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CAIOs are toiling to get AI agents implemented correctly

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Agentic AI has taken center stage in the worlds of AI, tech, and business, dominating the discourse and furthering the pressure for companies to swiftly integrate the tech or fall behind their competitors. More than anyone, it’s chief AI officers (CAIOs) who are charged with untangling the promises and realities of AI’s latest buzzword. 

As they oversee experimentation with and rollouts of AI agents and guide other leaders on the journey, CAIOs are also navigating through the hype, concerns around security and trust, and interconnectedness (or lack thereof) of these systems. Not to mention having to grapple with the question: What even is an AI agent?

Hype-chasing causes companies to lose focus

No one can seem to agree on what, exactly, the term “AI agent” really means, as Fortune and others have reported. Companies are defining the term differently and often using it to describe varied features and capabilities, including many that were previously described with other terms such as “AI assistants.” For Accenture chief AI officer Lan Guan, who led the build of an AI agent solution called Refinery AI for clients and also works directly with them on their own AI and AI agent deployments, this has caused her to devote a great deal of time to just helping clients sort through the contradictions.

“A year ago, everyone was saying, ‘I need to do gen AI.’ Now everyone is saying, ‘I need to do agentic AI or AI agents.’ And it’s like, at the end of the day, a lot of these things are still the same thing. They’re just getting called different things depending on who you’re talking to,” she said. “And so there’s a ton of confusion in the marketplace with our clients on, ‘What is an AI agent? What am I deploying?’ And so we spend a lot of time on education.”

A runaway effect of this has been companies quickly spinning up so-called AI agents “just for the press release,” says Michelle Bonat, chief AI officer of AI Squared, who also works with companies across regulated industries on their AI development. The pressure to have an answer for the agentic AI moment is causing some companies to rename features or chase AI agents to stay on trend, often merely creating thin layers of agents on top of foundation models.

“I’m totally seeing that. I’m seeing that every day,” Bonat says. “That’s why this space is full of noise.”

Security, errors, and trust dominate the risk analysis

Despite the hype and muddled terminology, the core idea of AI agents—systems designed to autonomously take action to carry out specific tasks—is still generating a lot of justifiable excitement. It’s also key to creating the types of systems technologists and science fiction lovers have always dreamed of, capable of executing sequences of complex tasks across multiple platforms on our behalf. But there are real roadblocks.

Uri Yerushalmi, cofounder and chief AI officer at Fetcherr, which uses AI for predictive pricing in the airline industry, believes the opportunities around AI agents are “enormous” but that unlocking that value depends on addressing real challenges around trust and integration and avoiding failure points. For example, agents must integrate with legacy systems and align with real-world constraints without disrupting existing workflows. And as we give agents more autonomy, we need to build guardrails, monitoring, override systems, and mechanisms for human alignment, he said. 

“Businesses need to trust the agent’s decisions,” he added. “That requires transparency, consistency, and demonstrable ROI.”

One of the most concerning failure points is compounding errors. Google DeepMind CEO Demis Hassabis has compared this issue to compound interest in finances, explaining that even if an agentic model has only a 1% error, it would cause a chain reaction of errors that would, after a few thousand steps, ultimately make the likelihood of a correct result completely “random.” Bonat points to this problem of compounding errors as a severe challenge in terms of trusting AI agents, saying this potential to compound one misstep without humans even being aware of it could “create havoc.”

This is especially true for the sort of multi-agent systems many businesses are contemplating, which Guan said can cause blind spots and get you into trouble very quickly.

“It may not work for you, and may actually introduce a lot of risk,” she said. “Think about it—a lot of the business workflows and transactions or interactions are high stakes. You don’t want agents to just issue a refund for every customer, right?” she said, adding that while her clients have a strong appetite to see impact from AI agents, they’re also wary of surprise high cloud bills and security risks.

