The Physical AI opportunity in India: An early-stage VC’s view on the next frontier

If the last decade of the evolution of technology was defined by software eating the world, the next decade will likely be defined by software moving the world.

Physical AI — software systems that combine intelligence with real-world action for robots — is emerging as the natural evolution of the AI wave. While large language models transformed how machines process text, images, and code, the next leap lies in enabling machines to perceive, decide, and act in physical environments.

Globally, this shift is already visible in the rise of companies such as Figure AI, Physical Intelligence, Covariant, and Agility Robotics, while Indian robotics companies, including CynLr in our own portfolio, are already being positioned in the same broader wave of “physical AI.”

At its core, Physical AI refers to the integration of robotics, perception systems, control algorithms, and increasingly, foundation models that enable machines to operate autonomously in the real world. This spans industrial robotic arms, autonomous mobile robots (AMRs), drones, agricultural robots, and even humanoid systems.

Unlike pure software AI, Physical AI requires tight coupling between hardware and intelligence — making it more complex, more capital-intensive, and ultimately more defensible.  That said, a global technology services and consulting giant argues that adaptive, context-aware robotics is moving into mainstream deployment.

This market is growing quickly, projected to hit $15 billion over the next five years.

India’s starting point in this domain is mixed. On the one hand, we have clear structural advantages. A deep pool of software talent, a growing base of AI researchers, and a globally competitive engineering workforce provide a strong foundation. Indian robotics companies are increasingly using simulation, edge AI, and digital-twin workflows.

NVIDIA, the predominant maker of chips for AI data centres, which also offers its Isaac robotics tool kit, notes in a blog post some examples of Indian robotics startups that are already part of this ecosystem.

On the other hand, gaps remain, including the lack of a mature supply chain for high-precision components such as actuators, sensors, and advanced electronics. Deep hardware R&D is still limited compared to the US or China, and patient capital for long-gestation technologies is relatively scarce. Policy discussions around deep-tech manufacturing increasingly emphasize domestic production of components such as sensors and precision actuators, underscoring the structural bottleneck.

From our point of view, as early-stage investors in deep tech, the opportunity lies not in replicating global frontrunners, but in identifying segments where India’s constraints become advantages.

Perhaps one of the most compelling areas is industrial automation for small and mid-sized enterprises (MSMEs). Unlike large factories in developed markets, Indian manufacturing is highly fragmented and cost-sensitive. This creates demand for low-cost, modular, and easy-to-deploy robotic systems. Warehouse automation and industrial autonomous vehicles are examples of this kind of demand in practice.

The rapid growth of e-commerce and third-party logistics has created operational complexity that cannot be solved just by throwing more people at it. Autonomous mobile robots, sorting systems, and AI-driven picking solutions are seeing increasing adoption.

Defense and dual-use robotics represent a structurally advantaged sector in India. With increasing geopolitical focus and government support for domestic manufacturing, startups building unmanned systems, surveillance robots, and autonomous navigation platforms are likely to see both funding and procurement tailwinds. In our own portfolio, Unmannd Autonomy is a great example.

India’s broader deep-tech push is also increasingly oriented toward strategic technology categories such as robotics, drones, and advanced manufacturing.

Agricultural robotics is a longer-term opportunity, but a significant one. Labour shortages, rising input costs, and the need for precision farming create demand for automation in spraying, harvesting, and monitoring. While adoption cycles may be slower, startups that can demonstrate clear returns for farmers or agribusinesses could unlock large markets.

Beyond end applications, the enabling layer is often underappreciated but critical. Startups working on perception stacks, simulation environments, robot operating middleware, and edge AI systems may offer more scalable, software-like economics, making the layer commercially relevant.

Again, CynLr stands out with its latest offering — an Object Intelligence Stack that the company’s two founders see as a precursor to a “manipulation operating system” for robots.

What has changed in recent years is not just the opportunity set, but the quality of execution. A new generation of founders — often with experience in ISRO (India’s space agency), global robotics labs, or deep tech startups — are building companies with a full-stack mindset.

Cynlr’s founders, for example, spent over a decade at National Instruments (now part of Emerson Electric, and widely respected for its virtual instrumentation and testing technologies for industrial engineering) and later consulted in the industry, before starting their own robotics company.

Prototyping cycles have accelerated due to open-source tools like ROS2, advances in simulation platforms, and improved access to components. At the same time, strategic capital from corporates in manufacturing, logistics, and defense is beginning to complement traditional venture funding.

Yet, the risks remain substantial. Physical AI startups operate on longer timelines than SaaS companies, often requiring years of iteration before achieving product-market fit. Hardware margins can be thin, and working capital requirements are significant. Go-to-market in India is particularly challenging, given fragmented customer bases and long sales cycles.

Perhaps most importantly, the talent pool for specialized areas such as controls engineering and mechatronics is still limited.

A venture-scale Physical AI startup in India is likely to exhibit a few defining characteristics. First, it will balance hardware and software intelligently — either by building full-stack systems with tight integration or by owning a critical layer in the stack. Second, it will prioritise recurring revenue models such as Robotics-as-a-Service (RaaS), reducing upfront costs for customers while improving long-term unit economics.

Third, it will often adopt an export-first or dual-market strategy, leveraging India for cost-efficient development while targeting global markets for scale. CynLr, based in Bengaluru and with customers in the US and APac, is an example of this approach, and in fact, their innovative playbook includes an advanced research lab in Switzerland.

The debate between full-stack tech versus component players remains open. Full-stack companies offer greater control and differentiation but require more capital and execution capability. Component or software-layer companies may scale faster but risk commoditization unless they achieve strong ecosystem lock-in.

Ultimately, the investment thesis for Physical AI in India is not about building the most advanced humanoid robot in the near term. It is about identifying where India can lead: cost-efficient, robust, and scalable automation solutions for real-world problems. In many ways, India’s constraints — price sensitivity, infrastructure variability, and fragmented markets — force startups to build more resilient and adaptable systems.

India may not produce the first generation of frontier humanoid platforms. But it has the potential to become the proving ground — and ultimately the production engine — for millions of intelligent machines deployed across the world.