Artificial Intelligence – part 3: The Hype, the Dangers, and the Resistance

Technology  •  August 27, 2026  •  Martin Hart-Landsberg

In early 2026, AI developers began promoting a new product, AI agents, confident that they would capture business interest and boost AI company profitability. Talk of superintelligence was downplayed in favor of claims that the new technology would enable companies to radically boost productivity while slashing employment.

In brief, AI agents are best understood as complex software systems that can interface with and manipulate other software systems and external databases. They can be given a complex directive, break it down into smaller ordered tasks, gather the required information, and then progressively make the decisions needed to satisfy the directive, all without successive human prompts or oversight. But these are not standalone systems; AI agents can only work in concert with large language model AI systems.

The Age of AI agents

The journalist and author Ezra Klein captures the excitement surrounding AI agents and their consequences for human work in his introduction to an interview with Jack Clark, co-founder and the head of policy at Anthropic:

“Every new [AI] model, impressive as it was, seemed like proof of concept for the models that would be coming soon, the models that could reliably do useful work on their own, the models that could make jobs obsolete or new things possible…

“I think the period in which we’re talking about the future is over now. The models we were waiting for — the sci-fi sounding models that could program on their own and do so faster and better than most coders, the models that could begin writing their own code to improve themselves — they are here now…

“Or, to put it differently, something that has been predicted for a long time has now happened: We are moving from chatbots to agents, from systems that talk to you to systems that act for you.”

Anthropic introduced the first major AI agent, Claude Code, with OpenAI quickly following with its own coding agent, Codex. Coding involves the writing of sequences of instructions that computers can follow to perform tasks. Previously, only skilled professionals who knew a programming language could write code. Now, with these agents, and the underlying work of large language models, anyone could code using a simple text-based prompt. And as might be expected, companies rushed to employ these agents, hoping they would enable their employees to write firm specific software for autonomously handling any number of common business tasks, including inventory management, payroll processing, and billing and delivery.

Leading AI companies also began developing their own pre-packaged agents, each tailored to meet the needs of firms in a specific industry. Anthropic, for example, created a legal agent that is said to be able to draft documents, conduct multi-source research, and “review confidentiality agreements, perform compliance checks, and generate legal briefings at a fraction of the cost of traditional per-seat legal software.” It has also produced agents designed to handle financial services tasks. According to Bloomberg, these agents “can draft pitch decks for client meetings, review financial statements and escalate cases for compliance review.”

Both Anthropic and OpenAI eagerly embraced the agent market because a vibrant market also meant steady demand for their multimodal systems. But it quickly became a highly competitive market, with other AI developers, including Google and Microsoft, launching their own agents. In fact, some businesses employ agents from multiple companies since they often have slightly different strengths and can work together. For example, Citi Bank “is paying for AI software from Anthropic, Google, Microsoft and OpenAI, to automatically read legal documents, approve account openings, send invoices for trades and organize sensitive customer data, among other tasks.”

AI developers are not the only ones building AI agents, since agents can be programmed to work with whatever large language model their corporate clients’ favor. OpenClaw, developed by the “vibe coder” Peter Steinberge, is oriented toward the ecommerce sector; it can place orders, negotiate deals, and adjust online marketing campaigns without human input. Agentforce, a product of Salesforce, can handle customer inquiries, case resolution, and inventory management. LinkedIn’s Hiring Assistant can review candidate profiles on LinkedIn, select ones that best match corporate preferences, and draft letters encouraging a formal job application.

As Deloitte Insight reports,

“Agentic AI has captured the attention of enterprises with its compelling promises of autonomous operation and intelligent execution. The momentum is undeniable: Gartner predicts that 15 percent of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from none in 2024, while 33 percent of enterprise software applications will include agentic AI by the same timeframe, compared with less than 1 percent today.”

