The new ThoughtWorks Tech Radar reads like a warning letter. Cognitive debt. Broken productivity metrics. Terms nobody agrees on. If you've been reading my posts, none of it will surprise you. It's always nice to get evidence that I'm not completely crazy and making all of this up.

The IT consolidation was coming long before ChatGPT. AI just gave it a narrative. And the people who actually understood the systems? They're updating their LinkedIn.

"Are we building the right thing?" is a question as old as engineering itself. GenAI made it easier than ever to skip. Time to start asking again.

Spec-Driven Development is the hot new thing in vibe coding. Write a perfect spec, let the agent work, sit back. Sounds familiar? We called it Waterfall. We hated it. We sucked at it. Why do we think we're suddenly ready for it now?

I listened to a podcast about "assisted migration" for trees and plants. A fantastic topic for a systems thinker. I listened to an AI podcast just before that. And then a few uncomfortable synaptic connections clicked.

OpenAI valued at $852 billion without profit. Sora lost $1 million per day. Big Tech spends $700 billion yearly, and most of it vanishes into chips and electricity. IPOs are coming. Before you buy in, remember T-Online. That was counted as a safe bet, too.

The S-to-P Jig is an advanced cognitive jig from DSRP. Scrum is a perfect example. And understanding it explains why good Scrum Masters adapt the framework instead of just following it.

"Where are your solutions?" Fair question. Honestly: I don't have any. What I have is tools for thinking. Systems thinking. Perspective shifts. I can't think for you. But I can show you ways to improve your thinking. I don't want you to share my thinking. That's my contribution. Small, but mine.

Heroes mask a system's weaknesses. When they're gone, things collapse. AI agents are the new heroes, flooding teams with output. But a healthy system doesn't need heroes. It needs balance.

Yesterday's garbage run by the river got me thinking. Biases aren't "just" glitches in our brains. They're stable systems with feedback loops. And they all have a social layer that makes them sticky. Find the loop, find the leverage point.

The simple answer wins. Always. Because we wanted it that way. We trained the algorithm, and now it trains us. Keep thinking. Leave the one-liners to the comedians

AI agents promise to write your tests for you. Sounds great until you realize: a test suite you didn't build is a test suite you don't understand. Don't let green checkmarks replace your own eyes.

AI agents are writing your test scripts now. So what exactly are you still doing here? If coding was the vehicle but you forgot the destination, you might be getting Pluto-ed. A post about dwarf planets, AI shepherds, and redefining your orbit.

AI coding agents produce the most probable code. Not the best. Not the most elegant. The most average. Without skill to guide them, you'll get a codebase that works, passes tests, and slowly becomes a maintenance nightmare. Your code deserves better.

A tester who doesn't think in systems is just poking at a surface. DSRP gave me a framework for what good testers do intuitively: draw boundaries, understand parts and wholes, stress relationships, switch perspectives. That's how you detect risks. That's the real job.

Test cases describe a desired reality. But what does that mean through a systems thinking lens? I have given it a try to explain my train of thought.

12 o'clock in Germany. Time to raise the gas prices. Do we get a new all-time high? Here is a rule from systems thinking that helps to understand situations like this. When the purpose of a system does not match its intention. Bonus post today, because of reasons...

In woodworking, glue strength depends on surface area. In teams, those surfaces are the moments where people connect. When we automate them away, we don't just remove work. We remove the thing that held the structure together.

We love labeling things as good or bad. Especially when testing. But labeling while building your mental model contaminates your map. Observe first, evaluate later. Bonus to The Six Moves series.

Same reality, seen differently. The P-Circle lays out who is looking, from where, and what they see. The real power? Noticing whose perspective is missing. Part 6 of the Six Moves.

Nature hides its secrets in relationships. So do the systems we build. Part 5 of the Six Moves grabs the relationship arrows and asks: what is this made of?

AI used well makes you faster, not lazier. Feed it stack traces, let it draft scripts, use it to learn. But stay in the loop. The moment you stop looking at the output, you're a spectator, not a professional. Here's a few examples that work for me.

Microsoft, Google, Apple, Meta. And now OpenAI and Anthropic. The speed of increasing dependency on big tech in the age of AI is getting higher and higher each day. Big tech has found a new drug to make us even more dependent. And we.... seem to accept it.

Often the beauty lies in the simplicity of a thing. AI allows people to add more without much friction. But is this really useful?

Happy Friday rant from yours truly: Testers build trust by understanding thought processes. LLMs 'explain' themselves too. But is it real reasoning or post-hoc storytelling? And where are the observability folks when we need them most? Asking not only for a regulated industry. Happy weekend!

