AI in Robotics and Physical Systems
Robotics, embodied AI, autonomous systems, manufacturing automation, drones, vehicles, and AI systems acting in the physical world.
K2 Raises $500 Million to Industrialize High-Power Satellite Production
K2 Space has raised $500 million to expand production of high-power satellites, arguing that spacecraft once built as multiyear, billion-dollar projects can be manufactured at far lower cost and on more reliable schedules. CEO Karan Kunjur says the company’s first 20-kilowatt satellite, now operating in orbit, has supplied the data for its Block Two design and helped drive more than $1 billion in signed contracts. The company’s next test is turning that first flight into repeatable delivery, while developing a larger platform intended to support compute-intensive infrastructure in space.
Qualcomm Targets $40 Billion in Non-Handset Revenue by 2029
Qualcomm CEO Cristiano Amon argues that memory shortages and higher prices—not weaker consumer demand—are depressing handset volumes and margins, while Apple’s faster move away from Qualcomm adds to the near-term pressure. He says the company’s growth case is increasingly tied to automotive, industrial AI and data centers, with non-handset businesses projected to generate $40 billion in revenue by fiscal 2029 and make up about two-thirds of the company’s sales.
Gemini Robotics 2 Targets Multi-Robot Physical Workflows
Google DeepMind positions Gemini Robotics 2 as a system for multi-robot collaboration: different robot types communicating to handle physical workflows beyond a single robot’s capability. Its examples focus on natural-language tasks involving objects, containers, locations and ordered actions, such as packing tools into a kit, closing it and returning it to a bin. While the material does not show how robots divide work or communicate, DeepMind’s argument is that these grounded, multi-step routines are the class of work collaboration is meant to address.
U.S. Restricts Chinese Humanoid Robots Before Domestic Demand Emerges
TBPN hosts John Coogan and Jordi Hays argue that the FCC’s restrictions on new Chinese humanoid robots may protect an emerging domestic industry, but cannot create the commercial demand or manufacturing capacity it still lacks. Their broader point is that control produces different trade-offs across the technology stack: hardware restrictions may give domestic producers room to build, while tighter access to frontier AI models can limit the smaller companies that need them even as it helps guard against distillation. eBay’s $56 million settlement over its harassment campaign against the publishers of EcommerceBytes, a blog critical of the company, offers a separate governance example of corporate power used to suppress criticism.
Decisive Force Now Depends on Distributed Precision and Rapid Replacement
Nadia Schadlow argues that cheap, precise drones are changing military mass from the physical concentration of forces at a single decisive point into coordinated pressure across a wider battlespace. But she contends that decentralized systems matter only if militaries can field, adapt and replace them at scale—and that they must complement, rather than replace, troops, logistics and high-end combined-arms capabilities.
Autonomous AI Agents Target Fewer Iterations Across Chip Design
NVIDIA argues that autonomous AI agents can reduce the repeated design cycles that slow development of increasingly complex AI-factory chips and their surrounding systems. The company positions Cadence, Synopsys and Siemens EDA tools across a connected workflow from RTL creation and verification to physical signoff, packaging, board design and thermal modelling, while retaining human supervision in Synopsys’s RTL-to-GDS flow. Its measure of progress is not AI-generated code alone, but whether designs require fewer iterations to meet performance, power, area and physical constraints.
NVIDIA Built Its Strategy Around Algorithmic Domains, Not Chips
Jensen Huang argues that NVIDIA’s strategy has never been simply to build better chips, but to identify algorithmic domains where new computing architectures can change what is feasible and then build the stack around them. Recounting NVIDIA’s early failure in 3D graphics, its interpretation of deep learning after AlexNet, and its push into agents and robotics, Huang makes the case that technical leadership depends on confronting wrong assumptions quickly, learning the underlying workload, and organizing close to the work.
RAM, Not Compute, Is Constraining Edge AI Deployment
Cormac Brick of Google AI Edge argues that RAM—not compute—is becoming the binding constraint on local AI, forcing developers to fit models within the memory budgets of phones, robots and low-cost devices. Models in the one-to-four-billion-parameter range can support prompt-based features on better-equipped hardware, he says, but broader deployment requires 50-million-to-500-million-parameter models fine-tuned for specific tasks. The trade is less generality for faster, cheaper offline capabilities such as voice control and dictation.
