Latest Posts (20 found)

our workplace LLM mass delusion

I can't help but wonder whether we will look back on this AI hype in the workplace with confusion and embarrassment. If we indeed progress into a future where the bubble will burst, models will further close up, become too expensive for the average user, enshittified, or really specialized for specific fields and most promises end up not fulfilled, how will employers everywhere play this off? How will employees recover from witnessing this cultish environment suddenly dropping off as if nothing happened? My employer, for example, struggles with funding. Open positions are not to be filled and will just fall away; employee bonuses for great work have been cancelled 2 years ago due to the tense financial situation; necessities fell away with a message to just " find a way to deal with it ". Several departments are completely overworked with no help in sight, and are just asked to cut corners. Important licenses and databases are just dropped to save money. This is the backdrop to our AI adoption in the workplace. Still, somehow, there is enough money to hire consultants that advise to go all in on AI for a possible future where money can be saved, and enough money to pay external companies for LLM workshops and seminars for employees for years, and enough money to pay for licenses of both ChatGPT and Copilot. That means: The employee bonuses that should go to all the hardworking employees, and the money to further support our work, is going to grifters, security risks, bad workshops that are not teaching anything remotely usable for our work, and technofascists. Not only that! We have recurring house-wide meetings where groups are asked to show off their LLM projects. They register them, try them out for a couple months, and then come back presenting their results. I have attended all of these meetings so far, and there was not a single one that actually worked out . All projects ended with the conclusion that this isn't workable, that this isn't saving time, or that it over-complicates things. Hundreds of people, different teams, people enthusiastic about AI, all kinds of projects, and there wasn't a single success . All kinds of workshops, "prompt-engineering", custom GPTs and skills, pre-prepared documents and templates could not make something truly effective and reproducible in our field of work (not anything coding related!). It was a messy gamble every time . It took a significant amount of time to fine-tune everything, to repeat the task, to verify the output, and correct mistakes before continuing with the rest of the workflow. Not considering this or that document, hallucinations, inability to fill in documents correctly or edit them were the biggest complaints. Even on an Enterprise license, the restrictions were too great. But wait, there's more! We also have house-wide meetings where employees show off how ChatGPT can be used regardless of specific projects; just general use cases for the workday. Let me tell you what great things were shown off. For one , it was shown that you can ask the bot how it feels today. That wasn't presented as a joke, or being sarcastic; no, it was shown very seriously, I guess under the guise of how cool and futuristic and human it is. I'm getting really upset here at the point of writing this, because I have to fight hard to get the funding for the database my team needs for my work and have to justify it every year, and I know that in any other contexts, or just 5 years ago, they would have laughed in your face if you suggested to get a subscription in the thousands to enable employees to have a pointless conversation with a bot. Hello, we have shit to do over here, departments are drowning in work, and you wanna have software that talks to you? That would have been the response, and it is the correct response still!! People like that need to be treated like the fools they are, and we need to challenge them more! Next up was the great use case of downloading the cafeteria menu (which is a 1 page nicely designed Excel sheet, like a timetable, showing the different options for each day) from the intranet, giving it to ChatGPT, and asking it what's for lunch on Wednesday. I wish I was joking . I WISH! The bot spat out a longer answer than reading the entire sheet would be. Downloading and uploading and writing the prompt took longer than just reading the sheet. You can see what's for lunch on Wednesday with one glance already. No bot needed! The other general use case presented to us (by our head of IT, no less) was that if we are not sure whether something is a spam mail, phishing attempt, a mail with a suspicious attachment, whatever... we should save it to our Desktop, upload it to ChatGPT and ask it. Good god. I am still in disbelief. I'm sorry, but I don't want the less technically inclined employees among us to save anything shady onto their work laptop. Come on now. What is happening? Have we lost our minds? Intentional or not, AI is seemingly great at amplifying the Dunning-Kruger-Effect in people, making everything they attempt with it seem smarter and justified to them, packaging every fart in a nice bow that makes it seem deep initially. People can pretend they’re now doing something really important and groundbreaking while using the tool for completely mundane and worthless tasks that are better handled differently. Defenders of the tech can feel like they’re part of something big and revolutionary and fantasize about the day they will be proven right and all the critics will shut up or apologize (like my conspiracy theorist dad, who still clings to the same prophecies after over a decade, hoping to be ahead of the curve and right for once in his disappointing life). It’s sad, because it feels like a completely out-of-control delusion; you see smart and capable people with lots of responsibility at work suddenly turn into a shill for these AI companies without any rhyme or reason. A highly qualified person, suddenly reduced to the same presence as a door-to-door salesman lying about how well the cleaning product really works, making up use cases that are neither useful nor working right. How is a person like you suddenly reduced to praising the option to ask a bot to summarize a damn 1 page lunch table and present it as a good use case in a company-wide meeting? What have you done to arrive at this point? It’s pure hype, eerily much so, and these people cannot possibly admit that. We have no specific problems it can solve in this workplace, at least 90% of the employees do not have work that would profit off of what Copilot etc can do; yet we attempt it anyway, each attempt worse than the prior one, inventing possible uses and creating problems where there are none, just to be able to burn tokens and justify a subscription, to cosplay the people in Sci-Fi media and have something to show upper management (" At least we tried "). We fall behind on our daily work to train an LLM, beg and plead with it and dance for it like court jesters, and poke around in the shit it spits out. If you ask around at my workplace, any use is good because it is a use and we are exploring and playing . It completely minimizes the time waste, the money sink, the effect of each use, and the powerful institutions behind these tools. And I just wonder: How did this befall us so quickly? There is never money for anything, but this unreliable tech with a huge upfront cost got through immediately? New tech usually passes the public sector by, but this one got all the attention? It takes years or even a decade to implement any sort of change or new ideas into this beast, yet we had the infrastructure and organizational bandwidth to deal with AI up within a blink of an eye? It is creepy to realize how capable a place really is, and how easily things can be implemented - if the leadership just wants to. It's a complete mask-off moment, underlining that it is never impossible or slow-going by default; it is intentional, by design, and could be improved any time. This is a completely trust-shattering moment for any employee. This is why I asked at the start of this post how we are supposed to move forward from this at some point. How are we all collectively supposed to forget and move past experiencing a point when the respectable elders in an institution have completely and totally embarrassed themselves in the name of "progress"? When all the gates and wallets have been opened for this utter disappointment, showing that the obstacles for implementing anything thought to be inherent and unavoidable in the organization are just a fluke, a lie, an arbitrary thing? How it all created a culture of feeling repeatedly gaslit over months about this whole assessment, as if you must be the one that is insane? I cannot forget this at all. This is my second Covid. As a final note: If none of that is happening in your workplace or life in general - genuinely good for you. Should be like that everywhere, hope that happens to me too eventually. I applaud you for the skilled and competent people in your life that choose AI use wisely, make the most of it, and offer good solutions. Happy for you if you work in an industry where its use makes sense and produces good output. But unfortunately, places and situations like the above exist, so let people commiserate about the insanity in them without attempting to deny what we’re experiencing. Reply via email Published 10 Jun, 2026

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Fable 5, Anthropic Alignment, AI Tiers

Fable 5 is the public version of Mythos, and while it is very capable it sets some troubling new precedents.

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Initial impressions of Claude Fable 5

I didn't have early access to today's Claude Fable 5 release, but I've spent the past ~5.5 hours putting it through its paces. My initial impressions are that this is something of a beast . It's slow, expensive and has been quite happily churning through everything I've thrown at it so far. As is frequently the case with current frontier models the challenge is finding tasks that it can't do. First, let's review the key characteristics. Anthropic claim that Claude Fable 5 offers the same performance as Claude Mythos 5, except with much more strict guardrails in place to prevent it being used for harmful things. Those guardrails trigger often enough that the Claude API has new mechanisms for letting you know when you hit them, and even has a new option to request it falls back to another model automatically if something gets rejected. Claude Mythos 5 is out today as well, Anthropic say it "Shares Claude Fable 5's capabilities without the safety classifiers". The models have a 1 million token context window, 128,000 maximum output tokens and a knowledge cut-off date of January 2026. They are priced at twice the price of Claude Opus 4.5/4.6/4.7/4.8: $10/million input tokens and $50/million output tokens. There's no increase in price for longer context usage. Other than that the upgrade guide is substantially thinner than the similar guide for Opus 4.8 . The best way to describe Fable is that it feels big . Not just in terms of speed and cost, but also in how much it knows. Here's an example prompt, which I used to compare Fable's knowledge with Opus 4.8 (typo included): Opus 4.8's reply started like this : I don't have a reliable, comprehensive, and date-verified list of all of Simon Willison's open source projects, and I want to be honest about that rather than risk giving you inaccurate dates or fabricated entries. Here's what I can tell you with reasonable confidence: Well-known projects by Simon Willison: LLM (~2023) – A command-line tool and Python library for interacting with large language models. Datasette (~2017) – A tool for exploring and publishing data, probably his most prominent recent project. sqlite-utils (~2018-2019) – A Python library and CLI tool for manipulating SQLite databases, often used alongside Datasette. Django (2005) – He is one of the co-creators of the Django web framework, along with Adrian Holovaty, originally developed at the Lawrence Journal-World newspaper. He also maintains a large ecosystem of Datasette plugins and many smaller utilities (often with names like , various tools for importing data, etc.). That's not bad. Here's the start of the response for the exact same prompt from Fable 5 - both models were running without access to search: I think you mean Simon Willison — the prolific open source developer, co-creator of Django, and creator of Datasette. An important caveat upfront: a truly complete list isn't feasible. Simon Willison is famously prolific — he has published hundreds of repositories (Datasette alone has 100+ plugins, many written by him), and my knowledge has a cutoff date, so I'll be missing his most recent work. Below are his notable projects with approximate initial release dates, most recent first: (Here's GPT-5.5 for good measure . It listed even more projects than Fable did!) In the past I've stated that I don't care about how much models know - I want them to be able to manipulate text and code in useful ways and actively look up the information they need via search tools, not bake it into their weights. But knowledge like this is a reasonably good proxy for model size - you can cram a whole lot more details about the world into a larger number of parameters. Does knowing more stuff mean the model is better at the tasks we pose to it? I can certainly imagine how a coding model with deeper knowledge of modern libraries and patterns could crunch through coding tasks more effectively. Is Fable really bigger than Opus? Anthropic haven't said anything about model size, so all we have are tea-leaves, but the speed, pricing and my own poking at its knowledge make me think that it's a large model. Maybe the largest yet from any vendor. Anthropic made Fable 5 available across all of their surfaces - the Claude.ai chat interface, Claude Code for web, Claude Code CLI and Claude Cowork as well. The model is available "until June 22nd" on the subscription plans (I'm on $100/month Max at the moment), after which it will be billed extra. Claude.ai is often under-estimated. Since September 2025 every chat has had access to a full container environment to run code, including the ability to install additional packages and even clone repositories directly from GitHub. Last week I released micropython-wasm , a Python library that uses wasmtime to run a custom build of MicroPython in WebAssembly to act as a sandbox for untrusted Python code. I decided to see if Fable could upgrade that to running full Python instead. I started with this prompt: Fable identified that it could use Brett Cannon's cpython-wasi-build builds for this, but was unable to download them itself due to environment restrictions. So I grabbed the two zip files from that page and uploaded them to Claude: ( , as attachments) And that was that. It churned away for a few minutes and got the entire thing working. Part of the response included: I tried the cleaner single-zip-stdlib approach to shrink the filesystem surface, but CPython's bootstrap fails to find from inside a zip without more prefix finessing — the directory-preopen approach works reliably, so that's what the PoC uses. The zip path is solvable but needs /frozen-getpath work. Then a little later: ... and it gave me this 13.9MB cpython_wasm-0.1.0-py3-none-any.whl file. You can try running Python code in a sandbox using that wheel URL and like this: Here's the full chat transcript . This was a very strong start. Before I'd realized it was Fable day, my stretch goal for today was to add a new feature to Datasette Agent : I wanted tool calls within that agent software to gain the ability to pause mid-execution and request approval directly from the user. This felt like a suitably meaty task to throw at the new model. Over the course of the day Fable not only solved that problem , it also identified and then implemented four issues in my underlying LLM library that would help support this kind of advanced pause-resume mechanism in tool calls. It got everything working first using somewhat gnarly hacks, but the moment I told it that changes to LLM itself were in scope it set to work unraveling the hacks and turning them into supported features of LLM instead. My stretch goal turned into LLM 0.32a3 , almost entirely written by Fable. Here are the release notes: Driven by the needs of Datasette Agent 's human-in-the-loop feature, made the following improvements to how tool calls work: I'm really impressed with the quality of API design, tests, code and documentation that Fable put together for this. I spent several hours on it today, but it feels like several days' worth of work. I recently started using AgentsView to help track my local LLM usage across all of the different coding agents. I published a TIL today about adding custom Fable pricing to that tool, which I expect will not be necessary in the very near future. After setting the price, I ran this command to start a localhost web server to explore my usage: Here's the treemap showing the breakdown of my Fable usage across various projects today: I used $110.42 worth of tokens today, all as part of my $100/month subscription. I ran "Generate an SVG of a pelican riding a bicycle" against all five thinking effort levels with Fable. Here are the results , including the token cost for each one: It's interesting that high ended up using fewer tokens than medium for this particular run. Here are the Opus 4.8 pelicans for comparison. You are only seeing the long-form articles from my blog. Subscribe to /atom/everything/ to get all of my posts, or take a look at my other subscription options . LLM (~2023) – A command-line tool and Python library for interacting with large language models. Datasette (~2017) – A tool for exploring and publishing data, probably his most prominent recent project. sqlite-utils (~2018-2019) – A Python library and CLI tool for manipulating SQLite databases, often used alongside Datasette. Django (2005) – He is one of the co-creators of the Django web framework, along with Adrian Holovaty, originally developed at the Lawrence Journal-World newspaper. files-to-prompt (April 2024) – concatenates files into a single prompt for LLMs datasette-extract (2024) – plugin for extracting structured data using LLMs LLM (May–June 2023) – CLI tool and Python library for interacting with large language models, plus a large ecosystem of plugins (llm-gpt4all, llm-mistral, llm-claude, etc., 2023 onward) symbex (June 2023) – search Python code for symbols ttok and strip-tags (May 2023) – CLI tools for token counting and HTML cleanup for LLM pipelines datasette-lite (May 2022) – Datasette running entirely in the browser via WebAssembly/Pyodide shot-scraper (March 2022) – automated website screenshots via Playwright s3-credentials (November 2021) – CLI for creating scoped AWS S3 credentials django-sql-dashboard (2021) – SQL reporting dashboards for Django Dogsheep suite (2019) – personal analytics tools: twitter-to-sqlite, github-to-sqlite, healthkit-to-sqlite, dogsheep-beta, etc. sqlite-utils (2018) – CLI and Python library for manipulating SQLite databases Datasette (November 2017) – his flagship project; tool for exploring and publishing data csvs-to-sqlite (2017) – convert CSV files to SQLite Various early tools (~2007–2010) – soupselect, json-head, geocoders, and others Django (developed 2003–2005, open-sourced July 2005) – co-created with Adrian Holovaty at the Lawrence Journal-World Tool implementations can declare a parameter named in order to be passed the object for the current invocation. This allows them to access the current . See Accessing the tool call from inside a tool . #1480 Every tool call is now guaranteed a unique - providers that do not supply one get a synthesized -prefixed ULID. #1481 Tools can raise a exception to cleanly pause the tool chain, useful for things like waiting for human approval. The exception propagates to the caller with and (completed sibling results) attached, and no model call is made with a placeholder result. See Pausing a chain from inside a tool . #1482 Failure semantics for concurrent tool execution: async sibling tool calls always run to completion before a pause or hook exception propagates. #1482 Chains can now resume from a history ending in unresolved tool calls: the calls are executed through the normal / machinery before the first model call, skipping any that already have results. The method also accepts a new optional argument for executing an explicit list of objects in place of the calls requested by the response. See Resuming a chain with pending tool calls . #1482 Fixed a bug where the async tool executor silently dropped calls to tools not present in - these now return results, matching the sync executor. #1483

