AI is too expensive

jqpabc123

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.

simianwords

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.

sailfast

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

hypnodrones

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

Havoc

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

overgard

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.

bensyverson

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.

dosinga

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.

changoplatanero

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.

hoansdz

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.

KoolKat23

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

eloisant

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.

czhu12

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

pingou

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.

skybrian

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?

slackfan

> If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, or $7 a month... This blog is too expensive too.

feverzsj

They are betting on AGI, in other words, Bullshit.

wagwang

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.

luk212

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.

autoexec

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.