SF Bay Area
Joined June 2024
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It's my great honor to lead Build into our next chapter: massive research bets and long-term partnerships in pursuit of physical superintelligence.
If I do my job, @eddybuild will go down as the greatest visionary of our generation.
a year ago, @buildpbc made a bet that human data scaling was the bottleneck to solving physical labor
it turns out this was a pretty good bet.
the next stage of this competition is driven by research. as such, i've promoted my co-founder and chief scientist @patrickrim to ceo!
patrick is a crazy man dressed up as a thoughtful researcher. once you talk to him, you'll see what i mean :)
data companies with exceptional researcher CEOs have critical advantages across hiring, sales, and data quality.
in the last 60 days, patrick:
- pushed sota on 3d pose (from scratch)
- built an outlier eng & research team in lightspeed
- assembled the largest academic consortium in physical ai
- won the largest contract in build's history
if things unfold as i predict, patrick will go down as an N-of-1 founder of our generation.
-eddy
founder & president, build ai
Incredible, wouldn't have bet against @kevinbanghe and glad to be proven right
oom more data coming in hot 🔥
Introducing Midcentury.
We’re building the data and simulation infra for physical AI.
Today, we’re coming out of stealth with a $15M Series Seed to scale robotics beyond polished demos.
We’re already supporting frontier labs with:
→ The world’s largest egocentric dataset: 2M+ hours, 50+ environments, 20,000+ tasks
→ Matrix: a frontier simulation platform scaled with our real-world data to evaluate and post-train policies at scale
throwback to when I collected a million distorted and occluded digits off football jerseys
models don't fail on clean data, they fail on the wrinkles. now we collect wrinkles at scale!
Patrick Rim retweeted
Gotta give props to Build AI for holding the record for so long on this too; it was an awesome early release for the open source community
Patrick Rim retweeted
pre-trained on Egocentric-1M data!
Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws:
• world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours,
• this human data scaling law implied a scaling law on never seen robot data,
• both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge
🧵
I’ve left DeepMind to lead research at Build AI
Our first bet: scale monocular vision. We haven’t even scratched the surface :D
Patrick Rim retweeted
1M hours of human video sounds insane.
That’s roughly 170 years of continuous waking experience.
If you had people wear cameras specifically to collect it:
1,000,000 hours ÷ 8 hours/day = 125,000 person-days
That’s ~343 people collecting 8 hours a day, every day, for an entire year.
That’s a massive data operation.
So I’m really curious: how exactly did Dyna-2 collect its 1M+ hours of egocentric human video?
play games, win prizes! 💫
SHOW3D challenge at ECCV 2026:
codabench.org/competitions/1…
What coding agents did to empower digital builders, physical AI will do for the blue-collar frontline:
Delegate manual labor and scale irreplaceable human know-how.
Patrick Rim retweeted
Jensen Huang: “The best career advice I got was from a gardener”
“Very few people know this but I don’t wear a watch,” Nvidia founder Jensen Huang begins. “And the reason I don’t wear a watch is because now is the most important time. Just dedicate yourself to now.”
Jensen explains by telling a story:
“The best career advice I got was from a gardener. I was on a family trip in Kyoto, and we went to the temple that had the largest moss collection in the world . . . All of the moss is perfect, and every species of the world’s moss is there. It was a hot summer day — anybody who’s been to Kyoto knows how incredibly hot it is during the summer — and my family walked by this old man who was squatted down working on the moss with a bamboo tweezer. His bamboo basket was nearly empty with only two or three small pieces of dead moss.”
“What are you doing?” Jensen asked the old man.
“I am taking care of my garden,” the old man replied.
The old man told Jensen that he has been working on the garden for almost 30 years.
“But this garden is so big and your tweezer and basket are so small. How can you take care of the whole garden?” Jensen asked.
“I have plenty of time,” said the old man.
Jensen reflects:
“That’s the best career advice I can give you. Most of the time I wait for things to come to me. I’m rarely chasing things. I don’t have a watch. I’m focused on now. I’m enjoying my job. I’m the longest-running tech CEO in the world . . . Dedicate yourself to learning all the time, doing the best possible work you can, and leave everything on the field. By the time I go to bed I’m exhausted, and I’m happy about my day because I did everything I could . . . You’ll be surprised. I’m not at all ambitious. I don’t aspire to do more. I aspire to do better at what I’m currently doing. I’m not reaching for more. I wait for the world to come to me.“
He continues:
“People who know me also know that Nvidia doesn’t have a long-term strategy. We have no long-term plan. Our definition of a long-term plan is, ‘What are we doing today?’ . . . You have plenty of time. Enjoy your work. Do the best you possibly can. Just keep learning every day, and good things will come to you.”