Investments in generative AI startups — those that are creating AI-powered products to generate text, audio, video and more — aren’t slowing down. But they’re being consolidated into a shrinking number of early-stage ventures.
In the first half of 2023, from January to July 16, 225 startups raised $12.3 billion from VCs, according to Crunchbase data shared with TechCrunch. Should the trend maintain, generative AI companies are on track to match or exceed the roughly $21.8 billion they raised in 2023.
Here’s how the H1 2024 total broke down by stage:
- 198 angel/seed deals: $500 million
- 39 early-stage deals: $8.7 billion
- 18 late-stage deals: $3.1 billion
Early-stage startups were the clear winners, like Elon Musk’s xAI (which raised $6 billion in May), China’s Moonshot AI ($1 billion in February), Mistral AI ($502.6 billion in June), Glean ($203.2 million in February) and Cognition ($175 million in April). According to Chris Metinko, an analyst and senior reporter at Crunchbase, investors appear to be betting on big startups they see as having a high chance of success while letting those they’re less sure about “wither away” at the earlier stages.
“Some VCs expect the legal and regulatory dilemmas AI companies could face in both the U.S. and overseas to lead to a slowdown in the flood of AI funding,” Metinko told TechCrunch. “Others point to the fact that when the mobile revolution occurred more than a decade ago, the biggest winners when it came to the foundational infrastructure layer ended up being well-established tech companies.”
To Metinko’s point, the fate of many generative AI businesses — even the best-funded ones — looks murky.
Generative AI models are typically trained on data like images and text sourced from public web pages, and companies assert that fair use shields them from legal challenges in cases where that data turns out to be copyrighted. But it’s not clear yet whether the courts will ultimately decide in favor of generative AI companies, which is probably why some have begun to ink licensing deals with copyright holders.
Regardless of the outcome of any one court case, high-quality training data is becoming harder and more expensive to obtain as startups exhaust the web’s supply and more creators opt to block crawlers from scraping their data. (One analysis estimates that the market for AI training data will grow from $2.5 billion to $30 billion within a decade.) The process of training models isn’t getting any easier or cheaper, either: Per a recent Stanford report, OpenAI’s GPT-4 cost $78 million to train while Google Gemini’s price tag came in at $191 million.
Unsurprisingly given the substantial upfront investment required to build flagship models, few generative AI startups are profitable — not even big guns such as OpenAI and Anthropic. According to The Information, OpenAI, which is reportedly generating around $3.4 billion in revenue, could end up losing $5 billion this year.
Investors in generative AI are playing the long game, it’d seem — particularly big tech investors like Google, Amazon and Nvidia, which see generative AI investments as strategic bets. But could the bubble burst soon? If generative AI startups aren’t able to overcome the existential challenges facing them, that seems like a real possibility.
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