Public market investors are facing a daunting challenge as AI agents become a significant source of revenue for software companies. The issue is not just about the growth rate, but about the underlying volatility profile of these AI-driven revenues. According to a recent article, traditional metrics like customer acquisition cost and lifetime value break down when customers are AI agents that have no loyalty or memory.
The Volatility Trap
Traditionally, companies could lose their biggest customer and take a revenue hit, but this was an expected and measurable phenomenon. Measures like retention and churn told us in aggregate what we should expect. However, AI-driven revenue concentrates differently. When coding agents select authentication providers or payment processors, they're following patterns learned during training by a handful of foundational models. For example, if OpenAI re-trains GPT and shifts toward a competitor's API, every application generated after that update defaults to the new choice. Revenue can shift substantially toward whoever appears most frequently in the newest training data.
This is not just an issue for developer tools. When a procurement AI used by large enterprises re-trains on updated vendor data, it can shift overnight from selecting one supplier to another. There's no loyalty buffer, no switching cost, no relationship to preserve. The selection follows training data patterns rather than economic indicators like recessions or recoveries. This makes stress-testing nearly impossible using conventional models.
The Transparency Gap
Investors evaluating a company with significant agent-driven revenue can't rely on historical recession performance or standard sensitivity analysis. But right now, there's no way to measure exposure because companies don't disclose it, and oftentimes don't measure it. Many companies cannot even tell you the disparity between human versus agent revenue because they aren't tracking it.
Segmented reporting would change this. I suggest companies break out revenue by source: human-driven purchases, agent-driven integrations, and hybrid transactions (where humans approve agent recommendations). For agent-driven revenue, they should disclose concentration across foundational models and estimate what percentage depends on current training data distributions versus locked-in enterprise contracts.
This will require analyzing integration patterns, tracking the transactions that originate from AI tooling, and estimating exposure to foundational models. Yes, it's complex, and some resistance is to be expected. Management teams may cite the high technical cost of tracking agent patterns. It might also just be impossible to measure unless the agentic "user" self-discloses.
Investor Implications
For public market investors, the practical question is how to price the current opacity. One approach is to apply industry comparables if a software company cannot or will not articulate what percentage of revenue comes from agent-driven integrations. The comparable could result in a discount or a premium. A discount reflects two risks: concentration exposure and the possibility that management doesn't understand their own customer base well enough to measure it.
A premium might be applied if it's clear that a company has some unfair advantage in agentic user acquisition or if their customers are retentive for some other qualitative reason. Vertically integrated software remains the exception. When a company sells directly to a single institutional customer, a hospital system, a government agency, or a large bureaucratic enterprise, the end user is still human. Those customers don't churn based on LLM training cycles.
However, even vertically integrated businesses may have products that include agent-driven components. A healthcare software platform, for example, might include AI assistants for physicians. Investors need to understand where the agents sit in the value chain and how much influence they have over which features get used. This is a new form of due diligence that will only grow in relevance.
Making Disclosure Standard
The gap between what companies report and what investors need to know is widening with agentic AI coming on stream. It is redefining competitive advantage, and the question for investors is how to properly price an asset when the user base is poorly understood. Waiting for FASB or the next market correction is a passive choice that creates opportunities for savvy investors.
I believe that more accurate reporting is better for our financial markets, because it will lead to more efficient capital allocation to the companies that deliver value to their customers. It's time for companies to take a proactive approach to transparency and provide investors with the information they need to make informed decisions.
Sources
This report was synthesised from the following coverage:
