Liquid Compute

There's understandably a lot of consternation about AI related debt out there. I read a NYT interview with Peter Orszag, tied to a paper he co-wrote on the new supply side era, where he compares the scale of AI capex debt to the fiber buildout of the 1990s. The scale of capex investment is enormous, but what is going to matter the most is the speed of adoption of AI, particularly at 'traditional' firms that are not VC or tech related. Whether real adoption happens over the next few years or the next decade, changes the return on capital completely. For the debt financing this capex, there is another question: who actually bears the adoption risk? If the contracts are long-term, take-or-pay and backed by strong counterparties, the pace of end-user adoption may matter much less to the lender. If the contracts leave utilization, pricing or renewals exposed to the market, that is a very different credit. Compute pricing is showing a similar pattern. Rent it now and it's expensive, because near term demand is the one thing everyone can see clearly. Go out 6 months, a year or two years and the compute pricing curve inverts, meaningfully, because nobody has real visibility into usage, adoption or which GPUs will be standard by then. Thanks @stanleylee21 for sharing this info. So if you're pricing any AI related financing, the pace of adoption is a key underwriting input. And just as important is determining who has contractually agreed to bear the risk if adoption takes longer than expected.

Part 1 of the GPU Offtaker Credit Map series