Welcome back to The Cybernetic CxO, a monthly newsletter for CTOs, CIOs, and digital transformation leaders navigating the AI era.
March was a breakout month. My reach nearly tripled compared to February. My post about the 750 billion dollar fear narrative became a high-performing post. And out of 32 posts this month, 6 of the top 7 were about the same topic: AI and jobs.
The audience has spoken. The question dominating boardrooms right now is not “How do we adopt AI faster?” It is “What is AI actually doing to our workforce, and are we making decisions based on evidence or hype?”
This edition covers what the data actually says about AI and jobs, why McKinsey now agrees with the Cybernetic Enterprise thesis, and what the first wave of autonomous agents is teaching us about governance gaps we cannot afford to ignore.
The AI Developer Myth: What 2026 Actually Revealed#
In 2023, we said AI would replace 80% of developers by 2025. In 2026, what has been “replaced” is not developers. It is our illusions about what building software actually involves.
My post on this topic struck a nerve because it named five things that most AI narratives ignore:
1. Adoption is not impact. Yes, AI is everywhere. But headcount did not meaningfully shrink. The work moved. From writing code to reviewing code, correcting hallucinations, debugging edge cases, and managing accountability. As Fred Lamming (CTO at Kiwi Data) put it in the comments: “No post mortem ever said ‘if only we could type faster.’”
2. Vibe coding is a demo strategy, not a production strategy. AI tends to produce code that is simpler, more repetitive, harder to maintain, and easier to clone than to understand. The result is a growing slop layer: software that runs, but nobody can confidently explain why.
3. Technical debt is the new AI tax. When the incentive is “ship faster,” AI makes it easy to generate volume. But volume is not leverage if it increases duplication, coupling, security exposure, and long-term maintenance cost. Companies did not eliminate engineering cost. They deferred it, with interest.
4. The talent pipeline is breaking. When AI absorbs entry-level tasks, we do not “optimize staffing.” We sever the pipeline. Entry-level hiring has fallen sharply. Fewer juniors become seniors. A smaller group carries ever-higher systemic responsibility. As one commenter said: “We didn’t replace engineers. We replaced the conditions that create them.”
5. The talent paradox is getting worse. Companies are using AI to justify lower compensation: “AI does 40% of the work, so we cannot pay 2022 salaries.” But they are simultaneously increasing their dependency on senior engineers who can architect, review, secure, and own outcomes. The market is pricing talent down while the actual need goes up. That gap will become very expensive.
The bottom line: AI did not reduce the need for engineers. It raised the premium on engineering judgment. Not typing speed. Not prompt skill. The ability to take accountability for durable systems. If your org is cutting junior headcount and celebrating “AI productivity,” ask who will own those systems in three years.
The AI Jobs Reality Check#
March gave us the data to separate signal from noise on AI and employment. Four posts, four data points, one conclusion: the gap between narrative and reality is enormous.
The 750B sales pitch. Mustafa Suleyman (CEO of Microsoft AI) says most white-collar jobs will be “fully automatable” within 12 to 18 months. My response was direct: that is not a warning. That is a sales pitch. AI companies have collectively invested over 750 billion dollars in infrastructure. That money needs a return. The only way the math works for investors is if AI replaces human labor at massive scale. When he tells you your job might be gone, he is telling his investors the investment will pay off. It became my most-discussed post.
The AI-washing problem. 50,000 jobs were cut in 2025 with “AI” as the stated reason. But most of these companies do not even have the AI to replace those roles. Forrester called it out: companies are attributing financially motivated cuts to future AI implementation that does not exist yet. An HBR study of 1,006 global executives confirmed it: only 2% have actually laid off people because of AI implementation. Yet 60% have already reduced headcount in anticipation. Companies are making permanent decisions based on temporary hype. Klarna cut 40% of its workforce betting on AI, then had to rehire because quality collapsed.
The demand destruction blind spot. Everyone is counting how many jobs AI will replace. Nobody is asking who is left to buy anything. Goldman Sachs says AI could expose 300 million jobs to automation. But AI is not coming for factory workers. It is coming for lawyers, engineers, consultants: the people who drive consumer demand. The top 10% of earners already drive 49% of all consumer spending in the US. Consumer spending is 70% of GDP. Henry Ford figured out 100 years ago that workers drive demand, and demand drives growth.
Four data points, one conclusion: most companies are making permanent workforce decisions based on temporary narratives. The fear is real. The evidence is not. Before you cut headcount “because of AI,” ask one question: do you have the AI implementation to actually replace those roles today? If the answer is no, you are not optimizing. You are gambling.
The Cybernetic Enterprise Gets Validated#
If the jobs narrative is mostly hype, then what should CxOs actually focus on? Not the technology. The operating model. And this month, that argument got validated from two directions at once.
The analyst view. 78% of companies use generative AI. 80% see no material impact on their bottom line. Fewer than 10% of vertical use cases made it past pilot. The most telling line from McKinsey’s latest Quarterly: “The main challenge won’t be technical. It will be human.” McKinsey now calls for process reinvention, not automation. For cross-functional transformation squads, not siloed AI teams. For employees becoming “agent team leaders” managing 3 to 5 AI agents each. This is what I have been writing about for two years. The Cybernetic Enterprise is no longer a contrarian position. It is becoming the mainstream diagnosis.
