The End of the "Fast Coder" Era: Why Systemic Adaptation Beats Raw Velocity

Kitze argues that the era of the "blazing fast" individual contributor is over. The competitive advantage has shifted from raw coding speed, once the hallmark of elite developers, to the ability to engineer self-correcting agentic loops. This shift exposes a hidden consequence: engineers who cling to their manual technical prowess are becoming the primary bottlenecks within their own organizations. The implication is a culling of talent where the ability to coast on legacy skills vanishes, replaced by a requirement to manage AI-driven workflows. For tech leads and engineers, the advantage now lies not in how fast you can type, but in how effectively you can architect systems that unblock themselves, a transition that requires shedding ego and embracing the discomfort of a different operating model.

The Hidden Cost of "Fast" Solutions

Most engineers view AI as a tool to speed up existing tasks, but Kitze argues this is a surface level application that ignores the deeper systemic shift. The real leverage is not in writing code faster; it is in building "police files," which are self-correcting loops that ensure an agent output is verified at every turn. When an engineer relies on manual prompting without these structural safeguards, they are essentially gambling on luck.

"Many people do it like maybe every day or every week or every month and then it is too late. You need it on every turn. The turn ends the agent self corrects everything."

-- Kitze

This creates a divergence in outcomes: those who treat the agent as a black box to be prompted repeatedly will eventually hit a ceiling of frustration. Those who treat the agent as a system to be engineered, by capturing frustrations as static rules and skill files, create a feedback loop that compounds in value over time.

Why Teams of Ten Are Collapsing into Teams of Two

The traditional organizational structure, where a team of ten engineers manages a codebase, is under pressure because the unit of productivity has changed. Kitze notes that in many environments, the work previously requiring five people can now be managed by one or two individuals who know how to drive agentic swarms.

This creates a downstream effect: organizations will naturally lean toward smaller, more capable teams that prioritize unblocking over traditional task execution. The rude awakening for many senior engineers is that their deep technical knowledge, if not paired with the ability to orchestrate these agents, becomes redundant. The system is responding by routing around the stubborn expert who refuses to adopt agentic workflows, effectively pushing them out of the value creation loop.

"Most of the companies are not fang... They are just regular companies who want to get shit done and those people if five of them were needed there now maybe one would be needed to drive agents."

-- Kitze

The 18-Month Payoff: Why Depth Beats FOMO

The most common trap for engineers today is the constant migration between models and tools, chasing the latest smarter release. Kitze warns that this shallow engagement is a competitive disadvantage. By constantly switching, you never build the internal mini-frameworks or refined prompt-engineering skills that turn a generic model into a specialized tool for your specific domain.

The advantage belongs to the engineer who picks one provider and goes deep. While the fast crowd spends their time migrating setups and relearning interfaces, the deep engineer builds a library of rules, memory files, and hooks that make their workflow increasingly autonomous. This is an uncomfortable investment, as it feels slower in the short term, but it creates a durable, compounding advantage that is impossible to replicate by simply jumping to the next hyped model.

Key Action Items

• Build Your "Police Files": Stop prompting in a vacuum. Start capturing every frustration or recurring error into a static rule file or skill document that your agent reads at the start of every turn. (Immediate)
• Stop Opening the Terminal: If you find yourself manually intervening in the terminal, treat it as a system failure. Ask yourself what rule or instruction is missing that would have allowed the agent to handle that task independently. (Over the next quarter)
• Commit to One Model/Provider: Ignore the "Composer 2.5" hype cycle. Pick one capable model and spend 3-6 months mastering its quirks and building your internal library of agentic structures. (12-18 months)
• Audit Your Ego: Identify where you are resisting AI because you enjoy the manual work (e.g., writing boilerplate or manual debugging). If you are not willing to delegate the fun parts, you are the bottleneck. (Immediate)
• Adopt the "Unblocker" Mindset: Shift your focus from being the person who writes the code to being the person who ensures the agentic workflow is never stuck. Your value is now defined by your ability to remove friction from the system, not by your lines of code. (Ongoing)