connected across channels.

Open by design

Twilio remains neutral by design. We start with the premise that you know your business. We aren’t here to prescribe a model, framework, or data strategy. We provide the infrastructure that helps you build customer engagement in the way that works best for your business. You pick the model and agent runtime. You own the data.

That doesn’t mean you need to start from scratch, either. We partnered with Microsoft, AWS, and others to create blueprints that support faster development. We are also introducing an open-source developer toolkit, Twilio Agent Connect (now generally available), that lets your teams connect agents built on any LLM or framework directly to Twilio’s infrastructure.

For developers, this means more flexibility. For businesses, it means less lock-in and the ability to get value from existing investments. For partners, it means more ways to build with Twilio.

A new front door

We are also introducing a reimagined Twilio Console, because as customer engagement grows more complex, managing the infrastructure behind it should feel effortless.

The new Console is a single mission control center that brings your communications, identity, and data into one experience: one login, consistent logs across every surface, an intelligent Console Assistant, transparent billing insights, and streamlined compliance workflows that no longer slow you down.

Over the coming months, we’ll roll out this new Console experience to customers automatically. You can also opt in to gain early access.

More channels, more control, smarter conversations

In addition to these launches, we are announcing several updates that expand customer reach, support enterprise requirements, and make it simpler to build on Twilio.

• Apple Messages for Business (Private beta) and Twilio Email (GA) give teams new ways to reach customers on the channels they already use.
• Data Residency for SMS (EU) (Public beta) enables teams to manage personal data locally to support regional data requirements.
• Conversation Relay enhancements add PCI compliance, HIPAA eligibility, Insights, and support for Deepgram Flux for smarter turn detection — helping AI agents better understand when a person has finished speaking.
• Stripe Projects integration enables developers and AI agents to seamlessly provision Twilio within Stripe Projects in a single, programmable CLI workflow.

Built with our customers

Bringing these new products to life required a close partnership with many beta customers and partners. This helped us understand real-world signals and needs to help make the capabilities robust from the start.

Among dozens of others, Centerfield, Constellation Dealerships, Car Finance 247, and Meera.ai leveraged Twilio to solve their own customer engagement challenges. These teams showed what is possible when businesses carry context forward, act on live conversation signals, and connect AI agents with human teams in the moments that matter.

Car Finance 247, a leading UK online car finance broker, is using Twilio to help recover stalled loan applications. When customers miss a field, need to correct information, or still need to confirm terms and conditions, AI-powered outreach across voice, SMS, and RCS, Conversation Memory tracks the application state. Conversation Orchestrator manages the outreach journey, and Flex helps bring in a human agent as needed. As Reg Rix, Co-Founder and CEO, shared:

“Because the platform remembers where each customer left off, we can pick up right where they stopped, helping them cross the finish line in a way that is modern, responsive, and genuinely helpful.”

Centerfield, a technology company powering AI-driven commerce, helps brands connect with consumers across digital and phone-based journeys. With Twilio, the team is connecting real-time conversation data with customer context to guide agents and AI systems in the moment, standardise what works, and improve performance at scale. As Aniketh Parmar, Chief Technology Officer, said:

“Performance comes down to how well every interaction moves a customer forward. We’re capturing each conversation in real time and applying what we already know about the customer to guide our agents and AI systems in the moment. With the Twilio Platform, including Conversation Orchestrator, Conversation Memory, and Conversation Intelligence, we can see what’s driving conversations so we can standardise what works, eliminate what doesn’t, and continuously improve outcomes at scale.”

Constellation Dealerships is using Twilio’s agent infrastructure to accelerate AI-powered engagement across its dealer network, moving from evaluation to measurable outcomes in days. As Richard Pineault, Director of R&D, shared:

“The value of this partnership is evident—our team progressed from evaluating Twilio’s agent infrastructure to realising measurable outcomes within days. This rapid speed-to-value exemplifies the agility and innovation required to propel the dealership industry into the future.”

Meera.ai is building on Twilio to modernise outbound engagement, replacing repeated manual follow-ups with always-on conversations across voice, SMS, and messaging. Vivek Zaveri, Chief Executive Officer, said:

“Meera.ai has partnered with Twilio since our inception to champion a conversation-first future for commerce. As the industry shifts toward real-time LLM-enabled interactions, Twilio’s Platform and the new Conversations products will help us reach customers in the moment.”

Together, these customers and partners show that the Twilio Platform can help businesses recover stalled journeys, improve live interactions, accelerate time to value, and create more connected experiences across AI agents, human teams, and every customer channel.

The next era of customer engagement starts here

As AI agents own more of customer engagement, businesses need infrastructure that keeps conversations connected across channels, systems, and teams. That means preserving context, coordinating handoffs, and acting on what is happening in real time.

That is what we are building with this next generation of the Twilio Platform: a new layer that connects channels, context, intelligence, and human and AI agents, helping businesses make every digital interaction more connected, more useful, and more amazing.

For 17 years, Twilio has helped builders create better ways for businesses to connect with their customers. In this next era, that connection matters more than ever.

Explore the new Conversations layer, try the products, and let’s build what comes next, together.

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It has been a year or so since Microsoft announced its plans to move TypeScript to a new, native runtime based on the Go language. Those first releases were unfinished (you had to compile them yourself) but showed promise, getting close to the expected 10x speed-up. That year has been one of steady progress, with Microsoft recently announcing the delivery of a release candidate build.

This release candidate is ready for use. It installs from npm like previous versions, and like earlier builds it works in much the same way as previous versions of TypeScript, checking types in your code, compiling it to run on ECMAScript-compliant JavaScript engines, and running just about anywhere. In addition, a native preview of the TypeScript language server for Visual Studio Code is available to help you write new TypeScript code and guide you through updating existing applications to the new language features.

All you need to do is enable the TypeScript 7 extension through the Visual Studio command palette and start coding. There’s a lot of work going on to get the new tooling ready for the final release of TypeScript 7, and new versions of the language server are being released almost daily. It’s certainly popular, too, with nearly half a million downloads at the time of writing.

What makes TypeScript 7 so much faster?

So how is this new TypeScript so much faster? Key to the improvements is a shift to a new native compiler built in Go. This has allowed the team to change how it operates, adding parallelization where possible. In some cases, this isn’t easy, such as when type checking large codebases split across many files.

Here TypeScript spawns a small number of checker workers that run across your codebase. They work independently, so can duplicate the work — though the output will be the same. You can choose your own number of checkers, but the more you use, the more memory and CPU will be required.

Large monorepos with many projects require a similar approach with independent builder workers. You’ll need to balance this with the number of checkers in use, as this can cause significant resource issues.

There are some significant language and configuration changes from TypeScript 5 (TypeScript 6 has the same changes, which makes it a useful tool for experimenting with migrations). It’s well worth reading the release candidate documentation to understand how these will affect your code, as well as using the TypeScript 7 extension for Visual Studio Code to identify where you need to make changes.

Working with users to build language tools

One important aspect to the development of TypeScript 7 has been collaboration with existing users of the language and its tooling, as well as using the existing suite of TypeScript test tools that have been used to evaluate other versions. As this update is primarily a port of existing code, rather than a bottom-up rewrite, the underlying language semantics and structure are the same as those used in the original JavaScript codebase, ensuring that code will quickly port from old to new versions.

A major internal collaborator was the Visual Studio Code team, who have been using TypeScript to develop the familiar cross-platform development tool. It’s an important partnership between tool and language, as VS Code is a key TypeScript development tool, hosting TypeScript’s language server and using its compiler to provide debugging and code completion features.

The VS Code team published a long blog post detailing how it has been working with the Go-based TypeScript. The team is both helping to develop the language and beginning the process of moving its codebase to the newer, faster, native platform.

How the VS Code team migrated is a useful case study, one that can help you move your TypeScript development more efficiently and with minimal risk. The team began working with extensions, using daily builds of TypeScript to ensure that bugs and issues could be reported as they occurred and would only have a limited impact as fixes could be rolled out quickly. At the same time, the VS Code team began using a preview version of the TypeScript 7 extension for VS Code, which was being built around the new compiler in parallel with its development.

Bridging development with TypeScript 6

The development of TypeScript 6 as a bridge between TypeScript 5 and TypeScript 7 allowed the VS Code team to transition to code that targeted a newer version of ECMAScript and provided more powerful checks. By moving code from TypeScript 5 to TypeScript 6, developers could validate it with what would become TypeScript 7 language features and get speed and performance boosts while doing so (though nowhere near what TypeScript 7 promised). By completing this first migration of the VS Code codebase, it was possible for developers to be confident that they were ready to shift to the Go-based version when it shipped.

