Question 1
Daniel Kokotajlo
x.com/DKokotajloAI Futures Project forecaster who studies how automating AI research could speed up progress and calls for a verified international slowdown.
How will AI change the world?
Across: his expressed Doom–Bloom outlook. Up: scale of transformation.
Doom–Bloom: 11 out of 100. Scale of transformation: 96 out of 100. Interpretation ranges: 0 to 25 horizontally, 91 to 100 vertically. These are interpretation coordinates, not event probabilities.
≈70%
“70% chance of something like AIs taking over”
AI takeover or a comparably very big catastrophe on the default path “if things don’t change”; explicitly not an extinction-only estimate
Transcript of Daniel Kokotajlo Interview: Diary Of A CEO Podcast · Jul 2026
A central assumption
The key mechanism is feedback: increasingly capable systems automate more of the coding involved in AI development; then they begin automating research itself—designing experiments, interpreting results, improving training methods and helping build their successors.Answer 1
If this assumption turned out differently, how would his outlook change?
An unresolved question
My median estimate for fully automated AI research is around the end of 2028, with substantial uncertainty.Answer 1
What would help him distinguish the plausible outcomes here?
What could change their mind
The biggest update would come from strong real-world evidence about whether AI can automate AI research without human bottlenecks.Answer 4
What evidence would be enough, and in which direction would it move his view?
More details
Several readings remain plausible: Substantial benefits are expected, with important conditions or distribution limits. / Limited or narrowly distributed gains are expected. / Transformative, broadly valuable gains are expected.
58 / 100
Interpretation range 33 to 100 on the qualitative scale.
Catastrophic or irreversible loss is central to the expected future.
99 / 100
Interpretation range 100 to 100 on the qualitative scale.
Human choices have meaningful but substantially constrained influence.
55 / 100
Interpretation range 50 to 75 on the qualitative scale.
AI is expected to remain bounded tools.
AI is expected to match people across most cognitive work.
Simulated position: AI is expected to substantially exceed people across cognitive work.
Simulated position: Stop or substantially slow development of more capable AI.
Continue development under stated safeguards.
Speed up development of more capable AI.
These interpretations keep his stated conditions. Benefits and harms can both be substantial. The ranges describe how we read his simulated answers, not statistical confidence intervals.
Similar worldviews
Thought leaders whose simulated worldviews are closest to Daniel Kokotajlo’s
What Daniel Kokotajlo has said about AI
Kokotajlo forecasts that AI companies could soon automate AI research, speeding up progress, and he calls for a verified international slowdown.
“Today’s AIs sometimes pursue goals other than the ones they were given, and sometimes hide that they are doing so.”
U.S. Senate subcommittee testimony “If I had to say one sentence, I would say: the trends seem to indicate that we’re just a couple years away from fully automating AI research”
80,000 Hours Podcast “I think it’s going to be very bewildering and scary. I think it could be really good. But it also could be really bad.”
80,000 Hours Podcast “We think there should be a deliberate effort to pace the frontier.”
Palisade Research podcast “I would say we do wanna build superintelligence eventually, but the way that we do it is extremely important.”
Lawfare, Scaling Laws podcast
Word for word from the linked sources, checked Oct 3, 2026
Sources
Articles, interviews, and writings used to ground this simulated user.
Use only Daniel’s labeled answers in the publisher transcript. He puts fully automated AI research around end-2028, rejects racing through an intelligence explosion, and argues that even a pause at human-level AI would radically transform the economy. Distinguish his forecasts from the host’s incident claims.

Coauthored latest forecast update: slightly shorter timelines, better evidence and modeling; combines coding uplift, revenue and time horizons. Estimates remain conditional on moving as fast as technically feasible. Distinguish Daniel’s parameters from Eli’s and Brendan’s.

Coauthored policy scenario advocating a verified international slowdown, transparent AI research and distributed power. It is a recommendation, not a prediction of AI arriving in 2040. He expects development sooner absent intervention; the concrete scenario uses another author’s timeline.

Team clarification: a slower transparent frontier can reduce power concentration and allow safety progress. China verification and government competence remain challenges. Sympathetic to full shutdown but concerned it may buy less alignment progress before agreements fail.

Thomas Larsen’s supplement to the coauthored plan, not a personal Daniel forecast. Explains public visibility into research and training activity while protecting model weights, reciprocal verification, outside scrutiny and checks against power abuses.

Thomas Larsen’s explicit assumptions, not Daniel’s personal numerical estimates. Separates confident high-level predictions and recommendations from uncertain dates, takeoff speed, alignment difficulty and ability to detect covert projects.

Historical update: Daniel moved Automated Coder median from late-2029 to mid-2028 after agentic-coding evidence and revised time-horizon estimates. Shows genuine updating; current answers should prioritize the subsequent August model and interview.

Coauthored self-evaluation grades concrete predictions rather than treating the scenario as established fact. Initial quantitative progress was slower than predicted; July amendment raises the estimated pace. Coding uplift and valuation lagged while revenue was stronger.

Coauthored correction of reporting that confused scenario years, modes, medians, raw model trajectories and different authors’ forecasts. They never claimed certainty about 2027. Superseded numerically by later quarterly updates.

Coauthored scenario linking coding automation to automated research, rapidly accelerating capabilities, misalignment and concentrated power. The scenario is a forecast exercise with branches, not an account of actual events. Later forecast updates supersede its dates.

Publisher’s speaker-labeled interview with Daniel and Scott Alexander. Use Daniel’s answers only: coding automation can remove research bottlenecks, government oversight and transparency counter secrecy and power concentration, and physical deployment still has bottlenecks. Timeline references are historical.

Third-party speaker-labeled transcript; use only Daniel’s answers, not Steven Bartlett’s framing. Asked whether we are heading somewhere bad if things don’t change, he says yes but he is not confident: something like 70%, because the current default path heads somewhere very scary. He corrects the host’s “70% chance of human extinction”: the figure is for AIs taking over or a comparably very big catastrophe, and AIs might take over without killing everyone. He does not think we are definitely doomed and could see it working out well.

Simulated Assessment
Question 2
Setting aside good or bad: how much do you think AI will ultimately change the world—a little, a lot, or completely?
Question 3
What’s your rough gut-feel chance that AI causes human extinction or a similarly permanent catastrophe? A number or “no idea” is fine.
Question 4
What discovery or event would most change your view of AI’s future impact?
Where do you land?
Map my worldview