That’s still a pretty early stage in the generative AI journey, and split perspectives are there when it comes to its returns. Anyway, the latest data from PitchBook says investors are feeling pretty comfortable with regard to that. For example, in Q3 2024 venture capitalists invested $3.9 billion in generative AI startups through 206 deals. The latter omits the primary round of $6.6 billion by OpenAI. The U.S. accounted for $2.9 billion in total investments across 127 deals.
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Several other startups emerged in this wave. Magic, a coding assistant, took in $320 million in August. Glean, an enterprise search provider took in $260 million in September, and Hebbia, a business analytics firm, took in $130 million in July. China’s Moonshot AI raised $300 million abroad in August, and Japan’s Sakana AI, a scientific research company, closed a $214 million round in September.
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Generative AI is, in fact, a whole family of technologies, including text and image generators, coding tools, and automated cybersecurity solutions. While a small number of experts wonder about its reliability and the legality of its use of copyrighted data to train, mainstream investors are betting on generative AI finding a niche in profitable sectors and that its long-term potential outweighs what is a credible concern at this point in time.
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Sourced from Forrester, 60 percent of the sceptics would use generative AI for better or worse tasks such as summarization and creative problem-solving. This is more positive than Gartner’s earlier view that 30 percent of the generative AI projects initiated at the pilot stage may be abandoned by 2026.
According to Brendan Burke, the senior analyst for PitchBook, most large companies are embracing hybrid systems integrated with both startup solutions and open-source AI models. He further mentioned that newer AI models promise substantial functionality in scientific research, data retrieval, and coding.
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However, the tremendous compute intensity is the major obstacle to the broader adoption of generative AI. According to a Bain study, companies could end up constructing gigawatt-scale data centres that are five to twenty times more power-hungry than today’s data centres-data that may continue to strain both labour and power supply chains.
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This increased demand for data centre energy is already extending the use of coal-powered plants. Morgan Stanley estimates that if this trend continues, the carbon emissions in a world with generative AI could reach three times the global carbon emissions that would be seen in a world without the rise of generative AI by 2030.
Major data center operators, such as Microsoft, Amazon, Google, and Oracle, are already building nuclear energy solutions. For instance, Microsoft has recently disclosed public commitments to using power from the Three Mile Island nuclear plant. These investments will likely take years to be fully effective.
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Even so, investments into generative AI startups are holding up well. ElevenLabs, the voice-cloning tool startup, has already entered talks to raise funding at a valuation of $3 billion. Black Forest Labs, which developed one of the most popular image generators, has begun negotiating a $100 million round.
Despite all the technical, ethical, and environmental challenges, the investors still believe great promise exists in generative AI transforming industries.
