Ideas Weave Every Narrative with AI.
Software delivery delays aren’t caused by lazy developers—they’re the result of broken feedback loops and systemic coordination failures. When companies add more process to fix bottlenecks, they only make the traffic worse. The real leverage point? Integrating Quality Assurance not as an end-of-line tollbooth, but as the engine of the entire pipeline.
Controllers were supposed to separate concerns, but they’ve become a procedural dumping ground for modern web apps. They entangle business logic with infrastructure, create testing nightmares, and generate endless boilerplate. It’s time to admit that the Controller pattern is a structural anti-pattern rivaling the billion-dollar mistake of null references.
Every product manager knows the visceral frustration of a client demanding impossible features by tomorrow. But what if their aggression isn’t an attack on your craft, but just unprocessed anxiety? Stop acting like a boundary-defending gatekeeper and start acting like a translator. Here’s how to turn chaotic demands into actionable frameworks.
Everyone is panicking about learning AI tools to avoid being replaced as a Product Manager. But that’s the wrong anxiety. AI isn’t eliminating PMs; it’s dismantling the low-value ‘middleman’ work of information transport. The future belongs to judgment-oriented PMs who can identify real problems and decide what *not* to build.
For years, emerging PLM vendors have tried to build cheaper versions of Siemens or PTC. That strategy is suicide. The next-generation PLM isn’t about managing drawings and BOMs—it’s about AI readiness, cross-disciplinary collaboration, and digital threads. If you’re competing on feature parity, you’ve already lost.
When AI makes implementation cheap, the product manager’s job shifts from managing resources to making high-quality judgments: what to build, when, and to what degree. Forget learning to code—become a judgment architect. This article breaks down 7 strategies from OpenAI’s Codex lead on how to thrive when building is abundant and taste is the only moat.
The most valuable output of a project isn’t the deliverable—it’s the conditional judgment of why it worked. Most teams ask if they should ‘productize’ a feature, but the real question is ‘under what conditions did this work?’ Misplaced knowledge does more harm than no retention at all.
Building an AI product isn’t just about prompt engineering. After developing Read-Box, an AI reading assistant with three collaborating Agents, I discovered that 30% of development time was swallowed by invisible, non-functional costs. Product managers must understand that architectural decisions—like choosing shared storage over event buses, or abstracting the LLM layer—directly dictate a product’s iteration speed, scope, and risk.
Alibaba just banned Claude and GPT internally, forcing 100,000 employees onto their own AI models. This isn’t a compliance move — it’s a brutal but brilliant product strategy. Dogfooding creates a high-density, zero-delay feedback loop that turns employee frustration into the most valuable product data. The short-term morale hit is the down payment on long-term market dominance.
AI isn’t going to replace all Product Managers, but it is going to wipe out the ‘low-resolution’ ones. As AI makes execution cheap and instantaneous, the cost of the wrong direction multiplies. The future belongs to high-resolution PMs who can define problems, expand solution spaces, and make high-stakes judgments.