Insights & Resources

Future earnings matter because they determine the present value of a company. The challenge—and the source of volatility—comes from predicting those future earnings. Investor opinions on the outlook can change daily and meaningfully.

Near-term earnings expectations are typically anchored in the previous quarter’s results and management guidance. In the U.S., companies stood out this season, reporting 14% earnings growth year-over-year, with over 70% delivering an earnings surprise. Yet even this strong performance failed to reassure investors of a bright future. Many have grown anxious due to speculation that AI agents could replace traditional software, leading to a 30% selloff in software stocks since the September 19, 2026, high. This is a bold claim that may or may not
materialize. History offers many examples of similarly dramatic predictions—most of which proved far less
extreme in reality.

A recent example was the market’s reaction to President Trump’s tariff announcements in April last year. Investors initially assumed the tariffs would be implemented in full and cause severe economic harm, including profit collapses and possible bankruptcies. In the end, the tariffs were smaller and less impactful. Moreover, the U.S. Supreme Court recently ruled aspects of the tariffs unconstitutional. Despite the early panic, 2025 ended up being one of the strongest years for equities, with global markets (MSCI World Index) returning 22%. Another familiar episode was the COVID-19 pandemic in 2020. Markets plunged on fears that society would not return to normal and that mass casualties were inevitable, drawing comparisons to the Spanish flu of 1918–1920. Obviously, today’s healthcare and medical capabilities are far superior to those of a century ago, but during moments of panic those advantages were not fully appreciated.

Thankfully, the worst-case scenario did not unfold, and stocks sharply rebounded, finishing the year with a 17% gain. Today, investors are pricing in a worst-case scenario for software companies but not applying the same assumptions to demand for AI hardware, resulting in simultaneous price declines for both AI disruptors (Nvidia, AMD, Broadcom, etc.) and the companies being disrupted (Microsoft, Salesforce, Palantir, etc.). This imbalance is putting valuation pressure across the broader tech sector. History suggests that once extreme scenarios are fully priced in, subsequent returns can be meaningful—so long as reality proves far less severe than feared.

What we do know is that fully replacing software with AI agents would require enormous computational capacity (GPUs) and substantial power infrastructure. Even if the technology were ready, scaling that infrastructure would take time. A useful comparison is full self-driving technology, which has struggled to become mainstream despite highly advanced underlying capabilities. In addition, many software companies operate with very little financial leverage, giving them the flexibility to invest and reinvent their business models when needed. It is also important to recognize that companies and sectors will not be affected uniformly. It is difficult to imagine a company like Microsoft losing 27% of its market value over a few months after repurchasing $18 billion of its own shares, paying $24 billion in dividends, introducing its own AI agent—Copilot—alongside enhanced capabilities across Windows and Teams, and most importantly, reporting record revenue. Software versus AI agents is a risk worth monitoring, but not a reason to panic.

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