The parallel development of the new language server and extension ensured that by late 2025 it was possible for VS Code development to shift to TypeScript 7, with TypeScript 6 used as a fallback if there were any issues. Those cases could then be reported back to the TypeScript team and used to prioritize development.

As the platform evolved, the use cases for the VS Code team changed. By early 2026 TypeScript 7 was stable and nearly feature-complete, so the team began to use it to build all of their own built-in extensions. This allowed them to rethink their toolchain, changing the bundler from webpack to the one built into esbuild, giving them another speed up. Once that process was tested and working, they could switch all development to TypeScript 7.

Having such a big project take on TypeScript 7 early reaped big rewards, as the resulting virtuous cycle allowed both VS Code and TypeScript to move forward together, fixing issues as they arose and providing valuable feedback. The results speak for themselves. Type checking the entire VS Code codebase is now 7x faster, with most extensions checked in under a second. The only exception was GitHub Copilot, which is almost as big as the editor itself, which type checked in 2.5 seconds.

Compilation has been sped up, dropping from 80 seconds to around 20 seconds. This may not seem a lot, but when you’re compiling and rebuilding and debugging, each change in your code now takes a lot less time. That improves developer productivity and ensures they stay in flow, rather than switching away to check email or Teams each time they start a new build. The same goes for using the language server, where loading the entire project (necessary for error detection and refactoring) now takes 10 seconds rather than a minute.

Lots of little time savings like this add up across a big project and a large team, helping developers stay focused and able to solve problems more effectively. The VS Code blog post notes that it cuts down on coffee runs, which take longer than the load or build that inspire a quick cuppa!

Getting ready for TypeScript 7 in your build pipeline

Microsoft is quick to point out that, while the TypeScript 7.0 release will be production ready, TypeScript 7 won’t have a full programmatic API until the release of TypeScript 7.1. As this won’t be for some time, Microsoft is providing a way to run TypeScript 7 side-by-side with TypeScript 6.

Installing the @typescript/typescript6 compatibility package alongside TypeScript 7 adds a new executable, tsc6, that allows you to modify code that uses the TypeScript 5 API to run using TypeScript 6, by renaming the calls to tsc in your scripts to tsc6. This should allow you to keep building to the latest releases at the same time as starting to experiment with using the new runtime.

It’s not a perfect fix. You do need to do some work to implement npm aliases that allow linters and other low-level tools to work with both versions. You can also provide two different dependencies in your package.json to allow TypeScript 6 (tsc6) and TypeScript 7 (tsc) to run side-by-side. The result is a way to help migrate TypeScript code to the newer platform, delivering more efficient code that runs on a more modern ECMAScript in the meantime.

TypeScript 7 will be a big upgrade, though it has taken surprisingly little time to deliver. With users like the Visual Studio Code team already building on the new release, it’s clear that beginning your own migration should be easier than you might have thought.

The final release is due sometime in July 2026. If you haven’t started looking at TypeScript 7, now is the time to start.

For workers who routinely spend hours a day interacting with various AI assistants, banging out prompts on a keyboard can quickly become a chore. 

“Whether it’s a coding task, helping write a document or think about strategy — there’s just so much typing and typing and typing you do as a part of that,” said Chris Patalano, chief technology officer at Thumbtack, an online marketplace for professional services.

With that in mind, Patalano and other senior colleagues last year began experimenting with new ways to interact with AI systems within Thumbtack. The idea was to test AI-assisted dictation tools developed by startups such as Monologue, Superwhisper, Willow Voice, and Wispr. 

Unlike previous generations of dictation apps that aimed to produce a verbatim transcript, newer tools rely on large language models (LLMs) to craft polished, edited text. The companies behind them claim users can produce text several times faster than typing, with greater accuracy than voice tools built into other apps.

That has sparked renewed interest in using voice prompts to carry out routine tasks in the workplace.

After one of Thumbtack’s principal engineers suggested Wispr Flow, Patalano kicked off a small pilot project with a handful of colleagues over a couple of months. The pilot was a success, and Wispr Flow is now available to more than 200 IT and engineering staffers; they use it for a variety of tasks, including interactions with AI assistants and drafting Slack messages to colleagues. 

Although Patalano said he still prefers typing for certain apps, Wispr Flow’s AI dictation tool has become a part of his daily workflow. “It’s becoming the primary interface that I have for any AI tools. It’s just so much more effective and efficient than having to type,” he said. 

“I’ve used it to help me build prototypes, explore the code base, help me explore my own technical strategy. I’ve used it to do analytics across data sets — even very specific acute things, like ‘What do I need to make sure is on my to-do list this week?’”

A new generation of dictation tools

Software that translates spoken words into text isn’t new to the workplace. Speech-to-text dictation tools have been around in various forms for decades. The earliest example dates back to 1952, when Bell Labs created Audrey, widely regarded as the first automatic speech recognition system. (Audrey could recognize the spoken digits 0-9 with 90% accuracy when used by the machine’s developer, HK Davis.) 

Commercial products appeared in the 1980s, with broader adoption in the 1990s via software such as Dragon Dictate. These were specialized — and expensive — applications with limited functionality, appealing mostly to professionals for whom dictation was already a part of their workflow, such as doctors and lawyers, rather than a wide range of office workers. 

In recent years, speech-to-text software has become more accessible, especially  with the integration of speech recognition into smartphones and computers by Apple, Google, Microsoft, and others. Deep learning has also significantly improved accuracy.

That’s made voice input more common in the workplace and an important accessibility tool for people who find typing difficult — even though the systems can still be “quite brittle,” said Benjamin Cowan, professor at the School of Information and Communication Studies at University College Dublin. That’s especially true of early voice input technology.

“Not only did they get things wrong all the time, they wrote everything you said — even if you didn’t want it to,” he said. “This meant that a lot of time was taken editing the notes after they were dictated.” 

Now, several startups offering AI dictation tools, including Wispr, aim to make voice a viable alternative to typing for everyday computer tasks. The key difference from earlier iterations of tools is the use of AI models to edit text in near-real-time, removing disfluencies such as “umms,” “ahhs” and filler words to create a polished sentence. 

In most cases, users can invoke an AI dictation tool across mobile and desktop applications with a text field – whether that’s a document editor, email client, a vibe-coding app or anything else – by pressing and holding a designated key or button while talking. Users can add words to the app’s dictionary so it can pick up on uncommon names, abbreviations, and industry jargon.

“The technology itself has improved dramatically” compared to previous tools that sought to transcribe speech verbatim and could be frustratingly inaccurate, said Maria Bell, senior research analyst at CCS Insight.  

“These modern systems are much more contextual; they understand your intent, they can help structure your thoughts and rewrite while you speak. They function more like writing assistants rather than just dictation.”

Wispr is among the best-funded startups in the market, having raised $81 million to date. Bloomberg reported in May that the company was in talks to raise a further $260 million at a $2 billion valuation. Other vendors have also attracted investor backing, with Willow Voice announcing a $4.2 million funding round last year.

The software is typically available via a freemium model, with a free tier offering basic functionality and usage limits alongside paid premium versions. Superwhisper Pro is $8.49 per user each month; Willow Voice’s Team Pro and Individual Pro are $10 and $12 per user each month, respectively; and Wispr Flow Pro costs $12 per user each month. Enterprise pricing is not publicly available from these vendors.

Larger tech firms have also invested in AI-assisted voice functionality. Apple, for instance, recently announced AI-powered dictation for its revamped Siri AI assistant, while Google is building in similar functionality for the Gboard keyboard on Android devices. Google is also developing a standalone AI dictation tool – Edge Eloquent – although its approach differs from that of startups in the space because the tool is not available across separate applications.

Why use AI dictation?

The key promise of AI dictation is that it can increase a knowledge worker’s words-per-minute (wpm) output versus typing. 

According to Superwhisper, most office workers can knock out between 40 and 70 words a minute on a  keyboard, though some can be much faster. (New York Times reporters vary from 36 to 134 wpm, according to a Times article earlier this year.) People talk much faster, at a rate of 160 to 180 wpm, and AI dictation app vendors promise low latency processing to turn speech into edited text (usually less than a second; some claim under 200 milliseconds).

It’s not just about speed: Willow Voice, for instance, claims its app is three times more accurate than dictation tools built into other applications. 

The prospect of accelerating routine writing and communication tasks has obvious appeal, particularly as AI threatens to increase rather than reduce the burden on office workers. “We all feel like we’re working faster – we have to do more with less time,” said Bell. 

“Employees are overloaded with communication work, and they’re spending huge amounts of time every day writing emails, messaging colleagues, using generative AI,” she said. “Voice tools are appealing because some feel they can do wor] faster. It reduces friction around all the tasks they’re being asked to do.”

