Johannes Schunter (@jschunter) header
Johannes Schunter (@jschunter) profile picture

Knowledge Management Advisor interested in #KM, #AI, #political cooperation, organizational change and #knowledgesociety. Opinions are my own

Washington, DC
Joined November 2007
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Why do orgs struggle to consistently achieve results in knowledge management? Because we fail to adapt the type of KM approach to the values & work conditions of the orgs it is applied in. Integral #KM might be the answer: linkedin.com/pulse/what-inte…
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Gene Roddenberry had a rule that drove the writers of Star Trek The Next Generation absolutely crazy: there can't be any interpersonal conflict amongst the crew. The result was a cultural product I consider almost unique: a story about good people trying to do the right thing that isn't boring. These days, moral ambiguity, twisted anti-heros and an apocalyptic vision of the future practially comes standard in our stories. Dark and complicated is synonymous with "smart," but in fact it's probably ten times harder to write a compelling story about principled, competent and psychologically healthy people. Star Trek TNG dared to imagine a future for humanity where we outgrew our petty squabbles and became our highest selves, where we travel the stars fulfilling our curiosity and genuinely making the universe a better place. Patrick Stewart's Captain Jean-Luc Picard was the indispensable anchor to this vision. Instead of William Shatner's campy swagger as James T. Kirk, Stewart's Picard made "tea, Earl Grey, hot" iconic. His depth and gravitas as a thespian specializing in Shakespeare sold the moral dilemmas that were so often at the heart of the best TNG episodes. The dramatic tension in TNG comes from people sharing the same ethical framework as a group facing difficult problems. The buck stops with the Captain. He listens to and respects his crew, but ultimately it is up to him to "make it so." For TNG to work as a show, Stewart had to take those very internal struggles between a man and his conscience and make it dramatic. He made Captain Picard's very moral seriousness charismatic. He made you believe that...yeah...of course there is a moral arc to the universe and it bends towards justice. Of course it is worth paying the price to stay true to our principles. And of course, the struggle to discern what is right action is actually much more interesting than wallowing in how broken everything and everyone is. Bon Voyage, mon Capitaine
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Johannes Schunter retweeted
What on earth do you mean "can't explain it". It's obvious. The publishing industry is serving them garbage, and ninety-five out of one hundred indie authors is a hack with delusions of adequacy. New authors can't find readers. Readers can't find new authors. They are hidden from each other by a whirling snowstorm of trash. This problem is fixable. Hell, I could fix this problem in two hours if I could get @JeffBezos to lock Kindle's senior management in a room with me and not let them out until they really listened. It works like this. Tradpub, in the 20th century, provided a variety of services for authors. For readers, it provided one important service. Curation. Publishers were investors. They invested money in manuscripts and authors, so they vetted those authors and manuscripts for what they thought people would actually read. The system worked. If you picked up a science fiction novel from a major science fiction imprint like DAW or ACE or Baen, there was a risk it might not be to your taste, but it wouldn't totally suck. Well, the tradpub business model is dead. There's no longer any point for new authors in getting a publishing deal, because they can't do anything for you that Amazon doesn't. They add no value. As an author, Amazon provides me what publishers once did.... a store to sell my books, and a press to print them. What they don't do is provide readers the service that publishers once did. They don't vet or curate novels. Why would they? They sell every novel ever, and putting up an Amazon page for your novel costs them less than a dime. You're the one doing most of the work. They have no financial interest in rejecting anyone ever, because unlike tradpub houses, Amazon is not an investor. They have no skin in the game. Now, there's a tradpub word for the unfiltered, unvetted, uncurated output of everyone who wants to be a novelist. They call it the slushpile. And their job, back when they actually used to do it, was to strain the slush for tiny flakes of gold. Now, Amazon, who replaced them, doesn't do that. Amazon just exposes readers directly to the slushpile. Now, about this moment, you might think I am complaining how a large corporation, or a billionaire, isn't fixing my problem for zero dollars. After all, we just got done spelling out that Amazon sells all the books. Why would they have any interest in promoting one over another? Well, that's where they're wrong. This occurred to me when I was writing my first novel. I happened to mention this project to a acquaintance who was highly placed at Amazon, and he told me it was a bad idea. "The book market is saturated," he said. "We get a lot of data from people's Kindle devices, and we know what they have on them, what they have read, what they haven't and how fast they read. These people have an average of five years of unread fiction on their devices. The demand simply isn't big enough to support more supply." I nodded, and smiled, and said "how interesting", and didn't argue. But in my head, at that moment, I invented an idea I call the Thiel Fallacy: the belief that because