Investments in generative AI startups reached $3.9 billion in the third quarter of 2024

Not everyone seems to be convinced about the return on investment in generative artificial intelligence. But many investors do, judging by the latest data from funding tracker PitchBook.

According to PitchBook, in the third quarter of 2024, VCs invested $3.9 billion in generative AI startups across 206 deals. (Not counting OpenAI’s $6.6 billion round). $2.9 billion of this financing went to U.S.-based corporations across 127 deals.

Big winners in the third quarter included coding assistant Magic ($320 million in August), enterprise search provider Glean ($260 million in September) and business intelligence company Hebbia ($130 million in July). Chinese company Moonshot AI raised $300 million in August, and Sakana AI, a Japanese startup focused on scientific discoveries, closed a $214 million tranche last month.

Generative AI, a broad cross-section of technologies that features text and image generators, coding assistants, cybersecurity automation tools and more, has its detractors. Experts query the reliability of this technology and – in the case of generative artificial intelligence models trained without permission on copyrighted data – its legality.

However, VCs are effectively betting that generative AI will gain a foothold in large and profitable industries and that the challenges it faces now is not going to impact its long-term development.

Perhaps they’re right. AND The Forrester report predicts 60% of generative AI skeptics will use this technology – consciously or unconsciously – for tasks starting from summarization to creative problem solving. This is a rather rosier result than Gartner’s forecast earlier this 12 months that 30% of generative AI projects can be abandoned after proof of concept by 2026.

“Large customers are deploying production systems that use startup tools and open source models,” Brendan Burke, senior emerging technology analyst at PitchBook, told TechCrunch in an interview. “The latest wave of models shows that new generations of models are possible that can excel in scientific domains, data mining and code execution.”

An enormous obstacle to the widespread adoption of generative AI is the enormous computational requirements of the technology. Bain analysts project in a recent release test that generative AI will drive corporations to construct gigawatt data centers – data centers that devour 5 to twenty times more energy than the average data center today – putting strain on an already strained labor and electricity supply chain.

There is already a requirement for data center power powered by generative AI extension life of coal power plants. Morgan Stanley estimates that if this trend continues, global greenhouse gas emissions by 2030 could possibly be thrice higher than they might be without the development of generative AI.

Several of the world’s largest data center operators, including Microsoft, Amazon, Google and Oracle, have announced investments in nuclear power to offset growing demand for non-renewable energy. (In September, Microsoft announced that it will draw power from the infamous Three Mile Island nuclear power plant.) But it could take years before these investments bear fruit.

Investment in generative AI startups shows no signs of slowing down – negative externalities be damned. ElevenLabs, the viral voice cloning tool, is reportedly searching for to lift $3 billion in funding, while Black Forest Labs, the company behind the notorious X image generator, is reportedly in talks for a $100 million funding round.