AI is too expensive (opens in new tab)

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

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.

If you're older than 30, you've seen this play out before… This is just how the VC game works. Cross-town Uber rides don't stay $5 forever.

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.

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.

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.

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 end goal of those companies is AGI, or even ASI. If you believe in that, and think they can do the job of a human for less money, you should put all your money into working towards that goal and buying as much compute as you can, especially 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?

The rug pull on users is bound to happen and it will involve advertising.

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

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

FANG et al, sit on trillions in capital..

If they are instead burning this on "hyper-scaling", and the bubble ends, and they have overinvested in AI as this blog post asserts - then they never get a return. OK so far.

Question: In the aftermath "AI" will still have utility, just like after the dotcom bubble. So what would happen to all of the assets and services of these AI companies?. If the cost has already been sunk, would they continue to benefit society? and would owners be incentivised to continue their operation to minimise their losses? Would they be cannibalised for parts? Would they become unviable to even break even at existing operation cost?

I don't understand the economic factors enough to see the possible futures clearly. But my general point is - maybe it's ok if all that capital is spent in this way if it benefits society overall and FANG never see a return. But I suspect I am missing other side-effects.

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 timing of this is great considering Google's rumoured Gemini 3.5 flash pricing spike.

It's just getting started. You won't find out what the real, actual cost is until after you build it into your workflow.

In other words; right now, we're still in the "bait" phase. The "switch" comes later.

If AI is too costly: bubble will burst because costs are unsustainable.

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.