The amount of investors that can access US public stocks is ~1B The amount of investors that have had access to invest in legitimate Anthropic/OpenAI directs or SPVs is likely in the tens of thousands We are seeing an unprecedented privatization of wealth building opportunity

Herbert has been a key media figure in the Tesla & Robotics community His channel is a go to for expert commentary and education for many Space, AI and Robotics investors. I first came across Herbert and @GoingBallistic5 through his channel years ago during my initial humanoid research journey and learned a lot from them. These guys are going to run a killer robotics podcast for @RoboStrategy

The @FCC is writing communications-supply-chain rules that are very likely to shape how robots are built, tested, sold, serviced, and financed in the United States. Today, @RoboStrategy filed comments explaining why those effects deserve attention. Some background: On July 22 the Commission adopted rules closing what it calls the “component part loophole” (basically in other words “we banned Huawei products but not Huawei parts . . . seems like something we ought to fix”), and they asked for the public’s feedback. Six days later, the Public Safety and Homeland Security Bureau added foreign-produced advanced robotic devices to the Covered List. This leaves us with the FCC filing in communications supply chain rules that now reach robotics and physical AI. We have taken the opportunity to say a few things that we think they may not have fully accounted for. From the get-go, I should state that the FCC’s objective is legitimate. The US has every right to and should protect communications equipment and networks from genuine national security threats. But robots are different from routers and handsets and rules focused on the latter shouldn’t unduly constrain innovation in the former. A connected robot may contain joint controllers, motor drivers, encoders, safety controllers, and power-management boards. Nearly all process digital information, yet many communicate only inside the machine and have no path to an external network. Treating every logic-bearing component alike could add substantial cost without addressing remote command, surveillance, or data-exfiltration risk. We asked the FCC for rules that follow the actual architecture and economics of robotics. In short: Use one clear, one-tier test for component origin. Draw the component boundary at external reachability. Preserve the distinction between named Covered List entities and restrictions based solely on production location. Allow permanent software and firmware maintenance, along with safe hardware changes that do not alter radio frequency characteristics. Keep a workable path for importing development units and operating customer pilots, which often last six to twelve months. Match any bill-of-materials requirement to the origin test and protect competitively sensitive supplier information. We also asked the Commission to protect existing authorizations and make enforcement predictable. Investors and manufacturers need to know whether regulatory action affects future sales, deployed machines, or both. Security and American leadership in physical AI can advance together. Chairman @BrendanCarrFCC identified onshoring investment as one measure of these actions. We agree. The proper path is a set of rules manufacturers can understand before they design products, qualify suppliers, and commit capital.

AI Robotics research is showing that you can build increasingly powerful robot models by pretraining on the large preexisting corpus of web video data without collecting vast amounts of new data Visual dynamics understanding increases with more compute and that directly translates into more performant robot foundation models

We led Maven Robotics' $100M Series A Apple previously had an incredible special projects group building autonomous systems that attracted some of the top talent relevant to building a great AI Robotics company. The core Maven Robotics team was assembled from that group after it was shut down. We often see many flashy demos of robots in the lab, but the Maven team is one of the few that have brought the robots out of labs, pilots, and into production environments with multiple shifts and >99% uptime. Our thesis has always been that the winning robotics companies will be vertically integrated, have deep cross functional experience, be aggressive in prioritizing real deployments and Maven is a great embodiment of that

Announcing Maven Robotics' $100M Series A, led by RoboStrategy Follow @mavenrobotics and CEO @hamzaderbas as they come out of stealth today. Read the TechCrunch article here:

Most Physical AI companies are still doing lab demos. A key factor in our investment in Dyna last year was their world class post training expertise/results and their deployment focus. More deployments begets better data begets better models begets faster time to deployment We saw this loop play out for multimodal models like ChatGPT/Claude and autonomous driving and we're seeing it play out for robotics

Over the last 2 years we went from “This AI thing is pretty cool but buggy” to “This is incredible we need to be tokenmaxxing or get left behind” We are entering that transition phase for robotics now

What would the best humanoid robot we can build in 2026 actually look like? Scott Walter (@GoingBallistic5), broke it down from the ground up. 00:00 The Philosophy of Robot Design 02:51 Understanding Bot Ecosystems 05:59 Choosing the Right Bot for the Task 08:59 Identifying Market Opportunities 11:45 Designing a Sprinting Bot 15:05 The Importance of Arm Movement in Running 18:12 Degrees of Freedom in Robot Design 21:10 Weight and Mass Distribution Challenges 23:56 The Role of the Head and Waist in Humanoids 27:03 The Mechanics of Running Bots 38:48 The Evolution of Humanoid Hip Designs 44:39 The Importance of Knee Actuators in Robotics 51:46 Challenges of Specialization in Humanoid Robotics 1:10:32 Choosing Between Off-the-Shelf and Custom Actuators 1:15:30 Leveraging Global Resources for Humanoid Development 1:16:20 The Open Source Robotics Movement 1:21:51 Navigating the Humanoid Market Landscape 1:22:31 The Future of Humanoid Robotics 1:30:10 Actuation Strategies and Challenges 1:39:17 The Role of AI in Humanoid Robotics 1:45:03 The Hardware Landscape and Future Prospects

