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All bills come due eventually, and artificial intelligence bills are mounting in two senses.
First, the capital investment into AI capacity keeps growing, and second, as a result the required pay-off keeps getting more demanding.
While capital markets can be notoriously generous at times, they also cannot function if they defer bills indefinitely.
More than two years into the so-called “AI era”, much is still not understood and the contours of the landscape are getting sharper.
The technology is very different from previous waves of digital disruption, in that it has high variable costs, very high capital intensity, and therefore does not scale in the same attractive way as past superstar digital businesses such as how Microsoft, Alphabet and Meta did.
For context, when Facebook had a similar number of monthly active users as OpenAI does now, it had a 25 per cent free cash flow margin, whereas OpenAI expects to have a minus 54 per cent FCF margin in 2025 — based on financials leaked to the press — despite massive subsidies from its partners and a reliance on non-cash expensing.
xAI, Elon Musk’s AI group, meanwhile, expects a minus 2,640 per cent FCF margin.
The big names at the heart of the AI category are burning cash at a historic rate
The big names at the heart of the AI category are burning cash at a historic rate.
This business model looks more like the Uber or WeWork generation of venture capital-funded “unicorns”.
Investors in AI companies are betting that the economics will look like Uber in the long run, but the model of continual price cuts makes it very hard to work out if there is any underlying pricing power or not.
Providers of compute power to the AI names are also seeing gross margins erode and capital intensity soar, as the recent Oracle results revealed — the big tech earnings growth cited above is significantly ahead of FCF growth.
The only big winner so far has clearly been Nvidia, whose graphic processing units are the default backbone on which the industry has organised itself.
It is significant that Nvidia’s marginal customer has shifted away from the cashed-up hyperscalers, to OpenAI’s new compute partners, who are highly levered and much more exposed to capital markets cycles, such as Oracle, CoreWeave and Soft Bank.
The other surprise relative to the expectations of 2023 has been the rapid penetration of consumer AI applications, but the relatively disappointing enterprise uptake.
As we learn more about the underlying tech, this is not that surprising.
The technology tends to be approximate and unreliable in a way that consumers can accept, but so far is prohibitive for businesses that carry strict liability and regulatory oversight.
While there are hopes that new developments within AI such as retrieval augmented generation, agentic models, and reasoning models can address these issues, the very high cost of these innovations brings us back to the exorbitant capital demands of the industry.
Recent deals, including Salesforce’s purchase of Informatica and Meta’s purchase of half of Scale AI, which we think are intended respectively to address the quality of retrieval data and post-training fine-tuning of models, highlight the steady inflation of the AI bill, while big revenue and profit pay-offs seem frustratingly always just around the corner.
This takes us back to the everyday reality of our portfolio and what we are doing.
We want to find companies where the competitive advantage is ideally improving at the margin, reflected in better pricing power and gross margins drifting gently upwards to enable more reinvestment in the competitive advantage.
As one well-respected information services company said to: “AI is a UX [user experience] not a product in itself.”
The natural language interface enabled by large language models can feel like an actual conversation, which makes it easier for non-technical users to surface content buried in large data sets.
This is naturally useful to companies that control big proprietary bodies of information, which are not available to the wider public and can be used to help businesses become more efficient.
We are well invested in this sector through the existing classic data companies.
There are many ways AI can improve the productivity of these companies’ clients.
For instance, Verisk is already selling a tool that allows faster review and processing of insurance claims documents. RELX similarly is selling tools that speed up the bread-and-butter tasks of lawyers in big white-shoe law firms, allowing quicker “Shepardisation” of legal cases to see if they are valid precedents or not.
Both have the common goal of improving the daily productivity of already highly skilled professionals.
We are also interested in companies that have large logs of transactional data from dealings that happen on their platforms, including Mastercard, CME, ICE and Marsh McLennan, which again can use these tools to aggregate insights from the data and make them available to clients.
Either AI will produce substantial and widely shared benefits for the wider economy, or it will be deprioritised and moved back to more niche use cases such as coding co-pilots, language translation and natural language search tools
The common thread binding these companies is that they are not building the infrastructure for AI because they do not have to.
The proprietary data they control cannot be reproduced with AI, so they can afford to be price makers on making AI tiers of content available to clients; in other words, they price for the value it creates today.
While this may be boringly conservative, we are not eager to take existential bets with client capital.
While there is a lot of lofty talk of superintelligence and the singularity looming around the corner, when and if we get the “wild abundance of intelligence and energy” recently promised in the chief executive of OpenAI’s blog, everyone will be a winner.
We focus on the scenario where this does not happen and continue to focus on balance sheets, cash flow and valuations.
For now, we remain in a world where money and intelligence are relatively scarce and valuable.
To be clear, we are not sceptics on the value of AI in its many guises. However, if it is to be a revolutionary technology, we assume it will benefit a broad range of enterprises and consumers.
Its real impact so far has been muted outside a small pool of picks-and-shovels names.
This is unsustainable. Either it will produce substantial and widely shared benefits for the wider economy, or it will be deprioritised and moved back to more niche use cases such as coding co-pilots, language translation and natural language search tools.
James Knoedler is a portfolio manager of the Evenlode Global Equity fund
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