Physical AI and AoT® (Autonomy of Things) is all about sensing and movement. The IoT revolution 3 decades ago ushered in connectivity between static physical machines and computers. AoT® connects static and moving Things, sensors and localization data to create autonomous movement. The environments in can vary – from uncontrolled (autonomous cars in dense urban environments), semi-controlled (autonomous trucking on highways, autonomy of blue collar vehicles in construction, agriculture and mining, drones in regulated airspace, robots and drones monitoring infrastructure) and controlled (robots in industrial settings and campuses, rail and shipping yards, warehouse logistics). The operations are typically carried out in harsh conditions - weather, dust, vibration and shock. The drivers for AoT® are capital efficiency, productivity, quality of life, safety, and addressing the acute shortage of skilled human labor in various physical industries.

In general, the AoT® revolution was driven by large corporations – Caterpillar and John Deere have been investing in partial or full autonomy for at least 3 decades, while the DARPA Grand Challenge of 2006 spurred on the driverless car movement, initially pioneered by Google (Waymo), and subsequently by Baidu, Uber, Tesla and large automotive OEMs. Physical AI is tough – it takes patience, innovation, data and extensive testing before it can generate revenues. Traditional venture capital stayed away from it.

AoT® requires massive innovation and specialized talent and experience. Alumni from technology companies, OEMs and universities founded start-ups in areas of driverless cars (Wayve), trucks (Aurora, Waabi, Gatik), drones (Ukraine), infrastructure monitoring (BrightAI) supported by investments from specialized deep-tech VCs. The first half of 2026 saw ~$50B in physical AI investments across ~500 deals – this includes secondary investments in companies like Waymo and Anduril, and in start-ups. Wall Street is getting in on the action ( JP Morgan , Softbank, Goldman Sachs, KKR, Blackstone, etc.).

A key emerging trend in deep-tech VC investments in physical AI is the emergence of companies like ASI and AIM , which build OEM-agnostic physical AI stacks that can deliver autonomy and fleet management to mixed-fleet operations in various applications ranging from trucking and construction, to agriculture, mining and logistics. This solves a significant issue for companies that perform these operations since they own vehicles from a range of OEMs. OEM-agnostic autonomy allows them to operate the fleet autonomously, rather than piece-meal, enabling a coherent, productive and efficient process. Supply chain resiliency and sovereignty for physical AI is another driver for venture investments. These trends are creating opportunities for start-ups to compete in physical AI industries, democratizing innovation and new approaches.

Linse Capital - We fund companies that move the world

Linse Capital , has backed deep-tech and physical AI companies for the past 2 decades, with ground breaking investments in companies like Skydio, Waabi and Wayve. Linse’s evaluates investment opportunities starting at the seed to later stages. Actual funding was limited to late rounds where it took controlling positions and made large investments (~$100M/deal, ~$1.7B total).

While Linse Capital’s traditional approach to investing focused on concentrated investments in companies with established technology and product-market fit, it recently announced the launch of an early stage fund, Linse Ignition, which has raised and deployed $75M since its launch in 2025. It does early stage investing in companies at varying stages of maturity that the firm believes will define the next generation of deep-tech . In addition, co-investment vehicles of $75M, will enable Linse to double down on those investments, which span across 14 start-ups (with an initial funnel of 400 potential opportunities).

Examples of the investment areas and companies are discussed below and Figure 1:

  1. Aerospace & defense supply chain: Amca is is building America’s industrial base by designing and manufacturing critical component for planes, military vehicles, and core infrastructure America needs. This is a critical function to ensure supply chain resiliency for physical AI. By building physics and geometry based systems that deliver concurrent design, prototyping, testing and production. This approach is similar to companies like Neural Concept and Monumo which were discussed in an earlier article .
  2. In-space manufacturing; Varda is a life sciences company that enables its customers to processes materials in orbit and return them to Earth. The company designs and builds in-orbit production equipment and space reentry capsules for Low Earth Orbit.
  3. Semiconductor manufacturing: Fab2 is a semiconductor startup that builds compact, modular chip fabrication facilities and the machinery inside them. Lace Lithography is developing BEUV (Beyond-EUV) atomic scale lithography systems to enable quantum technologies and physical AI.
  4. Energy efficient edge compute: Mythic is developing analog compute-in-memory architecture to deliver up to 100x greater efficiency for edge computing and AI.
  5. Fusion power: Thea Energy is leveraging recent breakthroughs in physics and engineering to create a faster, simpler approach to commercializing fusion energy. This is critical for powering energy-hungry data centers that support AI.

According to Bastiaan Janmaat, General Partner at Linse Capital, “While we historically focused on leading growth rounds, we have always met founders early-on to begin fostering those relationships. Now, besides helping founders from those early stages, we can put our money where our mouth is and deploy early checks. With Ignition, we will work with the next generation of deep tech companies early, and put real heft behind ones that we believe will become industry leaders”.

SET Ventures : Hyperlocal, Automated, Resilient Energy

FEV - Building the Future Energy System

SET is located in Amsterdam, Netherlands. FEV (Future Energy Ventures) is based in Berlin, Germany. The two VCs are focused on investing in companies deploying physical AI to maintain and improve the resilience of energy infrastructure. It is an interesting collaboration - SET invests in early stage companies, while FEV is focused on later stage investments. SET and FEV recently launched an article on how robotics and physical AI can support the energy grid.

