April 9, 2026
Warren Buffett’s observation that “price is what you pay; value is what you get” has never felt more apt. In a market still sorting through the aftermath of a historic AI-driven rally, the distinction between price and value is once again doing real work.
The first quarter of 2026 arrived against a backdrop of considerable uncertainty. Geopolitical tensions — including ongoing conflicts in Eastern Europe and the Middle East — have kept global risk sentiment unsettled. Inflation, while off its peaks, has proven stickier than many anticipated, and the path of interest rates remains a source of genuine debate as the Federal Reserve navigates a leadership transition. Meanwhile, the labor market is sending mixed signals: employment remains relatively firm, but anxiety about long-term job displacement — driven in part by automation and artificial intelligence — has begun to weigh on consumer confidence. Taken together, these forces have created an environment where investors are understandably cautious and increasingly selective about where they choose to take risk.
The first quarter of 2026 confirmed what the latter half of last year began to suggest: the narrow group of high-growth AI beneficiaries that dominated performance from late 2022 onward is no longer leading the market. That original surge ignited shortly after the release of the first widely adopted large-language-model interface in November 2022, unleashing enormous enthusiasm and capital flows into companies tied to artificial intelligence.
Over the past year, the market has been reassessing the durability of that leadership. One catalyst was the extraordinary level of capital spending required to build out next-generation data centers. Some analysts questioned whether the economics could justify the investment — especially if AI models ultimately became commoditized. We’ve already seen hints of that dynamic, with companies adopting more agnostic approaches rather than spending aggressively to develop proprietary frontier models.
Energy availability also entered the conversation. The power demands of AI infrastructure raised legitimate questions about whether existing grids could support the required build-out. At the same time, concerns about employment displacement created a more cautious public mood, prompting a second, more skeptical look at the long-term implications of AI adoption.
Competition added another layer of uncertainty. The rapid rise of new model developers challenged the assumption that early leaders would maintain their dominance indefinitely. And high-growth, long-duration companies most tied to the AI narrative depend on a backdrop of low inflation, low interest rates, and low macro uncertainty — conditions that have been in question for much of the past year.
Despite these headwinds, the overall market averages have remained resilient. What has changed is where investors are choosing to allocate capital. While many individual AI-linked stocks have experienced meaningful declines, money has rotated into companies with strong balance sheets, durable cash flows, and tangible economic value. Dividend payers, consumer staples, telecoms, miners, and industrial operators have all benefited from this shift — businesses grounded in real assets and essential services whose earnings don’t depend on uncertain technological outcomes.
As anxiety has grown about AI’s potential to replace everything from software engineers to radiologists, the value of work that is physical, skilled, and difficult to automate — whether extracting minerals, repairing infrastructure, or building homes — has become more apparent. These companies have regained relevance precisely because their earnings are visible, their assets are tangible, and their competitive positions are not easily disrupted by a software update.
Looking ahead, we do not see the underlying dynamics changing meaningfully in the near term. The macro uncertainties we described at the outset — inflation, rates, geopolitics, and employment — are unlikely to resolve cleanly in the months ahead, and markets will continue to price that ambiguity. Within the AI ecosystem specifically, the narrative is also shifting in ways that add a further layer of complexity. The first wave of AI enthusiasm was driven largely by GPU-intensive inference models, but many in the industry now believe the next phase — often described as “agentic” AI — may rely more heavily on CPU-centric architectures as models are asked to perform many simultaneous tasks rather than simply generate outputs. That evolution introduces fresh uncertainty about which companies and infrastructure players ultimately benefit, and how durable today’s leadership within the AI sector will prove to be.
Against this backdrop, companies with durable cash flows, strong balance sheets, and essential, real-world businesses continue to offer stability. We believe the market will keep rewarding those characteristics as we move through the year — and that Buffett’s distinction between price and value will remain a useful guide.
Ralph Scott
Chief Investment Officer
Craig Weston
Senior Managing Director
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L&S Advisors, Inc. (“L&S”) is a privately owned corporation headquartered in Los Angeles, CA. L&S was originally founded in 1979 and dissolved in 1996. The two founders, Sy Lippman and Ralph R. Scott, continued managing portfolios together and reformed the corporation in May 2006. The firm registered as an investment adviser with the U.S. Securities and Exchange commission in June 2006. L&S performance results prior to the reformation of the firm were achieved by the portfolio managers at a prior entity and have been linked to the performance history of L&S. The firm is defined as all accounts exclusively managed by L&S from 10/31/2005, as well as accounts managed in conjunction with other, external advisors via the Wells Fargo DMA investment program for the periods 05/02/2014, through the present time.
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