Enterprise

Cadence CEO: AI enhances chip design tools, doesn't replace them

Anirudh Devgan says the software maker's stock decline misses its essential role in designing increasingly complex semiconductors for AI and autonomous systems.

Omega Editorial· August 18, 2026· 3 min read

Cadence Design Systems has watched its stock fall 11% over the past year even as the semiconductor industry booms, caught in investor fears that artificial intelligence could disrupt traditional software businesses. CEO Anirudh Devgan says that concern fundamentally misunderstands his company's position in the chip supply chain.

"A good analogy for AI plus Cadence is like a turbocharger," Devgan told CNBC's Mad Money on Tuesday. "Our base tools are like the V6 or V8 engine, and AI by itself cannot do them."

The irreplaceable foundation

Cadence provides the software and tools that chipmakers use to design semiconductors, working with major players including Nvidia and Intel. Devgan argues that as chips grow more complex—some now containing 200 billion transistors at three-nanometer process nodes—the precise physics and mathematics required for design work cannot be automated away by AI alone.

Instead, the company is integrating AI into its existing products to help customers explore more design scenarios and optimize chip performance. "What the customers want is to improve the performance of the chip. If it's 3 gigahertz, they want 3.5 gigahertz. If it's 10 watts, they want 9 watts," Devgan explained. "It's an optimization problem, and AI can give more scenarios that we can run and improve the performance."

The company reported solid earnings in July, but its shares have been swept up in a broader software sector sell-off as investors reassess which companies face disruption versus opportunity from generative AI.

Beyond data centers

Devgan sees demand for Cadence's tools expanding beyond the current data-center AI buildout. He pointed to "physical AI"—artificial intelligence applied to machines interacting with the real world—as a significant growth driver.

Autonomous vehicles represent one major opportunity. "The amount of electronics and semiconductors in the cars is supposed to go up 10x in the next few years, so it creates a lot of new customers," Devgan said.

He views robotics as an even larger potential market, calling humanoid robots "the biggest product category of all time." These applications will require purpose-built chips with increasingly sophisticated electronics, all of which need to be designed using tools like those Cadence provides.

Why it matters

The disconnect between Cadence's stock performance and the semiconductor boom highlights a broader market struggle to distinguish between software companies vulnerable to AI disruption and those positioned to benefit from it. As more technology companies design custom silicon—a trend accelerated by AI workload requirements—the complexity and volume of chip design work is increasing rather than decreasing. Companies that provide the foundational tools for that work may be overlooked in the rush to identify pure-play AI beneficiaries.

Devgan maintains confidence in multi-year demand growth: "The demand for our products is only increasing, and will continue to increase for the next five to ten years."

These details were first reported by CNBC.

#cadence design systems#chip design software#semiconductor tools#eda software#physical ai#custom silicon

This is an original analysis by the Omega editorial team. Source reporting: AI Watch.

Want systems like this working for your business?

Book a Call

More in Enterprise

Enterprise· 4 min read

Dr. Martens Rebuilt Customer Service From Scratch After Years of Decline

The footwear brand consolidated fragmented systems across regions onto Salesforce and AWS, reversing a three-year slide in customer satisfaction within months.

Via Automation Watch · Sep 24, 2026
Enterprise· 4 min read

AI Coding Tools Added $942M to Hospital Bills Without Care Changes

Blue Cross Blue Shield Association analysis finds hospitals using automation to classify more cases as complex, driving up costs with no documented increase in treatment intensity.

Via AI Watch · Sep 24, 2026
Enterprise· 4 min read

AI Clinical Trial Endpoints Fail at Scale Without Data Harmonization

Analysis of over one million patient screenings reveals that AI validation in single sites masks critical performance drift across multi-site deployments.

Via AI Watch · Sep 24, 2026