The price of innovation
In the ever evolving world of investing, Artificial Intelligence (AI) has become a buzzword synonymous with innovation and the promise of high returns. But its integration into investment portfolios raises important questions: Should investors back companies adopting AI technologies (who isn’t, at this point?) because they might gain an edge? What about firms producing raw materials used to meet the increasing demand for infrastructure to power AI models? The list goes on.
Humans are drawn to good stories, and we're often willing to pay for the chance to be part of them. This tendency - driven by confirmation bias and optimism toward the familiar - is one reason investors gravitate toward companies with exciting narratives, hence pushing up their price relative to the underlying fundamentals of the company. These so-called ‘growth stocks’ are typically better-known, innovative, and headline-grabbing. In contrast, more traditional or overlooked businesses - perhaps due to the embedded risks inherent, or because they simply offer a less exciting story - are classified as ‘value stocks’ and generally priced more conservatively.
Understanding this dynamic is key to cutting through hype and setting appropriate long-term expectations for any innovative technology. The table below highlights relative pricing across familiar U.S. technology companies - some trading at elevated price-to-fundamental multiples, others appearing significantly cheaper.
Table 1: AI expectations embedded in market prices. Data source: iShares US Technology ETF as at 24/06/2025. Yahoo Finance. Price-to-earnings: The stock price as at the date displayed, relative to the earnings per share (EPS) over the trailing twelve-month period (TTM).
The challenge for investors is that even amazing companies with cutting-edge products don't always make great long-term investments. Markets are forward-looking - they build in expectations about the future quickly and efficiently. In today’s digital world, where news spreads quickly, stock prices adjust almost instantaneously as new information is released.
Take companies like NVIDIA, Broadcom, and Palantir - much of the excitement around AI is already reflected in their share prices. These stocks trade at high price-to-fundamental multiples, like price-to-earnings or price-to-sales, meaning investors are paying a premium today based on what they expect the companies to earn in the future. The higher the multiple, the more growth and profitability the company must deliver to justify the current price - and the smaller the margin for error. If expectations are already very high, future returns may be limited unless those companies exceed what’s already priced in.
Value stocks, such as Pintrest and HP, trade at lower prices relative to their fundamentals. This discount reflects higher perceived risk, but if the market is pricing that risk correctly, investors should expect higher returns over time to compensate them. Sticking to a disciplined strategy that leans toward these ‘boring’ stocks can be uncomfortable at times, but historically, it has rewarded patient investors willing to look past the hype.
Figure 1: Percentage of the time value stocks have beaten growth stocks, across regions. Source: Ken French Data Library © US (Jul-26), Developed ex-US (Jul-90) and Emerging (Jul-91) – three factor model to Apr-25.
In the end, the goal for systematic investors isn’t to avoid investing in innovative technologies altogether - it’s to benefit from their rise without chasing them at any price. A broadly diversified portfolio such as I advocate provides exposure to these winners as they grow, while a marginal tilt to value companies gradually reduces that exposure as expectations are lowered. This approach can feel counterintuitive, especially when a company is thriving, but it helps manage risk and stay grounded in long-term discipline. Investors still participate in the journey - just without relying on any single success story to carry their entire outcome.
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