He doesn't need AI background to commentate on financials. Your post reads like a personal attack. His record talking about stock market doesn't matter either. There is about 0 information in anyone talking about what stock market is going to do.
So his point is that big % of cloud revenue of Microsoft/Google/Amazon come from companies that:
1)are very unprofitable
2)need to raise staggering amount of capital to survive
3)are financed by their suppliers and that money is circling back to them
Your counter-argument is this:
>> It truly doesn't matter whether closed source Frontier lab models are spewing tokens or large foreign open weight models are doing it, the token factories will be just fine, and that's really all I care about.
This might be true but there are 2 majors questions here. One is exposure to Anthropic/OpenAI. If they go bust/can't IPO at expected price it's a big loss hyperscalars will need to admit. The second question is how much of that cloud revenue comes from training. This part of the demand is going shrink or disappear in the bad scenario.
He doesn't have a finance background either and it's quite a job going through the 7482 words or whatever he's rattled off this week to analyse where he's gone wrong.
His fundamental error I think, illustrated here https://www.youtube.com/watch?v=C0Gcx-6hJJw&t=196s is he thinks AI is just another tech product to hype rather than a comparable revolution to the industrial one.
The weak angle is on AI usability. I think he is wrong there; AI is clearly useful. Now, there is a discussion if it is multi-trillion dollar useful; I think it isn't, but it is useful nonetheless.
Now, there is a strong angle, which is the economic viability of AI, and the gargantuan amount of money being burned in what is a very risky bet. There, his arguments have proven so far rock solid.
The fact that you (as all his critics) chose to attack only the weak angle says something.
It's kind of complicated to argue on the economic viability as the spending does seem a bit excessive but if you assume an industrial revolution kind of situation it can be justified, if you assume it's just another tech product to hype then it isn't and will all fall apart. I think Zitron is wrong on that but can't prove it as I can't see the future.
You can look as past quotes like "that sound you hear is the slow deflation of the bubble I've been warning you about since March" in July 2024 and say he was over pessimistic on the economics but it's hard to prove he still is.
There's a fundamental issue that in standard finance the present value of an investment is the discounted value of the future cash flows which are hard to estimate with a developing tech like AI but Ed I guess thinks they'll be low and optimists high. I'm not sure he'd even be up on the concept of discounted future cash flows.
It's difficult to trust the viability of this when the amount of money that it needs to generate as revenue is in the "several trillions" ballpark just for "break-even".
If you assume that this will be "industrial revolution" situation... maybe? It doesn't help if this happy scenario takes 30 years to materialize. The money needs to be there by sometime in the next few years.
Also, every risky bet has a happy scenario that if successful, all the gamblers become gorillionaires. In the happy scenario, every GME hodler would have wife-changing money by now, with their shares in Gamestop worth infinite dollars.
I don’t trust someone who is dumb enough to think LLMs aren’t useful at all.
Of course, he may just be deceiving himself or his audience about this belief. I don’t think he is actually dumb. But this alternative is equally problematic.
This is a guy that comes purely from the financial angle as has no obvious ideological stance in being either pro or against AI. Also pretty balanced in his delivery.
Some numbers he brings up should make even AI hypers question the sanity of this whole thing.
The thing about "hyping AI way beyond its capabilities" is that the future capabilities will no doubt be greater than the current ones so they may just be talking about the future a bit.
There's a bit of a tipping point in usefulness between AI being a bit worse than humans and a bit better, like for mathematical theorems that's probably happened where being not very good was kind of useless and just recently they are proving lots of things. That will probably gradually happen in other fields creating a lot of economic value.
- AI may speed up on improvements and be a singularity moment, truly a new industrial revolution.
- AI may have only incremental improvements in the next few decades with diminishing returns.
- AI improvements may plateau and fizzle out.
All those are possible scenarios, and if you dig you may find evidence and historical precedence for all of those. I don't think making wild bets that can tank the whole economy based on a FOMO-fueled prediction that everything will work out in a best-case scenario is healthy.
I see your point but there's been a Moore's law like trend in compute/dollar that's progressed steadily for about a century and has a very high probability of continuing. How that maps into AI products is a bit uncertain but there's definitely a trend in that direction.
As an example of the predictability, Hand Moravec wrote quite a well argued paper in 1989 predicting human level hardware capabilities would be available in inexpensive machines around the mid 2020s which I think was fairly spot on. He also argued once those capabilities were there, software guys would figure things to use if for.
But why do you think everyone in AI is hyping it beyond its capabilities? That's a specific San Francisco-driven AGI cult and a handful of annoying billionaires. I'm doing what Patrick Boyle said is the biggest use case: running a one person consultancy on $400/month of AI services. It's working far better than I expected. And if the rates go up too much down the road, I'll pivot to running locally.
I suppose if you still consume influencer content, it's pretty bleak slop right now too, but then there are occasional slopcore geniuses that manage something entertaining so I say let the future work itself out. No worries, it will.
As for the sanity of the gold rush phase of anything... Are you kidding me? Really...
The global GDP annually is ~$120T. $2T is not that big a number at that scale. It's interesting that the critics stick to the domestic tech GDP when the global tech GDP is ~$20T to insist AI hitting $2T annually is impossible.
So his point is that big % of cloud revenue of Microsoft/Google/Amazon come from companies that:
1)are very unprofitable
2)need to raise staggering amount of capital to survive
3)are financed by their suppliers and that money is circling back to them
Your counter-argument is this:
>> It truly doesn't matter whether closed source Frontier lab models are spewing tokens or large foreign open weight models are doing it, the token factories will be just fine, and that's really all I care about.
This might be true but there are 2 majors questions here. One is exposure to Anthropic/OpenAI. If they go bust/can't IPO at expected price it's a big loss hyperscalars will need to admit. The second question is how much of that cloud revenue comes from training. This part of the demand is going shrink or disappear in the bad scenario.