This post is part of my "Minor but Important Red Flags Around AI" series...
AI may feel new and trendy, but it actually has the longest history of hype cycles in all of IT including two well-documented “AI winters” dating back to the 1960s. It might sound extreme to suggest we could experience another one, but the possibility is real.
From this perspective, the weak spot in the current AI megatrend isn’t the technology itself but rather penetration into everyday use at large businesses.
Yes, there are remarkable individual use cases, often coming from solo innovators or smaller businesses. At the other end of the spectrum, large enterprises are spending heavily: purchasing tools, signing contracts, and investing in the infrastructure. However, when you talk to people inside these big organizations, you often hear: “My boss bought this and I don't use it.”
This gap - between buying AI and using AI effectively - is where red flags start to appear. Worse, there is another kind of red flag that fits in a little to well with this one.
The big money investment deals at the top LLM startups have a couple odd regularities. This morning, you may have read about Nvidia making a large investment in OpenAI. In the fine print, much of that investment will flow right back to Nvidia purchasing infrastructure for data centers. Similarly, Microsoft’s investment in OpenAI was structured so a substantial portion returned to Microsoft through cloud computing credits. In many cases, these “investments” are explicitly services-for-equity.
Why should this concern business leaders?
In an environment where AI companies can attract positive attention generating enormous revenue without profits, there’s a risk of circular economics: I give you a crisp dollar bill, you give me the dollar back, !REPEAT! ... on paper, both of us show impressive revenue even though no real value was created.
This isn’t the same as a Ponzi scheme, and it doesn’t mean AI companies lack substance. But it does highlight a structural weakness. If too much of the industry’s growth is built on these circular deals, it creates an illusion of traction while masking the real question: Is AI adoption actually creating sustainable value in the broader economy?
That’s why I believe you should consider two superficially different metrics together:
The prevalence of circular investment deals. How often are dollars being recycled back to investors instead of funding real expansion and use?
Actual cubical-level adoption. Not just whether companies are buying AI solutions, but whether they’re embedding them deeply enough to drive ongoing business results.
We’ll learn more over the coming months and years. Some organizations will achieve real penetration, while others may decide AI isn’t the right fit for their operations.
In the meantime, leaders should keep a skeptical eye on the difference between buying AI and using AI, between creating value and swirling it round in a circle.