Parker Whitfill

Can you elaborate on what you think Anton Korinek does differently? 

Shouldn't we start to see the METR trend bending upwards if this was the case? Let T be time Horizon, A algorithmic efficiency, C training compute, E experimental compute, L labor and S speedup. 

Suppose, 

$T=(AC{)}^{\delta }$

$\dot{A}=A^{1-\beta }\left[\right(SL{)}^{\alpha }{E}^{1-\alpha }{]}^{\lambda }$

Then deriving the balanced growth path 

$g_T=\delta r{g}_S+\delta [r{g}_L+\frac{\lambda (1-\alpha )}{\beta }{g}_E+g_C]$

So if $g_S$ starting growing rapidly, we should see time horizon trend increase, potentially by a lot. Now maybe Opus 4.5 is evidence of this, but I'm skeptical so far. Better argument is that there is some delay from getting better research to better models because of training time, so we haven't seen it yet - though this rules out substantial speedup before maybe 4 months ago. 

I don't think this logic is quite right. In particular, the relative price of compute to labor has changed by many orders of magnitude over the same period (compute has decreased in price a lot, wages have grown). Unless compute and labor are perfect complements, you should expect the ratio of compute to labor to be changing as well. 

To understand how substitutable compute and labor are, you need to see how the ratio of compute to labor is changing relative to price changes. We try to run through these numbers here. 

I found this article very insightful, so I wrote a follow-up that tries to estimate $\rho$ in AI firms. 

Ben Todd here gets into the numbers a little bit. I think the rough argument is that after 2028, the next training run would be $100bn which becomes difficult to afford.