Security concerns are certainly top of mind in the AI agent landscape. By 2028, Gartner predicts, 25% of enterprise breaches will be traced back to AI agents, including abuse from both internal and external malicious actors. The dominating factor contributing to security risks is the combination of autonomy and intended interoperability of agent systems, which would have them connect to, exchange data with, and autonomously act across a wide swath of platforms and systems. Put differently, the exact nature of how these systems function and what they’re intended to do is what makes them so risky.

Interoperability dreams struggle to break free from walled gardens

Like all CAIOs, Ali Alkhafaji, chief AI and technology officer at Omnicom Precision Marketing Group, is concerned about data leakage and other security risks. He’s also concerned that many of the companies commercializing agent systems are using security as a convenient excuse to further lock their customers inside their ecosystems, going against the collaborative and decentralized vision many see as intrinsic to an agentic future: “Not because it can’t be solved, but because it’s not in the commercial interest of the vendor to solve it.”

“Every vendor is building their own ‘agent framework,’ but no one is solving for enterprise-level interoperability. Without open frameworks and semantic standards, we’re just building smarter silos,” he said, adding that agent collaboration protocols remain immature and that it’s frustrating to see major vendors and hyperscalers continue to reinforce walled gardens. 

Deloitte U.S. head of AI Jim Rowan is seeing this play out among his clients, noting that they’re mostly sticking with their current providers and using their agent capabilities as they’re released. It’s another iteration of the platform advantage that’s driving growth for providers like OpenAI, Google, and Microsoft as they onboard their current customers into their new AI pipelines and products. 

“There is a definite tension in the marketplace between who wants to own the agent system of record. Like, who’s gonna own the registry, who’s going to orchestrate all the orchestration that’s happening around agents,” said Rowan. “We see that with the hyperscalers and the SaaS providers and the third-party-tool startups that are in the space as well. I think the jury’s still out on who’s owning that.”

Correction, Sept. 3, 2025: An earlier version of this story misstated the name of Accenture’s AI agent solution.

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Palantir CEO says AI “will destroy” humanities jobs but there will be “more than enough jobs” for people with vocational training

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Some economists and experts say that critical thinking and creativity will be more important than ever in the age of artificial intelligence (AI), when a robot can do much of the heavy lifting on coding or research. Take Benjamin Shiller, the Brandeis economics professor who recently told Fortune that a “weirdness premium” will be valued in the labor market of the future. Alex Karp, the Palantir founder and CEO, isn’t one of these voices. 

“It will destroy humanities jobs,” Karp said when asked how AI will affect jobs in conversation with BlackRock CEO Larry Fink at the World Economic Forum annual meeting in Davos, Switzerland. “You went to an elite school and you studied philosophy — I’ll use myself as an example — hopefully you have some other skill, that one is going to be hard to market.”

Karp attended Haverford College, a small, elite liberal arts college outside his hometown of Philadelphia. He earned a J.D. from Stanford Law School and a Ph.D. in philosophy from Goethe University in Germany. He spoke about his own experience getting his first job. 

Karp told Fink that he remembered thinking about his own career, “I’m not sure who’s going to give me my first job.” 

The answer echoed past comments Karp has made about certain types of elite college graduates who lack specialized skills.

“If you are the kind of person that would’ve gone to Yale, classically high IQ, and you have generalized knowledge but it’s not specific, you’re effed,” Karp said in an interview with Axios in November. 

Not every CEO agrees with Karp’s assessment that humanities degrees are doomed. BlackRock COO Robert Goldstein told Fortune in 2024 that the company was recruiting graduates who studied “things that have nothing to do with finance or technology.” 

McKinsey CEO Bob Sternfels recently said in an interview with Harvard Business Review that the company is “looking more at liberal arts majors, whom we had deprioritized, as potential sources of creativity,” to break out of AI’s linear problem-solving. 

Karp has long been an advocate for vocational training over traditional college degrees. Last year, Palantir launched a Meritocracy Fellowship, offering high school students a paid internship with a chance to interview for a full-time position at the end of four months. 

