something I failed to consider on the compute overhang:

~3 years ago, if asked if it was good that frontier labs were pushing out models at their then-current pace I would have said :

it seems good  that we are maxing out our compute.

i now think a blind spot of mine was how much frontier labs’ demand for compute accelerated compute supply, therefore shortening timelines. 

To recount the compute overhang argument:

if we were to pause / proceed slowly, compute hardware would still improve, so we would get a spiky increase in capabilities. And without intermediate models to study, this would have been much more dangerous.

we did get intermediate models to study. But compute supply itself has also been accelerated. 

I think most people now agree that frontier labs’ compute demand has led to the building of more fabs by TSMC + memory makers.

vibe researching this: installed compute capacity was growing at ~2.5× per year before chargpt. Extrapolating that trend to 2027, we would have had roughly 1/4 of the installed compute capacity we now expect in 2027 if growth had remained at pre-chatgpt levels. So if you take pre-chatgpt levels as the counterfactual, in 2027, frontier  labs have accelerated installed compute by ~1-2 years.

I’m not sure how to value the extra ~1-2years. and whether that outweighs the value of the intermediate models we got to study.

but it definitely makes me less confident that the argument was sound, and makes me suspicious of similar arguments.

i updated on this take after reflecting on richard ngo's paragraph on compute overhangs.

in general if you are a big enough player (which most impactful people hope to be) , you should expect your actions to shape the environment quite abit.

website to sum up resources / tweet thread/ discussion for our introspection paper

https://modelintrospection.com

Some people (my mentor ethan perez ) said my weekly MATS research update slides were nice. Some rough tips i have:

• mentors often have alot of projects they are working on. at the start of your slides,  recap the takeaways from last week, and any jargon you might have.
• Keep graphs simple. As a rule of thumb, it gets quite confusing when you have >= 4 categories / colors to look at. Are all these categories important? Maybe just show the most important two. Keep the other categories as a backup slide in case ethan wants the breakdown. One graph, one story to takeaway
• Highlight what to look at in the chart. E.g if you have a line chart on model loss, draw a red arrow that say "Model loss goes down - thats what we want!".
• Show the prompt of whatever you are calling the model with
• If you have someone to show to (e.g.  random people over lunch), show your slides. These people are going to have much less context on what you are working on, so if they can actually understand your slides, its a great signal that ethan is going to understand it. showing it to other ethan collaborators also helps - ask them to model what ethan would say.
• when i first started working with ethan and improving my slides, it took me around 2-3 days to do it. I suggest starting early. This seems a long time, but it includes asking my collaborators to critique my slides, and from their feedback i improved my plots + run more experiments to address the critique. i think it was a worthwhile investment! (after awhile i got better at this so i take less time to iterate on this process)