Blue Raspberry Slush

Majority of new code is written by agents that begin each session without the theory the codebase depends on. This essay traces the resulting decay to a shift in the binding constraint, from writing code to judging it, and outlines the operating model for teams working this way.

Enterprises approve AI on cost savings but renew and expand it on growth, and those are two different decisions made by different people. Build a narrow, ROI-defensible entry point for procurement and an expansion surface that makes users more capable over time.

The standard narrative holds that junior workers are most exposed to AI-driven role change. Career stage predicts the outcome, but the order is approximately reversed. The mechanism is specific human capital: expertise accumulated through tenure that earns a wage premium precisely because it does not transfer, making seniority a proxy for how costly the transition is.

The management layer in product organizations translates information across specialist boundaries that engineers, designers, and product managers cannot cross directly. AI lowers the cost of a single person holding enough of each specialization to work without a translator, removing the function that justified the layer. What survives is the context-setter, a manager whose job is placing a small, capable team in the competitive environment it cannot observe from inside the build cycle.

Generative AI adoption in logistics operates at two thresholds that most analyses conflate: the sector-level barriers suppressing entry into adoption, and the intra-organizational barriers preventing adoption from compounding into systemic disruption once it begins.

When a user opens a new tool, pokes around for seven minutes, and closes the tab, product teams log it as churn. That reading is almost certainly wrong. This post argues that the real failure is in how the learning phase was designed, not the product itself.

Applying SaaS metrics to a platform produces measurements of the wrong thing: commercial metrics describe the outcome of market-clearing processes, not the processes themselves. Fill rate, utilization, and time-to-match capture whether markets are clearing; CAC, churn, and LTV cannot reach that question until failure has already compounded. The required discipline is market design, not product management.

Brand power is usually explained as storytelling, aesthetics, or emotional resonance, but none of those accounts predicts when trust holds under adversity or collapses without warning. The mechanism is constraint, the accumulated pattern of choices that could have gone otherwise and did not, which is what eliminates the need for re-evaluation at each encounter. The same logic governs character in individuals and, most legibly in current product design, the alignment decisions that determine what a conversational AI will and will not commit to.

High-performing teams report more problems than weaker ones. The standard explanation credits psychological safety, which is partly right but misses the mechanism. Communication's primary function is making reasoning visible enough that peers can correct it before it ships.

The most important product decisions are often made wrong, and the tell is how teams handle surprises. Slack, Twitter, and Instagram were all found by following something the team hadn't designed for. Rick Rubin describes why that's not luck, and why standard product methodology is built to prevent it.

Ken Kocienda’s Creative Selection is best read as a field report from Apple’s golden decade, when the iPhone and iPad were forged by half‑dozen‑person teams iterating on working software rather than circulating slide decks. The organizing principle is a tight four‑step loop -build, demo, critique, iterate - run by the same engineers and designers who will ultimately ship the code.

Exceptional work emerges through continuous iteration rather than a single flash of insight. Ed Catmull points to Pixar's practice of "reworking a scene until it sings" as evidence that greatness comes from active refinement.

Our day-to-day decisions are often guided by defaults. Defaults often go unnoticed, they shape our choices and limit our awareness. In a product management context, these impulses cause us to double down on a flawed hypothesis, dismiss ideas too quickly, or not consider possibilities that are known to us otherwise. Clear Thinking aims to solve that.