Do Not Learn To Code

“The work is mysterious and important.” — Harmony Cobel

“Learn to code” is a slogan, a bumper sticker, a meme. It is the mantra of departing gatekeepers mourning the evaporation of their moat.

Learning to code in order to fix errors in AI code, is like learning the law to correct your lawyer during trial. 

“Learn to code” is a necessary competence myth: the idea that in order to use a tool or supervise effectively, one must be able to replicate or outperform.

Someone doesn’t have to be:

• A chef to run a successful restaurant.

• A composer to direct an orchestra.

• A surgeon to run a hospital.

• A coder to deliver quality software

Learning to code will not account for the experience needed to know when and where to fix code issues. And even if someone learns to code, they’ll still use AI.

Don’t be intimidated by the insecurity of gatekeepers holding onto status. In two years, their resumes won’t matter. In five, they will be using AI.

Do Not Learn to Code, Learn to Lead

One of my proudest moments, while leading a team, was guiding Sid, a junior GoLang developer. His skillset didn’t align with his resume. But he was a nice person and a great teammate. 

I asked Sid if he’d be ok with doing some grunt work: documentation, Makefiles, creating firewall and defect tickets. He agreed. The goal was for him to understand the network, our process, and the codebase. The team was managing 40+ micro-services and web apps; running Go, JAVA, Typescript, Scala, YAML, and Shell scripts.

Some months on I asked Sid if he was ready for a Go project of his own. He was, and he did well. He never became a rockstar dev but he was a dependable, knowledgeable, contributor to the team.

Sid grew through foundational tasks. Gradually building competence and confidence. I didn’t need to “learn to code” in GoLang to help Sid become a solid GoLang developer.

AI is a lot like a junior developer with potential, but still needing iterative guidance, clear instruction, and interaction that matched its current capabilities.

Do Not Learn to Code, Learn The Process

In enterprise-level development: there are about 70 titled positions across the Software Development Lifecycle (SDLC). Only about 4 of those would be classified as coder/programmer.

Instead of learning to code, learn the SDLC. Learn how non-coding roles ensure high-quality software. Learn how to apply SDLC roles when building AI Coded Software (ACS).

Manage AI, Don’t Mimic It:

Product Manager (The What) — Give AI its task list and priorities.

• Role in Prompting: Breaks down high-level goals into features, components, modules, or user stories.

• Prompting Style: Outcome-oriented. Prioritizes clarity and deliverables.

• AI Role: Converts goals into actionable specs and todo lists.

Solutions Architect (The Why) — Give AI the blueprint and reasoning behind it.

• Role in Prompting: Explains how everything fits together. Provides context and justifications.

• Prompting Style: Conceptual and relational — focused on system interaction.

• AI Role: Structures systems, explains trade-offs, proposes integration strategies.

System Architect (The How) — Give AI the construction materials and wiring instructions.

• Role in Prompting: Defines tech stack, data flows, specifies constraints.

• Prompting Style: Technical, detail-rich, focused on constraints and interfaces.

• AI Role: Fills in scaffolding, defines inter-service protocols, optimizes backend logic.

• AI Style: Technical, constraint-driven.

UI Designer (The Seeing) — Give AI the paint, signs, and lighting to make the building inviting.

• Role in Prompting: Directs AI toward visual clarity, accessibility, and consistency across layouts.

• Prompting Style: Layout specific. Focuses on spacing, alignment, contrast, and consistency.

• AI Role: Makes visual decisions, adjusts contrast, spacing, typography.

Performance Tester (The Stress) — Give AI the load-bearing test to ensure the structure doesn’t collapse under pressure.

• Role in Prompting: Pushes AI to test limits of scalability, responsiveness, and endurance. Models failure scenarios.

• Prompting Style: Threshold defined by capacity, concurrency, and failure conditions.

• AI Role: Predict failure points, recommend tuning.

User Acceptance Tester (The Gut Check) — Give AI the final walkthrough checklist before opening the building to the public.

• Role in Prompting: Frames the prompt from the user’s perspective, often naïve or under pressure.

• Prompting Style: Goal aligned, with real-world usability and success criteria.

• AI Role: Emulate user thinking and report back in plain English.

Penetration Tester (The Ethical Hacker) — Give AI a crowbar and tells it to find every weak spot in the building’s security.

• Role in Prompting: Coaxes AI into adversarial mode. Simulates bad actors using real-world tactics.

• Prompting Style: Simulate edge cases and system entry points.

• AI Role: Reverse-engineer attack paths based on architecture.

DevSecOps Engineer (The Monitor) — Give AI the locks, alarms, and guardrails during construction.

• Role in Prompting: Uses AI to monitor pipelines and enforce policy. Embeds security checks within every automated step.

• Prompting Style: Lifecycle, enforcement, focused. Prioritizes automation of best practices and policy controls.

• AI Role: Points out secrets in logs, misconfigured ACLs, open ports.

Compliance Analyst (The Teacher’s Pet) — Give AI the building code and checks if every regulation has been followed before inspection.

• Role in Prompting: Translates legal/regulatory frameworks into system checks. Thinks in edge cases and legalese.

• Prompting Style: Standards alignment for audit readiness and policy adherence.

Don’t Learn To Code, Learn to Manage

We are the strategists, architects, managers — setting direction, identifying requirements, defining constraints.

AI is the executor, builder, producer — carrying out tasks, ideas, testing, writing, editing, analyzing.

• We are not the coder. We are the architect.

• We are not the engineer. We are the product owner.

• We are not the employee. We are the employer.

ACS requires leadership, vision, and systems-thinking. Not syntax memorization.

Learn to Manage Resources:

Money that could be spent on cloud services, tools, and freelancers are being consumed by compute.

• The longer the prompt, the more tokens.

• The longer the response, the more tokens.

• Every back-and-forth, correction, mistake, retry, or hallucination costs.

Do not learn to code. Learn to efficiently distribute limited financial resources. Costs can be minimized with proper planning, design, architecture, testing, deployment, and security. Not by learning to code.

Ask HyperGPT: Do Not Learn To Code any questions about to this article.

• How do I ‘manage’ AI effectively without technical expertise?

• Can you break down what the Software Development Lifecycle (SDLC) actually looks like?

• What’s the difference between managing AI and managing human developers?