DBJ Iceberg

[!Important] For rather excellent, more sober and very informative point of view, please see Keynote: After the AI Hype – What’s Real, and What’s Next - Richard Campbell - 2026 Geoffrey Hinton suggests that our current understanding of consciousness may be as fundamentally flawed as creationism once was. Ditto we can not see already existing intelligence in LLM’s. If consciousness is emergent rather than sacred, it may be a universal feature—not a biological accident. We aren’t just building machines; we might be building the architecture that allows consciousness to outgrow its biological constraints. Geoff claims. ...

That’s not a joke about forever confused CEO. It’s the default state of most AI hopefuls. The Perpetual State of Confusion Every vendor pitch, every all-hands, every roadmap slide is full of words everyone nods at: “agentic,” “AI-native,” “transformation.” Nobody stops the meeting to ask what they actually mean for this business, this process, this P&L line. So the nodding continues. Budgets get approved. (AI) Pilot gets funded. And six months later, nobody can explain why the thing doesn’t work — because nobody could explain, at the start, what “working” was supposed to look like. ...

The Flight Plan and the Autopilot The elusive goal of the “Competitive Advantage” What will “give” the “competitive advantage” is the Operational model, in order to navigate the whole organization around that huge iceberg of legacy. https://method.dbj.org/shop/ OP model introduction is not simple. But its worth it. Especially if organization is small(ish) and not too calcified. Just decide on one OP model, implement it and follow it. AI or no AI. ...

Summary “Workflow” is one word doing four jobs, and the collision is costing organizations clarity about what they actually own. Here’s the untangling. Just as we have different kinds of Architecture, we have different kind of Workflows. Again, DBJ Taxonomy brings order to the confusion. Feasibility replaces the struggle of intertwined responsibilities. And the required precondition of the functioning BPT (aka “the Loop”), of the DBJ Method Operating Model of the AI Readiness. ...

The Control Plane Your Platform Team Has Been Gluing Together Manually Platform engineering teams write a lot of glue. CI finishes — someone triggers a deploy script. An alert fires — someone pings Slack, opens a Jira, checks the last five deployments manually. A release train needs three repos to be green before the coordinated push — someone watches dashboards and hits the button when the moment looks right. This glue lives in a dozen places: GitHub Actions, bash scripts, cron jobs, Slack bots, Rundeck, internal wikis with “runbook” in the title. It works, until it doesn’t — and when it doesn’t, nobody knows which piece failed or why. ...

There’s a ladder. The rungs are labeled “AI Competence.” A novice climbs it, rung by rung, using a critical shortcut: “I didn’t learn, but the AI did.” It works. For a while it works great. The climb is fast, the view improves with every step, and the effort-to-altitude ratio feels like magic. Then the ladder ends. Not because the climber ran out of energy — because the ladder did. That point is the Danger-Kruger Peak: the spot where AI hallucinations start looking exactly like wisdom, because the climber has no competence of their own left to tell the difference. ...

The unicorn is still there. Exactly where it always was. But. Nobody is looking. A goat walked to the side of the moat and it’s fine up there — visible, unremarkable, good enough. The goat didn’t defeat the unicorn. It is just vibed up in greater numbers, at lower cost, faster than anyone could count. That might be the epitome of the AI slop. Not malicious. Not even bad. Just sufficient — produced at a volume and velocity that makes discernment feel like an unaffordable luxury. (yes I used that word) ...

Two teams. Same deadline. Different disasters. On the left: the coding monkeys. Keyboards rattling, errors scrolling, “It’s working! Maybe—” before the SHIP IT NOW and the inevitable ERROR. Fast. Confident. Wrong. On the right: the AI architects. Whiteboards full, meetings scheduled, not a single line written yet. “Don’t even start without four weeks on the spec.” Safe. Thorough. Also wrong. The cartoon is funny because both rooms exist in every organization that has ever touched software. ...

The senior engineer looks at the problem. Builds a mental model. Writes code that is simple and correct. Looks at it again, knows it’s right, moves on. The AI agent generates. Evaluates. Regenerates. Evaluates again. Tries a variation. Stumbles into something that passes the tests. Maybe. Same output, different paths. And the path matters — not for sentimental reasons, but for practical ones. What optimal actually means Optimal implementation is not the cleverest solution. It’s not the most elegant one. It’s the one that is correctly scoped to the problem, legible to the next person who touches it, and arrived at deliberately rather than by exhaustion of alternatives. ...

A Ferrari V12 engine dropped into a wooden farm cart. That is not satire. That is the exact situation most organizations are in right now. The engine is state-of-the-art. The cart is nineteenth century. The wheels are wooden. There is no drivetrain, no chassis rated for the load, and nowhere to sit. The moment you open the throttle, the cart disintegrates. What “AI Ready” Actually Means AI-readiness is not a technology procurement question. It is not about which model you license, which cloud you use, or how many GPU hours you can afford. Those are secondary. ...