State of AI coding ⬇️ Two days at the @aiDotEngineer Code Summit in NYC felt less like a conference and more like a working prototype of 2026. And 2026: The Year the IDE Died. That was literally the title of @Steve_Yegge and @RealGeneKim's talk. So what has actually changed in AI coding? ▪️ 1. From "The Diver" to "The Ant Swarm" We’ve hit the limits of the “bigger context window” dream. Steve Yegge’s “diver with an oxygen tank” metaphor nails it: even with a million tokens, the LLM-diver still runs out of air and slides into “nitrogen narcosis,” hallucinating and losing the thread. @dex_horthy calls this the “Dumb Zone,” the middle 40–60% of huge windows where reasoning collapses. "God Model" approach is dead. The future is an ant swarm. ⬇️ ▪️ 2. Context Engineering (the discipline everyone now practices) The winning pattern is now a factory of tiny specialists: - Planner Ant - Research Ant - Coder Ant - Tester Ant Each ant sees only what it needs. No pollution, no confusion, no Dumb Zone. Your job becomes Architecting the Ant Farm and keep the new canonical flow - RPI Loop: Research → Plan → Implement ▪️ 3. Proactive Swarms unlock true parallelism Agents will work while you sleep, updating deps, fixing breaks, and leaving verified PRs. You become the Orchestrator, defining constraints as dozens of agents run concurrently – a 100x leverage shift from typing to directing. ▪️ 4. Verification as the real moat @EnoReyes said: “Many tasks are easier to verify than to solve,” and @mkanat framed the enterprise challenge: “We spend more time reading code than writing it, and even more so now.” Models still lack verification context. The next decade of agentic infrastructure will be built around systems that can prove their own correctness. 🔹 5. Artifacts replace chat: The interface is shifting to living artifacts – diagrams, plans, and visual diffs – that evolve as agents work. --- If the last decade was about humans typing into buffers, the next one is about a new set of physics autonomous workers operating inside verification chains. The ant swarm is already here. But the thing is: missing context, flaky tests, and tangled dependencies turn AI into a chaos multiplier. Clean environments and engineers who can think like orchestrators and collaborate with swarms will accelerate the best. Read the full piece for more on how we can map this new reality into a stack: turingpost.com/p/aisoftwares…