The sigmoids won't save you

People who correctly cite the Lindy effect won't look like people who correctly cite the Lindy effect.

I felt the better takeaway from this was that it's impossible to know for certainty how long this will or will not continue regardless of the data or models you're using, because if you (or anyone else) could predict that accurately they'd be one of the richest people on the planet.

I don't know when (or if) AI will implode or succeed with any degree of provable certainty, because that's not my area of expertise. Rather, I can point out and discuss flaws in the common booster and doomer arguments, and identify problems neither side seems willing to discuss. That brings me cold comfort, but it's not enough to stake my money on one direction or another with any degree of certainty - thus I limit my exposure to specific companies, and target indices or funds that will see uplift if things go well, or minimize losses if things go pear-shaped.

I also think relying on such mathematics to justify a position in the first place is kind of silly, especially for technical people. Mathematical models work until they don't, at which point entirely new models must be designed to capture our new knowledge. On the other hand, logical arguments are more readily adapted to new data, and represent critical, rather than mathematical, thinking and reasoning.

Saying AI is going boom/bust because of sigmoids or Lindy's Law or whathaveyou is not an argument, it's an excuse. The real argument is why those things may or may not emerge, and how do we address their consequences within areas inside and outside of AI through regulation, innovation, or policy.

To make that argument you'd want to show some causal link which so far we haven't seen.

There was no implication of anything you're suggesting. It's a question of correctness (bias vs facts, predicting the sun will rise vs predicting the end of the world), whether you think it's important to be correct as a matter of reputation, and how correctness should be weighed if it is indeed important to one's reputation (a once-off comment vs a full report).

Not interested in further arguments about this.

First sigmoid was transformers allow us to rapidly scale to our already abundant data until we tapped it out, the second is/was reasoning, allowing us to scale to our available compute (and compute manufacturing capacity). Correct me if I'm wrong but we don't have candidate for the third sigmoid, and scaling inference is hitting real-world supply chain constraints - electricity and chips.

Short of a third sigmoid appearing in the ML CompSci space, perhaps in the form of ongoing, repeated step-optimisations which will also have diminishing returns, intelligence growth is now limited a few scaling problems that have already been worked on for a very long time.

Transistors, which have been doubling for almost a century now, but Moores Law has already plateaued and reached limits on energy efficiency, and simply building new fabs is not something that we can do exponentially. And the other growth limiter is electricity - there is no exponential supply of fossil fuels or power plants. Although manufacturing has scaled, PV tech improvements are also plateauing - and while storage is getting cheaper, it's still not economical vs fossil fuels (meaning: when we have to switch to it, the growth slows down further) and we are unlikely to see battery efficiency sigmoid enough to maintain the AI sigmoid.

I don't mean to be bearish here. There's so much money sloshing around that we can afford to put the smartest people, using unlimited tokens, on the task of finding small, incremental gains on the CompSci side of things that will have large monetary payoffs - hopefully allowing further scaling and increased emergent abilities of LLMs. Maybe we can squeeze the algos for quite a while. But I don't see that maintaining the same level of exponential as unlocking unlimited data or maxxing out the world's energy/fab capacity for long.

And I don't see why this is a massive issue except for the people who want to have some god-like super AI? Frontier LLMs are genuinely magic. Not "won't delete your production database" magic, but definitely a massive productivity gain for competent knowledge workers.