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Surprise! Pay $1000

My turn writing for the Forestwalk blog : Now typically, when you try a SaaS product for free without a credit card, and you hit the limit, you get cut off. Also known as “disruption to your service”. Instead, we were invoiced $1000, which was immediately overdue. Genuinely curious how common this practice is. Just because I was surprised by it, doesn’t mean it’s unheard of.

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Herman's blog Yesterday

Active recall

I'm currently reading What I talk about when I talk about running by Haruki Murakami (thanks for the recommendation, Rishi) and a line stuck out to me: Perhaps I'm just too painstaking a type of person, but I can't grasp much of anything without putting down my thoughts in writing. This line resonated with me because I've also found that the best way for me to understand a concept or idea is for me to write about it. Reading only gets me so far. The act of articulating what is stored in my brain into something legible and understandable is thinking and understanding. I worry about the education system, which has had the entire concept of writing as a form of study upended by AI, and universities have seen their first decline in literacy and comprehension ever recorded. But this isn't what this post is about, this post is about remembering things. I have often received complements from friends and strangers alike on how good my memory is. This tends to be complimenting what is generally considered a static attribute, since most people think of memory as being an immutable characteristic of a person. I can say with certainty that this isn't the case. The reason I remember things and confidently convey them is usually because I've written about them at some point. This isn't relegated to ideas and concepts, but events in my own life, since I keep a daily journal and have for over a decade now. I've written more about this here , here , and here . There's a study with the mouthful of a title Retrieval Practice Produces More Learning than Elaborative Studying with Concept Mapping by Karpike and Blunt (2011) that I read this morning where they had college students read short educational texts, then study them in different ways: rereading, building concept maps while looking at the text, and free recall (reading once, then writing down everything they could remember on a blank page without looking at the source material). On a delayed test about a week later they found that free recall produced better information retention than rereading or creating a concept map. There's an extra detail emphasised that's the real kicker: in one version of the experiment, the final test itself was to produce a concept map — yet the students who had studied by free recall still outperformed the ones who had studied by making concept maps. So even when the practice method (free recall) didn't match the test format (concept mapping), the act of retrieving from memory beat the more elaborate, intuitively "deeper" study technique. I realise that, inadvertently, this is exactly what I've been doing the entire time with my blog, notes, and journal. The reason I can recall information so well is because I have read, watched, experienced, or discussed a concept or idea, then wrote about it on my blog, or in my notes or journal. I was practising free-recall this whole time and managed to trick people around me into thinking I'm just unnaturally good at remembering things. This concept isn't new. The process of studying has historically always involved some form of note-taking and active recall. I wasn't a good student (at all) and never learnt how to study. The structure of school (and government school in South Africa, no less) was not set up in a way that allowed knowledge to stick. I feel like one minor tweak to the school system should be a course dedicated to how to study and effective note-taking. It's a pity I didn't have these skills in school because it would have made it a lot more pleasant. There's nothing quite as frustrating as having to learn something and despite many late nights studying, it refusing to lodge itself in my brain. To come full circle, there's nothing particularly special about the way that I take notes, write, or journal. It is the act of doing it which is important. I tend to journal about my yesterday each morning before getting into work. As for notes, whenever there's something interesting I'd like to remember I write it in my notes.txt file, which is a large file full or random scribbles. As for writing on my blog, I don't have a schedule or explicit plan, I just write when there's something I've been thinking about that I believe is interesting. The interesting part is that I don't really read my notes or journal. The act of writing is the important bit. In a nutshell: I write for you all, but I guess I also write for me.

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Unsung Yesterday

“Imagine being a pixel on an old Pac-Man game.”

1. In last year’s essay at Tedium, Ernie Smith investigated the rise and fall of screensavers , those pieces of software that peaked in the 1990s, originally meant to prolong the life of your display by kicking in after a period of inactivity, but eventually becoming “self-contained art projects.” As it always happens, what we thought was the first screensaver – Peter Socha’s SCRNSAVE – was far from the original idea: The accepted answer is often the easy answer, and when doing a little research, you can bust past that to the point of truth. [… But] while Socha deserves credit for popularizing the technique with a broad audience, the idea wasn’t totally new. See, during the 1970s and early 1980s, numerous hardware and software developers attempted to build things in the same wheelhouse as Socha’s early screen saver. The difference was, they weren’t for the IBM PC or even for a computer at all. Rather, they were for dumb terminals or video game systems. The prior art includes “attract mode” in arcade games, and is accompanied by the absolutely terrifying, jump-scare-adjacent photo of CRT burn-in you wouldn’t want to miss. 2. This is an enthralling 1-hour-long video by Savvy Sage that talks about the immense popularity of After Dark, a collection of screensavers for Macs and PCs, of the “flying toasters” fame: = 2x) and (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/yt1.2096w.avif" type="image/avif"> = 3x) or (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/yt1.1600w.avif" type="image/avif"> This video absolutely blew my mind. I had no idea the screensavers were so popular that they had their own (official) merch and (unofficial) guidebooks , and that the company that made them employed over 100 people – half of them artists – and had tens of millions of dollars in revenue. There’s tons of inevitable scope creep – screensaver remixers! screensavers with sound! interactive screensavers! licensed screensavers? – but also attempts to branch out to new ideas. The video is great in documenting everything so you actually see all that’s talked about, in copious detail. And since this is a blog about craft, obligatory caveat: most of these screensavers are absolutely garish , although one also has to account for state of the art of computer graphics at that time. 3. After Dark had a fish aquarium and so did competing products from Microsoft and Fifth Generation Systems – but in a moment likely recognizable to many people reading this blog, one person got fed up with how bad they all looked and created his own screensaver that became as well known as the flying toasters. This 16-minute video by LGR talks about the story of The Marine Aquarium Screensaver: = 2x) and (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/yt2.2096w.avif" type="image/avif"> = 3x) or (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/yt2.1600w.avif" type="image/avif"> This, too, had a lot more going on than I expected, including the eventual appearance of a hall-of-fame checkbox “Starfish allowed on glass.” 4. Another popular screensaver was Windows’s 3D Pipes, whose (much shorter) origin story is documented by Raymond Chen on his excellent and long-running The Old New Thing blog . But it’s the first comment there that steals the show: These were mesmerizing, but quite often IT folks would enable these on Windows Servers, and they would essentially “bring down the system.” See, they were CPU intensive and would take a tax on the system essentially stealing CPU time away from the business application running. […] I can recall the first time getting a call on this – and back then things were remote, etc. sometimes using PCAnywhere – and then I saw 3D Pipes running. Just told them to turn it off – and done. From that point forward the first question asked of our customers was “are you running any screen savers?” 3D Pipes also had some interesting lore behind it : A customer complained that they were losing productivity because employees were spending too much time running the 3D Pipes screen saver and waiting for teapots to appear. They requested an option to increase the likelihood of a teapot, so the employees would be placated more quickly and get back to their work. If this doesn’t remind you of that scene from The Office with another famous screensaver … 5. In Smith’s essay , he posts Socha’s recounting of the exact logic of his early screensaver: How does Scrnsave do all this? The clock inside your PC ticks 18.2 times per second. Scrnsave contains a three-minute counter that starts at 3276—the number of clock ticks for three minutes. On each tick of the clock, Scrnsave subtracts one from this count, and it turns off the screen when it reaches zero. […] Each time you push or release a key, the keyboard sends an interrupt signal to the PC. Scrnsave intercepts this interrupt; each time you push or release a key, Scrnsave resets its counter to 3276 (three minutes) before passing control to the ROM BIOS routines that read keystrokes. Scrnsave also resets its counter to 3276 every time a program sends characters to the screen. By intercepting these last two interrupts, Scrnsave can tell when you need to have the screen active, so it won’t shut out the lights unless you sit back or walk away for three minutes or more. It’s a very simple algorithm, but I was amazed by it, because that’s exactly the same algorithm you would use – in reverse – for any sort of debouncing that’s crucial in good front-end engineering; there is something kind of beautiful about these universal algorithms floating around, kind of like math quietly ruling the world around us. But on that note, one last video. Do you remember that well-known palette-cycling waterfall I posted some time ago ? = 2x) and (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/1.2096w.avif" type="image/avif"> = 3x) or (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/1.1600w.avif" type="image/avif"> This wasn’t as much a “prevent CRT burn in” screensaver as it was “a piece of standalone, repeating, interactive art” screensaver. It graced many an Atari ST display. Well, in April, Techmoan unpacked sort of a “prior art” to even that – a picture frame that simulates a waterfall (the relevant video segment starts at 6:04): = 2x) and (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/2.2096w.avif" type="image/avif"> = 3x) or (width >= 700px)" srcset="https://unsung.aresluna.org/_media/imagine-being-a-pixel-on-an-old-pac-man-game/2.1600w.avif" type="image/avif"> The art is (again) garish, and there is no screen to save here, but also curiously – there are no electronics at all, either. How was it made? I’ll let you click through to find out. It was fun for me to revisit this strange moment in time and learn more. It’s not just that there were tons of shared ideas, repeated algorithms, independent inventions, and one-upping each other. What stood out to me was also how many people engaged here did other things I used and admired – SCRNSAVE’s Peter Socha created the absolute 🐐 Norton Commander , Jim Sachs of the marine aquarium screensaver fame did graphics for the legendary Defender of the Crown game, a few people at After Dark also made the original zoom peek gesture before that, and the incredible The Incredible Machine after. It seems like a fascinating time that attracted people equally interested in tech as they were in its creative uses. #easter eggs #graphics #history #real world #youtube

