But also, the companies are buying up this infrastructure because whoever controls the infrastructure also controls the industry in around 5 years time.

However, clueless people who don't know how to optimize probably don't know where to spend money on optimization, either. So maybe it's just not a great fit for outsourcing, especially in a realm where there's no standard of correctness to measure the results of the supposedly "optimized" training against. And Warden seems to be pitching outsourcing rather than trying to get acquihired.

1. Token prices keep plummeting even as models are getting stronger.

2. Most models are being offered for free at a significant loss, so reducing costs would be critical to maintain some path to sustainability.

3. Every hyperscaler has been consistently saying for the past several quarters that they are severely constrained on capacity and in fact have billions in booked backlogs. That is, if they had more capacity they would actually be making even more billions.

I can totally imagine the smaller players renting these cloud resources for their private model uses to be rather inefficient (which is where the 50% utilization number comes from), probably because they are prioritizing time-to-market over other aspects. But I would wager that resource efficiency, at least for inference, is absolutely a top priority for all the big players.

OTOH garage-startup acquisitions are acquihires.

This eliminates the need for more specialized models and the associated engineering and optimizations for their infrastructure needs.

Recent generation llms do seem to have some significant efficiency gains. And routers to decide if you really need all of their power on a given question. And Google is building their custom tpus. So I'm not sure if I buy the idea that everyone ignores efficiency.

>I see hundreds of billions of dollars being spent on hardware

>I don’t see are people waving large checks at ML infrastructure engineers like me

Which seemed like a valid question mark until you look at the github. <1B Raspberry pi class edge speech models. That's not the game the hyperscalers are playing

I don't think we can conclude much of anything about the datacenter build out from that

That doesn't seem to be the case to me. I guess the author wants to do everything on his own terms and maybe companies aren't interested in that.

Spending a lot (on capex or opex) certainly is not providing any kind of signaling benefit at this level. It's the opposite, because obviously every single financial analyst in the market is worried about the rapid increase in capex. The companies involved are cutting everything else to the bone to make sure they can still make those (necessary) investments without degrading their top-line numbers too much. Or in some cases actively working to hide the debt they're financing this with from their books.

Even if we imagined that the author's conspiracy theory were true, there would still be massive incentives for optimization because everyone is bottlenecked on compute despite expanding it as fast as is physically possible. Like, are we supposed to believe that nobody would run larger training runs if the compute was there? That they're intentionally choosing to be inefficient, and as a result having to rate-limit their paying customers? Of course not.

The reality is that any serious ML operation will have teams trying to make it more efficient, at all levels. If the author's services are not wanted, there are a few more obvious options than the outright moronic theory of intentional inefficiency. In this case most likely that their product is an on-edge speech to text model, which is not at all relevant to what is driving the capex.