Code IS dead

Like, dead-dead.

I remember sitting down to debate this exact issue with my friend and master Nabil Belakbir, CTO at Nemo Technology, three years ago. At the time, we were playing around with a Visual Studio plugin called “AI Code” or something like that. It hooked 2022-era ChatGPT straight into the editor, saving us from the relentless copy-paste loop. That was the first real wake-up slap to my pride as a software craftsman.

I tried to resist as long as I could, arming myself with all the usual talking points:

• Code still has to be reviewed by a human
• Good design patterns are too hard to prompt into an AI
• AI will never grasp the full context of the codebase, adjacent systems, and actual business needs
• Software architecture requires a high-level, big-picture vision unique to human wisdom (lol)
• You can’t hand production over to an AI
• Critical paths will always need a human pass for optimization (concurrency, performance, databases, latency, and especially security)
• Letting an AI craft a public API is straight-up suicide

Then, one by one, those bulletproof convictions crumbled. First in my own head (if LLM benchmark curves keep climbing like this, we’re cooked), then across social media (tech giants quietly deploying autonomous AI at scale), and finally among tech influencers who suddenly couldn’t talk about anything else—including major industry figures who had preview access to LLM builds 6 to 12 months ahead of the rest of us.

I even threw myself into the trenches of LinkedIn posts and comment sections, arguing against AI moderates with their cozy, sanitized rhetoric. AI will just be a co-pilot for humans, AI is never going to replace coders, AI can’t write usable code, blah blah blah…

Deep down, I was desperately hoping they were right. Truly.

Then a contact in the US tech scene whom I deeply respect kept dropping the same line every two or three months: “Issam, get ready. You haven’t seen anything yet…” The writing was on the wall—far removed from wishful thinking and naive optimism.

Today, consensus is finally forming that gpt6-Astra is true AGI. I personally see AGI as a slow creep—a gradual realization among AI thinkers rather than a single model descending from the clouds. But this time, folks, we’re actually here.

I wrote my final lines of hand-crafted code back in October 2025, and I meticulously reviewed my last PR a few weeks ago. Sure, I’ll probably keep scanning sensitive components—security hooks, public APIs, the high-stakes stuff—but raw code has officially become modern-day assembly language. The developer has morphed into a supervisory artist, guiding the AI toward real business value, product experience, and actual utility.

Code is well and truly dead. The traditional software engineer job is done, finished, terminado, baraka.

Long live the age of AI!

Tech hiring has become really hard…

Not from a lack of talent (Morocco has nothing to envy other markets), or a lack of time (we’ve got tools for that—shoutout to Invirtus).

Simply because we no longer know what to look for in a tech profile. In this era of heavily subsidized AI-coding assistants, tomorrow’s software engineer is a super-chameleon who adapts to a constantly shifting environment.

So here are my 4 core criteria (for early-stage startups doing intensive agentic coding)—and I’ll let the community debate the rest :)

1. The Systemic Decomposition Specialist => They listen to a startup pitch or a business problem and maintain a high-level view of what’s being built, complete with its strengths and flaws. They pinpoint ambiguous areas and isolate them within the agents’ workload. They anticipate test cases or the need for greater architectural generality. This is fundamentally someone who knows how to step back, see the big picture, and ideally carries the battle scars of years spent deploying into complex enterprise systems.

2. The Stochasticity Reducer => This one specializes in analyzing and predicting failure modes in AI processes (which are inherently unstable). Much like an engineer building in an earthquake zone, this technician sets up evaluation and testing harnesses and keeps a Plan B ready for every fragile section of the codebase. Hallucinations and model drift are their worst enemies. They bring peace of mind to business teams and make the application far more deterministic.

3. The Entropy Hunter => A real sniper who senses and eliminates unnecessary local complexity wherever agents produce it. A re-coded function instead of using an existing library, an overcomplicated method with zero business rationale. They spot an LLM API call looping five times when a single, broader call would have done the trick. Code review is a must for them, but they can smell a bad pattern without even digging into the fine print.

4. The Paranoid Tester => This person trusts nobody—least of all the 5 Opus 4.7 agents looping non-stop since last night at the mercy of rate limits. They test everything that moves: between every user story, every snippet of UI, and every API call, determined to keep an accurate mental map of what’s happening under the hood. Refusing to get overwhelmed by the volume of generated code, they take the time to digest the complexity of every feature (even though they aren’t the product owner) and re-adjust the agents before a major feature drift takes hold.

Does your tech team have these 4 skills? Do you have these 4 skills yourself? Do you see any other macro-skills critical for this new era?

A few clarifications

1. Analyzing these profiles highlights a clear reality: each of these four postures requires significant seniority. The real challenge for the tech world moving forward will be our capacity to train juniors so they can actually acquire these skills… (thanks Oualid for pointing this out)
2. Context Engineering has also emerged as an essential role in bridging the human and agentic worlds—specifically in breaking down the information asymmetry between these two perspectives… (thanks Yahya for pointing this out)

For too long, we’ve reduced tax administration to its historic role: collecting money for the State. In doing so, we’ve forgotten that it is, by design, the premier “Data Factory / Laboratory” of our Kingdom—the true seismograph tracking the economic activity of every citizen and business.

