Not everyone is convinced of generative AI’s return on investment. But many investors are, judging by the latest figures from funding tracker PitchBook.
In Q3 2024, VCs invested $ 3.9 billion in generative AI startups across 206 deals, per PitchBook. (That’s not counting OpenAI‘s $ 6.6 billion round.) And $ 2.9 billion of that funding went to U.S.-based companies across 127 deals.
Some of the biggest winners in Q3 were coding assistant Magic ($ 320 million in August), enterprise search provider Glean ($ 260 million in September), and business analytics firm Hebbia ($ 130 million in July). China’s Moonshot AI raised $ 300 million in August, and Sakana AI, a Japanese startup focused on scientific discovery, closed a $ 214 million tranche last month.
Generative AI, a broad cross-section of technologies that includes text and image generators, coding assistants, cybersecurity automation tools, and more, has its detractors. Experts question the tech’s reliability, and — in the case of generative AI models trained on copyrighted data without permission — its legality.
But VCs are effectively placing bets that generative AI will gain a foothold in large and profitable industries and that its long-tail growth won’t be impacted by the challenges it faces today.
Perhaps they’re right. A Forrester report predicts 60% of generative AI skeptics will embrace the tech — knowingly or not — for tasks from summarization to creative problem solving. That’s quite a bit rosier than Gartner’s prediction earlier in the year that 30% of generative AI projects will be abandoned after proof-of-concept by 2026.
“Large customers are rolling out production systems that take advantage of startup tooling and open source models,” Brendan Burke, senior analyst of emerging tech at PitchBook, told TechCrunch in an interview. “The latest wave of models shows that new generations of models are possible and may excel in scientific fields, data retrieval, and code execution.”
One formidable hurdle to widespread generative AI adoption is the technology’s massive computational requirements. Bain analysts project in a recent study that generative AI will drive companies to build gigawatt-scale data centers — data centers that consume 5 to 20 times the amount of power the average data center consumes today — stressing an already-strained labor and electricity supply chain.
Already, generative AI-driven demand for data center power is prolonging the life of coal-fired plants. Morgan Stanley estimates that, if this trend holds, global greenhouse emissions between now and 2030 could be three times higher versus if generative AI hadn’t been developed.
Several of the world’s largest data center operators, including Microsoft, Amazon, Google, and Oracle, have announced investments in nuclear to offset their increasing nonrenewable energy draws. (In September, Microsoft said that it would tap power from the infamous Three Mile Island nuclear plant.) But it could take years before those investments bear fruit.
Investments in generative AI startups show no sign of decelerating — negative externalities be damned. ElevenLabs, the viral voice cloning tool, is reportedly seeking to raise funds at a $ 3 billion valuation, while Black Forest Labs, the company behind X’s notorious image generator, is said to be in talks for a $ 100 million funding round.
