I briefly discussed with Dwarkesh why I'm skeptical AI progress is heavily driven by scaling up spending on human experts labeling/making data. My main argument is that spending on researchers and experiment compute seem much higher. But I didn't say very much in the podcast.
More precisely, my view is that if spending on having human experts label/make individual data points were fixed at ~$100 million / year (per company), then AI progress would be <25% slower.
We didn't have time to get into everything in this podcast (and some content about this recorded at an earlier point was cut), so I'll spell out my view in a bit more detail here:
\- Spending directly on data (rather than on R&D about data) isn't growing that fast and isn't that high (relative to spending on researchers).
\- It's important to make a distinction between spending directly on making data and science about better processes for making data. E.g., better data mixes like FineWeb count as R&D (and the person doing the R&D needs almost no understanding of individual sequences).
\- AI automation seems differentially good at accelerating data generation, such that I think improvements in RL environments have mostly been driven by improved AI rather than spending on humans, and I expect this to continue. Like data stuff seems particularly amenable to acceleration from weaker AIs.
\- Structurally, most of what human data labeling does (though not all!) depends on having generally decent judgment rather than on having more expertise than the AIs being trained.
\- Transfer without domain-specific labels looks decent in practice. E.g., it doesn't seem like Anthropic is hiring a ton of mathematicians and cyber experts to do data labeling, and the AIs are still good and getting better at these domains. Maybe this depends on having labels in some domain, but so long as AIs can label in domains that transfer well enough and/or can make RL envs that don't require much labeling, that would be fine.
> **Herbie Bradley @herbiebradley** · 2026-08-11
>
> Ryan here seems to basically say "my sense here is that data isn't that important" but doesn't really go into why?
>
> I strongly disagree, for example, with his contention that scaling up data with human involvement in the loop hasn't been very important for AI R&D
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Had @RyanGreenblatt on to discuss/debate recursive self-improvement.
This might be the most important question in the world right now - whether within a year or so of achieving human level intelligence, you slingshot towards having 10s of billions of superintelligences, each of
> **Dwarkesh Patel @dwarkesh\_sp** · 2026-08-11
>
> Had @RyanGreenblatt on to discuss/debate recursive self-improvement.
>
> This might be the most important question in the world right now - whether within a year or so of achieving human level intelligence, you slingshot towards having 10s of billions of superintelligences, each of
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"relative to spending on researchers" is true, but I think the fraction relative to experiment compute is also relevant.
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\*such that I think improvements in RL environments have mostly been driven by improved AI rather than spending on humans
I mean "more has come from improved AI rather than scaling up spending on humans". R&D into how to make good data in general is also important.
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##### Comments
> **Auggie @aug5thmusic** · [2026-08-12](https://x.com/aug5thmusic/status/2087585530523611566)
>
> What do you think it would take to bring OMR (Optical Music Recognition) up to speed with OCR? One of my great disappointments with AI right now is that it can’t read music.
My median for full automation of AI R&D is around late 2030/early 2031. But my "modal"/best guess prediction for this milestone would be significantly earlier (mid 2029).
Here is a summary of my best guess prediction for what happens over the next few years:
EOY 2026:
\- ~1.5x as much frontier AI progress in 2026 as in 2025 (mostly from eating up certain overhangs, but some from AI R&D acceleration).
\- AIs accelerate AI R&D labor at Anthropic by ~2.5x (as in, as useful as making all researchers/engineers think/work 2.5x faster).
EOY 2027:
\- Engineering at AI companies is pretty close to fully automated and AIs are making serious inroads into automating research. AI R&D labor acceleration: ~8.5x.
\- Some people claim AI R&D is fully automated in 2027. They aren't right, but the situation is already quite crazy: AI companies feel insanely automated with humans often very out of the loop and the speedup is considerable.
\- ~1.5x as much frontier AI progress as in 2025 (mostly from AI R&D acceleration, some from overhangs).
2028:
\- Automated coder (AC) around April. (AIs that can basically fully automate research engineering / SWE.)
\- Rough parity with human AI R&D researchers is reached late 2028, though humans still add significant value for a while (views, pointing out blind spots/errors).
\- In the second half of the year, AI progress runs ~1.6x the 2025 rate: 6 months of calendar time yields ~0.8 years of AI progress.
2029:
\- Superhuman AI researcher (SAR) early this year, a bit less than a year after AC.
\- Progress is picking up with ~1.3 years of AI progress in the first half of the year (2.6x rate).
\- By EOY, significantly past top-expert-dominating AI (TEDAI), with ~2.5 years of AI progress in the second half of the year (5x rate). AIs are now very superhuman in many domains (though this varies).
2030 (??):
\- Mid: AIs are somewhere between TEDAI and wildly superhuman AIs (ASI). Crazy shit. Compute is maybe doubling every ~4 months (downstream of robots).
\- EOY: Singularity™. We've had a bunch of economic doublings. Compute is doubling every ~2 months (???).
2031 (??????):
\- Mid: doubling time is more like ~2 weeks. Truly insane new technology is coming online.
Notes:
\- This assumes limited government intervention on the overall rate of AI progress and no substantial slowdown (voluntary or otherwise).
\- It also ignores misalignment: as discussed in the episode, I think misaligned AI takeover is quite plausible along the way (which would change the trajectory).
