Cognichip Secures $60M to Develop AI-Powered Chip Design

The most advanced silicon chips have supercharged the development of artificial intelligence. Now, AI is poised to return the favor.

Cognichip is developing a deep learning model to collaborate with engineers as they design new computer chips. It aims to tackle a problem the industry has grappled with for decades: chip design is incredibly complex, prohibitively expensive, and painfully slow. Advanced chips take three to five years to progress from concept to mass production; the design phase alone can consume up to two years before physical layout even begins. Consider Nvidia's latest Blackwell GPU line, which packs 104 billion transistors—that's an immense amount to coordinate.

Cognichip's CEO and founder, Faraj Aalaei, notes that the market can shift dramatically during the lengthy chip creation process, potentially rendering massive investments obsolete. His goal is to bring the same AI-powered productivity tools that software engineers use into the semiconductor design arena.

"These systems have become intelligent enough that by simply guiding them and specifying the desired outcome, they can produce excellent code," Aalaei told TechCrunch.

He states the company's technology can slash chip development costs by over 75% and reduce the timeline by more than half.

The company emerged from stealth last year and announced on Wednesday a $60 million funding round led by Seligman Ventures. The round saw notable participation from Intel CEO Lip-Bu Tan, who invested through his venture firm Walden Catalyst Ventures and will join Cognichip's board. Umesh Padval, a managing partner at Seligman, will also join the board. Since its founding in 2024, Cognichip has raised a total of $93 million.

However, Cognichip cannot yet point to a new chip designed with its system and has not disclosed any of the customers it says it has been working with since September.

The company claims its advantage lies in using its own model, trained specifically on chip design data, rather than starting with a general-purpose large language model (LLM). This required obtaining domain-specific training data, a significant challenge. Unlike software developers who share code openly, chip designers closely guard their intellectual property, making the vast open-source repositories that typically train AI coding assistants largely inaccessible.

Cognichip has had to create its own datasets, including synthetic data, and license data from partners. The firm has also developed secure procedures that allow chipmakers to train Cognichip's models on their proprietary data without exposing it.

Where proprietary data is unavailable, Cognichip has relied on open-source alternatives. In a demo last year, the company invited electrical engineering students at San Jose State University to test the model in a hackathon. The teams successfully used it to design CPUs based on the open-source RISC-V chip architecture—a freely available design anyone can build upon.

Cognichip competes against established players like Synopsys and Cadence Design Systems, as well as a group of well-funded startups. These include Alpha Design AI, which raised a $21 million Series A in October 2025, and ChipAgentsAI, which closed a $74 million extended Series A in February.

Padval remarked that the current surge of capital into AI infrastructure is the largest he has witnessed in his 40-year investing career.

"If this is a super cycle for semiconductors and hardware, it's a super cycle for companies like [Cognichip]," he said.