Shelly Henry: Building Smarter Chips with Agentic AI

Bridging AI and Engineering to Change the Semiconductor Industry!

Waiting for something to work can be one of the most frustrating experiences. Everyone has felt it, staring at a screen, hoping the solution will appear, while time keeps ticking. For Shelly Henry, that feeling was a daily part of his work. He spent years at the intersection of semiconductors, software, and design, leading teams at Microsoft on complex chip architectures and AI workloads. Watching talented engineers pour effort into verification, only to face long delays and expensive errors, left him inspired but restless.

That restlessness grew into a vision. Shelly, along with his co-founders Shashank Chaurasia and Sirish Munipalli, has more than 45 years of semiconductor experience. They saw the same inefficiencies repeating everywhere, verification cycles stretching for months, bugs discovered too late, and costs stacking up. They realized there had to be a smarter way to build chips, one that could keep up with human ambition.

The solution came from a surprising place. Large language models, at that time, known mostly for summarizing code, had untapped potential. Shelly and his team imagined AI systems that could generate code, debug it, and reason through problems as engineers would, but faster and more precisely. That idea became MooresLabAI, a company built to bridge human creativity with AI speed, redefining the process of designing silicon.

For Shelly, every challenge he faced became fuel for change. The frustrations he once experienced inspired him to create tools that make engineering faster and smarter. Each breakthrough at MooresLabAI shows how technology can empower human skill instead of just replacing it.
Today, he is creating a new path for the semiconductor industry, proving that even the most complex problems can be solved with curiosity, patience, and determination. He is creating the future of chip design, turning the struggles of yesterday into the innovations of today.

Let us learn more about his journey:

The Drive Toward AI, VR, and Quantum Hardware Convergence

Shelly’s passion for solving difficult problems comes from a fascination with frontiers that seem separate until they suddenly connect. Shelly sees that AI, VR, and quantum hardware all face the same challenge: their progress depends on how fast hardware can evolve.

In 2015, while working on VR systems that required millisecond-level latency, he realized that the bottleneck was not software; it was silicon. That realization stayed with him.

Today, as the era emerges where AI models, Cloud Platforms, IoT devices and VR environments demand specialized compute, MooresLabAI’s architecture, with modular platforms like MooreCoreX for IP, MooreSOC for SoC designs, and MooreVal for validation, was deliberately built to adapt to that future. Shelly is building bridges between worlds that once ran on separate timelines.

Balancing Creativity with Engineering Precision in Product Innovation

When building products that drive massive market growth, maintaining the balance between raw creativity and precise engineering becomes crucial. At MooresLabAI, this balance is treated as an art of engineering leadership. Creativity is structured and guided, ensuring innovation occurs within clear boundaries. Their VerifAgent product, for instance, generates testbenches, test plans, and UVM verification environments directly from architectural specifications using EDA tools in the loop.

This framework allows creativity to flourish safely. AI Agents can imagine and iterate freely, knowing every idea is validated through compile-ready, simulation-driven code. In this environment, creativity does not compete with structure; it grows stronger because of it.

When Gut Instinct Outperforms Data

Shelly has experienced moments when trusting intuition over conventional data led to groundbreaking results. In one instance, the challenge was whether large language models could be integrated directly with EDA tools. Available data suggested the task was impossible, there was no existing training set and no proof that AI could understand SystemVerilog or UVM hierarchies.

He believed that embedding the LLM within the simulation loop and allowing it to learn through EDA feedback could close the accuracy gap. This insight led to the creation of VerifAgent’s multi-mode design, which now delivers 92–98% productivity gains and identifies critical bugs that human teams often overlook. The experience demonstrates that sometimes data must wait for pioneers to collect it.

How Experts Identify Future-Defining Tech Trends

When exploring emerging technologies, Shelly looks for signals that distinguish lasting innovations from fleeting hype. He observes that the most enduring breakthroughs come from persistent pain points that do not go away with scale. In semiconductors, these bottlenecks consistently revolve around time and talent, which is where durable innovation thrives.

