# Walking past the divide > Source: > Published: 2026-06-29 11:48:58+00:00 # Walking past the divide ### The future was never so unevenly distributed. Some are already 100% building with it. Some are trying to protect us from it. Both groups think the other doesn’t understand. The experiences keep being strange. Last week I was opening the VibeKode Munich conference with a keynote. It had two tracks running in parallel. One track: classic machine learning. The “old” hard core way of doing things. Just a couple o years, I went crazy when there way no application for ML in one of my clients’ product universes. But it was tough and hard core: training your own models, data engineering done the way it’s been done for ten years but deeper, better. A field with its own rigor, its own career paths, its own community. The other: vibe coding, context engineering, the full SDLC with agents in the loop. Autonomous pipelines. Local models running on a laptop at a level unimaginable 12 months ago. All vanity o small differences away, this was one direction. The direction of zero doubt, seeing valid, high quality results from the work they are doing. Solving the puzzle that remains. But the clarity and the evidence that this world exist is clear and there is no doubt. Just on example: At the social event on the first evening I ended up next to the CTO of a seventy-person company. Over the last year they’d completely pivoted from software for regulated industries like banking to building a harness for the full development lifecycle. Requirements, feedback loops, the whole thing. Supporting the AI based SDLC end to end. For builders, by builders. All in on the bet, which they don’t even see as that much of a bet. He’d gone deep. Learned the new techniques properly, been surprised how well they work. Then moved on. Not as a convert. As someone who checked, found out they work, and got back to building. As I said: just one example. But the whole conference was filled with examples like this. Small, medium, big companies already living that future. ## Looking at my feed, though … I’m not saying the feed is stupid. The concerns are based on legitimate concerns. Ongoing IP lawsuits about training data — court decisions that will reshape what’s legally possible. Well, beyond lawsuits, there is obvious, ruthless IP theft! The carbon cost of large training runs is documented and significant. In some sectors, entry-level content work has already disappeared. AI-generated content is degrading parts of the information commons in ways that are hard to reverse. These problems are real life, with super high social impact. They can’t be ignored. They are happening. And of that we don’t “know” the direction. And all of that comes with emotional baggage. People who spent years developing craft - writing, analysis, strategic thinking - are watching tools appear that can do a first raw draft of that work in seconds, ok minutes. A twenty-year career built on knowing something deeply can turn into a prompt. That’s not paranoia. It’s happening in real time.. The empirical claims of the loudest criticism are weaker, though, than research suggest. I read the research so you don’t have to. Brain rot memes - screenshots of study headlines, shared thousands of times. The lead researcher of the most-cited one said clearly: “we observed no brain rot”. These are memes, not findings. On the [main site documenting the research], the first FAQ item literally says the following: ”Is it safe to say that LLMs are, in essence, making us “dumber”?No! Please do not use the words like “stupid”, “dumb”, “brain rot”, “harm”, “damage”, “brain damage”, “passivity”, “trimming” , “collapse” and so on. It does a huge disservice to this work, as we did not use this vocabulary in the paper, especially if you are a journalist reporting on it.” So: careful with that axe, Eugene. The researchers say that because the memes and the superficial reporting are off. Research on the impact o AI on creativity research is also worth looking at more carefully. There are two types of studies. 1) AI is increasing individual creativity but decreasing collective creativity (by lowing the entry hurdle). 2) AI is lowering individual creativity but raising diversity in collective creativity. The trick of the memesters is to always use the negative part of each sentence. The [most famous experiment](https://pubmed.ncbi.nlm.nih.gov/38996021/) for research direction 1) was that the same prompt for getting started with writing an essay was given to 300 people. the same prompt. To 300 people. And surprise: The results converged. The overall verdict was: each individual for themselves wrote better stories, but overall the stories got more similar. That’s like handing 300 people a digital camera in front of the same landscape and being surprised that it’s easy to make a photo but that most photos roughly look alike. Other studies look at creativity as a systemic process in which AI can have a role. In which case the entry barrier is lowered without lowering the ceiling. Color (or later: digital photography) photography didn’t destroy photography. It changed the production process, changed what curation meant, and became its own art form. In hindsight all voices that warned of color photography were just wrong. Yes, a niche still favours black / white for individual taste, but there is no worse color art than black and white. One of the most famous color photographers - William Eggleston - made an art of using color photography to depict the mundane as an object