Parts look manageable in isolation. Connect them, and patterns emerge. Some of those patterns are not pretty. Part 4 of the Six Moves throws a Part Party.

They removed the wolves from Yellowstone and rivers eroded. Now companies remove people and wonder why teams fall apart. You're not cutting a role. You're intervening in a complex, organizational ecosystem that you don't fully understand.

Are they building a new AI data center in your vicinity? Then you are better not in urgent need of a plumber. There are relations in the larger system of AI that you have not even dreamed of before.

You can't understand a thing without understanding the systems it lives in. Zooming Out is the move that reveals the context. And context changes everything. Part 3 of the Six Moves.

We're building AI that explores applications. But when I automate tests myself, I learn the system deeply. Hand that to an agent, and the learning is gone. And where will the next generation of testers come from if we remove the bottom rungs?

Richard Feynman said "Everything is really interesting when you look close enough". In Part 2 of the series we zoom in. We look at the parts and the parts of parts. And the... you get the gist.

AI just ate the solution space for breakfast. Coding, testing, automation — all commodities now. The only skill left that matters? Understanding the actual problem.

Here's a quick Saturday night post, that just wanted out. Companies are trading their vision for margins, replacing people with AI, and calling it transformation. But a tool doesn't care about your vision. People do.

RAM prices exploded, laptops becoming unaffordable, Big Tech wants to push us into the cloud. But could scarcity bring back the lost art of efficient programming? Constraints breed creativity. They always have.

What happens when thousands of high earners suddenly stop spending? In my second post today, I zoom out of IT and into the wider economy. AI is triggering a reinforcing loop. Most of the dominoes haven't fallen yet.

Reinforcement loops are an interesting thing in systems thinking. This week when I heard the news from Atlassian another one of those love reality moments hit me. In today's post I try to roughly sketch the mental model of a system that unfolded in my brain.

A wooden bowl in a museum. You see the wood that's left. Not the tree, not the failed attempts, not the hands that shaped it. Systems thinking is about seeing what you don't see.

When everyone uses the word 'AI' and means something different, that's not a conversation. That's noise. Part 1 of the six moves of systems thinking applies the Is/Is Not List to the AI hype. Because boundaries matter.

It's time to go meta. I wrote a bunch of posts about AI risks lately. This is post explains the thinking behind the thinking. Applying M=IxO, the Love Reality Loop, and the "six moves" of DSRP that changed how I approach system thinking and in this case how I see the AI landscape.

What do railways and AI have in common? Massive capital, big narratives, broad application. The pattern repeats. Systems thinking explains why the hype always overshoots, and why the brakes never work in time.

I was just talking about this today. It's a toddler with a green crayon. Just because you see green everywhere doesn't mean it's good or it's in the right place. The code LLMs creates needs supervision and expertize to make sure it's not making things up or hiding when something isn't working.

You don't do everything by hand. You don't put everything through the machine. The hybrid woodworker knows the difference and understands what to use when. So should the developer, using AI, or basically any other tool.

Companies survived cloud outages because people understood the systems. Now we're firing those people and "replacing them" with AI. Here's my Ishikawa analysis of why that's a slow-motion train wreck. Love reality! Before reality stops loving you back.

AI turns the faucet wide open. More code, more output, better looking dashboards. But nobody's watching the bathtub. A systems thinking look at why more output isn't more value.

You prompted it. You shipped it. You own it. The lack of ownership is the real problem with AI vibe-coding. And it's also why quality suffers. Generating stuff by the dozens means nothing if you're absent from your own output.

AI gives your engineers x-time output. So you fire people. Push faster. Ship bugs with confidence. And watch your ops team burn at 2am. The defects didn't disappear. They just arrived later, at a higher cost. To the whole team and company. Slow down, zoom out and scale the whole system.

The best devs I know are lazy in the smart way. But AI is making us lazy in the dumb way. Like cleaning products that promise no scrubbing needed, we're skipping the elbow grease. Problem is, friction is how we learn. Time to learn how to distinguish necessary friction from unnecessary.

LLMs don't learn, they're not deterministic, and reviewing their output is a full-time job. Running multiple agents in parallel without proper supervision? That's not velocity. That's negligence with extra steps.

Vibe coding brought the old dev-tester silo back. Except now engineers are the ones getting half-baked deliverables thrown over the fence. One person + AI agents won't replace a cross-functional team. "You need the full package, man!" New post ✍️

Providing context to an AI is the same as describing the system in Systems Thinking. No context, generic answer. Good context, more useful answer. It's that simple. Because, you know, IT DEPENDS!