The VC slowdown is happening faster for AI startups

Get ready for a new watercooler topic: Venture investment may be trending down, but funding for AI is performing even worse.

Total VC deal count worldwide has maintained momentum from last year’s record highs, but so far in 2022, “deal value has declined rather significantly across all stages,” according to a PitchBook report—and AI funding in particular is falling faster than the market.

In Q2 2022, global AI funding plummeted by more than 44% year over year, from $33.6 billion to $18.8 billion, per Pitchbook data shared with Emerging Tech Brew. Over the same period, overall global VC funding fell by 25%, from $176 billion to $131.7 billion. On a quarterly basis, global VC funding for AI and machine learning was down more than 26% between Q2 and Q1, a slightly larger margin than the 20% drop for global VC as a whole.

“The market downturn has coincided with AI platform companies, including some of the largest ones, missing their revenue expectations, and demonstrating that AI across enterprise isn’t producing high growth yet,” Brendan Burke, senior analyst for emerging technology at Pitchbook, told us. “That’s forcing a reevaluation of some of those business models.”

Back to earth

For years, many VCs believed AI companies would figure out the path to profitability down the road, Shahin Farshchi, a partner at Lux Capital, told us. Today, investors want to see founders give more thought to how, exactly, they’ll build a sustainable business model around AI.

“This always happens—there’s sort of a new [tech] term that becomes a little bit trendy,” Colin Beirne, partner at Two Sigma Ventures, told us. He added, “It starts as a hobby, and it eventually becomes something very real, and then it becomes something that everyone needs and wants to use. I think AI has gone through that evolution.”

Now, Beirne said, VCs are less focused on which companies are the “very best executors of fancy technology.” He added, “That’s one thing, but that alone does not make a great company—you have to have all the other pieces on the product side, on the market side, on the customer-acquisition side.”

As a result, Burke said, venture investments are starting to trend toward AI applications with near-term commercial use cases, such as data preparation, robotic process automation, computer vision, database management, and natural language processing.

“The bull market in 2021 favored some longer-term sectors, such as autonomous vehicles, that led the vertical in exit accounts, or at least mega-exit count…in 2021,” Burke said. “But that mix is shifting in 2022 to focus more on smaller exits for more fundamental and near-term technologies.”

As is the case with overall funding in Q2, AI funding declined across all deal stages, per Pitchbook data.

Early-stage funding (excluding angel and seed rounds) hit $4.2 billion, a decrease from $5.6 billion in Q1—and down 35% from the same period in 2021. For its part, later-stage funding fell from $18.3 billion in Q1 to $13.4 billion in Q2, good for a 48% drop compared to last year.

In the months to come, Burke predicted, some AI startups will likely entertain the possibility of being acquired rather than attempting to fundraise in a market with limited exit opportunities.

“Some startups that haven’t raised recently will see that depressed funding environment and opt to become a part of larger platforms,” Burke said, adding that “this environment will be a catalyst for that.”