Monaco

@MonacoTrading

Oct 14

0xturbanurban

@0xturbanurban

Oct 14

Yesterday evening I had the pleasure of attending an interesting event hosted by @sfcompute and @jumptrading focused on the compute space. Some brief takeaways:

- There's an increasing understanding that greater liquidity in compute markets requires the construction of unifying hat bespoke/OTC markets can be priced/quoted as a diff to a shared set of benchmarks, similar to what exists in the energy complex already (think crude grades and power). Part of the discussion is whether this will organically emerge if more traditional players (Citadel, Jump, blue-chip banks) expand their OTC offering for bilateral hedges (using their own prices), or require an independent party (S&P, CME, etc.) to validate and give credence for broader adoption.

- Massive growth in the compute space is largely underpinned by structured financing trades with hedging mandates (similar to what you see in the oil and gas space). If someone with a sizable balance sheet just lent you a lot of money, who's likely to give you more aggressive pricing than their sales and trading arm?

- Funding from life insurers has been a key part of the data center buildout (you could call it duration matching), with an increasing number of insurers considering hedging compute. Some of this exposure sits in mortgage books, while the biggest bucket is via corporate bonds ($ORCL, $MSFT, $META, and $GOOG comprise nearly 80% of Athene's IG software debt).

- Quite a few folks seem offered over on the backend of the curve, largely driven by:
* Concerns that distressed sellers could flood the market if the AI growth story slows down (as there's a lot of GPU-backed debt to unwind)
* Natural hedgers are mostly sellers, as operators need contracted revenue to borrow, so they sell further out on the curve; buyers mostly lock in near-term rates w/ marginal demand coming from a handful of labs whose spending is contingent on their ability to still tap into financing
* Software improvements are reducing GPU-hour needs, all the while new chips are depreciating the value of old hardware per unit of compute: $NVDA is releasing a new gen roughly every year (Blackwell Ultra, Rubin, and Rubin Ultra). That said, it does look like old chips are staying useful longer than most depreciation models assume, since inference and smaller models still allow sufficient monetization of older hardware.

It'll be quite exciting to see how the space continues to mature, especially as new marketplaces begin to emerge for financially and physically settled compute. Huge thanks to @affaan and team for organizing!