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Accelerating Transactional Execution via Processing-In-Memory

Accelerating Transactional Execution via Processing-In-Memory André Lopes, Daniel Castro, and Paolo Romano EUROSYS'26 This paper describes a way to implement OLTP for a processing-in-memory architecture. As with other academic research, it uses UPMEM ( here are two summaries of papers that rely on UPMEM). Something I found surprising in this work is conflicts can cause transactions to abort, even if all transactions only access data in the same UPMEM bank. A UPMEM DIMM is like a DRAM DIMM, but each bank contains a multi-threaded in-order core which can access data from the bank it is co-located with. This paper calls these processors DPUs (some other papers call them IDPs). The only way for two DPUs to communicate with each other is for the host CPU to read data from one bank and write it into another. The system described in this paper is called PIM-TIDE. It assumes that transactions come pre-sliced into computational graphs comprising subtransactions. A single subtransaction only accesses data within one bank (and thus executes on a specific DPU). Users of PIM-TIDE do not need to know exactly which data words a subtransaction will access, but they do need to be able to restrict a subtransaction to only access data from a specific bank. This works well on TPC-C style transactions where most of the database can be partitioned on . The host CPU groups transactions into batches and sends work to the DPUs one batch at a time. Within a batch, transactions are categorized into two groups: Local transactions execute entirely within a DPU Distributed transactions execute across multiple DPUs All subtransactions associated with distributed transactions within a batch are assigned a unique sequence number, which determines the order in which the subtransactions will commit. Local transactions are not preassigned to a commit order. DPUs first process all distributed subtransactions in a batch and then execute all local subtransactions. Algorithm 3 illustrates PIM-TIDE’s concurrency control scheme for dealing with intra-DPU conflicts between subtransactions. is stored in fast on-chip memory and is indexed by a hash of the word address. If a transaction aborts, state can be rolled back and the transaction is retried. All transactions assigned to a DPU will commit eventually. Inter-DPU conflicts are handled by deterministic concurrency control (i.e., the pre-assigned sequence numbers). Source: https://dl.acm.org/doi/10.1145/3767295.3803621 Results Fig. 3 compares performance of PIM-TIDE vs a CPU baseline for a mix of TPC-C transactions, wow that is a significant speedup. I believe all transactions/subtransactions are written by hand in C code that is compatible with UPMEM DPUs. Source: https://dl.acm.org/doi/10.1145/3767295.3803621 Dangling Pointers TPC-C is easy to partition; I wonder how well PIM-TIDE does on workloads that are not as partitionable. Also, this scheme doesn’t seem to allow for interactive transactions, how important are those in the real world? Thanks for reading Dangling Pointers! Subscribe for free to receive new posts. Local transactions execute entirely within a DPU Distributed transactions execute across multiple DPUs

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Kev Quirk Yesterday

📝 2026-06-09 13:00

Yep, I definitely won't buy a bigger #3DPrinter. 🫣 Thanks for reading this post via RSS. RSS is ace, and so are you. ❤️ You can reply to this post by email , or leave a comment .

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Stratechery Yesterday

The iPhone’s Last Stand

Listen to this post : Apple fans would, for years and years, sneer at Microsoft’s penchant for talking about products that may or may not ship, deriding them as vaporware. After Apple’s bungled 2024 launch of Apple Intelligence and new Siri , however, vaporware is fair game, and just in time for this Article. Last week, at its annual Build developer conference, Microsoft put forth a vision for a new ecosystem of hardware devices under the banner of Project Solara : The concept — which isn’t entirely clear from that video, but was more fully explained on stage — is that in the future you will be surrounded by an ecosystem of devices, none of which stand alone, but are more like portals to interact with your agents, which live in the cloud. In other words, as I wrote in February, Thin Is In : This is even clearer when you consider the next big wave of AI: agents. The point of an agent is not to use the computer for you; it’s to accomplish a specific task. Everything between the request and the result, at least in theory, should be invisible to the user. This is the concept of a thin client taken to the absolute extreme: it’s not just that you don’t need any local compute to get an answer from a chatbot; you don’t need any local compute to accomplish real work. The AI on the server does it all. I made the case in that Article that server-side inference would dominate AI workloads, thanks in particular to increasingly high memory demands for agents. What I found intriguing about Microsoft’s vaporware, however, is that it showcased a use case wherein this thin client approach was compelling for reasons beyond KV cache. Specifically, for most of tech history computing has been indistinguishable from interacting ; that’s why we place so much value on new input methods, as they often set off new paradigm shifts. By the same token, the problem with wearables as the paradigm beyond the iPhone is that interacting with them generally sucks. Sure, you can imagine a future where voice interaction is completely seamless or where a device can “see” what you see, but anything longer than a few seconds is much less convenient than simply swiping on your phone. Agents, however, compute on your behalf, without any interaction necessary: a few seconds is all you need to get work done for hours — at least in theory. Apple, a company that can actually make devices, was under heavy scrutiny going into yesterday’s WWDC keynote for a different concern: can the company make AI? And, if your standards are the state of the art in AI circa June 2024, when Apple took their first crack at answering the question, they did quite well. The company’s pre-recorded keynote took great pains to show actual demos — spinning indicators and all — and they worked! Here was the first one of what Apple is calling “Siri AI”: What’s fascinating about this specific demo is that it also showed just how far behind Apple is. New head of Siri Mike Rockwell successfully used Siri to set a reminder to enter a lottery for concert tickets, demonstrating context awareness and the ability to interact with the Reminders app through Apple’s App Intents framework; what would have been state of the art would have been asking Siri to enter the lottery on his behalf when the time came. In other words, to act outside of the interaction paradigm that has traditionally defined computing, and which Apple has dominated. At the same time, the fact that Apple is behind the state of the art might not matter that much given Apple’s market and opportunity in that market. To start with the former, Apple is targeting consumers, for whom traditional chatbot functionality is probably sufficient for the vast majority of their AI needs. Siri will be able to give you recipes, tips on do-it-yourself projects, or generate images. Moreover, the fact that Siri will have access to your iPhone gives it all of the same advantages that made me optimistic about Apple Intelligence in the first place. From an Update after that initial June 2024 launch : The key part here is the “understanding personal context” bit: Apple Intelligence will know more about you than any other AI, because your phone knows more about you than any other device (and knows what you are looking at whenever you invoke Apple Intelligence); this, by extension, explains why the infrastructure and privacy parts are so important. What this means is that Apple Intelligence is by-and-large focused on specific use cases where that knowledge is useful; that means the problem space that Apple Intelligence is trying to solve is constrained and grounded — both figuratively and literally — in areas where it is much less likely that the AI screws up. In other words, Apple is addressing a space that is very useful, that only they can address, and which also happens to be “safe” in terms of reputation risk. Honestly, it almost seems unfair — or, to put it another way, it speaks to what a massive advantage there is for a trusted platform. Apple gets to solve real problems in meaningful ways with low risk, and that’s exactly what they are doing. Apple actually made this version of Siri much more capable in terms of accessing world knowledge and image generation, which should make the experience much more seamless, but the real differentiation will clearly be that access to your personal information. You can ask Siri about something you received in messages — or was it email, or a voicemail? — and it will actually find what you’re looking for; it can also “see” what you are looking at on your screen, and act on the information. And, to the extent that third-party apps offer up their data to the Spotlight semantic index, and make actions available via App Intents, Siri can actually operate across different services in a way other AIs can not, at least without making massive sacrifices in security on a local Mac or PC. These capabilities are genuinely useful, and there’s a good chance they’re enough, at least for now, and that’s because there is another aspect of the consumer market that is worth considering — beyond the fact that billions of consumers already have iPhones. Specifically, consumers don’t want to work, and don’t really care about being productive. This reality about the consumer market is a lesson that Silicon Valley has to re-learn every decade or so. Consider Dropbox, whose founder, Drew Houston, is in the process of stepping down . Dropbox was a category-defining product that had a viral hook — if someone signed up with your referral code, you got more storage — and grew extremely fast amongst consumers; the company then spent too long trying to actually build a business in the consumer space, before finally realizing that the only way to make money with what was ultimately a productivity product was by selling to enterprise. The reason is obvious when you think about it: enterprises are paying for their employees’ time, so of course they are willing to pay for tools that make those employees more productive; consumers, on the other hand, are mostly looking to waste time, which is why attention-harvesting advertising is the only software business model that works at scale for consumer services. The fact that Silicon Valley forgets this is downstream from Silicon Valley being a bubble; normal people aren’t looking for agents to buy them tickets to a concert. Still, the bubble was strong enough to convince OpenAI to make the exact same mistake Dropbox did: the company somehow convinced itself that it could make enough money selling subscriptions to consumers; Anthropic, meanwhile, realized that it was enterprises who were willing to pay for AI’s massive productivity benefits, even as OpenAI failed to capitalize on their consumer market penetration by refusing to build an advertising product . This is a long-winded way of saying that I don’t think that Apple’s agentic shortcomings are a big deal, at least for now. Agents help you do work and be more productive, and consumers don’t want to work or care about being productive. What they do want to do is watch short-form video, and an iPhone is simply much better at that than any other device ever will be; in that context, Siri being good enough is enough, and it appears that Apple crossed that bar. There are actually a lot of interesting technical details about how Apple rebuilt Siri, including expanding Private Cloud Compute to include Nvidia chips running in Google data centers, as well as a 20 billion parameter on-device mixture-of-experts model that selects the expert on a per-query basis (as opposed to on a per-token basis) so that it can run in an iPhone’s limited memory. The key strategic takeaway of these implementation details, however, is the centrality of the iPhone. Microsoft’s Project Solara obviously makes sense for Microsoft given the fact that the company missed out on mobile, but it also fits with the infrastructure of AI, which is in the cloud, and increasingly about compute happening without a human in the loop. Apple, in contrast, is heavily incentivized to preserve the iPhone’s importance, and by extension, to focus on use cases organized around human interaction. However, it’s too simplistic to reduce these approaches to a cynical analysis of incentives; both make sense in their own right. What makes me intrigued about Project Solara is the fact that Microsoft is positioning it as purely an enterprise play, which is important because an enterprise has context about the work being done, making it more viable to build long-running agents — which the enterprise is willing to pay for. That context would be far more difficult to build for consumers, given the need to tie together a huge number of services to get a coherent set of data over which to operate. Indeed, the only entities that can probably pull that off are Google and Apple via Android and iOS, respectively — and Google is always going to be focused more on its cloud services as the point of integration instead of the device. That leaves Apple as the only company truly — dare I say it? — thinking differently. And yes, the iPhone as the true core of Siri (which will work across your devices, but get its differentiated context first-and-foremost from your iPhone) just so happens to perfectly align with Apple’s business model and desire to not spend billions in capex, but that doesn’t mean it’s the wrong approach. You’ll be able to access all of that capex that other companies are building on your phone, you’ll just have to use an app; if you need to find something personal, or work across apps, Siri will be the only one who can pull it off — as long as it’s not vaporware (and it appears the second time is the charm).

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Farid Zakaria Yesterday

Relocatable Derivations

The earlier post on guix-transfer highlighted how we can use the tool to transfer derivations from to . It is always delightful when someone offers deeper insights into an idea I had put forward. I was so focused on the transfer from derivations from Guix, I failed to see the larger applicability of the tool. @tomberek shared with me the insight that the tool can be generalized to: “transfer derivations between realms” . Relocatable derivations. 💥 What does that mean? Perhaps the clearest concept to apply it to are deployments . You might have a derivation that you want to propagate through cascading deployment tiers: alpha, beta, and prod. You might have needed to painstakingly apply some logical firewall if all three realms used as their prefix to gate your deployment. By changing the prefix of each one, i.e. or , they are naturally segregated in Nix. How do we promote derivations from one realm to another? We could re-evaluate the Nix expression again against each new store or we can leverage guix-transfer . Why is this better than doing against these new store directories? What is our source of truth? The Nix files or the derivations? I posit that the derivations themselves are the source of truth. Furthermore, evaluation could be slow and requires the full source code (Nix expressions) and the entire evaluation environment (i.e. Nixpkgs, plugins, and overlays). By relocating at the derivation level, we move from Evaluation-based deployment (which is slow, requires source access, and may be prone to evaluation-time impurities potentially) to a Plan-based deployment . We now treat the build graph, via the derivations, as a portable artifact that can be relocated into any realm, regardless of whether that realm has the source code, the right version of Nixpkgs, or in the case of Guix, even speaks the same front-end language.