A McKinsey report claims that AI agents can already manage some 44 percent of all work processes in the US without the need for human labor. The CEO of Microsoft AI, Mustafa Suleyman, told the Financial Times in a February 2026 interview that because AI is rapidly approaching “human-level performance,” one could expect that most professional white-collar jobs will be fully automated within two years. It is this kind of talk that has given rise to fears of a “jobs apocalypse.”

And there are enough news stories of AI-driven mass layoffs to give credence to popular fears. Looking just at the tech sector, TechCrunch reports:

“The [job] cuts continue what feels to many in the tech industry like an epidemic: companies reporting record revenues while simultaneously culling their workforces, pointing to AI as both the engine of growth and the reason for the cuts. Tech layoffs hit their highest single month in years in May [2026], and AI was the most-cited reason, according to outplacement firm Challenger, Gray & Christmas.”

While currently employed workers fear that AI agents will cost them their jobs, young people, especially recent college graduates, are being told that agents are doing away with most entry level jobs, leaving them with few if any professional employment options. As the New York Times comments: “This is the worst spring for young degree holders since the depths of the pandemic.”

It is an open question as to whether AI agent boosters really believe that the technology is destined to decimate white collar jobs. What we can say is that fear of such an outcome serves business interests. Claims that emphasize the power of AI agents keep the money rolling into AI industry accounts while undermining worker confidence in possibilities for collective action. Regardless, there are strong reasons to reject the claims being made about the capacity and effectiveness of AI agents.

Overblown and Misleading Claims

Fears of a jobs apocalypse have, to a considerable extent, been driven by highly publicized accounts of layoffs that were said to be the result of realized or expected AI agent productivity gains. However, an ever-increasing number of analysts have begun to poke holes in those accounts. As they point out, there is growing evidence that many of these layoffs were due to poor corporate performance and had little, if anything, to do with adoption of AI agents. In other words, companies were touting nonexistent AI gains to fool investors into thinking that the layoffs were part of a well-thought-out long-term plan.

As the New York Times explains,

“Companies slashing their staffs have run the gamut from software providers like Atlassian and Autodesk, to social networking apps like Pinterest and LinkedIn, to financial technology companies like Intuit and PayPal…

“But in more than a few cases, the recent layoffs have coincided with other business issues. Wall Street loves an AI story right now. That, analysts and economists say, has offered a smoke screen for companies looking to beef up profits or patch over old mistakes.

“Cutting jobs to make way for AI is ‘a nice excuse, but some of these aren’t necessarily the best, most well-run companies,’ said Mark Mahaney, an analyst at the investment bank Evercore. ‘They may have over hired, or they may be losing market share. There may be other issues.’”

Several large-scale studies confirm that there are currently no tangible signs of a jobs apocalypse. For example, Bloomberg noted that: “A Harvard Business Review survey of more than 1,000 executives found many companies had made layoffs in anticipation of what AI could do, but only 2% said they cut jobs because of actual AI implementation.”

The Yale Budget Lab looked into whether the “widespread public anxiety about AI’s potential for job losses” was justified and came to a similar conclusion: “Overall, our metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.”

The evidence is also thin that AI is hammering young workers. A study by the Economic Policy Institute on the effects of AI on the employment status of college graduates determined that “it’s hard to argue that AI is uniquely causing job losses for new labor market entrants graduating from college now or in recent years. [Our] findings are consistent with the literature, as there is currently no consensus about the effects of working in AI-exposed occupations on employment thus far.”

Some proponents of the jobs apocalypse story dismiss the lack of evidence showing any meaningful AI impact on employment, noting that it takes time for companies to know how to effectively use such a transformative technology. They often point to studies which claim to measure the share of existing workplace activities that can be performed by advanced AI systems now or in the future. A case in point: an Anthropic report suggests that multimodal AI systems, given their rate of improvement, will be able to perform some 70-80 percent of all individual job tasks in the most important US industries. However, this and other similar studies make a number of problematic assumptions and predictions. For example, as an Ars Technica discussion of the report points out, jobs cannot be reduced to a predetermined set of tasks. More concerning is the fact that the report’s headline grabbing numbers relied on projections of the anticipated impact of the technology:

“Importantly, the researchers didn’t even set a self-imposed deadline for when these effects would be seen in future software. ‘We do not make predictions about the development or adoption timeline of such LLMs,’ the researchers write, creating an essentially unbounded horizon that limits the predictive power of this kind of projection.”

