Everybody talking about an AI bubble often imagines a dramatic bust. But economically a bubble simply means a market where supply outstrips demand—and that mismatch can occur even when something is genuinely valuable. TechCrunch
What complicates the AI story is the mismatch of timelines between software leaps and hardware/infrastructure build-outs. Software advances keep hitting at high speed, while building and powering huge data centres takes years. That means there’s huge uncertainty about how many servers or how much power will still be needed in three to five years. TechCrunch
Just because companies are making massive commitments doesn’t mean the demand side is locked in. For instance, one data-centre project reportedly drew $18 billion of bank financing, and organisations like Oracle, OpenAI and Meta Platforms are locking in cloud and infrastructure deals worth hundreds of billions. TechCrunch
But spending huge amounts now doesn’t guarantee usage will come later. Surveys show most large companies are using AI tools in some way—but few at meaningful scale. If the big customers don’t ramp up fast enough, the infrastructure could overshoot demand. TechCrunch
Also, even if demand is there, there are infrastructure constraints: power, cooling, site readiness. As the article quotes, it’s not just about chips—it’s about having “warm shells” ready to host them. TechCrunch
In short: framing the question as “Is this all hype?” misses the nuance. The article suggests the smarter way is to ask: Which parts of the AI stack are on solid ground, and which are vulnerable to timing, infrastructure or supply-chain risk? Good bets can still go wrong if they ignore execution realities. TechCrunch
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