Ten items are on today’s radar, and one challenged an assumption I had been carrying without questioning it. The rest of the list points in the same direction: fewer model announcements, more discussion about how to sustain what has already reached production.

In brief

Every AI feature has three cost layers: development, inference at scale, and retraining or adjustment when the model begins to drift. Prioritizing only by impact and effort leaves a relevant part of the equation out. For AI product work, the yardstick needs to include impact per inference call, model selection by stage, and controls for quality, feedback and governance.

One question has become routine in my roadmap discussions: how much does it cost to run this feature a thousand times a day?

Today I read an AI product strategy guide for 2026 that put its finger exactly on that sore spot. The central point is simple: every AI feature has three cost layers, and most teams see only one.

The first is development, which everyone estimates. The second is inference at scale, which almost no one includes when prioritizing. The third is retraining and adjustment when the model begins to drift, and that usually appears only after the feature is already in production.

This breaks an assumption we have carried for years. Traditional software has a near-zero marginal cost: if usage doubles, the cost barely moves. With AI, it does not work that way. Every call has a price.

I work on the product side of structured credit, and this logic changes how you look at a workflow. Automating the reading of a collateral document or checking an invoice can make complete sense in a pilot and fail to add up when volume multiplies. The reverse also happens: a low-visibility process, at the right volume, can pay for the whole operation by itself.

That is why the prioritization yardstick needs to change. It is not only impact over effort; it is impact per inference call. This is an AI product management decision: model the cost alongside the expected outcome instead of discovering the constraint after a feature goes live.

It is also worth choosing the model by stage, because many tasks do not need the top-of-the-line option. In composite workflows, that choice is part of the design of AI agents: each stage needs a model compatible with its volume, risk and type of decision.

The guide ends with a line that stayed with me: the challenge of 2026 is no longer adoption; it is execution. Without quality metrics, a feedback loop and AI governance, a feature leaves a beautiful demo and reaches production with unstable results and rising costs. The AI risk matrix helps calibrate controls, human confirmation and evidence when automation enters more sensitive workflows.

None of this makes me less excited about AI. It only makes the conversation more mature, and that is how these things become real products.

For anyone who wants to read the complete guide, it is worth the time: The 2026 AI Product Strategy Guide.

The rest of the radar

Kimi-K3 on Hugging Face — an open, frontier multimodal model with 1M context changes the build-versus-buy equation and the unit cost of AI features. Read more

Dynamic Workflows in Claude Code — automatic orchestration of parallel subagents shortens technical discovery and refactors, but drives inference cost up sharply. Read more

Step 3.7 Flash — another fast, low-cost model in the queue: latency and price per token continue to fall, so it is worth revisiting roadmap cost assumptions. Read more

Anthropic releases Opus 5 — near-frontier performance at half the price, but with high-error incidents reported on the same day; vendor dependence is a product risk. Read more

Robinhood lets AI agents trade stocks — a real case of an agent with permission to take irreversible action; a reference for how far to extend agent autonomy in your product. Read more

Noisy LLM evaluators still improve agents — challenges the excuse that “we do not have a good enough eval”: even a noisy metric can already guide roadmap decisions. Read more

AISlop, a CLI for detecting AI code smells — with AI-written features at scale, code quality becomes a future-speed risk, and it is now measurable. Read more

Study maps dark patterns in AI chatbots — optimized engagement in a conversational product can easily turn into manipulation: a brand, trust and regulatory risk. Read more

Final MCP spec, LangGraph 1.0 and Bedrock AgentCore in GA — the infrastructure layer for agents is becoming standardized, and architecture decisions made now become easier to reverse later. Read more

That is it for today. If you are changing prioritization today, bring the inference bill into the conversation.