of art. Huge criticism in the beginning, now one of the most influential photographers of all times. ## Lots of criticism is slop Looking deeper into the criticism, you can read from the contributions that the people never experienced first hand the good results you can achieve by going beyond chatting with an AI. They didn’t do the hard work of context engineering - in engineering or more general knowledge work. It means they’re judging the technology from the slop - that obviously exists. If you don’t know how to work with these tools, the output is hollow and generic. That’s a fact. You might convince your doc with a first ok diagnosis of your symptoms.But that’s it. It’s not what the tools do at depth. The people actually working with these tools aren’t debating whether they’re good or bad. They moved past that question months ago. They’re already asking the next questions: how far can local models go today in a high quality SDLC? What does the full SDLC look like when agents handle the loops? How do you supervise agents in production? What does judgment density look like when you’re not writing the code yourself? In my calls with the people learn gin AI augmented product management and in general client calls, this comes up. One participant, a product manager working through the course: *“I’m somehow lost in the universe — what ist Product Management? What is it and what not? What is the thing I am able of that the machines can’t do? ”* And then, a few minutes later, same conversation, higher abstraction level from a distance:: *“That’s what we did all the time — how cool, that there’s now a tool that glues it all together”* A digital PM at a mid-size e-commerce company, someone who’d had Claude for data work for over a year: *“In my Product-Management reality, it’s eye-opening. What you can now do with data - it’s a monster. Three years ago we had a Data Analyst in our team — but in practice that didn’t work half as good. Availability.”* A CPO managing several product teams, on the divide: *“There are some, who really already work in a totally different mode. Since months, 3 or 4 months. Others — I don’t know if they ever did anything with Claude Code yet.”* while others yet in my feed make funny, not helpful jokes on tokenmaxxing as a first approach on getting people onto the tools, sheer adoption. Not citing when tokenmaxxing was already stopped because it did it’s duty Two realities. The same industry. Sometimes the same company, the same org chart. ## Inverse time arrow: from forward directed to backward directed Something is historically strange about this moment. Technology has always been the thing that directed the arrow of time forward. For better or worse. Easy in hindsight. if we lied it or not, the direction was always the same: forward. The Luddites weren’t ignorant, they were skilled craftsmen who understood exactly what the mechanised looms would do to their communities. They broke the machines anyway (for the best reasons) but - again, I we like it or not, lost anyway. The carriage industry understood the car. Taxi drivers understood Uber. In every case, the people whose livelihoods were threatened knew precisely what was coming and fought it and the arrow moved forward regardless. The dissent always came from outside tech (simplification, sure) - from the people absorbing the costs, from communities being displaced, from regulators trying to catch up. The people building the technology believed in it. That was the structure of the argument. Now something different is happening. The people resisting - loudly, publicly, with manifestos - are not outside the industry. They’re inside it. They are, in many cases, the people who spent the last fifteen years evangelising change, embracing ambiguity, disrupting themselves. They were the apostles of forward. And now they are writing documents that say: stop. Protect. Don’t let this one through. And they feel like a majority. That’s interesting and new. I think it comes from the fear that the arrow might be turning — that this is not just another wave of automation but something that touches what people understood to be genuinely defining who we are as humans. The writing, the reasoning, the judgment. The fear is probably also very rational. A lot of what defines us now becomes automated, replaced, displaced. It touches our human exceptionalism. And the question of AGI - is this really human - is casually not helping but simply noise. The technology we have already automates a lot of what we felt human “good enough”. ## Two manifestos, one braking with the past, the other protecting from a future I keep coming back to two documents. The Agile Manifesto — 2001. Seventeen people in a ski lodge in Utah. Four values, twelve principles. The document broke cleanly with what came before. Heavyweight process, documentation over working software, contract negotiation over people. The manifesto didn’t even argue. It simply pointed forward. It polarised. Maybe unthinkable today after all the watering down of whatever agile was in the beginning. But the polarisation was because it wanted to get rid of the past. People felt the old way of doing things was a waste of valuable life time. So it was perceived as a threat. A small niche pointed towards a future. Enter [The Makers Manifesto](http://makersmanifesto.org). It mentions leaning on the Agile Manifesto. But the direction is inversed. Where the Agile Manifesto was a break with the past, the Makers Manifesto protects the past from the (bad?) influence of the present