SharePoint Dev/Architect at day, Azure, F# and Node.js hacker at night.

Malmö
Joined April 2009
Simon J.K. Pedersen retweeted
Este Fórmula 1 no es una grabación. Es un videojuego de 2014. Assetto Corsa con mods y DLSS 5, el renderizado neuronal de NVIDIA: la IA vuelve a dibujar cada fotograma en tiempo real. Hasta un diseñador profesional de videojuegos dudó si era real. Los estudios gastan cientos de millones persiguiendo el realismo. Aquí lo hicieron unos mods y una IA sobre un juego de hace 12 años.
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Simon J.K. Pedersen retweeted
IMO, Google doesn't try to compete with frontier LLMs because coding agents isn't what they want to do. It doesn't fit into their business model, and they don't feel they can disrupt the market with it the way they did with Search, Maps, Gmail, Android or even GCP. I think they see that a regular Google user doesn't notice any difference between the AI Overview quality (~GPT-3.5), the AI Mode quality (~GPT-4.0), and the frontier models. To be honest, I wouldn't see any difference between the models past o1 either if I didn't build with AI. So, Google is more interested in making GPT-4.0-level quality cheap than to beat it at any cost. Hence, the advancements in Flash and Flash Light and their Voice counterparts that would allow the users to talk to Google and that would be cheap enough to be paid for by the ads. Without a doubt, Gemini 3.7 Flash (as well as GPT-5.6 Luna) are the two best models for the money right now. I extensively tested all open-weight alternatives that are as expensive or cheaper, and none of them come close to these two models in terms of quality for money. I think it's the right strategy for Google. They don't lose casual users because those users don't see any reason to leave the Google ecosystem, Gemini is good enough as a voice interface to GCP and Gmail, and you can always easily distill your competitor's best model when that competitor reaches some sort of plateau, rather than try to beat all their intermediary versions for years for no practical reason.
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Simon J.K. Pedersen retweeted
ever since AI got smart enough to do shit autonomously i swear it has only made me dumber and lazier i cannot even get shit done anymore all i do is sit there staring at my screen watching AI do everything and is this really the same shit that was supposed to make humanity immortal and push us forward??? like bro what the fuck a few months ago i was moving between websites, opening 30 tabs, scraping for information and actually finding shit myself now i just type something into a box and wait there is no fun in this man i do not want to watch a machine use the internet for me i want to do shit with my own two fucking hands again i want to search, get lost, figure things out and actually feel something while i am online fuck being 10x more productive if i am not even the one doing anything i do not want it anymore man
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Simon J.K. Pedersen retweeted
An AI just read a stranger's mind through their skull, no surgery, no implants, and got roughly 45% of the meaning right on the first try. On a person it had never seen before. That's the tech an OpenAI researcher just quit her job for. Two weeks ago she walked out of the hottest company on earth to join Conduit, a startup collecting brain recordings in a San Francisco basement. Here's what happens in that basement. Paid volunteers put on four pound headsets covered in sensors and chat with an AI for two hours while it listens to their neurons fire. The signal is so faint the team shut off wall power to the entire building and runs on battery packs, because the hum from an ordinary outlet drowns out a human thought. They've collected 10,000 hours of brains so far. The largest dataset of its kind ever assembled. And here's the part that should make your hands sweat. Whisper needed 680,000 hours of audio to master speech. The moment someone scales brain data the same way, the decoding stops being 45% right. She says we're in the GPT-2 era of mind reading. GPT-2 was a joke in 2019. Five years later half the internet was written by its grandchildren. Her timeline says developers wear thought-streaming headbands by 2027. By 2035, AI stops feeling like a tool and starts feeling like a limb. Your inner monologue has been the one place nothing could touch for 300,000 years of human history. That run is ending in a basement in San Francisco, two hours at a time.