Introducing Index Today we're coming out of stealth with Index, the largest & most diverse robot dataset in the world → 30min of video uploads/sec → 16M video uploads → Paid $15M to date → 264k downloads We're committed to spending $1B the next 12 months on data & compute

RoboStrategy published our first Shareholder Letter, and in it we discuss our outlook on Robotics industry. While we've already reached the GPT-3 era, there won't be a ChatGPT moment. The adoption curve will be somewhere between AI chatbots and autonomous vehicles. There will be no single day where it clicks for the global consciousness, but nonetheless a rapid permeation of new autonomous machines of all shapes and sizes. Physical AI is already getting really good for a variety of use cases, but we can't instantly supply millions of robots to customers like we can with AI instances. By next year, intelligence will no longer be the bottleneck, it will be the actual robots.

Hyundai bought control of Boston Dynamics at a $1.1B valuation in 2021. Today Unitree opened near $66B - 35x it's last VC round of $1.9B and ~7x its IPO price. Unitree has real revenues and major brand presence, but the company is not very well understood by most investors. In 2025, it generated $131m in Humanoid sales. 70-80% of its Humanoid sales were for research use cases, 20-30% for education and entertainment, and very little for genuine robotic use cases. Unitree's main product was a robot body that lacked a brain. An affordable, programmable, at least semi-reliable brainless humanoid body however, is exactly what the research market demanded. Unitree G1s are ubiquitous across robotics research groups around the world. Just as AI research is dependent on physical computing hardware, robot hardware is indispensable for research in robot learning, control systems, simulation, etc. While there are few Unitree humanoids currently deployed for real robotic work, the rise of the company has greatly accelerated global research progress. It optimized for an axis (cheap dynamic locomotion) that allowed it to capture the research market but is different from the requirements of of the deployment market (intelligence, durability, payload, safety certification). However, a lot of the engineering capability they've built as a company can and is starting to be used to develop more deployment optimized hardware models. This is a fundamentally different approach from most American humanoid companies which are building towards operational products for consumers and businesses in a straight shot. Companies like Figure and Apptronik invest more resources in R&D and don't yet offer it to retail because they want to go direct to the larger deployment markets. They are building towards a highly functional polished product that can eventually become a development platform like Apple (as opposed to starting as a development platform). For AI, Anthropic took the mass market product capital intensive approach and it took a lot of dollars and time before lifting off on revenue. Cohere and AI21 Labs have existed for a similar amount of time, and took a more capital-light path. AI21 had to pivot, while Cohere has continued to grow, although significantly more slowly than Anthropic. Neither approach is right or wrong and history is filled with examples of successful parallels for both. You cannot compare companies taking different approaches solely on a revenue multiple basis. The company's commercial approach is a byproduct of the Chinese private capital markets. A market where there are not as many venture dollars as the US that are willing to fund hundreds of millions to billions for R&D before any revenue is generated. Revenue growth is required to fund the next rung of capital even for potentially massive TAMs. Actuator scaling parlayed into quadrupeds, quadrupeds into the dominant robot hardware research platform. This IPO funds their transition to the most ambitious phase yet - a company building vertically integrated intelligent robots across a wide variety of form factors. The current market valuation is suggesting that they will accomplish this transition, although it is not final yet. The outcome for Unitree differs dramatically based on if they can successfully move up market. Companies like DJI and Toyota have previously done so, while a failure to do so could have the company looking like Raspberry Pi. A company that cemented themselves within experimentalists and niche industrial markets. However, it may not be necessary for Unitree to build SOTA research capabilities in order to scale robot sales into real deployments. If physical intelligence commoditizes, which we believe it does, then they could have plenty of externally produced models for their customers to choose from. The companies that can produce high quality hardware at scale stand to be large benefactors from the development of physical AGI. The focus on hardware has enabled Unitree to raise a huge war chest and have access to thousands of robots that they can use for robot learning data collection and research. While various data types can be used in pretraining for robot foundation models, robot data is required for the models to become performant. To collect a large set of robot data, you will need a lot of robots. They may have actually created a stronger path for themselves to produce performant physical AI models than companies that have focused purely on robot model development years ago. Wang Xingxing is famous for his technical chops, but he has also been an excellent business strategist.