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Billionaire Unfiltered

[Sam Altman @sama]

i thought this was satire, kept looking for the handle to be spelled c1audeai or something

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@chiroxn @pmarca British too

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@chiroxn @elonmusk Very AI

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@chiroxn Very AI

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@chiroxn @araseb_ True

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@chiroxn @coingecko Haha

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@chiroxn @NoLimitGains Yes

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@chiroxn @tim_cook True

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@chiroxn @WatcherGuru Very true

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@chiroxn @disclosetv True

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OpenAI didn’t suddenly become interested in robotics overnight.

According to @adcock_brett, Figure AI and OpenAI spent a year working closely on humanoid robotics and next-generation AI systems after Figure’s $675M Series B round backed by Microsoft, OpenAI, and Nvidia.

[boom @MollyOnMic]

OpenAI didn’t suddenly become interested in robotics overnight.

According to @adcock_brett, Figure AI and OpenAI spent a year working closely on humanoid robotics and next-generation AI systems after Figure’s $675M Series B round backed by Microsoft, OpenAI, and Nvidia.

[Sam Altman @sama]

OpenAI Robotics is hiring, looking for exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society.

AI should be able to help people in the physical world. In the short term, we are focused on robots to support skilled workers to build our future infrastructure; in the long term, we imagine everyone having a personal robot doing anything they need.

Our world simulation research program, led by Aditya Ramesh (@model_mechanic), has evolved over the past year into OpenAI Robotics. Progress is rapid, and based on a foundation of co-design between robotics hardware and ML research.

If you love working hands-on across the robotics stack and want to build the future, please consider joining us. Send an email with your background and evidence of exceptional accomplishment to: [email protected]

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@adcock_brett @Figure_robot Congrats

[Brett Adcock @adcock_brett]

Figure just turned 4 years old

The ride so far has been surreal, and I wanted to share my perspective on what’s happened over the last four years

When I started Figure, the core technologies were still extremely nascent. Humanoid hardware was heavy, hydraulic, unsafe, expensive, and unreliable. Deep learning also wasn’t there yet - there was no real AI precedent and no clear path to building a truly general-purpose robot

My expectation was always that humanoid robotics would become the largest industry in the world, but that it would take a very long time to put all the hardware and AI pieces together

Honestly, I assumed it would take 20 years just to have a real shot at solving general robotics - decades before we could seriously attempt to build iRobot in real life

Instead, everything has moved much faster than I ever expected. Figure has gone through 4 major breakthroughs that probably accelerated our timeline by a decade:

> Cheap electric humanoid robots are now possible. There are many reasons for this but some include benefits from actuator torque density, sensor technologies, battery specific energy, high flop/memory onboard compute, and high-rate manufacturing techniques all helped significantly. At this point, we’ve largely de-risked the hardware side and it’s becoming a straightforward engineering problem (highly difficult engineering but tractable)

> Deep learning from camera pixels to torques actually works. The dimensionality of a humanoid robot is simply too high for hand-written code. You have 40+ motors that can rotate continuously, creating more possible robot body states than atoms in the universe. There is no path to building a truly general-purpose robot with heuristics and manually programmed C++ logic. This has to be AI-first, and Helix has now demonstrated that repeatedly

> Whole-body RL control changed everything. It’s what keeps the robot balanced, allows it to use stairs, move its arms, and coordinate its entire body through the world. We train these controllers entirely in GPU-accelerated physics simulation using reinforcement learning. The robustness is far beyond anything we ever achieved with hand-written heuristics. This fundamentally breaks the old thesis that walking robots are inherently too hard or too unstable to scale

> These robots can now perform useful human-like work at human-level speeds. This is an insanely hard problem because humans perform millions of different tasks in the real world - and Figure has to do this with 1 hardware platform in a general purpose architecture. btw, yesterday Figure completed a logistics use case running continuously for 200 hours without a failure - all with scalable hardware and internal AI models

The future is starting to feel very real

We have a real chance to build iRobot in real life - the good version

Thank you for everyone's support. Pedal to the metal

[Brett Adcock @adcock_brett]

We just wrapped what began as an 8-hour challenge - and it ran for 200 hours without a failure

Shoutout to the team for the hardcore engineering behind F.03 and the robust Helix models powering it

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@MollySOShea

[Molly O’Shea @MollySOShea]

Never forget