Introducing TRACE: deployment-time compute scaling for robot learning.
From a few Tutor demos, frontier LLM agents rebuild a robot's task in sim and train reliable, human-speed policies. Performance becomes a function of compute.
TI-0.1 is our first model trained with TRACE.
We raised a $75M Series B to deploy intelligent robots now, led by @a16z, with participation from @usv and @neo.
@tutorintel robots work real jobs across the United States, directed by human “tutors” and the foundation models we’ve trained on the work done. This round accelerates frontier model training, robot manufacturing, and deployment into industry.
We’re in almost 4x as many sites as a year ago; nearly a third of this year’s deployments went to existing customers coming back for more; and we’re on track to have more than 1,000 robots under contract by the end of 2027.
Our mission is to proliferate robotics for the common good. If you want to make robots reliably do things others think are impossible, for people who’ve never had access to this technology, join us.
There is a common view in the new right, particularly the downstream-of-thiel part of the new right, that the rise digital technology and financial services caused american stagnation. This was and I guess still is a fashionable view. “Innovation in bits, but not atoms”; “where is my flying car?” etc. I have heard it suggested, for example, that nuclear fusion could have happened sooner if the physics PhDs hadn’t disproportionately been tempted away from scientific R&D and into working on statistical-mechanics problems in finance and tech.
This view turned out to be almost entirely the product of negativity bias toward the status quo. If we solve fusion it will be paid for by the ai industry, and will also be aided by ai itself. If we cure cancer we will do it using chips designed originally to play video games. The achievement of almost all the technological dreams of the thielian dynamists will happen because of algorithmic research subsidized by, and in some cases done in service of, serving people digital ads and quantitative trading, the things the thielian dynamists say are so bad.
The tech industry was right, and the markets were right: the best use of those physicists’ time was not in the lab working on fusion reactors, it really was on the trading floor in New York, or the standing desk in Menlo Park.
95% chance of the kinds of social and economic disruptions that have toppled empires and caused world wars: i sleep
10% chance of some freaky AI overlord accidentally getting overly obsessed with paper clips: REAL SHIT