Podcast

Collins named what he observed incorrectly. "Humility" is really *integrity* — and this isn't a semantic issue. With renaming, Level 5 becomes applicable to the AI era: a showy CEO can still be Level 5 if they have integrity; a CEO who looks humble but isn't consistent with truth isn't.

Of the 11 companies Collins called 'great,' a third have collapsed or lost serious value since the book came out. Why? And does his definition of 'great' — durable, 3x market outperformance over 15 years — still make sense when AI-native companies run on acceleration rather than steady velocity?

I haven't read Good to Great and I don't intend to read it linearly. This is an experiment: reading books in the AI era — a few days of focused dialogue with AI, critique, distillation into blog posts, then listening back. Not lazy reading, but a new way to engage with knowledge for a time when there are too many books and too little time.

Biology and AI engineering have independently arrived at the same design principle: no system that needs to adapt can be allowed to converge permanently. The mechanisms behind hedonic adaptation in humans mirror the mechanisms behind exploration schedules in agents. Lessons for how you build organizations, products, and yourself.

Habits are not essence — they are weights, accumulated across tens of thousands of repetitions in your neural network. This essay places environment design, attention training, and cognitive reframing alongside Hebbian learning, predictive coding (Friston), constructed emotion (Barrett), and three distributed brain networks (DMN/SN/CEN) — to show why 'knowing but not doing' is not a bug, but a feature.

The company of one is no longer a lifestyle slogan. Some operators are running multi-million-dollar P&Ls alone with AI. But most of the supporting data is pre-AI, and the new configuration is shaping something not yet named: one person operating the scope of a multi-functional SME, while simultaneously birthing many new specialized roles. This essay is for Vietnamese tech operators considering leaving the CTO chair — a deep analysis of why this model is more viable than ever, why most who try will still fail, and what Vietnam's special position is.

The previous essay was about the fate of programmers. This one goes one step further: if writing code is just a small link in the chain of software production, what happens to the whole chain when AI swallows it link by link? And does the phrase 'software company' still mean anything?

Reasoning to the end: when AI writes code better than humans and humans lose incentive to develop the skill, what happens to humanity's understanding of software? And if you don't want to drift with the current, what should you invest in?

Smart people don't motivated-reason less — they motivated-reason more elegantly. IQ is a multiplier: it scales intent in whichever direction intent is already pointing, right or wrong. On the four forms of motivated reasoning in high-IQ people and why every purely-internal solution fails.

Most engineers who plateau at mid-level for a decade aren't held back by intelligence, time, or opportunity. They're held back by their inability to look honestly at what they're missing. This isn't a technical problem — it's an integrity problem. And in the age of AI, this problem is becoming dangerous in ways we've never seen before.

Inception walks us step by step through how an idea gets planted in the subconscious: why you have to go into a dream, the rule of the simplest possible version, layered architecture, the Mr. Charles gambit, the safe scene, totems, and the lesson of Mal. Each of Cobb's techniques maps to a concrete design decision for anyone building AI agents — and a concrete ethical responsibility.

Two old principles just became the most important skills of the next decade. AI amplifies good judgment and amplifies bad judgment. The difference is the ability to return to basic truths.