12 Oct 2026
When a growth theme becomes as ubiquitous as AI has over the past couple of years, niggling doubts about its sustainability begin to creep in. So how best to play the next stage of this trend?
Me and the other managers of the Artemis US Select Fund are doing exactly what we did before: looking for stocks where the potential upside outweighs the potential downside by a factor of at least 2:1.
Below we reveal the four groups of companies that we believe will be affected by AI in different ways and how our up/down framework has led us to an investment view on each one.
The first and most obvious way to play AI is to invest in the companies leading the charge: tech stalwarts such as Alphabet, Amazon, Meta and Microsoft.
The main problem with this approach is the excessive amount of investment required by these ‘hyperscalers’ to maintain a leading edge in the technology: AI infrastructure spending among this cohort has hit $1.1trn since 20231, leading to a collapse in short-term cashflows.
With no guarantee of a return from this enormous outlay, our search for the asymmetric risk/reward trade-off has led us to go underweight this group of companies until we see more evidence of who will come out on top and the likely impact on profits.
Another well-publicised method of playing the AI theme is through the ‘picks & shovels’ approach: the companies providing the goods and services on which the hyperscalers are spending such enormous amounts of money. In many cases, they can’t spend it fast enough, leading to a supply/demand imbalance which allows the providers to generate supra-normal profits.
The first beneficiaries of this trend were semiconductor designers (such as Nvidia) and manufacturers (such as Broadcom). These companies have high incremental margins (meaning any increase in revenue results in an exponential increase in profitability) so their cashflows spiked just as those of the hyperscalers were beginning to collapse.
Today we would also include providers of memory (Seagate), energy (Bloom), networking for data centres (Applied Optoelectronics and Coherent) and skilled labour (Comfort Systems).
We see nothing to suggest the supra-normal profits being generated by these companies will come under threat any time soon – especially if the hyperscalers’ spending commitments remain in place.
While we are largely positive on the prospects for companies in the AI supply chain, we are not the only investors who have come round to this view. As a result, few of them look especially cheap on conventional metrics such as the price-to-earnings ratio.
For investors looking for a margin of safety in the form of a lowly valuation, some of the biggest opportunities from AI may conversely be found in the companies thought to be most at risk of disruption from this technology.
Fears of obsolescence have pushed down valuations in areas such as software and consultancy, even though earnings growth has in many cases outpaced that of the market.
Should such fears turn out to be misplaced, investors will reappraise these as fast-growing companies generating healthy levels of free cashflow. Re-ratings will likely follow.
A good example of one of these companies is database software developer MongoDB, which was hammered in the ‘SaaSpocalypse’. We believe that as more businesses embrace AI applications and agents, they need to be able to access all the various types of information they hold in a timely and consistent manner. MongoDB’s database stores and organises a company’s data in such a way to most effectively provide access as AI proliferates across the business.
It seems many other investors have come around to our way of thinking – after its share price almost halved between January and April, it has since rebounded past its early-year peak.
One bizarre facet of the AI revolution is the hyperscalers that control it may see less net benefit from the technology than the customers they sell it to. And every company in our investable universe is a potential customer.
Take an industry such as healthcare. Perhaps the most exciting use-case for AI in this area is at the very start of the process: drug discovery. It is estimated the technology can reduce the time needed for a drug to become a pre-clinical candidate from about five years to between 12 and 18 months.
Going right to the other end of the patient-treatment process, AI is improving productivity even after the point when they have recovered.
If you undergo a medical procedure in the US, the hospital where it was performed can claim back the cost from your insurer by filling in a form. But the claim will be denied if any part of the form is filled in incorrectly – and funnily enough, the insurance companies have made these forms extraordinarily complex.
However, DNA testing provider Natera has developed AI tools that improve first-time approvals of claims by identifying errors and reducing the likelihood of immediate administrative rejections.
Like every other investor, we are still trying to work out all the ramifications of the AI revolution. But one thing we are already confident about is that a top-down view is not the way to play it. It is inevitable that some of the companies at the leading edge of this technology will flatter to deceive, while others deemed most at risk of disruption will find a way to survive and even thrive.
A continued focus on finding those companies where the potential upside outweighs the potential downside by a factor of at least 2:1 will help us limit the losses when we get it wrong – and just as importantly, maximise the gains when we get it right.
1. https://www.ft.com/content/dcf3873e-7b32-4a24-a90d-3bccf1d2c996?syn-25a6b1a6=1
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