Why an AI slowdown has markets “on edge”
Why a slowdown in model development might not mean a slowdown in AI cap-ex
Johnson posits a thought experiment. If AI models stop progressing and stay exactly where they are, will we see AI usage increase, decrease, or stay the same in five years’ time? He firmly believes that even if AI models stay unchanged, there will be more users. For the hardware and semiconductor stocks currently being rattled by this discussion of a slowdown, that means their GPUs, DRAM chips, and other necessary widgets will continue to be ordered.
Learmonth says that the pullback in hardware stocks does not represent a reversal of investor consensus around this issue. Rather, he sees this as investors re-timing the growth prospects for AI data centers. The expansion of AI services will continue, he says, but investors are beginning to think it will slow down somewhat. A slight slowdown will also potentially resolve some of the supply bottlenecks giving certain hardware companies so much pricing power right now. Learmonth adds, however, that scepticism around the sustainability of the AI buildout was already being considered in the prices of some of these stocks. He believes that even if the pace of AI growth slows or regulation comes into effect, that the economic incentives to build out more AI infrastructure remain.
That regulation could also prove incredibly challenging to write and implement. Johnson says that AI governance is not an obvious area that experts can tackle. Many of the examples of AI behaving in unsafe ways come from unexpected and unplanned for behaviours. Learning how to regulate that and ensuring that AI agents actually follow and hold to those regulations could prove remarkably challenging.
Managing big questions in portfolios
While leaders like Amodei and Altman may be talking at a very high level about questions of safety, agency, governance, and geopolitics, investors have to deal with the immediate ripple effects in their portfolios. Both Johnson and Learmonth say that in this environment, diversification continues to prove itself to be valuable. Learmonth emphasizes the need for a diverse exposure to the tech sector as a whole, including both hardware stocks and the software names that got some much-needed reprieve in the wake of this slowdown discourse.
Hard as it can be for any observer to parse between apocalyptic predictions about AI safety, an immediate pullback in certain stocks, and the long-term fundamentals for the space, both Learmonth and Johnson argue that a focus on those fundamentals can be effective.
