YOUR AI SHOULD KEEP A DIARY
An AI activity log makes invisible work easier to review and improve. It helps users delegate more effectively, preserve context, and build stronger AI habits.
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An AI activity log makes invisible work easier to review and improve. It helps users delegate more effectively, preserve context, and build stronger AI habits.
Chinese models are less of an economic threat than restrictive US AI policies. America should expand access to powerful models and support open innovation.
Leaders often reshape customer feedback to confirm existing beliefs. Teams should test their assumptions and consider that customers may mean exactly what they say.
Successful AI adoption requires technical understanding, problem knowledge, evolving practices, and trust. Without a credible social contract, resistance is rational.
Requirements often prescribe solutions instead of identifying real needs. Asking “why” and testing small increments helps teams preserve options and find simpler answers.
A good life is built through repeated action, not insight alone. We become better by practicing virtue, pursuing meaningful goals, and investing in relationships.
Fear often hides behind reasonable excuses.
AI's value is spreading beyond frontier labs.
AI will reshape product management, not eliminate it. A broader technology downturn poses a greater threat to PM jobs than automation.
AI makes convincing prototypes easier to build, but polish can create a false sense of product readiness. A clear decision framework helps teams define what each prototype can prove, gather the right evidence, and prevent experimental code from becoming an accidental production commitment.
Great products help users flourish rather than merely capture attention. Shared purpose, careful research, and continuous iteration keep teams focused on that goal.
AI is replacing routine PM work, not strategic product judgment. The PMs who thrive will focus on choosing the right problems and creating measurable value.
A strong product vision captures the lasting positive change a product aims to create, rather than describing features or business goals. Concise, emotionally engaging, and collaboratively developed, it motivates teams and provides stable direction for product strategy.
Customer data can reveal what to investigate, but rarely what to build. Rich, story-based interviews provide the context teams need to make better product decisions.
Early momentum can hide the details that determine whether a product succeeds. Rigorous testing and continuous iteration turn promising technology into something reliable.
Code Yellows help teams resolve serious engineering problems before they become emergencies. Clear ownership, measurable exit criteria, and transparent communication keep the response focused and temporary.
AI conversations reveal what users want and where products fall short.
Improving teams often uncover hidden demand, causing their backlogs to grow again.
Companies risk surrendering proprietary knowledge as their employees use and improve external AI systems. To retain that value, enterprises must control their data, feedback, evaluations, memory, and learning infrastructure while remaining independent from any single model provider.
AI's greatest promise is not simply saving time. It gives more people the ability to create, experiment, and turn their ideas into reality, making individuality more valuable than technical expertise, capital, or permission.
AI can generate endless design options, but it cannot determine which one best serves a product's ambition and context. Strong design still requires a responsible leader who can apply taste, resolve tradeoffs, and confidently choose a direction.
Graphlit built a powerful production platform but failed to become a scalable business because it lacked repeatable distribution and a clear, outcome-driven market position. Its core insight remains: agents need a portable context layer that stays separate from the runtime while supporting every stage of the work with durable organizational knowledge.
AI outputs vary from one interaction to another, so traditional feedback methods often provide an incomplete picture. Product teams should focus on users' next actions, review a representative range of real outputs, and capture the context behind poor results.
Low-margin businesses could become some of AI's biggest winners because modest cost reductions can dramatically increase profits. The greatest opportunity lies in embedding AI into existing operations to remove coordination work without requiring employees to change how they work.
AI's value depends on whether it moves software assets toward healthier economics, not simply whether it makes teams faster.
Clean, reliable data has little economic value unless it leads to better decisions. Data teams should define the decisions each product is meant to improve and measure that impact alongside traditional quality metrics.
AI makes execution cheap, so the advantage shifts to teams that manage attention and judgment well.
Meta pulled Muse Image after its lack of clear consent controls sparked immediate privacy and misuse concerns.
Meta should embrace AI to strengthen what it already does best: connecting, entertaining, and monetizing human attention.
AI makes coding faster, shifting the bottleneck to product discovery and specifications. Turning recorded conversations into human-reviewed specs helps teams build the right products at greater speed.
AI products win by turning usage and customer workflows into proprietary knowledge. Simple tools learn faster, but complex enterprise products can become much harder to replace.
As AI makes intelligence abundant, the ability to embrace mental effort will become increasingly valuable. People and institutions must use AI to strengthen independent thought while cultivating the curiosity, discipline, and ambition needed to pursue difficult work.
Product management judgment comes from experience: preserve key insights, work through the evidence, stay curious when priorities shift, and remain calm during organizational change.
Great teams solve problems, not specifications. They learn early, adjust scope, build working versions, and keep everyone aligned on the project's true state.
AI products should show essential outcomes and decisions first, while keeping settings, reasoning, and activity details one click away.
AI features gain adoption by delivering immediate value, fitting existing workflows, and making the first successful action easy.
Replace estimates with small, outcome-focused investments that grow only when results justify them.
Prioritize work that builds both impact and influence, and decline tasks that advance neither.
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AI made shipping 10x faster. But when teams can ship daily, the advantage is no longer speed alone. It's knowing what to build.
Product metrics are valuable only when they answer a meaningful question and lead to a clear decision. Instead of tracking generic or convenient numbers, teams should define specific measures tied to the user behavior and outcomes they want to change.
Rapid AI progress could unlock unprecedented abundance while also concentrating power, disrupting society, and creating major security and governance risks.
Understanding human nature helps you capture attention, communicate value, inspire action, and succeed across changing industries and technologies.
As AI makes building dramatically faster and cheaper, product work is becoming less about implementation and more about choosing the right problems, evaluating possibilities, and maintaining coherence and quality. The strongest contributors will pair deep expertise with agency, adaptability, and taste, using AI to explore broadly while applying human judgment to deliver exceptional products.
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Which flavor of barcode, ID, or label scanning best fits your use case? Scandit's Agent Skills answer that in your team's AI coding tool: describe what you're building ("warehouse picking app on Android"), get a recommendation grounded in 15 years of real integrations, then let the agent build it.
Burnout often stems from the belief that we should always be working, improving, or maximizing our potential. Recovery requires questioning that pressure, setting boundaries, resting without justification, allowing mental stillness, and deliberately deciding when something is complete.
As AI handles more defined tasks, career success will depend on choosing meaningful problems, building a strong reputation, and delivering exceptional results.
While AI might not accelerate design projects, it boosts productivity by helping teams align prototypes, specs, and documentation. This frees up time for better decision-making - a task where human judgment, context, and taste remain essential.
Deep thinkers thrive by separating self-worth from work, adapting with intention, and protecting the curiosity that makes their work meaningful.