monopolies have no competitors, they have no competition. Amazon, as an entity, was effectively bored with books, because they were convinced that, as the only game in town, they were selling all the books that could be sold. That they were selling as many books as people would read. A monopoly does not automatically have 100% of the potential market. It must compete with all of its opportunity costs, including non-consumption. In plain English, if you sell all the books there are, but people can't find a book they like, they're going to just chose not to read a book. Amazon was competing with nothing, and losing. If people have five years' untold novels on their ebook reader, this not mean they have too much stuff to read. It means they have not enough stuff that they want to read. If people could go on Amazon anytime they wanted, and easily buy a book they liked, they would have zero unread material on their Kindles, not five years' worth. So why can't people find a book they like? Because no one is sorting through the slushpile. It's just all being dumped on the readers. Imagine a store that sells everything in the universe, but it's all just dumped in a big pile. No one will buy anything, because no one can find anything. Amazon is like this. It gets an A+ at delivering you the book you ordered, but a D- at helping you find the book you want. So am I saying that Amazon should start rejecting books? Should build a whole publishing department and start reading the slushpile and rejecting things? No. Curation was just how tradpub helped readers find books they liked. It's not the only way or even the best. Amazon has data. Amazon has data from book sales. Amazon has data from Kindle. Amazon has giant computational clusters. Amazon has transformer AI technology. Amazon could build the best and most infallible book-reader matchmaking service in the history of the known universe. Omniscient, content-aware, and personalized. Then Amazon would sell more books. If you control 70% of the total book market, you don't sell more books by increasing your market share. You sell more books by increasing the size of the market. Right now, Amazon sells advertising. To authors. How retarded is that? Authors don't have money. If we did, we wouldn't be writing books and making a couple of dollars a sale. Instead, Amazon needs to advertise books for free. Not just the books that sell the most, but individually targeted marketing of the books each person would most enjoy. You don't even have to ask people which books they liked. They don't have to rate them from 1 to 5. Just look at the Kindles. Look at how fast they read it. Did they stay up until 2am reading The Hobbit? Okay, what else did people who stayed up late reading The Hobbit stay up to read? Build a correlation database. Turn AI lose on it. It should be Amazon's job, not the just the authors', to find readers for books. Because authors may be motivated, but Amazon has the data, and the eyeballs, and the money. They could single-handedly revitalize publishing, they'd make more money, authors would make more money, and readers would love them for it. All winners, no losers. All it would require is a development team, and an understanding of what the real problem is. Amazon's enemy isn't Kobo or Barnes and Noble. It's apathy.
I saw of video of Ethan Hawke lamenting how he's not reading half as much as he once did. This is a much deeper and darker pattern. Some of the most avid readers I know, lifelong readers, have basically stopped. They hate it and can't even explain it, the fire's just gone out
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I really first thought this quote was made up, but it’s actually legit. And it encapsulates perfectly why working with AI is really very close to how a creative director works. If you disagree, take it up with Hitchcock, one of the greatest directors in history.
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This guy blocked me not because I insulted him in any way or because I argued harshly, but simply because he didn’t want to have to hear a different viewpoint. He’d rather just not hear about what he doesn’t agree with. Debate culture is truly dead.
why not find a singer to work with then? rather than waiting 30 years for some machine to generate it? too human for you? >My soul is in what I gave into the machine, the machine just reflects it. genuinely very sad for you
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Johannes Schunter retweeted
The words "AI slop" were coined to describe an instinctive revulsion. But the people using don't know exactly what it is that's bothering them. It's not the quality of the visuals. It's not the ease of creation. And it's not any sort of style cue. It's the lack of artistic vision. What's artistic vision? It's the combination of two things: - An idea in my mind that I want you to see in the way that I see it. - A plan for how I'm going to get it across to you. Right now, an AI can't have artistic vision, because it has no object model, so it can't have an idea, and it has no theory of mind for you or anyone else, so it can't make a plan for how it's going to show you the idea that it doesn't have. So when you tell an AI "make me a short film about a cat playing basketball" you get slop. Lots of pictures and sound, no artistic vision. But when spend thirty-seven hours alternately banging your head against the desk in frustration, and wrangling the AI until it executes your exact idea and your exact plan, then what comes out will have artistic vision, because you supplied it. Someday, AI will be able to have a true artistic vision of its own. On that day, it will be a person. Until that day, AI is a terrible artist, but a wonderful brush.
