AIBubble

The “AI bubble” debate misses a key point: risk isn’t uniform across the stack — and that matters for both ML builders and the blockchain community.

We’re massively underinvested in the application layer, especially in agentic workflows that will power autonomous finance, supply chains, and on-chain intelligence.
Meanwhile, inference infrastructure is in a supply-constrained boom — token-generation demand (coding agents, decision engines, multimodal apps) is far ahead of available capacity.
The real overinvestment risk sits in training infrastructure, where hardware improvements and open-weight models continue to erode the frontier moat.

But here’s the intersection that the ML + Web3 world can’t ignore:
AI now needs verifiable trust, provenance, and decentralized access — and this is where blockchain has its moment. Tokenized compute markets, auditable data pipelines, and on-chain model governance can turn AI from a black box into an accountable system.
We’re not heading into an AI winter — we’re heading into an era where AI + blockchain converge into programmable, transparent infrastructure.

the opportunity isn’t in chasing parameter counts.
It’s in creating real, defensible, distributed applications that align intelligence, compute, and trust at scale.