This week, news sites and social media were abuzz with claims of Wall Street turning sceptical on artificial intelligence, citing recent analyses from Goldman Sachs, Sequoia Capital, and Barclays. As ever, a closer look reveals a different reality behind these sensationalised stories. These reports, far from representing a unified Wall Street perspective, stem from a handful of analysts whose views clearly don’t encompass the entire US financial sector, and certainly not those analysts who have even the most basic knowledge of AI’s development trajectory.
At the heart of their comments lies a basic argument: fear of missing out is driving big tech firms to ‘overbuild’ compute infrastructure creating a ‘bubble’. While it’s true that datacentre capex is above historical levels, and recent growth in cloud and AI business doesn’t yet show directly proportional revenue increases, the narrative of a bubble is an oversimplification. What seems to be happening here is that the comments of a few analysts are striking a chord with those who have yet to imagine what myriad uses powerful computational capacity can be put towards. This can be likened to IBM president Thomas J. Watson’s comments from the 1940s projecting a global market for “about five computers”. One can imagine there was a steam engine bubble, a railways bubble, an electricity bubble that formed in the minds of those still trying to grasp these general-purpose technologies, and perhaps confusing them with products and services.
Sequoia partner David Cahn’s analysis roughly estimates an annual gap of $600 billion between AI infrastructure spending and product revenue generation. This figure suggests some disconnect between investment and returns. Barclays analyst Ross Sandler draws parallels to the dot-com boom of the 2000s, where Internet bandwidth was deployed before business models for the Internet had been fully developed. However, this comparison overlooks crucial differences. Today’s tech giants, by contrast, enjoy robust financial positions and diverse businesses that provide a substantial cushion against slow take-off. The firms that collapsed during the dot-com era were in some cases spending more than 100% of their cash flow on building infrastructure (that in the end did prove highly valuable, albeit too late for those fragile businesses).
The concept of “over” building implies these analysts are able to predict future demand for AI capabilities and computational power – a notion their published comments fail to backup. Goldman Sachs’ Jim Covello, claims that AI isn’t designed to solve complex problems, citing experiences with “illegible and nonsensical results” in summarisation tasks. Such statements betray a surprisingly shallow understanding of AI’s current capabilities and potential. Barclay’s Sandler argues current AI capex will deliver compute in the next few years to power “12,000 new ChatGPT-scale AI products” – a capacity he deems excessive, again belying a limited understanding of the applications of intelligent compute that (as we have covered many times in this newsletter) go far beyond chatbots.
This narrow view stands in stark contrast to the perspectives of big tech leaders actively investing in the AI build-out. Amazon backed Anthropic’s CEO forecasts AI models costing $1 billion to train in the near term, with $100 billion models on the horizon. Microsoft’s CTO Kevin Scott continues to emphasise that he’s not yet seeing diminishing returns on AI scale-up, with each new generation of models bringing significant improvements in capability, cost-effectiveness, and robustness.
The impact of AI is already being felt across every conceivable sector, and manifests in digital systems, robotics, real-world and virtual-world simulations. While diffusion of these technologies isn’t immediate and adoption patterns are complex, dismissing AI’s potential on the basis of a few anecdotal experiences with the current generation chatbots and some back-of-a-napkin calculations is absurd. As with transformative technologies of the past – like steam, electricity, and internet bandwidth – the full scope of AI’s influence may take time to be effectively measured, but ‘artificial intelligence’ will be nothing other than intrinsically profound when it matures.
Takeaways: Businesses and investors should look beyond lazy headlines and limited analyst opinions when assessing AI’s potential. While caution on the scaling laws, energy and copper supplies, and the long-term effectiveness of current model architectures is warranted, dismissing the technology outright based on current revenue figures or isolated experiences with early applications will lead to missed opportunities. Instead, focus on understanding AI’s general-purpose capabilities, explore its novel use-cases and track its widespread adoption. Remember that new technologies often face scepticism in their early stages – opportunity awaits those who accelerate adoption.