By Igor Pejic, a technology strategist and the author of Tech Money
Picture by Grok
What a difference a few weeks can make. Experts who recently warned that AI was a dangerous bubble, pointing to exorbitant tech valuations as proof, are now calling many of those same stocks a bargain. They were wrong then, and they’re wrong now.
Like with most blunders in tech investing, the root problem lies in applying traditional value investing metrics to assess emerging technologies. At the moment, tech stocks appear to be cheap in terms of price to earnings ratios, despite an impressive earnings growth. This trend is primarily carried by semiconductor stocks. Within one year, Micron went up by 770%, Sandisk by nearly 4,500%, to mention just two prominent examples. Despite this exceptional growth, their forward P/E ratios are at 10 and 13 respectively, about 10 points below the S&P500 average. Even NVIDIA is cheaper than the market average. And the big five tech stocks together no longer enjoy a valuation premium over other stocks. So, the narrative sounds irresistible. Yet if something sounds too good to be true, it usually is.
When explosive growth stocks trade at modest multiples despite massive run-ups, the market isn’t being wrong or simply risk-off. It’s expressing skepticism about the growth trajectory. And growth is the heart of every technology asset. In this case, the market doubts whether the potential AI demand will be sufficient for the picks-and-shovels providers to earn back the massive capital expenditures. Do compressed upside expectations mean that the AI boom is over? Far from it. But they do mean that the growth momentum will soon be shifting to another part of the technology’s value chain.
In every technological revolution, there are two main, subsequent periods of growth. First, tremendous investments are poured into building out the infrastructure. In the 1990s companies that built high-capacity fiber-optic networks, routers, and switches took center stage. But once most of the groundwork is finished an inflection point comes at which infrastructure growth slows down. The application layer takes over. Another growth cycle begins. And this cycle promises even better returns. In the internet economy, for example, most value was captured by companies like Amazon or Google, not Cisco or Sun. In the case of AI, growth momentum is now shifting from chips and datacenters to vertical companies applying and monetizing the powerful models.
Evidence of this shift can be found in the P/E ratios of many companies that are building on the AI infrastructure to solve specific problems. Their valuations reflect expectations that the second growth phase is in reach. In the case of a general-purpose technology such as AI the list of such potential beneficiaries is long. Think Palantir (defense tech), Generate Biomedicine (biotech), or Intuitive Surgical (robotics). We don’t know if these companies will transform their momentum into good returns, but their valuations multiples move in lockstep with revenue growth. Obviously, investors have decided that the AI race is increasingly turning to the application of AI rather than building the base capacities.
Investor sentiment is a powerful indicator, which sometimes still can be wrong. Yet when it aligns so well with other trends as in this case, the fundamental shift is clearly starting. Technological advances in the base models are becoming smaller and more expensive. The gap between capital expenditure and model revenue is widening. Circular financing among AI heavyweights is paving the road to overcapacity. At the same time corporate adoption on the application layer is exploding.
The transition from the infrastructure to application era is a long process. Infrastructure giants like Intel are still priced as enormous growth opportunities. Unexpected technological breakthroughs could revert the trajectory. And many application players have their own challenges. Revenues are often still poor in absolute numbers. ROIs are poorer still. This makes investments into applied AI much more speculative. And while historically returns of top application performers eventually beat those of top infrastructure performers, the mathematical odds of backing a winner are significantly smaller. In every tech age a handful of competing giants builds the infrastructure, but a myriad of companies populates it.
So how then should investors act on the big beginning AI shift? The goal is to start rebalancing your portfolio slowly but consciously. Semiconductor champions and hyperscalers still deserve a prominent place in most tech portfolios but their contribution plays a different role. They are moving from the super-ambitious bucket to a more modest growth bucket. It is a similar development to what cloud giants went through a couple of years ago. After the cloud computing foundation was built, many SaaS stocks have skyrocketed in value. Yet up to this day the cloud market continues to enjoy double-digit yearly growth. Companies like NVIDIA, TSMC, or SpaceX have already soared to the top of the market cap chart. While total addressable markets in the tech world can swell quickly, it is hard to imagine another period of hypergrowth for these goliaths. They can thus no longer serve as asymmetric bets. Over the next months and years AI application players in various industries will increasingly rise to play that role.
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Igor Pejic is an author, keynote speaker, and banker. His latest book Tech Money uncovers the new rules of investing in the technology age and teaches investors and executives how to benefit from them. His previous title Blockchain Babel won the Independent Press Award and was profiled as a Financial Times book of the month. Pejic publishes the industry newsletter The New Frontier, and his articles and interviews regularly appear in media such as the New York Times, the American Banker, or Bloomberg. Pejic has held different management positions in banking and payments, currently at one of the largest banking groups in Europe.