Cognichip wants AI to design the chips that power AI, and it just raised $60 million to try it out

More advanced silicon chips have accelerated the development of artificial intelligence. Now, can artificial intelligence return the favor?

Cognichip is building a deep learning model to work alongside engineers as they design new computer chips. The problem you’re trying to solve is one the industry has had for decades: chip design is too complex, too expensive, and slow. Advanced chips take three to five years to move from concept to mass production; The design phase alone can take up to two years before actual planning begins. Keep in mind that Nvidia’s newest line of GPUs, the Blackwell, has 104 billion transistors — that’s a lot to count.

And in the time it takes to create a new segment, Faraj Alaei, CEO and founder of Cognichip, says the market could change and make all that investment waste. Aalaei’s goal is to bring AI tools used by software engineers to accelerate their work in semiconductor design.

“These systems are now smart enough that by just directing them and telling them what result you want, they can actually produce beautiful code,” Alaei told TechCrunch.

He says the company’s technology could reduce the cost of chip development by more than 75% and cut the timeline by more than half.

The company emerged from secrecy last year and said on Wednesday it had raised $60 million in new funding led by Seligman Ventures, with notable participation from Intel CEO Lip-Bu Tan, who invested through his venture firm Walden Catalyst Ventures and will join Cognichip’s board. Umesh Padhaval, managing partner at Seligman, will also join the board. Cognichip has now raised $93 million in total since its founding in 2024.

However, Cognichip cannot yet point to a new chip designed with its system and has not revealed which customers it says it has been collaborating with since September.

The company says its advantage lies in using its own model trained on chip design data, rather than starting with a general-purpose MBA. This requires access to domain-specific training data, which is not trivial. Unlike software developers, who share vast amounts of code openly, chip designers guard their IP addresses closely, making the kind of open source treasures that typically train AI coding assistants largely unavailable.

Cognichip had to develop its own datasets, including synthetic data and licensing data from partners. The company has also developed procedures to allow chip manufacturers to securely train Cognichip models on their own data without revealing it.

When proprietary data is not available, Cognichip has relied on open source alternatives. In a demonstration last year, ConiShip invited electrical engineering students at San Jose State University to try out the model at a hackathon. Teams were able to use the model to design CPUs based on the open source RISC-V chip architecture – a freely available design that anyone can build on.

Cognichip competes with existing companies such as Synopsys and Cadence Design Systems, as well as a host of well-funded startups. Among them: Alpha Design AI, which raised a $21 million Series A in October 2025, and ChipAgentsAI, which closed an expanded $74 million Series A in February.

Padvale said the current influx of capital into AI infrastructure is the largest he has seen in 40 years of investing.

“If it’s a supercycle for semiconductors and devices, it’s a supercycle for companies like them [Cognichip]He said.