The technology is potentially suited to a variety of jobs, said Cowan — not only those that require dictation — helping with tasks such as writing to-do lists and documents, or sending messages and emails. 

Accessibility is important, too. “These dictation tools also mean that people who find it hard to type or cannot type now have much better apps to help them with writing,” said Cowan. 

What’s holding the technology back?

Despite these potential benefits, the idea of talking to a laptop or smartphone throughout the day might not be appealing for a lot of people, particularly those in a busy office. 

“Some might find it embarrassing or uncomfortable, they’ll be worried about distracting colleagues or creating a disruption,” said Bell. “That’s still a major behavioral barrier that you have to overcome. 

“The technology is ready, but maybe workplace etiquette and culture is not necessarily there yet,” she said.

Working remotely, Patalano said he and his team can side-step some of this awkwardness. But it still took time to adjust to voice inputs. 

“Because we’re fully remote, we don’t have the challenge of everybody sitting side by side in an office talking into their computers, which would be more challenging, I suspect. But even getting comfortable with talking out loud alone in a room took a minute,” he said. 

As with any AI tool, there’s also the question of accuracy. 

Even if vendors promise a low error rate, LLM outputs can still have errors, requiring users to check the results. “They can still mis-recognize what’s being said,” said Cowan. Those in high-risk sectors such as healthcare still need to go through the AI-edited text and “double- and triple-check” the dictation. 

This friction means extra steps for a user working with the technology. 

It doesn’t take much to dissuade workers from adopting a new tool, said Jon Arnold, research analyst at J Arnold & Associates. “There’s definitely a lot of use cases where it would have a lot of value, but you’ve got to trust it — if it’s not giving what you think it will, you’re either going to fine tune it or go back to the keyboard and do it the old-fashioned way,” he said.

There are also privacy concerns. Because some tools send voice data to the cloud for processing, organizations in heavily regulated industries such as finance, healthcare and government might move cautiously.

Bell points to two types of privacy: social, such as “having colleagues overhear what you’re saying,” and digital privacy, which relates to who else can access the conversation data. 

App providers take different approaches; some process voice data on device, others send it to the cloud. That’s an important distinction for organizations with strict data protection requirements, said Bell. 

“Where’s the voice data processed? Where is it stored? How can it be accessed? Enterprises are very, very focused on governance and data security and data privacy,” she said.

Too soon to ditch the keyboard?

Despite growing interest in the technology, it’s still unclear whether a large number of workers will choose talking over typing. And remains to be seen whether startups that offer a best-of-breed AI dictation app can gain traction, or fade if the technology simply becomes embedded within the software ecosystems of larger tech firms.  

Workers are more familiar with voice technology, thanks to AI assistants in smartphones and smart speakers at home. That, said Bell, could improve the prospects of wider use in business settings. 

“Voice interaction feels less niche than it did about five years ago,” she said. “Overall, the technology is improving quickly…, but how we’re really going to determine success is whether we can change human behavior.”

Arnold is bullish about the use of voice technology in the workplace: “Five or 10 years [from now], we won’t think twice about it. It’ll just be the norm.”

Bell is more cautious.She sees potential for AI dictation as a supplementary tool for communication-heavy work. “I don’t think it’s going to replace the keyboard, but I do think it could become a secondary interface,” she said.

Even Patalano doesn’t expect AI-assisted voice dictation to entirely replace typing “Your speaking voice and your written voice will always, to some degree, be different, and that’s okay: we should probably lean into that,” he said.

“I think there will always be a place for wordsmithing, crafting, writing – and the same with coding, too. There’s going to be lots of cases where every single word matters.”

He plans to continue using AI dictation, whether with Wispr Flow or other similar tools that might emerge in the future.  

While a lack of accuracy slowed adoption in the past, continued advances could open the door to wider workplace uptake.  

“When I try to use a voice tool and it misses even once, you kind of throw up your hands and walk away, because the cost of having to correct it is way more than the benefit of using it versus typing,” Patalano said. “But, especially with the improvements in LLMs and AI models generally, the accuracy of these is going to keep getting better and better. 

“I’m already looking for more and more opportunities to use voice instead of having to type.” 

Microsoft has unveiled the Microsoft Frontier Company, a new operating business focused on delivering “frontier transformation” through AI for Microsoft’s customers around the world. Microsoft Frontier Company launches with a $2.5 billion investment from Microsoft and 6,000 industry and engineering experts who will be embedded with customers to co-design, co-innovate, deploy, and continuously improve their AI systems, Judson Althoff, CEO of Microsoft Commercial Business, said in a July 2 blog post announcing the new company.

Microsoft Frontier Company will provide a unique combination of skills that include deep industry knowledge, change management and continuous improvement experience, and enterprise-grade AI engineering expertise, Althoff said. Companies as part of the effort are being encouraged to establish an intelligence platform so their “unique IQ” — their proprietary data, expertise, workflows, and decision-making processes — compounds over time from within, using their choice of models to build AI solutions and workflows. At the same time, they need a trusted platform that allows them to observe, govern, manage, and secure AI solutions across every layer of the technology stack, Althoff said.

AI engineering expertise and deep industry knowledge are required to build a system that acts as a continuous loop of improvement between these two platforms. This is what Microsoft Frontier Company was built to do, Althoff said.

Microsoft will end support for its .NET 8 and .NET 9 platforms on November 10, 2026. The company advises current users of those platforms to upgrade to .NET 10.

After November 10, Microsoft will no longer provide servicing updates, security fixes, or technical support for .NET 8 and .NET 9. “We recommend upgrading to .NET 10, which is an LTS (long term support) release supported through November 2028,” said Rahul Bhandari, Microsoft senior program manager in a June 29 blog post. “By upgrading, you will continue receiving security updates and servicing fixes to keep your applications protected.”

Apps can be upgraded to .NET 10 by changing the value of the TargetFramework property in a project file to net10.0. Development and hosting environments will need to be updated. This process is covered in detail in Microsoft’s article, “Upgrade to a new .NET version.” .NET 10 can be downloaded from dotnet.microsoft.com.

 .NET 8 was released November 14, 2023, and .NET 9 was released November 12, 2024. .NET 10 became available November 11, 2025. 

Microsoft said users of .NET 8 and .NET 9 could expect the following after November 10:

• Applications built on .NET 8 or .NET 9 will continue to run.
• No new security updates will be issued for either version.
• Staying on an unsupported version means exposure to security vulnerabilities that will not be patched.
• Technical support will no longer be available for .NET 8 or .NET 9 applications.

Also, beginning with a future servicing update for Visual Studio 2022, the .NET 8 and .NET 9 components will be marked as out of support. The arrival of the production release of .NET 11, meanwhile, is expected this November.

The US government has reversed export restrictions on Anthropic’s frontier AI models Fable 5 and Mythos 5, allowing the company to resume global access after nearly three weeks of disruption triggered by concerns over the models’ cybersecurity capabilities.

“As of today, June 30, the export controls on Fable 5 and Mythos 5 have been lifted,” Anthropic said in a blog post. The company said Fable 5 will begin rolling out globally on July 1 across Claude Platform, Claude.ai, Claude Code and Claude Cowork, while access on Amazon Web Services, Google Cloud, and Microsoft Foundry will be restored “as quickly as possible.”

Commerce Secretary Howard Lutnick said the administration had worked with Anthropic before reversing the restrictions.

“Over the past two weeks, we have worked closely with Anthropic to analyze and approve Fable 5 to ensure alignment across the US Government and strengthen America’s leadership in AI,” Lutnick wrote in a post on X.

Anthropic had launched Fable 5 and Mythos 5 on June 9, and the US authorities restricted their export on June 12.

The Anthropic decision comes as other frontier AI developers are also operating under closer government scrutiny.

In announcing GPT-5.6 Sol, Terra, and Luna last week, OpenAI said it had previewed both its rollout plans and the model’s capabilities to the US government before launch and, “at their request,” was initially making the model available only to “a small group of trusted partners” whose participation had been shared with the government.

OpenAI said it would continue coordinating with government partners before expanding availability, but added that it did not believe “this kind of government access process should become the long-term default” because it keeps advanced AI tools from “users, developers, enterprises, cyber defenders, and global partners who need them.”

Analysts say the Anthropic decision reflects a broader shift in how frontier AI models are governed.

“The reversal is not the story; the instrument beneath it is,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. Washington, he said, has applied the long-standing “deemed export” doctrine to frontier AI models, signalling “negotiated oversight, conditional and monitored, rather than blanket prohibition.”

Immediate order created global disruption

Anthropic said the June 12 export controls required it to restrict access to foreign nationals.

