2023
Selekoa’s 356 records dated 2023, in date order, grouped by topic.
Selective: what the records hold, not a history of the year.
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People 46 records
Dated to 2023 only
· interval begins · dated to the year
Andrej Karpathy describes returning to OpenAI in 2023–2024 to build a midtraining and synthetic-data team. Evidence ↗· snapshot · dated to the year
Jiemian reported on 21 February 2025 that Google promoted Wu Yonghui in 2023 to Google Fellow and vice president of research at Google DeepMind. Evidence ↗· snapshot · dated to the year
Jiemian reported on 21 February 2025 that Zhu Wenjia led the creation of ByteDance’s first large-model R&D team in 2023 and became its head. Evidence ↗· event · dated to the year
Essential AI's press release of 12 December 2023 names Niki Parmar as a co-founder of the company. Evidence ↗· event · dated to the year
Essential AI's press release of 12 December 2023 names Ashish Vaswani as a co-founder of the company. Evidence ↗
February 2023
· snapshot · dated to the day · announced
Fortune, republishing Bloomberg, reported on 27 February 2023, attributing the information to a Meta spokesperson, that Ahmad Al-Dahle would lead Meta's new generative AI product group. Evidence ↗· snapshot · dated to the day
The first LLaMA paper, dated 27 February 2023, lists Timothée Lacroix under the Meta AI affiliation. Evidence ↗· snapshot · dated to the day
The first LLaMA paper, dated 27 February 2023, lists Guillaume Lample under the Meta AI affiliation. Evidence ↗· snapshot · dated to the day
The first LLaMA paper lists Hugo Touvron under the Meta AI affiliation. Evidence ↗
March 2023
April 2023
· snapshot · dated to the day
Google announced that Jeff Dean would become Google’s Chief Scientist in April 2023. Evidence ↗· snapshot · dated to the day
Google’s combination announcement stated that Jeff Dean would serve as Chief Scientist to Google DeepMind. Evidence ↗· snapshot · dated to the day
Google’s combination announcement stated that Jeff Dean would serve as Chief Scientist to Google Research. Evidence ↗· snapshot · dated to the day
Demis Hassabis stated that he would lead the combined Google DeepMind unit. Evidence ↗· snapshot · dated to the day
The April 2023 combination announcement identifies Demis Hassabis as DeepMind CEO. Evidence ↗· snapshot · dated to the day · announced
Demis Hassabis announced on 20 April 2023 that Eli Collins would join his leads team as VP of Product. Evidence ↗· snapshot · dated to the day · announced
Demis Hassabis announced on 20 April 2023 that Zoubin Ghahramani would join Google DeepMind's research leadership team, reporting to Koray Kavukcuoglu. Evidence ↗· snapshot · dated to the day · announced
Demis Hassabis announced on 20 April 2023 that a new Scientific Board for Google DeepMind would be led by Koray Kavukcuoglu. Evidence ↗
May 2023
· event · dated to the month
01.AI’s management-team page states that Kai-Fu Lee founded 01.AI in May 2023. Evidence ↗
June 2023
· snapshot · dated to the day
Meta identified Yann LeCun as its Chief AI Scientist in the I-JEPA account. Evidence ↗· snapshot · dated to the day
The Decoder reported at Reka’s emergence from stealth on 27 June 2023 that Cyprien de Masson d’Autume was its CTO. Evidence ↗· snapshot · dated to the day
The Decoder reported at Reka’s emergence from stealth on 27 June 2023 that Yi Tay was its co-founder and Chief Scientist. Evidence ↗· snapshot · dated to the day
The Decoder reported at Reka’s emergence from stealth on 27 June 2023 that Dani Yogatama was its CEO. Evidence ↗
July 2023
· snapshot · dated to the day
OpenAI’s Superalignment announcement names Ilya Sutskever as a co-lead of the new team. Evidence ↗· snapshot · dated to the day
OpenAI’s Superalignment announcement names Jan Leike as a co-lead of the new team. Evidence ↗· snapshot · dated to the day
Greg Yang appeared on xAI’s launch team roster in July 2023. Evidence ↗· snapshot · dated to the day
Guodong Zhang appeared on xAI’s July 2023 launch team roster. Evidence ↗· snapshot · dated to the day
Igor Babuschkin appeared on xAI’s launch team roster in July 2023. Evidence ↗· snapshot · dated to the day
Jimmy Ba appeared on xAI’s July 2023 launch team roster. Evidence ↗· snapshot · dated to the day · announced
Ars Technica reported on 12 July 2023 that xAI had announced that Dan Hendrycks, director of the Center for AI Safety, would be advising the team. Evidence ↗· snapshot · dated to the day
Ars Technica reported on 12 July 2023 that xAI's website listed Zihang Dai among the members of its team at launch. Evidence ↗· snapshot · dated to the day
Ars Technica reported on 12 July 2023 that xAI's website listed Kyle Kosic among the members of its team at launch. Evidence ↗· snapshot · dated to the day
Ars Technica reported on 12 July 2023 that xAI's website listed Manuel Kroiss among the members of its team at launch. Evidence ↗· snapshot · dated to the day
Ars Technica reported on 12 July 2023 that xAI's website listed Ross Nordeen among the members of its team at launch. Evidence ↗· snapshot · dated to the day
Ars Technica reported on 12 July 2023 that xAI's website listed Tony Wu among the members of its team at launch. Evidence ↗· event · dated to the day
Elon Musk publicly launched xAI on July 12, 2023. Evidence ↗· snapshot · dated to the day
Christian Szegedy appears on xAI’s launch-team roster. Evidence ↗· snapshot · dated to the day
Toby Pohlen appears on xAI’s launch-team roster. Evidence ↗
September 2023
· snapshot · dated to the day
The spin-out announcement identifies Paul Christiano as head of ARC. Evidence ↗· snapshot · dated to the day
Jingren Zhou is named with Qwen Team / Alibaba Group affiliation in the 2023 report. Evidence ↗· snapshot · dated to the day
Junyang Lin is named with Qwen Team / Alibaba Group affiliation in the 2023 report. Evidence ↗
October 2023
November 2023
· snapshot · dated to the day
Emmett Shear posted on X on 20 November 2023 that he had accepted the role of interim CEO of OpenAI. Evidence ↗· snapshot · dated to the day · announced
Sam Altman’s message of 29 November 2023, published by OpenAI, stated that Mira Murati would return to her role as CTO. Evidence ↗· snapshot · dated to the day
The FAIR retrospective identifies Joelle Pineau as Meta VP of AI Research. Evidence ↗
December 2023
Institutions 18 records
Dated to 2023 only
· event · dated to the year
Moonshot AI’s “About” page dates its formal establishment to spring 2023 (“2023年 春天 … 月之暗面（Moonshot AI）正式成立”). Evidence ↗· event · dated to the year
The ByteDance Seed Team states in its Seed-OSS model card (revision of 26 August 2025) that it was founded in 2023. Evidence ↗· event · dated to the year
Essential AI's press release of 12 December 2023 states that the company was founded in 2023. Evidence ↗· event · dated to the year
H Company’s about page states that the company was founded in 2023. Evidence ↗
February 2023
March 2023
April 2023
· event · dated to the month
Kechuangban Ribao (科创板日报) reported on 23 March 2024 that StepFun was established in April 2023. Evidence ↗· event · dated to the month
