Software: Collateral Damage of AI?

For several months, artificial intelligence has emerged as the dominant lens through which markets assess the technology sector. Software, in particular, is often portrayed as the likely casualty of this revolution, with some narratives going so far as to predict a broad, inevitable disruption of all existing models. This widely amplified view has helped drive a sharp dispersion in stockmarket performance: players seen as directly exposed to AI have been embraced, while more traditional software publishers have faced a clear loss of investor interest. Yet a more rigorous analysis suggests these fears are, in many cases, overstated.

The origins of this narrative largely rest on extrapolations drawn from consumer software, then indiscriminately applied to far more specialized segments. However, not all software follows the same economic logic. In particular, vertical markets, operate in environments where context, regulatory constraints and operational risks play a decisive role. Treating these segments like general-purpose applications is to ignore the real sources of their resilience.

AI undeniably changes the economics of code. It drastically reduces the cost and time required to develop, maintain or modernize an application. Interfaces, generic features and even certain workflows can be reproduced or improved with increasing efficiency. Taken in isolation, software as a product therefore becomes easier to copy. Although this often-cited observation leads to a hasty conclusion: confusing the ease of replicating an application with the ability to replace a critical customer relationship. 

Share-price performance of several software companies over the past 12 months:

In many specialized professional software categories, code is not the core competitive advantage. What keeps customers goes far beyond the tool itself. First, there is regulatory compliance, constantly updated and embedded into processes. In areas such as accounting, tax, payroll or administrative management, an error is not a simple technical glitch: it triggers immediate financial, legal and reputational consequences. Offloading that risk to a proven vendor represents major economic value - and AI does not eliminate it.

Historical data reinforces this inertia. They are theoretically transferable, but migrating them is complex, risky and operationally costly. Reconciliation risks, continuity breaks or non-compliance create friction that far exceeds the price of the software itself. In this context, offering an alternative that is marginally cheaper or technologically more modern is not a sufficient argument to prompt a switch. 

Workflows are another key point. AI can help reproduce standard processes, but in niche markets these workflows are often the product of decades of adjustments, field feedback and deep understanding of the profession. They reflect not only what customers do, but also how and why they do it. This embedded operational knowledge is difficult to formalize-and even harder to replace quickly, even with advanced AI tools.

Finally, trust remains central. In critical environments, customers above all seek reliability, accountability and continuity. Even if AI can theoretically master complex rules, few organizations are ready to entrust vital systems to a player with no track record, no proven support and no clearly assumed responsibility. Credibility built over time remains an intangible but decisive asset.

Overall, artificial intelligence weakens the value of code as a barrier to entry, but it does not erase the economic foundations that explain customer loyalty in vertical and specialized software. These players do not sell only an application: they sell compliance, domain expertise, institutional memory and a transfer of risk away from the client company. Under these conditions, AI looks less like an existential threat than a tool capable of improving existing products without undermining the deeper reasons for their relevance.

Viewing the entire software sector as uniformly threatened by AI reflects an overly simplistic reading. The real question is not whether software is technically reproducible, but what truly anchors the customer relationship. Where accountability, trust and operational risk take precedence, fears of widespread disruption appear largely exaggerated.