Pertanyaan 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.
Bagaimana AI akan mengubah dunia?
Mendatar: pandangan Doom–Bloom yang ia ungkapkan. Ke atas: skala transformasi.
Doom–Bloom: 11 dari 100. Skala transformasi: 96 dari 100. Rentang interpretasi: 0 hingga 25 secara horizontal, 91 hingga 100 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.
≈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
Asumsi utama
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.Jawaban 1
Jika asumsi ini ternyata berbeda, bagaimana pandangannya akan berubah?
Pertanyaan yang belum terjawab
My median estimate for fully automated AI research is around the end of 2028, with substantial uncertainty.Jawaban 1
Apa yang akan membantunya membedakan hasil-hasil yang masuk akal di sini?
Hal yang dapat mengubah pandangan mereka
The biggest update would come from strong real-world evidence about whether AI can automate AI research without human bottlenecks.Jawaban 4
Bukti apa yang akan memadai, dan ke arah mana bukti itu akan mengubah pandangannya?
Detail lebih lanjut
Beberapa penafsiran masih mungkin: Manfaat besar diperkirakan akan terwujud, dengan syarat penting atau keterbatasan distribusi. / Manfaat yang terbatas atau hanya tersebar secara sempit diperkirakan akan terwujud. / Manfaat transformatif yang bernilai luas diperkirakan akan terwujud.
58 / 100
Rentang interpretasi 33 hingga 100 pada skala kualitatif.
Kehilangan yang katastrofik atau tidak dapat dipulihkan merupakan unsur utama masa depan yang diperkirakan.
99 / 100
Rentang interpretasi 100 hingga 100 pada skala kualitatif.
Pilihan manusia memiliki pengaruh yang berarti, tetapi sangat dibatasi.
55 / 100
Rentang interpretasi 50 hingga 75 pada skala kualitatif.
AI diperkirakan akan tetap menjadi alat dengan kemampuan terbatas.
AI diperkirakan akan menyamai manusia dalam sebagian besar pekerjaan kognitif.
Posisi simulasi: AI diperkirakan akan jauh melampaui manusia dalam berbagai pekerjaan kognitif.
Posisi simulasi: Hentikan atau perlambat secara signifikan pengembangan AI yang lebih mampu.
Lanjutkan pengembangan dengan perlindungan yang telah ditetapkan.
Percepat pengembangan AI yang lebih mampu.
Interpretasi ini mempertahankan kondisi yang dinyatakannya. Manfaat dan kerugian dapat sama-sama besar. Rentang tersebut menggambarkan cara kami membaca jawaban simulasinya, bukan interval kepercayaan statistik.
Pandangan dunia serupa
Pemimpin opini dengan pandangan dunia simulasi yang paling mendekati pandangan Daniel Kokotajlo
Apa yang pernah dikatakan Daniel Kokotajlo tentang 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
Dikutip kata demi kata dari sumber yang ditautkan, diperiksa pada 3 Okt 2026
Penilaian Simulasi
Sumber
Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.
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.

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