Unless we are genuinely pushing to find AGI, at which point nothing matters, LLMs in their current form don't replace knowledge workers but are an effective force multiplier. How good is enough?

For instance, I pay about $1-2 a month for DeepSeek. It's not as sophisticated as Claude, but it still doubles my productivity as a SWE.

If Fable comes out and demands 50x the price of DeepSeek in order for Anthropic to make a profit on it, how much more productive would I be compared to my personal experience + DeepSeek? 3x? 50x?

Is it cost effective for a business to hire someone without SWE experience + Fable verses hiring someone with SWE experience and DeepSeek? When does R&D hit diminishing returns?

Whether it can physically be as all encompassing as it makes itself out to be or whether it will just be healthily profitable remains to be seen. Kind of like how Uber went from "We'll autonomously drive the world" to "Look, we deliver food, goods, and people to locations and we figured out how to do that in a way that makes profits. Also, ads".

If they manage to keep those customers for several years without more sales, that bit looks like a normal "high-touch" business.

They shouldn't look like a "high-touch" business, but their unitary numbers look way better than I expected. They just need to grow some 10 times to star making a profit... Maybe 100 to cover the opportunity cost of their capital.

It's just a matter of finding 5 billion people willing to pay US prices :)

But it is still better than I expected.

But: how are they calculating the cost of revenue? Do they have rapidly depreciating assets that are also needed to produce that revenue? (Starlink has this issue.) Will their cost per arithmetic operation for inference rise or fall? (Anthropic is paying xAI an absolutely insane amount to lease GPUs. They must be betting that they will not need to repeat that.) Is a large portion of the cost allocated to R&D actually being used to support their revenue?

I certainly believe that the cost of inference can be plenty low for them to make a profit, but a more granular breakdown would make it easier to evaluate.

With so many free models available the ai companies are going to struggle to convert active free users to paid.

So everything else is kind of academic. Of course they were losing money in 2025, they had a technology that was kind of cool - clearly eventually going to deliver something great, but they didn't actually have anything somebody should pay for. Now they have a thing that people will pay for. So who cares what they lost in 2025?

So what's important today is - how competitive are they with Anthropic in delivering that product. How do the economics of companies using AI agents for coding work. That's all. I don't think there's really an argument about them losing money on inference any more.

As OpenAI’s worth rose, the increased value of those investor rights created a roughly $30bn charge, added the person. The charge is not expected to recur following the restructuring, they said.

Stripping out the charge and other non-cash expenses, such as stock-based compensation of staff and computing credits from Microsoft, OpenAI’s losses were $8bn, according to the person.

Alphabet: ~$4.5T value / ~$403B revenue ≈ 11× revenue

Microsoft: ~$2.9T value / ~$282B revenue ≈ 10× revenue

OpenAI: ~$850B value / ~$13B revenue ≈ 65× revenue

Can someone explains that logic?

Operating loss went from ~$8.8B to ~$20.9B — roughly 2.4x.

Doesn't seem like a domesday scenario.

When I read "the worst possible thing for me to get" I had assumed it would be evidence that inference/Codex is fundamentally unprofitable (as Ed often blogs about) but there isn't enough information here to support that argument either: revenue is still greater than cost of revenue, and the major losses are clearly delineated.

If R&D costs don't go up - where does the moat come from? Cheaper players catch up with 'good enough' and will erode their revenue. Most of human tasks just don't require that much intelligence.

They're racing toward 'superintelligence' that recursively self-improves.

No indication we're anywhere close to reaching it.

Going to be an interesting year to say the least.

Look, for coding and a lot of other things, AI is awesome.

But the here's the killer. I have a dinky 16gb VRAM card, and that's kind of the sweet spot for the level of AI I actually want. I don't want it doing too much, I'd rather create slowly than have it one shot something that I have to then pore over later.

Feels like a company investing kazillions in, i don't know, air-conditioning or building wi-fi. Yes, it's going to be around, and also no one's gonna need THAT MUCH.

I don't like these products. I have several negative opinions on them. To the extent they work and there is a customer base what marketing could you /possibly/ be engaged in? Doesn't the product sort of market itself? Or another way is this a product that you can market to expand your MAUs?

It's so polarizing I can't imagine how that $5.7B is being spent.