Two weeks ago, I resigned from OpenAI to join Conduit as a founding researcher, where we're training models to non-invasively read the human mind. I've written some thoughts about what telepathy could look like by 2035 and how to get there:
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Simon J.K. Pedersen retweeted
Microsoft DELETED my account AND OneDrive!!?? After ACKNOWLEDGING that I’m the owner of the account and that it was compromised??? 25 fucking years of data, thousands of euros spended on games?? My son’s baby pictures? GONE! All because MICROSOFT couldn’t bring back a compromised account?? One of the biggest companies ever coulnd’t do that so they just deleted that shit like it was nothing?? Fucking shame on you!! @microsoftnl @MicrosoftHelps @MicrosoftHelpt @Microsoft #microsoft #hacked
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Simon J.K. Pedersen retweeted
No, French nuclear reactors did not shut down because they could not cool During the latest heatwave, several headlines made it sound as if French nuclear reactors had to close because the reactors themselves technically could no longer be cooled. That is not what happened. Some reactors were curtailed because of environmental regulation on river temperatures and water discharge. To protect downstream ecosystems, EDF must comply with strict environmental thresholds. Some plants are required to ensure that the river does not exceed a maximum temperature after cooling water is returned, even when the water on average is returned only around 0.2°C warmer. That is very different from saying the reactors could not technically cool themselves. Nuclear reactors have no problem using "hot" intake water as cooling. Coastal reactors are also not exposed to the same river-temperature constraints, why this "problem" mostly concern inland power plants. Looking at the actual generation data gives a useful sense of scale. From 1 to 16 June, French nuclear generation averaged 38.9 GW. From 17 to 30 June, during the heatwave period, it averaged 38.2 GW. In other words, French nuclear generation changed surprisingly little. Records from the French transmission system operator RTE Réseau de Transport d'Electricité show that nuclear capacity averaging 2.7 GW, equal to around 4.3% of the French nuclear fleet, was unavailable because of environmental regulation during the heatwave period (June 17 to June 30). But this did not translate one-for-one into lower French nuclear generation. Aggregate output stayed broadly steady, likely because other available reactors partly absorbed the effect and because unavailable capacity is not the same as guaranteed lost production. That does not mean there was no impact. A few GW of constrained nuclear capacity can matter when consumption rises during a heat wave. But it's not only nuclear that is affected. We could apply the same logic to other energy sources. Wind generation also dropped right as the heatwave began on 17 June. Solar panels become less efficient at high temperatures. Demand rises because of cooling needs. Heatwaves affect the whole energy system, not only nuclear power.
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Simon J.K. Pedersen retweeted
Palantir CEO Alex Karp on the Wealth Tax:  "It just punishes." "It starts with the billionaires." "Every single person at this table is going to be paying a wealth tax only to punish us." "And the reason for it is—these models have been completely, irresponsibly oversold." " And the sell is, 'It's dangerous for everyone—which is why I can give it to all your adversaries, but I can't give it to the Department of War." "Or, I can't safely give it to an enterprise in this country without being certain that the alpha of that business could transfer to this model tomorrow." "At every single enterprise I deal with, these people are livid." "They're like, 'I'm paying for tokens that create no value. These people are stealing the weights and alpha of my business, and they're creating a wealth tax that does not help the poor." Via @CNBC
Palantir CEO Alex Karp says enterprises want to "own the means of production" instead of "transferring their alpha" to OpenAI or Anthropic: "Why are [LLMs] charging for tokens if it's so valuable?" "If it was so valuable—let's say I can make you a billion dollars tomorrow. Wouldn't I say, 'I'll make you a billion dollars, and I want 30%?'" "Look at our financials. The reason why everyone is chillaxing with bad financials and growth while losing money, is the client refuses to pay the true cost." "The two places that actually make money—profit, free cash flow—are our application layer called ontology, and compute." "We can get the frontier application to be exactly the same as a frontier model without the risk of transferring the alpha of your business to another." Via @CNBC
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Simon J.K. Pedersen retweeted
Elon Musk built one of the largest AI compute clusters on earth. Yann LeCun just explained why xAI now rents it out to rivals instead of winning with it. Musk has antagonized so much AI talent he structurally cannot hire the people he needs. LeCun is not a critic on the sidelines. He won the Turing Award and ran AI at Meta for a decade. When he talks about who can and cannot build a frontier lab, he is describing his own world. His verdict on xAI was blunt. He called it kind of a failure and did not soften it. His reasoning had nothing to do with money. The founding team left or was fired. There is some uncertainty about which. Either way, the people who started the company are gone. That is the part that matters. A frontier lab is its researchers. Lose them and the compute is just hardware. By March, all eleven co-founders Musk recruited in 2023 had walked out. Researchers who came from DeepMind, Google, and OpenAI. Musk himself posted that xAI was not built right the first time and had to be rebuilt from the foundations up. So Musk is left with one of the biggest clusters on earth and no way to win with it. He rents it out to other companies to recoup the cost. The most expensive infrastructure in AI, built by someone who can no longer staff it. Asked directly if xAI can compete at the frontier, LeCun gave a one word answer. No. What do you guys think about this? Source: CNBC International Live
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Simon J.K. Pedersen retweeted
I admire Fabrice Bellard. He is almost certainly a better overall programmer than I am.
