待翻譯：The AI Hater's Manifesto

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

The AI Hater's Manifesto Ed Zitron Aug 25, 2026 50 min read If you liked this piece, you should subscribe to my premium newsletter. It’s $70 a year, $17 a quarter, or $7 a month, and in return you get a weekly newsletter that’s usually anywhere from 10,000 to 18,000 words, including vast, detailed analyses of NVIDIA, Anthropic and OpenAI’s finances, and the AI bubble writ large. My Hater's Guides To the SaaSpocalypse, Private Credit and Private Equity are essential to understanding our current financial system, and my guide to how OpenAI Kills Oracle pairs nicely with my Hater's Guide To Oracle, as well as the Hater’s Guide To Oracle (Part 2). Subscribing to premium is both great value and makes it possible to write these large, deeply-researched free pieces every week. This week's premium will be The Hater's Guide To Circular Financing - and how the AI industry is increasingly turning into a scheme to funnel money to NVIDIA and Broadcom at any cost. If you want to get in touch — and especially if you have any juicy information about Anthropic, OpenAI, or any other companies in the AI bubble — hit me up on Signal at ezitron.76. I’m also on IB on The Terminal. I’ve been writing about AI for the best part of three years. I’ll admit I was late, mostly because I was still trying to work out what it was I was doing with my life, let alone whatever it was I was “meant to cover” in a newsletter that started as a hobby on the side of another job I no longer really do. Things have changed a lot since then, mostly in that I’m near 115,000 subscribers, the premium newsletter and podcast are now my business, and I’ve had to learn more about economics, technology, power, construction, and the deep cynicism that drives the modern tech industry than I ever thought possible. It’s the greatest job in the world, and I’m very lucky to have it. Today, I want to put in clear terms how I feel about AI writ large, and how detestable this industry has become. Welcome to my Hater’s Manifesto. Modern Software Sucks, LLMs Are Interesting, And Their Cost Is Inexcusable Want a great example of why everybody’s pissed off at technology? I just tried to resize the above heading, and in doing so Google Docs for no apparent reason decided to make the entire paragraph below the size of a header. Modern software is inherently broken, a convoluted mess of different menus, tech debt, and poor design choices driven by the Rot Economy’s growth-at-all-costs mindset which demands constant change at all times, none of which ever seems to manifest as a “better” or “smarter” product. I think the vast majority of people want their software to work better, and one of AI’s most frustrating lies is that it sells itself as “autonomous” as it continues the depressing trend of software that blames the user for its failure to meet their needs. Microsoft, Google, Meta and Amazon have made their products increasingly-convoluted, then attached a supposedly-magical tool to them that somehow makes them more convoluted. You know what I’d love? Spell-check to work in Google Docs rather than putting a red squiggly line underneath and saying “yeah there’s probably something wrong with this, I dunno what though.” I’d like Microsoft Word to stop crashing because I have too many end-notes. I’d like Riverside to not have 10 different menus to click through to get to a link to send a person to join my podcast. I’d like my email to not be full of spam. I’d like things to “just work” rather than constantly fighting some sort of broken app or broken UX element or weird bug or intrusive pop-up about a feature that I don’t want. I’d like Slack or Discord to not feel like digital escher paintings of different notifications. LLMs are sold as some sort of magic tool that can fix “anything” without ever specifying what that thing might be, mostly because they cannot be trusted, even in things that they mostly get right, to do things right every time. While they can do “more” than they used to, the extent of that “more” comes with it the danger of giving a mindless software tool access to your computer’s files, which it may choose to delete in pursuit of “efficiency,” which makes investigating what they might be able to do equal parts convoluted and dangerous. One critique of my work is that I’ve never used LLMs. I have! I experiment with them from time to time to make sure I haven’t missed something. I used one to debug a problem with my son’s Minecraft add-on the other day, and it took 30 minutes of fucking around trying things to eventually sort of work it out. The other day I used one to install a Pokemon Minecraft mod, then when I asked it to make sure the PS5 controller worked with the menus it broke a bunch of stuff, though I’ll concede it was useful that it installed something and it sort of worked. The fun part of that paragraph is there are some that will think this is a grand victory for their technology, even though the result is decidedly mediocre. Four years into the AI bubble, and the best you’ve got is that a tool kind of worked after I bonked it on the head multiple times, and all it cost was a trillion-plus dollars in capex and tens of billions of dollars of training compute. I would never, ever trust this thing that deleted and added lines of code at random with anything mission critical, I could not trust software built with it, and I certainly couldn’t trust it with anything involving my personal data. And with all that said, the only real “use case” i’ve found for AI in my life have been three or four times where I’ve dumped a crash log into one of the tools and said “why broken” and got a result. Am I meant to be impressed? Sidenote: If your argument is “imagine what it could do in a year!” I just did so, and the answer was “the same thing, I guess?” Here’s how I feel about LLMs. In a vacuum, they’re an interesting technology that can do some interesting stuff, in the right