Hardik Kabaria is the founder and CEO of Vinci, a frontier lab building systems that make physical reality continuously computable.
While software has become programmable, physics has remained episodic—accessed through discrete simulations and approximations. Vinci is changing that. Under Kabaria’s leadership, the company is building the first system in what is emerging as continuous physics infrastructure, where physics is no longer simulated but continuously computed.
At its core is a deterministic, solver-grounded physics foundation model that operates directly on manufacturing geometry without reliance on customer data. The system is already embedded inside semiconductor engineering workflows, continuously computing thermal and mechanical behavior as designs evolve.
This eliminates simulation as a gating event in engineering workflows—shifting physics from a bottleneck to an always-on constraint, and enabling companies to reach manufacturable, high-yield designs in fewer cycles.
Tell us about your company.
At Vinci we have created what we believe is the first production-deployed physics foundation model for engineering workflows.
Our platform performs deterministic, solver-accurate physics simulation directly on native design and manufacturing geometry, enabling engineers to evaluate real semiconductor and electronics designs without the manual setup and simplifications that traditionally limit simulation workflows. In practice this allows teams to evaluate orders of magnitude more design scenarios — often up to 1,000× more simulations within the same engineering time window.
Today the platform is deployed at more than a dozen semiconductor and electronics organizations and is being used on real development programs.
The broader goal is not simply to make simulation faster. It is to make high-fidelity physics operational inside everyday engineering decisions.
In most organizations today, physics simulation remains episodic. It occurs at specific validation checkpoints, typically executed by highly skilled and scarce specialists, and often after major architectural or design choices have already been made. As systems become more complex, that model becomes increasingly difficult to scale.
Our view is that engineering is entering a new architectural phase where physics foundation models become a core layer of engineering infrastructure, much the way numerical solvers became foundational during the previous generation of CAE. In that world, engineers are no longer limited to isolated simulation runs — they can continuously query physical behavior across designs, materials, and operating conditions, making physics reasoning a far more accessible part of everyday engineering decisions.
What problems are you solving?
The fundamental constraint today is not whether physics simulation works. It does.
The constraint is who can use it, how often, and at what point in the workflow.
Across roughly $4 trillion of global hardware development, physical decisions are still gated by about one million specialists worldwide who know how to run complex simulation workflows. That creates a structural bottleneck.
Design teams are moving faster, even as the systems they build become dramatically more complex: single-digit nanometer features on centimeter-scale substrates, heterogeneous material stacks, and tightly coupled designs assembled from components supplied by multiple vendors. Yet the physics validation process remains largely manual, specialist-driven, and reliant on legacy simulation tools that were never designed to scale with the pace and complexity of modern hardware development.
The result is that teams explore less of the design space than they ideally should, and critical physical insights often arrive later than they need to. In practice this means engineers cannot evaluate as many package and die configurations as they would like, test boundary conditions and power envelopes systematically, or identify thermal sensitivities early in the design cycle. Reliability risks that could have been caught earlier instead propagate downstream into later validation stages, where they are much more expensive to diagnose and fix.
Our goal is to change the economics of physics reasoning.
On real semiconductor workflows, engineers using Vinci have been able to evaluate hundreds to thousands of design scenarios in the time traditional processes might support a single run. When that happens, physics stops being a bottleneck and starts becoming something teams can use continuously during design exploration.
That shift is ultimately much more important than raw simulation speed.
What application areas are your strongest?
Our strongest initial applications are in semiconductor and advanced electronics, particularly thermal and thermo-mechanical behavior. These are some of the hardest physics environments in engineering: extremely dense geometries, complex materials, tight tolerances, and direct consequences for performance and reliability. Thermal behavior, warpage, and mechanical stress are not secondary concerns in modern systems. They affect packaging, yield, reliability, and long-term performance.
We chose this domain intentionally because it is one of the most demanding proving grounds for physics computation. If you can run deterministic, solver-accurate physics on real semiconductor designs at manufacturing resolution, you are solving a problem class that sits near the frontier of engineering simulation. From there, the platform can expand across additional physics domains and into other hardware industries where similar bottlenecks exist.