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Xe Iaso Yesterday

Giving your Go apps Tigris superpowers

Tigris is S3-compatible, which means you can point the AWS SDK at it and most things just work. The catch is that the Tigris-exclusive features—bucket forking, snapshots, object renaming, and the like—need verbose workarounds because the AWS SDK doesn't know they exist. So we wrote a Go SDK that does. It comes in two flavors: the package is a drop-in replacement for the standard S3 client with first-class methods for the Tigris-specific operations, and is a higher-level client for the common single-bucket case that infers its configuration from the environment so you stop passing the same parameters over and over. You can adopt the Tigris features incrementally without refactoring your existing S3 code, and the simpler API still works against other S3-compatible providers. I wrote up how it works and why we built it over on the Tigris blog.

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LLMs and almost good code

TL;DR: My new prior is that top-of-the-line LLMs working on easy tasks generate code that is maybe 10 % more complicated than necessary. I also think we accept this complexity too easily, because it comes from code that is right here , right now , solving an immediate problem. This may have consequences for maintenance in the long term. The background to this discovery was that I needed to do some CRUD plumbing in a work project. It was a simple change that mostly mirrored existing functionality. This is a perfect fit for LLMs, in my experience, so I used a frontier model to generate the code for it. The change ended up being a total of just over 200 lines, mostly additions. The part of the generated code we’ll talk about is a 24-line function that converts an arbitrary (user-supplied) string to a safe HTTP header value. (Continue reading the full article on the web.)

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ava's blog Yesterday

the beach episode

Whenever I descend into a mental health episode (often, but not always, triggered by cycle-related hormonal changes), there’s very specific signs both my wife and I pick up on. She usually notices it earlier than me, though. The pattern is always the same: Because it is all so repetitive (been happening for over a decade) and well-known to me by now, I see it for what it is and it doesn’t actually cause any harm, as I do not end up even close to making these drastic decisions. I am still in control. At worst, I used to unfriend people or exit groups, but even that has stopped. I wouldn’t say my opinions snd obsessions during that time have no leg to stand on; I have a high standard for what my apartment is supposed to look like, and it’s not always up to par. I really wanna switch jobs, move, or change some blog design things some time. But how I feel about it is dramatically amplified, and introducing a very unfitting level of urgency, life-or-death panic about it all. It almost feels like it’s taking a similar pathway to a phase of hyperfocus, but in a cursed way. It also feels like seeking validation and good feelings/a feeling of control from changing things or cleaning, which would make sense for my brain to wanna do when I feel depressed. Like it’s searching for anything that could help. Since some people might be wondering: It doesn’t fulfill the criteria for mania. My anxiety just gets elevated, and my brain decides to handle it in a weird way. I don’t feel happy or energized. Since it is hormonal, I imagine it is related to what some pregnant people go through with their sensitivity to their surroundings. As this is a largely offline thing, I can usually isolate it well and not say a peep about it happening online. I hope I don’t seem weirdly different in my blog posts during it, I genuinely can’t tell. I’m actually going through it again right now, since last week. Dienogest (hormone medication for this mood stuff, PMOD and endometriosis) bought me a while of stability when I tried it from March to mid of May, and I appreciate that; it showed me how often it happened and how bad it really was. I stopped taking it since then because it caused some excessive bleeding, but am restarting it now after a doctor’s appointment today. It’s not all bad. The things I am so inappropriately upset about are true/exist, it’s just my reaction that is a problem. I can harness the sense of urgency and tackle these issues in a constructive way and get things done that I have put off for too long. For example, the sneakers I wear the most have been falling apart so much that walking in them causes blisters and chafing and stuff to poke me while walking. Still, I kept procrastinating on buying new ones. Today, after a particularly exhausting morning in this mental health episode, I bought two new pairs of shoes and was able to discard the broken ones. I also lost my mind about the clutter so much that I ended up discussing new shelf units with my wife, who kindly measured everything out and had good ideas, so we ended up ordering some together and will pick them up tomorrow. It wasn’t a compulsive purchase I’d regret for something I don’t actually need, but instead, I used my tumultuous emotions to finally tackle the issue of needing to use my space more efficiently now, and the decision was made deliberately over the span of a couple hours. I need more storage for more hobbies I acquired over the last years, and things my wife keeps here that don’t yet have their own proper spot. The way things are right now was supposed to be a temporary state until we can move somewhere bigger, but I’ve accepted that this could take years, so I wanna invest into making it comfortable now. In the near future, I also wanna have more storage in the kitchen and bathroom, finally donate the clothes I decided to get rid of, and tidy up the basement. I also felt equally compelled to switch email providers and get 98% of account/newsletter email switching done, updated all saved passwords, and removed saved passwords for accounts that no longer exist. I’m happy that I was able to use my struggle for good today. I still hate everything else about it, and that it’s so uncomfortable and stressful in my head right now, but at least I got that. Can’t wait until I’m stabilized again. Reply via email Published 08 Jun, 2026 Issues falling asleep, feeling inexplicably sad and without energy, not finding joy in anything I usually like, or possibly sleeping almost all day. Getting up the next morning with a very, very low tolerance for clutter, or even owning anything more than bare necessities. Being extremely sensitive to dirt, smells, messes, crumbs and so on. The entire living space and my body starts to feel contaminated. This causing me to spiral and feeling overwhelmed, ranting about how the apartment is hopeless, that I wish I could just leave it or burn it down, that I wanna sell everything, what needs to be done etc. I start cleaning and tidying up for hours in a really tense way, maybe even decluttering, if I can manage and am not frozen in overwhelm. If you step in at that moment to tell me it isn’t even that bad or that I should keep whatever I wanna throw out in that moment, my laser eyes will turn you into dust (I wish). Everything feels very urgent, like I need to do it now or else something really bad will happen; as if I will lose control and this is the last moment to turn it around. Being able to do nothing to alleviate the situation makes me feel sick. My thoughts race and I am stuck in a loop. I’m coping with fantasizing about drastic decisions: Emigrating to a different country, quitting my job, booking a trip on a whim, suicide, selling everything, rearranging my apartment, ending relationships or friendships, deleting accounts, redesigning my entire website and blog from scratch, exiting groups, and so on.

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Unsung Yesterday

“I’ve lost entire sentences of text to this incompetent implementation.”

John Gruber at Daring Fireball talks about a problem in SwiftUI-based macOS and iOS apps where undo is completely broken during text editing : Basic stuff that’s worked reliably for decades — some things that heretofore had worked forever — are dangerously broken. If you’re running MacOS 26 Tahoe, open Journal and make a new dummy entry. Type something like “The quick brown fox.” Then double-click on the word “brown” and delete it. Now invoke Undo. What you expect is for the word “brown” to reappear. What happens is ... the whole sentence disappears . Gone. Invoke Redo and you only get back to “The quick fox.” The word “brown” is just gone forever. It’s nowhere in the Undo stack. That’s just profoundly fucked up. I couldn’t believe it, but I reproduced it myself just now on my phone (my backup Tahoe-running Mac is in a closet not responding to pings, I am now assuming out of embarrassment): Gruber adds: Apple’s developer message used to be that it was not just easy to develop apps for their platforms, but that it was easy to develop good idiomatically native apps. You got the correct complex behavior — for things like Undo/​Redo — out of the box. That’s still true for AppKit and UIKit, but it’s never been true for SwiftUI, and SwiftUI is now seven years old . That’s too long for any excuses to hold water. I don’t want to automatically assume that this problem has existed for seven years (vs. being a more recent deterioration), and I don’t know exactly which native apps use SwiftUI, but either way, this reflects very poorly on Apple. Software engineering typically has some categories of bugs and failures that result in immediate action – a night shift, a war room, “sevs,” and so on. Those are, in my experience, things like: Depending on what you work on, this list will also likely include security problems, regulatory considerations, privacy-leaking bugs, and so on. In a more mature organization, these are all well documented, but even in early startups there is some shared understanding that some bugs are bigger than life and they take immense priority over pretty much anything else. At any company, a version of this list needs to exist for front-end and user-experience problems, and undo problems should be on top of that list. If you break undo, you drop what you’re doing to fix it . #apple #process #undo the app crashes, the site doesn’t load, there is data loss.

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Allen Pike Yesterday

Test Coverage Won't Save You

Forestwalk’s CTO Jenn Cooper shares what she’s been learning about tests , after a couple years of increasingly coding with agents: Most discussions about AI-native development jump from this problem – agents’ tendency to accumulate tech debt – directly to tests. … Tests verify that code does what it did before. Whether what it did was even the right way to do it is a separate question. She argues that while agents make it easy to have rigorous traditional test coverage, at best unit tests maintain local code cohesion. At worst, they can actually make it harder to improve what agents are worst at: the wider coherence of the entire codebase. So far I’ve been impressed with how effective the broader automated checks she describes can be to guard against agentic nonsense.

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iDiallo Yesterday

ppclp.ai announces 100x Productivity Gains

ppclp.ai, North America's third-largest AI-native manufacturer of premium wire-formed office fasteners, formerly known as Paper Clip Company, announced a landmark 100x improvement in its proprietary Organizational Productivity Index (OPI™), cementing what leadership is calling "a new era of operational excellence" and "a little bit of a miracle." The breakthrough follows an 18-month company-wide initiative called Project Streamline, during which all 340 employees completed mandatory efficiency training, adopted a new Jira-based workflow system powered by Rovo, and attended a two-day offsite in Scottsdale where a consultant named Derek asked everyone to "think about how they think about work." "I used to open fourteen browser tabs and just stare at them. Now I open fourteen browser tabs, and AI agents are looking at them for me. I've never felt more in control." — Sandra K., Senior Clip Assembly Coordinator, 11 years at ppclp.ai Central to the transformation is Rovo, Atlassian's AI, which the ppclp.ai Jira Center of Excellence team has deeply integrated into the company's ticket lifecycle. Rovo now autonomously opens tickets when it detects workflow friction, assigns them to the appropriate team, and, in what the Center of Excellence calls "the closed-loop moment", it closes them upon determining that sufficient time has passed. Ticket velocity has increased by 340% as a result. "Rovo doesn't wait for humans to decide a problem is solved," explained the Head of Delivery Operations, in a blog post titled We Taught Our Tickets to Heal Themselves. "It senses resolution. It acts. And then it documents the action in a follow-up ticket, which it also closes." The OPI™, aggregating over 200 signals including ticket velocity, standup attendance, emoji reaction latency in Slack, and what the methodology document calls "ambient focus energy," now shows a number in the top-right corner of the dashboard that is very large and going up. The dashboard itself, prominently themed in dark mode (naturally), required six months to build and is, by all accounts, extremely beautiful. "We are incredibly proud of this number," said CEO Bob Realman in a statement prepared by the communications team and reviewed by legal. "It represents the dedication, the hustle, and the genuine passion of every single person at ppclp.ai. And of Rovo, who we consider an honorary team member and who closed 1,400 tickets last Tuesday alone." When asked by a reporter at the earnings call whether the 100x productivity improvement had resulted in a corresponding increase in paperclip output, CFO Melissa Tran paused, smiled warmly, and said the question "reflected a pre-transformation mindset." She then advanced to the next slide, which was a photo of the team at the Scottsdale offsite. "Volume is a very legacy way of thinking about a fastener business. We've moved beyond units. We're measuring what matters: Agentic motion." — Bob Realman, current and former CEO, ppclp.ai The company did acknowledge, in a footnote on page 34 of the supplemental earnings materials, that paperclip production had declined approximately 20% year-over-year. The footnote attributed this to "macroeconomic headwinds, a challenging staple-adjacent market, and notable seasonality in Q2 clip demand," adding that the trend was "well within the range of normal paperclip seasonality" and "expected to self-correct, eventually." Analysts who requested a definition of "paperclip seasonality" were directed to a separate FAQ document that had not yet been written. A Rovo ticket to write it was opened and closed the same afternoon. ppclp.ai says it expects the OPI™ to continue improving through the end of the fiscal year. They are already exploring an OPI™ 2.0, an open source model that will incorporate biometric data, walking pace between meetings, and what the roadmap calls a "vibe coefficient." Production guidance was not provided, but the company noted that the dashboard remains, in their words, "extremely actionable," and that Rovo has already opened a ticket about it. OPI™ was developed in collaboration with Proxy Ai™ . ProxyAi, so you don't have to. About ppclp.ai: ppclp.ai has manufactured precision wire-formed fasteners since 1987, and rebranded as an AI-native company in 2024. The company serves offices, government agencies, and one very loyal stationery shop in Duluth. ppclp.ai employs 340 humans and Rovo, and is headquartered in a building with a lot of glass. More information is available at a website that is currently being redesigned by an agent. Forward-looking statements contained herein involve risks and uncertainties, including but not limited to: continued ambiguity about what productivity means, the possibility that Rovo opens a ticket about this press release and then closes it, and the risk that someone in finance eventually opens a spreadsheet unassisted. OPI™ is a trademark of ppclp.ai. "Seasonality" is used here in the broadest possible sense. Rovo is a product of Atlassian and is referenced here with the full confidence that it would autonomously resolve any objections. Past dashboard performance is not indicative of future paperclip output.