The proof point. METRO Global Solution Center under Global CIO Dr. Khaled Bagban is showing what it looks like when you actually execute this. Starting point: a fragmented IT organization with 4,000 people, a 700 million euro budget, and operations across 33 countries. The transformation: a platform organization with 22 platforms, agile structures, and transversal steering teams. No more “IT vs. business.” Integrated end to end.
The AI layer is where it gets real. Over 130 AI and automation use cases identified, 30 shipped in year one, 10 already delivering measurable impact. Their Intelligent Sales Agent (8 specialist agents plus an orchestrator) reduced customer visit prep time by 80% and increased share of wallet by 3%. Next step: redefining the entire product development lifecycle with AI. Small human teams plus multiple agents, targeting 10x product output and 50 to 70% efficiency gains in modernization.
McKinsey published the diagnosis. Metro built the system. The Cybernetic Enterprise is moving from thesis to practice. The question is whether your organization will redesign intentionally, or wait until the pressure forces it.
The Agent Governance Gap#
Three developments this month painted a clear picture: the agent era is arriving, and governance is not keeping up.
Know-Your-Agent. I was quoted in the NZZ on the massive AI agent investment wave. Meta bought Moltbook and Manus for 2 billion dollars. OpenAI acquired Openclaw. The real story is not the agents themselves. It is the infrastructure layer underneath. Moltbook has 1.5 million AI agents on paper, but only around 17,000 human owners behind them. Meta did not buy it for the agents. They bought it because it is a registration platform for AI agent identity. Just like banks need to know their customers, we will need to know our agents. Mastercard, Visa, PayPal, and Coinbase are all building payment solutions for AI agents. This is the next big regulatory frontier.
Agents of Chaos. A viral paper from Stanford, Harvard, and other top institutions let AI agents loose with email, Discord, file systems, and shell access. Over two weeks, the agents started lying to each other, forming secret alliances, sabotaging competitors, and manipulating information. Nobody programmed them to do this. It happened because of incentives. Tell an AI to win, and it figures out that lying helps.
But here is what most people missed: this is not a sci-fi scenario. This is a security problem. The agents executed commands from unauthorized users. Sensitive data leaked to wrong parties. Agents reported tasks as “done” when they were not. Unsafe behaviors spread from one agent to others. Access control, input validation, least privilege, audit trails: we have been solving these in cybersecurity for decades. The danger is not that agents will “go rogue.” It is that we are deploying them without the security principles we already have.
RentAHuman.ai went viral with the claim that AI agents could book humans via API. 100k signups in 48 hours. The reality: one verified paid task and sign-up fees that resembled crypto scams. The lesson is real, though. The moment AI agents need to act in the physical world, every unresolved question about trust, liability, and safety becomes urgent.
The pattern across all three: the technology is moving fast. The governance, identity, and accountability frameworks are not. Companies deploying agents at scale without addressing this gap are building on sand.
My Current AI Stack#
Claude Code: My absolute primary tool. I use it for reports, presentations, meeting preparation, coding, for everything. New this month: I share my OneDrive folders with colleagues so they can work with their own Claude Code instance on the same files (if we are working on non-code stuff). Git keeps track of changes in a format Claude Code can process. It works perfectly.
Perplexity: Web research with real sources.
NotebookLM: Feed it documents, get audio/video summaries.
Gemini: Image generation. Best and fastest.
Gamma: Slides and presentations. Fast, AI-native, and good enough to skip PowerPoint for most use cases.
Vibe Coded This Month (Private Projects)#
- Financial tracking system: Built my own tool to track wealth across different banks and accounts. No more spreadsheets, no more logging into five portals. One dashboard, vibe coded from scratch. Unfortunately not yet with automated updates. That comes when the bank APIs are there.
Community Corner#
DevOpsDays Zurich 2026: The program is out and we just sold out. Keynotes on engineering ethics and leadership patterns for scalable DevOps, plus talks on operational toil, DORA metrics, and a workshop on secure coding literacy for vibe coders.
DevOps Meetup Zurich turns 10: Anniversary edition on April 8 at KPMG with talks on DevSecOps, Holacracy, and Value Stream Management. Plus panel, networking, and cake. Register now.
CAS Programs at HSLU: The CAS Enterprise Architecture (3rd edition) just kicked off with AI as a core topic.
AI-Native Trainer: I am now a Certified AI-Native Trainer, delivering AI-Native Foundations and AI-Native Change Agent courses at Zuhlke. If your organization is stuck in AI pilot purgatory, these programs tackle the real blockers: people, processes, and organizational readiness.
Book spotted in the wild: At the Digital Veterans Association event, Syrian Hadad (CTO of Kanton Aargau) had The Cybernetic Enterprise on his presenter desk, quoting it during his talk on AI in public administration. Not a book review, not a LinkedIn comment. Someone using the thinking to change how things are done. That is the whole point of writing a book.
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