Mistral AI’s company page states that Mistral was born in April 2023. Evidence ↗· event · dated to the day
The Brain team from Google Research was announced as joining DeepMind in Google DeepMind. Evidence ↗· event · dated to the day
DeepMind was announced as joining the Brain team in a combined unit called Google DeepMind. Evidence ↗
May 2023
· event · dated to the month
01.AI’s company page states that the company was established in May 2023. Evidence ↗· snapshot · dated to the day
Alibaba’s May results identify Cloud Intelligence Group within its six-group holding-company structure. Evidence ↗
July 2023
· event · dated to the month
MIT Technology Review reported on 24 January 2025 that DeepSeek was founded in July 2023 by Liang Wenfeng. Evidence ↗· event · dated to the month
Sakana AI’s corporate information page gives its founding date as July 2023. Evidence ↗
September 2023
· announcement · dated to the day · announced
The article dated September 19, 2023 announces ARC Evals’ separation from ARC into a standalone evaluation organisation. Evidence ↗· snapshot · dated to the day
The 2023 Qwen report identifies Qwen Team under Alibaba Group. Evidence ↗
November 2023
· snapshot · dated to the day
Meta’s ten-year retrospective identifies FAIR as its Fundamental AI Research team. Evidence ↗
December 2023
· announcement · dated to the day · announced
The article dated December 4, 2023 announces ARC Evals’ adoption of the name METR, Model Evaluation & Threat Research. Evidence ↗
Ideas & lineages 114 records
January 2023
· publication · dated to the day
“Alexander and Yudkowsky on AGI goals” (2023-01-24) credits Eliezer Yudkowsky as one of its 2 authors. Evidence ↗· publication · dated to the day
The post “Alexander and Yudkowsky on AGI goals” (2023-01-24) contests Y05. Answering a question about whether AIs will internalise prohibitions the way most children do, he argues that children's moral learning relies on innate human mechanisms that AI training does not reproduce. Evidence ↗Alexander and Yudkowsky, Alexander and Yudkowsky on AGI goals (2023) →Claim page →
February 2023
· publication · dated to the day
The LLaMA paper discusses Hoffmann and colleagues’ compute-optimal training analysis while distinguishing inference budgets. Evidence ↗· publication · dated to the day
Meta published the LLaMA paper, whose first arXiv version is dated 27 February 2023; Meta’s announcement of 24 February 2023 introduced the work and links the paper. Evidence ↗· publication · dated to the day
The LLaMA paper reports using the Transformer architecture of Vaswani and colleagues. Evidence ↗· publication · dated to the day
Hugo Touvron co-authored the first LLaMA paper. Evidence ↗
March 2023
· announcement · dated to the day
Baidu announced ERNIE Bot on 16 March 2023, stating that it would be accessible to invited users from that day and that its API would be available on application to enterprise clients through Baidu AI Cloud. Evidence ↗· publication · dated to the day
“GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) credits Daniel Rock as one of its 4 authors. Evidence ↗· publication · dated to the day
“GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) credits Pamela Mishkin as one of its 4 authors. Evidence ↗· publication · dated to the day
“GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) credits Sam Manning as one of its 4 authors. Evidence ↗· publication · dated to the day
“GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) credits Tyna Eloundou as one of its 4 authors. Evidence ↗· publication · dated to the day
Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) bears on M02. The authors deliberately do not classify exposure as automation versus augmentation. Evidence ↗· publication · dated to the day
Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) bears on M03. The authors argue LLMs meet general-purpose-technology criteria of pervasiveness, improvement and complementary innovation. The authors note that co-invention requirements typical of general-purpose technologies may slow LLM diffusion. Evidence ↗· publication · dated to the day
Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) bears on M06b. The authors call for policy preparedness and point to prior work on education, training and safety-net reform, but decline to recommend specific policies. Evidence ↗· publication · dated to the day
Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) articulates M07. The authors estimate the share of US workers whose occupational tasks are exposed to LLMs, from O*NET task descriptions rated by humans and GPT-4. The authors describe their method: rating task exposure using a rubric applied by human annotators and GPT-4. Evidence ↗· publication · dated to the day
Eloundou et al., “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models” (2023-03-17) bears on M07. The authors caution that exposure measures technical feasibility, not realised productivity or automation. Evidence ↗· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) credits Eliezer Yudkowsky as its author. Evidence ↗· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y01. Argues that the dangerous thresholds lie beyond human-level intelligence and cannot be calculated in advance, so a lab could pass them unnoticed. Written in response to the March 2023 open letter calling for a six-month pause. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y07. In an op-ed arguing that a six-month moratorium is insufficient, he cites a leading lab's stated intention to have future AI do alignment work as evidence that there is no viable survival plan. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y14. Contrasts safety for superhuman intelligence with other challenges humanity has overcome by learning from mistakes and trying again. Written in response to the March 2023 open letter calling for a six-month pause. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) bears on Y17. Setting out what he thinks would actually need to be done, he calls for an indefinite worldwide halt to large training runs, backed by multinational agreements. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y17. Responding to an open letter calling for a six-month pause on training systems more powerful than GPT-4, he argues that the letter asks for too little. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y19. In a general-audience article he repeats the formula after saying that the likely outcome of current methods is an AI that does not care for humans. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y22. Concludes his TIME essay, which calls for an indefinite worldwide moratorium on large training runs, not a six-month pause; the same essay says anyone 'talking of arms races is a fool'. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y24. Within his proposal for a worldwide moratorium, he rejects framing AI development as a national arms race on the ground that everyone shares the outcome. Discussing a moratorium that might begin in the United States, he describes the technology as one that no country could own and that would kill people in both countries. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y26. Asks readers to picture a hostile superhuman AI as a fast alien civilisation initially confined to computers, then argues that such confinement would not last because molecular biology can be ordered online. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y27. Arguing that making superhuman AI safe could take decades, he says that failure on the first attempt leaves no one to learn from it. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
