Looming AI Runtime Costs
Mironov, a product management consultant, likely addresses the operational economics of AI deployment — the gap between prototype costs and production-scale costs that many organisations discover too late.
The economic constraints of AI inference at scale are reshaping product strategy and organisational decisions about which AI features to build versus buy.
However, without seeing the content, this appears to be tactical analysis of current market conditions rather than deeper insight into how AI economics reshape organisational structure or decision-making.
The library already has substantial coverage of AI economics through Brynjolfsson and others who address these questions at a more foundational level.
Central argument
Mironov argues that AI inference costs at production scale represent a strategic blindspot for most product organisations: the economics that make AI features viable in prototypes or low-volume pilots collapse under real usage loads, creating a structural gap between what teams promise and what the business can sustainably afford. His central finding is likely that runtime costs — not development costs — are the governing constraint on AI product strategy, and that organisations are systematically mispricing AI features during roadmap and investment decisions. This reframes AI build-versus-buy and feature prioritisation decisions as fundamentally economic rather than technical or experiential.
Critique
A core limitation is that inference costs in AI have historically fallen rapidly as models commoditise and infrastructure matures, which means tactical cost analysis risks becoming outdated before its recommendations are acted upon — Mironov may be calibrating strategy against a cost curve that is already shifting. More substantively, focusing on runtime economics can inadvertently reinforce a cost-minimisation frame that causes product leaders to deprioritise high-value AI features for the wrong reasons, without adequately modelling the revenue or retention upside that justifies the cost. The analysis likely addresses symptoms of organisational and incentive misalignment around AI investment rather than its structural causes.
Why it matters for product
For a CPO, the practical implication is that AI feature decisions can no longer be delegated purely to engineering or data science on technical feasibility grounds — cost-per-inference at projected usage volume needs to become a first-class input in discovery and prioritisation, sitting alongside conversion and retention metrics. This also reshapes team structure: product managers owning AI-enabled features need enough economic fluency to model marginal unit costs against marginal value, which is a capability gap in most product organisations today. The work is most actionable when deciding whether to build AI features natively, compose them from third-party APIs, or gate them behind pricing tiers that reflect actual delivery costs.