sjadler's Shortform

It’s interesting to me that the big AI CEOs have largely conceded that AGI/ASI could be extremely dangerous (but aren’t taking sufficient action given this view IMO), as opposed to them just denying that the risk is plausible. My intuition is that the latter is more strategic if they were just trying to have license to do what they want. (For instance, my impression is that energy companies delayed climate action pretty significantly by not yielding at first on whether climate change is even a real concern.)

I guess maybe the AI folks are walking a strategic middle ground? Where they concede there could be some huge risk, but then also sometimes say things like ‘risk assessment should be evidence-based,’ with the implication that current concerns aren’t rigorous. And maybe that’s more strategic than either world above?

But really it seems to me like they’re probably earnest in their views about the risks (or at least once were earnest). And so, political action that treats their concern as disingenuous is probably wrongheaded, as opposed to modeling them as ‘really concerned but facing very constrained useful actions’.

I believe we should view AGI as a ratio of capability to resources, rather than simply asking how AI's abilities compare to humans'. This view is becoming more common, but is not yet common enough.

When people discuss AI's abilities relative to humans without considering the associated costs or time, this is like comparing fractions by looking only at the numerators.

In other words, AGI has a numerator (capability): what the AI system can achieve. This asks questions like: For this thing that a human can do, can AI do it too? How well can AI do it? (For example, on a set of programming challenges, how many can the AI solve? How many can a human solve?)

But also importantly, AGI should account for the denominator: how many resources are required to achieve its capabilities. Commonly, this resource will be a $-cost or an amount of time. This asks questions like "What is the $-cost of getting this performance?" or "How long does this task take to complete?".

I claim that an AI system might fail to qualify as AGI if it lacks human-level capabilities, but it could also fail by being wildly inefficient compared to a human. Both the numerator and denominator matter when evaluating these ratios.

A quick example of why focusing solely on the capability (numerator) is insufficient:

• Imagine an AI software engineer that can do most tasks human engineers do, but at 100–1000× the cost of a human.
• I expect that lots of the predictions about "AGI" would not come due until (at least) that cost comes down substantially so that AI >= human on a capability-per-dollar basis.
• For instance, it would not make sense to directly substitute AI for human labor at this ratio - but perhaps it does make sense to buy additional AI labor, if there were extremely valuable tasks for which human labor is the bottleneck today.

The AGI-as-ratio concept has long been implicit in some labs' definitions of AGI - for instance, OpenAI describes AGI as "a highly autonomous system that outperforms humans at most economically valuable work". Outperforming humans economically does seem to imply being more cost-effective, not just having the same capabilities. Yet even within OpenAI, the denominator aspect wasn’t always front of mind, which is why I wrote up a memo on this during my time there.

Until the recent discussions of o1 Pro / o3 drawing upon lots of inference compute, I rarely saw these ideas discussed, even in otherwise sophisticated analyses. One notable exception to this is my former teammate Richard Ngo's t-AGI framework [AF · GW], which deserves a read. METR has also done a great job of accounting for this in their research comparing AI R&D performance, given a certain amount of time. I am glad to see more and more groups thinking in terms of these factors - but in casual analysis, it is very easy to just slip into comparisons of capability levels. This is worth pushing back on, imo: The time at which "AI capabilities = human capabilities" is different than the time when I expect AGI will have been achieved in the relevant senses.

There are also some important caveats to my claim here, that 'comparing just by capability is missing something important':

• People reasonably expect AI to become cheaper over time, so if AI matches human capabilities but not cost, that might still signal 'AGI soon.' Perhaps this is what people mean when they say 'o3 is AGI'.
• Computers are much faster than humans for many tasks, and so one might believe that if an AI can achieve a thing it will quite obviously be faster than a human. This is less obvious now, however, because AI systems are leaning more on repeated sampling/selection procedures.
• Comparative advantage is a thing, and so AI might have very large impacts even if it remains less absolutely capable than humans for many different tasks, if the cost/speed are good enough.
• There are some factors that don't fit super cleanly into this framework: things like AI's 'always-on availability', which aren't about capability per se, but probably belong in the numerator anyway? e.g., "How good is an AI therapist?" benefits from 'you can message it around the clock', which increases the utility of any given task-performance. (In this sense, maybe the ratio is best understood as utility-per-resource, rather than capability-per-resource.)