As the AI boom continues to surge, investors are starting to question whether it’s all just a bubble. The tech industry has invested hundreds of billions of dollars in AI, with Nvidia being the biggest beneficiary. However, demand for AI chips appears to be cooling slightly, and notable investors and analysts are starting to ask for more detail.
Sequoia partner David Cahn has revisited a nagging question he posed late last year: why is there a big gap between the revenue expectations implied by the AI infrastructure build-out, and actual revenue growth in the AI ecosystem, which is also a proxy for end-user value? Meanwhile, at Barclays, a group of analysts tried to run some numbers, estimating industry capital expenditure on AI and using research publications from Google to hazard a guess at how much all this new infrastructure can support, in terms of actual AI products.
Most notable is a research newsletter from Goldman Sachs, in which Head of Global Equity Research Jim Covello makes the case that the AI boom has a lot in common with the Dot-com bubble. Covello is broadly dismissive of AI in terms that probably feel familiar, but his most important claims are probably his most restrained: that the high level of investment in AI is largely about FOMO within the tech industry, and that investor pressure on companies outside of tech is driving companies with completely unclear uses for current AI technology to invest anyway, suggesting a rather classic investment bubble.
The Rise of AI in Investment Strategies
Historical Context
The rise of AI in investment strategies has been fueled by the belief that computing power is destiny. The biggest players in AI have been competing to design, train, and deploy the most capable AI models, primarily in the form of large, expensive, general-purpose “foundation” models, hoping that they’ll win customers through a combination of better engineering, better data, and smarter research or product bets. The tech industry’s hundreds of billions of dollars of investment in AI is largely an investment in Nvidia chips, hardware, and infrastructure needed to support and deploy Nvidia chips, and the power needed to power Nvidia chips.
Current Trends in AI Investing
As we approach the two-year anniversary of the release of ChatGPT, demand for AI chips appears to be cooling slightly, and notable investors and analysts are starting to ask for a little bit more detail. Sequoia partner David Cahn has revisited a nagging question he posed late last year. He noticed a big gap between the revenue expectations implied by the AI infrastructure build-out and actual revenue growth in the AI ecosystem, which is also a proxy for end-user value. A multi-hundred-billion-dollar race to build fundamentally similar supercomputers raises one extremely straightforward question that AI firms have been able to remain vague about for a couple of years now: For what? AI executives have treated the answer as too obvious to explain: AGI (artificial general intelligence) or more lately ASI (artificial superintelligence).
Meanwhile, at Barclays, a group of analysts tried to run some numbers, estimating industry capital expenditure on AI and using research publications from Google to hazard a guess at how much all this new infrastructure can support, in terms of actual AI products. Most notable, perhaps, is a research newsletter from Goldman Sachs, in which Head of Global Equity Research Jim Covello makes the case that the AI boom has a lot in common with the Dot-com bubble. Covello is broadly dismissive of AI in terms that probably feel familiar, but his most important claims are probably his most restrained: that the high level of investment in AI is largely about FOMO within the tech industry, which has struggled to articulate with any specificity, or demonstrate in the form of products, the actual trillion-dollar opportunity of AI; and that investor pressure on companies outside of tech is driving companies with completely unclear uses for current AI technology to invest anyway, suggesting a rather classic investment bubble.
In conclusion, the AI boom in investment strategies has been fueled by the belief that computing power is destiny, but notable investors and analysts are starting to ask for more detail as demand for AI chips appears to be cooling slightly. There are concerns that the high level of investment in AI is largely about FOMO within the tech industry, which has struggled to articulate with any specificity, or demonstrate in the form of products, the actual trillion-dollar opportunity of AI. Investor pressure on companies outside of tech is driving companies with completely unclear uses for current AI technology to invest anyway, suggesting a rather classic investment bubble.
Investor Sentiments and Expectations
Shifting Perspectives
As the hype around AI begins to cool, notable investors and analysts are starting to ask for more detail on the revenue growth and end-user value of the AI ecosystem. Sequoia partner David Cahn revisited a nagging question he posed last year, noting a big gap between the revenue expectations implied by the AI infrastructure build-out and actual revenue growth in the AI ecosystem. He is generally optimistic about the long-term potential of AI, which he describes as potentially a “generation-defining” technology wave, and suggests lots of potential upside for some investors. However, he warns of a dangerous “delusion” that has taken hold, that AGI is coming tomorrow, and we all need to stockpile the only valuable resource, which is GPUs.
Meanwhile, a group of analysts at Barclays tried to estimate industry capital expenditure on AI and how much all this new infrastructure can support in terms of actual AI products. Based on their estimates, the industry is assumed to produce upwards of 1 quadrillion AI queries in 2026. This would result in over 12,000 ChatGPT-sized products to justify this level of spending.
