AI is evolving faster than most people, even those building it, could even fathom, and its impact on the workforce and the economy is, at this point, really anyone’s guess.

Researchers from The Anthropic Institute are offering a few possibilities: They have built a nuanced framework looking at how AI might impact jobs, unemployment, and gross domestic product (GDP) growth between now and 2030.

They posit three potential scenarios for an AI-augmented future: “modest,” “substantial,” and “extreme,” and have created an interactive tool where users can explore how productive, or disruptive, AI will become in the workplace, based on their predictions of how they will work in 2030.

“Which of these worlds we are heading toward may become clearer within a year or two, and preparing for potential disruption seems to us the prudent course,” the researchers noted.

The goal of their work is to inform debate as AI becomes more powerful and capable. “AI is likely to reshape the US and global economies in profound ways in the coming decade, but how, and by how much, is extraordinarily uncertain,” they wrote.

How different scenarios could play out

If you add up every single task performed by people, machines, and software, the US has created a staggering $30 trillion in value over just the last year, the Anthropic researchers estimated. Their model and the corresponding tool are a way to explore how AI impacts tasks that contribute to the economy, the tasks it augments and creates, impacts on productivity, and speed of adoption.

“The answers to these questions have direct effects on GDP, the labor market, and the share of the pie taken home by workers,” they wrote.

Under their definition of “modest” change, AI will add less than half a point to GDP by 2030, meaning it will increase the growth rate of the national economy by just 0.5%, and will raise unemployment by just a tenth of a point, a minor shift. In this future, it’s difficult to see AI’s impact in macroeconomic data; change is steady but gradual, similar to that of the internet. “It drives real economic gains, but they’re within the historical norm for new technologies,” the researchers noted.

In the “substantial” scenario, AI will be capable of doing half of all knowledge work by 2030, the majority of it autonomously. Still, it wouldn’t be adopted for all work; in fact, most knowledge work tasks would still be completed without AI. Correspondingly, the economy would grow at twice its normal rate, but even as some non-knowledge workers see gains, wages for knowledge workers wouldn’t rise.

In this case, “AI makes a bigger impact than the internet, or the railroad,” the researchers wrote. Reallocation could be costly, but it is in line with what the US labor market has historically absorbed.

In the “extreme” scenario, of course, AI would be more productive than humans on the majority of knowledge work tasks, would do all of them autonomously, and subsequently would create no new knowledge tasks for humans.

The technology would “drive a completely transformed, unprecedented economy” arising from recursively self-improving AI. GDP growth would rise to 15% per year, but nearly one in five cognitive workers would be unemployed, and their relative wage would fall “immensely.”

The conundrum is that resources to compensate unemployed or under-paid workers will exist, but it’s unclear whether they would be fairly allocated. Mechanisms by which people can benefit from a much richer economy (retraining, income support, or universal basic income, for example) would become a question of economic policy.

“Whether and how those resources reach the people who bear the cost is not something growth delivers by itself,” the researchers wrote.

What users think

As well as developing the framework, the Anthropic researchers conducted a survey among roughly 11,000 Americans, asking them to predict AI use, productivity gains, automation versus augmentation, and displaced work.

They found that, in the main, public expectations land around the “substantial” scenario. That is, GDP would be 10% higher by 2030 than it would be without AI, and the overall unemployment rate would rise to around 5%.

Roughly 10% of respondents, on the other hand, had views in line with the “extreme” scenario.

Anyone can generate their own forecast using the researchers’ interactive tool, answering questions like: “Out of every 100 instances of a task AI can do in 2030, how many will AI actually be doing?”, “How many will be fully automated?”, or  “How much more gets done in an hour in 2030, compared with doing the tasks without AI?” The tool then responds, mapping their predictions to one of the three scenarios.

“Ultimately, what the economy looks like in 2030 depends on many factors, like what AI can do, and how companies and workers choose to adopt it,” the researchers wrote. “It also depends on how the financial benefit of this technology is shared.”

The between-the-lines reality

Sanchit Vir Gogia, chief analyst at Greyhound Research, emphasized that the Anthropic research “maps the conditions under which very different futures appear, it does not schedule destiny.”

