Comment by keeda
6 days ago
> That seems really large, but it's ~2-3x Walmart's yearly revenue, and OpenAI and Anthropic both have estimated valuations that compare to Walmart's market cap. ...
It's also before cutthroat pricing really kicks in.
Right, that's more of an estimate on the value proposition of the overall AI industry, rather than valuations of the industry or specific players. While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition (https://news.ycombinator.com/item?id=48040999
I was thinking of studies like https://www.science.org/doi/10.1126/science.adh2586 I may have misremembered the domain, or I may have neglected to remember the publishers. This time around I explicitly excluded any AI-tied sources (I don’t trust Anthropic to have an accurate study of productivity boosts).
> While I don't think OpenAI and Anthropic will capture all of the potential upside, I do suspect they will do much better than other players despite the competition
We may have a fundamental disagreement here because I think you see reasons for businesses to prefer LLMs beyond price, but my point was that I think a substantial portion of that top line number will be eaten up by needing to be cheaper than humans.
Like 30% cheaper than humans for the same task isn’t an unreasonable demand for onboarding, and that would lower the addressable market to .75-1.25 trillion. That’s also revenue, not profit. Their best case is probably 30-50% margins (even that might be high, I suspect it will become low margin as models are commoditized), so $600 billion-ish in profit spread across all the players and that feels pretty generous to me.
I suspect a lot of general knowledge work tasks could be done with fairly dumb local models. The small Qwens could probably respond to my Slack and emails for me.
> Typically yes, but there are reasons companies may be willing to pay the same amount or even more, such as "AI doesn't need sleep, holidays, insurance, or benefits" and "AI is easier to procure and replace than humans."
It’s also an enormous supply-side risk. Your AI provider could charge basically anything they want and you have to pay it, for the same reason people still pay bonkers Oracle licenses: too hard to migrate away.
Unless companies maintain their ability to swap providers, but then I can’t see why prices for inference don’t crash down to the cost of GPU time and electricity. If Claude doesn’t have some secret sauce that keeps me locked in, I don’t see why I wouldn’t use Deepseek or Qwen or one of the other dirt-cheap inference providers.
I just haven’t seen anything that seems to keep people locked in so far. Even the non-tech people I know that like AI are rotating providers to find their ideal price:performance ratio, many are finding success outside of Anthropic/OAI. I do know a couple holdouts that refuse to use anything Chinese. They mostly stick with OAI or Anthropic, though they do moan about paying more.
Agreed, there's nothing sticky about the models right now, but I see the big AI players making moves that hint at long-term success, and even dominance, in the enterprise space, which is where the real money is. As Microsoft has shown you can create stickiness for things that are already heavily commoditized. For instance, the FDE play itself could be a huge business.
Plus the big factor in my mind for why these frontier labs will succeed -- and this is very fuzzy and hand-wavy -- is that they are very business- and government-savvy and execute extremely fast. They have the most powerful AI models at their fingertips with sharp people who know how to use them, along with insane levels of funding, and are showing the world how a truly AI-empowered organization can operate. I suspect they will thrive despite all the forces arrayed against them.