Three Startups Target Oceans, Mining, and Nuclear Microreactors
Ulysses, Mariana Minerals and Radiant cast technology as a means of operating in, or remaking, large physical domains. Ulysses describes a round-the-clock network of surface and undersea vehicles, while Mariana Minerals CEO Turner Caldwell sees an opportunity to apply modern technologies more holistically across the minerals industry. Radiant extends the ambition furthest, arguing that mass-produced, reliable nuclear microreactors could ultimately enable humans to travel beyond the solar system.
Robot-Ready Blender Scenes Require Physics, Sensors, and Semantic Labels
NVIDIA argues that 3D scenes built for visual realism must also be given physical, sensor and semantic properties before they can train robots. Its Omniverse libraries, now part of the NVIDIA Agent Toolkit, are designed to bring that validation work into Blender: in the company’s demonstration, an agent adds rigid-body physics, LiDAR-style sensor output and object labels to a classroom before it is handed to Isaac Sim. The aim is to let creators prepare robot-ready environments without leaving their existing authoring tools.
NVIDIA Cosmos Uses Compute to Generate Training Data for Physical AI
NVIDIA argues that physical-AI developers cannot collect enough real-world data to cover an “infinite and unpredictable” environment, and that compute must therefore generate the missing training material. Its Cosmos model combines an autoregressive reasoner with a diffusion generator to process and produce pixels, actions, sound and language, generating physics-aware scenarios and action-conditioned futures. NVIDIA says post-training adapts the model into a world action model for particular vehicles, robots and other physical systems.
World Models Aim to Replace Robotic Trial and Error
Ankit Gupta and François Chaubard argue that AI needs world models—systems that predict how an environment will change after an action—to escape the poor sample efficiency of trial-and-error learning. They contend that passive video could provide broad knowledge of physical change, then be paired with smaller action-labeled datasets to train robots in simulated rollouts. But Chaubard says the approach still faces hard limits in large action spaces, rare safety-critical events, real-time planning, and adaptation when physical conditions differ from the model’s predictions.
Saronic Plans $3.2 Billion Texas Shipyard for Autonomous Vessels
Saronic Technologies plans to spend $3.2 billion building Port Alpha, a Brownsville shipyard for medium- and large-class autonomous surface vessels that CEO Dino Mavrookas says would address a severe shortfall in U.S. shipbuilding capacity. He says the greenfield project could create 10,000 jobs and eventually reach 2 million gross tons of annual capacity, while Governor Greg Abbott argues Texas can supply the workforce through its technical colleges and expanding Rio Grande Valley infrastructure. Mavrookas also casts the yard as a national-security asset following the reported combat use of Saronic technology in the Iran conflict.
NVIDIA Casts Japan as a Hub for AI Factories
NVIDIA argues that Japan’s manufacturing disciplines, engineering culture and long relationship with robotics make it a natural setting for AI factories—computing systems that produce intelligence for scientific, engineering and industrial work. The company presents this as the next phase of a three-decade relationship that began in gaming and later extended into accelerated computing, framing AI infrastructure and robotics as the future it seeks to build with Japan. It identifies no specific AI-factory deployments, however, instead making its case through industrial fit and ambition.
Stripe’s $53 Billion PayPal Bid Hinges on Operational Turnaround
John Coogan argues that Stripe’s $53 billion offer for PayPal is a wager that its consumer accounts, bank-linked relationships, checkout presence and cash flow can be worth far more under stronger management. He and Jordi Hays say the transaction depends less on identifying those assets than on whether Stripe, alongside Advent International, can restructure and integrate a 25,000-person legacy fintech without eroding them. The show applies a similar test to OpenAI’s reported AI speaker and Chip Motors’ autonomous neighborhood EV: appealing concepts still have to clear the harder work of building, operating and delivering them.
Adobe and Robotics Recast Ancient Craft as Future Infrastructure
At the Aspen Ideas Festival, artists Beatriz Cortez and Ronald Rael argue that ancient technologies should be treated not as primitive artifacts but as active systems of intelligence. Through Cortez’s work with migration, steel, plants, and Indigenous temporalities, and Rael’s experiments with adobe, robotics, and the U.S.-Mexico borderlands, they make the case that innovation depends less on leaving ancestral knowledge behind than on bringing it into contact with contemporary tools.
