In Meta commercial terms changes slated to kick in on October 30, the company will shift the liability for any mistakes made by its AI models to its enterprise customers. The new language also makes those IT executives responsible for anything done by a partner or contractor.

The changes also finally put a stake into the heart of robots.txt, with Meta granting itself the right to grab, and use for its own purposes, any content associated with a Meta account.

The language allows Meta to “use automated software such as web scrapers, bots, robots, spiders, crawlers, and user agents and create test credentials to access, index, cache, analyze, collect, retrieve, or crawl the domains, web pages, or URLs, and any content available through them, that are associated with your products or services,” according to the new language on its website on Monday. Meta also wants to be able to “periodically pull updates from such domains, web pages, or URLs in order to provide, develop, improve, research, and maintain advertising and business products including business, commercial and related products to help you create and deliver ads and other commercial content.”

Consultants said the three biggest concerns they had about the changes were the web scraping, data ownership, and AI liability transfer.

Web scraping and data ownership

However, Meta emailed a statement to CIO.com late Monday suggesting that it was rethinking its position on web scraping. Saying that Meta wanted “to clarify” the web scraping changes, Emil Vazques, a Meta public affairs manager, said, “the crawling terms we consolidated only applied to our ads and business products and we’ve clarified that in the terms themselves. It does not change how Meta uses web crawlers. As always, content owners can use robots.txt to signal their preferences. This is not about scraping news sites, and these updates don’t give us new rights that supersede those preferences.”

As of Monday evening, New York time, the proposed document’s wording had yet to be altered to reflect this, but Vazques said that a change was coming soon.

The data ownership issue sets Meta apart from other AI vendors in that it wants to train its models on anything it encounters. Then again, the nature of Meta businesses focuses on advertising and would therefore be expected to seek more data control.

The new proposed phrasing, which Meta could alter before it takes effect later this month, said that using any Meta product grants Meta “a license to content that is covered by intellectual property rights like photos or videos you share, post, or upload on or in connection with our Meta Company Products. That license applies to content you or someone on your behalf, such as your agency that places an ad for you or your service provider that manages your Page content for you, makes available on or in connection with any Meta Company Products. Without limiting anything in our terms and for clarity, Meta may use such content … to provide, develop, improve, research, and maintain Meta Company Products.”

AI liability

But the change that prompted the most raised eyebrows involved AI liabilities. 

The proposed language said, “AI features may make mistakes and may not always produce accurate, complete, or suitable results,” and customers “are solely responsible for and assume any and all risks associated with such AI features and any resulting content.”

That AI clause, said Frank Dickson, principal analyst at Dickson Research, “turns accountability upside down. Meta builds the AI features, admits they ‘may make mistakes’ and then assigns ‘any and all risks’ to the business.”

Mike Wilkes, enterprise CISO at Aikido Security, agreed that the move is perplexing.

“The new AI provisions shift substantial responsibility for inaccurate or unsuitable outputs onto customers. That makes no sense,” he said. “As an example, the ASPCA is going to be liable for Meta creating unsuitable results when they ask Meta to create an image of a dog. Only Meta has control of the model being used to create that image. I see a very high flight risk among enterprise customers when platforms impose such onerous terms and conditions around content usage.”

The October 30 date that the US-based Meta chose is ironic. That is the day before Halloween, which is known as mischief night in the US, when children play pranks on their neighbors. Given that, Dickson said, the date for these changes is appropriate: “Meta is handing out the tricks and keeping the treats.” 

He added that the data grab is frightening. “What Meta wants is content: what a business posts, uploads, or shares on or in connection with Meta’s products,” he noted. “The trouble is the phrase ‘in connection with,’ which can stretch as far as Meta needs it to. The list of ‘Meta Company Products’ lives on a separate page that Meta can revise at will. The license grows every time that list does.”

And, Dickson noted: “Meta may use the content to ‘provide, develop, improve, research, and maintain’ its products. Those five verbs cover nearly everything a technology company does.”

‘Legal sophistry’

One of the obvious defensive measures for an enterprise is to sever all relations with Meta. But beyond the pragmatic difficulties of doing that, given the powerful market share enjoyed by Facebook, WhatsApp, Instagram and Threads, such a move may not even solve the problem. That is because Meta made other contract changes in July that make Meta licenses “perpetual” and that said, “this license survives termination of these terms by any party for any reason.”

That, said Noah Kenney, principal consultant at Digital 520, means that the “the general content license ends when content is deleted from Meta’s systems,” not when the enterprise ends a contract.

One additional twist to the new language is that Meta had recently tweaked it to no longer explicitly say that it would train it models on enterprise data. But Thomas Randall, a research director at Infotech, said that the wording change made by Meta amounted to legal sophistry. “The revised version drops the word ‘train’ and states that collection is just for developing and maintaining advertising and business products, alongside protecting Meta products’ integrity and security.”

But dropping the word “train” is “not a substantive fix,” he pointed out. “The revised terms are still flexible enough to cover most AI work. When combined with other aspects of the revised terms — continued use equals consent, broad indemnity, AS-IS disclaimer, and Meta’s freedom to discontinue products without notice or liability — this is a one-way street. The difference between ‘train Meta products’ and ‘developing and maintaining Meta products’ is a matter of how you pronounce tomato.”

Could force IT strategy changes

In addition, Aman Mahapatra, chief strategy officer for technology consulting firm Tribeca Softech, stressed that this contract could be an industry admission that robots.txt no longer delivers any meaningful protections. Agentic systems had already made robots.txt close to irrelevant. 

“October 30 ends a thirty-year assumption that robots.txt protects what you publish. It never did,” Mahapatra said. “It was a courtesy convention the web agreed to honor, and Meta’s own framing of robots.txt as a place for site owners to register preferences tells you exactly how it will be weighed against a signed contract.”

