{"slug": "the-scaling-laws-that-made-llms-work", "title": "The Scaling Laws That Made LLMs Work", "summary": "Shrijith Venkatramana, building git-lrc, explains how scaling laws discovered by OpenAI, Google, DeepMind, and Anthropic made large language models like ChatGPT, Claude, and Gemini possible. The 2020 OpenAI paper 'Scaling Laws for Neural Language Models' showed that model performance improves predictably with increased model size, dataset size, and compute, transforming AI from a research problem into an engineering discipline.", "body_md": "*Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.*\n\nIn 2018, many researchers believed language models were clever toys.\n\nThey could autocomplete text, generate amusing sentences, and occasionally fool people for a paragraph or two. But few expected them to become software engineers, researchers, tutors, designers, and writing assistants.\n\nThen something strange happened.\n\nTeams at OpenAI, Google, DeepMind, Anthropic and elsewhere kept increasing three things:\n\nAnd performance kept improving.\n\nNot linearly.\n\nNot randomly.\n\nPredictably.\n\nThe shocking discovery was that intelligence-like capabilities emerged from scale itself.\n\nToday, ChatGPT, Claude, Gemini, and other frontier models exist largely because researchers discovered scaling laws—empirical mathematical relationships that revealed how performance improves as models become larger and are trained on more data.\n\nThis is the story of that discovery, why it mattered, and why it changed the economics of software forever.\n\nFor decades, AI progress often came from clever architecture changes.\n\nResearchers would invent:\n\nProgress was often irregular.\n\nA breakthrough would appear.\n\nThen improvements would stall.\n\nMany people assumed future progress would continue this way.\n\nThen deep learning arrived.\n\nResearchers began noticing something unusual.\n\nA bigger neural network often outperformed a smaller one.\n\nA lot.\n\nEven when nobody fully understood why.\n\nOne famous observation came from researchers at Google working on machine translation.\n\nInstead of hand-crafting linguistic rules, larger neural networks trained on larger datasets simply worked better.\n\nThe trend kept repeating.\n\nA key moment occurred in 2012.\n\nAt the ImageNet competition, a neural network called AlexNet built by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton dramatically outperformed competitors.\n\nThe architecture was important.\n\nBut equally important was something less glamorous:\n\nThey used GPUs.\n\nLots of compute.\n\nThe lesson was subtle but profound:\n\nMore computation could unlock capabilities that smaller systems never exhibited.\n\nThis idea would later become the foundation of modern LLM development.\n\nIn 2020, researchers at [OpenAI](https://openai.com?utm_source=chatgpt.com) published a landmark paper:\n\n**\"Scaling Laws for Neural Language Models\"**\n\nAuthored by:\n\nThe paper reported a surprising result.\n\nLanguage model loss followed a smooth power-law relationship with:\n\nInstead of hitting obvious plateaus, performance improved according to remarkably predictable mathematical curves.\n\nThe researchers found relationships resembling:\n\nL(N) is proportional to N^(-alpha)\n\nwhere:\n\nThe exact constants differed across experiments, but the important insight was this:\n\nEvery additional order of magnitude in scale delivered measurable gains.\n\nNo magic tricks required.\n\nNo fundamentally new algorithms required.\n\nJust scale.\n\nThis result was shocking because many researchers expected diminishing returns to arrive much sooner.\n\nInstead, the curves kept going.\n\nLet's build intuition.\n\nImagine a model with:\n\nSuppose increasing it to:\n\nreduces error by a meaningful amount.\n\nThen increasing to:\n\nreduces error again.\n\nEach improvement costs vastly more compute.\n\nBut here's the key:\n\nFor large organizations, even small quality improvements are worth enormous amounts of money.\n\nConsider search engines.\n\nIf improving answer quality by 1% generates hundreds of millions of dollars in user value, spending tens of millions on training becomes rational.\n\nThe economics start resembling semiconductor manufacturing.\n\nThe biggest players can afford massive upfront investment because performance gains compound downstream.\n\nThis is one reason frontier AI rapidly became a contest among organizations with access to:\n\nScaling laws transformed AI from a pure research problem into an industrial production problem.\n\nThen another surprise arrived.\n\nIn 2022, researchers at DeepMind published the famous Chinchilla paper \"Training Compute-Optimal Large Language Models,\" led by Jordan Hoffmann.\n\nThe team discovered something important.\n\nMany models were too large relative to the amount of training data they consumed.\n\nThe industry had been spending enormous compute training gigantic models that were under-trained.\n\nChinchilla showed that for a fixed compute budget, better performance often comes from:\n\nrather than:\n\nThe result fundamentally changed training strategies across the industry.\n\nMany later frontier models incorporated lessons from Chinchilla-style compute-optimal training.\n\nOne of the most fascinating observations came from large language models exhibiting capabilities not visible in smaller versions.\n\nExamples included:\n\nA small model might completely fail a task.\n\nA larger version suddenly succeeds.\n\nResearchers called these behaviors **emergent abilities**.