The author suggests that LLMs could be useful in reducing the cost of chip design, but this would only benefit niche markets that don't warrant hardware acceleration due to their small size. The author concludes that LLMs will primarily benefit large semiconductor companies, conventional chip startups, and EDA software startups selling LLM-based tools, but they won't enable hardware startups to tackle markets lacking hardware acceleration due to economic constraints.
Key takeaways:
- YC's proposal that large language models (LLMs) can significantly reduce the cost of chip design is flawed, as it underestimates the complexity of chip design and overestimates the capabilities of LLMs.
- Previous attempts to automate chip design, such as high-level synthesis (HLS), have failed to gain traction due to the reduced performance of the designs they produce.
- LLMs could potentially be useful in chip design for niche markets, but these markets are typically small and thus not economically viable for startups.
- LLMs could be valuable in the chip design industry by aiding in verification, a process that is currently facing a talent shortage, but ensuring that LLMs can understand and reason about chip designs is a significant challenge.