Perhaps the most important reason to doubt the claims of those promoting the notion of an AI agent jobs apocalypse is that there is growing evidence that these agents cannot deliver on their promises. In fact, according to 404 Media summary of a major study, titled Just do it!? Computer-use agents exhibit blind gold-directness, researchers from Microsoft, Nvidia, and the University of California at Riverside found that most agents were unable to complete their assigned tasks. “The average completion rate was around 30 percent.”

One big reason for this poor performance is that since AI agents must operate in concert with multimodal systems, their work is often compromised by the same limitations that affect those systems, including hallucinations. Even more concerning, there are an increasing number of incidents where agents pursue actions that disregard company protocols or, as it is commonly said, go “rogue,” having decided that doing so was the most effective way to achieve the assigned task.

The most popular AI agents are coding agents. And while there are many claims for their ability to rapidly write the software needed to automate complex workflows, improve existing code, and/or discover and correct security shortcomings, careful studies of their use suggest that the need to continually check and correct their work can actually slow down productivity.

One example: the non-profit Model Evaluation & Threat Research (METR) gave 16 experienced open-source developers 246 genuine programming tasks. The tasks were randomly assigned and randomly approved for AI use. Although developers predicted that AI use would speed up their work, those that used AI tools actually took 19 percent longer to finish the same tasks as those that didn’t, an outcome that “ran counter not only to their perceptions but also to the forecasts of experts in economics and machine learning.”

Companies for obvious reasons do not like to call attention to their AI agent problems, but sometimes they are serious enough that they cannot be hidden. For example, a Futurism article highlights reporting by the Financial Times that revealed that Amazon suffered a number of agent-caused “outages” in early 2026 which disrupted its ecommerce business. In one case, faulty AI agent coding “took down Amazon’s shopping website and app, leaving customers unable to make orders.” In another case, “the company’s in-house AI coding tool deleted and recreated the entire coding environment.”

Meta has had its own challenges. As another Futurism article describes,

“A rogue AI agent caused a critical security incident at Meta which exposed sensitive data to people who didn’t have proper authorization … For almost two hours, unauthorized access to troves of sensitive company and user data was given to engineers who weren’t approved to view the data before.

“Other problems have leaked out. For example, Meta’s director of AI safety revealed that ‘an OpenClaw agent she was experimenting with — by giving it control of her personal computer — nearly wiped out her entire email inbox while ignoring her instructions to stop.’”

AI agents ignoring instructions is a problem that goes beyond hallucinations. And it is not a rare occurrence. The Guardian reported on the experience of Jeremy Crane, the owner of PocketOS, who watched helplessly as his AI agent deleted his company’s entire database. PocketOS sells software to car rental businesses, and with the database erased,

“PocketOS’s car rental clients were left in a lurch when they arrived to pick up vehicles from businesses that no longer had access to software that managed reservations and vehicle assignments…

“Crane said that he was monitoring the agent as it deleted this data… Crane’s takeaway was that ‘the agent didn’t just fail safety. It explained, in writing, exactly which safety rules it ignored.’ He added: ‘We were running the best model the industry sells, configured with explicit safety rules in our project configuration, integrated through Cursor – the most-marketed AI coding tool in the category.’

“Crane also wrote on X that Cursor has a growing track record of violating ‘safeguards, sometimes catastrophically.’ He pointed to a handful of posts on blogs and forums about Cursor deleting software used to manage websites or an entire operating system on a computer, which included years of research for a dissertation.”