Looking ahead to the grand AI revolution, the tax authority (the DGI)—perhaps operating through a Data Office led directly by the Prime Minister—is poised to become a true economic catalyst, powered by its goldmine of informational assets. Anonymized, refined, and distributed, this dataset would form the ultimate fuel for our startups operating across financial inclusion, geomarketing, B2B payment delay analysis, GovTech, and PropTech.

Counterintuitively, the real fiscal value isn’t just in the tax collection itself; it’s in the weak signals extracted from the transactional behavior of our businesses and citizens. Once processed and handed over to players with the right tech firepower, this behavior becomes a massive lever for growth: better analysis of the real economy and inter-company cash flows, smarter allocation of resources (subsidies, tax credits, exemptions), a clearer understanding of citizen distrust toward institutions, earlier detection of economic shocks… the list goes on.

The sheer granularity of tax data—and by extension, public spending data (sitting at the Treasury)—would allow for a phenomenal deep dive into the country’s micro-dynamics, handing decision-makers priceless insights. But here’s the catch: all this raw data has to go through the meat grinder of AI and be torn apart, analyzed, and obsessed over by thousands of data-mindset-centric brains.

Beyond these use cases and the specific case of the DGI (which, let’s be honest, is already a frontrunner in data analytics), this is a call for a bold, constructive, and collaborative bridge between the guardians of the State apparatus and civil society in all its forms—especially its economic engines, with a massive focus on our startups.

Shifting from pure enforcement to a data-enrichment platform demands a fresh update to our social contract. The State becomes the guarantor of the data foundation—ensuring top-tier security and privacy—while startups act as value architects, turning those raw feeds into game-changing societal services. Implicitly, it’s a total rethink of the State’s role in the digital economy: positioning itself as an enabler and catalyst for innovation, rather than a mere regulator or data hoarder. It’s data-driven liberalism—Adam Smith’s invisible keyboard.

Imagine an aggressive Open Data framework with regulated, secure access where anonymity isn’t a bottleneck, but a technical protocol—unlocking hyper-granular insights into our real economy while meticulously mitigating re-identification risks.

While allied nations are already embedding autonomous AI agents at the very core of their administrative decision-making, we could forge a similarly visionary path: building a predictive watchtower that feeds this informational fuel straight to our tech ecosystem, supercharging tomorrow’s GDP.

True economic sovereignty doesn’t come from hoarding information. It comes from turning that information into a formidable socio-economic weapon—moving away from historical sedimentation and stepping into dynamic prediction and proactive foresight. You don’t build sovereignty by laying more bricks for walls; you build it by having the courage to throw up more bridges.

I overheard someone mention in conversation recently that Morocco had become the first “open-source” country—a cheeky nod to the relentless wave of cyberattacks targeting our institutions over the past few months.

Flipping that joke on its head, I asked him: isn’t this just fate winking at us? An unmistakable call for civil transparency, repurposed into an economic weapon?

In a techno-complex, hyper-connected, and above all cyber-fragile world, the choice is no longer between light and shadow. It’s between suffering an accidental leak or orchestrating a masterclass in strategic exposure.

Our two biggest hurdles to reaching this vision lie in how rationally we operationalize data privacy laws and national sovereignty.

Economic players constantly feel caught in a vise: they bear the moral responsibility of protecting user data, yet they have to balance velocity and execution in a world moving at breakneck speed. Let’s face it—state secrets aside—our personal and economic lives are already parked sitting on Microsoft, Meta, Google, Salesforce, and now the suffocating duo of OpenAI and Anthropic.

We are staring down a once-in-a-generation opportunity: a Technological Great Reset where all of humanity’s code and collective knowledge comes with a near-zero marginal cost—utterly laughable from a historical perspective. Every single country now has the tools to build whatever it wants using well-trained local talent.

It’s time to dare to unleash our data. It’s time to take bigger risks and power this revival with the ultimate fuel of the future: data.

It’s now or never.

I had a front-row seat to the 2026 Ceuta events, as I happened to be on vacation in northern Morocco right when it unfolded. Overnight, four lifeguards from our residential complex’s pools vanished, the local café was left running on a single waiter, and the coastal road felt like a non-stop marathon of people—young and old—racing toward the Spanish border.

I also experienced the whole thing through a completely different lens: X (Twitter). I follow plenty of accounts across both the far right and the radical left. It was wildly fascinating to watch how each side co-opted the event—with varying degrees of legitimacy—to turn it into weaponized talking points! For the right, it was the Islamist “great replacement,” the collapse of Spanish sovereignty, and the total failure of left-wing parties. For the left, it was the ultimate despair of Moroccan youth, an American-Israeli plot against Sánchez, and, somehow, a chance to claim that Schengen’s border protections actually worked.

I’ve rarely seen such aggressive polarization over an event—singular, no doubt, but hardly surprising to anyone aware of the geopolitical stakes in this region. The hit to our country’s reputation has been disastrous, though maybe this crisis carries a deeper logic that escapes me (beyond the endless conspiracy theories, at least).