A French engineer who lives quietly in Paris has spent 30 years writing software that the entire internet now runs on without knowing his name. He wrote the code that streams every YouTube video, every Netflix show, every TikTok clip. He wrote the code that runs the virtual servers underneath AWS, Google Cloud, and Microsoft Azure. He calculated more digits of pi than anyone in history. He has no Twitter. He has no marketing. He just keeps shipping. His name is Fabrice Bellard. Here is the story, because almost nobody outside the systems programming world knows what one man has built. Fabrice was born in 1972 in Grenoble, France. He studied at École Polytechnique, the top French engineering school. He never went to Silicon Valley. He never built a startup empire. He just wrote code. In 2000 he started a project called FFmpeg, an open-source multimedia framework for encoding, decoding, and streaming video. He was 28. The project did one thing nobody else had done well. It handled every video and audio format that existed, in one library, on every operating system. He led it himself for years. Today FFmpeg is the invisible engine of the internet. YouTube uses it. Netflix uses it. VLC uses it. Chrome and Firefox use parts of it. Every Android phone, every iPhone, every smart TV, every video editing tool you have ever touched runs FFmpeg somewhere underneath. If you have watched a video on a screen in the last 20 years, Fabrice's code processed it. He was not done. In 2003 he started QEMU, a machine emulator and virtualizer. He wrote it solo until version 0.7.1 in 2005. QEMU lets you run any operating system on any other operating system. It became the foundation of modern virtualization. KVM, the Linux kernel hypervisor, runs on top of QEMU. Every major cloud provider, AWS, Google Cloud, Microsoft Azure, IBM Cloud, runs virtual machines on infrastructure built around it. The Quick Emulator is the most cited piece of cloud infrastructure code on Earth. He kept going. In 2001 he won the International Obfuscated C Code Contest with a small C compiler that grew into TCC, the Tiny C Compiler. TCC can compile and boot a Linux kernel from source in under 15 seconds. In 2004 he calculated the most digits of pi ever computed at the time, using a personal desktop computer and an algorithm he derived himself called Bellard's formula. In 2011 he wrote a complete PC emulator in pure JavaScript that runs Linux in your browser, a project called JSLinux that engineers still cannot believe is real. In 2019 he released QuickJS, a small but complete JavaScript engine that fits where V8 cannot. In 2021 he released NNCP, a neural network based lossless data compressor that immediately took the lead on the Large Text Compression Benchmark. Then he turned his attention to large language models. He built TextSynth Server, a web server with a REST API for running LLMs locally. He released ts_zip and ts_sms, compression utilities that use language models to compress text and short messages at ratios traditional algorithms cannot reach. He released TSAC, a very low bitrate audio compression system. In December 2025 he released Micro QuickJS, a new JavaScript engine for microcontrollers, separate from QuickJS, designed for environments with almost no memory. Fabrice co-founded a telecom company called Amarisoft in 2012, where he serves as CTO. Amarisoft builds 4G and 5G base station software used by carriers and labs around the world. He has been running it for over a decade while continuing to ship personal projects from his own home page at bellard dot org He has no Twitter. He has no Instagram. He gives almost no interviews. His personal website is a flat list of projects with no styling, no fonts, no marketing copy. Just titles and links. A quiet French engineer who never moved to Silicon Valley wrote the code that quietly runs the internet. He is still shipping.
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Simon J.K. Pedersen retweeted
I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem. As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)! I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work. It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results? 88ms => 1.5ms 150K allocs => ~500 allocs Incredible right? Nope. My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path. This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput. The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity. Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.
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Simon J.K. Pedersen retweeted
At some point Anthropic talked to me informally about potentially joining their Board. I wasn’t interested and wouldn’t have been a good fit. But I did send Dario and Daniela a copy of Aristotle’s “Politics.” Unfortunately, I worry they’ve been too busy to read it.
Dario Amodei: Ideology Won't Survive the Reality of AI⁣ ⁣ "We're going to find that ideology will not survive the nature of this technology. The things I'm talking about are gonna become bipartisan and universal because everyone will recognize the necessity of it." — @DarioAmodei
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Simon J.K. Pedersen retweeted
Very true. Funny enough the opposite is also true. Complete dismissal for any use whatsoever.
