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Comment by treis | Hacker News Reader

They're both all in on being a starting point to the Internet. Painting with a broad brush that was Facebook or Google Search. Now it's Facebook, Google Search, and ChatGPT.

There is absolutely a moat. OpenAI is going to have a staggering amount of data on its users. People tell ChatGPT everything and it probably won't be limited to what people directly tell ChatGPT.

I think the future is something like how everyone built their website with Google Analytics. Everyone will use OpenAI because they will have a ton of context on their users that will make your chatbot better. It's a self perpetuating cycle because OpenAI will have the users to refine their product against.

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yeah but your argument is true for every llm provider. so i don't see how it's a moat since everyone who can raise money to offer an llm can do the same thing. and google and microsoft doesn't need to find llm revenue it can always offer it at a loss if it chooses unless it's other revenue streams suddenly evaporate. and tbh i kind of doubt personalization is as deep of a moat as you think it is.

Everyone could raise and build a search engine or social network. Many did and none of them dethroned Google or Facebook.

Google can offer their services for free for a lot longer than OpenAI can, and already does to students. DeepSeek offers their competitor product to ChatGPT for free to everyone already.

I don't think that's accurate. They're within the range of profitability on inference today and that's before theyve started selling ads.

On what basis do you say they're within the range of profitability on inference today? Every source I see paints a different story based on their own bias.

Ed Zitron has a bias and a narrative differing from OpenAI's bias and narrative: https://www.wheresyoured.at/oai_docs/

Sam Altman said they were.

Your article has 5 billion in inference cost vs 4.5 billion in revenue. That's within the range of becoming profitable.

You seem to have misread the article (which is not mine by the way), which makes the point that inference costs and revenue seem to scale with each other.

Social networks have network effects, the value comes from other people on the network, not the platform itself.

True enough, until it turns out that 90% of the people are AI bots.

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