A leading voice in artificial intelligence says the most widely repeated AI bubble narratives are missing the part of the ecosystem that could actually trigger a market shock.
In a new post on DeepLearning.AI, Andrew Ng, the co-founder of Google Brain and Coursera, outlines the three layers that make up the AI market.
He says each layer has its own fundamentals, capital needs and risk profile, and most conversations about a potential AI bubble fail to distinguish among them.
According to Ng, the first layer is the application tier, where the potential is much greater than most realize. Ng says he sees massive underinvestment in the application layer, as most investors tend to pour capital into the AI buildout.
“I have also spoken with many venture capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1 billion to build AI infrastructure is better understood… Overall, I believe there is significant underinvestment in AI applications.”
As for the second layer, Ng says the AI infrastructure for inference still needs significant investment as firms struggle to meet growing demand for processing power to generate tokens.
“As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5, and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow.”
Ng says these first two layers are not where the bubble risk sits. The danger, he says, lies in the third layer: model-training infrastructure. He calls it the only part of the stack where investment levels may be out of balance with returns.
“But of the three buckets of investments, this seems the riskiest. If open-source/open-weight models continue to grow in market share, then some companies that are pouring billions into training models might not see an attractive financial return on their investment.
Additionally, algorithmic and hardware improvements are making it cheaper each year to train models of a given level of capability, so the ‘technology moat’ for training frontier models is weak.”
Ng warns that a collapse in model-training infrastructure would have the potential to warp sentiment across the entire AI market.
“But what is the downside scenario — that is, is there a bubble that will pop? One scenario that worries me: If part of the AI stack (perhaps in training infra) suffers from overinvestment and collapses, it could lead to negative market sentiment around AI more broadly and an irrational outflow of interest away from investing in AI, despite the field overall having strong fundamentals.”
Disclaimer: Opinions expressed at CapitalAI Daily are not investment advice. Investors should do their own due diligence before making any decisions involving securities, cryptocurrencies, or digital assets. Your transfers and trades are at your own risk, and any losses you may incur are your responsibility. CapitalAI Daily does not recommend the buying or selling of any assets, nor is CapitalAI Daily an investment advisor. See our Editorial Standards and Terms of Use.