
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 […]
Compute has become the largest cost for companies building AI products, with hundreds of billions of dollars flowing annually into data centers and GPUs. However, there is currently no straightforward way to price compute or for firms to protect themselves against price changes. A startup has closed funding to create a reference price for GPU rental and an index that would underlie a Wall Street futures contract, with plans to launch compute futures trading pending regulatory approval.

TerraPower's nuclear power plant possesses a strategic advantage over competitors, especially when chasing after data center deals.

The chipmaker is tying its financial support to long-lived infrastructure and the residual value of the hardware that will run on it.

Relativity Networks deals in hollow-core fiber, a rarely deployed technology that allows data to be transmitted 30% faster than conventional fiber.
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven