{"componentChunkName":"component---gatsby-theme-spaceout-src-templates-article-template-tsx","path":"/agi-and-the-llm-wall","result":{"pageContext":{"article":{"id":"c3ce0c39-12cf-59ae-a2a3-bf935e2f5510","slug":"/agi-and-the-llm-wall","secret":false,"title":"The AGI Illusion: Why Scaling LLMs Won’t Get Us There (And What Actually Will)","author":"Luke Celitan","date":"September 7th, 2026","dateForSEO":"2026-09-07T00:00:00.000Z","timeToRead":7,"excerpt":"The industry keeps selling the idea that if we just add enough GPUs, next-token prediction will wake up. Here is why the LLM route to AGI is hitting a data wall, and what we actually need to build instead.","subscription":true,"body":"var _excluded = [\"components\"];\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n/* @jsxRuntime classic */\n/* @jsx mdx */\n\nvar _frontmatter = {\n  \"title\": \"The AGI Illusion: Why Scaling LLMs Won’t Get Us There (And What Actually Will)\",\n  \"excerpt\": \"The industry keeps selling the idea that if we just add enough GPUs, next-token prediction will wake up. Here is why the LLM route to AGI is hitting a data wall, and what we actually need to build instead.\",\n  \"date\": \"2026-09-07T00:00:00.000Z\",\n  \"hero\": \"agi_cover2.png\",\n  \"author\": \"Luke Celitan\",\n  \"category\": \"Post\",\n  \"tech\": [\"AI\", \"Architecture\", \"LLMs\"],\n  \"slug\": \"agi-and-the-llm-wall\"\n};\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n    props = _objectWithoutProperties(_ref, _excluded);\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"h1\", {\n    \"id\": \"the-billion-dollar-echo-chamber\"\n  }, \"The Billion-Dollar Echo Chamber\"), mdx(\"h2\", {\n    \"id\": \"introduction\"\n  }, \"Introduction\"), mdx(\"p\", null, \"If you sit in enough vendor meetings right now\\u2014or browse the timeline of any\\nheavily funded AI founder in 2026\\u2014you\\u2019ll hear the same underlying hum of\\nreligious certainty: \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"Artificial General Intelligence is just a matter of\\nscale.\")), mdx(\"p\", null, \"The pitch goes like this: we take an autoregressive language model, feed it the\\nentire internet, wire up a data centre the size of a small European country,\\nspend a billion dollars on training compute, and somewhere around the\\nhundred-trillion parameter mark, the model wakes up, stops hallucinating, and\\nstarts doing our jobs.\"), mdx(\"p\", null, \"I\\u2019ve been writing for a while about the gap between AI hype and engineering\\nreality. Most recently, I ran a benchmark proving that\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/ai-cant-count-give-it-a-calculator\"\n  }, \"your shiny new model can\\u2019t even add a column of numbers\"), \"\\nwithout simulating it like a child doing long division out loud. But the AGI\\nconversation is different. It\\u2019s not just a debate over whether a model got the\\nmaths wrong; it\\u2019s an architectural fallacy masquerading as a roadmap.\"), mdx(\"p\", null, \"Right now, the industry is confusing the map for the territory. We are confusing\\nthe ability to \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"describe\"), \" reasoning with the ability to \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"actually reason\"), \".\"), mdx(\"p\", null, \"So let\\u2019s do what we always do here. Let\\u2019s strip away the marketing, look at the\\nhard data from 2025 and 2026, examine the actual mechanics of Large Language\\nModels, and ask the uncomfortable question: how far along the LLM route to AGI\\nare we actually, and is it even the right road?\"), mdx(\"h2\", {\n    \"id\": \"defining-the-goalpost\"\n  }, \"Defining the Goalpost\"), mdx(\"p\", null, \"Before we talk about whether we can get there, we have to define \\u201Cthere.\\u201D\"), mdx(\"p\", null, \"The tech industry loves to define AGI as \\u201Cpassing the Turing test,\\u201D or \\u201Cscoring\\nin the 99th percentile on the bar exam,\\u201D or \\u201Cpassing the medical boards.\\u201D But as\\nI\\u2019ve learned from building\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/local-ai-agents-llama-cpp-rust-ai-tools\"\n  }, \"local, sovereign agents in Rust\"), \",\\npassing a static benchmark is a trick of memorisation and interpolation. If the\\nanswers exist in the latent space of the training data, the model will find\\nthem. That isn\\u2019t intelligence; that\\u2019s search compression.\"), mdx(\"p\", null, \"Real AGI isn\\u2019t a test-taker. It\\u2019s an entity that can:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Operate in novel environments\"), \" where the rules aren\\u2019t in its training\\ndata.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Learn continuously\"), \" without needing a full, billion-dollar weight update.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Reason non-linearly\"), \", backtracking, simulating outcomes, and altering\\nstate before acting.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Maintain agency\"), \" over long horizons without requiring a prompt to push it\\nforward.\")), mdx(\"p\", null, \"If you look at that list, you realise the problem: the standard LLM architecture\\nis fundamentally opposed to almost all of it.\"), mdx(\"h2\", {\n    \"id\": \"the-data-wall-reality-check\"\n  }, \"The \\u201CData Wall\\u201D Reality Check\"), mdx(\"p\", null, \"Let\\u2019s start with the fuel that runs the entire operation: data.\"), mdx(\"p\", null, \"Back in 2024, researchers at Epoch AI and other institutions started sounding\\nthe alarm about the \\u201Cdata wall.\\u201D The hypothesis was simple: we are going to run\\nout of high-quality, human-generated text on the internet to train these\\nincreasingly massive models.\"), mdx(\"p\", null, \"Fast forward to late 2026. The data wall isn\\u2019t a hypothesis anymore; it\\u2019s the\\ndefining constraint of the industry. The stock of high-quality public text is\\neffectively exhausted. You cannot scrape Reddit, Wikipedia, and Stack Overflow\\nagain and expect a 10x capability leap.\"), mdx(\"p\", null, \"To circumvent this, the industry pivoted to two strategies:\"), mdx(\"h3\", {\n    \"id\": \"1-synthetic-data-the-ouroboros\"\n  }, \"1. Synthetic Data (The Ouroboros)\"), mdx(\"p\", null, \"Labs are now using frontier models to generate training data for the next\\ngeneration of models. On paper, it sounds elegant. In practice, it\\u2019s a\\nstatistical trap. Training a model heavily on AI-generated data leads to \\u201Cmodel\\ncollapse\\u201D over multiple generations. The distribution tails shrink. The model\\nbecomes an amplified version of its own biases. It learns how to write perfect\\nboilerplate code, but it loses the messy, human edge required to invent a\\ncompletely novel architectural pattern. Synthetic data is great for fine-tuning\\nspecific tasks, but it does not expand the frontiers of general intelligence.\"), mdx(\"h3\", {\n    \"id\": \"2-inference-time-scaling-thinking-out-loud\"\n  }, \"2. Inference-Time Scaling (Thinking Out Loud)\"), mdx(\"p\", null, \"Because pre-training on raw data hit diminishing returns, the focus shifted to\\n\\u201Cinference-time compute.\\u201D Models like the OpenAI o-series (and now GPT-6) are\\ntrained to spend more time \\u201Cthinking\\u201D before they output a final answer. They\\nuse iterative reasoning, chain-of-thought, and self-correction.\"), mdx(\"p\", null, \"This is a massive improvement, and it\\u2019s why these models are so much better at\\ncoding and math than the models of 2024. But let\\u2019s be clear about what is\\nactually happening: the model isn\\u2019t gaining AGI. It is just being forced to\\ngenerate thousands of hidden tokens\\u2014simulating a reasoning loop\\u2014because its\\nunderlying architecture still requires it to predict the next word to function.\"), mdx(Mermaid, {\n    chart: \"graph TD\\nA[Prompt Input] --> B[Generate Hidden Token]\\nB --> C{Is task solved?}\\nC -- No --> D[Generate Next Hidden Token]\\nD --> B\\nC -- Yes --> E[Output Final Answer]\\nstyle B fill:#e6f3ff,stroke:#333,stroke-width:1px\\nstyle D fill:#e6f3ff,stroke:#333,stroke-width:1px\\nstyle E fill:#d4edda,stroke:#333,stroke-width:2px\",\n    mdxType: \"Mermaid\"\n  }), mdx(\"p\", null, \"Inference-time scaling is an admission that raw next-token pre-training hit a\\nwall. We are applying a brute-force patch to an architectural limitation.\"), mdx(\"h2\", {\n    \"id\": \"the-arc-agi-illusion\"\n  }, \"The ARC-AGI Illusion\"), mdx(\"p\", null, \"To understand how deep the illusion goes, we need to look at Fran\\xE7ois Chollet\\u2019s\\nARC-AGI benchmark.\"), mdx(\"p\", null, \"Unlike the Bar Exam or standard coding tests, ARC-AGI doesn\\u2019t test memorisation.\\nIt tests fluid intelligence. It presents the model with novel visual-logic\\npuzzles it has never seen before, requiring the model to deduce the underlying\\nrule from a few examples and apply it to a test case. It is designed to be easy\\nfor humans and incredibly hard for AI.\"), mdx(\"p\", null, \"In 2025, the top scores were hovering around the 24% mark. By September 2026,\\nthe headlines exploded: OpenAI\\u2019s GPT-6 Astra achieved a \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"99.9% score\"), \" on\\nARC-AGI-3.\"), mdx(\"p\", null, \"AGI achieved, right?\"), mdx(\"p\", null, \"Not exactly. If you dig into the paper, that 99.9% score was achieved using a\\nmassive, highly complex \\u201Charness\\u201D\\u2014a specialized scaffolding of software wrapped\\naround the model that forced it to generate code, test the code, verify the\\noutput against the puzzle, back-track, and try again, looping thousands of times\\nper puzzle.\"), mdx(\"p\", null, \"When researchers stripped away the scaffolding and ran the same GPT-6 Astra\\nmodel under a standard, minimal-interface harness, the score plummeted to\\n\", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"62.7%\"), \".\"), mdx(\"p\", null, \"The LLM itself wasn\\u2019t reasoning. The \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"scaffolding\"), \" was simulating reasoning by\\nwrapping the LLM in a brute-force search algorithm. The model was just\\ngenerating code variations. Hitting a benchmark through exhaustive programmatic\\ntrial-and-error is not AGI. It is just better interpolation, wrapped in a\\n\", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"while\"), \" loop.\"), mdx(\"h2\", {\n    \"id\": \"the-economics-of-the-plateau\"\n  }, \"The Economics of the Plateau\"), mdx(\"p\", null, \"This brings us to the financial reality, which is usually where the hype train\\nderails.\"), mdx(\"p\", null, \"The exponential cost of training these models is colliding violently with the\\nlogarithmic gains in capability. Let\\u2019s look at the estimated training costs for\\nflagship models over the last few years:\"), mdx(\"table\", null, mdx(\"thead\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"thead\"\n  }, mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Year\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Era\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Estimated Training Cost\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Notable Capability Leap\"))), mdx(\"tbody\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"2020\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"GPT-3\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"~$4 Million\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Few-shot prompting, basic code generation\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"2023\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"GPT-4\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"~$100 Million\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Advanced reasoning, passing standardized tests\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"2025\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"GPT-5 / o1\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"~$300 Million\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Inference-time reasoning, advanced math/logic\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"2026/27\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"GPT-6 / Astra\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \">$1 Billion\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Saturated static benchmarks, agentic workflows\")))), mdx(\"p\", null, \"Training costs are climbing by roughly 2.5x to 3.5x per year. We are approaching\\nthe billion-dollar threshold for a single training run. But are we getting a\\nbillion dollars worth of new intelligence?\"), mdx(\"p\", null, \"The industry\\u2019s own actions give us the answer. Notice how every major provider\\nnow ships \\u201Ctiered\\u201D model families? We have the Frontier tier (GPT-6, Claude 4.5\\nOpus), the Mid tier, and the Budget tier (Nano/Mini).\"), mdx(\"p\", null, \"Providers do this because the flagship models have become astronomically\\nexpensive to run, and for 95% of real-world enterprise tasks\\u2014routing data,\\nsummarising text, basic code autocomplete\\u2014the budget models from 2025 are\\nalready perfectly adequate. You don\\u2019t need a billion-dollar supercomputer to\\nparse a JSON payload.\"), mdx(\"p\", null, \"The performance at the very top of the leaderboard has converged. The massive\\ncapability leaps we saw from 2020 to 2023 have slowed into an expensive,\\nincremental grind. The industry is spending exponential capital for fractional\\ngains on edge-case reasoning, hoping that if they just push hard enough, the\\nmodel will suddenly learn to think.\"), mdx(\"h2\", {\n    \"id\": \"why-next-token-prediction-cant-reason-the-architecture\"\n  }, \"Why Next-Token Prediction Can\\u2019t Reason (The Architecture)\"), mdx(\"p\", null, \"Let me be precise about what an LLM actually is, because the anthropomorphism is\\nkilling our ability to engineer properly.\"), mdx(\"p\", null, \"An LLM is a phenomenal, high-dimensional statistical map of human language. It\\npredicts the most likely next token based on the context window. It doesn\\u2019t\\n\\u201Cthink.\\u201D It interpolates. When it gives you a brilliant architectural design,\\nit\\u2019s because a brilliant architectural design exists in the latent space it\\nmapped from human data.\"), mdx(\"p\", null, \"But true AGI requires breaking free from the context window entirely. Here is\\nwhy the architecture fails:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"The compute asymmetry:\"), \" As I showed in the\\n\", mdx(\"a\", {\n    parentName: \"li\",\n    \"href\": \"/ai-cant-count-give-it-a-calculator\"\n  }, \"counting experiment\"), \", LLMs have no\\ninternal state memory that persists outside of the context window. They\\ncannot update their internal model of the world without emitting a token. If\\nan AGI needs to silently simulate a complex 10-year business strategy, an LLM\\nhas to generate tokens for every single micro-step of that simulation. It is\\ncomputationally absurd.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"The novelty cliff:\"), \" LLMs interpolate beautifully between known concepts.\\nThey extrapolate terribly. Put an LLM in a genuinely novel situation with no\\nprior training data\\u2014like a newly invented programming language with zero\\ninternet footprint\\u2014and it hallucinates confidently. AGI requires robust\\nextrapolation.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"The static brain problem:\"), \" When an LLM learns something new in its context\\nwindow (e.g., you paste in documentation), that knowledge vanishes the moment\\nthe session ends. You can bolt on RAG (Retrieval-Augmented Generation) and\\nvector databases\\u2014which I advocate for highly in local setups\\u2014but that is\\nexternal tooling, not internal learning. The core weights remain frozen in\\ntime, unchanged until the next hundred-million-dollar training run.\")), mdx(Mermaid, {\n    chart: \"graph LR\\nsubgraph \\\"The LLM (Static)\\\"\\n    A[Context Window] --> B[Frozen Weights]\\n    B --> C[Next Token]\\nend\\nsubgraph \\\"True AGI (Dynamic)\\\"\\n    D[Environment State] --> E[Active Reasoning]\\n    E --> F[Weight Update / Learning]\\n    F --> G[Action]\\n    G --> D\\nend\\nstyle B fill:#ff9999,stroke:#333\\nstyle F fill:#99ff99,stroke:#333\",\n    mdxType: \"Mermaid\"\n  }), mdx(\"h2\", {\n    \"id\": \"what-actually-gets-us-there\"\n  }, \"What Actually Gets Us There?\"), mdx(\"p\", null, \"If LLMs are just the language centre of the brain, what does the rest of the\\narchitecture look like? We aren\\u2019t going to reach AGI by making the language\\ncentre bigger. We have to build the rest of the system.\"), mdx(\"p\", null, \"Here is the architectural shift I\\u2019m watching for over the next five years:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Neuro-symbolic systems:\"), \" We need to combine the fuzzy, probabilistic\\nbrilliance of neural networks with the hard, deterministic logic of symbolic\\nsystems. Give the AI a calculator; don\\u2019t ask it to be one. Let the LLM handle\\nthe semantic parsing, and hand the logic over to a deterministic solver.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Continuous local learning:\"), \" We need architectures where weights update\\ndynamically in real-time based on successful outcomes. An agent needs to\\nlearn that a specific bash command fails on a specific server, and remember\\nit tomorrow, without needing a billion-dollar cluster to re-train its\\nweights.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"True world models:\"), \" We must move beyond text prediction to predicting\\nphysics, state changes, and causal relationships. Video models are a baby\\nstep toward this, but they are still largely statistical. An AGI needs a\\ngrounded understanding that if it deletes a database table, the application\\ncrashes. Right now, it just knows that the word \\u201Cdelete\\u201D is often followed by\\nthe word \\u201Cerror\\u201D in its training set.\")), mdx(\"h2\", {\n    \"id\": \"final-thoughts\"\n  }, \"Final Thoughts\"), mdx(\"p\", null, \"Are we close to AGI? No.\"), mdx(\"p\", null, \"Are we close to highly capable, hyper-competent narrow agents? Yes. We are\\nalready there. If you build them right\\u2014with strict boundaries, typed toolchains,\\nand local control, exactly like we explored in the\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/local-ai-agents-llama-cpp-rust-ai-tools\"\n  }, \"Local AI Agents piece\"), \"\\u2014they will\\nabsolutely change how software is built and businesses are run.\"), mdx(\"p\", null, \"But the idea that an LLM will magically transition into Artificial General\\nIntelligence just by reading more synthetic data and running on a larger GPU\\ncluster is an illusion. We haven\\u2019t built a mechanical brain; we\\u2019ve built a\\nmechanical parrot with an eidetic memory and a silver tongue.\"), mdx(\"p\", null, \"The next leap in intelligence isn\\u2019t going to come from a bigger data centre.\\nIt\\u2019s going to come from an entirely new architecture that abandons next-token\\nprediction as the core engine of thought. Until then, stop waiting for the API\\nto wake up, and start building the guardrails.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"The AI industry might be stuck on the data wall, but your engineering doesn\\u2019t\\nhave to be. If you want to see how to actually wire up bounded, reliable agents\\ntoday, check out my deep dive on\\n\", mdx(\"a\", {\n    parentName: \"em\",\n    \"href\": \"/local-ai-agents-llama-cpp-rust-ai-tools\"\n  }, \"building a native AI platform in Rust\"), \".\")));\n}\n;\nMDXContent.isMDXComponent = 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everybody 👋 !  My name is Luke and I am the creator and owner of the spaceout.pl. Personally, I am a big geek with huge love for Star Wars, Marvel and DC comic books, Funk and 80s music. I take most of my design inspiration from offline experiences like going to art museums, galleries and working with other creative people on various projects. I also work with various Activist and NGO groups as a pro-bono designer and developer.\n","id":"f69d27c7-7f9a-5fbd-b05d-1a324fd10c0a","name":"Luke 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Will)","appDescription":null,"mailchimp":false,"next":[{"id":"aa978194-f0b0-57ef-a8ca-be817dbe5bba","slug":"/the-sovereign-workstation-agent-building-a-native-ai-platform-with-llama-cpp-rust-and-linux","secret":false,"title":"The Sovereign Workstation Agent: Building a Native AI Platform with llama.cpp, Rust, and Linux","author":"Luke Celitan","date":"July 20th, 2026","dateForSEO":"2026-07-20T00:00:00.000Z","timeToRead":6,"excerpt":"The industry keeps selling you bigger cloud models. Here's why I built my daily agent to run entirely on my own machine — in Rust, on llama.cpp — and what that buys you that no API ever will","subscription":true,"body":"var _excluded = [\"components\"];\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n/* @jsxRuntime classic */\n/* @jsx mdx */\n\nvar _frontmatter = {\n  \"title\": \"The Sovereign Workstation Agent: Building a Native AI Platform with llama.cpp, Rust, and Linux\",\n  \"excerpt\": \"The industry keeps selling you bigger cloud models. Here's why I built my daily agent to run entirely on my own machine — in Rust, on llama.cpp — and what that buys you that no API ever will\",\n  \"date\": \"2026-07-20T00:00:00.000Z\",\n  \"hero\": \"local_agent_cover2.png\",\n  \"author\": \"Luke Celitan\",\n  \"category\": \"Post\",\n  \"tech\": [\"AI\", \"Rust\", \"Linux\"]\n};\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n    props = _objectWithoutProperties(_ref, _excluded);\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"h1\", {\n    \"id\": \"who-owns-your-agents-brain\"\n  }, \"Who owns your agent\\u2019s brain?\"), mdx(\"h2\", {\n    \"id\": \"introduction\"\n  }, \"Introduction\"), mdx(\"p\", null, \"A while back I sat in a room while a very confident vendor explained that our\\nwhole engineering workflow should route through their API. Every prompt, every\\nfile scan, every \\u201Cjust check the logs for me\\u201D \\u2014 all of it, out over the wire to\\na server we didn\\u2019t own, billed per thousand tokens, gated behind a rate limit\\nwritten by someone whose incentives were not ours.\"), mdx(\"p\", null, \"Nobody in that room asked the question I\\u2019ve been asking my whole career, the one\\nI keep coming back to in\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/the-rise-of-the-solo-dev-army\"\n  }, \"The Rise of the Solo Dev Army\"), \": \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"who actually\\ncontrols this thing, and what happens to us when the control isn\\u2019t ours?\"), \"\\nBecause here\\u2019s the pattern I\\u2019ve watched play out a dozen times. The business\\nfalls in love with a capability. It gets wired into everything. And the\\narchitectural cost \\u2014 the dependency, the latency, the fact that your tool stops\\nworking the moment someone else\\u2019s dashboard turns red \\u2014 stays invisible right up\\nuntil the day it isn\\u2019t. Same story as technical debt: silent, compounding, then\\nsuddenly a rewrite.\"), mdx(\"p\", null, \"So I did the unfashionable thing. I stopped pointing my agent at a cloud\\nendpoint and built one that lives entirely on my own hardware. It\\u2019s called\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"https://github.com/MassivDash/ai_tools\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }, mdx(\"strong\", {\n    parentName: \"a\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"strong\"\n  }, \"ai_tools\"))), \" \\u2014 a local agent\\nplatform written in \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Rust (Actix-Web)\"), \" with an \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Astro + Svelte\"), \" frontend,\\ndriving local GGUF models through \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"strong\"\n  }, \"llama.cpp\")), \". This is a field report on how\\nit\\u2019s built, why \\u201Clocal\\u201D turns out to be an \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"architectural\"), \" decision and not just\\na privacy nicety, and how I taught it to actually touch my Linux box safely.\"), mdx(\"h2\", {\n    \"id\": \"the-bottleneck-nobody-puts-in-the-architecture-diagram\"\n  }, \"The bottleneck nobody puts in the architecture diagram\"), mdx(\"p\", null, \"Let me be precise about why cloud-bound agents hit a wall, because \\u201Cprivacy\\u201D is\\nonly the headline and it\\u2019s not even the most interesting part.\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Privacy and security boundaries.\"), \" You cannot pipe internal infrastructure\\ndetails, private keys, local file structures, or proprietary source into a\\nthird-party API without lighting up every compliance red flag in the\\nbuilding. Not \\u201Cshouldn\\u2019t\\u201D \\u2014 \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"cannot\"), \", if you work anywhere serious.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Latency and rate limits.\"), \" An agentic loop \\u2014 model decides to call a tool,\\nreads the result, decides the next step \\u2014 pays an HTTP round trip on \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"every\"), \"\\nhop. Multiply a few hundred milliseconds across a multi-step loop and the\\nthing feels like it\\u2019s thinking through molasses.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Cost anxiety on high-frequency work.\"), \" Polling, file scanning, background\\nmonitoring: exactly the boring, repetitive jobs an agent is best at are the\\nones that turn into a token meter you nervously watch tick upward.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"No native OS context.\"), \" A cloud model operates in a vacuum. It has no idea\\nwhat\\u2019s eating your RAM, which ports are open, or where that config file is\\nburied in your filesystem. It can \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"talk about\"), \" your machine; it cannot \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"see\"), \"\\nit.\")), mdx(\"blockquote\", null, mdx(\"p\", {\n    parentName: \"blockquote\"\n  }, \"The business only ever watches one dial \\u2014 velocity \\u2014 and cloud APIs are sold\\nas pure velocity. The costs above are the code smell you don\\u2019t notice until\\nthe codebase can\\u2019t move.\")), mdx(AgentToolCallLoop, {\n    mdxType: \"AgentToolCallLoop\"\n  }), mdx(\"p\", null, \"I\\u2019ve made this argument before in a different costume. Clean architecture, I\\nwrote once, is not decoration \\u2014 bad structure shows up as CPU, RAM, and server\\nbills, and eventually as a client walking away over the running costs. Renting a\\nremote brain for every keystroke is the same mistake at a new altitude. And if\\nyou\\u2019ve read \", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/beware-of-the-hype-train\"\n  }, \"Beware of the Hype Train\"), \", you already\\nknow my reflex: when the entire industry is shouting \\u201Cbigger, more parameters,\\nmore cloud,\\u201D that\\u2019s exactly the moment to ask what problem you\\u2019re actually\\ntrying to solve.\"), mdx(\"h2\", {\n    \"id\": \"the-ai_tools-platform\"\n  }, \"The \", mdx(\"inlineCode\", {\n    parentName: \"h2\"\n  }, \"ai_tools\"), \" platform\"), mdx(\"p\", null, \"The philosophy is boring on purpose: \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"high performance, zero external\\ndependencies for inference, strict type safety.\")), mdx(\"p\", null, \"Instead of reaching for a heavy Python framework, the runtime is pure Rust. Rust\\nhandles concurrent tool execution, WebSocket streaming, SQLite persistence, and\\nthe \", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"llama.cpp\"), \" server lifecycle with a memory footprint that lets the whole\\nplatform get out of the way of the model \\u2014 which is the only thing that should\\nbe eating your GPU.\"), mdx(Mermaid, {\n    chart: \"graph TD\\nA[Astro / Svelte Web UI] <-->|WebSocket & REST API| B[Actix-Web Rust Server]\\nsubgraph Agent Core Runtime\\nB --> C[Agent Orchestrator & Loop]\\nC --> D[SQLite Memory & History]\\nC --> E[ToolRegistry Dispatcher]\\nend\\nsubgraph Local Infrastructure\\nC <-->|OpenAI-Compatible Spec| F[llama.cpp / llama-server]\\nE --> G[ChromaDB Vector Store]\\nE --> H[Google Suite & Web Tools]\\nE --> I[Linux System Tools]\\nend\\nI -->|Tokio Command| J[Native Linux Kernel & Utilities]\\nF -->|GGUF Quants| K[Apple Silicon / NVIDIA GPU]\",\n    mdxType: \"Mermaid\"\n  }), mdx(\"p\", null, \"Three pieces do the real work:\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"strong\"\n  }, \"llama.cpp\"), \" engine control.\"), mdx(\"a\", {\n    parentName: \"li\",\n    \"href\": \"https://github.com/MassivDash/ai_tools\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"a\"\n  }, \"ai_tools\")), \" is a full control plane\\nover \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"llama.cpp\"), \". It scans your GGUF cache (\", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"~/.cache/llama.cpp/\"), \"), lets you\\ntune threads, context size, flash attention, and GPU-layer offload, and\\nbabysits the \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"llama-server\"), \" process for you. You own the model \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"and\"), \" the\\nknobs.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Two-tier memory.\"), \" SQLite stores complete conversation turns, active\\ncontext, and tool-call histories, durably. A local \", mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"ChromaDB\"), \" instance \\u2014 the\\nsame kind of embeddings-on-your-own-hardware setup I keep arguing for \\u2014\\nhandles semantic retrieval and long-term recall. Nothing leaves the box.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"A dynamic tool registry.\"), \" Tools register themselves into a central\\n\", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"ToolRegistry\"), \". The agent presents the available function definitions to the\\nmodel and executes the calls it decides to make.\")), mdx(\"p\", null, \"That last point is where an agent stops being a chatbot.\"), mdx(\"h2\", {\n    \"id\": \"the-toolbelt\"\n  }, \"The toolbelt\"), mdx(\"p\", null, \"An agent without tools is a very expensive autocomplete. Capabilities in\\n\", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"ai_tools\"), \" are organized into modules:\"), mdx(\"table\", null, mdx(\"thead\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"thead\"\n  }, mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Tool category\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Registered capabilities\"))), mdx(\"tbody\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, \"Database\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"chromadb\"), \" vector search & collection query\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, \"Development\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"github\"), \" repository & issue search\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, \"Financial & web\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Crypto prices, currency conversion, stock data, Google Books, uptime monitor\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, \"Utility & workspace\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Gmail (read/send), Google Docs, Drive, Sheets, Calendar, Tasks, YouTube, Weather, and a Human-in-the-Loop \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"ask_human\"))), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, \"System (Linux)\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Native OS control: \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"search_file\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"open_folder\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"list_top_processes\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"grep_search\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"system_status\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"network_ports\"))))), mdx(\"p\", null, \"Google APIs and vector search are useful. But the moment the whole thing clicked\\n\\u2014 the moment a local agent stopped being a novelty and became something I\\nactually reach for at 2pm on a Tuesday \\u2014 was when I let it touch the operating\\nsystem.\"), mdx(\"h2\", {\n    \"id\": \"giving-an-agent-hands-without-giving-it-a-knife\"\n  }, \"Giving an agent hands (without giving it a knife)\"), mdx(\"p\", null, \"There are two ways to give an agent system access, and one of them is a mistake.\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Unconstrained shell.\"), \" Hand the model \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"bash\"), \" and let it run whatever it\\ndreams up. Fast to build. Also one hallucinated \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"rm -rf\"), \" or one malformed\\nstring away from wrecking your environment. This is the \\u201Cwe\\u2019ll think about\\nsafety later\\u201D of agent design, and later never comes.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Structured native wrappers.\"), \" Expose discrete, safe commands with typed\\nparameters, hard timeouts, and truncated output. More work up front. Sleeps\\nat night.\")), mdx(GuardrailToggle, {\n    mdxType: \"GuardrailToggle\"\n  }), mdx(\"p\", null, \"I chose the second. Not because I\\u2019m cautious by temperament, but because I\\u2019ve\\nspent enough years watching \\u201Cmove fast\\u201D become \\u201Cexplain to the client why prod\\nis down\\u201D that I flinch at unbounded blast radius. Constraints \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"are\"), \" the feature\\nhere.\"), mdx(\"p\", null, \"Every tool implements one contract:\"), mdx(\"pre\", null, mdx(\"code\", {\n    parentName: \"pre\",\n    \"className\": \"language-rust\"\n  }, \"use async_trait::async_trait;\\nuse anyhow::Result;\\nuse serde_json::Value;\\nuse crate::api::agent::core::types::{ToolCall, ToolCallResult, ToolMetadata};\\n\\n#[async_trait]\\npub trait AgentTool: Send + Sync {\\n    fn metadata(&self) -> &ToolMetadata;\\n    fn get_function_definition(&self) -> Value;\\n    async fn execute(&self, tool_call: &ToolCall) -> Result<ToolCallResult>;\\n    fn is_available(&self) -> bool { true }\\n}\\n\")), mdx(\"p\", null, \"The system tool exposes six actions behind an enum-backed JSON schema, so the\\nmodel can only ever pick from a known menu:\"), mdx(\"ol\", null, mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"search_file\"), \" \\u2014 \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"find\"), \" files by pattern under a directory.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"open_folder\"), \" \\u2014 \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"xdg-open\"), \" a folder in the desktop file manager.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"list_top_processes\"), \" \\u2014 \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"ps -eo pid,ppid,cmd,%mem,%cpu --sort=-%cpu\"), \", sliced\\nto the top 15 \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"in Rust\"), \", not by shelling out to \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"head\"), \".\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"grep_search\"), \" \\u2014 \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"grep -rn\"), \" through text files.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"system_status\"), \" \\u2014 \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"df -h\"), \" and \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"free -m\"), \" for instant disk and RAM numbers.\"), mdx(\"li\", {\n    parentName: \"ol\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"network_ports\"), \" \\u2014 \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"ss -tuln\"), \" for listening TCP/UDP sockets.\")), mdx(\"p\", null, \"Here\\u2019s the shape of it (\", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"src/backend/src/api/agent/tools/utility/system.rs\"), \"):\"), mdx(\"pre\", null, mdx(\"code\", {\n    parentName: \"pre\",\n    \"className\": \"language-rust\"\n  }, \"use crate::api::agent::core::types::{ToolCall, ToolCallResult, ToolType};\\nuse crate::api::agent::tools::framework::agent_tool::{AgentTool, ToolCategory, ToolMetadata};\\nuse anyhow::Result;\\nuse async_trait::async_trait;\\nuse serde_json::json;\\nuse std::time::Duration;\\nuse tokio::process::Command;\\nuse tokio::time::timeout;\\n\\npub struct SystemCommandTool {\\n    metadata: ToolMetadata,\\n}\\n\\n#[async_trait]\\nimpl AgentTool for SystemCommandTool {\\n    fn metadata(&self) -> &ToolMetadata {\\n        &self.metadata\\n    }\\n\\n    fn get_function_definition(&self) -> serde_json::Value {\\n        json!({\\n            \\\"name\\\": \\\"system_command\\\",\\n            \\\"description\\\": \\\"Execute safe, everyday Linux system commands.\\\",\\n            \\\"parameters\\\": {\\n                \\\"type\\\": \\\"object\\\",\\n                \\\"properties\\\": {\\n                    \\\"command\\\": {\\n                        \\\"type\\\": \\\"string\\\",\\n                        \\\"description\\\": \\\"The command to run.\\\",\\n                        \\\"enum\\\": [\\n                            \\\"search_file\\\", \\\"open_folder\\\", \\\"list_top_processes\\\",\\n                            \\\"grep_search\\\", \\\"system_status\\\", \\\"network_ports\\\"\\n                        ]\\n                    },\\n                    \\\"path\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"Target directory.\\\" },\\n                    \\\"query\\\": { \\\"type\\\": \\\"string\\\", \\\"description\\\": \\\"File-name or grep pattern.\\\" }\\n                },\\n                \\\"required\\\": [\\\"command\\\"]\\n            }\\n        })\\n    }\\n\\n    async fn execute(&self, tool_call: &ToolCall) -> Result<ToolCallResult> {\\n        let args: serde_json::Value =\\n            serde_json::from_str(&tool_call.function.arguments).unwrap_or(json!({}));\\n\\n        let command = args.get(\\\"command\\\").and_then(|v| v.as_str()).unwrap_or(\\\"\\\");\\n        let path = args.get(\\\"path\\\").and_then(|v| v.as_str()).unwrap_or(\\\".\\\");\\n        let query = args.get(\\\"query\\\").and_then(|v| v.as_str()).unwrap_or(\\\"\\\");\\n\\n        let timeout_duration = Duration::from_secs(30);\\n\\n        let output = match command {\\n            \\\"list_top_processes\\\" => {\\n                let mut cmd = Command::new(\\\"ps\\\");\\n                cmd.args([\\\"-eo\\\", \\\"pid,ppid,cmd,%mem,%cpu\\\", \\\"--sort=-%cpu\\\"]);\\n                let res = process_output(timeout(timeout_duration, cmd.output()).await);\\n                // Safe slice in Rust memory \\u2014 no shell pipeline to `head`\\n                res.lines().take(16).collect::<Vec<&str>>().join(\\\"\\\\n\\\")\\n            }\\n            \\\"network_ports\\\" => {\\n                let mut cmd = Command::new(\\\"ss\\\");\\n                cmd.arg(\\\"-tuln\\\");\\n                process_output(timeout(timeout_duration, cmd.output()).await)\\n            }\\n            // ...search_file, open_folder, grep_search, system_status\\n            _ => format!(\\\"Unknown command: {}\\\", command),\\n        };\\n\\n        Ok(ToolCallResult {\\n            tool_name: self.metadata.name.clone(),\\n            result: output,\\n            tool_call_id: Some(tool_call.id.clone()),\\n        })\\n    }\\n}\\n\")), mdx(\"h2\", {\n    \"id\": \"the-three-decisions-that-matter\"\n  }, \"The three decisions that matter\"), mdx(\"p\", null, \"Skim past the boilerplate and there are exactly three choices doing the heavy\\nlifting, and each one is really a decision about \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"failure\"), \":\"), mdx(\"table\", null, mdx(\"thead\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"thead\"\n  }, mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Decision\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"What it prevents\"))), mdx(\"tbody\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"strong\"\n  }, \"tokio::process::Command\")), \" (non-blocking)\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"A shelling-out tool never stalls the Actix event loop or the WebSocket stream to other connected clients. One user\\u2019s \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"find\"), \" doesn\\u2019t freeze everyone else\\u2019s session.\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"strong\"\n  }, \"tokio::time::timeout\"), \" on every call\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"A command that hangs \\u2014 say, searching an unmounted network share \\u2014 gets cancelled at 30s instead of deadlocking the whole agentic turn.\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, mdx(\"strong\", {\n    parentName: \"td\"\n  }, \"No shell pipes; args passed to binaries directly\")), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": \"left\"\n  }, \"Nothing goes through \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"sh -c\"), \". Arguments are handed straight to \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"find\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"ps\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"ss\"), \", \", mdx(\"inlineCode\", {\n    parentName: \"td\"\n  }, \"grep\"), \". Shell injection isn\\u2019t mitigated, it\\u2019s \", mdx(\"em\", {\n    parentName: \"td\"\n  }, \"absent\"), \".\")))), mdx(\"p\", null, \"This is the constructive friction I keep banging on about, just moved into the\\ntype system. In a team, the raised eyebrow at code review is what stops the\\nreckless one-liner. When you\\u2019re a solo dev with an AI army \\u2014 the exact scenario\\nI worried about in the Solo Dev Army piece \\u2014 that friction has to come from\\n\", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"somewhere\"), \". Here it comes from an enum, a timeout, and a refusal to concatenate\\nstrings into a shell. The agent is fast because it\\u2019s local; it\\u2019s \", mdx(\"em\", {\n    parentName: \"p\"\n  }, \"trustworthy\"), \"\\nbecause the guardrails are compiled in, not prompted in.\"), mdx(\"h2\", {\n    \"id\": \"what-this-actually-feels-like\"\n  }, \"What this actually feels like\"), mdx(\"p\", null, \"Pair an open-weight model \\u2014 \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Qwen 2.5 14B/32B\"), \", \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"Llama 3.1 8B\"), \", a \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"DeepSeek\\nR1 distill\"), \" \\u2014 with local tool execution on Apple Silicon or a dedicated GPU,\\nand the developer experience changes character:\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Sub-second feedback.\"), \" An agent checking memory or finding a file answers in\\ntens of milliseconds. The only real latency left is token generation, which is\\n\", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"your\"), \" silicon\\u2019s problem to solve, not a queue in someone else\\u2019s data center.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Zero token-cost anxiety.\"), \" Let it run background loops all day. There is no\\nbilling dashboard climbing in the corner of your eye.\")), mdx(CostMeter, {\n    mdxType: \"CostMeter\"\n  }), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Actual autonomy.\"), \" \", mdx(\"em\", {\n    parentName: \"li\"\n  }, \"\\u201CMy machine\\u2019s running hot \\u2014 find the top process and\\nopen the project directory with the log file.\\u201D\"), \" The agent runs\\n\", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"list_top_processes\"), \", spots the runaway PID, calls \", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"search_file\"), \", and\\n\", mdx(\"inlineCode\", {\n    parentName: \"li\"\n  }, \"xdg-open\"), \"s the folder on my desktop. Seconds. No wire.\")), mdx(\"p\", null, \"None of these models is the biggest on the leaderboard, and that\\u2019s the point.\\nSame lesson I keep relearning: the boring, right-sized tool that runs where you\\nneed it beats the giant that lives somewhere you can\\u2019t reach. Efficiency is the\\nmoat now, not parameter count.\"), mdx(\"h2\", {\n    \"id\": \"final-thoughts\"\n  }, \"Final thoughts\"), mdx(\"p\", null, \"Local models stopped being the budget alternative to cloud APIs a while ago.\\nBolt one to a compiled, high-concurrency language like Rust and a local runtime\\nlike \", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"llama.cpp\"), \", and you get something the API-first crowd can\\u2019t ship you at\\nany price: \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"a sovereign workstation agent.\"), \" Fast because there\\u2019s no wire.\\nPrivate because nothing leaves the box. Trustworthy because the safety is in the\\ntypes, not in a hopeful system prompt.\"), mdx(\"p\", null, \"The business will keep watching the velocity dial, and the vendors will keep\\nselling more parameters. That\\u2019s fine \\u2014 let them. The quiet, unglamorous move is\\nto own the thing that runs on your own machine, structure its powers\\ndeliberately, and stop renting a brain by the token. That\\u2019s not a step down from\\nthe frontier. It\\u2019s just refusing to build your whole workflow on a foundation\\nyou don\\u2019t control.\"), mdx(\"p\", null, mdx(\"em\", {\n    parentName: \"p\"\n  }, \"If you want to kick the tires on \", mdx(\"a\", {\n    parentName: \"em\",\n    \"href\": \"https://github.com/MassivDash/ai_tools\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"a\"\n  }, \"ai_tools\")), \" or add a native tool of your own to the registry, check out the \", mdx(\"a\", {\n    parentName: \"em\",\n    \"href\": \"https://github.com/MassivDash/ai_tools\",\n    \"target\": \"_blank\",\n    \"rel\": \"noreferrer\"\n  }, mdx(\"inlineCode\", {\n    parentName: \"a\"\n  }, \"ai_tools\"), \" GitHub repository\"), \", clone it, and give \", mdx(\"inlineCode\", {\n    parentName: \"em\"\n  }, \"cargo run\"), \" a spin \\u2014 then tell me what you built. I read every reply.\")));\n}\n;\nMDXContent.isMDXComponent = 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Can't Count","author":"Luke Celitan","date":"July 16th, 2026","dateForSEO":"2026-07-16T00:00:00.000Z","timeToRead":8,"excerpt":"I benchmarked 21 language models on plain arithmetic. Only the slow, expensive ones could add a column of numbers — and here's why that should change how you build","subscription":true,"body":"var _excluded = [\"components\"];\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n/* @jsxRuntime classic */\n/* @jsx mdx */\n\nvar _frontmatter = {\n  \"title\": \"AI Can't Count\",\n  \"excerpt\": \"I benchmarked 21 language models on plain arithmetic. Only the slow, expensive ones could add a column of numbers — and here's why that should change how you build\",\n  \"date\": \"2026-07-16T00:00:00.000Z\",\n  \"hero\": \"aicount_cover.png\",\n  \"author\": \"Luke Celitan\",\n  \"category\": \"Post\",\n  \"tech\": [\"AI\", \"SoftwareEngineering\"],\n  \"slug\": \"ai-cant-count-give-it-a-calculator\"\n};\nvar layoutProps = {\n  _frontmatter: _frontmatter\n};\nvar MDXLayout = \"wrapper\";\nreturn function MDXContent(_ref) {\n  var components = _ref.components,\n    props = _objectWithoutProperties(_ref, _excluded);\n  return mdx(MDXLayout, _extends({}, layoutProps, props, {\n    components: components,\n    mdxType: \"MDXLayout\"\n  }), mdx(\"h2\", {\n    \"id\": \"can-ai-actually-count\"\n  }, \"Can AI actually count?\"), mdx(\"h3\", {\n    \"id\": \"introduction\"\n  }, \"Introduction\"), mdx(\"p\", null, \"Here\\u2019s the thing nobody tells you when they demo a shiny AI feature over a\\nspreadsheet: the model reading your data and the model calculating your data are\\nnot the same skill, and one of them is a lie.\"), mdx(\"p\", null, \"I\\u2019ve spent the last few years watching business stakeholders fall in love with\\nthe idea of pointing a language model at a table of numbers and asking it a\\nquestion. \\u201CWhat\\u2019s the total?\\u201D \\u201CHow many are overdue?\\u201D \\u201CWhich one is under\\nbudget?\\u201D It feels like magic. It\\u2019s fast, it\\u2019s cheap, it talks back in a full\\nsentence, and \\u2014 this is the part that should terrify you \\u2014 it never says \\u201CI\\ndon\\u2019t know.\\u201D It just returns a clean, confident, beautifully formatted number.\"), mdx(\"p\", null, \"The problem is that the number is frequently wrong. Not \\u201Crounding error\\u201D wrong.\\nWrong wrong.\"), mdx(\"p\", null, \"So I did what I always do when the hype and my gut disagree: I ran the\\nexperiment. I put 21 language models \\u2014 the full spread of GPT, Claude and\\nGemini, from the GPT-3.5 era all the way up to the latest GPT-5 and Claude\\nreleases \\u2014 through a benchmark whose only job was to answer one embarrassingly\\nsimple question:\"), mdx(\"p\", null, \"If I hand a model a list of numbers and ask for the total, how often is it right\\n\\u2014 and what does the right answer actually cost me?\"), mdx(\"p\", null, \"Over a thousand scored runs later, I have an answer. It is not the answer the\\ndemos promised.\"), mdx(KeyFindingsStats, {\n    data: keyFindings,\n    mdxType: \"KeyFindingsStats\"\n  }), mdx(\"h2\", {\n    \"id\": \"the-failure-mode-is-silent-and-thats-the-whole-problem\"\n  }, \"The failure mode is silent, and that\\u2019s the whole problem\"), mdx(\"p\", null, \"Let me start with why I even bothered, because it connects to something I\\u2019ve\\nbeen banging on about for years.\"), mdx(\"p\", null, \"In a normal engineering team, quality is defended by constructive friction \\u2014 the\\npull request that gets a raised eyebrow, the reviewer who says \\u201Cwait, are you\\nsure about that?\\u201D, the crucible of collaboration where a wrong answer gets\\ncaught before it ships. I wrote about how AI is quietly dissolving that friction\\nin \", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/the-rise-of-the-solo-dev-army\"\n  }, \"The Rise of the Solo Dev Army\"), \". Well, an LLM\\ndoing arithmetic is that dissolution taken to its logical, horrifying\\nconclusion.\"), mdx(\"p\", null, \"When a model can\\u2019t add a column, it doesn\\u2019t throw an error. A calculator that\\ncan\\u2019t compute shows you \", mdx(\"inlineCode\", {\n    parentName: \"p\"\n  }, \"ERR\"), \". A database returns null. A failing test goes red.\\nThe LLM does none of that \\u2014 it hands you \\xA34,182,940 in a tidy little sentence\\nand moves on. There\\u2019s no red flag, no raised eyebrow, no reviewer. The one\\nmoment where you most need someone to say \\u201Care you sure?\\u201D is the exact moment\\nthe machine is most confident and most alone.