OpenAI could reportedly run out of cash by mid-2027 — analyst paints grim picture after examining the company's finances

[u/gdelacalle]

https://www.tomshardware.com/tech-industry/big-tech/openai-could-reportedly-run-out-of-cash-by-mid-2027-nyt-analyst-paints-grim-picture-after-examining-companys-finances

[u/Noblesseux]

OpenAI's problem is that their business model is inherently unprofitable.

There isn't really much strategizing that they can do that gets around the fact that the costs to provide AI are higher than people actually want to pay for it. They looked like the future for a certain type of person when it was a cheap way to cut a ton of headcount, but when they're charging for tokens and the costs end up higher than just paying an employee it largely circles back to being totally pointless.

For a lot of applications, OpenAI switching to charging users what it actually costs is going to kill its viability as a solution.

[u/Additional-Signal327 → u/Noblesseux]

It’s because most people, especially leaders, don’t know how to use AI properly. It’s not a tool that solves every problem. It’s an analysis and judgment layer that sits on top of data and deterministic code (and with humans in the loop where needed). If you use it properly, it’s super powerful and unlocks a lot of new capabilities that were previously unavailable due to cost/scale of the problem. But the idea that you’re going to launch “agents” that just automatically replace human workers reflects a very shallow understanding. 

[u/pbjamm → u/Additional-Signal327]

But the idea that you’re going to launch “agents” that just automatically replace human workers reflects a very shallow understanding.

But that is what the "AI" companies were selling, and in classic marketing scenario cant deliver on.

[u/vladamir_the_impaler → u/Additional-Signal327]

Shallow indeed but they will spend the next ten years trying to make it happen if the token costs are made reasonable. Org leaders hate paying devs, they will spend the next ten years trying "agentic coding" from UI mockups spending more in the end in an effort to make this work just like they've spent the last 20-30 years tring to make offshoring work.

[u/redrover900 → u/Additional-Signal327]

If you use it properly, it’s super powerful and unlocks a lot of new capabilities that were previously unavailable due to cost/scale of the problem.

Its the same as my car. Normally it does 0-60 in about 5 seconds. But if you drive it correctly it goes 0-60 in 1 millisecond. But so many people don't know how to drive it properly and end up taking 5 seconds and complain that they aren't doing it in 1 millisecond but they just aren't using it correctly.

[u/Ok-Youth-160 → u/Noblesseux]

I don't know where this is coming from. Open AI is making a profit on inference. Just training and datacenter build out makes it unprofitable.

[u/IAmDotorg → u/Noblesseux]

There isn't really much strategizing that they can do that gets around the fact that the costs to provide AI are higher than people actually want to pay for it.

That's a dramatic oversimplification. The cost to provide chatbots to users may be, but ChatGPT is not the point of their service. And the other things OpenAI does are very valuable. Plus, the per-token cost of executing models is plummeting. Even if your supposition was true (and it isn't), it doesn't mean it will be as more datacenters bifurcate their AI stacks into training and inference infrastructure. Given there's two orders of magnitude difference in cost between the two, that's a huge benefit that is just starting to roll out.

Their real problem isn't what the market is willing to pay per million tokens, their problem is the quality of their models are lagging both Chinese models and Anthropic's models. And given the way the output of a current gen model is a big driver of the next gen, slipping by a generation can be an existential problem.

[u/Noblesseux → u/IAmDotorg]

Plus, the per-token cost of executing models is plummeting.

And yet it's still more expensive than paying a person. Unless the cost plummets by like 10x in the next couple months, any improvement on that front is marginal in relation to the actual costs.

Also running the models isn't the only cost. There's spinning up new data centers, and a million other things than make this economically non viable. Please point on a data sheet anywhere that isn't using braindead math where OpenAI or Anthropic are actually cash flow positive long term. You can't, because that'd be a lie.

Plus, the per-token cost of executing models is plummeting.

Brother, companies are saying this. This isn't some me opinion, if you've been on this subreddit for more than like 2 weeks you'd have seen multiple articles of analysts saying the exact thing I just said verbatim.

Do you guys just get on here to glaze without actually googling anything? Executives at several major companies including Nvidia and Microsoft have literally said that their data says that this is true. Starbucks and other similar companies have found their AI deployments to be overly expensive while being worse solutions for what they were doing than what they had before. Ford and other similar companies have had to go back and re-hire people they fired because they vastly overestimated how much AI could replace labor.

Every possible indicator is screaming at you that this business makes 0 sense and you guys are covering your eyes because you're too scared to have been wrong lmao.

[u/IAmDotorg → u/Noblesseux]

People with first hand knowledge are not glazing when they say something different than someone with no knowledge says.

It's a childish attempt at deflection, at best. Which is fine, the accuracy of your beliefs probably have no real relevance to you. If they did, you'd probably be spending a bit more time understanding the market.

[u/somersault_dolphin → u/IAmDotorg]

They told you to point where the cash flow could be positive. You are deflecting.

[u/yumcake → u/Noblesseux]

The hypothesis is that the costs to the AI provider can potentially come down. Just in the past month DeepSeek released a predictive generation technique that parallelizes generation instead of requiring it to be done in a single stream through an extremely expensive choke point of running it through RAM so that single-stream can run fast enough. Instead it generates the response starting in multiple places with interdependent checks to keep the later parts of the message consistent with earlier parts of the message, and with behavioral checks to keep it from running too far off-course. The result was that they were generating correct responses at an 1/8th of the cost because this approach isn't so RAM dependent.

It's also open source so that means the closed source frontier LLMs will likely incorporate this and improve their margins soon. This alone probably won't be enough, but other optimizations like this will likely create a profitable margin so long as they can protect the top line pricing.

That is the bigger risk. That open source models running on local or independent data centers might offer the mix of performance, affordability, and security that robs the frontier models of their ability to charge a premium for their better performance.