“Because the order took effect immediately and we had no reliable way to verify nationality in real-time, we suspended access to both models for all users,” it added. The company partially restored Mythos 5 last week for a limited group of US organizations.

Anthropic said the restrictions followed a report from Amazon researchers describing a technique that bypassed one of Fable 5’s cybersecurity safeguards. After reviewing the findings with Amazon and the US government, the company concluded the technique “did not expose any unique Mythos-level cyber capabilities” and instead represented “a borderline case for Fable 5’s safeguards.”

It subsequently retrained its safety classifier, saying the reported technique is now blocked in more than 99% of cases, while acknowledging that the stronger protections would increase false positives for some legitimate coding requests. Researchers at the US Department of Commerce’s Center for AI Standards and Innovation also evaluated the updated safeguards, Anthropic said.

Enterprises face a new continuity risk

Experts say the episode demonstrates that frontier AI’s availability can now be shaped by policy decisions as much as technical capability.

“Frontier access has become conditional infrastructure,” Gogia said. “The models went dark globally because nationality could not be verified in real time, so a control aimed at foreign nationals became an outage for everyone.”

He said enterprises should also not assume that deploying models across multiple cloud providers insulates them from regulatory actions affecting the underlying model provider.

According to Gogia, organizations should begin evaluating frontier AI platforms for regulatory interruption, cross-border identity restrictions, channel restoration delays, and trusted-partner eligibility alongside traditional security, commercial, and technical considerations.

Anthropic proposes a common jailbreak framework

Anthropic used the announcement to call for an industry-wide framework to assess AI jailbreaks, saying developers and governments currently lack a common standard for evaluating newly discovered techniques.

“There’s currently no consensus in the AI industry on how to describe, in objective terms, the severity of an AI jailbreak,” the company said. Anthropic said it is working with Amazon, Microsoft, Google, and other Project Glasswing partners on a framework for evaluating jailbreaks, while also expanding collaboration with the US government through pre-release testing of future frontier models, information sharing, and joint AI security research.

“Government involvement in AI releases requires a durable, transparent process that gives cyber defenders and others the certainty they need about access to powerful models,” Anthropic said. “These rules should be codified in strong regulation and applied equally across frontier model developers.”

Gogia said the broader lesson extends beyond Anthropic’s restored access. “Restored access is not restored certainty,” Gogia said. “Build for the detour, because the road now runs through policy.”

The US government has reversed export restrictions on Anthropic’s frontier AI models Fable 5 and Mythos 5, allowing the company to resume global access after nearly three weeks of disruption triggered by concerns over the models’ cybersecurity capabilities.

“As of today, June 30, the export controls on Fable 5 and Mythos 5 have been lifted,” Anthropic said in a blog post. The company said Fable 5 will begin rolling out globally on July 1 across Claude Platform, Claude.ai, Claude Code and Claude Cowork, while access on Amazon Web Services, Google Cloud, and Microsoft Foundry will be restored “as quickly as possible.”

Commerce Secretary Howard Lutnick said the administration had worked with Anthropic before reversing the restrictions.

“Over the past two weeks, we have worked closely with Anthropic to analyze and approve Fable 5 to ensure alignment across the US Government and strengthen America’s leadership in AI,” Lutnick wrote in a post on X.

Anthropic had launched Fable 5 and Mythos 5 on June 9, and the US authorities restricted their export on June 12.

The Anthropic decision comes as other frontier AI developers are also operating under closer government scrutiny.

In announcing GPT-5.6 Sol, Terra, and Luna last week, OpenAI said it had previewed both its rollout plans and the model’s capabilities to the US government before launch and, “at their request,” was initially making the model available only to “a small group of trusted partners” whose participation had been shared with the government.

OpenAI said it would continue coordinating with government partners before expanding availability, but added that it did not believe “this kind of government access process should become the long-term default” because it keeps advanced AI tools from “users, developers, enterprises, cyber defenders, and global partners who need them.”

Analysts say the Anthropic decision reflects a broader shift in how frontier AI models are governed.

“The reversal is not the story; the instrument beneath it is,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. Washington, he said, has applied the long-standing “deemed export” doctrine to frontier AI models, signalling “negotiated oversight, conditional and monitored, rather than blanket prohibition.”

Immediate order created global disruption

Anthropic said the June 12 export controls required it to restrict access to foreign nationals.

“Because the order took effect immediately and we had no reliable way to verify nationality in real-time, we suspended access to both models for all users,” it added. The company partially restored Mythos 5 last week for a limited group of US organizations.

Anthropic said the restrictions followed a report from Amazon researchers describing a technique that bypassed one of Fable 5’s cybersecurity safeguards. After reviewing the findings with Amazon and the US government, the company concluded the technique “did not expose any unique Mythos-level cyber capabilities” and instead represented “a borderline case for Fable 5’s safeguards.”

It subsequently retrained its safety classifier, saying the reported technique is now blocked in more than 99% of cases, while acknowledging that the stronger protections would increase false positives for some legitimate coding requests. Researchers at the US Department of Commerce’s Center for AI Standards and Innovation also evaluated the updated safeguards, Anthropic said.

Enterprises face a new continuity risk

Experts say the episode demonstrates that frontier AI’s availability can now be shaped by policy decisions as much as technical capability.

“Frontier access has become conditional infrastructure,” Gogia said. “The models went dark globally because nationality could not be verified in real time, so a control aimed at foreign nationals became an outage for everyone.”

He said enterprises should also not assume that deploying models across multiple cloud providers insulates them from regulatory actions affecting the underlying model provider.

According to Gogia, organizations should begin evaluating frontier AI platforms for regulatory interruption, cross-border identity restrictions, channel restoration delays, and trusted-partner eligibility alongside traditional security, commercial, and technical considerations.

Anthropic proposes a common jailbreak framework

Anthropic used the announcement to call for an industry-wide framework to assess AI jailbreaks, saying developers and governments currently lack a common standard for evaluating newly discovered techniques.

“There’s currently no consensus in the AI industry on how to describe, in objective terms, the severity of an AI jailbreak,” the company said. Anthropic said it is working with Amazon, Microsoft, Google, and other Project Glasswing partners on a framework for evaluating jailbreaks, while also expanding collaboration with the US government through pre-release testing of future frontier models, information sharing, and joint AI security research.

“Government involvement in AI releases requires a durable, transparent process that gives cyber defenders and others the certainty they need about access to powerful models,” Anthropic said. “These rules should be codified in strong regulation and applied equally across frontier model developers.”

Gogia said the broader lesson extends beyond Anthropic’s restored access. “Restored access is not restored certainty,” Gogia said. “Build for the detour, because the road now runs through policy.”

The article originally appeared on CIO.

US lawmakers on Thursday introduced a bill that would require developers of advanced AI models to report major safety and security incidents to the Commerce Department, establishing a federal oversight framework for high-risk AI systems.

The proposed AI Incident Reporting Act would mandate that developers of designated “covered models” disclose incidents within seven days of knowing, or reasonably believing, that one has occurred. For incidents posing an imminent or ongoing risk of serious harm, the Commerce Department would have to notify congressional leadership and the chairs of relevant House and Senate committees within 48 hours after receiving the report.

The bill directs the Secretary of Commerce to establish capability thresholds to determine which AI models and developers are subject to the reporting requirements.

“AI is a powerful engine of innovation, and I want to see it flourish, but not without accountability and not without human oversight,” Moran said in a statement announcing the legislation. “The rule of law should apply to this new frontier. This legislation ensures that when something goes wrong with a high-capability AI system, the US Government has the information needed to act quickly.”

Broad range of reportable incidents

The proposal identifies a broad set of incidents that would require disclosure to the Commerce Department.

According to the bill, developers would have to report attempts by covered AI models to evade human oversight, deceive operators, circumvent safeguards, resist shutdown, or obtain unauthorized access to systems or privileges.

The reporting requirement would also apply to theft or attempted theft of model weights, capabilities that could materially enable offensive cyber operations against important software or critical infrastructure, autonomous development of more capable AI systems, and capabilities that could accelerate the development or use of chemical, biological, radiological, nuclear, or explosive weapons.

The legislation also directs the Commerce Department to develop the capability thresholds in consultation with AI developers, academic researchers, cybersecurity experts, national security officials, and other stakeholders before issuing implementation guidance.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the proposal would make reporting serious AI incidents a legal obligation rather than a voluntary practice for developers of frontier AI models.

“The serious frontier developers already run the evaluations, the red-teaming and the escalation drills,” Gogia said. “What they have never faced at the federal level is a legal obligation to tell the government, on the clock, when a model behaves dangerously.”

