The push to build smarter AI systems has always depended on better chips. Now, there’s a growing effort to turn that equation around—using AI to make chip design itself faster and more practical.

That’s the idea behind Cognichip, a young startup trying to simplify one of the toughest problems in the tech world. Designing advanced semiconductors isn’t just complicated—it’s slow and extremely expensive. In many cases, it takes three to five years to bring a chip from an early idea to actual production, and a big chunk of that time goes into design alone.

The challenge keeps getting bigger. Modern chips pack in billions of transistors, and arranging them efficiently is no easy task. Even small improvements can take months of work.

Cognichip’s founder, Faraj Aalaei, believes this long timeline creates a real business risk. By the time a chip is ready, the market might have already moved on. His approach is simple in theory: use AI the way software developers already do—to speed things up and reduce manual effort. Instead of building everything step by step, engineers could guide an AI system and let it handle much of the heavy lifting.

The company says this could cut development costs dramatically and shrink timelines by more than half. That promise has already attracted attention from investors. Cognichip recently raised fresh funding, bringing its total to nearly $100 million since it started in 2024, with some well-known names backing the company.

Even so, it’s still early days. The startup hasn’t yet shown a complete chip built using its system, and it’s keeping details about its customers private for now.

One of the biggest hurdles is data. Unlike software, where developers openly share code, chip design data is usually locked away. To get around this, Cognichip has created its own datasets and worked with partners to access more information. It has also built ways for companies to safely use their own data without exposing sensitive details.

Where that kind of data isn’t available, the team has experimented with open-source alternatives. In one example, students used the system to design CPUs based on the RISC-V architecture during a hackathon, giving a small glimpse of what might be possible.

Cognichip isn’t alone in this space. It faces competition from established industry players as well as newer startups with strong funding. At the same time, the broader boom in AI and chip investment is creating the right conditions for ideas like this to take shape.

For now, the concept is still being tested. But if it works, AI may soon play a much bigger role—not just in running on chips, but in designing them too.

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