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Kev Quirk Yesterday

An open letter to office suite users, just before the Euro-Office announcement

by The Document Foundation The Document Foundation shares its history to rebuke claims made by Euro-Office about being Europe's first open-source office suite. They argue that by hiding its code provenance and defaulting to Microsoft's proprietary format, Euro-Office actually undermines European digital sovereignty rather than supporting it. Read post ➡ All this post does is make The Document Foundation sound petty and butthurt, especially this part: Euro-Office defaults to the fully proprietary OOXML document format, developed and controlled solely by Microsoft. This makes it a de facto ally of Microsoft in its content lock-in strategy, with control remaining firmly in Redmond and far from Europe. Saying that Euro-Office are an ally of Microsoft is a bit of a stretch. I assume they defaulted to MS format because that's what 99.9999% of the world uses, like it or not. If Euro-Office are going to be successful, they need to be compatible with MS out of the box. That's just a fact. Maybe that's why LibreOffice has never been able to eat Microsoft's lunch? I dunno. But this isn't a good look for TDF in my opinion. Sometimes it's just better to say nothing, yanno? Thanks for reading this post via RSS. RSS is ace, and so are you. ❤️ You can reply to this post by email , or leave a comment .

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ava's blog Yesterday

changed email address!

Just letting you know that my email address has changed from @pm.me to @tuta.com . The change is reflected in the footer and post template already. I don't know what I will do about the 400+ old posts that have the old email address attached yet; email forwarding has been set, but it will apparently run out next year as my subscription to Proton lapses. Currently in the process of exporting and importing mails, transferring files, and changing newsletters and accounts to use the new email address(es). I respect what Proton is trying to accomplish with their product suite, yet I feel like they are spread too thin, releasing unpolished product after unpolished product, and not putting enough effort into adequately supporting what they already have. On top of that, their questionable approach to politics as well as their recent sponsorship of a far-right French YouTuber was the final nail in the coffin for me. Their responses about this matter didn't cut it for me, as they evade the actual problem and responsibility. Personal politics (and the fact that I don't want my money to go benefit people who want to take away our rights) aside: If you lose sight of who you are sponsoring and where your money goes, and you cannot even clearly tell your customers what you have changed so that will not happen again, that is mismanagement I can't ignore. Reply via email Published 08 Jun, 2026

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Manuel Moreale 2 days ago

Fitness challenge underway

A few weeks ago, I was at my brother’s place, watching NBA, and amongst other things, I was teasing him about the fact that he’s putting up weight. Which is just a fact. But he’s also in his 40s, so that’s understandable. He pointed out that I’m also gaining weight (but I’m not in my 40s), and since it was a long time since I weighed myself, I decided to hop on a scale, and the number that came out was 89.6kg. Now, I’m 190cm tall, so being almost 90kgs isn’t really a tragedy but I told him «I’m gonna get back into shape» just so that I can keep continue teasing him and he won’t be able to say shit back. Isn’t brotherly love wonderful? I gave myself the “extended” whole summer (so till the end of September) to reach two goals. The first goal is to be lighter than I was 10 years ago. I had a smart scale for more than a decade, and the oldest measurement I have logged is 85.3kg, recorded on December 21st 2015. So I need to be below that by September 30th. The second goal is to be in better shape than I was 10 years ago. Now, this is a bit harder to quantify, but I’ll let my brother determine if I reached this goal or not. I did take a “before” picture at the beginning of June. It’s my intention to take an “after” one on September 30th, and we’ll compare and see if I made any progress whatsoever. Do I have a concrete plan for how to achieve these two goals? Absolutely not. Do I have a personal trainer guiding me through a training program? Hell no. Am I following a diet prepared by a nutritionist? Nah, what’s the fun in doing that! We’re in the “vibe” era, so I’m gonna vibe training and vibe dieting, meaning I’m doing things my way, trying random shit, going for silly walks, training however I want, and having fun in the process. Gonna be a fun summer. And since we’re talking losing weight, I’m gonna mention two of my blogging pals here, one mr Kev and one mr Luigi , both of whom are going through a similar challenge (for more sane and normal reasons, unlike myself, who is fueled by spite). I believe in you guys! Thank you for keeping RSS alive. You're awesome. Email me :: Sign my guestbook :: Support for 1$/month :: See my generous supporters :: Subscribe to People and Blogs

