The Case for and Against the Capex Supercycle

When a lab ships a new frontier model, the interesting question is rarely whether the benchmark went up. It is whether the price-performance curve shifted enough to unlock a category of application that was previously uneconomical. Watch the pricing page, not the leaderboard.

Open-weight models keep closing the distance to the closed frontier, and each release compresses the premium that proprietary providers can charge. That does not erase the moat — the frontier still leads on the hardest tasks — but it caps how much of the market the leaders can defend at the low and middle tiers.

The headline number everyone fixates on is training compute, but the margin story is increasingly about inference.

• Memory bandwidth has quietly become the constraint that dictates real-world throughput.
• You can stack more accelerators, but if the model cannot be fed fast enough, the extra compute sits idle.
• This is why the high-bandwidth memory roadmap is worth tracking as closely as the flagship chip roadmap.

Reading an AI earnings call is an exercise in separating booked revenue from backlog from ambition.

The bull case for the capex supercycle rests on durable demand and expanding use cases; the bear case rests on the possibility that a great deal of this spending is defensive, undertaken because no incumbent can afford to be the one that under-invested. Both can be true at once, and the timing of the reckoning is the whole game.

Advanced packaging is the constraint hiding behind the constraint.