Mahapatra said the language in the new Meta commercial terms could force a significant change in IT strategy.

“‘On your behalf’ extends the license to whatever your agencies and vendors upload. ‘Associated’ extends the crawl to domains nobody has defined, including the ones where marketing pages sit beside product documentation, research, and investor materials,” Mahapatra said. “Most CIOs cannot tell me how many Meta pages, ad accounts, and WhatsApp numbers carry their brand, because marketing and agencies own them and IT never inventoried them.”

There’s growing talk of digital sovereignty as enterprises rethink past consolidation decisions made in the name of simplicity.

Mozilla, developer of Firefox, argues that a “very specific, very important layer” has been mostly missing from that conversation: The browser.

To that end, the company has unveiled Firefox Enterprise in a bid to diversify the browser engine to support privacy, control, and security.

The new offering combines the Firefox browser with a management console that enterprises can run on their own infrastructure using their own tooling. Companies get a full view of their data and can govern how it moves, and IT teams can manage policy, access, and set controls around AI use.

Firefox is already deployed across thousands of organizations and used by roughly 200 million people each month, Mozilla said. It is built on Mozilla’s independent Gecko engine, the only major browser engine developed outside Google and Apple.

“If all your options are built with the same technology underneath, ‘choice’ is a lie. So is control,” Mozilla CEO Anthony Enzor-DeMeo observed in the blog post announcing the product.

The promise of Firefox Enterprise

“Firefox is hardly the first browser vendor to offer an enterprise-optimized choice,” said independent technology analyst Carmi Levy. “But it carves out a unique value proposition for IT organizations intent on maintaining independent control over how browsers are deployed, configured, secured, and managed.”

Firefox Enterprise keeps core management within an organization’s own environment; its data, keys, telemetry, and policy remain in controlled boundaries that the organization can run on-premises and audit itself. IT teams can gate access and control policies based on user and device. They can also export browser events and audit activity in their SIEM, and native DLP helps prevent data loss.

Mozilla is particularly gearing Firefox Enterprise to organizations with strict security, governance, and compliance concerns. The offering can help enterprises meet regulatory requirements like the General Data Protection Regulation (GDPR), the Digital Operational Resilience Act (DORA), the NIS2 directive, ISO27001, and SOC2.

IT teams can set external connection controls, allow or block organization-wide add-ons, and restrict browsing with a lockdown mode “so one zero-day doesn’t take down every browser in the estate,” the announcement said. AI use can be allowed, restricted, or disabled. 

From a security standpoint, enterprises can set data loss prevention (DLP) rules by domain or action, and integrate with existing security information and event management (SIEM) tools like Trellix or Broadcom to extend analysis into the browser.

IT teams can protect against phishing, malware, and malicious sites with native isolation, extension control, and managed security updates. Access can be gated based on user and device, without the need for virtual private networks (VPNs), remote browser isolation (RBI), and virtual desktops.

The company said that it will use a subscription model “that doesn’t depend on monetizing customer data or forcing a roadmap.” Firefox Enterprise includes Firefox Professional support; for businesses running the free Firefox browser without Firefox Enterprise, Firefox Professional support is available as a paid subscription.

Firefox Enterprise is now being introduced in Europe, initially across France, Germany, Austria, and Switzerland, with more markets to follow. Mozilla is also developing Thunderbird Enterprise, which will manage Thunderbird email, calendar, and contacts.

The case for enterprise browsers

Largely thanks to the shift to cloud-based computing, the browser has evolved from a simple internet gateway to the place where most workers spend virtually all of their time. This makes them “the very epicenters of modern enterprise digital security,” said Levy.

Thus it makes sense for IT to consider the merits of a dedicated enterprise browser. But, Levy said, “there are no ideal solutions in IT, and enterprise-class browsers are no exception.”

Employees may bristle at additional layers of controls and raise privacy concerns around centralized monitoring. Administrative complexity is also a concern, as existing identity management, endpoint management, monitoring, and data protection tools may not always “cleanly integrate” with a more robust browser, he noted.

Enterprise-class browsers also can’t stop “less-than-ideally-trained employees” from clicking on phishing links, and they also can’t protect against “every last malicious extension.” As always, bad actors can find ways to compromise sensitive corporate data or otherwise use AI toolsets in unsanctioned ways.

But these types of browser can help minimize AI-related risks as employees lean more heavily into chatbots and agents. Admins can craft more flexible rules of engagement; for example, workers could access a web app for deep-dive analysis, but humans and AI alike could be prevented from copying data externally. Enterprise browsers also allow more granular control of AI features, and even disengagement if they conflict with policy.

Key differentiators

Firefox Enterprise isn’t the only option when choosing an enterprise browser, however. Microsoft Edge for Business would likely still be the preferred choice for environments already heavily invested in Microsoft’s enterprise Azure and Microsoft 365 ecosystem, Levy pointed out. Similar logic would apply to Google Chrome Enterprise’s appeal for those already committed to Google’s cloud and productivity infrastructure. And the Prisma Browser from Palo Alto Networks integrates aggressive cybersecurity tools with monitoring of AI use, remote devices, and unmanaged devices.

Firefox Enterprise’s key differentiation is its use of the Gecko browser engine, and its management console that can be deployed on a company’s own infrastructure, Levy said. This might make it a good fit for shops that want to minimize implementation disruptions to existing data management, access control, and compliance protocols.

“Firefox Enterprise lets customers craft their own deployment roadmap, assuming they have the skills and resources to self-manage the process,” Levy said.

This article originally appeared on Computerworld.

Google seems to be making a strategic play to control the AI infrastructure layer, not just roll out another shiny new agent.

