Algerian Developers and the Five Levels of AI Coding: Where Are We?

At Anthropic, 90% of Claude Code’s codebase was written by Claude Code itself. The agent wrote the agent that writes the agents. At OpenAI, Codex is shipping features entirely built by its own AI agents. At StrongDM, a team of three engineers — just three — runs what they call a “dark factory” that turns markdown specifications into production software with no human writing or reviewing any code.

Meanwhile, most Algerian developers are still using GitHub Copilot to autocomplete their next line.

This is not a criticism. It is a diagnosis. The global software industry is stratifying into distinct tiers of AI-assisted development, and understanding where you sit on that spectrum — and what it takes to move up — is now the single most important career question for any Algerian developer. The gap between those who adapt and those who do not will determine who thrives in the next five years and who becomes professionally irrelevant.

The Five-Level Framework

Ben Shapiro, engineering lead at StrongDM, describes five levels of AI coding. This framework, which has resonated across the global developer community, provides the clearest language available for understanding the maturity curve. Here is what each level looks like — and where Algeria’s developers fall.

Level 1: Autocomplete. This is GitHub Copilot, tab-tab-tab. The AI suggests the next line. You accept or reject. The workflow is unchanged. The developer is still writing code — just writing it faster. Most of the global industry has adopted this level. Most Algerian developers who use AI tools at all are here. You have Copilot or Codeium installed. You accept the suggestions when they are right. You ignore them when they are wrong. Your fundamental relationship with code has not changed.

Level 2: Iteration. You start using AI as a conversational partner. You describe what you want. The AI generates a first draft. You refine it through multiple rounds of back-and-forth. Tools like Cursor, Windsurf, and Claude Code operate at this level. The AI writes meaningful chunks, but the human is still the primary architect. The code is still being reviewed line by line by a person. A growing number of Algerian developers — particularly freelancers working on international platforms and startup engineers in Algiers and Oran — are reaching this level.

Level 3: Delegation. Here is where the fundamental shift begins. You give the AI an entire feature, a complete module, and you evaluate the output as a whole. You are not reading every line. You are running it, testing it, checking whether it does what it is supposed to do. The human shifts from writer to evaluator. Very few Algerian developers operate at this level. The skills required — writing precise specifications, designing evaluation criteria, trusting code you did not write — are not taught in any Algerian university curriculum.

Level 4: Orchestration. You manage multiple AI agents working in parallel, each handling different parts of the system. You write specs, not code. You design evaluation criteria. You do not even check whether tests pass — you evaluate outcomes. This requires both a level of trust in the system and a quality of specification writing that almost nobody has developed yet, anywhere in the world. A handful of Algerian developers working at international companies may be approaching this level. Domestically, it is essentially nonexistent.

Level 5: The Dark Factory. No human writes code. No human reviews code. Specifications go in. Working software comes out. StrongDM operates here. Almost nobody else does. For Algerian development teams, this is a horizon to understand, not a near-term goal.

Where Algerian Developers Actually Sit

Be honest with yourself. If you are an Algerian developer, you are most likely at Level 1, with some reaching Level 2. Here is why that matters — and why it is not entirely your fault.

The cost barrier is real. GitHub Copilot costs $10/month. Cursor Pro is $20/month. Claude Pro is $20/month. These prices seem modest from a San Francisco salary perspective. For an Algerian developer earning 80,000 to 150,000 DZD per month (roughly $580 to $1,100), these subscriptions represent a meaningful percentage of income. And the most powerful tools — Claude Max at $100/month, Cursor Business at $40/month — are effectively priced out of reach for most Algerian individual developers. This creates a direct skills gap: the developers who can afford to practice with advanced AI tools get better at using them, widening the gap with those who cannot.

Internet reliability affects cloud AI tools. Level 2 and above require sustained, low-latency internet connections to AI model APIs. Cursor and Claude Code send code context to cloud servers, wait for generation, and stream results back. With Algeria’s internet infrastructure — improved but still inconsistent, particularly outside Algiers — interrupted sessions, timeouts, and lost context are common frustrations. A developer in Stockholm working with Cursor experiences a seamless flow. A developer in Sétif experiences interruptions that break concentration and reduce the tool’s value.