I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really). It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely. The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying. I worry.
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Simon J.K. Pedersen retweeted
Something strange is happening in tech. CTOs of billion dollar companies are quitting to take IC roles at Anthropic. Workday CTO -> MTS (Mar 2026) You[.]com CTO -> MTS (Mar 2026) Instagram CTO -> MTS (Jan 2026) Box CTO -> MTS (Dec 2025) Super[.]com CTO -> MTS (July 2025) Adept AI CTO -> MTS (Jan 2025) The mission is that real.
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Simon J.K. Pedersen retweeted
This is wild. Sam Altman deleted the AGI clause from the Microsoft contract today. The contract structure that replaced it makes any AGI announcement commercially worthless. The AGI clause was a legal singularity. Once OpenAI's board declared AGI achieved, Microsoft's IP rights would terminate. One sentence that could vaporize a $135 billion stake. That clause was OpenAI's only real leverage. They gave it up. The actual trade is buried in the new terms. Microsoft no longer pays OpenAI any revenue share on Azure-hosted sales. OpenAI keeps paying Microsoft 20% of all revenue through 2030, capped, "independent of OpenAI's technology progress." That last phrase is the entire deal. A company that thought AGI was 18 months away would never sign that. Independent of technology progress means: even if you cure cancer next quarter, you still owe us 20%. Even if you declare AGI tomorrow, the contract doesn't change. Microsoft converted AGI from a contingent event into a fixed date sunset. Microsoft's IP license runs flat to 2032. Same rights pre-AGI as post-AGI. The "appropriate safety guardrails" language from the October 2025 amendment is also gone, because guardrails imply triggers, and there are no more triggers. The cascade is what makes this clean. OpenAI signed $138 billion with AWS over the last six months. That alone broke Azure exclusivity. Microsoft had two choices: sue or renegotiate. They renegotiated and used the leverage to extract the AGI clause permanently. Sam and Satya did this personally over a few weeks. Markets priced it correctly. MSFT fell 2%. AMZN rose 1%. The exclusivity loss is real. The AGI risk removal is also real. Microsoft traded distribution rights for legal certainty on a 27% stake worth $135 billion. For OpenAI heading into IPO, the read is funny. AGI is now contractually meaningless. The board can announce it tomorrow and Microsoft still collects 20% through 2030. Declaring AGI before 2030 changes nothing in any major commercial agreement they have. The AGI clause survived six years because both sides believed AGI was a discrete event with contractual consequences. They both signed a piece of paper saying it isn't.
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Simon J.K. Pedersen retweeted
Ghostty is leaving GitHub. I'm GitHub user 1299, joined Feb 2008. I've visited GitHub almost every single day for over 18 years. It's never been a question for me where I'd put my projects: always GitHub. I'm super sad to say this, but its time to go. mitchellh.com/writing/ghostt…
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Simon J.K. Pedersen retweeted
OpenAI is shutting down text-embedding-3-small?!? I strongly believe that if you shut down a closed-source embedding model that you should open-source. Imaging the trillions of tokens that will no longer be queryable. cc @romainhuet
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Simon J.K. Pedersen retweeted
OpenAI's new Euphony tool works almost exactly the same way as my Codex transcript viewer tools.simonwillison.net/code…
OpenAI Developers
@OpenAIDevsApr 21
Introducing Euphony, an open-source tool for visualizing chat data and Codex session logs. Paste in a public URL or upload a local file, and Euphony turns the raw data into an easy-to-browse view. It supports translation, filtering, editing, and more.
