Great to see Jensen’s views align with us.
Four core values of @redglassvc
Curiosity
Intensity
Integrity
Humility
First two makes you technically smart
Next two make you a good human
When software shapes the car, what’s left for design?
The human. See how we’re building the next generation of intelligent cars with @Stellantis.
https://t.co/2NFEkVAu3K
"Startups win against incumbents not necessarily because they have the best technology, but because they move fast."
@bznotes on Fault Lines #10: 16 years in deep tech, Tesla's software premium, why a $150M fund could run 500 AI agents, and why robots should serve humanity
Today, our team is excited to announce @InspiredCap Fund IV, $450M in new capital to back early-stage founders.
There has never been a moment in history when more is possible. It takes just one person with exceptional ambition to take on the world’s problems and change billions of lives for the better.
Today, Dream Labs is out of stealth.
In February 2026, while we were at NVIDIA, @scott_e_reed@youliangtan@GeYunhao and I started calling a new kind of robot model a World Action Model, one that learns how the world moves from video, then uses that to decide how to act.
Today, we’re releasing Dream-1 through a public API that anyone in the world can use.
When you rent a car you’ve never driven, you don’t learn to drive all over again. Within a few minutes, it feels like your own. Robots should work the same way: a good model should be able to pick up a new robot and get to work, without being retrained from scratch.
That’s what we’ve spent the last 6 months on, and it’s what Dream-1 does. It controls bimanual robots with parallel grippers it has never seen before, zero-shot, on unseen tasks in unseen environments.
Generalization has been my North Star in robotics: first by distilling video world models into VLAs (DreamGen), then by learning to act from those world models directly (DreamZero), and now by transferring across robots (Dream-1). I’ve come to believe it’s the only direction that lasts. Pushing a narrow model from 90% to 99.9% on a single task is real work, but much of it gets washed away once a model that generalizes arrives.
Since the start of the company we’ve stayed single-minded about generalization.That focus is what got Dream-1 to control robots it was never trained on, on tasks it was never shown. And it’s what makes a public API possible at all: a model that only does one task on one robot can’t serve millions of users.
Dream-1 is still a research beta, and it will make mistakes. But it already drives cars it’s never seen.
Generalization wins in the end, and the end is closer than it looks.
Here’s Dream-1 taking the wheel of a robot it’s never seen. Full details 👇
Dream Labs CEO @jang_yoel reveals how Dream-1 can control robots 16-40x cheaper than LLMs from the biggest AI labs:
"It’s been five hours. Users from 22 different countries already signed up. They already made 70,000 inference calls to our model."
"Dream-1 controls robots that are unseen for unseen tasks, and it’s very cheap. We did a comparison, and there’s other models that can control robots, including LLMs from the big labs, but we’re 16 to 40 times cheaper. We need a robot native robot foundation model."
"We’ll go to mobile manipulation. Right now the robots can’t move around, but they’ll soon move around. Then dexterous hands once the hardware gets cheaper."
@dreamlabs_ai
Certain hate-posting accounts on this website must be really triggered by this disaster unfolding. Expect 8 more tweets from them on this topic every day for the next few days.
Ahead of launch, @robocurve evaluated Dream-1 independently. Result: a new Pareto frontier for robot control. 16x cheaper, 1.7x faster, and better performance (59 vs. 48 on RoboDojo-RC Tier 1) than the previous best alternative.
LLMs can plan. Robots still need a cheap, fast, and general System 1 to act.