University curricula are a generation behind. Algerian CS programs still teach instruction-era programming. Students learn to write code from scratch — data structures in C, object-oriented design in Java, database queries in SQL. These are foundational skills. They are also skills that AI now handles competently. What is not taught — specification writing, evaluation design, systems thinking, the ability to direct AI agents rather than be one — is precisely what Level 3+ requires. A graduate from USTHB or ESI leaves with strong algorithmic fundamentals and no training in the skills that will define software development for the next decade.

The METR Study: Why Getting Slower First Is Normal

Here is the finding that should both alarm and reassure Algerian developers. The METR organization conducted a rigorous study measuring experienced open-source developers on real tasks in real repositories — codebases they knew intimately. The developers were randomly assigned to complete tasks with or without AI tools.

The result: AI-assisted developers were 19% slower. Not faster. Slower.

And the developers predicted they would be 24% faster. They thought they were more productive while actually being less productive.

The researchers identified why. Developers spent substantial time formulating prompts, waiting for generations, correcting almost-right code, context-switching between their own mental model and the model’s output, and debugging subtle errors introduced by generated code that looked correct but was not. Forty-six percent of developers in broader surveys say they do not fully trust AI-generated code.

This is the J-curve of AI adoption. When you bolt an AI coding assistant onto an existing workflow, productivity dips before it rises. The dip happens because the tool changes the workflow, but the workflow has not been redesigned around the tool. You are running a new engine on an old transmission. The gears grind.

For Algerian developers, this J-curve has a specific implication: the initial experience with AI coding tools will feel disappointing. You will feel slower. You will be slower. And without understanding that this is a normal part of the adoption curve — a temporary valley, not a permanent destination — many developers will abandon the tools prematurely and conclude that AI coding assistance “does not work for them.”

It does work. But only after you push through the valley. And pushing through the valley requires deliberate practice, not just occasional use.

The Brownfield Reality

Most coverage of AI coding focuses on greenfield projects — building something new from scratch. This is where AI tools shine brightest. Give Claude Code a clean specification and an empty repository, and it will produce remarkably competent code.

But most Algerian developers do not work on greenfield projects. They work on brownfield codebases — millions of lines of existing code with no specifications, no scenario suites, no documentation, and institutional knowledge that exists only in the heads of engineers who may have left the company years ago. Government IT systems, Sonatrach’s internal tools, banking software, telecom billing platforms — these are legacy codebases where the gap between what the documentation says and what the code actually does is measured in years of undocumented patches and workarounds.

This is the brownfield problem, and it is where the transition from Level 2 to Level 3 gets genuinely hard. At Level 3, you need to delegate an entire feature to the AI. But delegation requires a specification precise enough for the AI to implement reliably. And writing that specification for a brownfield codebase requires understanding not just what the code should do, but what it actually does — including all the undocumented behaviors that have accumulated over years.

Most companies will spend the next three to five years building the infrastructure that lets AI agents work safely in codebases designed by and for humans. For Algerian enterprises sitting on decades of legacy code with minimal documentation, that timeline may be even longer. This is not a reason to wait. It is a reason to start the specification and documentation work now — because that work is the prerequisite for everything that comes after.

A Practical Path: Level 1 to Level 3 in Six Months

Here is a concrete progression for Algerian developers who want to move up the framework. This is not a theoretical curriculum. It is a practical path designed around the constraints that Algerian developers actually face.

Weeks 1-4: Master Level 1 properly. If you are using Copilot or Codeium, use it deliberately. Stop accepting suggestions automatically. Start evaluating each suggestion: is this what I would have written? Is it better? Is it subtly wrong? Build the habit of critical evaluation. This is the foundational skill for every level above.

Weeks 5-8: Enter Level 2 with intent. Start using Cursor or Claude Code (Claude’s free tier is sufficient to begin). Do not use it as a fancy autocomplete. Use it as a conversation partner. Describe what you want to build in natural language. Let the AI generate a first draft. Then iterate: explain what is wrong, what you want changed, what constraints the AI missed. The skill you are building is not prompting — it is specification. You are learning to describe what you want precisely enough for a non-human to build it correctly.

Weeks 9-16: Practice delegation on side projects. Take a small side project — a personal tool, a utility for your team, a contribution to an open-source project — and try to build it at Level 3. Write a specification first. Describe the feature completely: what it does, what edge cases it handles, what constraints it obeys. Give the specification to the AI and evaluate the output as a whole. Do not read every line. Run it. Test it. Does it do what you specified? If not, refine the specification and try again. The skill you are building is evaluation — judging output against intent without reading every line of code.

