AI: The ROI Runway Could Be Long Outside the Tech Sector

> If token costs converge toward zero for most AI use cases...

In the real world, token costs seem to be going up, as early stage pricing at a loss gives way to pricing that generates revenue.

Compute costs might go down a little over the next five years, but there's nothing coming along in hardware that leads to huge reductions in price. NVidia says don't expect better price/performance before 2030.

The models keep getting bigger, and people put loops around them which iterate, burning tokens.

Where is this cost reduction coming from?

Article is authored by a private credit firm who assigned absurd valuations to AI infra (see below as an even super recent example) - what is their intention by writing this (is it due to the AI fear narratives around software investments they also hold that drive ppl to withdraw their holdings in Apollo?)

From last month: https://peinsights.substack.com/p/apollo-and-blackstone-clos...

One thing I can't square: if the cost to build an application goes to zero, we should see a proliferation of apps, especially from the AI labs.

The fact that we aren't seeing an app explosion (I think) is evidence that building applications people will pay for is significantly more complex than just prompting claude/codex/etc

There is no reason to believe the ROI runway is not long inside the tech sector either. I mean, you cannot base that on claims made by the AI sub-sector of the tech sector; of course they are going to claim nothing other than that eating their dogfood is great ROI with a short runway.

Maybe there's an argument that a lack of rising profit margins in non-tech companies is a bad sign for AI, but this article doesn't make it. Why can't we have a red-queen's race where non-tech companies are implementing AI, but it's not increasing the total profits of those sectors, just meeting rising customer demands/fighting over the share of existing profits? (Never mind that if you look at that chart, profit margins aren't static to begin with, so you can't isolate AI impact from normal fluctuation).

Now, on the first order point, I agree that non-tech companies seem to be taking longer to see results from AI, even if the argument was bad.

I work on SaaS for the logistics space, and I feel like prior to the end of 2025, almost all the discussion about AI for logistics was vaporware, starting this year, companies are actually trying to deploy agents, and we'll start finding out what the ROI is later this year or next.

If you need immediate ROI (say, because you just invested a trillion dollars into datacenters) you may be out of luck.

And I don't think this is unusual. It took decades for previous technologies to be fully integrated into existing businesses. In the 80s you could see the IT revolution everywhere... except the productivity statistics, which didn't catch up until the 90s.

LLMs are still very new and have significant limitations (like prompt injection and high token costs) that are very likely solveable but will take time.

Microsoft, Amazon are all building forward deployment engineering teams - to increase AI adoption. It will take time, but it will happen.

The premise is flawed.

"The first chart below shows that so far there are no signs of profit margins rising outside the tech sector. This is ultimately what we are waiting for, because the value of AI companies today rests entirely on the promise that margins in the S&P 493 will eventually climb."

This is absolutely not necessary. The bull case is that AI will bring great efficiencies. The surplus profits from those efficiencies could easily be competed away by firms who have adopted AI. Those firms who do not adopt AI will have their margis crushed.

I don't understand why anyone insists that this needs more time. None of what we've seen in the past few years is new tech. It's more money and hardware thrown at the problem than ever before for diminishing returns.

The market has clearly spoken. Knowing what you're doing is much more valuable than just the doing. That still requires humans. This AI winter has already begun.

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