Recent market discussions around Salesforce have reignited a broader debate across the software industry. The issue is often framed as an “AI disruption problem,” but that framing is imprecise. What markets are reacting to is not a deterioration of software demand, but growing uncertainty around how software value will be monetized in an AI-driven operating model.
Morningstar analysts have noted that recent declines in software stocks reflect market overreaction to AI fears rather than weak fundamentals, with some well-known names trading at discounts to their fair value despite solid earnings and guidance.
In particular, Morningstar commentary highlights that:
- software stocks sold off dramatically on fresh fears that AI could upend traditional licensing models, even though fundamentals remain strong;
- many software companies look undervalued relative to fair value, and their selloff may represent buying opportunities once narratives shift;
These insights underscore the idea that the current sell-off is driven more by sentiment ripples than structural business decay.
However, this concern deserves to be taken seriously.
From Seat-Based to Value-Based Consumption
AI does not reduce the importance of software platforms. In many cases, it increases dependency on them. What it does change is how software is consumed.
AI-driven workflows:
- replace repetitive human interaction with automated execution
- shift usage from user-facing interfaces to system-to-system calls
- increase transaction volumes while reducing visible user counts
- raise the business criticality of software without increasing “seats”
From a financial perspective, this creates a mismatch. Revenue models built around users and licenses per employee no longer map cleanly to actual value delivered. Software may be more essential than ever, yet appear less monetizable under legacy pricing logic.
This is precisely the tension investors are reacting to.
The Financial Debate Is About Pricing Power, Not Relevance
The current market narrative often conflates two very different questions:
- Will AI make certain software categories obsolete?
- Will AI weaken pricing power for software vendors?
The first is largely unsupported by evidence. Enterprise software remains deeply embedded in business processes, data architectures, compliance frameworks, and operational risk management. Replacing these systems is costly, slow, and risky.
The second question, however, is valid.
If pricing remains tied to static metrics, like users, seats, or simple subscriptions, then AI-driven efficiency gains can indeed translate into downward pricing pressure. This is not unique to Salesforce. The same discussion applies to ERP, engineering software, automation platforms, and security solutions across the market.
What Changes in the Software Architecture Itself
Addressing this challenge is not a marketing exercise. It is an architectural one.
Software vendors must be able to:
- measure actual usage beyond human interaction
- differentiate features, capabilities, and consumption levels
- protect high-value functionality from uncontrolled use
- align pricing with outcomes, scale, and risk exposure
In short, monetization must follow how software is actually used, not how it was previously used.
This is where licensing, protection, and entitlement management move from being back-office mechanisms to strategic components of the product architecture.
Why This Matters for Software Companies Now
AI accelerates everything: deployment, automation, integration and also margin erosion if monetization does not keep pace.
Companies that fail to adapt risk a paradoxical outcome:
growing adoption, increasing dependency, and shrinking pricing leverage.
Companies that adapt can do the opposite:
turn automation, scale, and AI-driven usage into defensible, auditable, and monetizable value.
The Role of CodeMeter in This Transition
CodeMeter was designed for exactly this kind of transition.
By decoupling monetization from static user models, CodeMeter enables software vendors to:
- implement feature-based, usage-based, and consumption-based licensing
- protect intellectual property even in highly automated environments
- enforce licensing consistently across cloud, edge, and embedded systems
- retain control and transparency as software usage becomes less visible and more machine-driven
In an AI-driven world, the question is no longer whether software is used, but how, how much, and under what conditions. CodeMeter provides the technical foundation to answer those questions and to turn the answers into sustainable business models.
Closing Thought
The current financial debate is not a warning that software companies are losing relevance. It is a signal that software economics are evolving.
AI is not breaking software businesses.
It is breaking assumptions.
The vendors that recognize this early and align their technology, licensing, and monetization strategies accordingly will not just weather the transition. They will define the next phase of it.
This article is for informational purposes only and should not be construed as financial or investment advice. The author does not endorse or recommend any particular stocks, investments, or financial strategies. Readers should conduct their own research and consult with a financial advisor before making any investment decisions.