Garth Sheldon-Coulson, CEO of ocean compute startup Panthalassa, compares the energy requirements of reinforcement learning versus traditional model pre-training on Catalyst.
Assertion Not checkable as stated
Panthalassa wave data centers achieve 99.8 percent availability with minimal batteries
“In all of our optimizations, we can be achieving for payloads, you know, we can be achieving with very little battery, like, 99.5, 99.8% power availability with far less battery than you would need for an equivalent solar installation, for example.”
Assertion Not checkable as stated
Panthalassa designs deliver wave power at 3.5 to 4 cents per kWh
“We have designs that are two cent per kilowatt hour, On the power. And we, the optimum tends to be, you know, given everything I was just describing, the optimum tends to be in the four cent, three and a half to four cent per kilowatt hour range. But keep in m…”
Prediction Not checkable as stated
Panthalassa expects lower AI chip failure rates at sea than on land
“In many cases, we believe that the reliability of the chips, the failure rates will actually be lower on our platform because we can provide much colder cooling temperatures than is typical on land, and we have no oxygen. We eliminate the oxygen from the paylo…”
Prediction Not checkable as stated
Bulk of AI energy consumption will power long-running background processes
“There's a whole class of like super latency sensitive applications where you wouldn't want to use us. But that's not where the bulk of energy will be going. The bulk of energy will be going to very long running processes that are churning, churning, churning t…”
Insight
Ocean convective cooling eliminates traditional data center cooling infrastructure costs for Panthalassa
“We are actually in a resource that gives us free, extremely good convective cooling, and that's huge, because it essentially lets you eliminate the entire cost structure of the data center. And our object is actually replacing both power plant and data center,…”
Prediction Not checkable as stated
Sheldon-Coulson predicts AI hardware will shift away from failure-prone high-bandwidth memory
“There's lots of new accelerators that don't use as much high bandwidth memory and other components that are particularly failure prone, and that's where a lot of the industry will be going.”