The CEO of a $1 billion AI unicorn says his peers in Silicon Valley want you to fear for your job, but they’re actually first on the chopping block

Shumer warned that white-collar workers have to figure out plan B right now, because a Covid-like extinction event is coming for white-collar work. Almost simultaneously, Microsoft’s AI chief Mustafa Suleyman gave it 18 months before anyone looking at a computer for a living will be out of work within that timeframe. This was a revival of sorts for the sort of doomsday predictions that marked the first half of 2025 before going ominously silent. Anthropic’s Dario Amodei, for instance, predicted that AI would eliminate half of all entry-level white-collar jobs, while Ford CEO Jim Farley said it would wipe out half of white-collar jobs, full-stop.

Tanmai Gopal says these dire predictions are a classic case of Silicon Valley self-projection, even narcissism. The co-founder and CEO of PromptQL, a $1 billion-plus Bay Area unicorn that helps companies with AI adoption, told Fortune in a recent interview that the AI doomsday predictions definitely contain a grain of truth while also being massively overstated. “That’s 100% what’s happening where you have a bunch of … people who are in the hype cycle.” Gopal said his community in the valley is “feeling the awesomeness of this AI” but “we’re projecting that into domains that we don’t actually understand.”

“It’s like, oh, this is the problem for 7 billion people on the planet, because I’m in Silicon Valley, so I obviously know what’s best, right?” Gopal also noted that cynics have a point, with these doomsday predictions occurring right around the time of the next funding multibillion-dollar funding round for many AI start-ups that have yet to go public, offering a clear funding rationale that may not bear out. In general, he added, “Tech people… think like, this affects me. So it’s going to affect everyone like that.”

Actually, Gopal said, that’s just not the case. But when it comes to coders, even the senior software engineers, who are exposed to the “awesomeness” of the AI tools now available, he said those people are facing a paradigm shift.

The real jobs disruption is coming from inside the valley

Gopal was speaking to Fortune weeks after the “SaaSpocalypse” wiped out $2 trillion in software-as-a-service valuations, with investors realizing, as Bank of America Research recently put it, that AI is a “double-edged sword” and not purely an upside play. It could very easily “cannibalize” many businesses, BofA said, such as software that AI is advanced enough to write itself.

Economists have been puzzling over very noisy data over the last year or so, with the U.S. economy largely flatlining in job production while also facing elevated tariff costs and far fewer immigrants entering the workforce. Some AI thought leaders, notably Stanford’s Erik Brynjolfsson, looked closely at the data and saw productivity really starting to lift off in 2025. Writing in the Financial Times op-ed, Brynjolfsson noted the latest jobs report revised all job gains for 2025 down to just 181,000, while his own calculation projected productivity of 2.7% for the year, versus the 1.4% average over the past decade. Of course, this lends weight to the AI displacement theory, with even Federal Reserve Governor Michael Barr recently warning that millions could be “essentially unemployable” in the near future.

Gopal said it’s true that the tech industry has inadvertently automated itself, reaching the era of “baby AGI” (Artificial General Intelligence) specifically for coding. The latest AI models have the judgment and taste of an “average senior software engineer,” Gopal said, explaining that standard software engineering heavily relies on converting established business context into technical code and because AI excels at this translation, coding has become the first major domino to fall.

“What used to be kind of sometimes considered the epitome… of white collar was like high-grade software engineering,” Gopal noted. “That’s been all the rage for the last 30 years and I’m excited to see that go.” He explained that his excitement stems from the robotic nature of the jobs that robots are already starting to perform and what he’s seeing on the frontlines of his company, which helps Fortune 500 companies actually build AI tools and agents that are specialized to their business.

“What we’ve been doing over the last year is … we’ve been working exactly at that intersection,” Gopal said, and for the most part, he’s found that “AI is not useful” because it needs so much business context to be effective. “People keep thinking it’s a technical problem,” but it’s really about the difficult fact that AI can’t access business context that lives inside people’s heads and hasn’t been translated to data—and may never be. “People are thinking, ‘Oh, it’s like a semantic layer and a data problem and get your data ready and make it work and whatnot,” but the real issue is that data doesn’t exist for the most useful information that the AI needs. “Nobody wrote that down. And if nobody wrote that down, you can’t train AI on it.”

Paradoxically for an AI executive, Gopal said that arguably, many businesses exist that AI can never be trained on, “because this is real-life business that moves.” Real people who have conversations and continually update a business context will always be one step ahead of the machines, he explained. “Are you going to retrain for that one individual conversation for one day?” he asked, and then retrain on a rolling basis every time your business context changes?

Gopal agreed with his interviewer that journalism was an example of a profession that could resist automation, because readers are interested in human insight, deep sourcing and forward-looking analysis, things that AI can’t easily reproduce, if ever. He also mentioned salespeople, marketers and operations staff as examples. People in the field who have to make real-time decisions are inherently protected, in his view.

