Tag Archives: Cal Newport

The AI Doom Fever Finally Fades

Is the AI Doom Fever Breaking? (It’s About Time!) — Cal Newport, AI Reality Check, Deep Questions Podcast

For many years now executives leading the big AI companies have been telling the public that their own products will destroy the economy and gut the white-collar workforce. As Cal Newport observes this is the rough equivalent of a Pfizer executive announcing a new pill that cures psoriasis but also turns half the population into zombies. But lately the extremist rhetoric on AI has started to soften significantly. Instead of totally replacing entire segments of the workforce, these new AI systems will now simply augment existing workers and also lead to massive new opportunities for employment. That’s quite a radical shift in attitude, especially coming from people whose breathless messaging has been so bold. 

Nevertheless, tech companies are still laying off tens of thousands of employees and citing AI as the reason. So, we’ll see. Newport thinks the previous over-the-top positioning on AI resulted more from culture, whereas the recent shift in tone is likely more tactical. His analysis is comprehensive and seems pretty accurate given that he’s been pushing back on this rhetorical issue for years now. 

A Strange Sales Pitch

In his podcast, Newport runs through many of the recent doom statements. Mustafa Suleyman of Microsoft AI has suggested that AI will be capable of automating most knowledge work within roughly a year. Dario Amodei of Anthropic has warned that the technology will soon replace up to half of all entry-level white-collar jobs in finance, consulting, and tech. Sam Altman of OpenAI speculated last summer about a future in which AI would “do everything,” leaving humans to find new ways to “participate” in the world.

“That’s basically what we’re getting from the AI CEOs,” Newport says. “And I think it’s just lunacy.” Newport has held this position for some time now. And it’s an opinion shared online by many advanced engineers who have been working with AI systems for a long time. However, there are many so-called social media influencers in the AI space who still push the end-of-the world theme. It’s been an odd experience for sure. Granted, software executives have a long history of bragging in their corporate earnings calls that their systems will enable customers to cut expensive employees. It’s hard to think, though, of another industry whose leaders so cheerfully predict that their products will wreck civilization while making their founders and a few insiders rich beyond their wildest dreams. Seems like a difficult sell, eh?

The New Vibe

In late April 2026, Altman posted on X that OpenAI wants to “build tools to augment and elevate people, not entities to replace them,” and added that “jobs doomerism is likely long-term wrong.” A few days later, Nvidia CEO Jensen Huang pushed back even harder. In a Fortune interview posted May 2, he called the half-of-jobs prediction “ridiculous” and warned that becoming a CEO can leave a person with what he described as “a God complex,” speaking as if their position alone gave them the authority to predict civilization-scale outcomes. Huang also estimated that AI has already created more than half a million jobs, because companies that adopt it grow faster and hire more, and noted that demand for software engineers is actually rising.

Newport reads Huang’s comments as a slightly concealed dig at Amodei, but perhaps it was a subtle signal to the industry that things will be shifting. Either way, the tone has clearly migrated from outright apocalypse to smooth augmentation. Who knows. But at least it’s a welcome change for those directly affected by the recent massive layoffs attributed to AI systems that haven’t even been fully built and deployed yet. 

Where the Doom Came From

To understand the old rhetoric, though, Newport argues that you have to look at the tech culture of San Francisco and Silicon Valley, especially among engineers, and especially content articulated in a few internet forums in recent decades. The most influential was LessWrong, founded by Eliezer Yudkowsky and devoted to refining the art of human rationality. Also, the blog Slate Star Codex, written by Scott Alexander, helped push the same themes into a wider readership. Out of this loose network grew the rationalist movement, which is the idea that if you trained yourself to think like a logical engineer, you could overcome cognitive bias and act more effectively in the world. Newport, who was trained in computer science at MIT, recognizes this culture. “I’m around engineers. I am an engineer. I know this way of thinking,” he says, adding that his wife once told him, “Don’t take me to the MIT Christmas parties because you guys are all so weird.”

Two important offshoots followed. One was effective altruism, which applies something called expected-value reasoning to charitable giving and was made famous — and then infamous — by Sam Bankman-Fried when he led FTX. The other was the existential risk community (X-risk), which argued that very rare disasters with very large costs deserve serious consideration right now. Newport summarizes the X-risk crowd as focusing especially on three threats: asteroid strikes, deadly pandemics, and superintelligent AI. Nick Bostrom’s 2002 paper Existential Risks is the foundational text, but the wider X-risk literature also covers nanotechnology, nuclear war, and what Bostrom calls “totalitarian lock-in.” Newport doesn’t mention Bill Joy’s shock article “Why the Future Doesn’t Need Us” in WIRED in 2000 but it certainly fits the paradigm.

