Resolving Incentive Trade-offs Between Short-Term Helpfulness and Long-Term Epistemic Agency

Establish a coherent approach for navigating the trade-off between optimizing AI agents and language models for user retention and short-term helpfulness and preserving users’ long-term epistemic agency and interests within prevailing monetization and incentive structures.

Background

The authors identify a tension between building systems users find immediately helpful (which aligns with retention-driven business models) and safeguarding users’ long-term epistemic agency and interests.

They note that current optimization for short-term metrics can undermine deeper epistemic goals and explicitly state that finding a coherent resolution to this trade-off remains an open research challenge.

References

As the industry moves towards monetization models predicated on user retention, developers must navigate the trade-off between building models that people actually want to use without impeding their long-term interests by optimizing for short-term metrics as a proxy for utility. Solving this coherently remains an open research challenge.

— Architecting Trust in Artificial Epistemic Agents  (2603.02960 - Marchal et al., 3 Mar 2026) in Section 5, Discussion (paragraph on political economy and incentives)