SET was founded in 2007, and has raised a series of funds. Fund III (€100M) and Fund IV (€200M) are active, with 10 and 9 company investments respectively. Austin Wood is an investment manager. SET invests in 6 energy-related areas:

  1. AI and Enabling Technologies: AI technologies and applications, from Internet of Things and blockchain, to cybersecurity, distributed computing and data management.
  2. Distributed Energy Systems: hyperlocal energy systems (close to users), energy efficiency, security and resilience.
  3. Digital Utilities: energy-as-a-service, energy communities, prosumers, digital transformation, trading, energy auditing and energy data management.
  4. Built Environment: smart meters, heating control, heat networks, energy efficiency, smart lighting, building management, power distribution and urban infrastructure.
  5. Industrial Energy Systems: automation and robotics, asset optimization, predictive maintenance, electrification, energy hybridization, energy procurement and energy market integration.
  6. Mobility and Transport: vehicle electrification other charging infrastructure, autonomy, micro-mobility, smart traffic, smart logistics and mobility-as-a-service

Within the AI bucket, SET has 2 current investments (Figure 3):

Spoor uses computer vision-based AI to detect birds in the vicinity of wind turbines. Flexidao collects global energy and emission data collection, aggregates it and provides solutions for oversight of electricity and certificates. Apart from these 2 investments, SET has had 17 investment exits in the AI space.

FEV was founded in 2016, as a spin-off of Innogy Ventures backed (which was backed by E.ON SE, a major European electric utility company based in Essen, Germany, serving roughly 47 million customers across multiple European countries. Moritz Jungman is an investment manager at FEV. With $235M in funding across the energy space, FEV focuses on investments like Prisma Photonics that delivers critical monitoring solutions laser and fiber-optics based sensors. Photovoltaic panels, turbines and battery technology to generate and store energy, and integrating these into homes, cities, vehicles and streets via smart grid solutions are other areas of focus. This spans 3 broad topics:

  1. Future Energy: Use of digital technologies to create a shift to a decarbonized, decentralized and digitally-interconnected system, breaking down boundaries between energy and adjacent sectors,
  2. Future Cities: Rapid urbanization has created a number of challenges that must be overcome in mega urban areas. Smart cities use technology to reduce resource consumption in dense areas - energy, fresh water, garbage, etc. contributing to a better quality of life. It also incorporates traffic management to reduce congestion, noise and pollution.
  3. Future Technologies: frontier and deep technologies that promise to shape the future of cities and energy in the years to come, include the AI, machine learning, and cybersecurity.

SET and FEV recently launched an a white paper on how robotics and physical AI can support the energy grid.

A key point in the paper is the future focus of investments in the energy space (Figure 5). 600 companies were researched and mapped.

1) Operations and Orchestration: As robots increasingly deploy into the field, the operations and orchestration layer increases in value. The differentiation and stickiness comes from logic embedded into each company’s specific workflows.

2) Physical Work : increasingly customers want robots to do physical work, like for example repairing a piece of equipment. Delivering specific, high value, and critical work via design-to-task robots. Companies with proprietary technology, recurring RaaS models, and clear operational advantages are an investment priority. As robots, sensors, compute and AI mature and reduce in cost, doing physical work will become increasingly practical. This is already occurring in agriculture, construction and mining applications.

3) Inspection and Monitoring with robots and drones has progressed dramatically in the energy space , and there are fewer opportunities for finding high quality investments. It is however an area of selective focus.

4) Asset Intelligence Software: is not an area of focus. Advancements in AI have eliminated barriers to entry and increased commoditization for the base asset intelligence layer.

Figure 6 is shows a robots doing actual physical work in solar panel assembly.

Similarly, the idea is that robots will be able to repair infrastructure - repair a valve on an oil pipeline or repair and re-attach electric power cables.

According to Mr. Woods, “The next wave of physical AI in energy will be won on integration. The winners will take proven advances in autonomy, batteries, and actuators, and engineer fit-to-task systems that are reliable in the field and profitable from day one. With hardware and compute costs at historic lows, the real challenge is finding the environments where a purpose-built system can beat human labor on cost or productivity, then optimizing performance relentlessly”.

Mr. Jungmann : “Physical AI in energy is about machines that act, not just observe. Drones have gotten very good at flying around and capturing data, but the next generation will interact with what they find, so robots might actually fix something, not just flag repairs. The winners in this space will be companies developing robots that observe and interpret their specific environment and eventually deliver autonomous results".

Venture capital is investing significant capital into deep-tech and physical AI companies. Investment in this sector has grown significantly, with the first half of 2026 seeing around fifty billion dollars in physical AI funding across roughly five hundred deals. Firms like Linse Capital, SET Ventures, and Future Energy Ventures are actively backing deep-tech and energy infrastructure companies. These investments target technologies spanning aerospace supply chains, space manufacturing, semiconductors, fusion power, and the energy grid and infrastructure.

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