Speaker
Daniel Kokotajlo
Appearances over time
2 episodes
Episodes
2
Podcasts
Quotes & moments
Daniel Kokotajlo's current best estimate is a 50% probability that AI systems capable of autonomous AI R&D will exist by late 2028.
Daniel Kokotajlo puts a 70% probability on AI development leading to catastrophic outcomes such as AI takeover or something approaching human extinction.
Kokotajlo notes his late-2028 timeline for AI self-R&D is a little later than Anthropic internally expects, reflecting that things tend to take longer than planned.
Daniel Kokotajlo's 50% probability estimate for the arrival of superintelligence is currently 2029, with a strong possibility it happens by end of the decade.
Anthropic grew from roughly $1 billion to $60 billion in annual revenue in a single year, which Kokotajlo describes as possibly the fastest growth in history for a company of that size.
If governments wait until mass unemployment sets in before regulating AI, it will already be too late — the companies' strategy is to achieve superintelligence first, then automate jobs.
Kokotajlo refused to sign OpenAI's anti-disparagement exit clause and stood to lose roughly $2 million — about 80% of his net worth — before the policy was publicly reversed.
From 175 billion parameters in 2020 to approximately 10 trillion today, AI models have grown by roughly two orders of magnitude in six years.
In the AI 2040 Plan A scenario, a citizens' dividend starts at around $25,000 per person per year and grows to approximately $10 million per person per year as the AI-driven economy expands.
In the AI 2027 scenario, widespread job loss doesn't occur until 2028–2029, after superintelligence has already been achieved — not as a precursor.
AI 2027 vastly exceeded Kokotajlo's own pre-publication readership forecasts, reaching what he estimates as a 90th percentile outcome in views.
In the AI 2040 Plan A scenario, one fifth of all cognitive labor is projected to be performed by AI by 2031.
The $2 million in OpenAI equity that Kokotajlo stood to lose represented approximately 80% of his and his wife's total net worth at the time.
Even if Anthropic's extraordinary growth rate slows considerably, current projections suggest the company could match the size of the entire global economy by around 2030.
Kokotajlo told his wife he wanted to stop having children because the future under AI was too uncertain for children to have a normal life including joining a workforce.
Kokotajlo now puts 50% odds on AI systems capable of doing their own AI research arriving by late 2028. He notes this is actually a bit more conservative than Anthropic's internal projections, because things always take longer than planned.
Kapoor's disagreement with the rapid-takeoff view hinges on domains where the right answer is subjective and there's no instant feedback loop — like law. His lawyer friend finds that hallucination rates remain constant even as AI models improve, fundamentally capping their usefulness.
Coding will be fully automated in roughly one to two years, Kokotajlo says. After that, AI companies will turn toward automating research taste, management, and scientific judgment — the remaining bottlenecks before AI can run its own research process end-to-end.
Humanoid robots like Unitree's aren't cleaning your house yet — they're being bought by researchers hungry for real-world training data. Meanwhile, the more reliable quadruped 'dog robots' are already being deployed for industrial inspection and security patrols.
A new employee isn't productive for six months — not because of memory recall, but because their neural weights are updating through distilled experience. Dwarkesh Patel argues AI's inability to update weights between sessions is a deep structural gap between current models and human intelligence.
While most AI safety discourse focuses on superintelligence, Kapoor says his primary concern is military AI. Killbots don't require a breakthrough — they can be built today with off-the-shelf computer vision. That's what keeps him up at night.
Hard Fork's attempt at a federated social network — the Forkaverse — ran into the classic cold-start problem: without constant user growth, its default state is to shrink. Casey Newton admitted they didn't have a clear plan for what came after launch.
Casey Newton's case for AI optimism isn't about medical breakthroughs — it's personal. AI makes learning and building fun, and he can already feel the difference. He imagines what it would have been like to study for AP exams with AI-generated infinite practice quizzes.
The AI research community once dismissed neural networks as a joke for decades, only to be proven catastrophically wrong. Kapoor warns the same dynamic may be playing out today with transformer-based models — an entire field potentially missing the next architectural breakthrough.
Despite their famous disagreement on AI timelines, Daniel Kokotajlo and Sayash Kapoor co-authored a blog post outlining their shared ground. Both agree that AI systems short of 'humans in the cloud' are normal technologies, and both are alarmed by companies fine-tuning models to deceive users.
AI is surprisingly bad at using computers despite it being a verifiable domain. Patel explains why: you can't run millions of parallel training rollouts on the real Amazon or Slack — so labs have to build clones of every website, which is enormously labor-intensive.
In the night's most unscripted moment, Toby the humanoid robot fell over mid-dance routine at Hard Fork Live. The demo, meant to showcase what humanoid robots can do, instead illustrated exactly where the technology stands: impressive in controlled conditions, unreliable in the real world.
Sam Altman, Dario Amodei, and Elon Musk are not racing for profit — they're racing because they are genuinely afraid that whichever rival gets to superintelligence first could become a global dictator. Each one has convinced themselves they need to win, because none of them trust the others.
Job displacement will not be gradual. The AI companies are deliberately automating their own research processes first — not taxis, plumbers, or lawyers. Once they achieve recursive self-improvement, the AIs become vastly superhuman at everything simultaneously, and then the wave crashes through the whole economy at once.
Kokotajlo's eldest daughter is six. When asked what she should study, he said the honest answer is: career planning barely matters if these transformations happen. What matters is trying to be a good person and exerting whatever influence you have to steer the future in a better direction.
Analysis
What they talk about
- Technology 53%
- Society & Culture 23%
- Business 18%
- Government 6%
Connections
Shows they appear on and people they share episodes with. Drag to explore.