· publication · dated to the day
“Pausing AI Developments Isn't Enough. We Need to Shut it All Down” (2023-03-29) contests Y30. Opening his case against a six-month pause, he states what he expects from building superhuman AI under present conditions. Evidence ↗Yudkowsky, Pausing AI Developments Isn't Enough. We Need to Shut it All Down (2023) →Claim page →
April 2023
· publication · dated to the day
“GPTs are Predictors, not Imitators” (2023-04-08) credits Eliezer Yudkowsky as its author. Evidence ↗· publication · dated to the day
The post “GPTs are Predictors, not Imitators” (2023-04-08) contests Y09. Using the example of text listing products of primes followed by the primes, he argues that predicting text can demand more intelligence than generating it, so the task does not cap out at the level of the human writers. Concludes that predicting all Internet text, including the errors and private thoughts behind it, is a harder task than being a human, while conceding that GPT-4 is not yet as capable as a human in many ways. Evidence ↗Yudkowsky, GPTs are Predictors, not Imitators (2023) →Claim page →
· publication · dated to the day
The post “GPTs are Predictors, not Imitators” (2023-04-08) contests Y21. He argues that predicting human text requires modelling the processes behind it, a different and harder task than being human, so a predictor need not think like the humans it predicts. Evidence ↗Yudkowsky, GPTs are Predictors, not Imitators (2023) →Claim page →
June 2023
· publication · dated to the month
“The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) credits Matt Clancy as one of its 2 authors. Evidence ↗· publication · dated to the month
“The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) credits Tamay Besiroglu as one of its 2 authors. Evidence ↗· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) articulates G01. Speaker: Tamay Besiroglu. Besiroglu defines explosive growth as at least ten times the past century's annual growth rate, sustained for at least a decade, and gives a conditional probability. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) contests G01. Speaker: Matt Clancy. Clancy questions whether growth models can be extrapolated to growth far outside historical experience. Earlier in the debate he puts the chance of explosive growth at 10–20%. Clancy's answer to Besiroglu's 65%, given on the same condition of AI deployed throughout the economy. He adds that most of the probability is "clustered around the low end of explosive growth", and the same turn doubts that growth models can be extrapolated so far. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) articulates G02. Speaker: Tamay Besiroglu. Besiroglu argues that AI restores the population-ideas feedback that drove historical acceleration. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) articulates G03. Speaker: Tamay Besiroglu. Points to faster growth after the Neolithic and Industrial Revolutions, and to double-digit catch-up growth, as precedent. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) bears on G03. Speaker: Matt Clancy. Clancy questions whether past accelerations are informative about an acceleration driven by intelligence. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu (2023-06) articulates G04. Speaker: Matt Clancy. Clancy gives a verbal version of the Aghion, Jones and Jones task model applied to innovation: with every task required and automation advancing about 1% a year, growth stays steady; only faster or full automation gives explosive growth. Clancy says AI is "pretty likely to boost growth" and then lists bottlenecks that slow AGI's takeover of tasks: physical-world tasks, raw materials, training data, the time natural processes take, preferences for human performers, regulation and zero-sum competition. Clancy's closing turn: different sectors are held back by materials, time, data or regulation; sectors that automate easily become cheap and "no longer count for much of GDP". Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) contests G04. Speaker: Tamay Besiroglu. Besiroglu argues that with complementary tasks, scaling digital workers on automated tasks still raises output by one or two orders of magnitude unless a large share of tasks stays unautomated. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) contests G05. Speaker: Tamay Besiroglu. Besiroglu sets three conditions a resource bottleneck must meet (vital, hard to substitute, impossible to expand by an order of magnitude); he also argues that experiments can be parallelised and that regulation faces strong incentives to be bypassed. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) articulates G06. Speaker: Matt Clancy. Clancy answers Besiroglu's point about fast growth in AI training compute. Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the month
Clancy and Besiroglu, “The Great Inflection? A Debate About AI and Explosive Growth” (2023-06) articulates G09. Speakers: Tamay Besiroglu; Matt Clancy. Besiroglu's estimate conditional on AI automating the vast majority of tasks. In his closing turn he gives 25–75% "conditional on AGI". He separately puts the development of such AI this century at about 80%; he does not combine the figures. Clancy's estimate on the same condition, with "most of that probability clustered around the low end of explosive growth". Evidence ↗Clancy and Besiroglu, The Great Inflection (2023) →Claim page →
· publication · dated to the day
The preprint “Can large language models democratize access to dual-use biotechnology?” (arXiv v1, 6 June 2023) states that its results suggest that large language models will make pandemic-class agents widely accessible as soon as they are credibly identified, even to people with little or no laboratory training. Evidence ↗Can large language models democratize access to dual-use biotechnology? →Claim page →
· publication · dated to the day
The preprint "Can large language models democratize access to dual-use biotechnology?" (arXiv v1, 6 June 2023) states that the agents it discusses would become widely accessible as soon as they are credibly identified, even to people with little or no laboratory training, against the premise that laboratory execution is the binding barrier. Evidence ↗Can large language models democratize access to dual-use biotechnology? →Claim page →
· publication · dated to the day
