Resilient Content in the Age of AI: (Part 2) The New Valuation Logic of Media M&A

One question is becoming central for investors in media assets: How much value depends on content that will be disrupted by AI?

Part 1 of this series argued that generative AI is rapidly worsening the signal-to-noise ratio. The same stories can now be repackaged almost instantly by multiple outlets, sometimes directly from public sources such as government announcements and press releases. Traffic-dependent media businesses built around replicable content and weak user relationships are vulnerable.

The M&A landscape shows the cost of this perception: several high-profile digital media businesses once valued largely on audience scale have exited at levels materially below their peak valuations. Industry analysts now talk of publishing moving along a "distress curve", with restructuring increasingly priced into transactions.

There are important exceptions. Leading (and politically signifjcant) B2C publications with strong brands and paying audiences are still highly valuable. But such assets are scarce. Across the wider market, investor interest is shifting towards B2B events and intelligence products, both of which appear more resilient to AI-driven competition.

Value of select digital media company exits

Source: Axios research

What still commands a premium multiple

A closer look at deal data, however, shows dispersion widening. Investors still reward high-quality, recurring revenue and high-value audiences; but specialist communities, in-person experiences and proprietary data are becoming the main drivers of premium valuations. Because corporate clients rely on these products for critical decisions, they often become embedded in workflows and have lower churn. Their usage data can also improve the core offerings and support adjacent products such as benchmarks, alerts, APIs, events and advisory services.

S&P Global’s acquisition of With Intelligence is one signal of this shift. Despite its deep editorial roots including more than 100 specialist journalists, its transformation into a private markets data and analytics platform appears to have been the principal valuation driver. S&P Global reportedly paid $1.8bn, 13.8x revenue.

Informa TechTarget illustrates a different version of the same strategy. It converts specialist technology content and direct audience relationships into purchase-intent data, helping vendors identify where demand is forming, which accounts to target and which messages will resonate. It uses specialist content to maintain their event clients engaged throughout the year.

The emerging winners look less like conventional content businesses and more like proprietary information infrastructure. Their value lies in the functional utility it performs for customers.

Where the upside lies

Many publishers are weighing how to balance two opportunities: building their own intelligence products and licensing content to AI companies. Analysis prepared for the Professional Publishers Association (PPA) suggests, however, that licensing accounts for less than 3 per cent of publisher revenue on average - and players outside the top tier struggle to reach that level.

Deals with AI players are moving beyond one-off archive sales towards ongoing access arrangements with real-time feeds. Investors should compare the deals on the table against the potential value of new products and use cases that could feed from their content assets.

The opportunity extends beyond licensing. The process of collecting information, documenting its provenance and verifying facts (the fundamentals of reporting) increases confidence in the resulting data and creates monetisable value. Media businesses can also extract latent value from information they already generate: interview notes, archives, event registrations, session attendance, networking connections and commercial interactions. Combined with user-generated data, these assets can support predictive modelling.

The diligence questions

Investors should assess five dimensions of an information-based business:

• Content resilience. Does it offer proprietary information or trusted analysis? Does it anticipate trends? Does provenance matter?
• Product stickiness. Is it embedded into workflows or habits? Does it benefit from network effects or facilitate in-person relationships?
• Zero and first-party data. What is the strategy to for collecting and using this information? Can the business generate signals that demonstrate intent or have predictive value?
• Discovery risk. Which channels matter most? How dependent is the business on non-branded search referrals? Is it What would severe disruption to referrals from search mean for advertising and long-term subscription revenues?
• Potential for efficiencies and automation. How much of data gathering could be automated? How much of the content is actually creating value?