What follows is from an LLM session w/ Claude Code, where I was discussing some of the tensions in the current world. This accurately represents where I think things are heading. Is this a perfect analogy? Absolutely not. But it’s directionally interesting and indicative of the levels of change I think we’re looking at in the next 1-3 years.

A Tuesday in 2027

You open your morning dashboard. Not a PR queue - an outcomes board. Overnight, 47 changes shipped across three services. You don’t see diffs; you see deltas in error rates and latency percentiles. Two anomalies flagged. You don’t ask “what changed” - you ask “does it matter?”

One matters. You click through to the spec governing that service. It’s prose: “Payment authorization must retry idempotently on gateway timeout.” Below it, acceptance scenarios. Below that, revision history - not of code, but of the spec. Last human edit: six weeks ago.

You dig into the traces. The gateway client is creating fresh connections on every request - an agent optimization from last month that made sense for memory pressure but traded away latency. You add a constraint to the spec: “Prefer persistent connection pooling over per-request allocation.” An agent picks it up, confirms the approach, estimates 40% improvement, flags it rollback-safe. You approve. Twenty minutes later, it’s in prod. p99 drops.

The team room looks different. Whiteboards show boundary diagrams and capability maps, not class hierarchies. People argue about domain boundaries, failure modes, SLOs. Nobody argues about implementation patterns - that’s agent territory.

A feature request comes in: subscription gifting. The team sketches capability boundaries, amends existing specs, drafts new ones. A junior engineer pairs with a senior on the gifting spec. “You missed a race condition. What happens if two gifts target the same recipient simultaneously?” That’s the apprenticeship now - learning to specify clearly, anticipate edge cases, read telemetry. Problem decomposition and constraint articulation.

The specs feed into a bet register - goals ranked by expected impact, queued for agent execution. No Jira tickets for implementation. The orchestrator decomposes, farms out, coordinates.

The codebase is large. Nobody fully knows it.

There are zones of comprehension - core domain logic where humans still reason about implementation. And vast tracts of glue that work, are tested, are observable, and are effectively write-only. Nobody feels bad about this. It’s not tech debt. The code is clean. It’s just not human-legible as a primary concern.

When something breaks in the write-only zones, you don’t debug. You tighten the spec, add a constraint, regenerate.

Here’s the uncomfortable part: you ship code you’ve never seen to millions of users. You trust the specs, the tests, the telemetry, the rollback. You’ve traded comprehension for leverage.

The old guard calls it reckless. You call it the same leap we made from hand-optimized execution plans to SQL optimizers/query planners - you don’t debug the planner, you rewrite your query or add hints.

The question isn’t “do I understand this code?” It’s “do I have the right feedback loops to know if it’s working?”