The preprint of 6 June 2023 states that promising nonproliferation measures include pre-release evaluations of language models by third parties, curation of training datasets, and verifiable screening of all DNA generated by synthesis providers. Evidence ↗Can large language models democratize access to dual-use biotechnology? →Claim page →
· publication · dated to the day
Meta published an account of I-JEPA’s representation-prediction approach. Evidence ↗· publication · dated to the day
The I-JEPA account discusses self-supervised learning from unlabeled observations. Evidence ↗· publication · dated to the day
Sandbrink's preprint on AI and biological misuse (arXiv v1, 24 June 2023) states that large language models such as GPT-4 are already able to provide dual-use information that could have enabled historical biological weapons efforts to succeed. Evidence ↗Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools →Claim page →
· publication · dated to the day
Sandbrink's preprint (arXiv v1, 24 June 2023) differentiates large language models from biological design tools, stating that language models in particular lower barriers to biological misuse while design tools expand the capabilities of sophisticated actors. Evidence ↗Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools →Claim page →
· publication · dated to the day
Sandbrink's preprint (arXiv v1, 24 June 2023) states that enhanced screening of gene synthesis is among the measures key to reducing the risks it describes. Evidence ↗Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools →Claim page →
· publication · dated to the day
Sandbrink's preprint (arXiv v1, 24 June 2023) states that biological design tools will expand the capabilities of sophisticated actors, and proposes interventions to deter biological misuse by such actors. Evidence ↗Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools →Claim page →
July 2023
· event · dated to the day
OpenAI published Introducing Superalignment, setting out its new team’s alignment research programme. Evidence ↗· publication · dated to the day
Introducing Superalignment proposes AI assistance with evaluating other AI systems as scalable oversight. Evidence ↗· publication · dated to the day
Introducing Superalignment credits Ilya Sutskever as an author. Evidence ↗· publication · dated to the day
Introducing Superalignment credits Jan Leike as an author. Evidence ↗· publication · dated to the day
Introducing Superalignment argues that current alignment techniques including RLHF will not scale to superintelligence because they rely on human supervision that the authors expect to become unreliable for much smarter systems. Evidence ↗· publication · dated to the day
ARC Evals published an account of its autonomous-task pilot. Evidence ↗· publication · dated to the day
ARC Evals’ pilot announcement points to the GPT-4 system card as reporting earlier experiments. Evidence ↗ARC Evals autonomous-task pilot announcement (2023) →Claim page →
September 2023
· publication · dated to the day
Anthropic RSP v1.0 says its commitments are designed in the spirit of the Responsible Scaling Policy framework being developed by Paul Christiano and ARC Evals. Evidence ↗· event · dated to the day
The RSP announcement explicitly credited ARC Evals’ framework as inspiring Anthropic’s approach. Evidence ↗· publication · dated to the day
Jingren Zhou co-authored the Qwen Technical Report. Evidence ↗· publication · dated to the day
Junyang Lin co-authored the Qwen Technical Report. Evidence ↗· publication · dated to the day
The Qwen report says it uses PPO in reinforcement learning from human feedback. Evidence ↗· publication · dated to the day
Qwen Team published its 2023 technical report on pretraining and alignment. Evidence ↗· publication · dated to the day
"Open-Sourcing Highly Capable Foundation Models" (arXiv v1, submitted 29 September 2023) states that for some highly capable foundation models likely to be developed in the near future, open-sourcing may pose sufficiently extreme risks to outweigh the benefits, and names access to biological weapons knowledge among the dangerous capabilities at issue. Evidence ↗Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives →Claim page →
October 2023
· publication · dated to the month
The October 2023 NBER working paper “Economic Growth under Transformative AI (October 2023 version)” (2023-10) credits Anton Korinek as one of its 2 authors. Evidence ↗· publication · dated to the month
The April 2026 revision of the NBER working paper “Economic Growth under Transformative AI” (2023-10) credits Anton Korinek as one of its 2 authors. Evidence ↗· publication · dated to the month
The October 2023 NBER working paper “Economic Growth under Transformative AI (October 2023 version)” (2023-10) credits Philip Trammell as one of its 2 authors. Evidence ↗· publication · dated to the month
The April 2026 revision of the NBER working paper “Economic Growth under Transformative AI” (2023-10) credits Philip Trammell as one of its 2 authors. Evidence ↗· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI (October 2023 version)” (2023-10) articulates E01. Discussing AI safety, the authors say models in which capital recursively self-improves without human input are the most worrying for existential risk. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI (October 2023 version)” (2023-10) bears on E01. The abstract distinguishes AI letting capital self-replicate from AI letting it self-improve via knowledge production. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI” (2023-10) articulates E01. Abstract sentence contrasting automation of production (self-replication) with automation of R&D (self-improvement). Citing the high capital share in AI R&D (Besiroglu et al., 2024), the authors describe a feedback in which automating AI R&D speeds AI capability growth, then cite calibrated models of this feedback (Erdil et al. 2025; Lifland et al. 2025) as having 'speculative elements'. In the risk subsection, the authors contrast a scenario of acceleration from R&D automation alone with scenarios of automated physical production, each with distinct safety concerns. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023, revised 2026) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI” (2023-10) bears on E01. Opening paragraph: the authors say future models may reach AGI and, unlike earlier technologies, could automate R&D itself. The clause names AI creating better AI systems. The authors characterise the 'singularity' hypothesis and add that absent current economic effects cannot refute it because the theory predicts little advance warning. Second of the review's 'robust findings': automating R&D in isolation has ambiguous growth effects. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023, revised 2026) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI (October 2023 version)” (2023-10) articulates E02. Explaining their focus on average wages and the labour share, the authors state that the likeliest transformative long-run scenario is AI outsmarting all humans. