Investing in Ricursive Intelligence: AI for Chip Design and Chip Design for AI

From workhorse GPUs to purpose-built inference ASICs, the rapid acceleration of modern AI has been fueled by the steady drumbeat of improvements in underlying compute power.

Despite this, chip design is a painfully manual, slow, and costly process.

The most performant silicon takes large teams of engineers upwards of two to three years and hundreds of millions of dollars to design. These costs accrue in part to EDA tooling, but the bulk of semiconductor research and development spend goes to labor.

In other words, people are iterating on semiconductor designs, over and over again, to try to achieve “design closure.” An integrated circuit that meets all of the necessary constraints and desired objectives of the chip.

In a world where software is evolving as fast as we can describe it in natural language, this creates a profound mismatch between the pace of AI innovation and the infrastructure needed to support it. Moreover, it limits the benefits of custom silicon to only those companies able to resource large teams and afford expensive licenses to legacy EDA tooling.

Imagine what could happen in a world where we could compress silicon design time from years to weeks.

Ricursive Intelligence is a frontier AI lab optimizing the full chip design process to do just that.

Ricursive is building a recursive loop between hardware and software: AI that enables performance gains through silicon built for specific AI workloads, which in turn accelerate the design of even more efficient compute. This co-evolution will unlock custom silicon impossible with traditional methods and democratize access for companies without massive design teams.

As audacious as this idea is, it has already been demonstrated at production scale in the real world.

Co-founders Anna Goldie and Azalia Mirhoseini pioneered the field of AI-driven chip design with their groundbreaking work on AlphaChip at Google Brain. Much like its gameplaying namesakes, AlphaGo and AlphaZero, AlphaChip is a deep reinforcement learning (RL) agent. It frames the physical design of chips as an RL problem, “learning by doing” as it iteratively adds semiconductor components and receives a reward signal based on the predicted placement quality. This algorithm can not only outperform human layout designs, but also close design criteria in a matter of hours.

Over the last four years, AlphaChip has driven real world impact. Google has leveraged it across four successive generations of its flagship TPU, using the approach to design increasingly larger portions of the chip with superhuman layouts. Outside of Google, some of the leading semiconductor firms, such as MediaTek have also adopted the technology.

This is the team that blazed the trail of AI-enabled chip design. Now they’re building the full-stack platform to operationalize it at scale and usher in a Cambrian explosion of custom silicon.

That’s why we’re thrilled to be leading Ricursive’s Series A.

Coming soon after the company’s launch, this round is a testament to the strength of the founding team, the size of the opportunity, and the density of talent that Anna and Azalia have assembled from Google DeepMind, NVIDIA, Apple, and Cadence. Moreover, it reflects the rapid technical progress Ricursive has made since inception—and the years of work and production-scale impact behind it.

The recursive loop between AI and hardware is an infrastructure bet that could redefine the industry—and we’re humbled to partner with Ricursive as they advance this frontier.