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by Vishakha Agrawal, Algon
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AI safetyExistential riskForecastingAI forecastingArtificial intelligence
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This is a linkpost for https://aisafety.info/questions/5633/When-do-experts-think-human-level-AI-will-be-created

This is an article in the featured articles series from AISafety.info. AISafety.info writes AI safety intro content. We'd appreciate any feedback. 

The most up-to-date version of this article is on our website, along with 300+ other articles on AI existential safety.

On the whole, experts think human-level AI is likely to arrive in your lifetime.

It’s hard to precisely predict the amount of time until human-level AI.[1] Approaches include aggregate predictions, individual predictions, and detailed modeling.

Aggregate predictions:

Individual predictions:

  • In a 2023 discussion, Daniel Kokotajlo, Ajeya Cotra and Ege Erdil shared their timelines to Transformative AI. Their medians were 2027, 2036 and 2073 respectively.
  • Paul Christiano, head of the US AI Safety Institute, estimated in 2023 that there was a 30% chance of transformative AI by 2033.
  • Yoshua Bengio, Turing Award winner, estimated “a 95% confidence interval for the time horizon of superhuman intelligence at 5 to 20 years” in 2023.
  • Geoffrey Hinton, the most cited AI scientist, also predicted 5-20 years in 2023, but his confidence is lower.
  • Shane Legg, co-founder of DeepMind, estimated a probability of 80% within 13 years (before 2037) in 2023.
  • Yann LeCun, Chief AI Scientist at Meta, thinks reaching human-level AI “will take several years if not a decade. [...] But I think the distribution has a long tail: it could take much longer than that.”
  • Leopold Aschenbrenner, an AI researcher formerly at OpenAI, predicted in 2024 that AGI happening around 2027 was strikingly plausible.
  • Connor Leahy, CEO of Conjecture, gave a ballpark prediction in 2022 of a 50% chance of AGI by 2030, 99% by 2100. A 2023 survey of employees at Conjecture found that all of the respondents expected AGI before 2035.
  • Holden Karnofsky, co-founder of GiveWell, estimated in 2021 that there was “more than a 10% chance we'll see transformative AI within 15 years (by 2036); a ~50% chance we'll see it within 40 years (by 2060); and a ~⅔ chance we'll see it this century (by 2100).”
  • Andrew Critch, an AI researcher, estimated in 2024 that there was a 45% chance of AGI by the end of 2026.

Models:

  • A report by Ajeya Cotra for Open Philanthropy estimated the arrival of transformative AI (TAI) based on “biological anchors”.[3] In the 2020 version of the report, she predicted a 50% chance by 2050, but in light of AI developments over the next two years, she updated her estimate in 2022 to predict a 50% chance by 2040, a decade sooner.
  • Tom Davidson's take-off speeds model somewhat extends and supersedes Ajeya Cotra's bio-anchors framework, and offers an interactive tool for estimating timelines based on various parameters. The scenarios it offers as presets predict 100% automation in 2027 (aggressive), 2040 (best guess), and never (conservative).
  • Matthew Barnett created a model based on the “direct approach” of extrapolating training loss that as of Q1 2025 outputs a median estimate of transformative AI around 2033.

These forecasts are speculative,[4] depend on various assumptions, predict different things (e.g., transformative versus human-level AI), and are subject to selection bias both in the choice of surveys and the choice of participants in each survey.[5] However, they broadly agree that human-level AI is plausible within the lifetimes of most people alive today. What’s more, these forecasts generally seem to have been getting shorter over time.[6]

Further reading

  1. ^

    We concentrate here on human-level AI and similar levels of capacities such as transformative AI, which may be different from AGI. For more info on these terms, see this explainer.

  2. ^

    Metaculus is a platform that aggregates the predictions of many individuals, and has a decent track record at making predictions related to AI.

  3. ^

    The author estimates the number of operations done by biological evolution in the development of human intelligence and argues this should be considered an upper bound on the amount of compute necessary to develop human-level AI.

  1. ^

    Scott Alexander points out that researchers that appear prescient one year sometimes predict barely better than chance the next year.

  2. ^

    One can expect people with short timelines to be overrepresented in those who study AI safety, as shorter timelines increase the perceived urgency of working on the problem.

  3. ^

    There have been many cases where AI has gone from zero-to-solved. This is a problem; sudden capabilities are scary.

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GregorBln11
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Thank you very much for the review and aggregation of all these forecasts! Very nice!

I just have one point to add:

As the first aggregate prediction, you mention the AI Impacts’ 2023 survey of machine learning researchers. Your post gives the impression that it produced an aggregate forecast of 50% by 2047 for human-level AI. I think this is at least imprecise, if not incorrect.

AI Impacts asked about the timing of human-level performance by asking some participants about how soon they expect “high-level machine intelligence” (HLMI) and asking others about how soon they expect “full automation of labor” (FAOL). The resulting aggregate forecast gave a 50% chance of HLMI by 2047 and a 50% chance of FAOL by 2116. In your post, you ignore that AI Impacts uses two different concepts for human-level AI and just report the aggregate forecast for HLMI under the headline of human-level AI. 

I think this is unfortunate because this difference matters. One of your main points is that you claim that experts think human-level AI is likely to arrive in your lifetime. However, most of us will probably not be alive in 2116.

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Vasco Grilo🔸5
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Great point, Gregor! Tom Adamczewski has done an analysis which combines the answers to the questions about tasks and occupations. Here is the mainline graph.

Tom aggregates the results from the different questions in the most agnostic way possible, which I think is the best one can do.

I achieve this by simply including answers to both questions prior to aggregation, i.e. no special form of aggregation is used for aggregating tasks (HLMI) and occupations (FAOL). Since more respondents were asked about tasks than occupations, I achieve equal weight by resampling from the occupations (FAOL) responses.

Here is how Tom suggests people describe the results.

Experts were asked when it will be feasible to automate all tasks or occupations. The median expert thinks this is 20% likely by 2048, and 80% likely by 2103. There was substantial disagreement among experts. For automation by 2048, the middle half of experts assigned it a probability between 1% and a 60% (meaning ¼ assigned it a chance lower than 1%, and ¼ gave a chance higher than 60%). For automation by 2103, the central half of experts forecasts ranged from a 25% chance to a 100% chance.2

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StevenKaas2
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Thanks! We've edited the text to include both the FAOL estimate that you mention, and the combined estimate that Vasco mentions in the other reply. (The changes might not show up on site immediately, but will soon.) To the extent that people think FAOL will take longer than HLMI because of obstacles to AI doing jobs that don't come from it not being generally capable enough, I think the estimate for HLMI is closer to an estimate of when we'll have human-level AI than the estimate for FAOL. But I don't know if that's the right interpretation, and you're definitely right that it's fairer to include the whole picture. I agree that there's some tension between us saying "experts think human-level AI is likely to arrive in your lifetime" and this survey result, but I do also still think that that sentence is true on the whole, so we'll think about whether to add more detail about that.

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Alex (Αλέξανδρος)1
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This timeline is very interesting, and it leads to a question beyond just when: what happens when AI is capable of independent thought and goal-setting? We're focused on mitigating risks, which is crucial. However, we should also consider the moral implications of creating beings capable of:

a) thinking independently, beyond merely fulfilling human-designed requests

b) setting their own goals

How can we ensure a future where humans and advanced AI can co-exist, minimizing suffering for both and maximising the potential benefits of collaboration – from scientific discovery to solving global challenges?

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