Weeks 17-24: Apply Level 3 to real work. Take a real feature from your actual job and attempt the Level 3 workflow. Write the specification. Delegate to the AI. Evaluate the output. You will encounter the brownfield problem here — the AI will not understand your existing codebase’s undocumented conventions. Document those conventions. Add them to the specification. Each iteration makes the specification better, and that specification becomes a permanent asset for your team.

Throughout this process, track your time honestly. You will be slower at first. The METR study says 19% slower. You might be 30% slower. That is the J-curve. Push through it. By week 24, the developers who persist will be operating at a level that most of their peers — and most of their competitors on international freelance platforms — have not reached.

The Freelancer’s Competitive Edge

For Algerian developers working on international freelance platforms — Upwork, Toptal, Freelancer — the AI coding level framework is not academic. It is directly competitive. Clients are increasingly expecting AI-assisted development speeds. A developer who can deliver a feature in two days using Level 3 workflows will outcompete one who takes a week at Level 1, regardless of where either developer is located.

This is simultaneously a threat and an opportunity for Algerian freelancers. The threat: international clients will increasingly expect AI-augmented productivity, and developers who cannot deliver it will lose contracts. The opportunity: Algerian developers who reach Level 3 can compete on output quality and speed with developers anywhere in the world, at Algerian cost structures. The combination of Level 3 productivity and Algerian pricing is a formidable competitive position.

But it requires the investment. The six-month progression outlined above is not optional for freelancers who want to remain competitive. It is the minimum required professional development for the next era of software.

The University Gap

Algeria’s CS departments face an uncomfortable reckoning. The skills that Level 3+ requires — specification writing, evaluation design, systems thinking, the ability to direct AI implementation rather than perform it — are not taught in any Algerian university program. Curricula are still organized around the assumption that the developer’s primary skill is writing code.

The junior developer pipeline is collapsing globally. Entry-level coding jobs have dropped roughly 50% in two years across major job boards. If AI can handle implementation, the entry-level skill of “write simple code” is no longer a differentiator. The junior developers of the future will not learn by writing code. They will learn by writing specifications and evaluating output. The mentorship model changes from “teach them to code” to “teach them to specify and evaluate.”

Algerian universities that adapt their curricula to this reality will produce graduates who are immediately competitive in the global market. Those that do not will produce graduates trained for jobs that are rapidly disappearing. ESI, USTHB, the University of Constantine, and the university system as a whole need to recognize that the shift from implementation to specification is not a trend to monitor — it is a transformation to lead.

The Restructuring That Is Coming

The organizational implications are stark. Cursor has about 50 engineers shipping software at a rate that would historically require 200 to 300 people. Anthropic’s engineering team spends more time reviewing and directing AI output than writing code. The 500-person engineering organization of 2023 is becoming the 50-person engineering organization of 2027.

For Algerian software companies and IT departments, this means the value of individual developers will increasingly depend on their level in the framework. A Level 3 developer is worth multiple Level 1 developers — not because they work harder, but because they operate at a fundamentally higher level of abstraction. Engineering managers, scrum masters, and coordination roles will evolve or disappear as teams shrink and specification replaces implementation as the core activity.

The demand for software will not shrink. It will grow. When software becomes cheaper to build, more of it gets built. Every Algerian enterprise that could benefit from custom internal tools — and that is all of them — will be able to afford them. The demand for what software does never saturates. What changes is who builds it and how. Algerian developers who position themselves on the right side of that change will not lack for work. They will have more of it than ever, at higher value.

The question is which level they will be operating at when the demand arrives.

Sources & Further Reading

• StrongDM Software Factory and Five Levels of AI Coding — Ben Shapiro, Engineering Lead
• METR Study: AI-Assisted Developers 19% Slower on Real Tasks, 2025
• Anthropic: 90% of Claude Code Written by Claude Code
• OpenAI Codex: Features Built Entirely by AI Agents
• Cursor — AI-Native Code Editor
• GitHub Copilot Usage Statistics 2025
• Algeria ICT Infrastructure Report — MPTIC
• USTHB Computer Science Department — Curriculum Overview
• ESI (Ecole Nationale Supérieure d’Informatique) — Programs
• Stack Overflow Developer Survey 2025: Junior Developer Job Market
• Gartner: Software Development Productivity and AI Tools, 2025