Reporting timelines and enforcement

The bill requires covered developers to submit an initial report within seven days of discovering a reportable incident and supplemental reports as additional information becomes available.

The legislation also authorizes the Commerce Department to investigate compliance, issue subpoenas, require corrective action, and impose civil penalties of up to $2 million for violations. Each day of a continuing violation would constitute a separate violation, the bill states.

Gogia said implementation could hinge on how regulators define reporting triggers.

“Capability thresholds are the visible difficulty, and not the deepest one. Thresholds decide which models enter the regime. Discovery decides whether the regime ever sees the fire,” he said.

Drawing a comparison with cybersecurity regulations, he said reporting requirements should clearly define when an incident becomes reportable.

“Cyber reporting has already taught the lesson. A vague trigger produces either silence or noise: firms stay quiet until they are certain, or they file everything and bury the signal,” Gogia said.

Filling a gap, a recent dispute exposed

The bill follows a US government action that exposed the absence of any such process. On June 12, the Commerce Department took action against the latest models from Anthropic, a US AI developer, on national security grounds, prompting the company to disable global access to those models.

“Export control was the sledgehammer. This proposal is the search for a scalpel,” Gogia noted. The measure is a narrower alternative to the Great American Artificial Intelligence Act, a broader discussion draft released earlier in June that also routes critical safety incidents to Commerce.

The Commerce Department’s Center for AI Standards and Innovation has separately signed agreements to evaluate leading models before deployment.

Compliance burden falls on enterprises

Gogia said the legal duty falls on the developer, but the operational cost reaches the customers. “Regulation may name the lab, but the bill for poor visibility is settled downstream,” he said.

He said the hardest question is not which models qualify but when a reporting clock starts. “Thresholds decide which models enter the regime. Discovery decides whether the regime ever sees the fire,” he said, adding that a model can pass laboratory tests yet behave differently once connected to live tools and enterprise data.

The bill exempts submitted reports from public disclosure requirements and states that submitting a report would not waive trade secret protections or attorney-client privilege.

“The instinct behind this bill is sound, but the balance cannot be scored from a press release,” Gogia said. “The wording will decide everything.”

US lawmakers on Thursday introduced a bill that would require developers of advanced AI models to report major safety and security incidents to the Commerce Department, establishing a federal oversight framework for high-risk AI systems.

The proposed AI Incident Reporting Act would mandate that developers of designated “covered models” disclose incidents within seven days of knowing, or reasonably believing, that one has occurred. For incidents posing an imminent or ongoing risk of serious harm, the Commerce Department would have to notify congressional leadership and the chairs of relevant House and Senate committees within 48 hours after receiving the report.

The bill directs the Secretary of Commerce to establish capability thresholds to determine which AI models and developers are subject to the reporting requirements.

“AI is a powerful engine of innovation, and I want to see it flourish, but not without accountability and not without human oversight,” Moran said in a statement announcing the legislation. “The rule of law should apply to this new frontier. This legislation ensures that when something goes wrong with a high-capability AI system, the US Government has the information needed to act quickly.”

Broad range of reportable incidents

The proposal identifies a broad set of incidents that would require disclosure to the Commerce Department.

According to the bill, developers would have to report attempts by covered AI models to evade human oversight, deceive operators, circumvent safeguards, resist shutdown, or obtain unauthorized access to systems or privileges.

The reporting requirement would also apply to theft or attempted theft of model weights, capabilities that could materially enable offensive cyber operations against important software or critical infrastructure, autonomous development of more capable AI systems, and capabilities that could accelerate the development or use of chemical, biological, radiological, nuclear, or explosive weapons.

The legislation also directs the Commerce Department to develop the capability thresholds in consultation with AI developers, academic researchers, cybersecurity experts, national security officials, and other stakeholders before issuing implementation guidance.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the proposal would make reporting serious AI incidents a legal obligation rather than a voluntary practice for developers of frontier AI models.

“The serious frontier developers already run the evaluations, the red-teaming and the escalation drills,” Gogia said. “What they have never faced at the federal level is a legal obligation to tell the government, on the clock, when a model behaves dangerously.”

Reporting timelines and enforcement

The bill requires covered developers to submit an initial report within seven days of discovering a reportable incident and supplemental reports as additional information becomes available.

The legislation also authorizes the Commerce Department to investigate compliance, issue subpoenas, require corrective action, and impose civil penalties of up to $2 million for violations. Each day of a continuing violation would constitute a separate violation, the bill states.

Gogia said implementation could hinge on how regulators define reporting triggers.

“Capability thresholds are the visible difficulty, and not the deepest one. Thresholds decide which models enter the regime. Discovery decides whether the regime ever sees the fire,” he said.

Drawing a comparison with cybersecurity regulations, he said reporting requirements should clearly define when an incident becomes reportable.

“Cyber reporting has already taught the lesson. A vague trigger produces either silence or noise: firms stay quiet until they are certain, or they file everything and bury the signal,” Gogia said.

Filling a gap, a recent dispute exposed

The bill follows a US government action that exposed the absence of any such process. On June 12, the Commerce Department took action against the latest models from Anthropic, a US AI developer, on national security grounds, prompting the company to disable global access to those models.

“Export control was the sledgehammer. This proposal is the search for a scalpel,” Gogia noted. The measure is a narrower alternative to the Great American Artificial Intelligence Act, a broader discussion draft released earlier in June that also routes critical safety incidents to Commerce.

The Commerce Department’s Center for AI Standards and Innovation has separately signed agreements to evaluate leading models before deployment.

Compliance burden falls on enterprises

Gogia said the legal duty falls on the developer, but the operational cost reaches the customers. “Regulation may name the lab, but the bill for poor visibility is settled downstream,” he said.

He said the hardest question is not which models qualify but when a reporting clock starts. “Thresholds decide which models enter the regime. Discovery decides whether the regime ever sees the fire,” he said, adding that a model can pass laboratory tests yet behave differently once connected to live tools and enterprise data.

The bill exempts submitted reports from public disclosure requirements and states that submitting a report would not waive trade secret protections or attorney-client privilege.

“The instinct behind this bill is sound, but the balance cannot be scored from a press release,” Gogia said. “The wording will decide everything.”

The article originally appeared on CSO.

AMD has acquired memory optimization startup MEXT, bringing predictive memory optimization software into its AI infrastructure portfolio as enterprises look for ways to manage increasingly memory-intensive AI workloads without continually expanding expensive DRAM capacity.

The technology uses AI to intelligently move frequently accessed data between flash storage and DRAM, enabling organizations to increase effective memory capacity while reducing infrastructure cost and power consumption, according to AMD.

“Memory has become a critical constraint across cloud and enterprise environments,” AMD said in a blog announcing the acquisition, adding that traditional approaches of simply adding more DRAM are becoming increasingly costly and power-intensive.

Financial terms of the acquisition were not disclosed. AMD did not immediately respond to a request for additional comment.

AI boom reshapes memory economics

AMD’s move comes as AI infrastructure demand is reshaping the memory market and forcing enterprises to rethink how they scale AI deployments.

According to IDC, AI infrastructure is driving a strategic reallocation of memory production toward enterprise-grade components, with 2026 DRAM supply growth expected to remain below historical norms at 16% year over year, creating pricing pressure across the market.

Gartner has separately forecast a 130% increase in combined DRAM and SSD prices by the end of 2026, warning that higher memory costs will increasingly influence enterprise technology investment decisions.

Against that backdrop, MEXT’s software is designed to address a growing enterprise challenge by using predictive algorithms to identify frequently accessed data and proactively move it between flash storage and DRAM, extending usable memory capacity without requiring proportional hardware expansion, the blog added.

“Memory prices have seen an unprecedented growth, nearly going 4x since 3Q25, making memory one of the most contested chips in the AI infrastructure story,” said Shrish Pant, director analyst at Gartner.

Pant said higher prices and constrained supply are reviving interest in software-driven memory optimization strategies that received little attention when memory was abundant and inexpensive.

AI infra competition moves up the stack

The acquisition also reflects a broader shift in how AI vendors are competing for enterprise workloads.

While the first phase of the AI race centered on securing GPUs and compute capacity, vendors are increasingly investing across networking, software, and infrastructure optimization to improve overall system efficiency.

“We can safely say that we are beyond ‘chips wars’ and have already entered into an ‘Infrastructure optimization war’, and software-based memory optimization is just one of many moving pieces which will determine winners for the AI race,” Pant said.

AMD’s acquisition expands its AI infrastructure portfolio beyond processors into software that optimizes memory utilization, mirroring a broader industry trend toward integrated hardware and software stacks rather than standalone silicon performance.