Another clue lies in solutions that drastically reduce costs. He points to artificial intelligence applied to silicon as one of those rare cases, where verification costs fall by 85% and time-to-market accelerates 7x. Such developments represent a new physics of productivity rather than a passing fad.

Keeping the Spark of Innovation Alive in Scaling

As ideas move from prototype to global production, the challenge lies in preserving the original spark of innovation. The spark fades when teams stop owning the why.

When VerifAgent scaled from pilot to enterprise deployments, the product was built around human-in-the-loop design modes: “start from scratch,” “edit,” and “complete.” Those modes mimic how real engineers think and create. It kept the product flexible, alive, and deeply human, even as it became enterprise-grade.

Aligning Diverse Teams Around One Goal

Leading teams that bring together physics, design, and business presents a complex challenge. Shelly has learned that alignment does not come from process; it comes from purpose. He states, “Our shared purpose is clear: Reinventing SiliconEngineering with Agentic AI. Whether one is a DV Engineer or an ML scientist, the mission serves as a common point around which all can rally.”

He also emphasizes structured freedom, enough discipline to ensure reproducibility, enough freedom to chase the unexpected. Shelly tells his teams: “Innovation happens when a physicist feels comfortable challenging a data scientist, and both laugh about it later.”

Leadership Tested Through a High-Stakes Product Pilot

Among the biggest product journeys in his career, one experience tested his leadership the hardest. During the first major pilot with a leading NPU provider, the team promised results that sounded almost unbelievable: a 90% reduction in verification time. Midway through, early models failed during mid-sim-reset. He recalls thinking, this could break us.

Instead, the team built a self-debugging agent inside the platform. It became one of their core differentiators. That project went on to save the customer 150+ hours and uncover a critical bug missed by human teams.

The experience taught him that leadership is not about having all the answers. It is about creating a culture where failure becomes an invention lab.

Quantum Hardware as a Catalyst for AI and Virtual Reality

When considering the growing role of quantum hardware in technology, Shelly envisions it as the ultimate accelerator for AI training and simulation. Verification loops that take hours today could collapse into minutes as quantum solvers evaluate trillions of states simultaneously.

AI will no longer just run on hardware — it will co-design it in real time.

At MooresLabAI, the team is preparing for this world. Their architecture already supports modular AI agents that can plug into hybrid compute environments, classical, GPU, and quantum. When that convergence happens, AI will do more than describe the future of silicon; it will invent it.

Turning Failures into Fuel for Learning

Shelly believes that every innovation story includes failures along the way. He explains that failure is simply unscheduled learning. In the first six months, multiple VerifAgent prototypes failed integration with simulation tools.

Instead of patching around the issue, the team turned it into a debugging feature, which they now call EDA-in-the-loop validation. Those “failures” became their moat. Today, that architecture ensures their AI never hallucinates, because it compiles every result through the customer’s own EDA environment.

The Evolution of Leadership

Shelly, having gone through several waves of technological disruption and therefore change, thinks back and sees how his concept of leadership has been transformed throughout the years.

In the early stages of his career, leadership meant certainty, the ability to make fast, correct decisions. Over time, it means curiosity. It is about asking the right questions and empowering brilliant people to find unconventional answers. As CEO, his role centers on setting the vision and then stepping aside. True innovation happens when teams feel both safe and accountable, safe to try, accountable to results.

Keeping Security at the Core of Innovation

In the race for breakthroughs and market dominance, ensuring security remain central to innovation is a key challenge. Shelly states, “We built security into the architecture. Our on-premises deployment model gives semiconductor companies full control of their intellectual property while still accessing enterprise-grade AI through secure, sandboxed environments. And we only integrate APIs that meet stringent compliance standards, including OpenAI’s SOC 2 and FedRAMP frameworks. Ethical innovation is about designing systems where risk is mitigated by transparency and control.”

The Future of Adaptive Chips

Shelly identifies Self-optimizing silicon as the hardware breakthrough that could redefine how humans live and connect. He envisions chips that continue to evolve after manufacturing, reconfiguring logic blocks and power paths using embedded agentic AI.

These chips would learn from real-world workloads, becoming smarter every day. According to him, this development marks the logical next step after Moore’s Law: chips that grow, not just shrink.

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