\"), mdx(\"p\", null, \"That\\u2019s not a bug you can grep for. It\\u2019s a category of failure that looks exactly\\nlike success.\"), mdx(\"h2\", {\n    \"id\": \"the-experiment\"\n  }, \"The experiment\"), mdx(\"p\", null, \"I kept the setup deliberately boring so the results couldn\\u2019t be argued with:\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"21 models, three vendors\"), \" \\u2014 OpenAI, Anthropic, Google \\u2014 all called through\\none identical, OpenAI-compatible interface so nobody got a home-field\\nadvantage.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Five task families\"), \", from dead-simple counting to multi-step financial\\nreasoning.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Deterministic ground truth.\"), \" Every \\u201Cright answer\\u201D was computed in code \\u2014 a\\nplain reduce over the array for sums, a code tally for counts. The model was\\nscored against math, not against a vibe.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Strict scoring.\"), \" Exact match or it fails. \\u201CClose\\u201D is a fail. A wrong total\\nis a wrong total.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Full cost telemetry\"), \" \\u2014 every call logged its input/output tokens, latency,\\nand an estimated dollar cost.\")), mdx(\"p\", null, \"The five families ranged from a pure sum benchmark (add 25 to 500 numbers, up to\\nseven digits each), through counting and grouping tasks (tally rows by status,\\nbucket events into a timeline), up to genuine calculation and reasoning (derive\\nratios, compare one property\\u2019s rent and value against a peer at easy/medium/hard\\ndifficulty).\"), mdx(\"h2\", {\n    \"id\": \"two-regimes-and-a-cliff-between-them\"\n  }, \"Two regimes, and a cliff between them\"), mdx(\"p\", null, \"The headline finding is that models don\\u2019t have a level of numeric ability. They\\nhave two, and there\\u2019s a cliff in the middle.\"), mdx(\"table\", null, mdx(\"thead\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"thead\"\n  }, mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Task family\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"What the model must do\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Best result\"))), mdx(\"tbody\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Work-order timeline\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Group events by creation date\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"88%\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Work-order counts\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Tally rows by status\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"85%\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Property KPIs (easy)\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Compute a ratio when told the formula\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"100%\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Sum benchmark\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Add a list of numbers\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"30%\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Fund financial KPIs\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Derive ratios unaided\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"5\\u201324%\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Property KPIs (hard)\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Choose the method yourself\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"0%\")))), mdx(TaskFamilyAccuracyChart, {\n    data: taskFamilyAccuracy,\n    mdxType: \"TaskFamilyAccuracyChart\"\n  }), mdx(\"p\", null, \"Read that top to bottom and you can see the personality of these things emerge:\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Counting and categorising holds up beautifully\"), \" \\u2014 85 to 100%. Ask a model\\nto find things, route things, group things, and it\\u2019s genuinely excellent. This\\nis reading comprehension over structured data, and it\\u2019s what transformers are\\nactually good at.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, mdx(\"strong\", {\n    parentName: \"li\"\n  }, \"Calculation collapses\"), \" \\u2014 0 to 30%. The moment the task requires arithmetic\\nthat isn\\u2019t in the text, the floor gives way.\")), mdx(\"p\", null, \"My favourite result in the whole study is the \\u201Chard\\u201D property KPI: every single\\nmodel got the magnitude of the answer right and then flipped the sign. They all\\nknew the two numbers were \\xA3200k apart. They just couldn\\u2019t reliably decide\\nwhether the property was over or under the peer. Right maths, wrong direction,\\ndelivered with total confidence. If you want a one-line summary of where we are\\nwith AI and numbers, it\\u2019s that.\"), mdx(\"p\", null, \"Give the model less hand-holding and the cliff gets steeper. Both KPI families\\nfall apart the moment the prompt stops spelling out the formula:\"), mdx(KpiTierChart, {\n    data: kpiTierData,\n    mdxType: \"KpiTierChart\"\n  }), mdx(\"h2\", {\n    \"id\": \"ai-cannot-count-the-sum-test\"\n  }, \"\\u201CAI cannot count\\u201D: the sum test\"), mdx(\"p\", null, \"The pure addition test is where it gets brutal. Across 21 models and 189 cases,\\nonly 30% were correct (56 out of 189). And the accuracy wasn\\u2019t evenly spread \\u2014\\nit decayed predictably as the task got bigger:\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"On short lists of 25 numbers, models managed 43%.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"Scale that to 500 large numbers and it falls to 14%.\")), mdx(\"p\", null, \"More numbers means more carries to track. More digits means longer arithmetic.\\nBoth hurt, and the errors aren\\u2019t random noise \\u2014 models confidently round, drop,\\nor straight-up invent digits to produce a plausible-looking total.\"), mdx(AccuracyDecayChart, {\n    data: accuracyDecayData,\n    mdxType: \"AccuracyDecayChart\"\n  }), mdx(\"p\", null, \"But here\\u2019s the split that actually matters for anyone choosing a model:\"), mdx(\"p\", null, \"Zero of the 13 non-reasoning models could add at all. GPT-4o, GPT-4.1, GPT-3.5,\\nthe standard Claude models, GPT-5 Chat \\u2014 0 out of 9 each. The only models that\\ncould reliably add were the \\u201Creasoning\\u201D models (the GPT-5 and o-series), which\\nscored 6 to 9 out of 9. Here\\u2019s every model in the study, ranked:\"), mdx(ReasoningVsChatChart, {\n    data: sumAccuracyByModel,\n    mdxType: \"ReasoningVsChatChart\"\n  }), mdx(\"p\", null, \"Let that sink in. The friendly, fast, cheap chat model that every product team\\nreaches for first? It cannot add a column of numbers. Not \\u201Csometimes gets it\\nwrong.\\u201D Never gets it right.\"), mdx(\"h2\", {\n    \"id\": \"why-being-right-is-so-expensive\"\n  }, \"Why being right is so expensive\"), mdx(\"p\", null, \"This is the part I find genuinely beautiful, in a bleak sort of way. It explains\\neverything.\"), mdx(\"p\", null, \"An LLM has no calculator inside it. There is no arithmetic logic unit hiding in\\nthe weights. The only way a model can be right about a sum is to simulate the\\narithmetic in generated text \\u2014 to literally write out \\u201Ccarry the one\\u201D step by\\nstep, one token at a time. And generating tokens is exactly the part that costs\\nmoney, time, and energy.\"), mdx(\"p\", null, \"Look at what separates a right answer from a wrong one:\"), mdx(\"table\", null, mdx(\"thead\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"thead\"\n  }, mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Metric\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Gets it RIGHT\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Gets it WRONG\"))), mdx(\"tbody\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Output tokens per sum\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"6,000\\u20139,000\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"~4\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Time per answer\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"40\\u2013130s\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"~1s\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Method\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Full step-by-step working\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Single blurted guess\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Result\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Correct\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Wrong, every time\")))), mdx(\"p\", null, \"The wrong models are cheap and fast because they\\u2019re guessing. The right models\\nare slow and expensive because they\\u2019re doing the equivalent of long addition on\\na whiteboard, out loud, and you\\u2019re paying by the word. Correctness isn\\u2019t a\\nsetting you toggle \\u2014 it\\u2019s compute you buy.