He sees the distribution result, rather than the unemployment result, as the serious finding. In the extreme case, GDP is 32.4% above the no AI path, and the cognitive wage bill is 31% below it. Labor’s share of income falls from 60% to 45.2%, and capital income rises 81.4 %. That means a full 15% of GDP is captured as ROI rather than being paid out in labor costs.

In other words, he pointed out: “A richer economy is not automatically a fairer one.” Capability, diffusion, productivity, automation, and occupational friction all have to arrive together.

“AI will touch a large and rising share of knowledge work and will execute a much smaller share under independent authority,” he said. There is no single honest adoption percentage, because worker use, company use, technical exposure, and executed task instances are four different measurements.

Lessons from the research

Enterprises can take important lessons from the research as they deploy AI and consider its impact on their systems, workflows, and workforce, Gogia said.

“For enterprises, the binding variable is permission to delegate,” he noted. “A model that can draft a payment instruction is not thereby permitted to move money.”

His firm identifies five recurring concerns that come up in enterprise conversations: Durable returns after the full cost of deployment, control over authority being granted, augmentation quietly becoming substitution, erosion of professional formation, and fairness of how gains and risks land.

Some of those changes are progressing faster than the governance around them, he observed. Once a system can inspect customer data, change configurations, or act on workforce records, autonomy has stopped being a feature and has instead become an allocation of institutional authority.

“And the tasks easiest to automate are frequently the tasks through which judgement is learned,” he noted.

This article originally appeared on Computerworld.

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.

Traditional operating systems (OS) were built to manage hardware, files, apps, and users on a device, but Cloudflare says the agentic AI era requires a whole new format.

The company this week announced Cloudflare OS, which connects AI agents, enterprise data and context, internal systems, and workflows together in one secure workspace. It is open source and browser-based, sparing companies the need to build all-new infrastructure.

The OS is launching alongside several other new security, identity, spending, and user insight tools that Cloudflare has built for the AI-based workplace.

“Cloudflare OS isn’t a traditional desktop OS,” said Rita Kozlov, VP of product at Cloudflare. “It reimagines the workplace computing environment for AI.”

Open source OS runs in a browser

Cloudflare OS serves as a secure, AI-equipped workspace that is plugged into internal company systems. Available now through Cloudflare’s open source repository, it is accessible directly in a browser, and runs inside an enterprise’s Cloudflare account.

“It is a browser-based workspace that begins with a conversation,” Kozlov explained. Users can ask an agent to research, create slides, spreadsheets, and documents, build full-stack apps, or automate workflows without the need for a terminal. Those outputs are then shareable, but kept in isolated databases with access controls.

Enterprises will soon be able to access the OS directly through Cloudflare or via a “select group” of partners that will build tailored offerings on Cloudflare’s architecture, the company says. Because it is open source, organizational processes, internal system connections, and context aren’t locked into a vendor product or AI model provider. Customers can use whatever models they choose.

Cloudflare OS is built on Cloudflare Workers, Dynamic Workers, Durable Objects, and Access, the company’s zero trust network access (ZTNA) tool that verifies every user and request. Agents start with zero permissions by default and are only granted access to tools required for a specific task. Organizations configure their own Access policies, models, branding, skills, and integrations, Kozlov explained.

Governed connectors known as gatekeepers give admins control over what AI can see, what it can change, and when the system needs human sign-off. They can also control budgets, set rate limits, and delegate tasks to different models.

“Because agents act on people’s behalf and produce work others can access and modify, they require a new security model,” Kozlov said. Thus, Cloudflare OS tracks the resources an agent requires so the right access controls follow its work when it is shared.

Cloudflare initially built the OS for internal use, and employees “across every team” use it daily. Kozlov estimated that, over the last 30 days, internal users have used it to create more than 4,000 apps, automations, and tools. Over that same period, she claimed, the company’s sales team saved an estimated 10,000 hours by automating previously manual tasks like territory planning and proposal creation.

“We open sourced Cloudflare OS so any organization can build ‘Your Company OS,’” Kozlov said. Open source is critical because “you cannot put your company into software you do not own. Organizations need to be able to inspect the platform, customize it, connect their own systems, and make it their own,” she explained.

A more cohesive bundle

Cloudflare deserves credit for packaging Cloudflare OS as an operating system, noted tech analyst Carmi Levy.

