1000 Iterations Beat One Perfect Plan
00<br>A Paradigm Shift for AI Engineers<br>The operating model for AI engineers is changing because three things moved at the same time: frontier models became good enough to produce serious code, developer tokens became too cheap to optimize around, and iteration speed collapsed the cost of being wrong.<br>Key shift: the highest-leverage AI engineer is no longer the one who writes the most complete plan and one-shots anything. It is the one who can produce 1000 real working artifacts fast, extract signal from them, and cherry-pick the best ideas into a high-quality final product they understand and own.<br>Cheap tokens change the engineering loop. Developer inference is mostly a one-time build cost, not a user-scaled runtime cost.<br>Many attempts beat one perfect plan. You learn more from ten working artifacts than from one static spec.<br>Quality still needs ownership. Vibe mode is how you discover the solution; quality mode is how you ship it.
01<br>Fast AI Generated Slop Is the New Engineering Superpower<br>The old framing was that AI generated code was disposable: fast, plausible, and usually not something you wanted to own. That was fair for a while. It is becoming less true for coding. Strong frontier models can now produce real, production-shaped code at a speed that changes how we should work.<br>By slop, I mean fast AI-generated artifacts that may not be production-ready yet, but are useful enough to learn from, compare, and mine for code.<br>The speed is the key. If an agent can produce a serious working artifact in minutes, the output does not need to be perfect to be valuable. It can reveal the shape of the problem, expose hidden gaps, suggest architecture, and give you code worth cherry-picking into the version you actually ship.<br>This is not a side tool you occasionally reach for. It changes how you explore, evaluate, discard, and rebuild software. You should be building your entire workflow around tons of AI generated slop. The key is separating vibe mode from quality mode. Vibe mode generates breadth and information very quickly; quality mode takes the vibed outputs and turns that into an extremely high-quality production output you understand and own. This separation is the key.<br>The useful framing is simpler: frontier models can now produce high-quality code very, very quickly. That speed is the advantage. If you build a workflow around it instead of resisting it, the leverage can feel like 100x.
02<br>Be Comfortable With Major Rewrites<br>New paradigm for AI engineers: implementation is no longer precious by default. If agents can produce serious alternatives quickly, your job is to generate evidence, reverse bad decisions, and restart from the best information available.<br>Disassociate yourself from the implementation. Each architectural decision carries less sunk cost and less tech debt than it used to. Ask one question: does this decision produce the highest-quality system? If not, change it, even if that means a major rewrite. Your architecture is much more fluid now.<br>This reverses a lot of old software instincts. Build first, inspect the artifact, then decide whether the architecture deserves to live. If the database, framework, data model, or product shape is wrong, change it. A decision that used to feel permanent can now be tested with a serious alternative in 10 minutes.<br>Database choice: spend 30 minutes trying a second database instead of debating it for half a day.<br>Framework choice: generate the same feature in two stacks and compare real code, not opinions.<br>Full rewrite: restart cleanly, cherry-pick the useful 90% of components, tests, API shapes, copy, styles, and edge cases, and discard the wrong structure.<br>The math is simple: a messy end-to-end artifact in 30-60 minutes plus a clean rewrite with references can beat hours of trying to plan the perfect architecture upfront. The first version is not the asset; the learning, references, and cherry-pickable code are the asset. Do not over-optimize decisions upfront when you can reverse them in 10 minutes.
03<br>One-Shotting Is Holding You Back<br>Optimizing for one plan, one spec, and one implementation is increasingly a self-imposed bottleneck. If generating implementations is cheap and fast, the winning move is not to make one output perfect. It is to build 100 outputs, inspect them, and cherry-pick the best ideas from each one.<br>The key part: it does not take 100 times longer to implement 100 solutions instead of one. Agents can explore in parallel, in branches, in sandboxes, or overnight. The constraint moves from typing the code to knowing how to judge the outputs.<br>In my opinion, 100 working outputs that you can compare and steal from will almost always beat one precious output that had to be right from the beginning. Every artifact teaches you something: a better abstraction, a cleaner UX, a sharper data model, a hidden edge case, or a direction you should avoid.<br>We are entering a time where generating 100 implementations is...