He predicts this will become standard across all enterprises.
I told @HarryStebbings on @20vcFund that @mercor_ai now spends more on tokens for our internal agents than we do on headcount.
In a few years, every enterprise will too. We already see that the companies pulling ahead are running like AI labs: an eval for every workflow, agents trained like you'd train an employee, models swapped in and out on price and performance.
This is what organizing human intelligence looks like.
i notice a lot of divergence in agent usage across companies. at minimum, it just makes sense to have a shared best practices md file/skill vs a dozen different workflows across a team
I told @HarryStebbings on @20vcFund that @mercor_ai now spends more on tokens for our internal agents than we do on headcount.
In a few years, every enterprise will too. We already see that the companies pulling ahead are running like AI labs: an eval for every workflow, agents trained like you'd train an employee, models swapped in and out on price and performance.
This is what organizing human intelligence looks like.
He predicts this will become standard across all enterprises.
I told @HarryStebbings on @20vcFund that @mercor_ai now spends more on tokens for our internal agents than we do on headcount.
In a few years, every enterprise will too. We already see that the companies pulling ahead are running like AI labs: an eval for every workflow, agents trained like you'd train an employee, models swapped in and out on price and performance.
This is what organizing human intelligence looks like.
Positive users view Mercor's AI token spend exceeding headcount as a real signal of AI-native org progress and managed intelligence, while negative users criticize the CEO's podcast focus over AGI priorities.