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI” (2023-10) bears on E02. Definition of AGI used in the review's introduction. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023, revised 2026) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI (October 2023 version)” (2023-10) articulates E03. The authors frame AI safety as a public good to which public-goods and climate-risk economics could apply. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI” (2023-10) articulates E03. Concluding subsection placing AI existential risk within the paper's framing of economic transformation; it goes on to say the scenarios carry different risk profiles. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023, revised 2026) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI” (2023-10) bears on E04. Closing sentence of the dangerous-AI subsection. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023, revised 2026) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI (October 2023 version)” (2023-10) articulates E05. Closing their review, Trammell and Korinek say that existential risk from AI, central in futurist discussion, is almost absent from economics, because existing growth models offer no obvious mechanism for it. The authors attribute the profession's inattention to growth singularities to a modelling norm tied to the Kaldor facts, not to the models themselves. Footnote dating Sandberg's 2013 overview of singularity models to before mainstream economists' engagement with transformative AI. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI (October 2023 version)” (2023-10) bears on E05. The review opens by dating economists' awareness of AI's transformative potential to Simon's prediction. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023) →Claim page →
· publication · dated to the month
Trammell and Korinek, “Economic Growth under Transformative AI” (2023-10) articulates E05. Cites Jones (2024, 2025) and Shulman and Thornley (2025), and Trammell and Aschenbrenner (2026) on precaution possibly increasing long-term risk. The authors introduce their synthesis of 'the most influential papers on the topic' of transformative AI and growth. Footnote: most models Sandberg summarised did not spell out how AI would fit standard economic models; 'Our review fills this gap.'. Evidence ↗Trammell and Korinek, Economic Growth under Transformative AI (2023, revised 2026) →Claim page →
· publication · dated to the day
“Explosive growth from AI automation: A review of the arguments” (2023-10-01) credits Ege Erdil as one of its 2 authors. Evidence ↗· publication · dated to the day
“Explosive growth from AI automation: A review of the arguments” (2023-10-01) credits Tamay Besiroglu as one of its 2 authors. Evidence ↗· publication · dated to the day
Erdil and Besiroglu, “Explosive growth from AI automation: A review of the arguments” (2023-10-01) articulates G01. Opens the paper's summary of its assessment: substitution of AI for labour on most or all tasks makes explosive growth the default prediction of growth models. Evidence ↗Erdil and Besiroglu, Explosive growth from AI automation (2023) →Claim page →
· publication · dated to the day
Erdil and Besiroglu, “Explosive growth from AI automation: A review of the arguments” (2023-10-01) contests G04. Based on a CES task model calibrated with a range of substitution parameters: fast automation of most tasks can give a large transitory boost even with bottlenecks. Section 3.1 treats the objection that "the training or deployment of AI systems will be sufficiently impeded by regulation"; the authors argue that incentives for AI adoption resemble those for nuclear proliferation, so coordination to slow it is likely to be imperfect. Evidence ↗Erdil and Besiroglu, Explosive growth from AI automation (2023) →Claim page →
· publication · dated to the day
Erdil and Besiroglu, “Explosive growth from AI automation: A review of the arguments” (2023-10-01) contests G05. Accepts eventual limits and contests their timing, with an estimate of returns to scale. Evidence ↗Erdil and Besiroglu, Explosive growth from AI automation (2023) →Claim page →
· publication · dated to the day
Erdil and Besiroglu, “Explosive growth from AI automation: A review of the arguments” (2023-10-01) articulates G09. The authors' unconditional estimate. The same paragraph says high confidence "seems unwarranted". Evidence ↗Erdil and Besiroglu, Explosive growth from AI automation (2023) →Claim page →
November 2023
· publication · dated to the month
“The A.I. Dilemma: Growth versus Existential Risk” (2023-11) credits Charles I. Jones as its author. Evidence ↗· publication · dated to the month
“Can Socially-Minded Governance Control the AGI Beast?” (2023-11) credits Joshua S. Gans as its author. Evidence ↗· publication · dated to the month
Jones, “The A.I. Dilemma: Growth versus Existential Risk” (2023-11) bears on E01. Cites Aghion, Jones and Jones (2019), Trammell and Korinek (2020), Davidson (2021), Nordhaus (2021), Erdil and Besiroglu (2023). Evidence ↗· publication · dated to the month
Jones, “The A.I. Dilemma: Growth versus Existential Risk” (2023-11) articulates E02. Preceded by 'many experts worry that these advances entail existential risk:'. Jones reports warnings from 'a substantial contingent of the A.I. research community, including leading researchers at OpenAI and Google'. Evidence ↗· publication · dated to the month
Jones, “The A.I. Dilemma: Growth versus Existential Risk” (2023-11) bears on E02. Jones's own statement of possibility, followed by growth models in which machines produce ideas. Evidence ↗· publication · dated to the month
Gans, “Can Socially-Minded Governance Control the AGI Beast?” (2023-11) bears on E03. Refers to the 'potential existential risks' that motivated OpenAI's funders; the model then assumes the risks are real. Evidence ↗Gans, Can Socially-Minded Governance Control the AGI Beast (2023) →Claim page →
· publication · dated to the month
Jones, “The A.I. Dilemma: Growth versus Existential Risk” (2023-11) articulates E03. The abstract sets growth gains against existential risk, which it attributes to 'many experts', and frames the paper as a welfare analysis of whether to continue or stop. First of four insights: 'With log utility, the models are remarkably unconcerned with existential risk'. Insight 2 says CRRA utility with γ of 2 or more is 'quite conservative'. Key Point 3: with γ ≤ 1 any risk short of sure annihilation is acceptable for an immediate singularity; with γ = 2 or 3 the cutoffs are much smaller. The 'key exception' to the conservative result: mortality gains are 'in the same units' as existential risk. Evidence ↗· publication · dated to the month
Gans, “Can Socially-Minded Governance Control the AGI Beast?” (2023-11) articulates E04. Abstract: a model under idealised conditions (AGI risks real, safe AGI possible, socially-minded funders exist) motivated by OpenAI's governance. Case in which the for-profit entity is viable in direct competition; 'they' is the socially-minded entity. Conclusion; Gans adds that whether national or global intervention is needed is left to future research, while the results point to the latter. Evidence ↗Gans, Can Socially-Minded Governance Control the AGI Beast (2023) →Claim page →