Head of Global Equity Research Jim Covello at Goldman Sachs makes the case that the AI boom has a lot in common with the Dot-com bubble. He believes that the high level of investment in AI is largely about FOMO within the tech industry and that investor pressure on companies outside of tech is driving companies with completely unclear uses for current AI technology to invest anyway, suggesting a classic investment bubble.
Performance Metrics
The tech industry’s investment in AI is largely an investment in Nvidia chips, hardware, and infrastructure needed to support and deploy Nvidia chips, and the power needed to power Nvidia chips. The clearest winner of the recent AI boom is Nvidia, which designs and sells the best chips for training and running modern AI models. As of June 2024, Nvidia is the most valuable company in the world.
Arguments about AI have a tendency to slide into abstract, speculative territory, converting narrow questions about reducing errors in LLMs into online fights about the nature of intelligence. Allegedly more sober claims made by tech companies follow much the same pattern, diverting questions and criticism into fuzzy conversations about inevitable progress toward human-level machine intelligence and higher productivity, with occasional calculated performances of grave humility about the economic disruption such inevitabilities imply. This has been a useful rhetorical strategy for AI firms in general, as they raised early money, dealt with the press and critics, and had their first encounters with dazzled regulators. Most importantly, it helped produce the aforementioned FOMO.
If investor confidence falters, however, and if this really is the moment when VCs and major banks start to speak more cautiously about AI, then the tech industry may need to provide more concrete answers to the question of what all this investment in AI is really for.
Challenges and Risks
Ethical Considerations
Investors are grappling with the challenges and risks of the fast-growing AI market, particularly with regards to ethical considerations. Leading investors are already using generative artificial intelligence (genAI) to source opportunities and manage assets. However, investors view responsible AI as critical to capturing the technology’s value and mitigating risk [1]. As AI becomes more prevalent in society, ethical considerations surrounding its use become increasingly important. Investors are mindful of the potential for AI to be used in ways that could infringe upon privacy, perpetuate biases, or cause harm to individuals or society as a whole. As such, responsible AI practices are becoming a key factor in investment decisions.
Market Volatility
Investors are also starting to wonder if the recent AI boom is just a bubble. Notable investors and analysts are starting to ask for more detail on the industry’s capital expenditure on AI and the actual revenue growth in the AI ecosystem [2]. The tech industry’s investment in AI is largely an investment in Nvidia chips, hardware, and infrastructure needed to support and deploy Nvidia chips, and the power needed to power Nvidia chips. A multi-hundred-billion-dollar race to build fundamentally similar supercomputers raises one extremely straightforward question that AI firms have been able to remain vague about for a couple of years now: For what? [3]
Investors are concerned about market volatility and the potential for overbuilding things the world doesn’t have use for, or is not ready for. The more time that passes without significant AI applications, the more challenging the AI story will become. If important use cases don’t start to become more apparent in the next 12-18 months, investor enthusiasm may begin to fade [4]. The boom in AI investment has a lot in common with the dot-com bubble, and overbuilding things that the world doesn’t have a use for or is not ready for typically ends badly [5].
In summary, investors are grappling with the ethical considerations of AI and the potential for market volatility and overbuilding. Responsible AI practices are becoming a key factor in investment decisions, and investors are starting to question the industry’s capital expenditure on AI and the actual revenue growth in the AI ecosystem.
Future Outlook
Technological Advancements
The current state of AI investment is largely focused on building and deploying the most capable AI models, primarily in the form of large, expensive, general-purpose “foundation” models. The biggest players in AI have all been competing to design, train, and deploy the most capable AIs, hoping to win customers through a combination of better engineering, better data, and smarter research or product bets. However, a lot of players in AI believe that computing power is destiny, and are hoarding as much physical hardware as possible, and building facilities to contain it.
As AI chips become more powerful, the potential for AGI (artificial general intelligence) or ASI (artificial superintelligence) becomes more realistic. The long-term potential of AI is still optimistic, but investors are starting to ask for more detail. Notable investors and analysts are starting to wonder if the high level of investment in AI is largely about FOMO within the tech industry, which has struggled to demonstrate the actual trillion-dollar opportunity of AI.
Regulatory Landscape
The regulatory landscape for AI is still evolving. There are concerns about the potential impact of AI on jobs, privacy, and security. Some countries have already implemented regulations to address these concerns, while others are still in the process of developing guidelines.
Investors in AI should be aware of the regulatory landscape and how it may impact the industry. As AI becomes more advanced and widespread, it is likely that regulations will become more strict. Companies that are able to navigate the regulatory landscape will be better positioned to succeed in the AI industry.
In summary, the future outlook for AI is still optimistic, but investors are starting to ask for more detail about the potential return on investment. The regulatory landscape for AI is still evolving, and companies that are able to navigate it will be better positioned to succeed in the industry.