Editor’s note: This article is based on insights from our podcast series. The views expressed in the podcast reflect the views of the speakers and do not necessarily represent the views of this publication. Readers are encouraged to explore the entire podcast for additional context.
What will it take to make AI not only powerful but practical?
This question was at the center of a compelling live conversation between Pris Banerjee, Synopsys’ Senior Vice President of Innovation, and host Sanjay Puri at the India Innovation Impact Summit.
Broadcast on the CAIO Connect podcast, the discussion moved seamlessly from semiconductor design to farmers in rural India, making a powerful case for inclusive AI.
Banerjee’s thesis was simple but ambitious. India has built digital public infrastructure like Aadhaar and UPI to transform financial inclusion. We now have the opportunity to build on that foundation with an AI layer that will serve 1.5 billion people.
Much of the conversation focused on the strategic logic behind Synopsys’ acquisition of ANSYS. Historically, Synopsys has helped design some of the world’s most advanced semiconductor chips. But today, chips don’t exist alone. They power complex software-defined systems, self-driving cars, aircraft, robots, and spacecraft.
ANSYS brought advanced physics simulation capabilities. The combined company can now model the entire system, not just the chip, but the software running on it, the physics around it, and the real-world environment in which the chip operates.
Read: Anu Bradford on why India should build a hybrid AI governance model (May 11, 2026)
As Banerjee explained, the industry is moving from designing the “brain” of a machine to optimizing the whole organism. This means digital twins of semiconductor factories, full mission simulations of spacecraft, and integrated modeling of software-defined vehicles.
The most interesting thing about Mr. Banerjee’s appearance on “CAIO Connect” was his explanation about “physical AI.”
Traditional large-scale language models use words as tokens. Image models use pixels. The video model uses frames.
Physical AI will continue to evolve.
Use real-world variables such as pressure, temperature, air flow, electromagnetic fields, and 3D spatial mechanics as training inputs. These are often referred to as “world models.” Physical AI doesn’t just predict text, it also learns how reality works.
Through high-fidelity simulation, companies like Synopsys can generate synthetic, physically-based data to train robots, autonomous systems, and AI-powered machines without having to measure every physical variable in the real world.
This represents the next frontier of embodied intelligence.
Read: ‘It’s not a shiny new toy’: Thomas Davin on the role of AI in education and child protection (May 8, 2026)
In a conversation with Banerjee, the AI ecosystem was outlined as a five-layer stack.
- Infrastructure (GPU)
- cloud platform
- basic model builder
- Workflow and agent AI
- application
His advice for Indian entrepreneurs in particular was clear. Don’t compete at the infrastructure layer. Instead, build at the application layer. “You don’t have to work at the lowest level, the GPU level, the cloud level, the model level. Work at the application level. Impact society. See if you can impact farmers, patients, or these children.”
Create AI solutions for farmers that optimize crop yields. Developing an AI tutor for rural children who cannot afford private coaching. Build AI diagnostic tools to empower physicians in underserved communities.
Each use case can be a million-dollar opportunity. But millions of such use cases exist. Multiply this and you see a multi-trillion dollar opportunity in the application layer.
Banerjee concluded the discussion with a policy message. He said AI, like electrification, is transformative, but like nuclear power, it must be managed responsibly.
Governments need a framework to prevent abuse. At the same time, AI cannot become a tool available only to the elite.
Just as Aadhaar enabled financial participation on a national scale, AI needs to be made widely available, especially across the Global South.
Ultimately, as Banerjee emphasized, the true measure of AI success is not the size or valuation of the model. The question is whether it will bring about tangible changes to people.
