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.
You could imagine a Moore’s Law-esque cheapening of the tech that coincides with the business raising their prices. That might look like a continuation of simply “using the tools” on the surface, but on the inside it would spell a gradual, meaningful increase in margin
The subsidies went away gradually and the prices leveled out in a spot where the services are heavily used. Uber became profitable. Ride sharing is affordable.
I think our $20/month plans might become a little less generous and the $200/month plan won’t always allow non-stop vibe coding, but I don’t think the prices are going to rise so much that users are priced out. Like Uber, customers will grumble for a while and then adapt to the new normal.
The big difference is that compute hardware is getting better. I think we might overshoot with data center buildouts to the point that compute becomes cheap, while hardware improvements continue to lower the cost of serving models. Over time the same service becomes cheaper to operate, opposite of Uber where driver wages are creeping upward.
Hell yeah obviously. There's close to no doubt. So why do we think its not true now?
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.
More realistically, it seems like someone calculated that it could still be profitable up to several hundreds of billions of dollars which explains the initial investment. And continued investment can be explained by trying to salvage the existing capital spend. But even if it's the best option those companies have now as far as a hypothetical goes, it still might not have been worth it.
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 am exclusively using 5.4 because its only 1x and very good, but the github calculation showed my once $40 become a $680 billing
That is too expensive and not worth paying
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
jqpabc123•38m ago
In other words; right now, we're still in the "bait" phase. The "switch" comes later.
happyPersonR•35m ago
ReptileMan•30m ago
happyPersonR•5m ago
1) someone deepseeks deepseek lol:
Generates their own weights and figures out a way to determine all of the intermediate states.
2) places realize there’s real risk with using a model that might have things baked into it that produce specific flaws that could be security bugs, but only under certain conditions.
gdulli•15m ago
If people's dependence on their streaming service keeps them captive, just wait until people have gone 5 years without doing real work.
b65e8bee43c2ed0•33m ago
citrin_ru•28m ago
han1•21m ago
larme•9m ago
They also publish papers talking about how to save kv cache and computation powers. Because currently they don't have the most powerful nvidia cards, training and inference efficiency is very import for them.