At its Gemini at Work 2026 event this week, the tech giant unveiled what it calls a “single, universal agent” and API in Gemini that can be kicked off from a simple prompt box. Like other available agents, it is plugged into skills, tools, and enterprise systems, can answer questions, create media, and write and run code, return finished work via of documents, inboxes, and developer environments, and spin up sub-agents to help it do its job.

But Google isn’t just looking to go toe-to-toe with existing offerings, or trying to convince enterprises its models are qualitatively and quantitatively superior, said independent tech analyst Carmi Levy.

“Instead, it’s trying to establish itself as the gatekeeper of the new enterprise operating system, powered by whatever underlying model makes the most sense,” he said.

‘Omnipresent access,’ orchestration capabilities

Google says its Gemini agent works autonomously from a single interface, and can function as a personal assistant or team member, akin to a project manager or analyst. Coworker agents can perform tasks across days, sessions, and shifting responsibilities; they have dedicated identities, and are given their own @agents.company.com emails and persistent storage.

However, Google says, they only have access to specifically-defined data and channels. Enterprise-dictated security, governance, and cost controls are built-in.

“You give it objectives, not instructions,” Google Cloud CEO Thomas Kurian said in his keynote. “Work now starts in the prompt window.”

The agent runs in the cloud and has “omnipresent access,” meaning it can work on nearly any device, including iOS and Android phones or Windows and Mac desktops, he said. It can also be accessed through any channel, including command line interfaces (CLIs), Google Workspace, Microsoft 365, or Slack channels, and when integrated into third-party apps. When needed, it also works as a headless agent, or even without a dedicated user interface.

Working autonomously, the Gemini agent can spin up temporary or job-specific underlying agents and communicate and coordinate workflows with them to orchestrate multi-step tasks. These include parallel and sequential steps that might run for hours or days, Google said.

Notably, the agent is given the flexibility to choose which model should drive different workflows, to improve quality and reduce cost; right now, those models include the Gemini family and Claude, but Google says it will soon expand capabilities to other leading private and open models.

This is important because the leading model changes every few months, Kurian noted, and “the best model for the task is not always the largest one.”

Choice a ‘unique value proposition’

Mahmoud Ramin, a research director at Info-Tech Research Group, noted that Google’s announcement does not create a whole new category, since OpenAI, Anthropic, Microsoft, and Meta have already positioned multiagent capabilities in their platforms.

What is interesting with Gemini, though, is that not only can it operate directly in multiple Google products like Gmail, Drive, Sheets, and Slides, it also connects to external systems. It is, of course, a good fit for those already using the Google environment, he noted, and the strongest use cases are around repetitive tasks and information synthesis, such as that described in the research performed by project managers, analysts, and marketing specialists.

Levy agreed that Google’s unique value proposition revolves around choice. “It separates the agent from the underlying model, and allows enterprises to use whatever model makes sense for a given workload,” he said.

Its role-based agents are also “compellingly unique,” because they allow enterprises to create agents optimized for specific roles; meanwhile, more granular permission-setting addresses growing concerns about automated bots going off the rails.

While OpenAI and Microsoft have made similar claims, Google is “arguably leading the conversation around identity, audit trails, and who, or what, is ultimately responsible for workflows,” Levy said.

Google and others looking to be ‘gatekeepers’

As the AI industry pivots beyond mere chatbots toward its “inevitably agentic future,” the role of companies like Google, OpenAI, Anthropic, Microsoft, and others in the role of gatekeepers comes into sharper focus, Levy noted. The defining layer has shifted from operating systems to browsers, apps, cloud platforms, and now large language models (LLMs).

In the agentic rush, key players are racing to establish their role in enterprise workflow infrastructure. “The agent layer could be the ultimate prize of the AI era, and it largely explains why every vendor announcement seems to essentially claim the same thing,” Levy said.

But Google has to convince IT decision makers that its agentic roadmap is better than the competition’s; this means shifting the AI conversation to autonomy, rather than just basic tasks.

Like virtually every other vendor’s agent, Google’s Gemini agent can write an email, tweak a spreadsheet, and build a presentation. “However,” Levy said, “whether CIOs and CISOs can trust it to run mission critical business processes indefinitely without doing something stupid enough to generate damaging headlines is another story altogether.”

“Will enterprises care? If they’re already heavily invested in Google’s workplace stack, this could be an easier sell in the C-suite,” he noted.

Raising important governance questions

Info-Tech’s Ramin noted that whatever platform enterprises choose, the front-and-center focus going forward should be who controls AI agents.

“As organizations unleash the power of agent autonomy, it raises the pivotal and non-negotiable requirement of enforcing governance,” he said, noting that with basic AI systems, risk is “much more contained” compared to that of an autonomous agent that has access to multiple business systems and can communicate with other agents.

In the latter case, enterprises should significantly enforce guardrails, including permissions, identity, human oversight, and system audits, he said. They should “clearly outline which decisions the agent is allowed to make, what data should be accessed, and how performance will be monitored.”

This article originally appeared on Computerworld.

The theft of FBI employee data by hacking group ShinyHunters, and the subsequent shutdown of the FBI’s Peoplesoft-based jobs portal, is causing concern for enterprise users of the Oracle product, with analysts recommending extreme measures in response.

Law enforcement has made some progress in its pursuit of the cyber criminals involved, but there has still been no official word from either the FBI or Oracle about Peoplesoft’s alleged involvement.

On Saturday, Reuters reported that a suspected member of ShinyHunters had been arrested in Jordan and has been cooperating with law enforcement to help identify other member of the group. The arrest comes a week after the arrest of another suspected ShinyHunters member by the Dutch National Police. In a video about that arrest, an FBI official warned gang members that arrests have a way of changing who is willing to talk, concluding, “You know how to find us, and we know how to find you.” Before the arrests, the gang told The Register it had discovered a new PeopleSoft zero-day hole and leveraged it to break into an FBI system, stealing information on all FBI employees. The FBI has since confirmed the breach without providing details.

