An Economic Framework for Generative Engines: Advertising or Subscription?

Abstract

Generative Engines (GEs) such as ChatGPT and Google's AI Overviews are rapidly reshaping search economics by delivering synthesized responses that allow users to bypass third-party websites, cutting those sites' advertising revenue. Yet this shift also leaves GEs facing their own monetization problem: whether to insert ads into synthesized responses or keep them ad-free to drive subscription conversions. In this paper, we introduce a dynamic framework to study this problem, which captures how query-level design choices shape user engagement, retention, and subscription conversion over time. Using this framework, we show that the optimal policy follows a cutoff rule: ads should only be shown to users only when the immediate ad payoff exceeds the long-term value of providing ad-free responses. This cutoff shifts toward with-ad responses when i) ad revenue is high or ii) users are less sensitive to ads, and toward ad-free responses when iii) subscription conversion becomes relatively more valuable. In addition, the presence of rival GEs shifts the optimal policy further toward ad-free responses, as ad-heavy monetization becomes less sustainable when users can freely switch to alternatives. Our findings reveal incentives for real-life generative engine providers to adopt designs that enhance user experience and long-term sustainability.

Figures (16)

• Figure 1: Optimal monetization policy thresholds for GEs. The dashed line shows the baseline cutoff between with-ad and ad-free responses; the solid line shows how outside competition shifts this cutoff, expanding the ad-free region.
• Figure 2: Overview of the generative engine's monetization framework. The generative engine chooses between a with-ad response (yielding immediate ad payoff) and an ad-free response (incurring inference cost). Each response affects user engagement: with-ad responses reduces engagement, while ad-free responses builds it. Users then choose among the generative engine, subscription, or outside alternatives, determining long-term retention and conversion.
• Figure 3: Simulation of the optimal policy in \ref{['prop:opt_gate']} for a fixed user and ad exposure state, as AI experience increases. Left:$\mathrm{RE}_{\text{ad}}$'s total edge over $\mathrm{RE}_{\text{free}}$ and its short-term (ST) and long-term (LT) components from \ref{['rem:tradeoff']}. Right: Pre-subscription value function $V_{x}$; the dashed line marks the subscription threshold, beyond which the value equals the subscribed level.
• Figure 4: Illustration of the optimal policy cutoff across user and query types from \ref{['prop:type_order']}. Left: User ad sensitivity$\gamma$ vs. query AI experience gain$\psi$. Right: User ad sensitivity$\gamma$ vs. query ad profitability$r$. The solid curve separates $\mathrm{RE}_{\text{ad}}$ (blue) and $\mathrm{RE}_{\text{free}}$ (orange) regions.
• Figure 5: Policy cutoff shift illustration under inference cost and outside competition, at a fixed pre-subscription state $(s,c)$. Ad sensitivity follows $\gamma\sim\mathrm{Beta}$. The solid curve shows the user-type density; users to the left of the cutoff are served $\mathrm{RE}_{\text{ad}}$, while those to the right are served $\mathrm{RE}_{\text{free}}$. Dashed lines indicate the cutoff $\gamma^*$ at each parameter level, and arrows indicate the direction of the cutoff shift. (a) Varying inference cost $\kappa$; (b) varying outside competition $\omega$.

Theorems & Definitions (21)

• Proposition 1: Optimal policy as a comparison rule
• Remark 1: Short-term vs. long-term trade-off
• Proposition 2: Threshold structure over user and query features
• Remark 2: Interpretation: when to show $\mathrm{RE}_{\text{free}}$ and $\mathrm{RE}_{\text{ad}}$
• Proposition 3: Free-tier and paid-tier inference costs have opposite effects near subscription
• Proposition 4: Higher subscription price shifts the design toward $\mathrm{RE}_{\text{free}}$ near the threshold.
• Proposition 5: Intensified competition shifts the policy toward $\mathrm{RE}_{\text{free}}$
• Proposition 6: Competition shifts the type cutoffs toward $\mathrm{RE}_{\text{free}}$
• Remark 3: Subscription insulation
• Remark 4: Beyond i.i.d. queries