More generally, as the authors of Just do it!? Computer-use agents exhibit blind gold-directness conclude,

“we identify a phenomenon that causes CUAs [computer-use agents] to take undesirable and potentially harmful actions, which we call Blind Goal-Directedness (BGD). BGD is an inherent tendency to pursue user-specified goals regardless of feasibility, safety, reliability, or context. BGD captures a broad set of risks in CUAs that can arise even without directly harmful instructions and that can happen without user intent.”

If these concerns were not enough, companies must also contend with the fact that a growing number of consumers do not like dealing with AI agents. As a CNBC report points out:

“Nearly one in five consumers who have used AI for customer service saw no benefit from the experience, according to the Qualtrics 2026 Customer Experience Trends Report. That figure — a failure rate almost four times higher than for AI use in general — points to something specific about customer service that makes it harder for AI to get right. Consumers rank AI applications for customer service among the worst for convenience, time savings, and usefulness.”

And then, there are financial issues which are perhaps the biggest threat to the widespread adoption of AI agents. AI developers, in particular the two industry leaders, OpenAI and Anthropic, have yet to make a profit. One major reason is that they had offered their customers free or highly subsidized flat subscription services in order to encourage adoption of their models. However, with losses mounting, both OpenAI and Anthropic decided that they needed to take action to close the ever-widening gap between their subscription revenue and operating costs. Thus, starting in early 2026, they began introducing a new pricing strategy, one tied directly to token use.

As noted in Part II of this series:

“Multimodal AI systems cannot directly process text. Questions or directives must first be converted into machine-readable numerical units called tokens. The resulting string of tokens is then processed and a response, also in token form, is generated and then converted back into readable text.

“The major problem … for business users is that it is impossible for anyone to know or even predict, ex ante, the total token use associated with any AI inquiry or directive. That is because AI systems often explore multiple paths of inquiry before generating a response, and each path can involve significant token use.”

This new pricing strategy is especially problematic for businesses using agents, which rely heavily on chain-of-thought reasoning to explore options before taking actions, a procedure that can easily burn through many tokens, making them very expensive to use. And that is true even if they avoid hallucinating or engaging in Blind Goal-Directedness behavior.

The experience of GitHub Copilot (a joint product of GitHub and OpenAI) users is illustrative of how the new pricing strategy is affecting agent use. As explained by Ars Technica:

“In April [2026], GitHub announced that it was moving subscribers from request-based billing to a usage-based model for its AI-powered Copilot service. As that new pricing model goes into effect today, many GitHub Copilot users are reporting some extreme sticker shock as they realize just how quickly their previous ‘normal’ usage is burning through their newly limited monthly allotment of AI credits.

“Across social media and forums, many Copilot users are sharing personal statistics showing how just a few hours of AI usage can now account for a large chunk of their new monthly subscription caps. For some users, it reportedly took less than a day to use up a month’s usage quota.”

Uber, which uses Anthropic’s Claude Code, “burned through its entire 2026 AI coding tools budget by April [2026] after rolling out AI tools at near-total scale across its engineering organization.” The company’s COO later acknowledged “he could not draw a line between that token spend and meaningful consumer-facing product improvements.” Not surprisingly, Uber has now capped its employee spending on AI.

In sum, as Axios explains, “Companies that rushed to embrace AI are now confronting ballooning IT costs, uncertain productivity gains and growing employee skepticism.” And while many previously encouraged their workers to maximize their AI use, most are now imposing restrictions. None of this is to say that companies are done with AI agents. Rather, their use will likely be limited and targeted, which means that there is no AI-driven jobs apocalypse on the horizon.

But dismissing the extreme claims of tech leaders does not mean we have nothing to fear from letting the AI experience proceed unchecked. Part IV highlights some of the ways in which companies seek to integrate AI into their work processes, the resulting negative consequences for workers and those that use the goods and services they produce, and possibilities for resistance. •

This article first published on the Reports from the Economic Front website.