This wouldn’t be the first flaw ShinyHunters had found in PeopleSoft. It discovered a PeopleSoft zero-day hole in June that resulted in a flurry of extortion attempts, long after Oracle said it had patched the bugs.

Oracle did not respond to a request for comment on the alleged new vulnerability.

Significant risk exposure for enterprises

Analysts and consultants played up the risk exposure from the potentially new vulnerability even as they sharply played down the implications of the arrest.

Frank Dickson, principal analyst at Dickson Research, recommended that PeopleSoft users take immediate action.

“If it is a new flaw, the patch is necessary but not sufficient. PeopleSoft customers should pull the Environment Management Hub and the Integration Broker off the public internet,” he said. “Doing so does not break normal user sessions.”

He also encouraged users to quickly implement many of the recommendations from Google unit Mandiant’s report late last month on PeopleSoft security issues, especially “hunting for the web shells and outbound traffic Mandiant describes.”

“For PeopleSoft shops, the to-do list is short,” Dickson said. “Apply Oracle’s patch. Disable or remove the Environment Management Hub if you do not use it. Search your logs for encoded variants of the PSEMHUB path, not just the literal string. If you find a web shell, treat the server as compromised and rotate every credential it could read.”

This is a dangerous situation, he said: “An unpatched PeopleSoft flaw is a Sword of Damocles over every PeopleSoft shop

But despite the extreme risk, even after the patch was released, rather than applying it, some organizations have opted for other mitigations. “Many organizations chose to block the vulnerable endpoint with web application firewall rules rather than patch,” Dickson said. “The attackers walked past those rules by changing a single character in the web address, writing ‘%50’ in place of the letter ‘P.’”

Could signal a bigger problem

However, IDC Research Director Philip Harris encouraged CISOs to view the FBI’s confirmation of its breach with caution, given the attack vector was not revealed.

“ShinyHunters says it used a second, previously undocumented PeopleSoft preauth RCE zero-day, distinct from the CVE-2026-35273 flaw exploited in the earlier campaign,” he said. Yet, he pointed out, “no CVE has been assigned to it, it has not appeared on CISA’s Known Exploited Vulnerabilities catalog and Oracle has not commented on it. Every outlet covering this is explicit that the zero-day claim comes only from the threat actor, not from independent forensic confirmation or from Oracle. That distinction matters.”

But if the hole is as the group has suggested, Harris said it signals a much bigger problem.

“Assuming it is real, this would mean PeopleSoft has a second critical, unpatched preauth RCE in the same window as CVE-2026-35273, a pattern of recurring critical exposure rather than one isolated bug,” he said. “ShinyHunters’ own claim that they are already using it against other, unnamed Fortune 500 targets would mean every PeopleSoft customer is currently exposed to an unpatchable, undisclosed flaw with no vendor guidance to act on, a meaningfully worse position than the WAF-bypass story, since there is no mitigation to attempt while waiting on Oracle to confirm something it has not acknowledged.”

Jeff Valdes, a director at consulting firm Acceligence, added that Oracle’s silence is unwarranted.

“I think customers reasonably want more communication from the vendor,” he said. “They want to understand whether Oracle’s original guidance remains sufficient, whether there are additional mitigations customers should implement, whether Oracle is seeing anything through its own support organization that changes the risk picture, and whether there are configurations or architectural choices that materially increase exposure. Those are operational security questions that can be addressed without discussing attribution, arrests, investigative techniques or anything else that might interfere with an FBI investigation.”

This article originally appeared on CSOonline.

OpenAI says true agentic AI has finally arrived, pushing beyond the trivial capabilities of early-stage virtual assistants.

This week at OpenAI Dev Day, the company introduced Dots, which CEO Sam Altman described as “remarkably capable, always-on” agents powered by GPT‑6 Astra. Dots have their own cloud computer, can be accessed in ChatGPT, Slack, or Teams, and plug into more than 4,000 apps based on human-set boundaries.

In a keynote, Altman said using it will feel like “a whole new way to work with AI.”

“It can hugely magnify your ability and expand your potential, working 24/7 to take on things that you don’t want to do, so you can focus on higher-level work,” he said.

OpenAI also announced a slew of other new capabilities and tools, including GPT-6.1 Sol, a cheaper and faster sibling of GPT-6.1 Astra, which was shelved earlier this week due to safety concerns. Additionally, Codex is also now in the cloud, Codex CLI has voice control and can help developers inspect, edit, and run code locally, and a new Agents API supports computer use and provides components like harnesses, hosting, memory, and multi-agent controls.

Dots goes beyond ‘trivial’ tasks

Early virtual assistants have been useful for quick handoff tasks like booking reservations, but that is “trivial relative to what the technology can do,” Altman contended in his keynote.

Taking that to the next level, OpenAI’s new Dots can understand what’s happening in a user’s workflows and learns over time without requiring constant human instruction, he explained. Soon, users will be able to access Dots outside ChatGPT by interacting with it through text messages or even by calling the agents on the phone to discuss updates, changes, and weigh in on decision-making.

“You can delegate ambitious pieces of work the way you would to a high agency engineer or a chief of staff,” Altman said. Dots can monitor incoming bug reports and propose fixes, draft pull requests (PRs), or take on tasks like kicking off the next year’s budget planning cycle.

Altman provided the example of migrating an app off a legacy API. This can be difficult, because the API might be used all over a code base, he said, and it could break as developers start making changes. But Dots can trace dependencies and determine what needs to change, run tests, and suggest next steps.

“Think about how you would have done that the old way,” Altman noted. “How many people would have been involved? How much time would it have taken them? What could they have done instead?”

