Shamoon Siddiqui

They did WHAT to robotics?!

This is Genie 4, our most realistic and interactive world model yet. 🌐 Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.

When we started @SereactAI , people told us returns were impossible to automate. This is Cortex handling @Zalando returns at a @cevalogistics fulfillment center. It’s live and in production, every shift. Proud of the team that made this real.

One question I get from business owners all the time: How to turn everyday work into robotics training data? Now, I finally write an article about that. The best way to "catch up" on the physical AI transformation is to embrace it.

LLMs are magical alien technologies we’ve managed to conjure up from a distillation of the internet. Even if progress completely stalls, we have years worth of value to harness from what we already have. In other words, these lego bricks still haven’t been put together in the most obvious combinations.

1. Humans will not stop creating text, images, and music. We will always do this for joy and self-expression. 2. AI will create most content in the future, and it will displace many humans doing so, just as machines did for physical products. The result will be an abundance of affordable, high-quality text, images, etc. for everyone to use and enjoy. 3. You might be sad about this. I won't tell you that you are wrong, because I don't police people's feelings. Regardless, this transformation will be, on net, a very good thing. I understand if it's emotionally hard to process, though.

The AI boom has created insatiable demand for data centers. Nobody knows how long it will last. Everything that goes into data centers, from cement, to the labor that builds them, to the electricity that runs them, to the chips that provide their intelligence, is overpriced by a large factor. Gas turbines are on track to triple in price from 2019 levels. An electrician wiring a data center makes about double what electricians make everywhere else. Nvidia sells its chips at a 75% gross margin, as if it were a software company. Companies making these investments are drawing straight lines into the future.

Thanks to @Nasdaq for celebrating our 100M ARR milestone in Times Square.

Announcing the beta release of the Pi API. We’ve partnered with companies across diverse industries to adapt our models to their robots and applications, from harvesting strawberries to assembling servers. Now we’re opening access to more teams.

150 GW of new production brought online over 20 years, or 7.5 GW per year. Most of it would be wind power. Offshore turbines currently generate about 25 MW, so they would have re-industrialized Glasgow's dockyards to produce about one per day. This is about the same rate that the Kaiser shipyards produced finished liberty ships, which are substantially more complex. One turbine per day doesn't sound like all that much, but do it for a decade and Britain doubles its power production capacity. Or stay poor forever.

sat in a room full of robotics founders in SF tonight. everyone agrees VLAs continuously fail, so real deployments rely on reasoning models and vision. most demos can be vibe coded on GPT 6.1 Sol every company has the same progress. only the ones in the media look ahead.

Most robot policies treat time implicitly, using sequence position as a proxy for physical time. But real robot systems rarely run on a fixed clock. Sensors run at different rates, observations arrive with delays, and actions take time to compute and execute. Successful control depends not only on what the robot observes but also on the timing of observations and actions. We introduce 🕒 Chronos Policy, a diffusion policy that explicitly conditions on observation capture times and requested action execution times. We use time-aware positional encodings to make attention depend on elapsed physical time. Because timestamps let the policy interpret each sequence's timing, we can randomize sensing and action timing during training without making those variations ambiguous. This exposes the policy to a wider range of timing conditions, improving robustness to timing changes at deployment.