问题 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.
AI将如何改变世界?
横向:他表达的 Doom–Bloom 前景看法。 纵向:变革程度。
Doom–Bloom:100 中的 11。变革程度:100 中的 96。解读范围:横向为 0 至 25,纵向为 91 至 100。这些是解读坐标,而不是事件概率。
≈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 · 2026年7月
一个核心假设
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.回答 1
如果这个假设实际并非如此,他的展望会如何变化?
一个尚未解决的问题
My median estimate for fully automated AI research is around the end of 2028, with substantial uncertainty.回答 1
什么能帮助他区分这里各种合理的结果?
什么可能使其改变看法
The biggest update would come from strong real-world evidence about whether AI can automate AI research without human bottlenecks.回答 4
什么证据才足够,又会让他的观点朝哪个方向转变?
更多详情
仍有几种解读是合理的:预计将带来显著益处,但受到重要条件或分配方面的限制。 / 预计收益有限,或仅分布在较小范围内。 / 预计将带来具有变革性且广泛有价值的收益。
58 / 100
在定性尺度上,解读范围为 33 到 100。
灾难性或不可逆的损失是预期未来的核心。
99 / 100
在定性尺度上,解读范围为 100 到 100。
人类的选择具有实质性但受到很大制约的影响。
55 / 100
在定性尺度上,解读范围为 50 到 75。
预计AI仍将是能力有限的工具。
预计AI将在大多数认知工作中达到人类水平。
模拟位置:预计AI将在认知工作中大幅超越人类。
模拟位置:停止或大幅放缓开发能力更强的AI。
在落实所述保障措施的前提下继续开发。
加快开发能力更强的AI。
这些解读保留了他陈述的条件。益处和危害都可能很大。这些范围描述的是我们如何解读他的模拟回答,而不是统计置信区间。
相似的世界观
模拟世界观与 Daniel Kokotajlo 最接近的意见领袖
Daniel Kokotajlo关于AI说过的话
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
逐字引自所链接的出处,核对于 2026年10月3日
模拟评估
来源
用于为此模拟用户提供事实依据的文章、访谈和著述。
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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