Nvidia’s $589B DeepSeek rout

There's a Jevons Paradox article up now and I'll put most of my thoughts there: https://news.ycombinator.com/item?id=42863808>

If you care to respond though, my first question would be what examples of falling input prices not subject to the Jevons Paradox are. Several of the more notorious ones involve energy, and that was Jevons's principle topic of study (The Coal Question most notably).

I've got my own theory of how technological mechanisms function, with an ontology of nine elements. Fuels are one of those, information is another. See prior comments: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...>

As might be pertinent to AI and LLM, whilst fuels and power applications seem to scale linearly against input (constant slope, if not 1:1 relation), information processing delivers far more variable returns, often with critical thresholds. Network effects and Metcalfe's Law are the best known of these (if highly inaccurate themselves, see Tilly-Odlyzko's refutation), but another is the limited returns of predictive and targeting applications.

For the latter, the 18 order of magnitude increase in computing power from 1965--2025 (60 years, about 20--30 Moore's Law cycles) has roughly doubled the length of accurate long-term weather forecasting from roughly 5 days to 10. It's made possible fully-resuable first-stage boosters for orbital spaceflight, which is visually impressive, but has only resulted in a five-fold reduction ($1,400/kg vs. $5,400/kg) in low-Earth orbit (LEO) launch costs (Falcon Heavy vs. Saturn V). SpaceX are looking for another factor of 2--4 reduction (to $250--600/kg), but that's still far less improvement than we've seen in raw compute. At some point orbital physics, the rocket equation, and fuel chemistry simply dominate other considerations.

Similarly, AdTech makes possible far more targeted advertising, but to heavily diminishing returns, the core result has been an abandonment of non-targetable media by advertisers, notably print and broadcast, as well as an arms-race between the browser (for a very small fraction of the market) and advertisers (the largest of which also has the largest browser marketshare), and a concentration of advertising revenue amongst two online entities, Google (a/k/a Alphabet) and Facebook (a/k/a Meta).

Which makes me wonder what applications AI LLMs might practically be put to. Advertising, manipulation, fraud, and propaganda certainly seem to be benefiting.

Given they've reproduced earlier model's and vetted it - I think it's probably safe to assume that these new models are not out of thin air - but until somebody reproduces it, it's up in the air.

My original point was that when PG is writing outside of his core competencies, it's usually about subjective opinions. Such as, is wealth tax a good or bad thing? That is a subject about which you can have an opinion, but there is no absolute.

My concern is that when people say "he doesn't know anything when he talks about, say, economics or politics" what they really mean is "I disagree with him and therefore he is wrong." (I disagree with PG on many such things, but that's like, just my opinion, man)

I guess I'm invested in the debate because I want people to be more open-minded and charitable and not less.

There are basically two reasons for consultants:

Either you need some once-removed thing (be that you selling/buying some part to/from a competitor, accounting, lawyering). That part sensible people will not replace by LLMs. But here it isn't that you yourself lack the expertise at all. Here it is absolutely necessary, that someone else does the actual implementation.

Or you have some general "we need to do better" feeling. And here you have again two options: 1) you know _what_ your problem is and you just need the best solution there is. This is (obviously somewhat tongue-in-cheek) essentially corporate espionage. Again you cannot replace that with an LLM (or maybe you can I don't know), but you will pay a lot of money for it. Or 2) you don't know what the problem is. Now you are competing in finding an appropriate starting point with 20-somethings fresh from university that are not wanted in your organisation and therefore won't get access to the relevant information anyhow. So yeah, I'm willing to believe that the typical success rate of those consultancy projects is low to negative.

If you are only given a 3 week crash course in $BUSINESS, you won't be able to produce much more than a generic set of "have you thought about that?". And THAT is something that I believe LLMs to be reasonably good at. And they are dirt cheap and instantly available compared to any kind of human consultant.

Now I don't think that will necessarily a net-negative for consultants in general. I do think, similar to ATMs, that consultations are mostly becoming cheaper by that.