Manish Rawat, semiconductor analyst at TechInsights, said memory is increasingly becoming a strategic constraint for enterprise AI deployments.

“As enterprises deploy larger models and scale user workloads, memory limitations often constrain performance and GPU utilization before compute resources are fully exhausted,” Rawat said.

Memory is evolving from a supporting hardware component into a strategic enabler of AI scalability, performance, and cost optimization, he added.

Software aims to delay expensive DRAM upgrades

AMD said MEXT’s predictive memory tiering technology intelligently places frequently accessed data in high-speed memory while shifting less active data to lower-cost flash storage.

That approach is intended to increase infrastructure efficiency and reduce the need for continual DRAM expansion as enterprise AI workloads grow, analysts said.

Rawat said software-based memory optimization offers enterprises a practical way to delay expensive hardware upgrades rather than eliminate the need for DRAM.

Although the technology cannot replace high-performance DRAM for latency-sensitive applications, it can improve data center efficiency, lower total cost of ownership, and help organizations maximize returns from existing infrastructure investments, he said.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the industry is entering a phase where infrastructure orchestration will matter as much as compute performance.

“The GPU is the engine. Memory is the road, the fuel line, and occasionally the traffic jam,” Gogia said.

Production AI workloads place sustained demands on parameters, embeddings, and cached context, making memory performance a business issue rather than simply a hardware specification.

Gogia said predictive memory tiering addresses inefficiencies that often leave expensive DRAM underutilized, but cautioned that optimization should complement, rather than replace, sound infrastructure design.

“Predictive tiering attacks the waste inside that reflex,” he said, referring to the tendency to address performance challenges by purchasing more memory instead of improving utilization.

Rawat said organizations that optimize compute, memory, storage, and software together are likely to scale AI deployments faster, lower operating costs, and generate stronger returns on AI investments than those relying primarily on increasing hardware capacity.

When SpaceX on Tuesday officially announced its plan to purchase AI coding startup Cursor for $60 billion in stock, as it had predicted it would do in April, it presented CIOs and developers with a little good news, a little bad news and a massive pile of uncertainty. 

The details of the proposed acquisition were virtually identical to the terms announced in April, even retaining the $10 billion consolation prize for Cursor should SpaceX back out of the deal.  

But for CIOs and developers, the increasing probability of the deal happening is forcing them to make long-term decisions without many long-term answers about what is likely to happen to Cursor, which says its coding agents are used by 64% of Fortune 500 companies.

But whether the deal is good news for Cursor’s customers, or even those of its rivals, is an open question.

Arnal Dayaratna, research VP for software development at IDC, is firmly in the good news camp. He argued that the main element holding back the company, which he estimated brings in about $2 billion in annual revenue, was access to GPUs. And, he said, SpaceX’s xAI has the ability to resolve that problem.

It’s all about the GPUs

“The implication is that Cursor can get better because it will get better access to more compute, in the form of GPUs. That was what Cursor was struggling to obtain,” Dayaratna said, adding that Cursor joins both Anthropic and Google in aligning with xAI. 

“It is bad news for Anthropic,” he said, pointing out, “[Elon] Musk figured out that GPUs were the limiters, the bottleneck. Not human talent: compute. And it’s not only about GPUs. It’s about the data centers,” as well as the mechanisms needed to support data centers, including electricity access, cooling apparatus, and zoning rights.

“Zoning rights are very difficult to obtain in the United States, and Elon has been able to leverage his political capital” to obtain those rights, Dayaratna said.

Dayaratna also commented on the highly unusual tactic of a company pre-announcing an acquisition and then announcing it “officially” two months later. The reason, he suggested, is that Musk didn’t want the acquisition to delay the SpaceX IPO, but he did want potential investors to know about it. “It gave the IPO more legs, even though it was not formally a part of the IPO. He wanted to socialize it before it happened,” he said.

Jason Andersen, principal analyst at Moor Insights & Strategy, took a far more cautious approach and predicted, “enterprise customers will be very concerned.”

Time for CIO due diligence

“xAI’s models and treatment of guardrails are very different than what Cursor has stood for,” Andersen said, and the key issue involves model choice. “Will Cursor be able to point at models other than Grok? If that is the case, it could go towards helping customers be OK with things for a while,” he said. 

But Andersen stressed the words “for a while.” He argued that Cursor rivals have gotten more sophisticated, which could give enterprise CIOs more alternatives.

“The tooling options from the classic enterprise vendors have gotten really good, and also have a number of features that enable better governance and teaming. For example, Kiro from AWS is already a worthy competitor to Cursor, but there have also been major improvements from Google and Microsoft this year,” Andersen said. “I’d venture to say that Cursor was already facing a substantial competitive challenge in the enterprise, especially as the vibe coding movement lost some steam towards agents that enable so much more developer capability.”

Andersen added, “A lot of SpaceX’s valuation is pinned to xAI and there wasn’t enough IP there for it to be long-term competitive with Anthropic or OpenAI without rapidly injecting new technology.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, said the current period of due diligence gives CIOs opportunities to ask difficult questions.

“I do believe that enterprise customers will start to ask harder questions now, especially if more of the processing starts moving through Grok or xAI infrastructure,” Bellamkonda said, but he stressed that he does see a SpaceX deal helping Cursor.

“Cursor is already carrying a lot of LLM cost and if SpaceX and xAI can give it access to more compute and a faster, improved Grok model, customers could eventually get better performance at a lower cost. Grok is getting faster and more accurate, and under SpaceX there may be more enterprise appetite for xAI than xAI had on its own,” Bellamkonda said.

Questions about data privacy

But Bellamkonda also pointed to the trust issue, specifically asking whether Cursor would be allowed to continue its zero-data-retention policy under the new ownership.

“Enterprise users with sensitive codebases will want to understand exactly where their data is going, who has access to it, and whether any prompts, code, metadata, or embeddings are touching SpaceX or xAI systems,” Bellamkonda said. “Zero data retention is only valuable if customers believe the controls still apply across the new infrastructure. If they can preserve Cursor’s privacy posture while using xAI and SpaceX compute to improve the product, this could be a very strong outcome for customers. If the data governance is unclear, enterprise buyers will hesitate.”

Justin Greis, CEO of consulting firm Acceligence, echoed Bellamkonda’s concerns.

“For many enterprise customers, Cursor’s zero-data-retention policy was not simply a security feature. It was a foundational part of the procurement and approval process,” Greis said, pointing out that security teams, legal departments, compliance officers, and executive leadership all became more comfortable adopting AI-assisted development because they believed their source code, prompts, and proprietary intellectual property would not be retained, stored, or repurposed.

“Whenever ownership changes, customers naturally revisit those assumptions,” he said. “I would expect enterprise customers to immediately seek clarity on whether existing zero-data-retention commitments remain unchanged, whether those commitments continue to be contractually enforceable, and whether any future modifications could be introduced through changes to terms of service or product strategy.”

Reassessing platform risk

Another concern is whether the move will create greater vendor concentration, and whether that will increase risk exposure. 

“Many organizations are already concerned about becoming overly dependent on a small number of AI providers. If Cursor becomes more tightly integrated into a broader SpaceX technology ecosystem, some enterprises may worry about reduced flexibility and fewer alternatives. I suspect this will be a continued question with SpaceX’s recent IPO and growth plans,” Greis said.

Additionally, he pointed out, “AI coding platforms are rapidly becoming part of critical software delivery infrastructure. When organizations standardize on these tools, they are making a long-term platform decision that influences developer productivity, software quality, security processes, and engineering workflows. A change in ownership naturally prompts a reassessment of platform risk, roadmap alignment, and long-term strategic direction.”

As well, Sanchit Vir Gogia, chief analyst at Greyhound Research, noted, “zero data retention survives only where it stays contractual, auditable, enforceable and fenced from affiliate use. The deeper point is what Cursor has become. It is no longer a developer convenience. It sits inside the act of software creation, close to the intellectual-property bloodstream of the enterprise. That is a control plane, and control planes change hands rarely and consequentially.”

Gogia added that even before an acquisition, Cursor was already weakening its data guarantee. “Even now the promise is layered. Cursor’s own disclosures show that standard Privacy Mode still permits some code data to be stored for product features, while only the stricter legacy setting retains nothing. Zero is the exception, not the default.”

This article originally appeared on InfoWorld.

When SpaceX on Tuesday officially announced its plan to purchase AI coding startup Cursor for $60 billion in stock, as it had predicted it would do in April, it presented CIOs and developers with a little good news, a little bad news and a massive pile of uncertainty. 

The details of the proposed acquisition were virtually identical to the terms announced in April, even retaining the $10 billion consolation prize for Cursor should SpaceX back out of the deal.  

