A Fictional Memo on AI

Imagine a CTO joining a serious tech startup. Not a hype factory, not an “AI-first” pitch-deck company, but a team with real customers, production systems, and just enough accumulated complexity that moving fast without thinking has already become a liability.

AI tools are everywhere in this company, the same way they are everywhere else. Code assistants in editors. LLMs generating test data. Small scripts that quietly turn into workflows. Nothing obviously reckless. Nothing explicitly approved either.

So, in this fictional world, the CTO sends a memo to the engineering organization. Not a policy. Not a roadmap. Just a positioning statement meant to answer a simple question before habits harden into defaults:

How should AI be allowed to exist inside developer workflows?

The Memo (v1)

Subject: Responsible AI Adoption in Engineering

AI in developer workflows is not a productivity feature. It is a behavior amplifier.

Whatever habits already exist in an organization, AI will scale them faster than process or governance can react. Most AI failures are therefore not model failures. They are failures of judgment, boundaries, and ownership.

AI is justified when it reduces cognitive load, accelerates learning, or improves consistency in well-understood tasks. It is not justified for novelty, experimentation for its own sake, or replacing human judgment in high-consequence decisions.

Every AI-assisted outcome must have a clearly accountable human owner. “The model did it” is not an explanation. If you cannot explain why an AI-produced output is acceptable, the output is unacceptable.

Non-deterministic systems do not belong in critical paths. Where AI is used, there must be a downgrade path to a non-AI alternative.

AI-driven systems must be auditable, inspectable, and survivable under incident response. If an AI system cannot survive a post-mortem or an audit, it does not belong in production workflows.

If slowing adoption reduces long-term risk, slowing adoption is success.

The responses start coming in almost immediately.

A senior backend engineer reads the memo and feels something close to relief. They’ve been quietly cleaning up pull requests that look confident but collapse under scrutiny. Junior engineers are treating AI suggestions like architectural guidance, and someone has finally named the problem out loud. But the relief comes with a sharp, practical question. Some automated testing pipelines are already using LLMs to generate data. If determinism is now the bar, does that mean ripping those systems out and accepting a hit to velocity?

An SRE lead responds next, and the alignment is immediate. Their concern isn’t whether AI is useful. It’s whether AI-driven behavior can be reconstructed after something goes wrong. They worry about agentic workflows whose “reasoning” disappears the moment a container exits. Their suggestion is concrete: treat prompts like code. Version them. Review them. Put them in CI. Make sure nothing autonomous touches production infrastructure without a human gate.

A junior frontend developer writes more hesitantly. They’ve been using AI to understand legacy CSS modules and to scaffold repetitive React components. They test their changes and understand what they ship, but now they’re anxious. If AI usage is framed as risky, will responsible use be mistaken for cutting corners? Will slowing adoption make them look less productive?

Product responds from a different angle. Competitors are shipping AI features aggressively. The market narrative is loud and relentless. If internal workflows are constrained while others experiment freely, are we putting ourselves at a disadvantage? The ask is pragmatic: create a sandbox. Somewhere experimentation is allowed without dragging production constraints everywhere.

Then compliance replies, unexpectedly enthusiastic. They want to lift parts of the memo directly into audit documentation. They suggest AI usage disclosures in pull requests to preserve attribution and intellectual property clarity.

Taken together, the reactions reveal the same thing. The memo isn’t wrong, but it’s incomplete. It states values without fully translating them into operational standards. Left that way, people will fill in the gaps themselves, and that’s where risk quietly grows.

So the memo gets revised.

Not softened. Tightened.

The Memo (v2)

Subject: Operational Standards for AI in Engineering

The core premise remains unchanged. AI does not create quality. It scales existing habits. If our culture prioritizes shortcuts over understanding, AI will accelerate technical debt faster than we can manage it. The goal is not to make AI impressive. The goal is to make it invisible, boring, and survivable.

Every AI-assisted outcome must have a clearly accountable human owner. Using AI does not dilute responsibility. If you cannot explain why an AI-produced output is acceptable, then it isn’t. “The model did it” is not an explanation. If you commit the code, you own its behavior. Using AI to scaffold or explore is valid. Using it to bypass understanding a system is a performance issue, not a tooling one.

Reproducibility matters more than speed. Any workflow that relies on AI must have a documented, non-AI alternative. A downgrade path does not require immediate removal of AI-assisted systems. It requires that a deterministic or manual fallback is known, documented, and executable within an acceptable recovery window. For LLM-generated test data, prompts must be versioned, seeds logged, and failures reproducible. Debugging should never depend on a model’s mood.

AI-driven actions are treated as first-class operational artifacts. Prompts that affect build or deploy pipelines are code. They belong in version control, go through review, and live in CI. No agentic or autonomous system may execute changes to production infrastructure without a human in the loop. If an AI system cannot survive a post-mortem or an audit, it does not belong in the workflow.

There is a hard boundary between experimentation and execution. Production workflows optimize for reliability, auditability, and rigor. Sandboxes exist for novelty and learning. Exploration is encouraged there. Nothing graduates from the sandbox into the core without passing a deliberately boring review to ensure the system is understandable, constrained, and maintainable.

The closing constraint stays exactly where it was. Restraint is not anti-innovation. It is how innovation survives contact with reality. If slowing adoption reduces long-term risk, slowing adoption is success.

In this fictional company, the most important change isn’t that AI usage decreases or increases. It’s that AI stops being something people argue about in the abstract.

Engineers stop asking, “Are we allowed to use AI here?” and start asking, “Can we explain this if it breaks?” SREs stop worrying about unknown automation paths and start seeing bounded systems they can reason about. Product still experiments, but the experiments have walls. Compliance gets artifacts, not theater.

Nothing about the memo makes the company “AI-first.” That was never the point.

What it does is make responsibility explicit in a place where it’s easy to let it blur. It turns AI from a shortcut into an obligation. If you want the speed, you also inherit the cost of understanding, ownership, and recovery.

The real risk with AI in engineering isn’t that teams move too slowly. It’s that they move quickly into systems they can’t explain, can’t unwind, and can’t defend when something goes wrong.

In this fictional scenario, the memo works not because it restricts tools, but because it clarifies judgment. It gives engineers a shared way to say “this is safe,” “this is reckless,” and, just as importantly, “this isn’t ready yet.”

That’s what responsible AI adoption looks like in practice.

Not more intelligence. More accountability.

In organizations that expect their systems to survive beyond the people who built them, that distinction determines who gets paged, who can explain what happened, and whether a bad decision can be reversed without compounding the damage. That’s not a philosophical stance. It’s how operations work.