· publication · dated to the month
Jones, “The A.I. Dilemma: Growth versus Existential Risk” (2023-11) bears on E05. Distinguishes the paper from Jones (2016) on growth versus lives lost; earlier the section lists Joy, Bostrom, Rees, Posner, Yudkowsky et al. and Ord as having highlighted AI existential risk. Evidence ↗· publication · dated to the day
xAI’s initial agenda proposes integrating formal verification. Evidence ↗· publication · dated to the day
xAI published an initial research programme alongside its Grok announcement. Evidence ↗· publication · dated to the day
xAI’s initial agenda discusses scalable oversight assisted by tools and human feedback. Evidence ↗· publication · dated to the day
The preprint "Towards Responsible Governance of Biological Design Tools" (arXiv v1, 27 November 2023) states that biological design tools pose distinct regulatory challenges and that regulatory proposals tailored to large language models may be less effective for them. Evidence ↗Towards Responsible Governance of Biological Design Tools →Claim page →
· publication · dated to the day
The preprint of 27 November 2023 states that the design capabilities of biological design tools could be used to evade DNA screening techniques, against the premise that screening at synthesis is a dependable control point. Evidence ↗Towards Responsible Governance of Biological Design Tools →Claim page →
· publication · dated to the day
The preprint of 27 November 2023 identifies, among the challenges of regulating biological design tools, the potential for safeguards to be circumvented by open-sourcing model weights. Evidence ↗Towards Responsible Governance of Biological Design Tools →Claim page →
· publication · dated to the day
Meta published a retrospective locating FAIR’s launch in late 2013 and describing a decade of open research. Evidence ↗
December 2023
· publication · dated to the month
CSET's explainer "AI and Biorisk" (December 2023) states that the barrier to scientific information is not high even for beginners, and that feasibility depends on both information and hands-on production success, without stating which of the two limits biological misuse. Evidence ↗· publication · dated to the month
CSET's explainer of December 2023 treats biological design tools as a separate class from chatbots, stating that their use in its current state requires both scientific and programming expertise. Evidence ↗· publication · dated to the month
CSET's explainer of December 2023 states that the biorisk landscape is not uniform and that specific scenarios and actors should be assessed individually, without stating that assessment must cover actors who already hold relevant skills. Evidence ↗· event · dated to the day
Google DeepMind published its Gemini research introduction through CEO Demis Hassabis, on behalf of the Gemini team. Evidence ↗· publication · dated to the day
OpenAI’s Weak-to-strong generalization announcement presents the linked paper as the Superalignment team’s first paper. Evidence ↗· publication · dated to the day
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision cites Supervising strong learners by amplifying weak experts. Evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision →Claim page →
· publication · dated to the day
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision cites Self-critiquing models for assisting human evaluators. Evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision →Claim page →
· publication · dated to the day
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision cites AI safety via debate. Evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision →Claim page →
· publication · dated to the day
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision cites Scalable agent alignment via reward modeling: a research direction. Evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision →Claim page →
· publication · dated to the day
Weak-to-Strong Generalization distinguishes generalizing beyond weak supervision from improving supervision through scalable oversight. Evidence ↗Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision →Claim page →
Capital 4 records
January 2023
· event · dated to the day · announced
Microsoft announced a multiyear, multibillion-dollar investment in OpenAI. Evidence ↗
May 2023
· announcement · dated to the day · announced
Liang Wenfeng publicly identified High-Flyer as one of DeepSeek’s financial backers. Evidence ↗
September 2023
· event · dated to the month · invested
Amazon reported making an initial $1.25 billion investment in Anthropic in September 2023. Evidence ↗· event · dated to the day · announced
Anthropic announced that Amazon would invest up to $4 billion in the company. Evidence ↗
Compute 7 records
January 2023
· event · dated to the day · announced
Microsoft’s partnership announcement described Azure as OpenAI’s exclusive cloud provider. Evidence ↗
February 2023
· announcement · dated to the day · announced
Anthropic announced its selection of Google Cloud for training, scaling and deploying its AI systems. Evidence ↗
May 2023
· announcement · dated to the day · contract
Helion announced on 10 May 2023 an agreement to provide Microsoft electricity from its first fusion power plant, expected online by 2028 and targeting 50 MW or more after a one-year ramp-up, with Constellation as power marketer. Evidence ↗· snapshot · dated to the day · stated as in operation
Meta reported completion of RSC’s second phase with 16,000 NVIDIA A100 GPUs. Evidence ↗
September 2023
· event · dated to the day · announced
Anthropic announced AWS as its primary cloud provider for mission-critical workloads. Evidence ↗
December 2023
· snapshot · dated to the day · stated as in operation
Google DeepMind’s Gemini team reported training Gemini 1.0 on Google’s infrastructure using TPU v4 and v5e. Evidence ↗· snapshot · dated to the day · deployed
The Gemini v1 technical report described TPUv4 SuperPods of 4,096 chips in Gemini Ultra’s multi-datacenter training infrastructure. Evidence ↗
Governance 167 records
Dated to 2023 only
March 2023
· snapshot · dated to the day
Inflection AI stated on 8 March 2023 that it had decided to make Inflection a Public Benefit Corporation. Evidence ↗· announcement · dated to the day
Alibaba announced a six-business-group structure with group-level CEOs and boards. Evidence ↗
May 2023
June 2023
· publication · dated to the day
1 provision record of UK Biological Security Strategy
July 2023
· publication · dated to the day
The Ministry of Commerce and the General Administration of Customs issued joint Announcement No. 23 of 2023, dated 3 July 2023, imposing export controls on gallium- and germanium-related items with effect from 1 August 2023. Evidence ↗· publication · dated to the day