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マリウス 2 days ago

Bureaucracy is Eating the World

Disclaimer: This is an opinion piece. It is also a long one, because the subject is too tangled to compress without losing the thread. I have tried to look at the matter from different perspectives and include the strongest counter-arguments where I saw them. As this write-up had been in the works for a very long time, some of the referenced data isn’t the absolute latest data available today, which however does not impact the underlying message. As usual, summary at the end. A few weeks ago I sat down with a friend who, after twenty years in a steady job, had decided to start a small business in the European Union. Nothing exotic, just a one-person operation, selling a thing they had been making in their spare time for years and that other people kept asking to buy. By the time we were done, we had identified the trade register filing, the tax office registration, a separate VAT registration with its own threshold rules, the obligation to issue invoices in a specific format, the e-invoicing mandate, the beneficial-ownership disclosure under the EU’s AML regime, the bank’s own KYC questionnaire, the data-protection obligations under GDPR even though the operation collected practically no personal data, the CE marking requirements, the extended-producer-responsibility packaging registration, the WEEE registration, the social-security contributions for self-employed individuals, the mandatory professional liability insurance for the relevant guild, and the local trade-tax filing. None of these are illegitimate and most of them, taken in isolation, sound reasonable. Together, however, they constitute a mountain that my friend, who is a competent adult with a real product, was now expected to climb before they sold the first unit. That is when it occurred to me that the story I had been telling myself for years, that it has always been like this and every generation thinks the system is rigged , might not actually be true, and this post is the result of that thought. I want to walk through roughly three and a half centuries of how easy or hard it has been, in the western world, to simply do something economically. From the period when an Englishman with a ship and a bond could legally attack Spanish merchants for a living, through the early 1900s when an entrepreneur could incorporate a company on four pages, to the present, when the same kind of operation requires a stack of filings most people will never finish reading. I am going to argue that we have drifted, slowly and with the best intentions, into a regulatory state where the friction of doing anything new is high enough that the people best positioned to absorb it are no longer the small operators that once founded the today’s behemoth companies. I want to be clear up front that I am not writing a “libertarian manifesto” . There are regulations I am glad exist, including most of the worker-safety, environmental, and consumer-protection regimes that the post-war west put in place. The argument is narrower than abolish the rules , and it is roughly that the cumulative weight of three centuries of mostly well-intentioned rule-making (almost none of which was ever repealed, btw) has reached a point where it disproportionately punishes the small and rewards the large. That, I think, is a problem regardless of where you sit politically. Let me repeat: This post is not about political ideology and I urge you to read it as apolitical as humanly possible and focus on the real-world implications rather than some abstract political ideas. Also: While I’m no historian, I tried my best to investigate and find reliable information, which I linked where necessary. Anyhow, let me start with one of my favorite periods to dwell on, which is the time … Of course, we’re talking about the era historians loosely refer to as the Golden Age of Piracy (roughly 1650 to 1730). Back then, the relationship between private business and the state in the Atlantic world was so different from ours that it can feel almost like science fiction. If you were an English merchant in 1690, and you wanted to make money by attacking Spanish (or French) shipping, you did not have to do it in secret. You could go to the Lord High Admiral , or one of the Commissioners acting on his behalf, and apply for what was called a letter of marque . The application named your vessel, its tonnage, its armaments, the owner, and the intended crew. You posted a bond promising to observe the laws and treaties of England, and you got, in return, a piece of paper that legalised an activity that, without the paper, would have made you a pirate . The captures were later judged in admiralty courts, the Crown took a percentage (usually 10%, though Queen Anne later waived even that tiny bit of tax as an incentive ) and you kept the rest. This was not an obscure backwater of business, but in fact an arrangement that some of the most celebrated figures in English history operated under. Sir Francis Drake , whose 1577–80 circumnavigation predates the Golden Age proper but established the template, gave investors a return on capital that contemporary sources placed in the order of forty-seven pounds for every pound invested , with Elizabeth I ’s share alone reportedly enough to retire the Crown ’s debt. The exact figures are deliberately obscured in the surviving records ( Elizabeth had diplomatic reasons not to be specific), but I don’t think any historian disputes that the venture was extraordinarily profitable, and that it was funded by something close to a venture-capital syndicate of nobles and merchants. A century later, in 1695, William Kidd received a privateering commission from the Admiralty Commissioners , plus a special commission under the Great Seal , to seize French ships and pirates. His subsequent hanging in 1701, however, was less about the business model than about the fact that he attacked the wrong ships. Whoops , I guess. Note: It wasn’t only privateering that was comparatively easy to establish and run. In one of my favorite books, Moby-Dick , Herman Melville roughly describes how the economics of whale hunting worked at the time. The capital for the endeavour came from the town. A vessel like the Pequod had a couple of principal owners, the retired Captains Peleg and Bildad in the novel, but the rest of the ship was parcelled out among ordinary Nantucket citizens, a crowd of old annuitants; widows, fatherless children, and chancery wards , each one owning the value of a timber head, or a foot of plank, or a nail or two in the hull . A widow could put her late husband’s savings into a sliver of a whaler the way one might today buy a few shares of an index fund, and when the ship came home three years later heavy with oil, she retrieved her portion of the proceeds, minus the owners’ cut for fitting her out. The crew, meanwhile, drew no wages at all. Every man from the captain down to the greenest hand was paid in what was called a lay , a fixed fraction of the voyage’s total net profit, the size of the fraction set by his skill and rank. The smaller the number, the larger the slice, so a seasoned harpooner like Queequeg was signed at the ninetieth lay, while Ishmael , who had never so much as touched a whale, was first offered the seven-hundred-and-seventy-seventh by the pious and tight-fisted Bildad before Peleg talked it up to the three-hundredth. Nobody was paid for showing up, you were paid, if at all, only when the casks were full and sold, which meant every soul aboard owned a piece of the outcome and bore a piece of the risk, no payroll department required. This was not merely a novelist’s imagination, but it was how the real Nantucket and New Bedford fisheries actually worked. Ships were financed by pooling fractional shares among the townsfolk, every hand from captain to greenhorn took a lay instead of a wage, and historians today describe the whole arrangement as a precursor to modern venture capital, obviously with significantly less bureaucracy involved. Looking at the possible growth and power, the chartered companies of the same era operated on a scale that no modern private corporation could legally match. The English East India Company , chartered by Elizabeth I on the last day of 1600, was eventually granted the right to acquire territory, mint coinage, command fortresses and standing troops, form alliances with foreign powers, make war and peace, and exercise civil and criminal jurisdiction over its holdings. The Dutch East India Company ( VOC ) , chartered in 1602, went further and was given an explicit twenty-one-year monopoly, the right to wage war, sign treaties with sovereign powers, build forts, appoint governors, and mint its own coins. At its peak the VOC employed roughly twenty-three thousand people in Asia, fielded somewhere between one hundred and fifty and two hundred and fifty ships at any one time, kept a standing army of around ten thousand soldiers, and over its life sent close to a million Europeans to Asia on nearly five thousand ships. The Hudson’s Bay Company , chartered by Charles II on May 2, 1670, was granted absolute Lords and Proprietors status over Rupert’s Land , an area of roughly 3.9 million square kilometres, or about 40% of modern Canada, again with monopoly trade rights, lawmaking, civil and military jurisdiction, and the authority to wage war. For the ordinary merchant, who was neither a Drake nor a director of the VOC , the bureaucratic environment was correspondingly easy to navigate. There was no income tax, since Britain’s first income tax was a Napoleonic-era invention from 1799 , and there was no business registration in the modern sense. There was no payroll tax, no compliance officer, no insurance mandate, no occupational licensing, and certainly no equivalent of GDPR or beneficial-ownership reporting. The state extracted revenue mostly through customs and excise on specific goods, the Land Tax on real property, the Hearth Tax from 1662 to 1689 (two shillings per fireplace), and the Window Tax from 1696 onwards. In the American colonies in particular, enforcement of even the limited Navigation Acts was famously weak under what historians call salutary neglect , to the point that the Hoover Institution notes colonists in the late seventeenth century killed three customs officers, imprisoned two others, tried one for treason, and persuaded one to join them . That’s a level of “customs compliance” that would definitely not pass a modern audit. :-) Note: Of course there is a romanticised version of this period that isn’t as rosy as it first seems. For example, within chartered English boroughs (London especially), domestic trade was seemingly gated by guilds and the Freedom of the City . The 1562 Statute of Artificers required a seven-year apprenticeship for most trades, which was apparently the only national apprenticeship law in pre-modern Europe, and admission fees in the 16th and 17th centuries ranged from under one pound to twelve pounds and more, which was a substantial sum at the time. Guild monopolies were seemingly a real form of bureaucracy, just not a state-run one. So the open a shop with no paperwork framing is more accurate for rural England, the frontier American colonies, and overseas trading ventures than for established urban commerce. It was the commercial ventures specifically (the ships, the colonies, the trading expeditions) that operated with the kind of low-friction freedom we no longer have, not the neighbourhood bakery in seventeenth-century London, although that bureaucracy was still well below what we are facing in today’s world. The point I want to draw from this period is not that we should bring back privateering or massive chartered companies that can wage wars , which would be both impractical and politically unattractive, but that the basic relationship between private enterprise and the state was, at least for novel ventures, permissive by default . You could simply go ahead , and the state involved itself only when you crossed a specific line that had been drawn in advance. Yet, despite the lack of all modern bureaucracy, regulation and compliance, civilization evolved and societies developed, maybe at times even at a faster pace than we’re seeing it today. Skip forward two and a half centuries, and the world is unrecognisable in almost every way except that starting and running a business is still extraordinarily light on paperwork, even by modern standards. In 1896, New Jersey passed the first enabling general incorporation statute, allowing a company to be formed by simple administrative filing rather than by a special act of the legislature. Delaware followed on March 10, 1899 , and the modern American corporation was born. Before then, every incorporation in the United States required its own legislative act, but after it, you just mailed a form , figuratively speaking. The cleanest illustration of how thin that paperwork was is the document that incorporated the Ford Motor Company on June 16, 1903 . Henry Ford and twelve co-investors signed a four-page Articles of Association in Detroit . The document covered the name of the corporation, its purpose, its place of operation, its capital stock ($28,000), its term, and its stockholders. It was notarised, mailed to the Michigan Secretary of State , and the company was legally constituted by June 17, 1903. The same Ford Motor Company would go on, over the next four decades, to produce the Model T , build the Highland Park assembly line, employ tens of thousands of workers, and become one of the largest industrial enterprises on the planet, all without anyone needing to file beneficial-ownership disclosures, complete a Customer Due Diligence questionnaire, or commission a Data Protection Impact Assessment . The tax environment was also correspondingly simple. The 16th Amendment to the U.S. Constitution was ratified in 1913, and the Revenue Act of 1913 introduced a federal income tax with a 1% normal tax on net income above $3,000 (single) or $4,000 (married), with a graduated surtax topping out at 7% on income above $500,000. Approximately 3% of the U.S. population was even subject to the tax, and under 1% paid anything at all. In Britain, income tax averaged 2% to 3% of GDP from 1900 through 1913, and the super-tax , the precursor to surtax, was only introduced in Lloyd George ’s 1909 People’s Budget . Value-added tax, the workhorse of modern European public finance, did not exist anywhere in the world until Maurice Lauré’s reform was signed into French law on April 10, 1954, and was not mandated EEC-wide until two directives in April 1967. Note: If these are too many numbers and dates and words and you only want to remember one single thing from this chapter, then remember the following: Taxes, as we know them today, did not exist around a hundred years ago, and many of them only go as far back as ~70 years. It is also worth noting that even the way taxes were collected was different. Tax withholding at source, the now-ubiquitous mechanism by which your employer hands a slice of your salary to the state before you ever see it, was introduced in the United States only in 1943 with the Current Tax Payment Act , as a wartime measure to fund the war effort and to broaden the tax base from the wealthy to ordinary workers. Before 1943, Americans calculated their taxes annually and wrote a cheque, which is why tax-day filing was, for most of history, a relatively low-frequency interaction between citizen and state. Many EU member states introduced their withholding tax regimes only between 1952 and 2013. The withholding mechanism is, in many ways, the backbone of the modern administrative state. It works only because there is a persistent identity attached to every worker, a bank account they are paid into, and a payroll system that can route the deductions automatically. Banking, in the same period, was also fairly accessible. There was no formal Know Your Customer regime in the sense we now use it. The Bank Secrecy Act , which is the foundation of the modern American anti-money-laundering regime, was passed in 1970. KYC as a structured set of rules was not codified federally until the U.S.A. PATRIOT Act of 2001 introduced the Customer Identification Program under Section 326 . The Foreign Account Tax Compliance Act ( FATCA ) , which today shapes the experience of Americans abroad and the willingness of foreign banks to serve them at all, was only enacted in March 2010. If you were a working person in 1920 and you wanted a bank account, you walked into a bank, gave your name and address, signed a card, and received an account. There was no requirement to hand over a utility bill, to document the source of your funds, no electronic identity verification, no sanctions screening, and no algorithmic suspicious activity detection. De-banking , in the modern sense of having a financial institution close your account because of who you are or what you do, was not really a phenomenon at all in the early twentieth century. The Oxford English Dictionary records the verb debank as far back as 1929 , but the meaning that contemporary readers will recognise is essentially post-2014. This matters because the people who built the post-war economy did so in exactly this environment. The men and women of the GI Generation (born roughly 1901–1927), the Silent Generation (1928–1945), and the Baby Boomers (1946–1964) came of professional age in a regime where you could open a bank account in an afternoon, file a four-page incorporation document, hire and fire on a handshake, pay relatively low effective taxes, and grow a business through several decades without anyone asking for a beneficial-ownership statement, a tax-residency certificate, a data-protection impact assessment, or a sustainability report. This is not a moral observation about that generation, it is an observation about the environment they built businesses in. Before I move on to what changed, I want to take one short detour east, because it is the cleanest case I know of how much the regulatory environment can matter to outcomes. In 1953, at the end of the Korean War , South Korea had a GDP per capita of roughly sixty-seven U.S. dollars, which made it one of the poorest countries in the world , poorer than most of sub-Saharan Africa and on a par with Haiti. Seoul, its capital, had a population of about one million people and had been substantially flattened during the war. The country had no significant industrial base, no natural resources to speak of, and no obvious path forward. Seventy years later, South Korea’s GDP per capita is roughly thirty-three thousand dollars, the Seoul Capital Area is home to roughly twenty-five million people, and the country is a global leader in shipbuilding, steel, electronics, automobiles, semiconductors, and increasingly cultural exports. This is one of the most extraordinary economic transformations in recorded history. The popular story of Korea being a free-market miracle, however, is half right at best. The serious academic literature, particularly Alice Amsden’s Asia’s Next Giant and Robert Wade’s Governing the Market , makes clear that the Park Chung-hee regime (1961–1979) was an authoritarian developmental state , not a libertarian paradise. It directed credit, picked sectors, suppressed labour, and tolerated heavy chaebol concentration. What the regime did not do, however, was load new ventures with the kind of compliance and regulatory machinery that the modern OECD economies were already accumulating. New industries could be built quickly, factories could be thrown up, ports expanded, ships launched, because the bureaucratic overhead was thin and the political will to remove obstacles was high. I am definitely not holding Park -era Korea up as a model, as the political costs were severe. What I am pointing at is how fast a country can transform when the regulatory friction on building things