Gopal isn’t the only executive who recognizes that AI requires human deployment to function. Tatyana Mamut, a former Salesforce and Amazon Web Services executive who now offers AI agent-monitoring purposes through her startup Wayfound.AI, told Fortune that “we need to stop talking about AI like tools. It is not a tool, right? It’s not like a hammer.” Rather, she argued, it’s more like a hammer “that thinks for itself, can design a house, can build a house better than most people who work in the construction industry can build a house.” It still needs to be shown the construction plans, though.

Regarding business context, Mamut said she thinks “very few” people really understand how to make this work with AI. “You need like real tools and mechanisms to capture that contextual learning.” Companies with different brands, different systems and different processes all have different context that need to be captured by AI, she said, predicting that the smart SaaS companies will pivot into this territory. Instead of software-as-a-service, she said expert services will be delivered via agents with proper context capture.

Gopal was bearish about how much this context can be captured, estimating that 70% of the effort required to make AI useful relies entirely on unwritten business context that exists only in human heads. “You fundamentally cannot train a system” on this fluid daily reality, Gopal explained, noting that real-life business constantly changes based on individual conversations and human interactions. While AI can automate tasks at the absolute top (coding) and the absolute bottom (physical robotics), the vast middle ground of knowledge work requires human context.

Ed Meyercord has been deploying machine learning processes for over a decade at Extreme Networks, a networking company that powers pro football and baseball stadiums and draws in over $1 billion in revenue. He told Fortune in a recent interview that he sees dynamics similar to Gopal’s on the operator’s side of the table. His teams already use agents to design networks, spot failures before they happen, and even communicate with other agents in systems like ServiceNow, but he is adamant that there is always a human in the loop to review the work when the stakes are critical infrastructure.

“A network is critical infrastructure, so we have to be right,” Meyercord said. Extreme has built an agentic core into its platform, he added, “but effectively what that’s allowed us to do is to be highly, highly accurate.” Because accuracy is so paramount, he said, “we always want to have a human in the loop, show all the work that we’re doing.”

Like Gopal, Meyercord said he doesn’t believe AI can simply “take our jobs” outright; the role of the human is shifting from doing every task manually to orchestrating agents, gathering the right context, and deciding which problems to point the machines at. He said his job as CEO is, in many ways, to surround himself with specialists “a lot smarter than I am” while using AI as another hyper‑fast teammate rather than a replacement.

On the other hand, anything that can be automated is already vulnerable to AI, Gopal said, nodding to the “SaaSpocalypse” in markets that is brutally punishing software-as-a-service stocks, insurance, wealth management and customer service. By the end of the year, he said, this will be even more visible in company valuations, as robots hoover up the work of anything that doesn’t require business context. The exciting thing, he added, is what this means for work.

The white-collar worker shift

This symbiotic relationship between the human worker, who has a business context, and the AI, which can work faster and even smarter but lacks the input, will define the future of white-collar work that Shumer has warned about, according to Gopal. “You have to pick and choose the context and you have to keep capturing the context, right? And I think that’s really what the shift is for the average white-collar worker is that they have to understand.”

Gopal related an anecdote from his team, expressing frustration with a mediocre software engineer now that they have AI coding tools. “We’re like, ‘Man, like, it’s just more expensive to talk to you than it is to do it myself. Like, to explain what I need built on the product takes more time than me just slamming it out of AI on the side.’” The time it takes to talk to a mediocre engineer could be spent managing an AI output instead, he added. He likened this to every employee having a personal technical co-founder by their side at all times, potentially enabling them to produce 20 times as much work.

Meyercord agreed, saying that computer-science graduates don’t need the same skillset as before, but they will “need a different skillset.” He said he’s already starting to see new skillsets develop, not necessarily all liberal arts graduates who are deeply trained in critical thinking, but more a sense of “people that are helping us develop.” He needs people who can delegate work to AI agents, talk with agents, vet their work, and oversee workflows. It sounds a lot like what Gopal predicted.

The job of the human has to evolve to feed the proper inputs to the AI agents that will power the business, Gopal predicted, and he put a name on it. “Our job as humans and people is that we are now context gatherers instead of just workers.” Most people have taken this for granted up until now, he said, because they didn’t have AI agents to work alongside. “What makes us good at our job, and what gives us promotions, and what makes us more impactful is actually that ability to gather context. That’s what makes us good.”

The only people who genuinely need to fear for their jobs, Gopal warned, are those who are “refusing to grow” and deny this new reality. If everyday workers fail to adopt these tools, they risk handing all economic power to a select few who do understand the technology, potentially creating a dystopian wealth gap. But for those willing to adapt, the future is incredibly bright. “I don’t think AI will just come and take our jobs,” Gopal said. “That’s not even kind of possible”.

Meyercord said his business is still growing, and he argued that the AI job-loss narrative misses the forest for the trees. “On the one hand, you can do a lot more with less,” he said, “or you could do more with the same [number of workers]. Or you could do a lot more with a little more, right?” If you hire the right context gatherers, Meyercord added, you can really grow your business. “It’s like, how do you think about what you want to try to accomplish? We want to do a lot more.”