Dots is now available to all ChatGPT Pro, Business Premium, and Enterprise users. OpenAI is also now previewing specialist Dots for enterprises, and is working with Microsoft to integrate Dots into Agent 365. These will serve as virtual teammates to help scale more intensive work in accounting, marketing, or legal, Altman explained.

Carmi Levy, an independent technology analyst, said of everything announced at DevDay 2026, Dots might be the most impactful, as it represents OpenAI’s definitive move into AI agents.

While Meta has been grabbing headlines with its app store-topping Muse, OpenAI’s offering is potentially more relevant to enterprise decision makers who are still trying to figure out how agents can fit into their corporate AI development and deployment roadmap, he noted.

“It allows enterprise customers to build on their pre-existing investments in OpenAI’s stack, and represents a familiar adoption path for developers,” he said.

Cheaper and faster sibling of Astra

OpenAI launched GPT-6 Astra to a limited group of users earlier this month, calling it state-of-the-art in computer use, browsing, software engineering, cybersecurity, science, and professional work. But its generally availability was canceled this week after the model failed critical internal safety and alignment tests.

OpenAI learned from the experiment, Altman said, with early testers demanding: “make it cheaper and make it faster.” The answer to that is GPT 6.1 Sol, which provides near Astra level intelligence at one-fifth of the price. Cached inputs cost $0.10 (US) per million tokens, 95% less than standard input pricing. This makes it cheaper for agents to work through longer tasks, reuse context, and ruminate on solutions. Example uses include multi-step business workflows, code writing and debugging, and document analysis.

GPT 6.1 Sol is “smarter than Astra in some ways, and it will become your daily workhorse,” Altman said. It is now available to all Plus, Pro, Business, Enterprise, and Edu users of ChatGPT Work and Codex, and will soon be offered in an ‘Ultrafast’ plan, with up to 8X faster token generation.

Levy noted that GPT 6.1 Sol reduces the cost curve for enterprises looking to deploy AI agents at scale, all while “avoiding some of the privacy/safety/integrity baggage of the now-stillborn GPT 6.1 Astra.” It effectively shifts the economics of AI agents by dropping the price of resource-intensive development activities like querying repositories, running tests, and iterating code.

“It doesn’t have quite the power of Astra, but that was never what the average developer was looking for, anyway,” Levy noted. “Affordability still drives development decisions, and by leading with Sol, OpenAI removes one more barrier to broad-based adoption.”

New Codex capabilities, assurances around safety

In a much-anticipated move, OpenAI has introduced Codex Cloud, giving developers the ability to move coding tasks between devices without losing threads. “You don’t have to worry about closing your laptop in the middle of a run,” Altman said. “Your agents just keep working, wherever you are.”

Meanwhile, new Codex CLI capabilities allow developers to start, steer, delegate, and track tasks with voice commands, and a new Codex Security Cloud helps defenders identify vulnerabilities and prepare fixes.

Altman addressed the top-of-mind security and governance issue in his keynote. “The best version of AI is not about making people cogs in a giant machine,” he said, noting that there are some things that should not be automated.

“People often talk about AI as a new industrial revolution. I find that quite off-putting,” Altman said. “The future, if we get this right, can be much more like a new renaissance than a new industrial revolution.”

This article originally appeared on InfoWorld.

Meta is once again repositioning itself to target the enterprise market.

This week, the company announced the Meta Enterprise Platform, which it says will evolve its AI stack into products and services that enterprises can deploy into operations, rather than just using them for advertising and social media campaigns.

While the company provided few details about the offering, Meta CEO Mark Zuckerberg called the platform “the next major pillar” of its business, saying it will feature a connected suite of products including Muse agent, Meta Business Agent, Muse API, and Muse Code.

MongoDB’s Chirantan “CJ” Desai will step in as chief enterprise platform officer to lead the effort.

The question is whether this endeavor, once fully launched, will be more lasting than Meta’s previous attempts in the enterprise.

“The issue of trust remains,” said Armaan Sandhu, research analyst in the AI practice at Info-Tech Research Group.

The shutdown of previous products like Workplace and Horizon Workrooms indicate that Meta tends to “abandon the enterprise products as soon as its vision of the future changes,” he said. “Companies will therefore require hard evidence that Meta will stick to its future plans and continue investing in their development.”

Meta’s growing AI ambitions

Meta has been working to reorganize its AI efforts since founding its Meta Superintelligence Lab in 2025. Muse Spark was the first new model to come out of that initiative; launched in April 2026, the agent supports multimodal inputs, multiple reasoning modes, and can spin up sub-agents in parallel to handle more complex tasks.

Next, Meta Business Agent, launched in June, directly targets enterprises, providing them with AI assistants to automate customer interactions, support sales, and drive leads across platforms including WhatsApp, Messenger, and Instagram. The company followed that up with Muse Agent, an autonomous business and personal agent that can handle multi-step background tasks.

Muse Code is designed to handle complex software workflows across large codebases, while Muse API gives developers direct access to the Muse AI stack.

Abandoned efforts

However, Meta has so far struggled to gain a strong foothold in the enterprise market, and in some cases has abandoned previous attempts after they failed to take off.

For instance, Workplace, launched in 2016, supported communication and information sharing across organizations, but Meta announced in 2024 that the service would be discontinued, even as it reported millions of users. After a 10-year run, Workplace officially shut down in May 2026.

Then there are the company’s virtual reality efforts, including Horizon Workrooms, a collaboration app used with Quest headsets. It was offered for five years before being retired in February 2026.

Different positioning

But the company is positioning Meta Enterprise Platform differently; it will play on strengths that “few other companies have” and leverage its experience connecting millions of businesses and customers, Zuckerberg said, touting the company’s “advanced models, leading agents, large-scale infrastructure, and years of working closely with many businesses.”

