Grow the Pie or Take a Slice: Question Facing AI Chip Startups?

"Startups" in semiconductor chip design space had been a rarity since the dot-com crash in the early 2000s. Chip design requires massive development cost as design cycles are multi-year long with dependence on (1) expensive EDA (Electronic Design Automation) tools for design and (2) foundries for manufacturing -- both of which are highly advanced technologies with very few players in the world. Long design cycles from the conception of an architecture specification to its tapeout (tapeout is when a chip design is frozen & sent to a semiconductor foundry for manufacturing) plus time it takes to develop a SW stack to program new architectures further delays the point of revenue generation for such companies. Initial high investment costs with delayed revenue and delayed improvement in gross-margin had caused major market consolidations after the 2000 dot-com crash and had made semiconductor chip startups less attractive for venture capital funding. However the advent of AI in the last 8 years with its unique computational requirement has exposed newer opportunities for domain-specific ASICs to be, once again, a high-risk-high-gain proposition for venture funding. Introduction of Tensor Processing Unit (TPU), which is a chip designed specifically for Deep Learning (DL constitutes most of AI these days), by Google in 2017 demonstrated the possibility of building a domain-specific chip solution by a new player (new in terms of building ASICs) and cross validated the presence of a lucrative market for investors.