Editor’s note: Today’s post is by Jonathan Woahn, Co-founder of Cashmere.io, which helps publishers safely and responsibly monetize their content with artificial intelligence. Reviewer credit to Chef Tim Vines.

In Part One, The Missed Expectation, we argued that AI training will not become the durable revenue engine publishers hope for. While uncomfortable, this conclusion isn’t pessimistic. The real economic opportunity lies not in how models are trained, but in how they are used.

In this context, usage is the inference stage: the moment an AI model applies its training to solve a specific user problem. Clients pay to have these problems solved, not for the training data itself.

But inference monetization doesn’t arrive by default. Unlike the lump-sum payouts and corpus delivery for training deals, inference happens when the AI accesses and processes individual articles; capturing this usage requires new infrastructure and norms. Monetizing inference requires a clear understanding of how value is created and captured when content no longer reaches users in its original form.

In this second part, we focus on the path forward. Not a theoretical one, but one grounded in historical precedent and emerging reality. AI is not destroying the content economy; it is forcing it to evolve. To understand that evolution, we must first look to the past.

The Great Reallocation

When technology disrupts an industry, the way the product is consumed and the way consumption is paid for fall out of sync. Demand may even rise while revenue from established payment routes collapses. Only later do new revenue models allow producers to recapture value.

Music is the canonical example. Napster greatly expanded listening, but demolished revenue from CDs and records. So even though musicians continued making music, and users listened more than ever, the industry was unable to monetize this new form of consumption. Finally, after a period of turbulence, services like Spotify and Apple Music emerged with models that reconnected listening and compensation under a new consumption paradigm.

This pattern now applies to scholarly and professional content. Demand for authoritative knowledge has not declined: researchers, students, and professionals still rely on trusted sources to do their work.

What has changed is how that knowledge is delivered. Instead of directly reading articles, users increasingly ask AI systems to locate, synthesize, and report back on relevant work. Like the music industry in the Napster era, the delivery system has changed, but the revenue models are yet to stabilize.

Publishing is not at the end of its economic arc; it is in the turbulent middle of a reallocation.

Reallocations are often framed as cataclysmic and are very uncomfortable for those inside the affected industry. In practice, they are remarkably consistent. The table below shows how this reallocation unfolded in music and online video — and how it is now unfolding in publishing.

Table 1: The Reallocation Pattern

This Reallocation Is Different

In every prior reallocation, the decisive moment came before monetization stabilized — when infrastructure was beginning to form, but norms had not yet locked in. What followed depended on whether industries correctly understood the shift they were actually facing.

AI places publishing at that same inflection point, and where we go from here will depend on how well we understand the shift we are facing. This time, it is not merely one of distribution.

Previous reallocations changed how content was packaged and distributed, but the content itself still reached the end user largely as the creator intended. A song moved from vinyl to CD to streaming, but listeners still heard the original recording. A film moved from theaters to DVDs to online video, but viewers still watched the work as originally produced.

AI breaks that continuity.

Content can now create value without ever being presented to the user in its original form. Ideas are extracted, combined, and reassembled into new outputs. Users benefit from the underlying works without directly encountering them. When value is created this way, revenue from that value does not automatically flow back to its sources.

History offers a playbook for how platform transitions unfold. What it does not offer are solutions for the latest reallocation, the route to reconnecting how product is paid for with how it is consumed. That is the problem the next section addresses.

Three Leading Economic Models Emerging for Publishers

If history is a guide, the market will not converge on a single economic model. Instead, it will stabilize around a monetization mix — coexisting approaches aligned to different sources of demand and control.

That mix is already forming in AI-driven inference. While implementations vary, three approaches are now emerging with clarity.

1. Pay-Per-Use (PPU)

The PPU model itself is straightforward. When a user consumes content through an AI platform, the platform pays the relevant publisher for the content that informed the response.

Crucially, the platform is not the economic consumer of the content. Users are. Through their queries, engagement, subscriptions, or attention, users signal which sources matter. The platform aggregates those signals and compensates publishers accordingly.

This is how Perplexity’s Premium Data for All program operates. When licensed publisher content is used to generate an answer, Perplexity compensates the publisher on a per-use basis. The license is narrow and temporary, but it is paid — and it scales with demand. Better sources improve answer quality, which drives usage and, in turn, supports the subscriptions and upgrades that fund content access.

