The Neuron: AI Explained
The Humans Behind AI: How Invisible Technologies Trains 80% of the World's Top Models
03 Nov 2025
Ever wondered who's actually teaching ChatGPT and Claude how to think? Meet Caspar Eliot from Invisible Technologies - the company behind 80% of the world's top AI model training. In this eye-opening conversation, we uncover the massive human workforce behind "artificial" intelligence, why your League of Legends skills might land you an AI job, and the shocking mistakes enterprises make when deploying AI.We discuss:• How AI models really learn (hint: it's not just scraping the internet)• Why data quality beats data quantity every time• The Charlotte Hornets' revolutionary AI scouting system• Whether robots will actually take your job (spoiler: probably not)• The $14.8 billion Scale AI valuation and what it means• Why Mark Andreessen thinks VCs won't be automatedPlus: Caspar reveals the #1 mistake companies make with AI deployment and why "AI-ifying" your current process is doomed to fail.Subscribe to The Neuron newsletter: https://theneuron.aiConnect with Caspar on LinkedIn: https://uk.linkedin.com/in/caspar-eliot-46b9a55aLearn more about Invisible Technologies: https://invisibletech.ai?utm_source=neuron&utm_medium=podcastPlease check out the sponsor of this video, Warp.dev: https://warp.devSo who is Invisible Technologies? In four words: they make AI work. Their platform cleans, labels, and structures company data so it’s ready for AI. It adapts models to each business and adds human expertise when needed — the same approach used to improve models for over 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.Their successes span industries from supply chain automation for Swiss Gear, to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets. And get this: Invisible has been profitable for over half a decade, was ranked #2 fastest-growing AI company in 2024, and recently raised $100M to advance its platform technology.Check them out at Invisible Technologies: https://invisibletech.ai?utm_source=neuron&utm_medium=podcast
Full Episode
So behind every AI response, there's an invisible army of humans who trained it, labeling images, rating answers, and teaching these models right from wrong. Today, we're going to talk to Casper Elliott from the company that's trained 80% of the world's top AI models. Welcome, humans, to the latest episode of the Neuron Podcast.
I'm Corey Knowles, and we're joined, as always, by Grant Harvey, writer of the Neuron Daily AI newsletter. And today, we're diving deep into the human side of AI with Casper Elliott from Invisible Technologies. Casper, thanks so much for joining us.
Pleasure to be here. Thank you for having me. So, Caspar, Invisible Technologies just raised $100 million. Invisible also says it has trained 80% of the world's top AI models. Both of those are incredible stats. Can you just tell us a little bit more about what that process actually looks like?
The way I think of it is, so large language models, they're not like traditional machine learning, right? They're non-deterministic. They're based on neural nets. They do some funky things. I think of a large language model like an enthusiastic teenager. Yeah, he really wants to answer questions, and he wants to get smart.
But if you want a teenager to learn something, like there are some ways you can teach them, right? You could, for example, you could take them to a library, or you could give them a load of homework, or you could set them a test. We kind of do all those three things.
When you hear supervised fine-tuning or reinforcement learning about human feedback or evaluations, that's actually one of those three things. So supervised fine-tuning is giving a model loads of real high-quality examples of data sets to look like. That's taking your model to the library and saying, here's some textbooks to read. It's going to read the textbooks. They'll tell you what's true.
Reinforcement learning is, okay, you're going to give the model some questions. It'll give some answers, and you're going to say if those answers are good or not. Like you might ask the model to write me a poem about...
Russia and then you'll you'll check that poem and you'll have something about what if that poem is good or bad and you'll give it so great and it'll learn from that and so that's reinforcement learning or you can call it reward modeling you're basically allowing you to change the way you reward your model for different types of answers and then evaluation is building the like the test the model has to take to understand if it's good because companies will release loads of different versions of models and they've got to understand if it's better or worse like I mean you've seen the news about track dbt5 dbt5 they released it
It was obviously better in some metrics, but the audience wasn't happy. And that's why human evaluation is so necessary because people are not deterministic too. People like to have opinions on things and you can't just be like, well, this was better than all our benchmarks. If it feels different to someone and the user doesn't like it, it doesn't matter if it's better. And that's evaluation.
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