Since the launch of the smart phone and the creation of the network opportunity across billions of connected devices, one of the much vaunted characteristics of the most successful companies has been their capex light models. The concept that it was an advantage not to own assets but rather to be the platform for the assets of others created a new breed of highly profitable companies generating market leading returns in less than a decade of existence.
Google and Meta, in particular, built businesses that have taken well over 50% of the global advertising market in a very short space of time, creating combined revenues that grew from about $150 billion in 2017 to a forecast $640 billion in 2026. However, the era of AI has changed the dynamic for return creation going forward and, with it, a degree of uncertainty about the incremental returns that can be earned on substantial capital expenditure increases. The chart below shows the level of capex/revenues for 2017 and 2026 estimated. Capital intensity has increased dramatically, making yesterday’s asset light business models into the smoke stake companies of the AI world – in fact, they are now some of the most capital intensive companies in any sector globally.
Source: Bloomberg, Liontrust, June 2025
The question that remains hotly contested is: will these investments in substantial amounts of AI infrastructure generate a better return than if the spending was not targeted at this area or even to capital expenditure projects at all. Apple now generates over 60% returns on invested capital (ROIC) through its capital allocation policy to return money to shareholders.
As the chart below shows, while returns remain solid, increased capital spending has started to dampen ROIC for the mega scalers. Meta is the exception, suggesting that where a company has sufficient proprietary data and this data set is sizable, it is possible to earn a return on investment.
Return on invested capital (%)
Source: Goldman Sachs, Consensus estimates, June 2025
The critical piece of the puzzle now in determining what happens to the ROIC looking forward is being able to see what revenues can be generated from the AI spending frenzy. There is very little information on which to make this analysis. Microsoft has indicated that AI ventures have already generated $13 billion in annual revenue and Open AI has stated that annualised revenue is currently running at $10 billion. While this is impressive on one level, as new software companies or revenue streams accelerate to multi-billion dollar levels in a short period, the capital expenditures being undertaken to deliver this are just off the scale. UBS forecasts total AI capex of $360 billion in 2025, rising to $480 billion in 2026. Much of this sits in the hands of a very few mega scale companies that are flush with cash and quite publicly state the risk of missing out far exceeds the risk of losing money. Luckily, these companies are in a position to make that call with plenty of cash available, but whether shareholders will see any benefit remains to be seen.
It is worth reminding ourselves of the 1999-2000 capex binge that led to miles and miles of surplus cable capacity being built for the expansion of the Internet. Much of that capacity is still redundant today. The charts below from Goldman Sachs show the returns required back then and today from capital intensity. The realised return on investment (ROI) of -120% for telecoms in 2001 is a salutary reminder that trees don’t grow to the sky.
Source: Compustat, Goldman Sachs Global Investment Research, June 2025 - R&D = Research and Development
Source: Compustat, Goldman Sachs Global Investment Research, June 2025 - EPS = Earnings Per Share
In the AI iteration of capital intensity, we believe the playbook will definitely look different. First, the spending to date has been largely driven by a small number of cash-rich companies that can afford to take the risk. The worst outcome will not be the bankruptcies of the 2000s, where companies could not service their debts. It will instead be writing off investments with commensurate lowering of the ROIC.
Markets do not necessarily care that the impact on returns and shareholders has been negligible – Meta spent over $10 billion on VR (Virtual Reality) and AR (Augmented Reality) in its last big expenditure gamble. In the AI world, all capital expense is, at the very least, a useful expense in the sense that the capacity will be used, unlike after the cable mania of the 2000s. However the counter to this is that without significant AI revenues, the cost of AI will be pushed down to almost zero. This creates an exciting new world for the users of AI and maybe the mega scalers see that in their spending – Meta, Google, Amazon and Microsoft all have massive, unique data lakes that allow them to extend their competitive advantages.
It is for this reason that we continue to favour those companies that use AI effectively across all sectors as well as those that both build it and use it like the hyper scalers. What makes us more nervous are those companies that sit in the capex supply chain or those which only offer AI cloud infrastructure for rent, such as CoreWeave that came to the market recently. Telecom companies learned in the 2000s that it was not sufficient to own the pipes alone; we expect that AI providers will discover the same this time around. It is ownership of data that will define the winners.
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