The same pay-per-use logic can be funded by advertisers rather than users. ProRata.ai offers a clear example. ProRata applies an ad-supported model to AI inference: ads are displayed contextually alongside AI-generated answers, generating revenue on every query. That revenue is split 50:50 with publishers whose content contributed to the response. Users receive free access, advertisers fund the system, and publisher compensation remains tied directly to per-query usage.

In both cases, the unit of value is the same: content contribution at inference time. What differs is the source of funding.

While the economics are simple, the infrastructure is not. Attribution, contribution weighting, fraud resistance, and transparent reporting must operate reliably at query-level granularity. The closest analogue is pay-per-click advertising, as implemented by Google. As with PPC, Pay-Per-Use can scale quickly — but only once pricing logic, attribution, delivery, and enforcement infrastructure are in place.

2. Bring-Your-Own-License (BYOL)

BYOL models work differently. Here, the user already holds rights to the content — typically through an institutional or professional subscription — and chooses to connect those rights to an AI platform.

A clear example is Wiley’s partnership with Perplexity. Institutions that already license Wiley content can authenticate within Perplexity and access the material their existing agreements permit. In this arrangement, the AI platform acts as an interface layer, not a reseller, and the commercial relationship between publisher and customer remains unchanged.

The defining feature of BYOL is portability. The publisher sells the license to the customer; the customer decides where to exercise those rights. Like Netflix, a subscription is not tied to a single interface or device. BYOL extends that portability into AI-driven workflows, preserving pricing, trust, and institutional relationships while making platforms more useful to licensed users.

The constraint is reach. BYOL inherits the limits of existing entitlements, which means the completeness of AI-generated answers may vary across users or institutions depending on what content their subscriptions allow the system to access. In research settings, this creates a real risk: relevant work can be silently excluded if it falls outside the licensed corpus.

For that reason, BYOL works best as a portability layer for known, licensed collections — not as a complete solution for discovery when access must be created at the moment of need. That gap is what the next model addresses.

3. Licensing on Demand (LOD)

The third model centers on unlocking content at the moment it is discovered. While specific implementations are still emerging, several major publishers are actively developing Licensing on Demand offerings and are expected to bring them to market soon.

In Licensing on Demand, users do not need to hold licenses in advance, as with BYOL, nor rely on their AI platform to have pre-negotiated access, as with PPU. When valuable content surfaces during an AI interaction, access can be licensed immediately, in context, and under terms set directly by the publisher.

The core shift is timing: discovery comes first, and licensing follows immediately thereafter. Instead of forcing users to leave the AI experience or guess which subscription might apply, access is created precisely when the need becomes clear.

If Pay-Per-Use monetizes what users already consume, and BYOL extends licenses users already hold, Licensing on Demand enables access exactly when users — via AI-powered discovery — realize they need it.

Norms Worth Locking In Early

The AI reallocation right now offers publishers a rare opportunity: a chance to set the rules before they harden.

For the last two decades, search engines like Google and marketplaces like Amazon established defaults that publishers largely had to accept after the fact. AI is different. The ecosystem is still forming, and the terms are not yet fixed. That creates an opening to agree on how value should move.

From the publisher viewpoint, these norms should be stated early and often:

Inference on premium content should be paid.
If authoritative content improves AI’s answers, the publisher should be compensated when the content is used.

Attribution should be standard.
Source visibility benefits users, preserves trust, and reinforces publisher value.

Usage should be transparent.
Markets work when participants can see what is happening. Measurement enables pricing, investment, and accountability.

Direct relationships should remain intact.
AI can intermediate access without permanently distancing publishers from their audiences or customers.

These norms are straightforward. They align incentives, support sustainable markets, and reflect how publishers already operate elsewhere.

AI systems depend on high-quality content, and publishers still control the creation of that content. The earlier these expectations are reinforced across the industry, the more likely they are to become the defaults that shape what comes next.

Conclusion

AI is not going to pay for content the way many publishers once hoped. Training AI models will not become a broad, recurring revenue engine. AI will pay for the information it needs when conducting inference — when it accesses high quality information and brings that value to the end user.

The publishers who engage now — by aligning on shared expectations, supporting emerging models, and investing in common infrastructure — will help shape how knowledge is discovered, credited, and paid for in the AI era.