However, noted Info-Tech’s Sandhu, Meta’s key advantage is not its technology; rivals such as Microsoft, Google, Amazon, and Salesforce also offer capable models and agents. Its differentiator is that it owns the platforms where users familiarize themselves with brands, interact with businesses, and make purchases. Meta can therefore link advertising, customer discovery, messaging, service, and commerce through AI agents running across WhatsApp, Instagram, and Messenger.

“Few rival companies can boast of such an effective combination of AI and access to customer channels,” Sandhu said.

Enterprise appeal may not be there

Still, Meta has challenges to overcome as it seeks to compete with well-entrenched tech giants and move beyond its consumer market origins.

Said independent technology analyst Carmi Levy, “It’s difficult to understand the appeal to enterprises, as Meta has never been known as a significant player in the enterprise market.”

Its data stewardship track record is also “somewhat checkered,” he pointed out, citing the infamous Cambridge Analytica scandal that Meta is now answering for, as well as a number of other high-profile data stewardship and privacy cases in the US and UK.

He contended that the company’s core competency revolves around harvesting data to fuel growth, hardly a desirable trait in the enterprise space. Workplace “never quite realized its early promise,” and it’s unclear whether the Meta Enterprise Platform will suffer the same fate.

“Time and again, Meta’s behavior betrays its inability to craft the kind of iron-clad enterprise-grade infrastructure and organizational competency that corporate decision makers value,” he said, noting that even if its offerings were years ahead of the competition, these trust issues should give buyers pause.

In addition, Meta Enterprise Platform loosely ties together a host of offerings originally released into the mass consumer market, yet positions Muse as the pillar of its future enterprise strategy, he pointed out. If anything, Muse’s “early teething troubles,” including privacy violations and serious zero-day vulnerabilities, should be the only warning enterprise buyers need.

If Meta did manage to turn a consumer-architected collection of offerings into something somewhat more enterprise-ready, its “indifferent level of commitment” to earlier enterprise offerings raises the question of whether it intends to support such a platform well into the future. “If the commitment isn’t there, neither is the trust,” Levy added.

However, if enterprises do decide to evaluate the product, Sandhu advised that they pay attention to pricing, integration with other systems, and technical features. And initially, they should choose a narrow field of application, “with the goal of maximum benefit without making this technology a company’s key system.”

The fact that almost one-fourth of Anthropic’s revenue last year came from just two customers, and that many of its largest customers have not signed long-term contracts, could translate into a much stronger negotiating position for enterprise CIOs.

According to a detailed analysis by Reuters, the company’s confidential prospectus depicted a company that might be suffering from a lack of revenue diversification, with Anthropic, like other AI vendors, needing enterprise revenue more than enterprise CIOs might need that particular AI vendor.

Analysts and consultants said the results of the analysis might be good news for enterprise CIOs, but events still need to play out. 

The key risk factor for Anthropic, according to the prospectus, is the revenue concentration, said Frank Dickson, principal analyst at Dickson Research.

“Nearly a quarter of revenue came from two customers, and Anthropic itself warns that many large clients are not locked into long-term contracts. That cuts both ways,” he said. “CIOs have more leverage than they think, because the vendor needs them more than the pitch deck admits. Negotiate price protection, advance notice before models are retired, and data portability now, while you are the customer they cannot afford to lose.” Or, for that matter, a prospective customer it absolutely needs to attract.

The newly-disclosed information also underscores the AI risks that every enterprise is absorbing, and Dickson said there are clear ways for CIOs to try to minimize that exposure. 

“The CIO who hardwires one model into every workflow has not bought AI. He has bought a dependency,” he warned. “CIOs should build for portability, creating an abstraction layer between applications and models, evaluations you own, and prompts and data you can move. The exit plan is part of the purchase.”

Jack Collier, CMO at io.net, said the revenue numbers suggest that Anthropic’s concentration problems go beyond over-reliance on a small number of clients. 

“Strip away the existential language and the filing reads like a case study in concentration,” he said. “Nearly a quarter of its revenue last year came from two clients, its seven co-founders hold majority voting control over the company’s decisions, and about 80% of its $518 billion infrastructure bill is non-cancellable or payable regardless of usage. If the best-funded lab outside Big Tech can only secure compute on take-or-pay terms, [Anthropic has] almost no leverage at all.”

And, said Scott Bickley, advisory fellow at Info-Tech Research Group, the vast control that a relatively small number of AI vendors hold adds to the AI complexity for enterprise IT executives.

“The problem is that every time Anthropic’s revenues beat expectations, their costs beat them by more. This is a structural feature of a frontier growth model,” he said. “Adding insult to injury is [the fact] that Anthropic and OpenAI now capture 89% of all revenue generated by the 34 leading AI native startups, and this is increasing.”

This, he pointed out, is another reason that enterprise CIOs are going to have to carefully negotiate the future AI landscape.

“At least half of the $2 trillion revenue backlog for Google, Amazon, Microsoft, and Oracle traces back to OpenAI and Anthropic,” Bickley said. “This is a narrow base of customers and price increases should be expected and built into any ROI forecasts.” Thus, he said, CIOs should negotiate for shorter-duration contracts while pushing for flat per-seat pricing where possible for larger organizations.

“This market is extremely fluid,” he noted, “so reserving the ability to switch vendors is key to retaining leverage and optimizing spend in a nimble manner.”

However, Arnal Dayaratna, a research VP at IDC, stressed that open source is most likely to influence, if not outright dictate, the direction of enterprise AI for the next couple of years, putting pressure on companies like Anthropic.

In fact, he said, Anthropic’s long-term viability depends on its ability to demonstrate measurable differentiation from open models on predictability, latency and total cost of ownership at scale.