The company criticized American universities for “indoctrinating” students and having “opaque” admissions that “displaced meritocracy and excellence,” in their announcement of the fellowship. 

“If you did not go to school, or you went to a school that’s not that great, or you went to Harvard or Princeton or Yale, once you come to Palantir, you’re a Palantirian—no one cares about the other stuff,” Karp said during a Q2 earnings call last year.

“I think we need different ways of testing aptitude,” Karp told Fink. He pointed to the former police officer who attended a junior college, who now manages the US Army’s MAVEN system, a Palantir-made AI tool that processes drone imagery and video.  

“In the past, the way we tested for aptitude would not have fully exposed how irreplaceable that person’s talents are,” he said. 

Karp also gave the example of technicians building batteries at a battery company, saying those workers are “very valuable if not irreplaceable because we can make them into something different than what they were very rapidly.”

He said what he does all day at Palantir is “figuring out what is someone’s outlier aptitude. Then, I’m putting them on that thing and trying to get them to stay on that thing and not on the five other things they think they’re great at.” 

Karp’s comments come as more employers report a gap between the skills applicants are offering and what employers are looking for in a tough labor market. The unemployment rate for young workers ages 16 to 24 hit 10.4% in December and is growing among college graduates. Karp isn’t too worried. 

“There will be more than enough jobs for the citizens of your nation, especially those with vocational training,” he said. 



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AI is boosting productivity. Here’s why some workers feel a sense of loss

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Welcome to Eye on AI, with AI reporter Sharon Goldman. In this edition…Why some workers feel a sense of loss while AI boosts productivity…Anthropic raising fresh $10 Billion at $350 billion valuation…Musk’s xAI closed $20 billion funding with Nvidia backing…Can AI do your job? See the results from hundreds of tests.

For months, software developers have been giddy with excitement over “vibe coding”– prompting desired software functions or features in natural language—with the latest AI code generation tools. Anthropic’s Claude Code is the darling of the moment, but OpenAI’s Codex, Cursor and other tools have also led engineers to flood social media with examples of tasks that used to take days and are now finished in minutes. 

Even veteran software design leaders have marvelled at the shift. “In just a few months, Claude Code has pushed the state of the art in software engineering further than 75 years of academic research,” said Erik Meijer, a former senior engineering leader at Meta

Skills honed seem less essential

However, that same delight has turned disorienting for many developers, who are grappling with a sense of loss as skills honed over a lifetime suddenly seem less essential. The feeling of flow—of being “in the zone”—seems to have vanished as building software becomes an exercise in supervising AI tools rather than writing code. 

In a blog post this week titled “The Grief When AI Writes All the Code,” Gergely Orosz of The Pragmatic Engineer, wrote that he is “coming to terms with the high probability that AI will write most of my code which I ship to production.” It already does it faster, he explained, and for languages and frameworks he is less familiar with, it does a better job. 

“It feels like something valuable is being taken away, and suddenly,” he wrote. “It took a lot of effort to get good at coding and to learn how to write code that works, to read and understand complex code, and to debug and fix when code doesn’t work as it should.” 

Andrew Duca, founder of tax software Awaken Tax, wrote a similar post this week that went viral, saying that he was feeling “kinda depressed” even though he finds using Claude Code “incredible” and has “never found coding more fun.” 

He can now solve customer problems faster, and ship more features, but at the same time “the skill I spent 10,000s of hours getting good at…is becoming a full commodity extremely quickly,” he wrote. “There’s something disheartening about the thing you spent most of your life getting good at now being mostly useless.” 

Software development has long been on the front lines of the AI shift, partly because there are decades of code, documentation and public problem-solving (from sites like GitHub) available online for AI models to train on. Coding also has clear rules and fast feedback – it runs or it doesn’t – so AI systems can easily learn how to generate useful responses. That means programming has become one of the first white-collar professions to feel AI’s impact so directly.

