We raised our $750 million Series D at a $20 billion valuation from @benchmark, @generalcatalyst, @nvidia, @salesforce, and @zoom.
@mercor scaled from $1M to $3B in run-rate revenue in 27 months, the fastest in history to $3B.
For 2 years, we've organized human intelligence to build evals and training data for every frontier lab.
The labs are the leading indicator for AI startups, then the Fortune 500.
Our growth has accelerated over the past 6 months as enterprises start evaluating and training their own models. For many, owning their intelligence is existential.
I might be missing something obvious, why are we optimizing for humanoids so much (easy data?) rather than completely new form factors for general purpose?
Isn't consistently walking "correctly" much harder than doing algebra for humans, and AI models
AI can "do tasks", people's value in workplace is not just what we can execute mindlessly, it's the business, people and technology context that we add on top of all the things that get done
Very exciting direction of work by @aden_barton and @victorbarres_, congrats!
AI models are rapidly improving on benchmark tasks, and that progress is often read as an answer to a bigger question: can AI do your job?
But benchmarks don’t measure jobs; they measure tasks. Most economically valuable work takes place within companies, where people must coordinate, decide and negotiate to get things done. Jobs are messy in a way that tasks are not.
In a new paper, Mercor Research's @aden_barton and @victorbarres_, along with @NicolaiFoss and Henrik M. Nielsen, develop a vocabulary for this messiness, which they call organizational difficulty, and apply it to ~500 prominent AI benchmarks.
Read the blog post: https://t.co/1hcP4bjKE0
Read the paper: https://t.co/WWMcyRIjDD