IT is a perpetual cycle of creating new features (and thus new problems) and then troubleshooting them, often at inconvenient times. The seamless experience of a simple app like TikTok masks immense, layered complexity that requires constant maintenance and problem-solving.
Related Insights
To prevent burnout from constant AI model releases, GitHub's product leader treats his team like athletes who need rest for peak performance. This includes rotating high-stress roles, proactively increasing headcount, forcing focus on only the top three priorities, and enforcing recovery periods.
Overly structured, workflow-based systems that work with today's models will become bottlenecks tomorrow. Engineers must be prepared to shed abstractions and rebuild simpler, more general systems to capture the gains from exponentially improving models.
Artist's CPO notes that while frameworks and processes can feel productive, the best product work is often messy and uncomfortable. It involves fighting with stakeholders and making bets on uncertain features rather than fixing known, smaller issues. This contrasts with the idealized view of smooth, process-driven development.
According to the 'dark side' of Metcalfe's Law, each new team member exponentially increases the number of communication channels. This hidden cost of complexity often outweighs the added capacity, leading to more miscommunication and lost information. Improving operational efficiency is often a better first step than hiring.
Products are no longer 'done' upon shipping. They are dynamic systems that continuously evolve based on data inputs and feedback loops. This requires a shift in mindset from building a finished object to nurturing a living, breathing system with its own 'metabolism of data'.
Visionary creators are often tortured by their own success. By the time a product launches, they are already deep into developing its superior successor and can only see the current version's flaws. This constant dissatisfaction is the engine of relentless innovation, as seen with Walt Disney.
Saying yes to numerous individual client features creates a 'complexity tax'. This hidden cost manifests as a bloated codebase, increased bugs, and high maintenance overhead, consuming engineering capacity and crippling the ability to innovate on the core product.
A killer app for AI in IT is automating tedious but critical tasks. For example, investigating why daily cloud spend deviates by more than 5%. This simple-sounding query requires complex data analysis across multiple services—a perfect, high-value problem for an AI agent to solve.
Companies racing to add AI features while ignoring core product principles—like solving a real problem for a defined market—are creating a wave of failed products, dubbed "AI slop" by product coach Teresa Torres.
The era of winning with merely functional software is over. As technology, especially AI, makes baseline functionality easier to build, the key differentiator becomes design excellence and superior craft. Mediocre, 'good enough' products will lose to those that are exceptionally well-designed.