“The assumption that open models are cheaper than their frontier counterparts is a fallacy in many enterprise contexts,” he noted, “because open models can be significantly more difficult to operationalize at scale. Anthropic needs to make that case clearly and consistently to sustain its pricing and its position.”

Gartner on Wednesday said that it expects 30% of the positions eliminated by AI-related layoffs to be refilled by 2029, suggesting that the initial terminations were ill-advised and excessive.

“When business and IT executives look back on the early AI era, they will realize their greatest mistake was believing that work automation was the point, when workforce amplification was the opportunity,” said Tori Paulman, VP analyst at Gartner. “The competitive advantage will go to the CIOs and business executives who build an AI-shaped organization where AI value compounds by reshaping roles and allowing workflows to cross traditional boundaries, increasing velocity and reducing friction.”  

The Gartner report noted that it is finding that the cuts “deplete talent pipelines and erode institutional knowledge.” Beyond the immediate workforce disruptions associated with any mass layoff, companies will also face steep increases in costs for recruitment, training, and onboarding.

It also predicted that, by 2027, “75% of organizations that prioritize capturing AI productivity gains as cost savings will be eclipsed by competitors that aggressively reinvest those gains into innovation, modernization and upskilling.”

In an interview with Computerworld, Paulman said that the 30% figure represents the average impact on organizations of all sizes; they estimate that the layoff boomerang for enterprises would be even higher, roughly 40%. 

Paulman said that Gartner’s research found a lot of what they called “AI washing” by executives who want/need to do layoffs for purely budgetary reasons, and will falsely blame AI for the reductions because it makes them look better.

“More than 50% of our enterprise clients have been given a number [by their bosses],” Paulman said, and have been told by senior management to find that percentage of savings from AI.

But despite widespread evidence of problems due to AI-related layoffs, such job cuts are still increasing. 

Layoffs were ‘excessive’

Other analysts and consultants agreed with the Gartner suggestion that many of these job losses attributed to AI are going to be walked back, but questioned the specific statistic. Some also noted that 70% of the AI-attributed layoffs may remain in force, which would suggest that the original terminations were mostly justified. 

However, Frank Dickson, principal analyst at Dickson Research, argued that a lot of the layoff reversals will occur in a variety of ways that will obscure the fact that they are restoring a terminated role. 

“A lot of that 70% never shows up as a clean rehire even when the original cut was wrong,” he said, pointing out that some of the losses caused service to quietly get worse, and stay poor, some of the work was contracted out or offshored, some of the roles were reconstituted with a different position or title, and some was covered by the remaining staff absorbing the load. This,” he noted, “shows up later as burnout and attrition, not as a line item on this report. None of that gets counted in the 30%, and none of it is evidence the original call was sound.”

Melody Brue, principal analyst for Moor Insights & Strategy, added that the 70% scenario “could show that a substantial share of the AI-related workforce reductions is durable,” but, she stressed, “it shouldn’t be mistaken for endorsement of how those layoffs were made. What it doesn’t show is whether the organization captured the full economic value it expected. A lower headcount is not by itself evidence of a successful AI transformation.”

Valence Howden, advisory fellow at Info-Tech Research Group, questioned the methodology behind the calculation of Gartner’s 30% figure, but he agreed with the overall sentiment that layoffs attributed to AI have been excessive.

“I’m not sure we can substantiate those numbers, since it’s much more of a guesswork statement than anything else,” he said. “I do believe the current trend is going to lead to rehiring, especially as AI governance requirements ramp up and given AI’s lack of contextual semantic understanding. We know AI has not provided the value proposition that it has been sold as providing, and unless costs are controlled, it will be cheaper to use humans to perform some of the advanced work.”

Supporting data

Dickson also raised questions about the Gartner report because it lacked comparative layoff statistics. 

“Gartner doesn’t say what the reversal rate looks like for ordinary layoffs, the ones that have nothing to do with AI,” he said. “Suppose normal cuts get walked back at 10% to 15% in a typical five-year window, which is plausible given ordinary churn and business-cycle rehiring. A 30% rate specific to AI-driven layoffs would still run well above that, and that’s a damning number. Without that comparison, 30% is just a figure floating with no anchor.”

However, Dickson pointed to various datapoints supporting the position that AI layoffs have been excessive, noting that Forrester reported that 55% of businesses “already regret AI-driven cuts and are predicting half of those layoffs get quietly reversed.” 

“Robert Half puts it at a third of hiring executives who eliminated roles for AI having already rehired. Ford, IBM, Booz Allen Hamilton, Alphabet and CSX have all walked back cuts or announced rehiring drives,” Dickson said. “Gartner’s 30% by 2029 sits comfortably inside that range.” Klarna has also walked back AI layoffs. 

A ‘major indictment’

He added that many AI layoffs amounted to a corporate version of a crash diet. “You cut fast, you look great on the next earnings call, and eighteen months later, the weight is back, plus interest, because nobody fixed why the cut was made in the first place.”

Gartner’s Paulman agreed, noting, “business and IT executives who use AI primarily as a tool for cost cutting risk making reductions that are too deep and too soon, affecting their ability to innovate their business model and compete in new markets as AI continues to mature.”

Mike Wilkes, enterprise CISO at Aikido Security, said that even if the 30% figure turns out to be accurate, it is a major indictment of the layoffs. 

“If 30% of AI-driven layoffs must be reversed, that is an enormous error rate for a strategic workforce decision,” Wilkes said. “Imagine any other major capital decision where nearly one-third had to be unwound at a premium three years later. No CFO would call that a strong outcome.”

This article originally appeared on Computerworld.

Nvidia and six financial partners are creating a $500 billion investment pool to help Nvidia customers including frontier AI labs, AI clouds, and other enterprises buy its chips on credit. 