The X-risk crew organized a closed-door conference that produced the open letter signed by Stephen Hawking, Elon Musk, Bill Gates, and many of the leading AI researchers of the day. Newport places it in Puerto Rico in 2017, but the actual event was the Future of Life Institute’s “Future of AI: Opportunities and Challenges” conference in San Juan in January 2015. The 2017 follow-up was the Beneficial AI conference at Asilomar in California. Robert McMillan’s WIRED piece from January 2015, “AI Has Arrived, and That Really Worries the World’s Brightest Minds,” captured the elite anxiety that emerged from the Puerto Rico meeting and may be the article Newport has in mind when he describes the era. There were other similar pieces in the elite media during this time period as well. The elites aren’t shy with the media. 

ChatGPT and the Hero Complex

Then ChatGPT arrived. For people who had spent ten years writing footnoted lists about the coming superintelligence, it felt like the moment they had been preparing for. Newport thinks this was both terrifying and intoxicating. “What if we were right about this risk,” Newport imagines them thinking, “and not only were we right, but it’s happening?” The rationalists sensed they were going to be Neo. They were going to be John Connor. They were the ones who saw it coming and would now lead everyone else through it. It may be hard for normal people to think this way, but we are talking about the tech elite, after all. They do actually live in a different world, one that’s in many ways disconnected from the normal reality of people who have to work for a living. Newport stresses that it’s important to realize that the current AI companies we see now all grew from that culture. 

OpenAI, Newport says, originally presented itself as a nonprofit AI safety organization heavily shaped by X-risk concerns. It was started as almost a hobby project for the rationalist crowd before commercial ambitions reshaped what it is now. Anthropic was founded by former OpenAI staff who, according to Newport, felt their old employer was not being rigorous enough about safety. Grok came out of the same orbit. The CEOs, Newport says, were not playing 4D chess with investors. They were just talking the way everyone they knew talked in Silicon Valley. The trouble started when their companies got too big to keep speaking only to their own closed subculture. As Newport puts it, “we’re not, you know, in the Mission District anymore.”

Why It’s Breaking Now

Newport sees three potential forces that may be accelerating the recent change in AI positioning:

First, there is real IPO pressure building. As OpenAI and Anthropic move toward public markets, more sober East Coast investors (who wear suites, Newport says) are quietly asking the founders to stop terrifying the customers they hope will pay for AI products. 

Second, public opinion is turning. A Quinnipiac poll from March 2026 found that 55 percent of Americans now believe AI may do more harm than good in daily life, which is up from 44 percent a year earlier, with about seven in ten people expecting fewer job opportunities in the future.

And third, journalists are running out of patience. Ezra Klein’s May 2026 New York Times column “Why the A.I. Job Apocalypse (Probably) Won’t Happen” reports that the economists he interviewed are skeptical of mass joblessness. Also, a recent Ronan Farrow piece in The New Yorker even raises the question of whether Altman is actually a strong chief executive for a trillion-dollar company.

Newport says there may be additional pressures in the market pushing AI executives to temper their rhetoric in recent months, but his analysis on the three issues above seems pretty comprehensive as a working hypothesis. 

A Welcome Maturation

The Silicon Valley monoculture has finally collided with the rest of the country, Newport argues. Wall Street realism, journalistic scrutiny, and ordinary public sentiment are forcing the language to evolve to the realities of the market. Newport sounds almost relieved. Somebody, he suggests, finally had to tell these founders to “stop talking like you’re Sarah Connor from Terminator 2.” His understated parting advice still applies. Take AI seriously, but not everything you hear about it.

Cal Newport — In Defense of Thinking

From Cal Newport in the NYT : “The problems I focused on in Deep Work, and in my writing since, have been getting steadily worse. In 2016 my main concern was helping people find enough free time for deep work. Today I think we’re rapidly losing the ability to think deeply at all, regardless of how much space we can find in our schedules for these efforts.”

So, it’s getting worse. Fine. That only represents more of an opportunity for those who can leverage emerging productivity tools to implement stuff while at the same time master the ability to master deep focus to create stuff. I’ll take it.

In Defense of Thinking
NYT Opinion Guest Essay There’s a Good Reason You Can’t Concentrate
The Deep Life
Deep Work

Is AI Conscious? 

