Parker Whitfill

More generally, the whole idea of communicating things is very hard to pin down in economic terms, since any individual sentence is not a credible signal of the meaning of that sentence without some sociopolitical context that incentivizes honesty (unlike, say, an antelope jumping, which is a credible signal of its health).

There's a big 2000 onwards econ game theory literature on communication and information design.

Maybe the seminal modern paper here is https://web.stanford.edu/~gentzkow/research/BayesianPersuasion.pdf

Don't have opinions on how insightful... (read more)

FWIW I think many of these sociopolitical ideas have been absorbed into mainstream academic economics already e.g. 

• work on national identity/culture/social trust being very important for growth and cooperation 
• work on game theory on credible commitments and their variations in repeated games
• meta-rationality/rational inattention on sometimes computing how valuable something is might not itself be rational. 

I think it would be more precise to say this against classical or 1960s econ ideas. 

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... (read more)

Seconding that updating the data on Github would be useful. 

A few months ago, I had a similar concern, and redid the METR plot but estimates time horizon by the following algorithm. 

1. Loop through a fine grid of times (1 min, 2 min, 3 min, etc) 
2. For each time, use a local linear regression to estimate average probability of success at this time (any differentiable function is locally linear, so local linear is a good nonparametric estimator) 
3. If you cannot reject that this estimated probability is 50%, then add it to the time horizon

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.