Are We in an AI Bubble?

I don't know if we're in an AI bubble. And this is definitely not investment advice (it's napkin math).

If I had to guess, I'd estimate a 70% chance that we're in a bubble: current valuations seem to have priced in an impossible level of optimism. But AI is here to stay and the real wealth will be created over the next decade whether there's a crash or not.

We might not be in a bubble

• If AI takes jobs, the best defense is to own it. Owning potentially overpriced shares of AI companies isn't an investment — it's a hedge and a necessity. When you risk losing your income, owning AI ensures you at least will profit from it.
• If AI companies have durable moats and their customers profit, current valuations are justified. But both assumptions must be true. Without moats, AI companies become commodities — margins disappear and valuations tank. If end users don't find AI profitable, they won't increase spending, and revenue won't grow.
• Productivity gains in specific contexts are compelling. Many software engineers have completely stopped writing code manually. Customer support has been reinvented from the ground up. It's possible the economic gains are so substantial that we haven't yet quantified them in a way that justifies current prices — but the market's collective wisdom is correctly pricing them in.

We're likely in a bubble:

• AI spending is circular. Most revenue originates from investment that becomes the revenue of some downstream company. OpenAI spent $8.7 billion on Azure inference through Q3 2025 — and projects $8 billion in operating losses for 2025. Anthropic projects just 40% gross margins as cloud costs grow alongside revenue. AI startups received roughly $200 billion in funding in 2025, with a significant portion flowing directly to cloud providers. Much of reported cloud "AI revenue" is VC money recycled through the ecosystem.
• Frontier AI companies spend more on compute than their entire revenue. They'll eventually need to raise prices or switch to lower-cost models. Higher prices means lower demand. Lower-cost models means competing with smaller, less capable alternatives — including open-source. Either way, they become a commodity business with no moat. Open-source models now trail proprietary frontier models by only 3 months on average, and DeepSeek trained a frontier-competitive model for approximately $5.6 million under an MIT license.
• Current investment levels imply massive customer adoption that isn't materializing. Sequoia Capital calculates that end-user AI revenue would need to reach $600 billion+ annually to justify current infrastructure spending. J.P. Morgan says the industry needs $650 billion in annual AI revenue in perpetuity to earn a 10% return on projected cumulative spending through 2030. The demand side tells a different story. McKinsey's 2025 survey found only 7% of organizations have fully scaled AI across their enterprise. Just 6% qualify as "AI high performers." And 42% abandoned most of their AI initiatives in 2025. MIT found 95% of enterprise generative AI pilots fail to deliver measurable P&L impact. Only 23% of organizations can even accurately measure their AI ROI. The investment side is pricing in deep, universal AI adoption — but the data suggests we're still in the experimentation phase, with most companies struggling to extract real value. Either adoption accelerates dramatically, or the math doesn't work.
• AI reliability constrains the agent vision that drives much of the investment thesis. At 95% per-step accuracy, a 10-step agent workflow achieves only ~60% reliability. In enterprise CRM tasks, goal completion rates sit below 55%. Gartner predicts 40% of agentic AI projects will be canceled by 2027. Only 5.2% of organizations have AI agents live in production.

TL;DR

Current AI investment implies that it will be highly profitable for end-customers to adopt AI and that AI companies will have strong, defensible moats. In my opinion, it is extremely unclear that either of these assumptions is true.