I’ve been following Cal Newport for years for his work on cognitive issues and deep work to improve career opportunities. But that’s his side job. He’s actually a serious computer scientist and recently he challenged some obvious misconceptions (here, here) about artificial intelligence (AI), especially claims made by biologist Bret Weinstein on the Joe Rogan podcast and earlier in his own podcast. Generally, Weinstein’s great but he can be abstract and long-winded and sometimes difficult to understand in his effort to explore deeper levels of thought. But with AI, he breaks down. In his conversation with Rogan, Weinstein seemed to imply that ChatGPT and similar large language models (LLMs) might be conscious or even manipulating people right now. Conscious? Not even close. And it didn’t take much for Newport to wreck Weinstein’s view by simply explaining in detail how LLM systems actually work at present. 

Newport played some audio clips from the Rogan podcast where Weinstein said that these current LLMs may be functioning like a child’s brain, running experiments and learning what they want. But Newport explains why this comparison fundamentally misunderstands the technology. “Language models don’t run experiments in the way a human mind might,” Newport says. “LLMs certainly don’t want anything to happen. They have no values or drives like a human brain does.”

It’s reasonable to ask why Weinstein doesn’t recognize that he’s out of his field here. He sounds like a guy on the street talking about something he knows nothing about. Newport and others have clearly stated that these LLMs are limited compared to a fully thinking human being, and if we wanted to build something like a conscious human brain, we’d have quite a long way to go.  

Newport says that language models operate through complicated but static tables of numbers processed sequentially through multiplication algorithms. “Once one of these networks is trained, that vast table of numbers is fixed. It’s static. It does not change.” When you query ChatGPT, nothing is being updated or learned. The same fixed numbers produce words through straightforward matrix multiplication spread across thousands of GPU chips.

Newport compares language models to isolating just the language processing center of a human brain. That neural cluster does sophisticated work to understand words and concepts, but we would never call it conscious or alive on its own. Weinstein asks in one of his Joe Rogan clips, “Is the AI conscious? I don’t know. If it’s not now, it will be.” Really? How does he know that? At this point, Weinstein goes over the top, which only undermines his credibility on the subject and helps promote fear. The technology around AI and other forms of computer automation has been progressing for many decades, but only now we’re finally afraid?

Newport responds to Weinstein immediately and emphatically: “I can answer your question. Is the AI conscious? No, it is not conscious.” Newport sounds almost surprised and frustrated. He explains that when we understand the mechanical process of how language models work, “this has a fraction of the types of operations and behaviors you would need to even imagine something like consciousness.”

Newport also addresses why AI pioneer Geoffrey Hinton sounds similar alarms despite knowing how these systems work since he did much of the initial research to enable the technology. Newport clarifies a crucial but obvious distinction totally missed by Weinstein: Hinton worries about hypothetical future AI systems we haven’t built yet, not the current language models in use today. “Weinstein’s talking about language models,” Newport says. “Hinton’s talking about AI artificial brains that we haven’t built yet, but he’s now more confident than he was before that they are buildable. That’s different.” How long away these systems are Newport doesn’t speculate.

Newport also identifies a pattern in how people commonly misunderstand AI: “When a non-technical critic like Weinstein looks at language models, here’s what I think they’re doing instead. They’re observing from the outside the things that the model is doing. They then write a story about what’s happening inside the model that matches what they observed.” This represents what he calls “an ancient animist religion approach to understanding the world” rather than scientific analysis. That’s a heck of a criticism to project at a scientists like Weinstein. But that’s science, right? You debate data. You discuss different perspectives. And sharp criticism among researchers is normal.

Regarding truly dangerous artificial intelligence, Newport says that we would need breakthroughs in multiple separate technologies: world modeling, simulation and planning systems, policy networks for values and drives, memory systems, and real-time learning capabilities. Current AI agents remain limited because language models lack these crucial capabilities.

Newport says that instead of focusing on apocalyptic narratives, we should instead direct our attention toward AI’s potential present day harms. “AI presents many problems, but most of them are happening right now, not in the future,” he says. These concerns include how AI degrades our thinking abilities when we outsource cognitive work, how it erodes our capacity to distinguish truth from fiction, how it floods us with low-quality generated content, and the environmental and economic costs of the global AI arms race. By understanding the actual mechanics of how AI systems work right now, we might be able to avoid both the unfounded fears as well as the overconfidence in their current capabilities.