The Cyberspace Administration of China and six other departments issued Order No. 15, the Interim Measures for the Management of Generative Artificial Intelligence Services (生成式人工智能服务管理暂行办法), dated 10 July 2023, to take effect on 15 August 2023. Evidence ↗· publication · dated to the day
11 provision records of Interim Measures for the Management of Generative Artificial Intelligence Services (生成式人工智能服务管理暂行办法), Order No. 15
· event · dated to the day
H.R. 4702 of the 118th Congress, the Securing Gene Synthesis Act, was introduced in the House of Representatives on 18 July 2023 by Representative Anna G. Eshoo, referred to the Committee on Energy and Commerce, and referred to its Subcommittee on Health on 21 July 2023. Evidence ↗· publication · dated to the day
Meta announced Llama 2 availability for research and commercial use. Evidence ↗· event · dated to the day
S. 2399 of the 118th Congress, the Artificial Intelligence and Biosecurity Risk Assessment Act, was introduced in the Senate on 19 July 2023 by Senator Edward J. Markey, with Senator Ted Budd as an original cosponsor, and read twice and referred to the Committee on Health, Education, Labor, and Pensions. Evidence ↗· event · dated to the day
S. 2400 of the 118th Congress, the Securing Gene Synthesis Act, was introduced in the Senate on 19 July 2023 by Senator Edward J. Markey and read twice and referred to the Committee on Health, Education, Labor, and Pensions. Evidence ↗· publication · dated to the day
1 provision record of S. 2399 (118th Congress): Artificial Intelligence and Biosecurity Risk Assessment Act, as introduced in the Senate
· publication · dated to the day
2 provision records of S. 2400 (118th Congress): Securing Gene Synthesis Act, as introduced in the Senate
· publication · dated to the day
The White House named Amazon among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
The White House named Anthropic among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
The White House named Google among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
The White House named Inflection AI among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
The White House named Meta among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
The White House named Microsoft among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
The White House named OpenAI among the seven companies it convened on 21 July 2023, announcing that it had secured voluntary commitments from them to help move toward safe, secure and transparent development of AI technology. Evidence ↗· publication · dated to the day
On 21 July 2023 the White House published “Ensuring Safe, Secure, and Trustworthy AI”, a preamble and eight voluntary AI commitments under the headings safety, security and trust, with a fact sheet announcing that seven companies had chosen to undertake them immediately. Evidence ↗· publication · dated to the day
5 provision records of Ensuring Safe, Secure, and Trustworthy AI: voluntary AI commitments (July 2023)
August 2023
· effective · dated to the day
1 provision record of MOFCOM–GAC Announcement No. 23 of 2023 on export controls on gallium- and germanium-related items (商务部 海关总署公告2023年第23号)
September 2023
· interval begins · dated to the day
Anthropic’s restated certificates of incorporation since March 2023 give holders of Class T Common Stock, voting as a separate class, the right to elect one director from 6 September 2023, two from the Second Phase-In Date and three of five from the Final Phase-In Date. Anthropic states that the Class T stock is held exclusively by the Long-Term Benefit Trust and gives it authority to elect and remove a number of board members that phases in by time- and funding-based milestones. Evidence ↗· publication · dated to the day
The White House named Adobe among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
The White House named Cohere among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
The White House named IBM among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
The White House named Nvidia (NVIDIA) among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
The White House named Palantir among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
On 12 September 2023 the White House announced that it had secured a second round of voluntary commitments from eight companies and linked a commitments text headed “Voluntary AI Commitments”, with the same eight commitments and a scope clause measured against each company’s own most advanced model. Evidence ↗· publication · dated to the day
The White House named Salesforce among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
The White House named Scale AI among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
The White House named Stability AI (as “Stability”) among the eight companies from which it announced on 12 September 2023 that it had secured a second round of voluntary AI commitments. Evidence ↗· publication · dated to the day
2 provision records of Voluntary AI Commitments (September 2023)
· event · dated to the day
Anthropic announced publication of its initial Responsible Scaling Policy. Evidence ↗· effective · dated to the day
Anthropic’s Responsible Scaling Policy v1.0 states that its ASL-2 deployment measures overlap substantially with the White House voluntary commitments that Anthropic and other companies made in July, which it says it continues to maintain. Evidence ↗· publication · dated to the day
Anthropic published a description of its Long-Term Benefit Trust on 19 September 2023. It said the Trust’s Class T stock would let it elect a number of directors phasing in to a board majority within four years, named five initial Trustees chosen by its board (Jason Matheny, Kanika Bahl, Neil Buddy Shah as chair, Paul Christiano and Zach Robinson), and said that “failsafe” provisions let large enough supermajorities of stockholders change the Trust and its powers without the Trustees’ consent. Evidence ↗· snapshot · dated to the day
Anthropic's announcement of its Long-Term Benefit Trust on 19 September 2023 names Kanika Bahl, CEO and President of Evidence Action, as an initial Trustee. Evidence ↗· snapshot · dated to the day
Anthropic's announcement of its Long-Term Benefit Trust on 19 September 2023 names Jason Matheny, CEO of the RAND Corporation, as an initial Trustee. Evidence ↗· snapshot · dated to the day
Anthropic's announcement of its Long-Term Benefit Trust on 19 September 2023 names Zach Robinson, Interim CEO of Effective Ventures US, as an initial Trustee. Evidence ↗· snapshot · dated to the day
Anthropic's announcement of its Long-Term Benefit Trust on 19 September 2023 names Neil Buddy Shah, CEO of the Clinton Health Access Initiative, as an initial Trustee and as the Trust's Chair. Evidence ↗· effective · dated to the day
29 provision records of Responsible Scaling Policy (2023)
· publication · dated to the day
1 provision record of Amended and Restated Certificate of Incorporation of Anthropic, PBC (filed 2023-09-21)
October 2023
· publication · dated to the day
