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You know the drill: ideas in Slack, context buried in docs, and Jira tickets you swear you saw somewhere. That was Maple too. See how they rolled out Jira Product Discovery to get ideas and prioritization into one place, plus the month-by-month playbook they used to go from one team's pilot to an org-wide standard.
AI makes execution cheaper and experimentation faster, but product expertise still matters. The advantage shifts toward judgment, customer understanding, taste, and verification.
AI vendors are increasingly marketing behavioral science, emotional calibration, and conversation design as core capabilities.
Good naming reflects clear thinking. Precise language makes systems easier to understand and can expose deeper design flaws.
AI agents have created "slop-creep" - the accumulation of unnecessary features, abstractions, and infrastructure built simply because they are cheap to produce. Without implementation friction, weak ideas survive longer, especially in large companies where software persists.
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Product managers should build engineering understanding with AI as product and engineering roles converge.
AI may create more analytics questions than it eliminates. The winning tools could look more like Excel than SaaS, giving agents a portable workspace to explore data and compound insights.
TLDR is hiring its first PM to help build the agent-first operating layer used across the company. We’re looking for a builder who has shipped real products / systems with LLMs. Click here to learn more [13].
AI can increasingly handle execution, but humans still choose the next hill to climb. Consumer AI's biggest opportunity may be helping people feel happier and more connected, not merely more productive.
AI creates little value when it simply speeds up a broken process. Companies should redesign the workflow first, then use software, agents, and humans where each is best suited.
Evals tell you whether AI works, not whether it creates value.
Workplace conformity does not always mean agreement.