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Industry leaders facing funding reckoning.

Wiggers 24 (Kyle Wiggers is a senior reporter at TechCrunch with a special interest in artificial intelligence.) “Investors are growing increasingly weary of AI” April 15, 2024 https://techcrunch.com/2024/04/15/investors-are-growing-increasingly-wary-of-ai // ZX

After years of easy money, the AI industry is facing a reckoning.

A new report from Stanford’s Institute for Human-Centered Artificial Intelligence (HAI), which studies AI trends, found that global investment in AI fell for the second year in a row in 2023.

Both private investment — that is, investments in startups from VCs — and corporate investment — mergers and acquisitions — in the AI industry were on the downswing in 2023 versus the year prior, according to the report, which cites data from market intelligence firm Quid.

AI-related mergers and acquisitions fell from $117.16 billion in 2022 to $80.61 billion in 2023, down 31.2%; private investment dipped from $103.4 billion to $95.99 billion. Factoring in minority stake deals and public offerings, total investment in AI dropped to $189.2 billion last year, a 20% decline compared to 2022.

Yet some AI ventures continue to attract substantial tranches, like Anthropic’s recent multibillion-dollar investment from Amazon and Microsoft’s $650 million acquisition of Inflection AI’s top talent (if not the company itself). And more AI companies are receiving investments than ever before, with 1,812 AI startups announcing funding in 2023, up 40.6% versus 2022, according to the Stanford HAI report.

So what’s going on? Gartner analyst John-David Lovelock says that he sees AI investing “spreading out” as the largest players — Anthropic, OpenAI and so on — stake out their ground.

“The count of billion-dollar investments has slowed and is all but over,” Lovelock told TechCrunch. “Large AI models require massive investments. The market is now more influenced by the tech companies that’ll utilize existing AI products, services and offerings to build new offerings.”

Umesh Padval, managing director at Thomvest Ventures, attributes the shrinking overall investment in AI to slower-than-expected growth. The initial wave of enthusiasm has given way to the reality, he says: that AI is beset with challenges — some technical, some go-to-market — that’ll take years to address and fully overcome.

“The deceleration in AI investing reflects the recognition that we’re still navigating the early phases of the AI evolution and its practical implementation across industries,” Padval said. “While the long-term market potential remains immense, the initial exuberance has been tempered by the complexities and challenges of scaling AI technologies in real-world applications … This suggests a more mature and discerning investment landscape.”

Other factors could be afoot. Greylock partner Seth Rosenberg contends that there’s simply less appetite to fund “a bunch of new players” in the AI space.

“We saw a lot of investment in foundation models during the early part of this cycle, which are very capital intensive,” he said. “Capital required for AI applications and agents is lower than other parts of the stack, which may be why funding on an absolute dollar basis is down.”

Aaron Fleishman, a partner at Tola Capital, says that investors might be coming to the realization that they’ve been too reliant on “projected exponential growth” to justify AI startups’ sky-high valuations. To give one example, AI company Stability AI, which was valued at over $1 billion in late 2022, reportedly brought in just $11 million in revenue in 2023 while spending $153 million on operating expenses.

“The performance trajectories of companies like Stability AI might hint at challenges looming ahead,” Fleishman said. “There’s been a more deliberate approach by investors in evaluating AI investments compared to a year ago. The rapid rise and fall of certain marquee name startups in AI over the past year has illustrated the need for investors to refine and sharpen their view and understanding of the AI value chain and defensibility within the stack.”

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