I was watching a World Cup match last week and one of the TV ads during half time was something to the tune of ChatGPT being used by kids to improve their street soccer skills. This was Brazilian TV. Anyone even remotely familiar with Brazil would find this ad deeply, thoroughly out of touch. I can't think of a worse chatbot pitch than that.

It will become profitable. Local models and local on-laptop inference will get good enough. This argument has been made for decades. It's not like everyone is walking around hosting email and photos on their personal machines. Sometimes it takes a large investment to make servers and clouds for this stuff possible.

We need to get away from this idea that in order for one thing to succeed, the other must fail. We also need to stop thinking in binary and accept that all these things (profitability, local models, powerful laptops, etc.) can all happily coexist.

Glad to see more sane takes in the comments. All these articles on their current financials are missing the point.

OpenAI is doing pretty well.

Capital expenditure is required to deliver on 1) better models 2) better infra and 3) better products. Insane CapEx is required to do all the above + compete with Google, Meta, Microsoft, Apple, Anthropic, etc. etc. etc. who are all trying to do the same. These financials are sane, considering the scenario.

People ignore all his horrendous takes from last year and still eat this years “analyses” like it’s Gods words.

He has been predicting the doom for years and years now and it is strange to see HN still putting credence here.

This is what he said around a week back

“ One of my sources has come forward and brought me a story that will possibly burst the AI bubble. The reason they brought this to me is that I’ve shown — and will continue to show — that I actually give a shit about this industry and the people in it.

If you’re wondering what the story is, know that it’s the information I’ve wanted for years, delivered as I have always wanted it, and I will treat it with the reverence it deserves. Imagine what the worst possible thing for me to get would be and you’re probably close.

I expect it to be out in the next two weeks, and you’ll know exactly when it runs. There’ll be a podcast and a newsletter, and very likely follow-on coverage elsewhere.

I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.”

This is qanon tier stuff. He’s been pulling this shtick for a while and people still haven’t caught on.

I feel like the 1080 ti is like a prophet of the current crisis, these companies are buying $10k paperweights per user to MAYBE... LUCKILY... charge what... $200 a year? and that is for every 1/100 users.

this same 10k hardware will be outdated in a couple of years...

It just doesn't make financial sense, if you couldn't sell standalone GPUs that people PAID for with HBM in them, what makes you think that you can sell a POSSIBLE subscription utilizing a $10k+ GPU?

This is the most obvious bubble of all time.

"According to the narrative story in Genesis 11, the city received the name "Babel" from the Hebrew verb bālal,[e] meaning to jumble or to confuse, after Yahweh distorted the common language of humankind.[11] According to Encyclopædia Britannica, this reflects word play due to the Hebrew terms for Babylon and "to confuse" having similar pronunciation.[7]" (Wikipedia)

Basically, it's a company that's not sustainable for two separate reasons. The first one is that they have an extremely high overhead. SG&A of 55% is really bad. The seconds reason is that their R&D costs are truly astronomical. They could probably cut those costs to some extent, but they're not going to cut them to nothing. They're already losing ground to Anthropic even with this much R&D.

To put it differently, even if OpenAI cut its R&D and inference costs by half, they would still be leaking money like a sieve.

Just as with the two previous bubble, we’re seeing companies hemorrhaging huge amounts of money, and when the dust settles the market is going to crash big time like it did with the two previous bubbles.

Unlike previous bubbles, this bubble isn’t giving people high paying jobs until everything crashes (programmers with the dot-com bubble; construction people during the real estate bubble), but it very annoyingly is making memory and SSD storage cost far too much causing computers to cost about 150% as the cost two years ago before the AI bubble was in full force, forcing Apple to make a “MacBook Neo” model with the absolute minimum of ram and SSD storage space.

Like the dot-com bubble, we will have very few winners left (with dot-com, the big winners were Amazon and Google) but unlike the previous bubble, it’s incredible how political this particular bubble is (i.e. the controversy around Grok).

AI companies are black holes for money the way delivery companies are (or were, considering the money people are willing to pay these days).

Most of them will disappear alongside the money people have bet on them.

The whole point of the company is that they are investing a huge amount of money upfront in order to make models that are better and better, and thus have a higher productivity multiplier.

They are very profitable on inference, they just know that the race to AGI requires a huge amount of investment, compute, getting the best researchers, etc.

That ship has sailed long ago into the IPO sunset.