These are all just bets that eventually someone wins anyway, right? Adoption is good but marginal revenue doesn’t matter if and when these models and solving world hunger - or have created the next yakuza mega corp that governs the world - right?
Feels like an unspoken rule here. Everyone wants to own a chunk of nuclear weapons and it doesn’t matter whether it’s profitable. You just need the nukes to survive and have a seat at the table
It’s a similar bet as Uber. They also started out with numbers that make no sense - overpaying drivers and undercharging users
The math may look questionable but there are also senior people talking of automating all white color work in the next couple years. Even if that estimate is miles off on both time and % it’s still trillions. So crazy as the numbers seem it could still work out
More people need to read Ed, especially tech journalists. I feel like he's one of the rare few people that are actually speaking about the industry honestly.
The counter argument (not mine): Software Engineers are willing to spend their own money on AI. The same people that wouldn't pay 10 dollars for code if there was a workaround that took hours.
Imagine you were looking at Google, a sustainable and profitable business, and you thought you saw a once in a lifetime opportunity to compete with them and take their position as a leading tech company. How much money would you need to spend to make a credible attempt?
Google has had decades to accumulate intellectual and physical capital. Catching up quickly means spending >500 billion. If you can actually dethrone Google (admittedly not an easy task) then it will have been worth it. If not, I suppose it's wasted investment.
Now what happens when three or four startups vie for this opportunity at once? Well that's how you get $2 trillion in captial investments per year.
The cost of tokens used by AI in many fields is even greater than the cost of human services; people are experiencing FOMO, but once the wave passes, the market will stabilize.
The thing that everyone seems to be missing is that the US AI companies are focused on the frontier models, that are very expensive for diminishing returns.
If suddenly the money craze stops, meaning (1) AI companies investors want them to become profitable and (2) clients start being cost-sensitive to AI bills (which they are absolutely not currently), then everyone will switch to smaller, cheaper models that are enough for a lot of use case.
Sonnet instead of Opus. GPT 5.4 instead of 5.5.
Chinese models.
People keep comparing to Uber but Uber can't suddenly make it cheaper to operate.
I really respected Ed Zitron, but I feel like he's very much lost the plot on AI.
Scroll back not too far and he was publishing criticisms that no one wants to spend actual money AI. Anthropic has shattered all notions of that since then.
Then there was the idea that even if people want it, we have way too much GPU capacity to ever be saturated. Now almost all providers are hitting limits.
Now, its the next iteration that even if people want to spend money and GPU's are at capacity, its just never going to be profitable. This may or may not be true, especially with more capable open source models that can be served at cost. But at this point, he mostly just brings up anything possible to downplay AI
Providers have been hitting limits and growing backlogs -- i.e. real money customers had committed to paying them but could not be realized due to lack of capacity -- to triple-digit billions EACH for multiple quarters now.
People are only just noticing the capacity crunch now because they're being directly impacted by Claude crapping out so much. But a superficial glance at the quarterly earnings of any of the hyperscalers shows that the AI compute crunch and revenues have only been growing pretty much since the AI boom took off. That alone should have raised questions about the bubble narrative.
Yet commentators like Zitron have been crying "bubble" all that time. I guess there's real money... er... engagement to be farmed by playing up the AI backlash. The backlash is real and understandable, but these narratives only serve to muddle useful discourse in exchange for some cheap rage-views.
why does it sound like the crux of your argument against Ed is "why is he evolving his position"? Are we supposed to respect someone less for not being dogmatic and holding onto an opinion against mounting evidence?
The end goal of these companies is AGI, or even ASI. If you believe this is around the corner, and think AI can do the job of a human for less money, it makes sense to put all your money into working towards that goal and buying as much compute as you can. This is especially true since whoever gets there first (or is simply ahead and can use their AI to get even better) gets a big advantage.
I’m paying API prices for my hobby coding due to the coding agent I use. So far I’ve switched from Opus to Sonnet to GLM 5.1. Looks like it’s about 25% of the cost and quality seems good enough so far.
I think competition is going to keep customer costs low if you’re willing to switch. Maybe people on expense accounts won’t care, though?
You have to look at use cases and there are a bunch of slam dunk use cases that are wildly profitable at todays token prices, whether we keep finding use cases as intelligence goes up is another story.
A lot of the current AI economics seems to depend on three assumptions being true at once: 1. inference costs fall fast enough 2. usage grows into very large recurring revenue 3. customers don't cut once handed the bill
We should draw a distinction between "AI is valuable" and "AI justifies its current investment levels." There's real productivity value in AI, especially for things like search, boilerplate, tests, refactoring, etc...BUT that doesn't mean every enterprise should let token spend grow without strict telemetry, cost-attribution and outcome-based measurements.
The teams that win here will not be the ones using the Most AI, but the ones that treat it like any other expensive production dependency, which means measuring unit economics, cap runway usage, properly align models with tasks(not just Opus everything), and scale workflows with ROI in mind.
If you think it's expensive now, imagine what they'll do if they get their way and people become dependent on it. Once they've got businesses and consumers over a barrel the gloves will come off and they'll skip the lube. The good news is that we can decide we don't actually need it. Maybe it'll take a generation or two to recover the skills and mental abilities we lost by outsourcing everything to the bots but accepting shitty results in exchange for getting them faster and easier is a choice, and it's up to us to decide when it isn't worth it anymore.
