TLDR Product Management 2026-05-12

Happening this Wednesday: join Kyle Poyar and Scott Woody for a lively discussion on how to translate agentic outcomes into revenue opportunities.

• Live teardowns of AI pricing strategies from Clay, Figma, PostHog, and others

• Why the agentic buyer requires a shift from monthly recurring revenue to real-time value exchange
• Designing platform-level monetization for agentic scale

Chat with Scott and Kyle live on 05/14

Product transformations fail when teams chase OKRs, discovery, strategy, or AI before fixing delivery. If teams cannot ship quickly and reliably, everything else becomes slower, weaker, and harder to trust.

AI makes it easy to build too much, too fast. Product teams need clearer outcomes, direct customer validation and the discipline to stop work that does not move toward a Minimum Productive Outcome.

AI makes prototyping fast, but speed is no longer the designer's advantage. The real edge is intent: understanding the problem clearly before using AI to build.

Application performance is a product requirement. Product and engineering must define what “fast enough” means for key user journeys, then balance performance work against the need to keep the system changeable.

Many ID verification providers are "black boxes" built on third-party wrappers, leading to hidden latencies and brittle integrations. Learn why a vertically-integrated stack is essential to eliminate vendor-on-vendor lag, ensure architectural integrity, and truly own your user's trust by verifying the tech behind your verifier. Read the blog here.

Computers can now talk to us and do real skilled work. Software, teams, and organizations must adapt to a world where agents are no longer just tools, but active participants in how systems get built and used.

Agent harnesses are hidden AI technical debt. As models improve, today's prompts, workflows, and tool wrappers will become obsolete, so teams should keep harnesses thin, invest in durable skills and evals, and build systems they can replace easily.

Organizations need enough consistency to stay aligned, but not so much that teams lose flexibility. AI can help translate across different ways of working, as long as it does not replace the human sensemaking that creates real alignment.

AI agent orchestration can become “doom building,” draining attention while feeling productive. Most agent work should be supervised through clear scopes and review blocks, while staying deeply involved only in the creative work that needs human judgment.

Executives are investing heavily in AI, but many product teams still struggle to execute. New research shows where delivery breaks down and what high-performing teams do differently.See the data

Old work hides in backlogs, roadmaps, and defect trackers, increasing WIP and slowing delivery.

AI coding agents are shrinking months of roadmap work into days.