Valuation for AI related firms are starting to heat up

The appetite for investment in AI has entered a new paradigm for private equity and VC firms. But there are fears that valuations for AI-related firms are starting to become too high, driven by a stampede of investors afraid of missing out. Will AI see a repeat of the dotcom boom and bust of the late 1990s?

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PE and AI

Few would doubt that the landscape and appetite for investment in AI related areas has now entered a new paradigm. There had been a huge boom of interest by private equity firms in anything to do with AI but, says Andy Jones, CEO of Private Equity Info, it has abated somewhat now as investors are realizing that actually there’s a lot of competition: “The valuations are way too high and it’s the Wild West out there!” The dotcom boom of the late 1990s was followed by the bust which led to European pension funds in particular, shying away from investments in VC for the next couple of decades, despite the transformational changes that became evident through the global adoption of the internet. Will AI follow the same path?

The release of a demo version of ChatGPT 4 by OpenAI on November 30th 2022, opened the floodgates to interest and usage of AI. But whilst that event created widespread public knowledge of the capabilities of AI, the development and investment in AI had been going on for well over the previous decade. Private Equity Info’s database of PE/VC investments in AI and machine learning (ML) from 2008 till August this year shows a significant increase in 2018 and a peak in 2021.

Historically, the total AI/ML investments have so far been mainly in technology and software according to Private Equity Info’s database of 650 or so transactions. But since Q1 2023, ie after the release of ChatGPT, the most prevalent sub-sector of AI has been developer tools and health/bio according to Freddie Macpherson and Michael Joyce of Isomer Capital. VCs across all geographies and most notably in France, they say, have shown significant interest in Large Language Models (LLMs) writing, debugging, and (eventually) deploying code. But AI applications within health/bio have shown the largest dispersion on entry valuations within their portfolio and “appear to exhibit the most hype and competition” whereas developer tools, by contrast, have seen a much tighter dispersion on entry valuation despite drawing significant media attention.