One example, to add a little concreteness: suppose that the path to AGI is to scale up o1-style inference-time compute, but it requires multiple OOMs of scaling. So it no longer has a relatively-short stream of "internal" thought, it's more like the natural-language record of an entire simulated society.

Then:

• There is no hope of a human reviewing the whole thing, or any significant fraction of the whole thing. Even spot checks don't help much, because it's all so context-dependent.
• Accurate summarization would itself be a big difficult research problem.
• There's likely some part of the simulated society explicitly thinking about intentional deception, even if the system as a whole is well aligned.
• ... but that's largely irrelevant, because in the context of a big complex system like a whole society, the effects of words are very decoupled from their content. Think of e.g. a charity which produces lots of internal discussion about reducing poverty, but frequently has effects entirely different from reducing poverty. The simulated society as a whole might be superintelligent, but its constituent simulated subagents are still pretty stupid (like humans), so their words decouple from effects (like humans' words).

... and that's how the proposal breaks down, for this example.

I haven't decided yet whether to write up a proper "Why Not Just..." for the post's proposal, but here's an overcompressed summary. (Note that I'm intentionally playing devil's advocate here, not giving an all-things-considered reflectively-endorsed take, but the object-level part of my reflectively-endorsed take would be pretty close to this.)

Charlie's concern isn't the only thing it doesn't handle. The only thing this proposal does handle is an AI extremely similar to today's, thinking very explicitly about intentional deception, and even then the proposal only detects it (as opposed to e.g. providing a way to solve the problem, or even a way to safely iterate without selecting against detectability). And that's an extremely narrow chunk of the X-risk probability mass - any significant variation in the AI breaks it, any significant variation in the threat model breaks it. The proposal does not generalize to anything.

Charlie's concern is just one specific example of a way in which the proposal does not generalize. A proper "Why Not Just..." post would list a bunch more such examples.

And as with Charlie's concern, the meta-level problem is that the proposal also probably wouldn't get us any closer to handling those more-general situations. Sure, we could make some very toy setups (like the chess thing), and see what the shoggoth+face AI does on those very toy setups, but we get very few bits, and the connection is very tenuous to both other threat models and AIs with any significant differences from the shoggoth+face. Accounting for the inevitable failure to measure what we think we're measuring (with probability close to 1), such experiments would not actually get us any closer to solving any of the problems which constitute the bulk of the X-risk probability mass. It's not "a start", because "a start" would imply that the experiment gets us closer, i.e. that the problem gets easier after doing the experiment. If you try to think about the You Are Not Measuring What You Think You Are Measuring problem as "well, we got at least some tiny epsilon of evidence, right?", then you will shoot yourself in the foot; such reasoning is technically correct, but the correct value of epsilon is small enough that the correct update from it is not distinguishable from zero in practice.

The problem with that sort of attitude is that, when the "experiment" yields so few bits and has such a tenuous connection to the thing we actually care about (as in Charlie's concern), that's exactly when You Are Not Measuring What You Think You Are Measuring bites real hard. Like, sure, you'll see this system do something in the toy chess experiment, but that's just not going to be particularly relevant to the things an actual smarter-than-human AI does in the situations Charlie's concerned about. If anything, the experimenter is far more to likely to fool themselves into thinking their results are relevant to Charlie's concern than they are to correctly learn anything relevant to Charlie's concern.

I think this misunderstands what discussion of "barriers to continued scaling" is all about. The question is whether we'll continue to see ROI comparable to recent years by continuing to do the same things. If not, well... there is always, at all times, the possibility that we will figure out some new and different thing to do which will keep capabilities going. Many people have many hypotheses about what those new and different things could be: your guess about interaction is one, inference time compute is another, synthetic data is a third, deeply integrated multimodality is a fourth, and the list goes on. But these are all hypotheses which may or may not pan out, not already-proven strategies, which makes them a very different topic of discussion than the "barriers to continued scaling" of the things which people have already been doing.

Some of the underlying evidence, like e.g. Altman's public statements, is relevant to other forms of scaling. Some of the underlying evidence, like e.g. the data wall, is not. That cashes out to differing levels of confidence in different versions of the prediction.

Oh I see, you mean that the observation is weak evidence for the median model relative to a model in which the most competent researchers mostly determine memeticity, because higher median usually means higher tails. I think you're right, good catch.

FYI, my update from this comment was:

• Hmm, seems like a decent argument...
• ... except he said "we don't know that it doesn't work", which is an extremely strong update that it will clearly not work.

Still very plausible as a route to continued capabilities progress. Such things will have very different curves and economics, though, compared to the previous era of scaling.

I don't expect that to be particularly relevant. The data wall is still there; scaling just compute has considerably worse returns than the curves we've been on for the past few years, and we're not expecting synthetic data to be anywhere near sufficient to bring us close to the old curves.

unless you additionally posit an additional mechanism like fields with terrible replication rates have a higher standard deviation than fields without them

Why would that be relevant?