Right now, a lot of the costs (especially the environmental ones) are mostly hidden from and removed enough from users that "fast and easy" is still very tempting. People are still learning for themselves what the limitations are and how different what AI delivers is from what they were promised. There's plenty of time for people make a lot of money and cause a lot of harm before the bubble bursts, companies realize AGI isn't going to happen, and the true costs get properly factored in.
The AI related companies seem to be doing ok. Google profits $132bn Microsoft $102bn. Anthropic losing about $10bn but on revenue of $30bn up from $14bn a year or so ago. I don't think it's all going bust too quickly.
OK, Question: Would this outcome still benefit society overall?
In the aftermath of this bubble "AI" will still have utility, like the dotcom bubble. So lets say FANG doesn't make a return, how much should we care? How much of this investment is sunk cost that would continue to provide value, and how much of it is operation costs just keeping the lights, I mean GPUs on, that would become unviable post-bubble? As an immediate effect, what happens to these AI companies? or if they become insolvent, what happens to the assets and tech? and what are the secondary economical effect to society if FANG doesn't get their ROI?
Although the data centers are probably optimized for AI workloads; they can probably be used for all kinds of computing tasks. If AI revenue does not meet projections, the hardware is not going to be unused.
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[ 4.0 ms ] story [ 49.9 ms ] threadIn other words; right now, we're still in the "bait" phase. The "switch" comes later.
If AI is too cheap: bubble will burst because you can run them locally and data centrs are not needed.
If is it in-between, AI companies make too much money and they make too much profit which is bad!
I don't think this guy is a serious commentator.
Feels like an unspoken rule here. Everyone wants to own a chunk of nuclear weapons and it doesn’t matter whether it’s profitable. You just need the nukes to survive and have a seat at the table
The math may look questionable but there are also senior people talking of automating all white color work in the next couple years. Even if that estimate is miles off on both time and % it’s still trillions. So crazy as the numbers seem it could still work out
The bright side is: this is a golden era of subsidized tokens. It will not always be like this, so now is the time to churn out your passion projects.
Google has had decades to accumulate intellectual and physical capital. Catching up quickly means spending >500 billion. If you can actually dethrone Google (admittedly not an easy task) then it will have been worth it. If not, I suppose it's wasted investment.
Now what happens when three or four startups vie for this opportunity at once? Well that's how you get $2 trillion in captial investments per year.
If suddenly the money craze stops, meaning (1) AI companies investors want them to become profitable and (2) clients start being cost-sensitive to AI bills (which they are absolutely not currently), then everyone will switch to smaller, cheaper models that are enough for a lot of use case.
Sonnet instead of Opus. GPT 5.4 instead of 5.5.
Chinese models.
People keep comparing to Uber but Uber can't suddenly make it cheaper to operate.
Scroll back not too far and he was publishing criticisms that no one wants to spend actual money AI. Anthropic has shattered all notions of that since then.
Then there was the idea that even if people want it, we have way too much GPU capacity to ever be saturated. Now almost all providers are hitting limits.
Now, its the next iteration that even if people want to spend money and GPU's are at capacity, its just never going to be profitable. This may or may not be true, especially with more capable open source models that can be served at cost. But at this point, he mostly just brings up anything possible to downplay AI
Providers have been hitting limits and growing backlogs -- i.e. real money customers had committed to paying them but could not be realized due to lack of capacity -- to triple-digit billions EACH for multiple quarters now.
People are only just noticing the capacity crunch now because they're being directly impacted by Claude crapping out so much. But a superficial glance at the quarterly earnings of any of the hyperscalers shows that the AI compute crunch and revenues have only been growing pretty much since the AI boom took off. That alone should have raised questions about the bubble narrative.
Yet commentators like Zitron have been crying "bubble" all that time. I guess there's real money... er... engagement to be farmed by playing up the AI backlash. The backlash is real and understandable, but these narratives only serve to muddle useful discourse in exchange for some cheap rage-views.
I think competition is going to keep customer costs low if you’re willing to switch. Maybe people on expense accounts won’t care, though?
This blog is too expensive too.
We should draw a distinction between "AI is valuable" and "AI justifies its current investment levels." There's real productivity value in AI, especially for things like search, boilerplate, tests, refactoring, etc...BUT that doesn't mean every enterprise should let token spend grow without strict telemetry, cost-attribution and outcome-based measurements.
The teams that win here will not be the ones using the Most AI, but the ones that treat it like any other expensive production dependency, which means measuring unit economics, cap runway usage, properly align models with tasks(not just Opus everything), and scale workflows with ROI in mind.
Right now, a lot of the costs (especially the environmental ones) are mostly hidden from and removed enough from users that "fast and easy" is still very tempting. People are still learning for themselves what the limitations are and how different what AI delivers is from what they were promised. There's plenty of time for people make a lot of money and cause a lot of harm before the bubble bursts, companies realize AGI isn't going to happen, and the true costs get properly factored in.
OK, Question: Would this outcome still benefit society overall?
In the aftermath of this bubble "AI" will still have utility, like the dotcom bubble. So lets say FANG doesn't make a return, how much should we care? How much of this investment is sunk cost that would continue to provide value, and how much of it is operation costs just keeping the lights, I mean GPUs on, that would become unviable post-bubble? As an immediate effect, what happens to these AI companies? or if they become insolvent, what happens to the assets and tech? and what are the secondary economical effect to society if FANG doesn't get their ROI?