The Administration for Strategic Preparedness and Response published the Screening Framework Guidance for Providers and Users of Synthetic Nucleic Acids in the Federal Register of 13 October 2023, stating that it sets out baseline standards for the gene and genome synthesis industry and best practices for all entities involved in the provision, use and transfer of synthetic nucleic acids. Evidence ↗· publication · dated to the day
The 2023 Screening Framework Guidance for Providers and Users of Synthetic Nucleic Acids states that advances in the field motivated the United States Government to review and revise the 2010 Screening Framework Guidance for Providers of Synthetic Double-Stranded DNA, and cites the 2010 guidance at 75 FR 62820. Evidence ↗Screening Framework Guidance for Providers and Users of Synthetic Nucleic Acids →Claim page →
· publication · dated to the day
4 provision records of Screening Framework Guidance for Providers and Users of Synthetic Nucleic Acids
· publication · dated to the day
The Bureau of Industry and Security published the interim final rule "Implementation of Additional Export Controls: Certain Advanced Computing Items; Supercomputer and Semiconductor End Use; Updates and Corrections" in the Federal Register of 25 October 2023, effective 17 November 2023, revising the advanced computing controls of 7 October 2022. Evidence ↗· publication · dated to the day
1 provision record of Implementation of Additional Export Controls: Certain Advanced Computing Items; Supercomputer and Semiconductor End Use; Updates and Corrections (interim final rule)
· snapshot · dated to the day
Google DeepMind described its Responsibility and Safety Council as an internal governance body. Evidence ↗Google DeepMind Responsibility and Safety Council →Claim page →
· event · dated to the day
Google DeepMind published its response describing safety and responsibility processes for the UK AI Safety Summit. Evidence ↗· publication · dated to the day
The G7 Leaders' Statement on the Hiroshima AI Process of 30 October 2023 welcomes the International Guiding Principles and the International Code of Conduct for Organizations Developing Advanced AI Systems, attached to it. Evidence ↗G7 Leaders' Statement on the Hiroshima AI Process →Claim page →
· signature · dated to the day
Executive Order 14110, "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence", is dated and signed at the White House on 30 October 2023. Evidence ↗· publication · dated to the day
4 provision records of Hiroshima Process International Code of Conduct for Organizations Developing Advanced AI Systems
· publication · dated to the day
1 provision record of G7 Leaders' Statement on the Hiroshima AI Process (30 October 2023)
November 2023
· event · dated to the month
The UK AISI report credits work with METR to showcase autonomous-agent demonstrations at the Safety Summit. Evidence ↗UK AISI approach to evaluations (February 2024) →Claim page →
· snapshot · dated to the day
Under the heading 'The countries represented were:', the GOV.UK text of the Bletchley Declaration lists 29 entries, 28 countries and the European Union (count made by Selekoa; the text states no number). Evidence ↗· publication · dated to the day
The Bletchley Declaration was published on GOV.UK on 1 November 2023 (the page’s “First published” date) by the Prime Minister's Office, the Foreign, Commonwealth & Development Office and the Department for Science, Innovation and Technology. Evidence ↗· publication · dated to the day
Executive Order 14110 was published in the Federal Register of Wednesday, 1 November 2023 (Vol. 88, No. 210, beginning at p. 75191), as FR Doc. 2023-24283. Evidence ↗· publication · dated to the day
3 provision records of The Bletchley Declaration by Countries Attending the AI Safety Summit, 1–2 November 2023
· publication · dated to the day
11 provision records of Executive Order 14110: Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
· snapshot · dated to the day
The chair's statement on safety testing of 2 November 2023 names world leaders representing ten countries and the European Union, and industry leaders of ten companies, including Anthropic, Google DeepMind and 'Open AI', as having recognised the importance of collaborating on testing next-generation AI models. Evidence ↗Bletchley chair's statement on safety testing (2023) →Claim page →
· publication · dated to the day
The chair's statement of session outcomes on safety testing from the AI Safety Summit was published on GOV.UK on 2 November 2023 by the Prime Minister's Office, the Foreign, Commonwealth & Development Office and the Department for Science, Innovation and Technology. Evidence ↗· publication · dated to the day
3 provision records of Safety Testing: Chair's Statement of Session Outcomes, 2 November 2023
· event · dated to the day
OpenAI’s board announced Sam Altman’s removal as CEO. Evidence ↗· snapshot · dated to the day
OpenAI announced Mira Murati as interim CEO, effective immediately. Evidence ↗· announcement · dated to the day
The board of directors of OpenAI, Inc. announced on 17 November 2023 that Sam Altman would depart as CEO and leave the board of directors. Evidence ↗· announcement · dated to the day
The same announcement of 17 November 2023 stated that Greg Brockman would step down as chairman of the board and remain in his role at the company, reporting to the CEO. Evidence ↗· snapshot · dated to the day
OpenAI’s announcement of 17 November 2023 lists Helen Toner as an independent director on the board of OpenAI, Inc. Evidence ↗· snapshot · dated to the day
OpenAI’s announcement of 17 November 2023 lists Tasha McCauley as an independent director on the board of OpenAI, Inc. Evidence ↗· snapshot · dated to the day
Sam Altman announced his return as OpenAI CEO. Evidence ↗· snapshot · dated to the day
OpenAI announced on 29 November 2023 that Adam D’Angelo would be a member of its new initial board, chaired by Bret Taylor. Evidence ↗· snapshot · dated to the day
OpenAI announced on 29 November 2023 that Larry Summers would be a member of its new initial board, chaired by Bret Taylor. Evidence ↗· announcement · dated to the day
OpenAI’s announcement of 29 November 2023 stated that Ilya Sutskever would no longer serve on the board. Evidence ↗· snapshot · dated to the day
OpenAI announced on 29 November 2023 that its new initial board would consist of Bret Taylor as chair, Larry Summers and Adam D’Angelo. Evidence ↗
December 2023
· event · dated to the month
Anthropic's Long-Term Benefit Trust page states, in an undated footnote, that Jason Matheny stepped down from the Trust in December 2023; Anthropic stated that he did so to preempt potential conflicts of interest with RAND Corporation's policy-related initiatives. Evidence ↗· publication · dated to the day
OpenAI’s Preparedness Framework (Beta) describes the framework as one more way in which OpenAI is meeting the voluntary commitments on safety, security and trust in AI that it made in July 2023. Evidence ↗· publication · dated to the day
OpenAI published Preparedness Framework Beta, identified by its publication date of 2023-12-18. Evidence ↗· publication · dated to the day
25 provision records of OpenAI Preparedness Framework — Beta