Okay, this is the best way to explain why AI Slop is not what you think. By using AI Denzel Washington to explain it all. This is actually really good. Well written, directed and generated. It's about you not needing the studios anymore.
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Welcome to the “Consciousness is primary” club @RaoulGMI! Once you see it everything changes. I would suggest looking into this excellent lecture from physicist Thomas Campbell on what this paradigm shift means & how it explains the nature of reality: youtu.be/T1vYHOPFgcg?is=osyF….
The Universal Code. Consciousness is the substrate. Everything else, physics, biology, markets, AI, is just compute running on the same operating system. New episode is up. As ever, please enjoy!
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If you meet any Germans today... #GERPAR #VAR
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Johannes Schunter retweeted
We’ve automated every single thing we can @every with AI agents. And yet there’s way more human work to do than ever. We’ve gone from 4 -> 30 human employees since GPT-3. I wrote a report on the structural reasons: how AI makes expert competence cheap, why that drives up demand for experts, and why the dynamic only intensifies as we approach AGI. After Automation: every.to/p/after-automation
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Johannes Schunter retweeted
"Wish servers" 💀
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Johannes Schunter retweeted
There are multiple realities of AI right now. And what you have access to drastically changes your workflows, trust in AI, and ability to adapt to the future. Here’s the briefest state of the AI world for business professionals (not engineers) ⬇️ Free AI - you use free ChatGPT or Claude. You think AI is decent for simple tasks but nothing to restructure your day around. You’re worried it hallucinates too much to trust with real work. On a daily basis you treat it like an intern who needs to be checked on everything. Paid AI - you pay for one (maybe more?) AI subscriptions. You’ve had at least a few moments where AI genuinely surprised you with what it could do. You use it daily for writing, research, and analysis and you’ve stopped asking “can AI do this” and youve started asking “how do I get AI to do this BETTER.” Super AI - you have multiple paid subscriptions, you either use Cowork or Claude Code for daily tasks, Claude Code or Codex to build tools for yourself. You treat AI like a teammate with a role, not a tool you open sometimes. You’ve automated things your coworkers are still doing manually. You have genuine opinions about which model is best for which task. AI isn’t just a productivity boost for you anymore. It’s infrastructure. You’re completely reinventing the way your work gets done. Basic enterprise AI - your IT team approved one AI platform (probably Microsoft Copilot, ChatGPT Enterprise, or Gemini for Workspace) in the last few years, and rolled it out with clear-ish guardrails. You have access but you also have restrictions on what data you can put in. You use it for safe, surface-level tasks and your high-stakes work still happens the old way because nobody’s built the bridge between your AI tool and your real enterprise systems (ex: email and docs). Super enterprise AI - your company has access to agentic harnesses (Codex, Claude Code) across different functions, each chosen for what it does best or just giving your employees more flexibility. Your teams have AI embedded in their actual workflows, not bolted on. Engineers have been using Claude Code/Codex/Cursor for months or years. Operations use agents for real processes. Leadership tracks AI adoption the way they track revenue (ie not just usage). The gap between your company and competitors who handed out a single Copilot license is getting wider every month. I would go so far as to say that companies not giving their employees the best AI today are shooting themselves in the foot because their employees are losing more AI trust, putting them in a worse position for eventual (hopeful) company-wide transformation.
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Johannes Schunter retweeted
karpathy is showing one of the simplest AI architectures that actually works.. dump research into a folder, let the model organise it into a wiki, ask questions, then file the answers back in. the real insight is the loop...every query makes the wiki better. it compounds.. now thats a second brain building itself. i think this is so good for agents if applied right instead of pulling from shared memory every session, they build a living knowledge base that stays. your coordinator is not just coordinating tasks anymore.. it is maintaining institutional knowledge so every execution adds something back to the base. the bigger implication is crazy tho. agents that own their own knowledge layer do not need infinite context windows, they need good file organisation and the ability to read their own indexes. way cheaper, way more scalable, and way more inspectable than stuffing everything into one giant prompt.
LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
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Happy to share the first guest post of the #SiaSays series!! This one is from Phelim (8 years old), the son of one of my former colleagues! Remember: Send me your kid's funny saying, and if I turn it into a comic, you'll get $10! #AIart
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