or future. It lists things we should hold onto - craft, judgment, real understanding - against the threat of a new technology, the future. The argument is: don’t let this compromise your values, your work, what makes you *you*. There are a million issues with the manifesto and I don’t want too much of a focus. The main thing is: It wants to keep the present present. Things should not change *so much*. While the Agile manifesto said: here’s what’s next. The other says: hold your horses. Tha name itself is another problem. A makers manifest that task exclusively about validating but no opening discovering. Which asks for validation through paying customers and thus probably excludes real makers working simply on open software or whatever. It’s a really cool, legit Product Management manifesto in a very specific Horizon 1 scenario of Product Management. It does that well. I only mention it becasue it signifies this moment. And I understand both impulses completely. The fear is real. The things being protected are real. And simultaneously, when I sit with a CPO whose team is splitting in half — some in a completely different mode for months, some who haven’t touched it once — I can see the gap opening faster than any conversation about protection can close it. And the gap is not helpful Each organisation needs to cleary define their own future. You will not survive a divide for long. That the bubbles exist and are seemingly polarised is tough but you won’t survive it in your org. ## Quality is not the bottleneck any longer, explainability is Six months ago the question in my sessions was *whether the output was any good.* Now the quality is premium — the quality is better than expected, sometimes uncomfortably good. And that has created a different problem - in my life and bubble. When I hand someone a finished output — a strategy analysis, critical assumptions about your market, a simulation of a workshop you haven’t run yet - the push back is because the analysis is too clear. Not because it’s wrong. It’s too uncomfortable without human trabalstion layers and buffers. In knowledge work outside of coding. if you don’t watch it happen, you can not process the sense making. It’s as if you missed the meeting. *How did you get there? Why should I believe this? You need to see the agents n discussion, discovering the verdict. * In a real workshop that doesn’t happen. People share in the room, argue, ight over wordings, yet and yet again. It’s part of the alignment. The outcome is theirs — even if a twenty-minute simulation would have reached the same conclusions. They saw it happen. If they gave one idea into the simulation, they own it. I run simulations of strategy workshops before the actual sessions. The simulation is often sharper and faster than the human discussion. That’s not because humans are weak, it’s because the role of a workshop is not exclusively “thinking hard” but also “alignment”. Simply handing my agent output, of course, does not help. As a participant said: *“Handing over the protocol of the simulation helps a lot, to follow the thinking.”* Not just the result — the path to the result. While it’s clear that this happens, it also shines a light on the other aspect: A huge function of us humans mangling wordings is emotional or sometimes vanity, not factual. It’s cool. It’s fine, but it is what it is. Quality is fine. The hard part in 2026 is: how do you make results that nobody watched being produced feel like something people can stand behind? **Walking past the divide** I understand every protection instinct. I honestly do. The concerns about IP, about ecology, about what happens to the people whose entry-level work has already disappeared, none of this gets resolved by building faster. They require political decisions, legal frameworks, redistribution of benefit. I’m welcome all of them. And I understand the sense that the time arrow is turning. That for the first time, the technology feels like it could take the thing you thought was yours. That this is the matrix, the singularity. (Well, if it is, we have bigger problems anyway ;) At the same time: I no longer have energy to spend on the question of whether this really works. If it’s really true that Boris Cherny works on loops that write the prompts, that he does not write the prompts for his agents anymore, while weeks ago the reckoning was that he does not look at the code and weeks before that, that he did not write any code by hand any longer. The CTO I met at the conference checked, found out it works, and got back to building. The people in my sessions who are four months in are operating in a different world from the people in the same company who haven’t started. The gap between those two groups is not closing on its own. And I decided not to convince people on “how well this works”. Who wants to see it, learn it: welcome. Who doesn’t: great personal decision. I can’t argue against it. Who am I? The open questions are: how do you build trust in outputs people didn’t watch being produced? How do you bring a team across when some are deep in and some haven’t started? How do you talk to a C-level who saw a demo and now thinks everything is trivial? How far can local models go? As a company: define your future clearly and make it happen. Wherever in the spectrum you want to be. But don’t have it figured by your employees. The number one hint I have for you: Create that clarity and make it happen. The rest follows. The other discussion, for a couple of months, had its place. I leave it behind.