But for CIOs and developers, the increasing probability of the deal happening is forcing them to make long-term decisions without many long-term answers about what is likely to happen to Cursor, which says its coding agents are used by 64% of Fortune 500 companies.

But whether the deal is good news for Cursor’s customers, or even those of its rivals, is an open question.

Arnal Dayaratna, research VP for software development at IDC, is firmly in the good news camp. He argued that the main element holding back the company, which he estimated brings in about $2 billion in annual revenue, was access to GPUs. And, he said, SpaceX’s xAI has the ability to resolve that problem.

It’s all about the GPUs

“The implication is that Cursor can get better because it will get better access to more compute, in the form of GPUs. That was what Cursor was struggling to obtain,” Dayaratna said, adding that Cursor joins both Anthropic and Google in aligning with xAI. 

“It is bad news for Anthropic,” he said, pointing out, “[Elon] Musk figured out that GPUs were the limiters, the bottleneck. Not human talent: compute. And it’s not only about GPUs. It’s about the data centers,” as well as the mechanisms needed to support data centers, including electricity access, cooling apparatus, and zoning rights.

“Zoning rights are very difficult to obtain in the United States, and Elon has been able to leverage his political capital” to obtain those rights, Dayaratna said.

Dayaratna also commented on the highly unusual tactic of a company pre-announcing an acquisition and then announcing it “officially” two months later. The reason, he suggested, is that Musk didn’t want the acquisition to delay the SpaceX IPO, but he did want potential investors to know about it. “It gave the IPO more legs, even though it was not formally a part of the IPO. He wanted to socialize it before it happened,” he said.

Jason Andersen, principal analyst at Moor Insights & Strategy, took a far more cautious approach and predicted, “enterprise customers will be very concerned.”

Time for CIO due diligence

“xAI’s models and treatment of guardrails are very different than what Cursor has stood for,” Andersen said, and the key issue involves model choice. “Will Cursor be able to point at models other than Grok? If that is the case, it could go towards helping customers be OK with things for a while,” he said. 

But Andersen stressed the words “for a while.” He argued that Cursor rivals have gotten more sophisticated, which could give enterprise CIOs more alternatives.

“The tooling options from the classic enterprise vendors have gotten really good, and also have a number of features that enable better governance and teaming. For example, Kiro from AWS is already a worthy competitor to Cursor, but there have also been major improvements from Google and Microsoft this year,” Andersen said. “I’d venture to say that Cursor was already facing a substantial competitive challenge in the enterprise, especially as the vibe coding movement lost some steam towards agents that enable so much more developer capability.”

Andersen added, “A lot of SpaceX’s valuation is pinned to xAI and there wasn’t enough IP there for it to be long-term competitive with Anthropic or OpenAI without rapidly injecting new technology.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, said the current period of due diligence gives CIOs opportunities to ask difficult questions.

“I do believe that enterprise customers will start to ask harder questions now, especially if more of the processing starts moving through Grok or xAI infrastructure,” Bellamkonda said, but he stressed that he does see a SpaceX deal helping Cursor.

“Cursor is already carrying a lot of LLM cost and if SpaceX and xAI can give it access to more compute and a faster, improved Grok model, customers could eventually get better performance at a lower cost. Grok is getting faster and more accurate, and under SpaceX there may be more enterprise appetite for xAI than xAI had on its own,” Bellamkonda said.

Questions about data privacy

But Bellamkonda also pointed to the trust issue, specifically asking whether Cursor would be allowed to continue its zero-data-retention policy under the new ownership.

“Enterprise users with sensitive codebases will want to understand exactly where their data is going, who has access to it, and whether any prompts, code, metadata, or embeddings are touching SpaceX or xAI systems,” Bellamkonda said. “Zero data retention is only valuable if customers believe the controls still apply across the new infrastructure. If they can preserve Cursor’s privacy posture while using xAI and SpaceX compute to improve the product, this could be a very strong outcome for customers. If the data governance is unclear, enterprise buyers will hesitate.”

Justin Greis, CEO of consulting firm Acceligence, echoed Bellamkonda’s concerns.

“For many enterprise customers, Cursor’s zero-data-retention policy was not simply a security feature. It was a foundational part of the procurement and approval process,” Greis said, pointing out that security teams, legal departments, compliance officers, and executive leadership all became more comfortable adopting AI-assisted development because they believed their source code, prompts, and proprietary intellectual property would not be retained, stored, or repurposed.

“Whenever ownership changes, customers naturally revisit those assumptions,” he said. “I would expect enterprise customers to immediately seek clarity on whether existing zero-data-retention commitments remain unchanged, whether those commitments continue to be contractually enforceable, and whether any future modifications could be introduced through changes to terms of service or product strategy.”

Reassessing platform risk

Another concern is whether the move will create greater vendor concentration, and whether that will increase risk exposure. 

“Many organizations are already concerned about becoming overly dependent on a small number of AI providers. If Cursor becomes more tightly integrated into a broader SpaceX technology ecosystem, some enterprises may worry about reduced flexibility and fewer alternatives. I suspect this will be a continued question with SpaceX’s recent IPO and growth plans,” Greis said.

Additionally, he pointed out, “AI coding platforms are rapidly becoming part of critical software delivery infrastructure. When organizations standardize on these tools, they are making a long-term platform decision that influences developer productivity, software quality, security processes, and engineering workflows. A change in ownership naturally prompts a reassessment of platform risk, roadmap alignment, and long-term strategic direction.”

As well, Sanchit Vir Gogia, chief analyst at Greyhound Research, noted, “zero data retention survives only where it stays contractual, auditable, enforceable and fenced from affiliate use. The deeper point is what Cursor has become. It is no longer a developer convenience. It sits inside the act of software creation, close to the intellectual-property bloodstream of the enterprise. That is a control plane, and control planes change hands rarely and consequentially.”

Gogia added that even before an acquisition, Cursor was already weakening its data guarantee. “Even now the promise is layered. Cursor’s own disclosures show that standard Privacy Mode still permits some code data to be stored for product features, while only the stricter legacy setting retains nothing. Zero is the exception, not the default.”

HPE has rolled out a super-sized package of hardware and software aimed at helping enterprise customers build and manage large AI infrastructures from the data center to the edge.

At its Discover event in Las Vegas this week, the vendor announced HPE Juniper Networking QFX switches aimed at inferencing and scale-up architectures. It also deepened integration of its Juniper Networking data center switching and operations into its Mist AI engine and launched a unified, AI-native SASE platform.

“AI requires a solid architectural foundation, and the success of agentic AI in the enterprise depends on a modern networking foundation built for autonomous workflows, where network performance, reliability, and intelligence determine the effectiveness of the entire AI architecture,” Rami Rahim, executive vice president, president and general manager of networking for HPE, told journalists and analysts in a briefing before the event. “HPE is delivering that foundation, letting enterprises deploy agentic AI with greater control, confidence, security, and operational simplicity.”

On the hardware front, the company introduced the HPE Juniper Networking QFX5140 switch, aimed at the booming demand for AI inferencing and edge AI use cases. 

The 1RU, 16T QFX5140 fixed-configuration data center switch is designed for AI fabric, spine, leaf, and border leaf deployments. Its port density can be configured to support 24× 400G QSFP112, 8× 800G OSFP800 and 2x SFP28 ports, and it includes support for RDMA over Converged Ethernet (RoCEv2). Additional capabilities include congestion management features such as Priority Flow Control and Explicit Congestion Notification and dynamic load balancing — all features that enable effective GPU-to-GPU communications, according to HPE CTO Fidelma Russo.

The box fills out the mid-tier of the HPE Juniper QFX family, which includes the high-end 102T QFX5240/QFX5250 and entry-level 100GBE QFX 5100.

HPE also announced the QFX5252 module for its 72GPU-per-rack AMD Helios turnkey package aimed AI training and high-volume inferencing. The package combines CPUs, GPUs and open Ethernet networking technology into a unified, high-end AI platform. 

The QFX family is part of a significant move aimed at tightening the integration between the networking gear HPE acquired from Juniper and its existing HPE AI infrastructure offerings. Specifically, HPE’s data center management platform, Data Center Director, now includes the QFX switch portfolio. 

The idea is to offer data center customers a more integrated, automated, and centralized view of all their network components to improve network visibility and speed troubleshooting, according to Russo.

HPE Mist integration

Continuing that integration theme, HPE said it will integrate Juniper’s natural language Mist AI into HPE Aruba Central and vice versa, all fed by its core AIOps Marvis AI engine. Marvis collects telemetry and user state data from Juniper’s routers, switches, access points, firewalls, and applications to detect and resolve a broad range of enterprise networking problems. A key part of Marvis is its AI-based Marvis Actions component, which identifies and prioritizes network problem remediation.