is set close to zero. It is much, much faster than people who have only experienced modern OECD economies typically realise. The generations that built the post-war west ( GI , Silent , Boomer , and to a lesser extent early Gen X ) accumulated (or inherited) their wealth in this lighter regulatory regime. The generations that came after (later Gen X , Millennials , Gen Z , …) are trying to do the same thing in an environment that has significantly changed under their feet. The U.S. Federal Reserve’s Distributional Financial Accounts are the authoritative source, that shows, that as of late 2024, Millennials and Gen Z together represented 35.1% of U.S. households but owned only 10.1% of total household wealth , roughly 71% less than their household-share would predict. By contrast, Boomers in 1989, at a roughly comparable average age, held 19.5% of wealth while making up 42.2% of households. In other words, younger Americans today are more under-represented in wealth than Boomers were at a comparable point in their lives. Pew Research similarly found that the median net worth of households headed by Millennials aged 20–35 in 2016 was roughly $12,500, compared with $20,700 for Boomers at the same age in 1983, in constant dollars. That is, the Millennial household had about 60% of the inflation-adjusted net worth that the Boomer household had at the same stage of life . Homeownership data tells the same story. Apartment List ’s analysis of homeownership rates at age 30 finds that 55% of Silents owned a home by that age, falling to 48% of Boomers , 42% of Gen X , and just 33% of Millennials . In the United Kingdom, the Office for National Statistics reports that in 2024 the median home in England (£290,000) cost roughly 7.7 times median full-time annual earnings (£37,600), and the Resolution Foundation has shown that it now takes a typical young first-time buyer roughly 18 to 19 years to save a deposit from disposable income, compared with about 3 years in the mid-1990s . The Joint Center for Housing Studies at Harvard reports that in 2022 the U.S. median home price reached 5.6 times median household income, the highest ratio on record going back to the early 1970s. Note: I want to recognize that the wealth-comparison story is more nuanced than the Millennials are screwed narrative that I’m partially presenting here. For example, the St. Louis Fed has also found that, on a per-household basis, Millennials and Gen Z have been catching up rapidly since 2019. Critics, including New America and others, point out that this relies on average rather than median wealth and is heavily skewed by a thin slice of high-earning younger households. Both stories are simultaneously true, depending on which slice of the distribution you look at. The median younger household is materially behind, the average younger household less so. I am framing the argument around the median because that, in my view, is the more relevant indicator of broad opportunity. In addition, the crises that bracketed Millennial , Gen Z and later generation’s lives were not randomly distributed across generations. Those generations experienced the post-9/11 wars in Iraq, Afghanistan, and the broader counterterrorism campaign , the 2008 Global Financial Crisis , the COVID-19 pandemic , the Ukraine war , and the recent war in Iran with all its economic impact slowly unfolding. The cost of these, in the form of debt, inflation, and asset-price inflation through quantitative easing , has fallen disproportionately on the people who were/are not yet old enough to own assets when these events occurred. In fact, the Bank of England ’s own analysis of the distributional effects of quantitative easing acknowledged that a large share of the wealth gains flowed to households that already owned assets, and a 2023 Oxford Bulletin paper found that the asset-price channel of QE increases wealth inequality across most countries studied. There is a counter-argument from central banks that the alternative (a deeper recession) would have hit younger workers even harder through unemployment, and I think this counter-argument is partially correct, but the cumulative effect, on top of the housing-supply story documented by Glaeser and Gyourko , is a generational asset-price gap that compounds. I’m trying to be careful not to slide into a Boomers caused this framing, because that doesn’t appear to be what the data ultimately says, despite everything visually pointing towards this narrative. Boomers themselves appear to be highly stratified, with the median Boomer being noticeably less wealthy than generational averages seemingly suggest. The Urban Institute ’s research on the Great Inequality Transfer emphasises that policy regimes, not generational malice, are the proximate cause. What is true, however, is that the policy choices of the past several decades, made disproportionately by people who were already established in the post-war environment, accumulated into a stack of rules, asset prices, and compliance requirements that the people coming up behind them now have to navigate. In that sense, the current environment can in fact be attributed to the decisions made by Boomers , as well as the generations in their immediate proximity. Which brings me to the actual point about how big that stack of rules has become, and what its distribution of cost is. The U.S. Code of Federal Regulations , which is the codified body of federal regulatory rules, was a thin pamphlet at its origin in 1938. It is now, depending on how you count, somewhere north of 190,000 pages . The Mercatus Center ’s RegData project, which counts regulatory restrictions defined as instances of words like shall , must , and may not , finds that the federal CFR contained roughly 835,000 such restrictions in 1997, rising to over 1.08 million by 2019 and continuing to climb. The Federal Register , which is the daily journal in which new federal rules are first published, totalled 9,562 pages in 1950 across 15 volumes, and hit 86,356 pages in 2020 , the second-highest count ever recorded. Meanwhile, the European acquis communautaire , the body of cumulative EU law, has followed a similar trajectory, with estimates of the active acquis range from an 80,000-page figure to over 170,000 pages, depending on how you count, with more than 100,000 of those pages produced in the prior decade alone . The cumulative legislation since 1957 is on the order of 666,879 pages. The cost of complying with all this is, as you might have guessed, not evenly distributed. The widely cited 2010 SBA Office of Advocacy study by Crain and Crain estimated that U.S. small firms with fewer than 20 employees paid about $10,585 per employee per year in regulatory compliance, compared to $7,755 for firms with more than 500 employees, which is roughly a 36% gap. A more recent 2023 National Association of Manufacturers study put the total federal regulatory cost at $3.079 trillion in 2022 (about 12% of GDP), with small manufacturers paying $50,100 per employee per year compared to $24,800 for large manufacturers , which is roughly a 100% gap. Note: The Crain and Crain methodology has been criticised by the Congressional Research Service and others as including economic-impact estimates rather than just direct compliance costs, and using a cross-country regression that some economists consider unreliable. The NAM is an industry association with an obvious incentive to report large numbers. However, even if we discount both estimates substantially, the basic shape (that smaller firms pay disproportionately more per employee to comply with the same rules) is consistent across studies and across methodologies. A fixed cost of compliance simply hits a smaller firm harder, in per-employee terms, than a larger one. A few specific recent regulations are worth naming, considering their (cost-)impact: I will stop the list there, but the pattern is clear, and adding DAC6 , DAC7 , the DSA , the DMA , the CSRD , the CSDDD , UKCA marking, REACH , MDR , IVDR and the rest does not improve the picture. Each of these has a defensible rationale, and most of them addressed a real problem, but the cumulative burden, however, is significant . Sadly, there is no agency anywhere whose job is to look at the total weight of regulation on a small business and ask whether it is still proportionate. However, there are agencies, in many jurisdictions, whose job is to add to it. Tax complexity has followed the same arc. The U.S. Internal Revenue Code runs to roughly 2.4 million words, or about 10 million if you include Treasury regulations and IRS guidance. Wolters Kluwer ’s Standard Federal Tax Reporter , the practitioner’s reference, has grown to roughly 80,000 pages from a thin volume in 1913. The U.K.’s Tolley’s tax handbooks have grown from about 5,000 pages in 1997 to over 21,000 pages in current editions. The IRS Taxpayer Advocate , who is statutorily independent of IRS political leadership, has reported that Americans spend roughly several billion hours per year complying with the federal tax code. Meanwhile, the OECD ’s tax-to-GDP statistics show that the average tax-to-GDP ratio across member countries rose significatnly from 1965 to 2022. For example, France went from ~33% to ~46%, Denmark from ~29% to ~42%, the U.K. from ~30% to ~35%, Germany from ~31% to ~38%, Spain from ~14% to ~36%, and the U.S. from ~23% to ~28%. In other words, across most of the developed world, the share of economic activity passing through tax authorities has grown by roughly a third over six decades, and the complexity of the path it takes through those authorities has grown by considerably more. The crucial point, for my argument, is not about whether taxes are too high in some absolute sense, or whether taxation as such is actual theft , as that is a separate political question on which reasonable people disagree, but the point is that the complexity has grown to the level where it is itself a significant input cost, and that cost is again non-linear. A small business that needs to interpret 80,000 pages of tax guidance has to either hire someone to do it, or do it themselves at the cost of not running their business for the time that it takes. A multinational has a tax department, and frequently has the resources to make the complexity work in its favour. Which brings me to the most important part of the picture, which concerns tax *cough* planning . The Institute on Taxation and Economic Policy documented in its 2021 study, that 55 of America’s largest corporations paid $0 in federal income tax on $40.5 billion of pre-tax income in 2020. A 2024 update found 109 large profitable U.S. corporations paid 0% federal income tax in at least one year between 2018 and 2022, with an average effective rate of about 14.1% against a statutory rate of 21%. A 2022 study from the U.S. Government Accountability Office on large profitable corporations found an average effective federal rate of about 9% over the period 2014 to 2018, well below the statutory rate of the time. The mechanisms by which large multinationals (and wealthy individuals) achieve these rates are well-documented, largely legal and, most importantly, only available to companies (and individuals) of equal size and accounting firepower , and definitely not to your mom-and-pop-shop next door. The Double Irish with a Dutch Sandwich , used by Google , Apple , Facebook and others, was estimated by economist Gabriel Zucman to have shifted more than $100 billion per year at peak. Ireland closed it in 2014 with a phase-out completed by 2020. The European Commission ruled in August 2016 that Apple owed €13.1 billion in back taxes to Ireland, and the Court of Justice of the European Union finally upheld this on September 10, 2024 in Commission v. Ireland (C-465/20), eight years after the original ruling. The Tax Justice Network ’s State of Tax Justice 2023 report estimates that countries collectively lose roughly $472 billion per year to tax abuse, of which about $311 billion is corporate. It’s worth mentioning that the TJN’s methodology is contested by the IMF and others, and that the figure should be treated as an upper bound, but even at half that figure, the disparity is striking. The OECD’s BEPS Pillar Two , the global minimum corporate tax of 15% beginning in 2024, is estimated to raise corporate income tax revenue by roughly $155 to $192 billion per year, but it does nothing for the structural disparity between a small business (or regular individuals) that cannot relocate its profits and a multinational (or wealthy individuals) that can and, on the contrary, is likely to introduce even more bureaucracy for small businesses in future iterations of the code. There are honest reasons the system has ended up where it has, including the difficulty of taxing economic activity that crosses borders, but the lived effect is that a self-employed plumber in Paris or a small bakery in Chicago pays a higher effective tax rate than Apple or Amazon does on income shifted through the right holding structure. There is one more thread that I want to pull on, because it has changed character significantly in the past decade and is, I think, undertreated in the broader conversation, which is the slow conversion of banks from financial-services providers into compliance gatekeepers. One (in)famous exampale for this is Operation Choke Point , a U.S. Department of Justice initiative running from 2013 to 2017, that pressured banks to drop high-risk merchants, including payday lenders, firearms dealers, and adult-industry workers. The program was officially terminated in August 2017 after the FDIC settled lawsuits and pledged to cease informal or unwritten suggestions to banks, but the label Operation Choke Point 2.0 has since been applied to alleged debanking of crypto firms after the March 2023 collapse of Silvergate , Signature Bank , and Silicon Valley Bank . However, the evidence for a coordinated Choke Point 2.0 operation remains contested , and the framing has been used by politically interested parties on both sides. Less contestable, on the other hand, is the Nigel Farage / Coutts case, in which the U.K. private bank Coutts closed Farage ’s account, and an internal 36-page Reputational Risk Committee dossier from November 2022 cited his political views as “at odds with our position as an inclusive organisation” . The CEO of parent group NatWest resigned in July 2023, and the U.K.’s Financial Conduct Authority subsequently reviewed account closures across multiple banks, finding roughly 343,000 personal and business accounts were closed in 2021 to 2022 alone. Banks self-reported that few were for political views, but the FCA also noted significant data-quality problems. Disclaimer: I have no skin in the U.K.’s political game and I do not care about Farage as a political figure or even as an individual. However, the Farage / Coutts case is one of the most prominent cases, which is why I picked it up to give an example. Make no mistake to believe that de-banking is solely an issue on one side of the political spectrum , as it is clearly not. The volume of Suspicious Activity Reports filed by U.S. financial institutions to FinCEN has risen from about 1.3 million in 2014 to roughly 3.6 million in 2022 , and Currency Transaction Reports run at about 20.5 million per year. The European Banking Authority ’s 2022 opinion on de-risking is even explicitly acknowledged that AML rules are causing unwarranted account closures across NGOs, money-service businesses, and correspondent banks. To understand the real world implications of how these account freezes and closures impact even regular people on a day to day basis it’s enough to look at individual institutions’ bad ratings on any unbiased review site, e.g. for (Transfer)Wise on ConsumerAffairs . The mechanism here is, again, one I think is poorly understood. Banks are one part malicious, injecting their own policy and beliefs into their decision-making, and one part cautious, as they face fines for AML failures. For example for laundering $881 billion, the HSBC paid $1.9 billion in fines in 2012. But don’t worry, nobody at HSBC went to jail. Similarly, the U.S. Treasury Department settled with the Standard Chartered for $1.1 billion, for violations of multiple sanctions in 2019. On the other hand, however, AML over-compliance effectively carries no penalty at all, e.g. for closing the account of a legitimate small business or unbanking an innocent individual. The economically rational response is to weight profit vs. risk and to interpret risk conservatively for any account that does not pay enough to justify the regulatory exposure . The result is that the marginal small business, the freelancer with an unusual revenue pattern, or the person whose work happens to fall in a politically sensitive category, finds banking impossible, without any of this being written into any law as such. It is the emergent behaviour of a stack of regulations, none of whose authors probably intended this outcome. Add to this the ever changing regulatory environment and banking suddendly becomes yet another bureaucratic burden for small and medium businesses, let alone lower- and middle-class private individuals. The deeper irony is that this regime consistently fails at the very thing it was built to do. The list of major money-laundering scandals over the past two decades, including HSBC ’s settlement for laundering Mexican cartel money , Credit Suisse ’s Suisse Secrets leak revealing accounts held by criminals and corrupt politicians, the 1MDB scandal in which billions of dollars flowed through major Western banks, the CumEx tax fraud in which European treasuries lost an estimated €150 billion, and the Wirecard collapse in which one of its senior executives simply vanished, all happened despite KYC, AML, beneficial-ownership disclosure, and the rest of the modern compliance stack, without any of the involvement of cryptocurrencies or any other modern technologies, that are usually politically vilified for enabling these sort of schemes. The people the regulatory regime is supposedly catching are, with rare exceptions, not being caught. The people most affected are those who do not have a tax planner, a private banker, a trust fund, or a corporate structure that can absorb the friction and make issues simply go away . Take the popular case of Flipper Devices , the small hardware company behind the Flipper Zero , a multi-tool aimed at hardware hackers, penetration testers, and electronics hobbyists. The product’s Kickstarter campaign in 2020 was extraordinarily successful, raising nearly five million dollars from tens of thousands of backers. In late 2022, the company publicly reported that PayPal had frozen approximately $1.3 million of its funds, applying the platform’s standard 180-day review hold and citing only generic suspicious activity as justification. After significant media attention the funds were eventually released, but for a small hardware company in the middle of manufacturing and fulfilling international orders, going six months without access to over a million dollars of customer money is plainly a near-fatal event. Flipper survived because the tech press noticed and because they had alternative revenue streams, but the experience is far from unique. Most small companies that find themselves on the wrong side of a payment processor’s algorithmic risk score do not have a public profile large enough for anyone outside maybe their accountant or, ultimately, their lawyer to care. I believe that what is happening here is important and underappreciated. The bureaucratic delegation of compliance enforcement to private financial institutions has handed banks, payment processors, and similar gatekeepers a degree of power over private economic life that, historically, simply did not exist at this scale. A bank has the unique ability to create a business, by extending it credit on terms no ordinary lender would offer, or to destroy one, by