What keeps your customers up at night?
Physical validation is non-negotiable in hardware development, but the workflows around it do not scale well with the pace and complexity of modern design. Each design change can trigger a chain of setup work, coordination between teams, and delays before engineers receive a trustworthy physics answer. As systems become more complex, those delays become more costly and limit how often teams can explore the design space. Engineers usually know the physics questions they want to ask, but they cannot ask them frequently enough or early enough in the design cycle. What they ultimately want is not just faster simulation, but physics that is reliable, repeatable, and available early enough to shape design decisions rather than simply validate them afterward. That is the gap Vinci is focused on closing.
What does the competitive landscape look like and how do you differentiate?
There are three broad approaches emerging in this space.
First are the traditional CAE and EDA simulation platforms that have powered engineering analysis for decades. These systems are extremely capable, but they were architected for workflows where simulations are run intermittently by specialists rather than continuously across the development process.
Second are research efforts and startups applying machine learning techniques to physics problems, often by training surrogate models on large datasets generated from simulations or experiments. These approaches can work well in narrow problem domains, but they typically require significant training data and retraining as conditions change. That can be challenging in semiconductor environments where design data is highly sensitive and design spaces evolve rapidly.
The third category now emerging is physics foundation models designed to operate as engineering infrastructure, and that is the direction we are focused on. The idea is not to build a separate model for every domain or dataset, but a single physics model capable of deterministic reasoning across many designs and operating conditions. Our platform runs directly on native design and manufacturing geometry, produces solver-grade deterministic results, and can be deployed securely behind the customer’s firewall without requiring training on proprietary design data.
That distinction is critical. Engineering organizations cannot rely on tools that behave probabilistically or require retraining on sensitive design data to produce credible results. For physics to become operational inside real development workflows, the system must maintain the determinism, traceability, and validation standards engineers expect from established solvers, while also delivering the advantages AI makes possible: dramatically greater simulation throughput, the ability to explore much larger design spaces, and physics reasoning that can be applied continuously across many scenarios rather than only during isolated analysis runs. That is the challenge we have focused on solving.
You mention Physical AI. Can you explain the difference between that and what you provide with Physics AI?
The terms sound similar, but they refer to two very different parts of the technology stack.
Physical AI generally refers to AI systems that perceive and act in the physical world — robotics, autonomous vehicles, drones, and other embodied systems. The focus there is decision-making and control in real environments.
Physics AI, by contrast, is about computing how physical systems behave. It applies machine learning and advanced numerical methods to predict thermal behavior, structural stress, fluid dynamics, electromagnetics, and other physical phenomena inside engineering designs.
In practice, physics AI sits earlier in the lifecycle. Before a robot moves, a vehicle drives, or a chip is manufactured, engineers need to understand how the system will behave physically under real operating conditions. Physics AI helps compute those outcomes before hardware is built.
Our focus at Vinci is on that second category — enabling engineers to reason about physical behavior directly on their designs with deterministic, solver-grade accuracy.
What new features or technologies are you working on?
Our roadmap is centered on three areas: scale, physics coverage, and operational integration.
First is scale. We want engineers to be able to evaluate far more scenarios than has historically been possible. When teams can run orders of magnitude more simulations, they can explore the design space much more thoroughly.
Second is expanding the physics domains we support while maintaining solver-grade fidelity.
Third is reducing the operational friction between design inputs and validated outputs so the platform can be used naturally inside real engineering workflows.
The long-term direction is straightforward: physics that is continuous, composable, and operational across the development lifecycle, rather than an isolated analysis activity.
How do customers normally engage with your company?
Most engagements begin with a specific workflow bottleneck.
A team will identify an area where physical validation is too slow or difficult to scale — often in thermal or thermo-mechanical analysis.
We then run Vinci directly on their real designs and compare the outputs against the baselines they already trust. That step is important because engineers want to verify that the results are deterministic and consistent with established methods.
Once that trust is established, the conversation usually shifts.
Instead of asking whether a single simulation can run faster, teams begin asking how much more physics they could evaluate if the bottleneck disappeared.
That is typically when the broader implications of the platform become clear.
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