The impact of such a cash infusion on enterprise AI is uncertain, but analysts fear that it could both further increase enterprise AI infrastructure costs and exacerbate the shortage of AI chips for data centers. 

The announcement from Nvidia and financial partners Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR said that their memorandums of understanding describe a fund “to establish the first compute financing platforms of their kind at global scale to enable the AI infrastructure buildout across Nvidia’s ecosystem, including leading frontier AI labs, enterprises and AI clouds.”

The group added that the fund would “create dedicated pools of capital at significant scale at attractive rates for Nvidia customers.”

Although the statement said the goal was to help AI infrastructure “across Nvidia’s ecosystem, including leading frontier AI labs, enterprises and AI clouds,” analysts and consultants agreed that it is highly unlikely any of these funds would be dispensed directly to enterprises, but would instead impact the overall AI supply chain.

Even the precise amount of money earmarked for the fund was unclear, with the statement merely saying that the amount would be more than $500 billion. 

Nvidia did not respond to requests for clarification about details of the proposed fund, but one financial partner did comment on the amount.

“We can clarify that this is a number that’s been totaled up by Nvidia,” said Simon Maine, managing director for communications at Brookfield Asset Management, in an email. “The finance partners are not collaborating together on this, but rather it is a series of individual partnerships. We therefore cannot comment on how the total figure has been arrived at.”

CIO concerns: chip pricing, availability

The top concern for CIOs around such a fund is the question of whether it would impact chip pricing along with that of components and devices using those chips, and if it would impact chip availability. Almost all of the analysts and consultants willing to speculate on that agreed that it would likely increase prices and worsen chip shortages. However, one optimistic interpretation of the fund was that it could help reduce chip shortages. 

“It remains to be seen what kind of downstream impact this initiative will have on enterprise spend,” said Ashish Nadkarni,  a group VP for IDC, but “there is an assumption here that these investments will go toward building fab capacity, and that the fabs will produce chips to address a chip shortfall.”

Other analysts disagreed, and argued that the fund would likely make the chip shortage worse, at least initially. 

“The current chip demand is taking all of the capacity and there is only so much chip fabrication capacity available,” said Mark Tauschek, a distinguished analyst at Info-Tech Research Group. “It will also take years to build new chip fabs. [The proposed Nvidia fund] will probably exacerbate the shortage.”

In fact, for enterprises, Tauschek projected a 15%-20% cost hike.

Sanchit Vir Gogia, chief analyst at Greyhound Research, agreed with Tauschek on both counts. 

He estimated that AI chip prices would be roughly the same for another year, and increasingly only at a good price if customers sign a long-term commitment. “The discount for committing is shrinking, not growing,” he said.

Thus, he pointed out, “more financing therefore means more new capacity is spoken for before it exists, and the open market gets whatever is left. The queue is no longer sorted by who can pay. It is sorted by who will commit.”

But he added that these numbers will likely improve eventually. “It does add real capacity in the end,” he noted. “Enterprises planning for 2028 will benefit. Those reacting to 2026 will not.”

Mike Wilkes, enterprise CISO at Aikido Security, said that one of the key impacts of the fund will be the way in which enterprises should view AI financing. That change, he argued, is both good and bad.

The improved availability of funds could “finance the AI buildout at much greater scale. That could accelerate enterprise access to compute, but it could also connect AI infrastructure much more tightly to the financial system,” Wilkes said. “This financing is likely to lower the cost of getting access to AI infrastructure in the near term, but not necessarily lower the price enterprises ultimately pay for AI.”

He suggested that potential enterprise impact will vary over time.

For large enterprises, the infusion of funds would allow their infrastructure vendors to build large datacenters without requiring financial help from the enterprise, he said, and this added capacity should eventually put downward pressure on the unit cost of compute.

“But,” he noted, “in the next few years, I would expect vendors to use cheaper financing primarily to build faster and lock customers into longer-term capacity contracts, rather than simply pass all of those savings through. In other words, enterprises may get more AI for the same dollar before they get the same AI for fewer dollars. So I think the biggest effect on enterprise readers is that this could remove one bottleneck while creating another.”

He expects that capital may cease to be the limiting factor in building AI infrastructure, giving CIOs considerably more capacity and more financing options available to them. “But,” he said, “the resulting competition may increasingly focus on who can persuade enterprises to make the longest and largest commitments to future AI consumption. If hundreds of billions of dollars of infrastructure are financed based on assumptions about future utilization, somebody ultimately has to pay when those assumptions prove wrong.”

Things will brighten, but not for awhile

Justin Greis, CEO of consulting firm Acceligence,  agreed that the short-term impact of this agreement may not be good for enterprise IT. 

“My view is that this financing will accelerate the creation of AI infrastructure, but it will not provide meaningful near-term price relief for most enterprises. In fact, I think the next 12-18 months could remain a period of elevated costs and constrained availability as the market absorbs this new wave of investment,” Greis said. The reason, he noted, is that the limiting factor today is not capital, it is the physical capacity to manufacture components.

“Adding hundreds of billions of dollars of available financing will create more buyers with the ability to compete for those resources,” he said. “My expectation is that the largest AI infrastructure providers and hyperscalers will continue to secure a significant share of available capacity because they have the scale, existing relationships, and ability to commit to long-term purchases.”

But eventually he believes that the enterprise picture should brighten. 

“Where I do expect enterprises to see benefits is further out. As this capital turns into actual infrastructure capacity, the market should become more competitive and enterprises should have more options in how they access compute,” Greis said.

“From a CIO perspective, I would not respond to this announcement by trying to secure more hardware. That is likely to become an expensive race that most enterprises cannot win.”

This article originally appeared on NetworkWorld.