freezing or even closing its account. If a major institution decides a particular firm is strategically valuable, it can throw virtually endless money at it through revolving credit facilities, underwriting commitments, intra-day liquidity, and market-making support, propping up balance sheets that, on the merits, would have folded years earlier. The opposite operation is just as easy, and considerably faster, as a compliance officer flagging a customer as inconvenient can, overnight, sever that customer from the payment rails on which essentially all modern economic activity runs. There is no court, no due process, and no meaningful right of appeal, and the customer typically receives a single boilerplate letter that does not even state the reason. This is a degree of power over individual livelihoods and corporate existences that, in democratic societies, used to require a court order. It is now exercised routinely by salaried risk officers operating under regulatory pressure that strongly incentivises closing first and asking questions later never. The same dynamic falls, sometimes even more starkly, on individuals, where a debanked person can find themselves locked out of housing, employment, and even the ability to receive their own salary, with no agency, court, or ombudsman that they can effectively appeal to. It’s this exact same financial-control infrastructure that has been used for instance by the Canadian government in response to the Freedom Convoy protests , or by the German government in response to Antifa . By freezing bank accounts associated with protest participants and donors, without conventional court orders, these individuals were cut off of modern life in an instant. Regardless of the underlying political debate and whatever you think of each of these cases individually, the precedent (specifically, a government using existing AML infrastructure to remove citizens from the financial system as a form of political pressure) is now established, in a world in which even the everyday use of cash, which is the official government-issued currency , has been recoded as suspicious in many jurisdictions. Large-cash deposits trigger reports, jewellers and car dealers face mandatory reporting thresholds, and several EU member states have explicitly capped what can be paid in cash at all. Going back to the more general topic of bureaucracy , especially in the context of business activity, I think the outlook is worrying when we project forward on the current curve. The U.S. Census Business Formation Statistics show that applications surged after 2020 from roughly 3.5 million per year to about 5 million per year, which is generally a welcome thing. But the underlying business dynamism has been declining for decades. Decker, Haltiwanger, Jarmin and Miranda ’s Brookings work documents that the U.S. startup rate fell from roughly 13% of all firms in the 1980s to about 8% by the 2010s. Additionally, multiple studies show that the share of employment at firms with fewer than 20 employees fell significantly in the past fourty years, and the OECD ’s Entrepreneurship at a Glance series finds similar declining startup rates across most member economies. So the recent surge in applications is encouraging, but it is seemingly happening against a multi-decade trend of declining small-firm employment and declining new-firm formation relative to incumbents. One plausible end-state, which I do not think is inevitable but which is clearly the trajectory we are on, might look something like this: Large incumbents accumulate the regulatory, tax, and compliance machinery (at their scale, the per-employee cost of compliance falls). Small entrants either do not start, start under the radar in regulatory grey zones (if even possible, see below), or get acquired before they can scale. The marginal new business is increasingly a side-hustle on a platform owned by an incumbent (think Amazon FBA , TikTok Shop , etc.), where the platform absorbs the compliance burden in exchange for a (hefty) cut, but also sets the terms unilaterally. The number of independent businesses falls, the share of actual, as well as paractical employment in incumbents rises, and the regulatory state both causes and justifies this concentration, on the grounds that fewer, larger firms are easier to oversee, while simultaneously likely to be corrupted bought lobbied by these exact firms. For most of the past century, the infrastructure of compliance was built and operated by bureaucrats , namely people sitting in offices, processing forms, applying judgement within the limits of their statutory remit. The friction generated by that arrangement was high, but the friction was also a feature, in a way, as it built in a small reserve of leeway , in the form of a human who could lose a form, mark a case as borderline, or exercise discretion. The trajectory we are now on is the replacement of that human by a system, with the bureaucratic structure inherited intact and made fully queryable . The bureaucrats built the cathedral, and the techno-/algocrats are now installing the 24/7 surveillance and the “AI” . Decades of beneficial-ownership filings, KYC records, CRS and FATCA exchanges, DAC7 platform reports, payment-service-provider data, e-invoicing pipelines, real-time VAT reporting (Italy’s SDI , Spain’s SII , Hungary’s RTIR , the impending EU-wide ViDA regime), property and Land Registry records, vehicle registries, customs data, and social-security feeds, all sitting in databases that, for most of their existence, were used in isolation, as data-silos. The techno-/algocratic project, broadly speaking, is to wire those databases together, to make them fully searchable, and to layer pattern-detection and “AI” on top. Make no mistake in believing that this is only hypothetical. The U.K.’s HMRC Connect , which is operational since 2010 and built for the tax authority by BAE Systems Applied Intelligence at a reported cost in the tens of millions of pounds, already cross-references dozens of distinct sources, including bank statements, social media, Land Registry filings, DVLA vehicle records, Companies House data, PayPal transactions, and offshore-account exchanges. On September 8, 2023, the U.S. IRS announced a major expansion of audit activity targeting large partnerships and high-income individuals, with Inflation Reduction Act funding and an explicit reliance on “AI” to identify return patterns that human reviewers would not have spotted. The OECD ’s forum on tax administration has, for years, been pushing member states toward real-time compliance and data-driven audit as the operating model of choice. With all of this the direction of travel is clearly set, and the technical capacity to act on it has now finally caught up. The cautionary tales of what happens when this approach is rolled out at scale, without the institutional caution that has historically slowed bureaucratic overreach, are already with us. For example Australia’s Robodebt scheme , which was in operation from 2016 to 2020, used automated income-averaging to issue hundreds of thousands of debt notices to welfare recipients on the basis of data-matching alone. The scheme was found unlawful by the Federal Court in November 2019, the government settled a class action for AUD 1.8 billion, and the Royal Commission that reported in July 2023 concluded the programme was crude, cruel, and produced disproportionate harm, including contributions to suicides . Also, the Netherlands’ toeslagenaffaire , in which the Belastingdienst ’s self-learning risk model wrongly accused tens of thousands of mostly immigrant-background parents of childcare-benefits fraud, demanding repayments that drove families into bankruptcy and, in over a thousand documented cases, the loss of custody of their children, was serious enough to bring down the third Rutte cabinet on January 15, 2021. Finally, the Dutch SyRI system , a separate algorithmic fraud-detection tool deployed in low-income neighbourhoods, was struck down by the District Court of The Hague in February 2020 for violating Article 8 of the European Convention on Human Rights on grounds of opacity and disproportionality. AlgorithmWatch ’s Automating Society reports have catalogued dozens of similar cases across European member states, in welfare, policing, education, and employment. What unites these cases, and what should worry anyone trying to run a small business or live a private life, are the integral features of the techno-/algocratic arrangement, which are that the system acts at machine speed, that the cost of being wrongly flagged is borne entirely by the citizen, and that the institutional pathway for appeal is essentially the same slow, human bureaucracy that has now been deprioritised in favour of the system that flagged you. The political-philosophy literature on algocracy , or governance by algorithm, has been making this point for over a decade, but has only recently moved it from academic concern into operating practice. The borderline situation, namely the small business that took a contested deduction, the freelancer with an unusual revenue mix, the side-hustle whose VAT return is six weeks late, or the consultant whose payment patterns trip an unexplained risk score, that, twenty years ago, would have been quietly tolerated, mis-filed, or simply missed becomes discoverable instantly. Once discovered, it is sanctionable with no human in the loop, and the same asymmetry that already shapes the regulatory landscape applies here, as large multinationals have the budget to deploy their own AI , hire armies of tax engineers, and structure around the algorithmic detection, while small businesses inherit the algorithmic enforcement without the means to defend against it. The leeway that used to exist in the system, a function of human inattention, finite processing capacity, and occasional judgement, is being engineered out as a deliberate goal, and sold as efficiency and fairness . The people who lose the most when that leeway disappears are precisely the ones who needed it most. I think this is sadly where we are headed if nothing changes, and I think it should worry people across the political spectrum, in every developed and developing nation. Probably the strongest argument against this is, that the reason 1700 looked deregulated is, that it was also worse on almost every dimension of human welfare. Workers had no protection, children worked in mines, the seas were full of slaves, rivers were poisoned, and banks collapsed routinely and took depositors’ savings with them. The reason the modern regulatory state exists is that the previous arrangement was actively killing people, and the slow accumulation of rules is the price of a society in which most people “no longer” die at work , drink contaminated water , or lose their savings to a bank’s bad bets . That’s at least what the bureaucrats , and technocrats , and algocrats keep telling us. In principle, cumulative regulation is not a flaw of the system, it is a record of every preventable disaster the system has tried to prevent from recurring, and removing the rules in aggregate would remove the protections in aggregate. And don’t get me wrong, I am glad when workers have meaningful safety protections, when consumers can sue over defective products, when depositor insurance exists, when environmental externalities are at least partially priced in, and when a small number of bad actors no longer get to externalise their costs onto everyone else. I do not think that the counterfactual world in which we kept the freedom of 1700 and added the income from 2026 is a world I or anyone else would probably want to live in. What I do think, however, is that there is a meaningful difference between essential protections (that actually work) and accumulated regulatory drift . The basic worker-safety rule that says you have to provide fall protection above a certain height is essential. The 47-page guidance document about how to file your beneficial-ownership disclosure for a single-member LLC is drift. The basic principle that consumers should be told what is in their food is essential. The 200-page set of EU labelling rules covering every permissible variation of free-range is drift. And the regulatory process, almost everywhere, is asymmetric, as rules are added much more easily than they are removed. The U.K.’s one-in, two-out initiative was an attempt to rebalance this, and was quietly abandoned. The EU’s REFIT programme was modest in ambition and modest in delivery. The basic dynamic, that political incentives strongly favour adding rules in response to any given incident and very weakly favour removing them in response to cumulative drift, is the big issue here. If you can grant me that distinction, between essential protection and accumulated drift , then I think the current state of the western regulatory system contains a lot more drift than its defenders are willing to admit, and removing some of that drift would not actually require dismantling any of the protections that make modern life better than in 1700. To wrap this up with something approaching constructive thoughts, I think we should want a regulatory state that protects against catastrophic externalities (pollution, fraud, systemic financial collapse, occupational deaths) while making it easy to start the kind of small, independent business that built post-war prosperity. I think we should want a tax system that is fair and simple enough that a person running a one-person operation can comply with it in an evening, and graduated enough that a multinational cannot escape it through accounting geography . Speaking of which, I also think that we should want a levelled playing field, in which accounting geography is either impossible, or possible for anyone regardless of the depth of their pockets or the political and economical influence they might have. I think we should definitely want banking that is open to anyone who has not been individually adjudicated to have done something wrong, and not closed to people on the basis of category-level reputational risk. And we should almost certainly want this lifeblood of our modern life to require a lot more effort to be simply turned off in an instant than it does today. I think we should want a habit, in the political class, of asking what should we remove this year , with the same energy we currently bring to the question what should we add . None of this is a return to 1700, but it is more like a return to the economic environment of, say, the 1950s, 60s, 70s and maybe even 80s, in which the post-war west had built a real welfare state and meaningful worker protections, but had not yet loaded on top of that the accumulated drift of seventy further years of rule-making. That is not a libertarian fantasy, and it may in fact be living memory for some of the people reading this. It was the environment in which their parents or grandparents started shops, opened bakeries, built small factories, and made middle-class lives. None of the rules that I think constitute drift were added in bad faith and probably each of them was a response to a real problem, designed by people who (in most cases) meant well, and voted for by representatives whose constituents wanted that specific problem solved. The cumulative effect was nobody’s intention, it is just what happens when a system has a stronger tilt towards adding than towards removing, and that runs on such an imbalance for several decades. Therefor, I think the next political project that is worth caring about, more than most of the noise that currently passes for debate, is the project of deciding what to keep, what to remove, and how to build an institutional habit of asking that question regularly. I do not have a clear policy proposal for how to do that, as I am far from being truly politically and economically knowledgeable, but I am reasonably sure, that the current trajectory ends somewhere I would rather not arrive at, and that the people who will pay the highest price for arriving there are the people who were not yet born when most of the drift was added. If you take only one thing from this post, take this: The bureaucratic weight that we treat as an immovable feature of modern life is, in fact, a very recent construction. It was built in living memory, by people we can name, in response to problems we can list, with consequences they did not entirely foresee or willfully ignored. It can be rebuilt, lightened, simplified, and reformed, in the same way it was built up. The question is whether we have the political stomach to treat accumulated rules with the same scepticism we currently treat proposed rules , and to remember that every line of regulation, however well-intentioned, is also a small tax on every future person who has to read it before they are allowed to do something useful. I would like to think we still have that stomach, but I am not certain we do. Footnote: The cover artwork is a real painting by Heather Castles . Sarbanes-Oxley ( SOX ) from 2002 added 3,000 to 10,000 compliance hours per company per year, with average direct cost of $1.7 million for accelerated filers, and average annual SOX cost for a small public company rose from about $1.5 million in 2001 to over $2.8 million by 2007. This is a significant part of the reason that the number of U.S. publicly listed companies has roughly halved since the 1990s. Dodd-Frank from 2010 required, at peak, around 398 distinct regulatory rulemakings, and the American Action Forum estimated cumulative compliance burden at over $36 billion and 73 million paperwork hours by 2016. The 2018 Economic Growth, Regulatory Relief, and Consumer Protection Act rolled back some of this for regional banks, but the substantive bulk remained. MiFID II from January 2018 cost the financial industry roughly $2.1 billion in first-year implementation costs and several hundred million in ongoing costs, with the top ten sell-side firms each spending more than $50 million. A documented side-effect, perhaps an unintended one, was a substantial fall in EU sell-side research analyst headcount over the following two years. The General Data Protection Regulation ( GDPR ) , in force from May 2018, has been one of the most consequential pieces of EU legislation of the past decade. Industry surveys at the time put average Fortune 500 GDPR spend in the mid-eight-figure range, with substantial recurring annual costs thereafter. More than a thousand U.S. news sites blocked European users entirely after GDPR came into effect, including major publications like the Los Angeles Times and the Chicago Tribune , and many of those blocks remain in place years later. GDPR was a serious response to a real problem, but the way the cost falls is itself an externality of how the rule was designed. Strong Customer Authentication ( SCA ) under PSD2 , enforced from 2021, made online payments in the EU more secure but, by Stripe ’s estimate, also lost European e-commerce roughly €57 billion in sales in the first year. The U.S. Corporate Transparency Act , effective January 1, 2024, requires beneficial-ownership disclosure on roughly 32.6 million existing entities plus another 5 million new entities annually, at first-year compliance cost estimated by FinCEN itself at approximately $22.7 billion . The constitutionality is currently in litigation, but the regulatory intent tells you a lot about where the trajectory is. The EU AI Act , passed in 2024 and phasing in through 2027, will impose high-risk AI compliance obligations whose cost the Commission estimated at roughly €29,000 per system, but which independent analyses (such as CEPS ) suggest could run closer to €400,000 for a small company deploying a high-risk system. The order-of-magnitude disagreement is itself a sign that nobody really knows what this is going to cost yet, which is not a reassuring property of a major piece of legislation.

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