\"), mdx(\"p\", null, \"Which leads to the numbers that made me sit up. The peak cost for a single\\ncorrect sum in my run was $0.92 \\u2014 one addition problem \\u2014 from o1 grinding\\nthrough 500 seven-digit numbers. That\\u2019s roughly 360\\xD7 more expensive than a fast,\\nwrong answer at about a quarter of a cent. Across the study the gap between\\nright and wrong ran from 100\\xD7 to 700\\xD7.\"), mdx(CostPerSumChart, {\n    data: costPerSumData,\n    mdxType: \"CostPerSumChart\"\n  }), mdx(\"p\", null, \"You are, quite literally, paying two to three orders of magnitude extra for the\\nprivilege of a correct total that a for loop would have produced for free.\"), mdx(\"h2\", {\n    \"id\": \"you-dont-get-what-you-pay-for\"\n  }, \"You don\\u2019t get what you pay for\"), mdx(\"p\", null, \"If you think price signals quality \\u2014 that the expensive flagship must be the\\nsmart one \\u2014 the arithmetic results will disabuse you fast. I sorted every model\\nby its output-token list price and lined it up against how many sums it got\\nright:\"), mdx(\"ul\", null, mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The two most expensive models per output token (a top-tier Claude Sonnet at\\n$75/1M, GPT-4 at $60/1M) scored 0 out of 9.\"), mdx(\"li\", {\n    parentName: \"ul\"\n  }, \"The two cheapest (GPT-5 Nano at $0.40/1M, GPT-5 Mini at $2/1M) scored 8 and 9\\nout of 9.\")), mdx(PriceVsAccuracyChart, {\n    data: costVsAccuracyData,\n    mdxType: \"PriceVsAccuracyChart\"\n  }), mdx(\"p\", null, \"The price tag reflects the vendor\\u2019s positioning, not the model\\u2019s ability to\\ncount. The single best value in the entire study was GPT-5 Nano \\u2014 the cheapest\\nthing I tested \\u2014 landing 8 of 9 at about $0.004 per sum. The premium tier\\u2019s best\\neffort, o1, managed 6 of 9 for up to $0.92 \\u2014 beaten, badly, by a model costing a\\nfraction of a penny.\"), mdx(\"p\", null, \"This is the AI equivalent of a lesson I keep having to relearn on the business\\nside. Over in \", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/mastering-agile-estimation\"\n  }, \"Mastering Agile Estimation\"), \" I\\ncomplain that your business overlords don\\u2019t care about the code, only the\\nfeatures, and they\\u2019ll happily equate \\u201Cexpensive\\u201D with \\u201Csafe.\\u201D Same trap here.\\nNewer and pricier does not mean it can do the maths. Sometimes it means the\\nopposite.\"), mdx(\"h2\", {\n    \"id\": \"claude-vs-openai-a-closer-look\"\n  }, \"Claude vs OpenAI: a closer look\"), mdx(\"p\", null, \"One question I kept getting when I shared early results internally: is this just\\nan OpenAI-favours-OpenAI story? Not quite. Restricting the comparison to\\nOpenAI\\u2019s and Anthropic\\u2019s standard models (Gemini set aside for this one, and\\nClaude\\u2019s extended-thinking variant wasn\\u2019t available through our gateway, so this\\nis standard Claude vs the full GPT lineup including its reasoning models):\"), mdx(ClaudeVsOpenAIChart, {\n    data: claudeVsOpenAIData,\n    mdxType: \"ClaudeVsOpenAIChart\"\n  }), mdx(\"p\", null, \"OpenAI wins the overall pass rate \\u2014 54% vs 40% \\u2014 and wins five of the seven\\nbenchmarks outright. But Claude isn\\u2019t uniformly behind: it actually \", mdx(\"strong\", {\n    parentName: \"p\"\n  }, \"beats\"), \"\\nOpenAI on the work-order timeline task, 93% to 85%. The one place OpenAI\\u2019s lead\\nturns into a rout is arithmetic itself \\u2014 32% vs 13% on the sum benchmark \\u2014\\nbecause OpenAI\\u2019s reasoning models (GPT-5, o-series) were reachable through the\\ngateway and Claude\\u2019s equivalent extended-thinking mode wasn\\u2019t part of this run.\\nBest at counting, no contest: GPT-5 at 9/9 versus Claude 4.6\\u2019s 5/9.\"), mdx(\"h2\", {\n    \"id\": \"isnt-this-just-ai-hype-cycle-whiplash\"\n  }, \"Isn\\u2019t this just AI-hype-cycle whiplash?\"), mdx(\"p\", null, \"Fair challenge. I\\u2019m the one who wrote\\n\", mdx(\"a\", {\n    parentName: \"p\",\n    \"href\": \"/beware-of-the-hype-train\"\n  }, \"Beware of the Hype Train\"), \", so let me apply my own\\nskepticism in both directions.\"), mdx(\"p\", null, \"No \\u2014 this isn\\u2019t me saying \\u201CAI is useless.\\u201D It emphatically isn\\u2019t. Reading,\\nrouting, summarising, grouping, explaining: models are genuinely, reliably good\\nat all of it, and that\\u2019s a real superpower. The hype I\\u2019m puncturing is narrower\\nand more specific: the pervasive assumption that because a model can talk about\\nnumbers fluently, it can compute them correctly. Fluency is not accuracy. A\\nmodel that describes your portfolio in flawless prose and then fumbles the total\\nis not \\u201Calmost there\\u201D \\u2014 it\\u2019s doing two completely different jobs, and it only\\npassed one of them.\"), mdx(\"h2\", {\n    \"id\": \"so-what-do-you-actually-do-about-it\"\n  }, \"So what do you actually do about it?\"), mdx(\"p\", null, \"The fix is refreshingly unglamorous, and I like it all the more for that. Don\\u2019t\\nlet the model be the calculator.\"), mdx(\"table\", null, mdx(\"thead\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"thead\"\n  }, mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Do\"), mdx(\"th\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Don\\u2019t\"))), mdx(\"tbody\", {\n    parentName: \"table\"\n  }, mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Compute numbers in code or a tool call\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Ship a raw model total to a user\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Use models to read, route, explain\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Assume a bigger model fixes maths\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Reserve reasoning models for high stakes\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Use a chat model where exactness matters\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Validate against deterministic ground truth\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Trust a confident, formatted number\")), mdx(\"tr\", {\n    parentName: \"tbody\"\n  }, mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Log tokens and cost\"), mdx(\"td\", {\n    parentName: \"tr\",\n    \"align\": null\n  }, \"Ignore the compute spend\")))), mdx(\"p\", null, \"That last row matters more than it looks. Because correctness is bought with\\noutput tokens, and tokens are compute, and compute is energy, \\u201Cget it right\\u201D and\\n\\u201Cuse less energy\\u201D pull in opposite directions when you make the model do the\\nmaths. Push the arithmetic down into code and you\\u2019re not just more accurate and\\ncheaper \\u2014 you\\u2019re greener. The deterministic path wins on every axis at once.\\nIt\\u2019s the same instinct as wiring a SonarQube quality gate into CI: you don\\u2019t\\nhope the number is right, you make it impossible for a wrong one to get through.\"), mdx(\"h2\", {\n    \"id\": \"final-thoughts\"\n  }, \"Final Thoughts\"), mdx(\"p\", null, \"We spent decades building machines that were flawless at arithmetic and hopeless\\nat language. Then we built one that\\u2019s wonderful at language and, it turns out,\\nhopeless at arithmetic. The mistake is asking the new machine to do the old\\nmachine\\u2019s job.\"), mdx(\"p\", null, \"So the whole thing collapses to one sentence: AI can\\u2019t count \\u2014 so give it a\\ncalculator. Let the model do what it\\u2019s brilliant at \\u2014 reading, routing,\\nexplaining \\u2014 and hand the actual maths to code that provably can\\u2019t get it wrong.\\nReserve the slow, expensive reasoning models for the rare case where exactness\\nis genuinely worth the cost, and verify every number before it reaches a human.\"), mdx(\"p\", null, \"Because the one thing this whole experiment confirmed is the thing I opened\\nwith: nobody in that loop is going to raise an eyebrow for you. You have to\\nbuild the eyebrow in.\"));\n}\n;\nMDXContent.isMDXComponent = 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