NVIDIA Frames Physical AI Startups as the Next Industrial Stack
NVIDIA’s GTC Taipei 2026 startup showcase argues that the next industrial AI cycle will be built around “physical and sovereign AI”: systems that combine accelerated compute, domain models, simulation, robotics, healthcare, quantum workflows, and network infrastructure. Through Inception companies including Tricuss, FindingsTech, Nexuni, RLWRLD, Quantum Brilliance, and SynaXG, NVIDIA presents its hardware and software stack as the means to move AI from prototypes into deployed industrial systems.
Ancient Material Knowledge Is a Blueprint for Future Technology
Artists Beatriz Cortez and Ronald Rael argue that ancient material knowledge should be treated not as artifact but as technology still capable of shaping the future. In a discussion at the Aspen Ideas Festival, they make the case through steel, adobe, volcanic ash, border wall fragments, seeds, caves, and robots: older forms of intelligence can work with contemporary tools rather than be displaced by them. Their shared target is the assumption that innovation means leaving ancestral knowledge behind.
Rocket Lab Buys Iridium to Enter the Space Applications Market
John Coogan frames Rocket Lab’s $8 billion agreement to buy Iridium as a bid to move beyond launch and into the space applications business SpaceX has already defined: spectrum, operating satellites, customers and recurring connectivity revenue. Peter Beck, Rocket Lab’s chief executive, makes the same case in the company’s announcement, arguing that Iridium gives Rocket Lab the scarce assets needed to become a vertically integrated space operator rather than just a launch and spacecraft supplier. The rest of the discussion tests that stack-ownership logic against messier cases, from Comcast unwinding content and connectivity to Meta’s uncertain prediction-market ambitions.
Frontier AI Scrutiny Risks Spilling Over Onto Open Source
Hugging Face chief executive Clem Delangue told Bloomberg Technology that government scrutiny of Anthropic’s Mythos model reflects a dynamic frontier AI labs helped create by marketing their systems as exceptionally powerful and risky. Delangue argued that a “too dangerous” label can aid enterprise sales for large closed-model companies, while regulation aimed at those firms could damage startups, researchers and open-source developers that lack the same resources and provide more transparency.
Board Bets Tabletop Touchscreens Can Make Video Games Social Again
Brynn Putnam, founder and CEO of Board, argues that video games can become more social by moving the screen from individual devices to a shared tabletop. In a This Week in Startups interview, she describes Board as a $399 “face-to-face gaming console” that uses a 24-inch touchscreen and physical pieces to combine the interactivity of video games with the accessibility of family board games. The broader case is that category-creating hardware has to make the experience legible before the market is obvious.
Scientific Discovery Is Being Rebuilt Around Models, Agents, and Lab Automation
NVIDIA argues that scientific discovery is being reorganized around a new bottleneck: instruments and simulations now generate more biological data than researchers can interpret manually. Its case for the “agentic AI era” is that accelerated computing, open biological models, AI agents, and lab automation are becoming a single discovery stack, compressing simulation timelines and shifting more of the work of reading, design, analysis, and execution onto computational systems while scientists define the questions.
Midjourney Medical Extends Image-Generation Ambitions Into Full-Body Ultrasound Scanning
TBPN hosts John Coogan and Jordi Hays read Midjourney Medical as a continuation of David Holz’s long-running work on sensing, interfaces and machine perception, rather than a sudden move from image generation into healthcare. Their account argues that Midjourney’s unusual business — bootstrapped, community-driven and cash-generative — has given Holz room to attempt a capital-intensive ultrasound scanning system with ambitions far beyond a conventional clinic device. The episode pairs that bet with OpenAI’s hiring of Noam Shazeer and Dean Ball as evidence that technical talent, policy capacity and institutional advantage are converging in AI.
Anti Fund Raises $100M to Back AI, Defense, and Robotics
Jake Paul and Anti Fund co-founder Geoffrey Woo argue that their venture firm is moving beyond celebrity access into institutional frontier investing, with an oversubscribed $100mn-plus growth fund and a focus on AI, defense, robotics, energy, hardware and other capital-intensive technologies. In a TBPN conversation, Paul frames his media career and boxing promotion business as evidence that he can help technical companies build distribution, while Woo says the firm’s thesis is shifting toward AI infrastructure and the physical world.