“This very much is not Windows, macOS, or Linux, and it isn’t an operating system by its common definition,” he said. “But Cloudflare’s use of this terminology implies familiarity to enterprise IT buyers.”

This makes for an easier discussion as enterprises struggle to understand how to best incorporate AI-related platforms and workflows into infrastructure that wasn’t initially designed for it.

Microsoft has marketed the combination of its Azure, Entra, Fabric, Windows, and Microsoft 365 offerings as an operating system of sorts, but hasn’t pulled all the pieces into a common brand, Levy said. And Google’s Gemini, Workspace, Vertex AI, and Cloud Run are “circling similar territory.”

But, he noted, Cloudflare OS is “more cohesively bundled” and infrastructure-focused, offering a single pane of glass platform for buyers worried about stitching together otherwise disparate AI-aware networking pieces. The company recognizes that AI introduces new architectural realities such as inference and model routing “over and above” traditional OS core competencies.

“While competing offerings generally leave the infrastructure heavy lifting to enterprise decision-makers, Cloudflare is marketing itself as a single-source vendor, which potentially frees IT planners from having to integrate all the AI pieces on their own,” Levy said.

An infrastructure-first, application-agnostic approach means Cloudflare OS can coexist with whatever AI applications already exist in an enterprise, he said. It will “play nice” with OpenAI, Anthropic, Google, Microsoft, Meta, or open source layers, allowing employees to begin working in familiar workflows after sign-in.

“Its open-source architecture also minimizes the potential for vendor lock-in as enterprises gradually figure out how to evolve their stacks to align with new AI-era realities,” Levy said.

Managing identities and budgets for both humans and AI

As AI agents emerge across the enterprise, tracking their use can be challenging, causing problems from both a security and a spend standpoint. Along with Cloudflare OS, the company has launched a way to address this issue with its new Identity-Aware AI Gateway, now in beta.

Also integrated with Access, the offering gives admins visibility into what users (both human and AI) are requesting from AI models. It allows security teams to set up custom domains in front of their gateways and replace shared API keys by integrating with their identity provider, like Okta or Entra, and ZTNA infrastructure, Cloudflare explained.

Every request is tied to Access-verified identities, and enterprises can filter each user’s logs, analytics, and spend. IT teams can track redundancies, limit usage rates, and apply filters that strip out employee names, passwords, and other sensitive data before requests go to outside model providers.

A companion feature, AI Spend, tracks every user’s behavior over time to create a baseline of normal AI usage. When spending deviates from that pattern, the system alerts the IT team.

A new tab, User Insights, tracks cost and identifies over-spend caused by activities such as low cache-hit rates or oversized context windows. The capability scores sessions and compares them against account history using a 95th percentile session cost over the previous 30 days, Cloudflare product managers Ming Lu, Kenny Johnson, and Ayush Kumar explain in a blog post. Anything above 2x an account’s 95th percentile is a “strong candidate for anomalous behavior.”

For instance, one Cloudflare customer had an employee who left a rogue AI session running, generating a $30K bill. “User Insights helped them identify the problem and shut off access before the problem was further exacerbated,” Kozlov said.

Cloudflare is also building prompt classification functionality that sorts requests into categories such as coding or writing. This can help enterprises understand what AI is being used for.

“Once business traffic is separated from everything else, personal use becomes visible,” the project managers explained. “From the outside, someone running a side hustle on company time and someone quietly moving data out through a model look the same. Telling them apart is central to catching insider risk.”

Looking at the bigger picture

Identity-Aware AI Gateway and AI Spend address the visibility problem that has dogged so many recent AI deployments where enterprises failed to monitor usage, Levy noted. Projects “crashed and burned” as users unwittingly blew through token allocations.

These platforms provide single-point visibility into what is being used, how it’s being used, and where the potential lies for raising the productivity bar, he said. They overlay with existing models; in doing so, they enhance security with more precise control over resource allocations, and via automated anonymization protocols that prevent inadvertent sharing of sensitive data.

Ultimately, he said, vendors who free IT from having to independently assemble the pieces of their own AI implementations, and who assist them with answers to AI-specific questions, “will gain advantage over vendors that aren’t looking at the bigger picture.