Analysis, case studies, and news radar on artificial intelligence for product managers and product leaders. We cover frontier models, autonomous agents, governance, FinOps, and AI adoption in technology and fintech products.
Artificial intelligence in the context of product is the use of machine learning models — especially LLMs — embedded in features that solve real user problems. It is not about AI as an isolated product, but as a capability that improves existing workflows: document analysis automation, intelligent triage, content generation, and decision support.
PMs should evaluate AI through three lenses: technical feasibility (does the model solve the problem with the required accuracy?), business viability (do unit economics work with token and infrastructure costs?), and operational viability (is there governance, human review, and a defined process to scale?). The most common mistake is looking only at the first lens.
Costs vary dramatically depending on the use case. LLM calls can cost fractions of a cent per request, but scaling to the entire user base can reach tens of thousands of dollars per month in tokens. The Lovable case shows $85K in tokens to scale coding agents. The right question is not how much it costs to implement, but how much value each call generates.