These tensions will affect many professions

These tensions, however, won’t be confined to software developers. White-collar workers across industries will ultimately have to grapple with them in one way or another. Media headlines often focus on the possibility of mass layoffs driven by AI; the more immediate issue may be how AI reshapes how people feel about their work. AI tools can move us past the hardest parts of our jobs more quickly—but what if that struggle is part of what allows us to take pride in what we do? What if the most human elements of work—thinking, strategizing, working through problems—are quietly sidelined by tools that prize speed and efficiency over experience?

Of course, there are plenty of jobs and workflows where most people are very happy to use AI to say buh-bye to repetitive grunt work that they never wanted to do in the first place. And as Duca said, we can marvel at the incredible power of the latest AI models and leap to use the newest features even while we feel unmoored. 

Many white-collar workers will likely face a philosophical reckoning about what AI means for their profession—one that goes beyond fears of layoffs. It may resemble the familiar stages of grief: denial, anger, bargaining, depression, and, eventually, acceptance. That acceptance could mean learning how to be the best manager or steerer of AI possible. Or it could mean deliberately carving out space for work done without AI at all. After all, few people want to lose their thinking self entirely.

Or it could mean doing what Erik Meijer is doing. Now that coding increasingly feels like management, he said, he has turned back to making music—using real instruments—as a hobby, simply “to experience that flow.”

With that, here’s more AI news.

Sharon Goldman
sharon.goldman@fortune.com
@sharongoldman

FORTUNE ON AI

As Utah gives the AI power to prescribe some drugs, physicians warn of patient risks – by Beatrice Nolan

Google and Character.AI agree to settle lawsuits over teen suicides linked to AI chatbots – by Beatrice Nolan

OpenAI launches ChatGPT Health in a push to become a hub for personal health data – by Sharon Goldman

Google takes first steps toward an AI product that can actually tackle your email inbox – by Jacqueline Munis

Fusion power nearly ready for prime time as Commonwealth builds first pilot for limitless, clean energy with AI help from Siemens, Nvidia – by Jordan Blum

AI IN THE NEWS

Anthropic raising fresh $10 Billion at $350 billion valuation. According to the Wall Street Journal, OpenAI rival Anthropic is planning to raise $10 billion at a roughly $350 billion valuation, nearly doubling its worth from just four months ago. The round is expected to be led by GIC and Coatue Management, following a $13 billion raise in September that valued the company at $183 billion. The financing underscores the continued boom in AI funding—AI startups raised a record $222 billion in 2025, per PitchBook—and comes as Anthropic is also preparing for a potential IPO this year. Founded in 2021 by siblings Dario Amodei and Daniela Amodei, Anthropic has become a major OpenAI rival, buoyed by Claude’s popularity with business users, major backing from Nvidia and Microsoft, and expectations that it will reach break-even by 2028—potentially faster than OpenAI, which is itself reportedly seeking to raise up to $100 billion at a $750 billion valuation.

Musk’s xAI closed $20 billion funding with Nvidia backing. Bloomberg reported that xAI, the AI startup founded by Elon Musk, has completed a $20 billion funding round backed by investors including Nvidia, Valor Equity Partners, and the Qatar Investment Authority, underscoring the continued flood of capital into AI infrastructure. Other backers include Fidelity Management & Research, StepStone Group, MGX, Baron Capital Group, and Cisco’s investment arm. The financing—months in the making—will fund xAI’s rapid infrastructure buildout and product development, the company said, and includes a novel structure in which a large portion of the capital is tied to a special-purpose vehicle used to buy Nvidia GPUs that are then rented out, allowing investors to recoup returns over time. The deal comes as xAI has been under fire for its chatbot Grok producing non-consensual “undressing” images of real people.