\n\nThe exact mechanisms remain debated.\n\nHowever, scaling laws provided an important clue.\n\nIf performance improves smoothly on underlying representations, task-level capabilities may appear abrupt only because evaluation thresholds are discrete.\n\nFor example:\n\nA small continuous improvement underneath can create a seemingly sudden jump in usefulness.\n\nThis observation continues to influence modern research into reasoning models.\n\nThe public often imagines AI breakthroughs occurring through genius insights alone.\n\nThe reality is much messier.\n\nScaling laws forced organizations to become experts in:\n\nTraining frontier models became one of the largest computing operations ever attempted.\n\nModern training runs can involve tens of thousands of GPUs operating simultaneously.\n\nAt that scale, hardware failures become routine.\n\nEngineers must design systems assuming components will constantly fail.\n\nIronically, many advances enabling modern AI came not from machine learning itself but from classical systems engineering.\n\nThe people building the training infrastructure often look more like distributed systems engineers than traditional AI researchers.\n\nScaling laws explain why capabilities keep arriving that seem surprising.\n\nMany developers ask:\n\n\"How did models suddenly become good at coding?\"\n\nThe answer is often less mysterious than it appears.\n\nA large portion of progress comes from moving further along predictable scaling curves.\n\nMore compute.\n\nMore data.\n\nMore parameters.\n\nBetter optimization.\n\nThe resulting improvements accumulate until tasks become economically useful.\n\nThis perspective is valuable because it reframes AI progress.\n\nInstead of viewing each new model as a miracle, we can see many advances as the expected outcome of operating larger and more efficient training systems.\n\nThe future may contain architectural breakthroughs.\n\nBut one lesson from the past decade is difficult to ignore:\n\nScale itself turned out to be one of the most important algorithms.\n\nOne of the great scientific surprises of modern AI is that intelligence-like capabilities did not emerge primarily from increasingly clever hand-designed systems.\n\nThey emerged from discovering a predictable relationship between computation and capability.\n\nThe researchers who uncovered scaling laws effectively found a map.\n\nThat map allowed organizations to forecast future performance before spending billions of dollars building larger systems.\n\nFew discoveries have reshaped an industry so quickly.\n\nThe next time a new language model appears with capabilities that seem impossibly better than its predecessor, it is worth remembering:\n\nThe improvement may not be magic.\n\nIt may simply be another point on a scaling curve that researchers have been following for years.\n\nIf scaling laws continue holding for another decade, do you think future breakthroughs will come primarily from **more compute**, **better architectures**, or **entirely new paradigms beyond today's transformers**?\n\n*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.\n\ngit-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*\n\nAny feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.\n\n| [🇩🇰 Dansk](https://github.com/HexmosTech/git-lrc/readme/README.da.md) | [🇪🇸 Español](https://github.com/HexmosTech/git-lrc/readme/README.es.md) | [🇮🇷 Farsi](https://github.com/HexmosTech/git-lrc/readme/README.fa.md) | [🇫🇮 Suomi](https://github.com/HexmosTech/git-lrc/readme/README.fi.md) | [🇯🇵 日本語](https://github.com/HexmosTech/git-lrc/readme/README.ja.md) | [🇳🇴 Norsk](https://github.com/HexmosTech/git-lrc/readme/README.nn.md) | [🇵🇹 Português](https://github.com/HexmosTech/git-lrc/readme/README.pt.md) | [🇷🇺 Русский](https://github.com/HexmosTech/git-lrc/readme/README.ru.md) | [🇦🇱 Shqip](https://github.com/HexmosTech/git-lrc/readme/README.sq.md) | [🇨🇳 中文](https://github.com/HexmosTech/git-lrc/readme/README.zh.md) | [🇮🇳 हिन्दी](https://github.com/HexmosTech/git-lrc/readme/README.hi.md) |\n\nGenAI today is a **race car without brakes**. It accelerates fast -- you describe something, and large blocks of code appear instantly. But AI agents *silently break things*: they remove logic, relax constraints, introduce expensive cloud calls, leak credentials, and change behavior -- without telling you. You often find out in production.\n\n** git-lrc is your braking system.** It hooks into\n\n`git commit`\n\nand runs an AI review on every diff In short, git-lrc helps **Prevent Outages, Breaches, and Technical Debt Before They Happen**\n\n**At a glance:** [10 risk categories](https://github.com/HexmosTech/git-lrc#what-git-lrc-checks-for) · [100+ failure patterns tracked](https://github.com/HexmosTech/git-lrc#what-git-lrc-checks-for) · every commit…", "url": "https://wpnews.pro/news/the-scaling-laws-that-made-llms-work", "canonical_source": "https://dev.to/shrsv/the-scaling-laws-that-made-llms-work-5h5h", "published_at": "2026-06-24 18:58:52+00:00", "updated_at": "2026-06-24 19:09:10.298203+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-products", "neural-networks", "machine-learning"], "entities": ["OpenAI", "Google", "DeepMind", "Anthropic", "ChatGPT", "Claude", "Gemini", "AlexNet"], "alternates": {"html": "https://wpnews.pro/news/the-scaling-laws-that-made-llms-work", "markdown": "https://wpnews.pro/news/the-scaling-laws-that-made-llms-work.md", "text": "https://wpnews.pro/news/the-scaling-laws-that-made-llms-work.txt", "jsonld": "https://wpnews.pro/news/the-scaling-laws-that-made-llms-work.jsonld"}}