\- Milestones (AC, SAR, TEDAI) are roughly as defined in the AI Futures Model.
\- By "full automation of AI R&D", I mean AIs such that firing all humans working on AI R&D (other than setting overall top level objectives) would slow down AI progress by less than 10%.
\- Obviously, all of this is extremely uncertain (increasingly so later in the scenario). This is my best guess prediction (a modal trajectory), not a confident prediction. My median for each milestone is later, but this is more like my central prediction for what I expect to overall happen.
> **Dwarkesh Patel @dwarkesh\_sp** · 2026-08-11
>
> Had @RyanGreenblatt on to discuss/debate recursive self-improvement.
>
> This might be the most important question in the world right now - whether within a year or so of achieving human level intelligence, you slingshot towards having 10s of billions of superintelligences, each of
---
##### Comments
> **Liam Rosen @Liface** · [2026-08-12](https://x.com/Liface/status/2087343717732462768)
>
> For 2027 I assume you mean 1.5x 2026, not 1.5x 2025, right?
>
> > **Ryan Greenblatt @RyanGreenblatt** · [2026-08-12](https://x.com/RyanGreenblatt/status/2087345663512289515)
> >
> > Nope, I'm using 2025 ai progress as a benchmark / measure of AI progress. I'm using that year because it's a kinda normal year of the reasoning model era without that much AI R&D acceleration.
> **Suketu Patel @SuketuPatel23** · [2026-08-12](https://x.com/SuketuPatel23/status/2087346545322561607)
>
> Does this trajectory assume the backbone stays a decoder-only transformer with a pre/post-training pipeline the whole way through?
>
> Continual learning is a requirement for AC → SAR. Nothing in the current stack updates weights online or carries memory across sessions. If that
> **Lars Holm Tjessem @t4intelligence** · [2026-08-11](https://x.com/t4intelligence/status/2087293495572668624)
>
> The exact dates almost don’t matter.
>
> If this trajectory is even directionally right, the critical transition happens before “ASI” — when AI begins materially accelerating the research process that produces better AI.
>
> At that point, capability growth increasingly becomes
> **Soroush Pour @soroushjp** · [2026-08-12](https://x.com/soroushjp/status/2087362019037151463)
>
> What gives you the relatively short timelines to robots?
>
> Do you think AI takeover is plausible or likely even before AI is sufficiently capable enough to sustain itself, which I believe given DC maintenance is downstream of robotics?
> **Michael Tontchev @MichaelTontchev** · [2026-08-11](https://x.com/MichaelTontchev/status/2087307006696735170)
>
> "Distributions account for this", I know, but I anyway want to register that having that happen mid-2028 seems to very plausibly be in the books, and that near-future AIs may well be able to simulate research taste through brute force (imagine 500 hierarchically debating agents).
> **David Johnston @OrionJohnston** · [2026-08-11](https://x.com/OrionJohnston/status/2087310134104260887)
>
> Do you expect the ECI slope to bend upward soon?
> **Plastic Soldier @PlastiqSoldier** · [2026-08-12](https://x.com/PlastiqSoldier/status/2087354484707950666)
>
> For the life of me, I don't get how you believe we will have multiple 2025s of AI progress before we even get an automated coder.
> **Shinka - AI @ShinkaIoT** · [2026-08-11](https://x.com/ShinkaIoT/status/2087322452854812874)
>
> An AI safety researcher calmly calendaring the exact month his own job gets automated is the most collected resignation letter on X.
##### Conversation
[Luke Drago reposted](https://x.com/luke_drago_)[Ryan Greenblatt](https://x.com/RyanGreenblatt)[@RyanGreenblatt](https://x.com/RyanGreenblatt)
An economist and a futurist walk into a bar. The economist takes a sip of his drink. "Ugh, if only people understood basic economics. High-skilled immigration alone would do wonders for US growth." Futurist: "Oh yeah? Say 100 million immigrants moved to the US, each matching the best human experts in every economically relevant field. Big deal?" Economist: "Massive. Transformative." Futurist: "What if they also worked longer hours and faster than any American?" Economist: "Even better." Futurist: "What if they were extremely frugal — consuming only the bare minimum needed to keep working?" Economist: "A near-100% savings rate? Better still!" Futurist: "What if they were very clumsy and physically weak, so they could only do some kinds of work?" Economist: "They could still do all cognitive labor — that's over half all wages! Somewhat less good, sure. Still transformative." Futurist: "What if their skin was grey, almost metallic, from some kind of accident?" Economist: "Who cares?!" Futurist: "What if they were AIs?" Economist: "3% growth per year, tops. There'd be bottlenecks. Honestly, the people predicting explosive growth from AI should learn some economics."[KC K](https://x.com/KCMartinK)[@KCMartinK](https://x.com/KCMartinK)
[1m](https://x.com/KCMartinK/status/2079622589421584494)
Are we surprised, the study of economics is about people and how they organize around value. All disciplines have to adapt to the physics of our time. Future -> Consilience.[1](https://x.com/KCMartinK/status/2079622589421584494/analytics)
These AI "immigrants" sound more like offshore labor taking all the jobs and paying no taxes to the US. Hasn't that already been tried? How did it go?
banger