Overall, the move integrates AIOps across wired, wireless, and SD-WAN environments to proactively resolve issues, including trusted actions such as wired port remediation, to further extend autonomous operations across the HPE networking portfolio Rami said. He also noted that Marvis Actions will be extended to Aruba Central by the end of the year. 

Further, HPE said it will be melding the HPE Aruba CX switching portfolio with HPE Mist, giving CX customers capabilities such as AI-native visibility, zero-touch provisioning, wired assurance for Layer 2 access, service-level insights, and HPE Marvis AI-driven support, Rami said.

HPE also expanded Mist data center capabilities to include predictive analytics for proactive maintenance of network components. For example, Mist can now use AI/ML technology to predict potential optics failures that would cause network outages.

Mist also can now use an advanced reasoning AI agent for high-confidence remediation, Rami said. Agentic AI is used to continuously and autonomously reason across diverse data streams, including millions of TAC cases and a contextual graph database from HPE Networking Data Center Director, to deliver precise root cause analysis inside of the data center. 

“So think of this as Marvis AI engine for data center operations. So, here we’re combining telemetry, application flows, operational context, historical knowledge to understand rapidly the root cause and recommend next steps,” Rami said. “So the problems that once took hours if not days to diagnose can now be resolved literally in minutes or even proactively before anybody understands that there is an issue. Together these capabilities bring the self-driving network from where it started inside the campus to now inside the data center.”

The most important takeaway is that there’s real momentum behind HPE’s acquisition strategy to leverage the core of the Mist platform by expanding Marvis as the AI engine across the portfolio, Mike Leibovitz, senior director analyst in Gartner’s business technology and innovation group, told Network World. “They are continuing to innovate while simultaneously integrating that model into Aruba networking, into the data center, and out the branch.”

“Agentic NetOps is the most exciting area of innovation in enterprise networking in more than 20 years, as organizations move toward increasingly hands-off operations. HPE is well aligned with Marvis as the AI engine across its portfolio,” Leibovitz said. “Most enterprises are starting this journey with their existing infrastructure vendors like HPE, Cisco, or Arista, but those with more heterogeneous environments are also investigating a growing set of infrastructure-independent software providers. The space is moving quickly, and leadership is still very much up for grabs.”

Unified SASE efforts

HPE is also trying to simplify WAN access to data center resources. That’s the driving idea behind a new SASE Orchestrator package. It ties together the vendor’s SD-WAN and SSE with cloud security and a unified policy engine that will use AI to manage branch, remote user, and cloud connectivity from one place. With the policy engine, customers can set security policies once, for example, and deploy them across many sites.

“The Orchestrator promises simpler operations with AI operations, faster zero-trust adoption, and a better user experience through intelligent traffic steering and application awareness,” Rami said.

Nvidia updates

Beyond hardware and software enhancements, HPE also tightened its integration with core partner Nvidia, via its HPE Private Cloud AI, a turnkey AI factory co-engineered with the Nvidia.

HPE Private Cloud AI delivers a preconfigured hardware and software stack featuring the latest Nvidia AI Enterprise software and blueprints. The package is being enhanced to help manage AI agent by adding support for Nvidia’s Agent Toolkit software, including Nvidia’s Nemotron open models, NemoClaw, and OpenShell secure runtime, to provide an an agent operating system that reasons, lets customers monitor agent behavior, enforces policies, and reduces deployment risk, according to HPE.

HPE is also bringing Nvidia Confidential Computing to the HPE AI Factory through HPE Services. Nvidia Confidential Computing protects models and private data during execution for on-premises or sovereign deployments, according to HPE.

HPE Zerto and HPE Morpheus additions

A new release of HPE Zerto Software lets customers identify any rogue agent action and utilizes data protection to rewind to a clean slate. The Private Cloud AI package now also supports secure local agent registration, providing customers with the ability to approve AI models, skills, and tools while adhering to centralized governance and security policies.

Changes are also in store for HPE Morpheus, which lets enterprises manage virtual machines, containers, and cloud resources across multiple environments from a single control plane. Often positioned as an alternative to Broadcom’s VMware, HPE announced if customers own or buy HPE’s VM Essentials package for a year, it won’t charge anything.  

“We are announcing that as a customer goes through this transformation with HPE Morpheus VM Essentials, you don’t pay for the first year of licenses. You will get Zerto migration licenses during that period to help you move. And so what this does is it helps mitigate the double-bubble cost problem that customers see as they are looking to migrate from one platform to another,” Russo said. Zero-interest financing for HPE cloud ops software over three years further supports customer migration, Russo added. “This cost mitigation aims to accelerate customer adoption and ease transitions from legacy platforms,” Russo said.

Microsoft has open-sourced an AI evaluation framework that converts natural-language requirements into executable tests, expanding its push into enterprise AI governance as organizations struggle to validate agent behavior before production deployments systematically.

The framework, called ASSERT (Adaptive Spec-driven Scoring for Evaluation and Regression Testing), generates evaluation scenarios, datasets, metrics, and scorecards from written specifications, product requirements, and governance documents, Microsoft said in a blog post announcing the release.

“Agents fail in ways that are hard to see,” Microsoft wrote in the blog post. “They drift from policy, produce unsafe outputs in edge cases, and behave differently in production than they did in testing. Generic benchmarks do not catch these failures because they are not built around your policies, your agent, or your use case.”

Rather than requiring developers to manually create evaluation suites, ASSERT translates written intent into reusable tests that can be integrated into AI development pipelines, the company said in the blog post.

With ASSERT, Microsoft is entering an increasingly competitive AI evaluation market that already includes platforms such as LangChain’s LangSmith, Braintrust, Patronus AI, Galileo, Arize AI’s Phoenix, and Promptfoo, which help enterprises benchmark, monitor, and validate large language model applications.

Behavioral testing remains immature

The release comes as enterprises rapidly expand AI agent deployments while formal evaluation practices remain the exception rather than the rule.

“Most organizations, in fact, 99% of them, do not evaluate any AI agents pre-production,” said Anushree Verma, senior director analyst at Gartner.

According to Verma, the industry’s next competitive advantage will depend less on advances in reasoning models than on how effectively organizations simulate and stress-test AI agents before deployment.

“The next competitive moat in agentic AI is not about the sophistication of reasoning models or the underlying architecture,” she said. “It will be about the depth and realism of the training environment through agentic simulation, particularly for mission-critical deployments.”

Gartner estimates that by 2029, more than 75% of domain-specific agents designed without agentic simulation in regulated industries will fail to deliver value.

Forrester sees enterprises moving toward behavioral evaluation but says most organizations have yet to make it a formal production requirement.

“Most enterprises are still in an intermediate stage where behavioral evaluation is inconsistently applied rather than treated as a formal production gate,” said Biswajeet Mahapatra, principal analyst at Forrester.

According to Forrester data, more than 45% of organizations are already using AI agents, and another 25% are piloting them, yet many continue to struggle with scaling because of immature governance and limited operational rigor.

“The net is that behavioral evaluation is becoming important, but for most organizations it is still ad hoc or tool-driven rather than a standardized release gate enforced across the lifecycle,” Mahapatra said.

AI judges still need human oversight

Microsoft said ASSERT uses large language models as judges, with model-generated evaluations agreeing with human reviewers 80% to 90% of the time in the company’s internal validation.

That level of agreement can help automate large portions of AI testing, but should not be treated as a standalone governance mechanism, Mahapatra said.

“An 80% to 90% agreement rate with human reviewers indicates strong alignment but is not sufficient as a standalone control for governance or compliance,” he said.

Instead, enterprises should adopt layered oversight where AI evaluates AI at scale while humans retain supervisory accountability for high-risk, regulated, or ambiguous scenarios. Buyers should also watch for bias, consistency issues, and overreliance on a single model acting as both generator and evaluator, he added.

Open source reduces lock-in, not governance risk

Microsoft released ASSERT under the MIT open-source license, allowing organizations to inspect, modify, and integrate the framework into existing AI development workflows.

But open sourcing a framework does not eliminate questions around evaluation neutrality, Mahapatra said.

“Open sourcing under an MIT license reduces lock-in concerns and enables broad interoperability across model ecosystems,” he said. “However, it does not fully eliminate trust or conflict-of-interest questions because the originating vendor still influences how evaluation criteria, scoring logic, and definitions of acceptable behaviour are encoded.”

Instead of relying on a single evaluation framework, enterprises should validate AI systems against multiple evaluation approaches and retain ownership of internal evaluation policies, he said.