Nvidia’s (NASDAQ:NVDA) supremacy in building computer chips for artificial intelligence has cooled venture funding for potential rivals, according to investors, with the number of deals in the U.S. this quarter dropping 80% compared to the previous year.
The Santa Clara, California-based company dominates the market for chips that power large-scale language data processing. AI generative models become increasingly intelligent through exposure to more data, a process called training.
As Nvidia strengthens its position in this field, it becomes more challenging for companies attempting to build competing chips. Seeing these startups as riskier bets, venture financiers have recently been unwilling to provide large injections of capital. Advancing the design of a chip to a functional prototype can cost over $500 million, so the retreat has quickly threatened the prospects of startups.
“Nvidia’s ongoing dominance has made clear how difficult it is to enter this market,” said Greg Reichow, partner at Eclipse Ventures. “This has resulted in a pullback in investment in these companies, or at least in many of them.”
U.S. AI chip startups raised $881.4 million through the end of August, according to PitchBook data. This compares to $1.79 billion in the first three quarters of 2022. The number of deals dropped from 23 to four by the end of August.
The information comes from Reuters. Nvidia declined to comment.
AI chip startup Mythic, which raised around $160 million in total, ran out of cash last year and was nearly forced to shut down operations, technology news site The Register reported. However, it managed to secure a relatively modest $13 million investment several months later, in March.
Nvidia indirectly contributed to the broader challenges of AI chip fundraising because investors want “home run investments with a big investment and a huge return,” said Dave Rick, CEO of Mythic.
Difficult economic conditions have contributed to the recession in the cyclical semiconductor industry, Rick said.
A secretive startup called Rivos, which is working on chip projects for data servers, had difficulty raising funds recently, said two sources familiar with the company’s situation.
A spokesperson for Rivos said that Nvidia’s dominance in the market has not hindered their fundraising efforts, and that their hardware and software “continue to excite our investors.”
Rivos is involved in litigation with Apple, which has accused Rivos of stealing intellectual property, which has exacerbated fundraising challenges.
DEMANDING INVESTORS
Chip startups seeking to raise money are facing tougher demands from investors. They require companies to have a product that is only a few months away from launch or is already generating sales, said sources.
About two years ago, new investments in chip startups used to be $200 million or $300 million. That amount has dropped to around $100 million, according to Brendan Burke, an analyst at PitchBook.
At least two AI chip startups have overcome investors’ reluctance by touting potential clients or their relationships with well-known executives.
To raise $100 million in August, Tenstorrent boasted CEO Jim Keller, a nearly legendary chip architect who designed chips for Apple, Advanced Micro Devices, and Tesla.
D-Matrix, which projected revenue of less than $10 million this year, raised $110 million last week, backed by financial support from Microsoft and the Windows maker’s commitment to testing D-Matrix’s new AI chip after its launch next year.
While these chip makers struggle in Nvidia’s shadow, AI software startups and related technologies do not face the same restrictions. They raised about $24 billion in funding this year through August, according to PitchBook data.
Despite Nvidia’s dominance in AI computing, the company does not have an impregnable lock on the sector. AMD plans to launch a chip this year that will compete with Nvidia’s, and Intel made a leap in development by acquiring a rival product. Sources see this as having long-term potential to become alternatives to Nvidia’s chip.
There are also adjacent applications that could open opportunities for competitors. For example, chips that perform data-intensive computing for prediction algorithms are an emerging niche. Nvidia does not dominate this area and is ready for investment.