christian@curious_vii
"...frontier models are useful for producing capital assets. Open weights models are useful for operating them..."
Here's how I'm thinking about frontier vs. open weights models atm:
As long as the Labs can maintain a meaningful, if relatively small in terms of the time it takes to catch up, lead versus the open weights models on the margin, it'll often be the economically rational decision to pipe your problem in context through OpenAI or Anthropic's latest offering and not the open weights models.
That being said, I do suspect there's room in the real economy for both. One way of thinking about this is that frontier model tokens and the complementary labor that uses them should probably be capitalized as you take good judgment and scarce context and turn it into a capital asset, e.g. an intangible full-stack software application that unlocks capacity across the business, making a subject matter expert's logic and data accessible to team members asynchronously and in an AI-native way via MCP.
On the other hand, open weights models would seem to make more sense as OPEX spend once something is known and scaled. If there is some probabilistic inference that needs to happen at runtime to make that capital asset available to a larger audience, then using those open weights in production might be the move.
So, frontier models are useful for producing capital assets. Open weights models are useful for operating them.
