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The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes.
Silicon Data just closed a $30 million Series A to change that. The startup aims to become the reference price for GPU rental and an index that a Wall Street futures contract would settle against. The company plans to launch its compute futures trading on the CME October 5th, pending regulatory approval.
On this episode of TechCrunch’s Equity podcast, Rebecca Bellan is joined by Steve Hou, head of research at Silicon Data, to discuss the health of the AI buildout, and why the data is telling a different story than the doom and gloom headlines about depreciating chips and stalled data centers.
Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod.
Chapters:
00:00 Intro
0:59 Futures contracts explained
4:01 Can GPU compute actually be a commodity?
7:10 Who’s really trading these: hyperscalers, AI labs, and market makers
14:32 Locking in prices to make data center debt easier to swallow
18:21 Inside the pricing data
20:25 The A100 mystery: why an old chip is still in high demand
22:56 Do GPUs depreciate as fast as everyone thinks?
23:20 Making sense of the Texas and New York data center pauses
25:40 What the futures curve says about where prices are headed
28:01 Silicon Data’s $30M Series A and what’s next
30:36 Outro
