Brett Adcock
@adcock_brett
@figure_robot (AI robots)
@hark_labs (AI lab)
@cover_thz (weapon detection)
@flyArcher (flying cars)
24 Following 867.9K Followers
Helix 2.5 is crazy by @Figure_robot
Here’s my outlook on humanoid robots. There are four stages of maturity, and each is gated by progress in the stage before it
First, you have to build great humanoid hardware
Second, that hardware needs to work with a whole-body, AI-first architecture
These first two chapters can’t be brute-forced with capital. They require deep engineering across hardware, controls, AI, and systems integration
The third chapter is scaling intelligence. The primary bottlenecks become data and compute. I believe humanoid robotics will ultimately require far more of both than LLMs
The final chapter is scaling manufacturing and integrating humanoids into the economy at massive scale
These last two chapters - intelligence and manufacturing - can be accelerated with capital. Doing them correctly will require tens, and eventually hundreds, of billions of dollars
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If you want to watch something really boring: here's 4 hours of our humanoid doing zero-shot work across 30 rental homes
Watch 4 straight hours of Helix 2.5 at work in 30 homes, with no additional training.
Our humanoid robots are learning from the largest installed dataset in the world: humans operating in the real world
Index stats from this week:
→ 53 minutes of real-world data uploaded every second
→ 2.4M video uploads this week alone
→ 115,000 weekly active users
This is the unfair advantage of humanoids: they can learn directly from humans, with high transfer to the robot
No other robot form factor can do this at the same scale
This is the data flywheel powering Helix
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I come in around 17 minutes to talk about Helix 2.5.
Helix 2.5 was built to answer a harder question: can a humanoid enter a home it has never seen and immediately get to work, with its whole body, on its own?
We posted a detailed write-up:
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Here's 4 hours of our robot doing zero-shot work in 30 rental homes
The holy grail for robotics is being able to generalize: doing work in unseen places
We rented 30 homes in the Bay Area and are doing tasks without any new training
We’ve had an AI breakthrough at Figure and will be showcasing this tomorrow
Robots are cool
We just visited Figure and it was 94 degrees out and the robots were everywhere outside and had no issue with the heat
These robots are built to last
max AGI is a humanoid robot that can do everything a human can
This is the part of humanoid robotics that gets interesting fast.
@Figure_robot already has multiple robots moving around its San Jose HQ without someone sitting there controlling every step.
The goal isn’t just making a robot perform a demo. It’s getting to the point where robots can operate on their own in the real world.
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Figure campus is year 2050
Gotta love @Figure_robot
At its San Jose headquarters it has a bunch of robots walking around with no nerds watching them.
Figure is scaling up data in a big way. Last week we hit 69,900 weekly active users
Users are uploading 35 minutes of data every second across the world
Looking for some help - has anyone ever used a US-based QA firm that puts real humans on software/website testing?
Needs to be US-based and a firm that's done this before. Please add any recs in the comment 🙏
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There are chapters of the humanoid race you can't buy with unlimited money: the deep engineering of the hardware and designing the right AI recipe
And then there are chapters money can buy. Figure has entered the chapter of scaling intelligence through data and compute
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@Figure_robot just stacked the two halves of the Physical AI bottleneck a week apart.
First Index: a creator network collecting real-world task video at roughly half an hour of uploads every second, across homes and workplaces in 100+ countries. That is the long-tail data you do not get from a lab teleop setup.
Then Nscale: $3.5B of compute to start, intent past $6B, up to 100,000 NVIDIA Vera Rubin GPUs.
@adcock_brett said they are largely constrained by data and compute. Index is the data bet. Nscale is the compute bet. Figure's own writeup says the quiet part: data alone cannot solve this.
My take: Compute is a prerequisite. Collecting more data is critical. But finding the 1% of data that actually teaches the model is what compounds. Search and curation, not just capture. That's the problem we are focused on solving at @Foxglove.
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People ask us why Figure is moving so fast
We didn't chase perfection on v1. We're iterating toward perfection across generations of robots
You will see perfection in F.04
Yesterday, Figure announced it will be working with Nscale to deploy up to 100,000 GPUs on the NVIDIA Vera Rubin Platform
I’m excited to be working with Josh and the Nscale team
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I’m watching Helix doing things you wouldn’t believe
Figure is going all-in
buying a house on mortgage in San Francisco