scenarios, but never in a way that involves you fully surrendering your actual work product to it. As a way of speeding up small units of work in ways that are manageable both technically and cognitively, LLMs can be useful. The further you stretch yourself away from having complete clarity and industry over every element of the output’s purpose, the more likely you are to fall foul to a technology that is mathematically certain to make mistakes, and if you feel insecure reading it, you know that you are, on some level, embarrassed to have used AI. I don’t tell everybody about the weird keyboard I use, nor do I judge them despite how incredibly fast it makes typing for me, likely far faster than my competition, allowing me to operate at great speed. Who gives a fuck? In any case, it is impossible to view LLMs in a vacuum, because their existence demands hundreds of billions of dollars. Every data center is incredibly expensive, offensive-sounding and looking, and their existence is explicitly to enrich some sort of Patagonia-gargoyle at an asset management firm, all sold under the auspices of “investing in American infrastructure,” whatever the fuck that means. Their existence is a monument to the worst excesses of growth-at-all-costs capitalism — a technology that appears to coddle the user but ultimately lulls it into endlessly defending its fuckups under the flimsy pretense of “one day becoming perfect,” though woe betide you if you ever set perfection as the target, because that’s too unreasonable, as humans make mistakes. Actually, that’s a good point! Stop Comparing LLMs To Humans Unless You Are Ready To Demand Trillions Of Investments In Social Services and Labor Rights Please, point to the time in history when we have invested a trillion fucking dollars in making human workers better. Point to a time when we have taken the idea that managerial culture is a performative fuck-fest built to enrich and empower business idiots that make important-sounding projects and con other people into doing the actual work. Where is mentorship in corporate America? Where are labor standards? Where are the social services that would make human workers truly excel at their jobs — a good night’s sleep, a healthy body, a good income, basic fucking dignity in the workplace, and their labor respected and empowered. I’m old enough to remember when everybody was chiding workers for “quiet quitting” — by which I mean “doing the work you are asked to do and not taking on extra responsibility for free.” I’ve read article after article insisting that we do not need medicare for all, that Universal Basic Income is a bad idea, that we must means test welfare, that people must have a “good work ethic” and that ultimately someone’s worth is derived from their contribution to the economy, hundreds of thousands of words dedicated to critiquing and prodding and judging every kind of worker other than the vaunted Chief Executive Officer or the Glorious Startup Boys. Everyone seems so obsessed with sinking billions of dollars into the theoretical chance that machine learning might be able to replace human beings, and that more money makes it “smarter” and “better” at tasks, but the idea of unionization, healthcare as a right, investing in the education, and actual talents of the workers would be communism. Yet for some reason — because it’s a product, I guess? — we should as a nation, society and media ecosystem should do everything we can to assure that as much money as possible is invested in fucking large language models so that they can become something they are not. There is no AGI coming. There is no conscious computer. LLMs have gotten “better,” but the “better” is not the kind of “better” that actually makes “economic sense for literally anyone involved.” Your best case scenario is that these things can do some coding work for you, in a controlled manner, in a way that’s safe, or alternatively face the professional harm that’s already befalling basically anyone getting caught using LLMs outside of coding, and even then, those within software engineering who are over-LLM’d are mocked. It’s also becoming increasingly more-difficult to understand both what has made an LLM “better” for both the people using them and the people making them, and there has been little-to-no headway made in making a meaningful impact in other industries. You can jerk your bingus all you want about benchmarks or case studies or some anecdote you heard on a Subreddit, but AI products are just not very good at stuff. Those who boast of “massive productivity gains” from AI have found them only after endless hours of tinkering (or “Jarvising” as I’ll get to later), and in every single case their work reads or looks like crap, unless of course they’re somebody using LLMs as tools rather than a replacement for their miserable little mind. LLMs can help out with lots of small things, get worse as they try and do real things, and do not need to speak like people. They do not need to be in anything near healthcare or finance or mental health or, really, people. The anthropomorphism and overpromising about these technologies has suffocated and obfuscated what they can actually do in pursuit of endless growth, and the only reason they can do anything is that OpenAI and Anthropic were allowed to annihilate hundreds of billions of dollars on training, along with very real harms and systemic risks that have emerged as a result. Sidenote: The “well human beings make mistakes too” argument is very stupid on its own — after all, human beings can learn on the job at speed, and can self-correct in a way that LLMs are incapable of doing. I’ll also add that the way that people frame how “often” LLMs make mistakes is utterly flawed too. A human being might make a mistake but largely get the facts and techniques correct while meaningfully understanding the purpose and developing their approach over time. An LLM can keep a text document or look at files and [truncated for AI cost control]