Reading Notes from My 2023 Shelf: Strategy, Curiosity, and Better Product Decisions
How revisiting strategic classics like Good to Great, Start with Why, Range, and classic sci-fi sharpened my framework for technical leadership and AI product engineering.
When technological paradigms shift as rapidly as they have with generative models and autonomous tooling, it is tempting to consume only the latest documentation, arXiv preprints, and developer blogs.
Yet whenever I feel overwhelmed by the velocity of technical change, I return to my bookshelf. In late 2023 and throughout 2024, revisiting a handful of foundational books on business strategy, human range, and purpose gave me the mental scaffolding needed to make clearer, more durable engineering decisions.
Here are the key frameworks from that shelf and how they directly influenced our software architecture and product strategy.
1. The Engineering Flywheel: Good to Great by Jim Collins
In Good to Great, Jim Collins describes the Flywheel Effect: greatness is not achieved through a single miraculous innovation, dramatic reorganization, or sudden breakthrough. It is the result of pushing a heavy, massive flywheel turn after turn, building steady momentum that eventually compounds into unstoppable velocity.
In software engineering, teams frequently fall into the trap of looking for silver bullets: rewrites, new frameworks, or throwing AI at every legacy workflow.
Applying the Flywheel concept to our iOS codebase meant focusing on compounding fundamentals:
- Fast, Reliable CI/CD: Shaving two minutes off the pull request build time pays compound dividends on every developer interaction.
- Strict Concurrency Hygiene: Eliminating data races systematically prevents intermittent Heisenbugs that derail sprints months down the line.
- The Stockdale Paradox: Confronting the brutal facts of our current architecture (such as technical debt or flaky UI tests) while maintaining unwavering faith that our disciplined system will prevail.
2. Purpose Over Tooling: Start with Why by Simon Sinek
Simon Sinek’s Start with Why argues that people and organizations do not truly buy what you make; they buy why you make it.
During the height of the AI frenzy, nearly every engineering team was asking: What cool AI capability can we add to our app this week?
Sinek’s framework forced us to flip the question on its head:
- Why does this user open our app in the first place?
- What friction or anxiety are they trying to resolve?
- Is an LLM actually the best way to solve this, or would a well-indexed search database and a polished three-tap interface serve them better?
Starting with the user’s Why saved our team hundreds of hours of wasted engineering effort. We deliberately scrapped two speculative AI prototypes because they were impressive technical solutions looking for a non-existent user problem.
3. The Power of Broad Thinkers: Range by David Epstein
In Range: Why Generalists Triumph in a Specialized World, David Epstein demonstrates that in complex, uncertain domains (which he calls “wicked learning environments”), individuals who have broad experience across diverse disciplines outperform narrow, over-specialized experts.
Modern mobile and AI engineering is the quintessential wicked environment. You are not just writing Swift; you are balancing:
- On-device Core ML neural engine thermal limits.
- Backend streaming WebSocket protocols.
- Human-computer interaction (HCI) and visual feedback loops.
- Business unit economics and cloud API token costs.
Encouraging “range” inside our engineering organization meant actively supporting engineers in cross-training: iOS developers learning cloud infrastructure, backend engineers exploring SwiftUI ergonomics, and technical leads studying behavioral psychology. The resulting cross-pollination produced far more resilient architectural designs.
4. Cosmic Perspective: The Last Question by Isaac Asimov
While not a business book, Isaac Asimov’s short story The Last Question explores humanity’s multi-generational relationship with artificial intelligence and universal entropy. It is a sobering, philosophical reminder of the long arc of computational evolution.
Reading Asimov keeps daily engineering stresses in perspective. A failed build, an unexpected API regression, or a stressful sprint is just a tiny data point in a vast technological continuum. It reminds us to approach our craft with curiosity, playfulness, and patience.
Conclusion
The greatest risk for software engineers in an era of rapid technological acceleration is becoming hyper-competent at building the wrong things.
By grounding our work in proven principles of compounding momentum (Good to Great), clear customer purpose (Start with Why), and cross-disciplinary curiosity (Range), we can build software that stands the test of time.