I previously noted that we’re seeing a pretty sharp divergence between the measured capabilities of models and their real-world impacts, which should suggest that something weird is going on.
There’s something there but I think its scary to reason from the the lack of normal economic impacts because they’re such a lagging indicator.
I think the current situation is:
Frontier AI labs are really getting to the point where the nature of their work is fundamentally changed and productivity really is up by a large fraction (cf recent Ant, OAI acceleration posts)
In most other places the impact is pretty modest
AI labs are willing to spend hundreds of thousands of dollars per member of staff on compute to boost productivity
AI labs have a combination of beliefs and competitive pressures (including deep throughout the company) that allow them to aggressively redesign workflows around new tools
A large amount of the training effort of labs goes into the kinds of work done by these companies, not least just because the feedback loop from the massive usage volume is so tight
Pretty much no other workplaces have this combination (maybe like Jane Street/Citadel? would be very curious to hear how much AI is accelerating them)
I think this means that it makes a lot of sense that we could see limited global economic transformation but AIs really are capable of speeding up the labs by a huge and increasing factor, and potentially going FOOM. By Christmas per Alex’s above post still seems aggressive but it seems more likely than after 2030 (conditioned on no slowdown).
I partly agree on lagging factor, but I disagree on the acceleration condition being unique to labs.
First, AI labs do not have a monopoly on using expensive coding agents to develop. OpenAI reported their research team uses ~$600/day/person, which is still in the range of an AI-forward SF company (I agree the majority of older software companies do not do this). Note that OpenAI’s spend might be slightly exaggerated by virtue of likely not caring about internal costs and not doing the most mild things to reduce it (e.g. smaller auto-compact windows, cheaper models for tasks that require less intelligence, etc.). Additionally, given the labs are locked into themselves, their capabilities might not be that far ahead of what the public can use. (Was OpenAI only having Astra in August that much SOTA over the public who was already using Fable 5 and Opus 5?).
In my domain, I’d ballpark engineering productivity up ~80%+ compared to early 2025, but it’s nuanced how that manifests itself. Onboarding (both as a new employee and into new functional areas) are very fast. But ultimately software engineering is under Jevon’s Paradox—our additional productivity translates to making better software (less bugs, more features), which all of our competitors also do as well.
Additionally, there’s a feeling of diminishing returns to higher model intelligence. My read is that what is really happening is Amdahl’s Law at play—the models aren’t getting better fast enough at the non-verifiable tasks so further intelligence gains (in the METR horizons sense) are less translating to productivity gains. Models translate clean greenfield specs very well into code, but at a larger (dozens of engineers) company, work is often brownfield: “I’m building a new feature X, how should this play with Y feature that I didn’t even know existed until I started implementing X”—today, that typically requires human judgement and as models accelerate coding, is taking more and more of my time percentage-wise.
There’s something there but I think its scary to reason from the the lack of normal economic impacts because they’re such a lagging indicator.
I think the current situation is:
Frontier AI labs are really getting to the point where the nature of their work is fundamentally changed and productivity really is up by a large fraction (cf recent Ant, OAI acceleration posts)
In most other places the impact is pretty modest
AI labs are willing to spend hundreds of thousands of dollars per member of staff on compute to boost productivity
AI labs have a combination of beliefs and competitive pressures (including deep throughout the company) that allow them to aggressively redesign workflows around new tools
A large amount of the training effort of labs goes into the kinds of work done by these companies, not least just because the feedback loop from the massive usage volume is so tight
Pretty much no other workplaces have this combination (maybe like Jane Street/Citadel? would be very curious to hear how much AI is accelerating them)
I think this means that it makes a lot of sense that we could see limited global economic transformation but AIs really are capable of speeding up the labs by a huge and increasing factor, and potentially going FOOM. By Christmas per Alex’s above post still seems aggressive but it seems more likely than after 2030 (conditioned on no slowdown).
I partly agree on lagging factor, but I disagree on the acceleration condition being unique to labs.
First, AI labs do not have a monopoly on using expensive coding agents to develop. OpenAI reported their research team uses ~$600/day/person, which is still in the range of an AI-forward SF company (I agree the majority of older software companies do not do this). Note that OpenAI’s spend might be slightly exaggerated by virtue of likely not caring about internal costs and not doing the most mild things to reduce it (e.g. smaller auto-compact windows, cheaper models for tasks that require less intelligence, etc.). Additionally, given the labs are locked into themselves, their capabilities might not be that far ahead of what the public can use. (Was OpenAI only having Astra in August that much SOTA over the public who was already using Fable 5 and Opus 5?).
In my domain, I’d ballpark engineering productivity up ~80%+ compared to early 2025, but it’s nuanced how that manifests itself. Onboarding (both as a new employee and into new functional areas) are very fast. But ultimately software engineering is under Jevon’s Paradox—our additional productivity translates to making better software (less bugs, more features), which all of our competitors also do as well.
Additionally, there’s a feeling of diminishing returns to higher model intelligence. My read is that what is really happening is Amdahl’s Law at play—the models aren’t getting better fast enough at the non-verifiable tasks so further intelligence gains (in the METR horizons sense) are less translating to productivity gains. Models translate clean greenfield specs very well into code, but at a larger (dozens of engineers) company, work is often brownfield: “I’m building a new feature X, how should this play with Y feature that I didn’t even know existed until I started implementing X”—today, that typically requires human judgement and as models accelerate coding, is taking more and more of my time percentage-wise.