The AI conundrum – build and they will come….maybe?

In December 2001, Michael J Cooper published an article entitled A Rose by Any Other Name in the University of Utah Journal of Finance that showed a strong correlation between companies that added the .com or .net ending to their corporate name or added the word internet! They found that on average these companies added 53% to their share price for the first five days post announcement. Manias and bubbles are not new, nor are the efforts that are often made by companies to ensure they are seen in the brightest light as a new, exciting technology emerges.

Since the third quarter of 2022 and the launch of ChatGPT, AI has been the exciting new technology lighting the touchpaper on a new wave of disruptive excitement. If corporate earnings calls are anything to go by, then AI is in full-on hype mode. According to FactSet, almost half of all S&P 500 companies mentioned AI on their first quarter 2024 earnings calls, well up from the 10-year average of 50. The biggest spenders on AI hardware have become evangelical – Meta mentioned AI 95 times on their call, Nvidia 86 times and Microsoft 74 times.

What has really changed? In 2017, Accenture published a report on AI saying that it would add $14 trillion to economic activity. A 2015 research paper by PNAS showed that it took only 200 likes for Facebook to know you better than you know yourself on certain criteria – that is 10 years ago.

Use of AI is not new but what seems to have changed is the perception. There is a perception that it will dramatically change our lives in untold ways and at speed. General AI tools like ChatGPT and DALL-E 3 make the vision clear for everyone in a way that was not clear pre the GPT public launch although many developers will say they had been using these tools for years already. Being able to create human-like text, images from text and even music evokes excitement and emotion. The chart below from McKinsey shows the sharp increase in adoption caused by the GPT launch after years of little progress.

AI adoption worldwide has increased dramatically in the past year, after years of little meaningful change

Organisations that have adopted AI in at least one business function,¹ % of respondents

Source: McKinsey Global Survey on Al, 1,363 participants at all levels of the organization, 22 February – 5 March 2024. 1. In 2017, the definition for Al adoption was using Al in a core part of the organization’s business or at scale. In 2018 and 2019, the definition was embedding at least one Al capability in business processes or products. Since 2020, the definition has been that the organisation has adopted Al in at least one function.

However, revenue generating use cases are more steady to build. ChatGPT was forecast to have 77.2 million subscribers in June 2024 and 3.9 million paid users (a US market forecast by BackLinko). At $20 per user per month this is just $1 billion in recurring revenue.

Compare the revenue from use cases to the investment in AI infrastructure and the equation just does not balance up. IDC forecasts that infrastructure investment was $154 billion in 2023 and that it will rise to $300 billion in 2026. Much of this is spent on data centre chipsets and Nvidia GPUs in particular. Jensen Huang, Nvidia’s ebullient CEO, forecasts a total $1 trillion data centre investment wave in short order. Could this be a re-run of the fibre overbuild in 1999/2000 that left many operators facing bankruptcy or is it really a case of ‘This time it’s different’ (incidentally, quoted by John Templeton as the four most dangerous words in investing)? We just don’t know but it is highly likely that the current trajectory in investment is unsustainable. Over 50% of Nvidia chipsets are being purchased by hyper-scalers, companies with huge cash piles that can afford to take the risk. However, this is making them look more like capital intensive smoke stack factory operators than the more traditional capex-lite software models that investors are used to.

Source: Bloomberg; Company data. Past performance does not predict future returns.

The table above shows the capital expenditure to sales ratios in 2015 and 2024 expected. Meta in particular has driven capital intensity higher, spending an estimated 23% of sales for 2024, which is more than either US Steel or Exxon Mobil by a factor of two to three times!! All the mega-scalers have increased their capital intensity substantially over the last 10 years, and the question will soon be asked: can they generate a sensible return on the additional invested capital?

The irony of this relationship is that mega-scalers are spending huge amounts on Nvidia chipsets – cash that earns Nvidia over-sized profits which is then multiplied many times in the eyes of investors when valuing Nvidia’s shares. $1 billion spent by a mega-scaler with Nvidia becomes at the margin (70%), and at a 45x PE multiple, $31.5 billion of added market value to Nvidia’s capitalisation. This monopoly has to come under pressure at some point – the buyers are working hard on alternatives, writing code to try and break the CUDA dominance and extending the life of the chipsets to reach a place where spend could fall meaningfully.

Hardware spending is not like recurring revenue streams. It is cyclical and at some point the insatiable demand for chipsets will abate. Already ChatGPT is seeing a waning of user interest – April 2024 saw two billion visits/month; this has fallen to 600 million most recently. Time will tell but we would rather find the big inference winners using the technology to permanently increase productivity or garner additional revenues than those continuing to pump out the bricks that build the factories.

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