I often get asked whether I think AI is a bubble. Usually because I work in an AI-native company. I took Andrew Ng’s machine learning course back when he still used Octave, and started experimenting with agentic frameworks 2 years ago. My primary thought: this is a revolution and we don’t yet know what the effects will be. No, “AI” is not a bubble. But…

Yes, there is an AI bubble.

The bubble isn’t inherent to the technology; the allocation of capital is in line with the potential. The transformer revolution has irrevocably changed the scope of what we can use digital computers to do.

The capex spent on GPUs and data centers is questionable in cases, but we’re encountering some (healthy) limits on growth and I am optimistic we’ll make use of any spare computing power. We’re just scratching the surface of what’s possible with inference, and training will continue being critical for businesses even if open-weight models provide a capable enough foundation.

The bubble I see is opinions presented as wisdom.

AlexNet was almost 14 years ago, but most of the “AI experts” on LinkedIn didn’t notice the thaw even 9 years ago when the nerds realized what was becoming possible with natural language.

I don’t mean to be a graybeard that discounts newcomers, and I certainly don’t claim much wisdom myself. While most people only started paying attention when OpenAI showed that autocomplete was good enough to retire the Turing test, some began paying very close attention and experimenting. We can all benefit from following their critical thoughts on how machine learning and cheap, capable agents may shape the future and when.

What I do discount, and hope you will, is those who are happy to present with confidence the right way to deploy AI in products, the best methods to adapt workflows to agents, and predictions for who will win and lose in the uncertain future.

I’ll indulge myself in a prediction, though: this bubble will not pop suddenly. It may shrink, but only when a more attractive trend surfaces.