The rise of generative artificial intelligence initially promised to transform industries, in the manner of the advent of microcomputing in the 1990s. However, the gap between massive investments and effective financial returns poses serious economic challenges.
Tech giants such as Amazon, Microsoft, Google, and Meta plan to invest up to $200 billion in generative AI infrastructure by 2024, largely due to the high cost of Nvidia's processors. However, current revenues—such as OpenAI's $3.4 billion and Anthropic's targeted $850 million for 2024—fail to justify the $600 billion in investments required, according to David Cahn of Sequoia Partners.
The sector has seen a surge in investments, with $27 billion raised last year, often based on valuations that don't reflect economic realities. Adam Selipsky of Amazon Web Services and Emad Mostaque of Stability AI have expressed concerns about a potential bubble, citing possibly unrealistic valuations for startups in the sector.
Faced with a market demanding tangible economic results, some companies must adapt or sell to larger competitors. OpenAI, for example, had to reduce its token prices in response to competition, illustrating the difficulties in maintaining viable pricing amid growing operational costs, especially in computing power and energy. Stability AI's modest revenues of $8 to $11 million demonstrate the challenge of generating significant financial returns.
Despite these challenges, generative AI has significant transformative potential, with expected advances that could, in the long run, align results with initial investments. However, businesses need to adjust their business models to meet current economic realities, focusing on developing more effective monetization strategies and adaptive cost management.
At Strat37, we believe that AI is a short and medium-term bet, and that early adopters will have the most advantages.
Economic history shows that major innovations, such as railways and the Internet, did not immediately yield returns. Both required significant infrastructure investments and time to attract enough users to generate substantial revenue. This lesson applies to AI, which, like railways and the Internet, represents a major paradigm shift rather than just a new product.
Investing in AI today means betting on the future potential of this technology, even if returns on investment are not immediate. To remain competitive, it is essential to continue supporting generative AI with investment strategies that exceed immediate benefits. This perspective will allow businesses to fully leverage significant technological advancements as they mature.
Generative artificial intelligence remains a promising field but faces significant challenges in terms of profitability and economic viability. Industry players need to carefully assess their investment and growth strategies, considering economic realities to avoid the pitfalls of previous technological innovation cycles. Strategic adjustments, both in terms of operations and financial expectations, will be essential to fully exploit the potential of this emerging technology.
Marie
AI consultant
marie.wald@strat37.com
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