ChatGPT Fever Has Investors Pouring Billions Into AI Startups, No Business Plan Required

Amid broader venture-capital doldrums, it is boom times for startups touting generative artificial intelligence tech. From a report: Before their startup had customers, a business plan or even a formal name, former Google AI researchers Niki Parmar and Ashish Vaswani were fielding interest from investors eager to back the next big thing in artificial intelligence. At Google, Ms. Parmar and Mr. Vaswani were among the co-authors of a seminal 2017 paper that helped pave the way for the boom in so-called generative AI. Earlier this year, only weeks after striking out on their own, they raised funds that valued their fledgling company -- now called Essential AI -- at around $50 million, people familiar with the company said. While most of Silicon Valley's venture-capital ecosystem remains in the doldrums, investors this year have been pouring funds into companies like Essential specializing in generative AI systems that can create humanlike conversation, imagery and computer code. Many of the companies getting backing are new and unproven.

Analysts at research firm PitchBook predict that venture investment in generative AI companies will easily be several times last year's level of $4.5 billion. That is driven in part by Microsoft's $10 billion investment in January into OpenAI, the startup behind the wildly popular ChatGPT bot. In comparison, such investment totaled $408 million in 2018, the year OpenAI released the initial version of the language model powering ChatGPT. Entrepreneurs and their backers are hoping generative AI will change business activities from movie production to customer service to grocery delivery. PitchBook estimates the market for such AI applications in enterprise technology alone will rise to $98 billion in 2026 from nearly $43 billion this year. As with the recently ended bull run of broader startup investing, though, investors often are jumping into AI startups even when it isn't clear how they will make a profit -- especially since the computational power required to train AI services can sometimes amount to tens of millions of dollars a year or more. The sudden influx of capital is also encouraging many AI researchers, some without management or operations experience, to start their own companies, adding to competition.

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