contact: jurkovich.nikola@gmail.com
Nikola Jurkovic
Karma: 2,159
it has to go to infinity when we get AGI / superhuman coder.
This isn’t necessarily true, as even an AGI or a superhuman coder might get worse at tasks-that-take-humans-longer compared to tasks-that-take-humans-shorter (this seems pretty likely given constant-error-rate considerations), meaning that even an extremely capable AI might be like 99.999% reliable for 1 hour tasks, but only 99.9% reliable for 10,000 hour tasks, meaning the logistic fit still has an intercept with 50%, it’s just a very high number.
In order for the 50% intercept to approach infinity, you’d need a performance curve which approaches a flat line, and this seems very hard to pull off and probably requires wildly superhuman AI.
I think a 1-year 50%-time-horizon is very likely not enough to automate AI research. The reason I think AI research will be automated by EOY 2028 is because of speedups from partial automation as well as leaving open the possibility of additional breakthroughs naturally occurring.
A few considerations that make me think the doubling time will get faster:
AI speeding up AI research probably starts making a dent in the doubling time (making it at least 10% faster) by the time we hit 100hr time horizons (although it’s pretty hard to reason about the impacts here)
I think I place some probability on the “inherently superexponential time horizons” hypothesis. The reason I think it is because to me, 1-month-coherence, 1-year-coherence, and 10-year-coherence (of the kind performed by humans) seem like extremely similar skills and will thus be learned in quick succession.
It’s plausible reasoning models decreased the doubling time from 7 months to 4 months. It’s plausible we get another reasoning-shaped breakthrough.
So my best guess for the 50% and 80% time horizons at EOY 2028 are more like 10yrs and 3yrs or something. But past ~2027 I care more about how much AI R&D is being automated rather than the time horizon itself (partially because I have FUD about what N-year tasks should even look like by definition).
Thoughts on extrapolating time horizons
Forbes: Fear Of Super Intelligent AI Is Driving Harvard And MIT Students To Drop Out
Grok 4 is slightly above SOTA on 50% time horizon and slightly below SOTA on 80% time horizon: https://x.com/METR_Evals/status/1950740117020389870
I heard it from someone who works at xAI
xAI’s safety team is 3 people.
I would have taken this class had I not graduated this spring!
A few suggestions:I would like to cover the various ways AI could go wrong: malfunction, misuse, societal upheaval, arms race, surveillance, bias, misalignment, loss of control,...
Talk about predictions for the future, methodologies for how to come up with them.
Some technical components should include: evaluations
All of the above said, I get antsy if I don’t get my dosage of math and code- I intend 80% of the course to be technical and cover research papers and results. It should also involve some hands on projects.
Another thing I consider really important: many of the students will be like “Holy shit, AGI is happening! This affects my life plans!” and will want advice. I think it’s good to have something to point them to, like:
Good luck running the course!
The US Secretary of Energy says “The AI race is the second Manhattan project.”
https://x.com/SecretaryWright/status/1945185378853388568
Similarly, the US Department of Energy says: “AI is the next Manhattan Project, and THE UNITED STATES WILL WIN. 🇺🇸”
https://x.com/ENERGY/status/1928085878561272223
Agreed, thanks! I’ve moved that discussion down to timelines and probabilities.
I don’t think I make the claim that a DSA is likely to be achieved by a human faction before AI takeover happens. My modal prediction (~58% as written in the post) for this whole process is that the AI takes over while the nations are trying to beat each other (or failing to coordinate).
In the world where the leading project has a large secret lead and has solved superalignment (an unlikely intersection) then yes, I think a DSA is achievable.
Maybe a thing you’re claiming is that my opening paragraphs don’t emphasize AI takeover enough to properly convey my expectations of AI takeover. I’m pretty sympathetic to this point.
Outcomes of the Geopolitical Singularity
Survey of Multi-agent LLM Evaluations
One analogy for AGI adoption I prefer over “tech diffusion a la the computer” is “employee turnover.”
Assume you have an AI system which can do everything any worker could do, including walking around in an office, reading social cues, and doing everything else needed for an excellent human coworker.
Then, barring regulation or strong taste based preferences, any future hiring round will hire such a robot over a human. Then, the question of when most of the company are robots is just the question of when most of the workforce naturally turns over through hiring and firing, because all new incoming employees will be robots.
Of course, in this world, there wouldn’t just be typical hiring rounds, and there would probably be massive layoffs of humans to replace humans with robots. But typical hiring rounds provide an upper bound on how long the process would take. If the only way the company to “adopt” AGI is to hire human-shaped things, then the AGI will be human-shaped.
This is not what automation will actually look like, it’s just an upper bound on how long it’d take. In practice the time between ASI and 90% US unemployment ignoring regulation and x-risk would be more like 0-2 years because a superintelligence could come up with very quick plans to automate the economy, and the incentives will be much stronger than in typical hiring/firing decisions.
Another consideration is takeoff speeds: TAI happening earlier would mean further progress is more bottlenecked by compute and thus takeoff is slowed down. A slower takeoff enables more time for humans to inform their decisions (but might also make things harder in other ways).
Forecasting time to automated superhuman coders [AI 2027 Timelines Forecast]
The base models seem to have topped out their task length around 2023 at a few minutes (see on the plot that GPT-4o is little better than GPT-4). Reasoning models use search to do better.
Note that Claude 3.5 Sonnet (Old) and Claude 3.5 Sonnet (New) have a longer time horizon than 4o: 18 minutes and 28 minutes compared to 9 minutes (Figure 5 in Measuring AI Ability to Complete Long Tasks). GPT-4.5 also has a longer time horizon.
Thanks for writing this.
Aside from maybe Nikola Jurkovic, nobody associated with AI 2027, as far as I can tell, is actually expecting things to go as fast as depicted.
I don’t expect things to go this fast either—my median for AGI is in the second half of 2028, but the capabilities progression in AI 2027 is close to my modal timeline.
Note that the goal of “work on long-term research bets now so that a workforce of AI agents can automate it in a couple of years” implies somewhat different priorities than “work on long-term research bets to eventually have them pay off through human labor”, notably:
The research direction needs to be actually pursued by the agents, either through the decision of the human leadership, or through the decision of AI agents that the human leadership defers to. This means that if some long-term research bet isn’t respected by lab leadership, it’s unlikely to be pursued by their workforce of AI agents.
This implies that a major focus of current researchers should be on credibility and having a widely agreed-on theory of change. If this is lacking, then the research will likely never be pursued by the AI agent workforce and all the work will likely have been for nothing.
Maybe there is some hope that despite a research direction being unpopular among lab leadership, the AI agents will realize its usefulness on their own, and possibly persuade the lab leadership to let them expend compute on the research direction in question. Or maybe the agents will have so much free reign over research that they don’t even need to ask for permission to pursue new research directions.
Setting oneself up for providing oversight to AI agents. There might be a period during which agents are very capable at research engineering / execution but not research management, and leading AGI companies are eager to hire human experts to supervise large numbers of AI agents. If one doesn’t have legible credentials or good relations with AGI companies, they are less likely to be hired during this period.
Delaying engineering-heavy projects until engineering is cheap relative to other types of work.
(some of these push in opposite directions, e.g., engineering-heavy research outputs might be especially good for legibility)
Thanks for the post!
I didn’t mean to suggest this in my original post. My claim was something more like “a naive extrapolation means that 80% time horizons will be at least a month by the end of the decade, but I think they’ll be much higher and we’ll have AGI”