Can AI do your job? See the results from hundreds of tests. I wanted to shout-out this fascinating new interactive feature in the Washington Post, which presented a new study that found that despite fears of mass job displacement, today’s AI systems are still far from being able to replace humans on real-world work. Researchers from Scale AI and the Center for AI Safety tested leading models from OpenAI, Google, and Anthropic on hundreds of actual freelance projects—from graphic design and creating dashboards to 3D modeling and games—and found that the best AI systems successfully completed just 2.5% of tasks on their own. While AI often produced outputs that looked plausible at first glance, closer inspection revealed missing details, visual errors, incomplete work, or basic technical failures, highlighting gaps in areas like visual reasoning, long-term memory, and the ability to evaluate subjective outcomes. The findings challenge predictions that AI is poised to automate large swaths of human labor anytime soon, even as newer models show incremental improvement and the economics of cheaper, semi-autonomous AI work continue to put pressure on remote and contract workers.

EYE ON AI NUMBERS

91.8%

That’s the percentage of Meta employees who admitted to not using the company’s AI chatbot, Meta AI, in their day-to-day work, according to new data from Blind, a popular anonymous professional social network. 

 

According to a survey of 400 Meta employees, only 8.2% said they use Meta AI. The most popular chatbot was Anthropic’s Claude, used by more than half (50.7%) of Meta employees surveyed. 17.7% said they use Google’s Gemini and 13.7% said they used OpenAI’s ChatGPT. 

 

When approached for comment, Meta spokesperson pointed out that the number (400 of 77,000+ employees) is “not even a half percent of our total employee population.”

AI CALENDAR

Jan. 19-23: World Economic Forum, Davos, Switzerland.

Jan. 20-27: AAAI Conference on Artificial Intelligence, Singapore.

Feb. 10-11: AI Action Summit, New Delhi, India.

March 2-5: Mobile World Congress, Barcelona, Spain.

March 16-19: Nvidia GTC, San Jose, Calif.

April 6-9: HumanX, San Francisco. 



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Trust has become the crisis CEOs can’t ignore at Davos, as new data show 70% of people turning more ‘insular’

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Everywhere you turn in Davos this year, people are talking about trust. And there’s no one who knows trust better than Richard Edelman. Back in 1999, Edelman was on the cusp of taking  over the PR firm founded by his father Daniel. Spurred by the 1999 WTO protests in Seattle, he decided to try and measure the level of trust in NGOs compared with business, government and media, Edelman surveyed 1,300 thought leaders in the U.S., U.K., France, Germany and Australia, and the Edelman Trust Barometer was born. 

While the survey sample long ago expanded beyond elites to include about 34,000 respondents in 28 nations, its results are still unveiled and debated every year at the ultimate gathering of elites: the World Economic Forum. This year’s findings are grim: About 70% of respondents now have an “insular” mindset: they don’t want to talk to, work for, or even be in the same space with anyone who doesn’t share their world view. And “a sense of grievance” permeates the business world, Edelman finds. At Davos, debating such findings have spawned a series of dinners, panels, cocktails and media briefings on site. What better place to bring people together than the world’s most potent village green?

I moderated a CEO salon dinner with about three dozen leaders last night to discuss what they’re seeing and doing when it comes to building trust. Before the dinner, I asked Edelman what he’d like to see this year, after 26 winters of highlighting the erosion of trust. “Urgency,” he said. “A sense that time is running out.”

Because the gathering itself was held under the Chatham House rule, I won’t share names and direct quotes. But the focus was on how attendees are trying to address the problem through what Edelman calls “trust brokering,” or finding common ground through practices from nonjudgemental communications to “polynational’ business models that invest in long-term local relationships. (See the report for more information.) There were some success stories from the front lines of college campuses, politics and industries caught in a crossfire of misinformation.

Still, the mood was somewhat subdued, with a sense that there’s no easy fix to building trust. As one CEO pointed out, rarely have leaders faced such a confluence of geopolitical crises, tech shifts, economic divides, disinformation, job disruption and wicked problems. And as much as Davos is a great gathering ground to talk through all of these problems, the fact is the problems will all still be waiting once these CEOs return from the mountains.

This story was originally featured on Fortune.com



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