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Simon J.K. Pedersen retweeted
This is the moment NVIDIA should be seriously worried. In the next couple of weeks DeepSeek V4 will be launched. It’s a direct attack on the entire AI stack that American companies have spent years locking down. Full “de-NVIDIA-ization”, a complete shift away from CUDA into Huawei’s CANN ecosystem, running on Huawei Ascend chips. That means one thing, breaking the dependency that made NVIDIA untouchable. 35x faster inference vs early versions. Nearly 3x the performance of NVIDIA’s H20 on a single card. 40% less energy consumption. Over 95% CUDA compatibility with migration times collapsing from months to hours. Even Jensen Huang has already admitted it. If this works at scale, it’s a “terrifying outcome” for US companies. Because here’s the real problem, this isn’t happening in isolation. Chinese tech giants like Alibaba, ByteDance, and Tencent are already ordering hundreds of thousands of Ascend chips. Market share is shifting fast, domestic chips now at 41%, NVIDIA slipping to 55% in China’s AI server market. Additionally DeepSeek V4 is reportedly offering API costs at a fraction of US competitors. $300 for massive workloads that would cost $2,500+ on OpenAI models, or even $5,000 on Anthropic. So this isn’t just about one model. It’s about China building a fully independent AI stack, chips, frameworks, models, and applications. Completely outside of US control. NVIDIA doesn’t just lose sales. It loses its grip on the global AI standard.
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Simon J.K. Pedersen retweeted
Microsoft sold every spare CPU it had to Anthropic and OpenAI. Amazon tripled its CPU buys year over year and still can't keep up. Two of AWS's biggest customers asked Andy Jassy if they could buy the entire 2026 production run of Graviton chips. He said no. The ratio inside an AI datacenter used to be 100 megawatts of GPUs to 1 megawatt of CPUs. CPUs handled storage, checkpointing, pre-processing. Light work. GPUs did the actual training and inference. Then OpenAI shipped o1-preview in September 2024. RL post-training went from "check the model output with a regex" to "run classifiers" to "compile the code and run the unit tests" to "spin up a sandbox, call three databases, run a physics simulation, verify the answer." Every rollout now needs a CPU-backed environment to verify against. Codex 5.4 runs agentically for 6-7 hours at a time. Each database call, each cron job, each scraped URL is CPU work. Coding agent revenue went from a couple billion to north of $10B in six months. That compute is sitting on CPUs. The CPU to GPU ratio is now approaching 1:1. The entire global cloud was built for 1:8. That's why GitHub has been unstable for weeks. Nvidia and Arm both announced they're entering the server CPU market in March. TSMC will only meet 80% of server CPU wafer demand this year. High-end server CPU prices are already up 50%. When the GPU king and the IP licensor both pivot to CPUs in the same month, the boring chip isn't boring anymore.
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Simon J.K. Pedersen retweeted
I don't think people understand how big Lithography is for AI and basically every piece of technology on earth ASML (a single company in the Netherlands) makes 100% of the machines required to produce EVERY SINGLE cutting-edge chip on this planet. Lace Lithography plans to crack this monopoly by using a helium atom beam instead of light to make chips. A human hair is 100,000 nanometers wide. ASML's most advanced beam is 13.5 nanometers. Lace's beam is 0.1 nanometers. The width of a single hydrogen atom. 135x smaller than the best technology on earth today. The technology is called Beyond-EUV. Maskless. More energy efficient. Feature sizes 10x smaller than anything currently possible. Chips printed at what the CEO describes as "ultimately atomic resolution." Microsoft's venture arm is backing it. and their pilot tool is going in a real fab by 2029. Pretty neat
Introducing Ⓛ 𝗟𝗔𝗖𝗘 𝗟𝗜𝗧𝗛𝗢𝗚𝗥𝗔𝗣𝗛𝗬 A novel approach to chip-making that can extend Moore's Law 10x beyond what is possible with light — to atomic resolution. News today: "Manufacturers use light-based lithography systems made by the Dutch company ASML, which dominates ​the market. Lace has developed a new approach. Instead of ​light, Lace's engineers have made a form of lithography that uses a helium atom beam. With that, the Norwegian company will be able to create chip designs that are 10 times as small as what is currently possible" "The main advantage of the helium atom beam is the industry could create features such as transistors, ‌the ⁠building blocks of modern chips, an order of magnitude smaller to an "almost unimaginable" degree, according to John Petersen, Scientific Director of Lithography at Imec, a research and innovation hub for the chip industry. The beam Lace will use to make chips is about the width of a single hydrogen atom, or 0.1 nanometer. ASML's lithography tools use ​a beam of light that ​is about 13.5 nanometers; ⁠a human hair is about 100,000 nanometers wide. Smaller transistors and other features would give chipmakers the ability to ramp up the performance of advanced AI processors well beyond ​the current capabilities. Lace's technology would enable chip manufacturers to print wafers at ​what is "ultimately atomic ⁠resolution" — reuters.com/world/asia-pacif… Now hiring in Bergen and Barcelona: LaceLithography.com
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