Google's NEW Simula is INSANE!

Hinzugefügt: 2026-05-05

[00:00:00]Google's new Simula is insane. Today, I'm going to show you Google's craziest AI drop of the week. It's called Simula, and it just broke how the whole AI world thinks about training data. It builds fake data out of thin air. Leaks, scraping, risk. And the best part? It's already powering stuff on your phone right now. Stick around because what I'm about to show you is wild. Here's the problem Google just solved. Every AI model on the planet needs data to learn, like tons of it. Chatbot you use, tool you love, the agent that writes your emails. They all learn by eating up huge piles of text and images from the internet. Oh, simple, easy. Here's the scary part. We are running out of good data, data in general, specialized data, the stuff that actually matters. Think medical files. Think legal cases. Think cybersecurity attacks. Think banking fraud. That kind of data is locked up.

[00:00:48]It's private. It's rare. Or it just doesn't exist in big enough piles. And you can't train a smart AI agent on data it never sees. This is called the data wall. And until now, it was blocking the next wave of AI, especially for anyone trying to build a niche tool in a specialist space. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. As he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Trust me, this one is massive. Julian Goldie reads every comment, so make sure you comment below and tell me what you think about Simula, which is basically every serious business using AI today. So Google and a team from EPFL teamed up, and they built something that breaks that wall. Called it Simula. And what Simula does is kind of nuts. Creates fake training data that is so good models trained on it can beat models trained on real data in some cases. Let me say that again. Data built by a reasoning model that works better than real data without needing any real data to start with. Seeds, copies, scraping. No humans writing prompts by hand. Just pure logic and structure. And it goes big, crazy big. Team scaled it up to 512,000 data points across things like cybersecurity and law without ever touching real protected data from those fields. You might be thinking, "Okay, but how does it actually work?" Cuz this sounds like magic. It's not magic. It's smart. And this is where it gets really interesting. Most AI data tools work one prompt at a time. You ask a model to make one example, and another, and another. That's like a factory worker making one shoe at a time with no plan.

[00:02:16]Sometimes you get great shoes. Sometimes you get a pile of the same left foot.

[00:02:20]Simula is different. Simula treats the whole data set like a product you design on purpose. Google calls this mechanism design. That's a fancy way of saying they plan the whole thing from the top down before they make anything. And they break it into three big stages. Let me show you how this thinking is going to change the game for business owners. And if you want to stay ahead of all these AI updates and actually use them inside your own business, come join the AI Profit Boardroom. It's the best place online to scale your business, save hundreds of hours with AI automation, and learn how to use tools like Simula-style data pipelines, Claude agents, Gemini workflows, and everything else to get more customers and grow faster. Link is in the comment and description. Come say hi inside. Stage one is called global diversification.

[00:03:00]This is where Simula maps out the whole domain first, drawing the full map of a country before you start placing cities.

[00:03:06]It uses a taxonomy. Think of that as a giant menu of every possible topic and subtopic in the area you care about. So if you're building a cybersecurity dataset, it first lists every type of attack, every type of defender, every type of system, every corner of the space. This step makes sure your data covers the whole subject, not just the stuff that shows up most online. Stage two is local diversification. This is where the system zooms in and creates lots of different examples inside each spot on the map. It uses what's called one-of-n meta prompting. That just means it keeps generating many different versions of each scenario to make sure they don't all sound the same. And then on top of that, it runs complexification. That's where it takes simple examples and pushes them to be harder, trickier, more nuanced, like leveling up a video game. Easy, medium, hard, boss fight. So your AI model learns the whole range. Stage three is the dual critic filter. This is the quality check. Two different critic models look at each example and decide if it's good enough to keep. If the data is weak or repetitive, it gets thrown out. And the numbers here are eye-opening. In the legal dataset they tested, the critic rejected 61% of what was made, meaning more than half the data was trashed for not being good enough. That's a serious filter, and that's why the final data quality is so high. Here's where it gets really exciting for anyone building a business with AI. Simula splits quality, diversity, and complexity into three separate knobs. You control them one by one, not all tangled together. That means if you need super diverse but simple data, you can do that. If you need super complex but narrow data, you can do that, too. This is huge because in the real world, you don't always need the same thing. A legal AI might need super careful, narrow, complex cases. A customer service bot might need huge variety of simple chats. One size does not fit all. And Simula finally gives you separate controls for each. Here's the part that blew my mind. Google is already using this in real products you might already have on your phone. Simula is helping power AI scam detection for Android calls. That's the feature that warns you when a phone call sounds like a scam. It's also helping with spam filtering inside Google Messages. So when a shady text tries to reach you and gets blocked, Simula is part of why.

[00:05:06]Think about that for a second. You can't train a scam detection model on real scam data. It's illegal. It's private.

[00:05:12]It's risky. But you can train it on Simula data that was built from first principles. So the AI learns the shape of a scam without ever needing a real victim's real message. That is the whole point of this tech. Unlocks AI for places where real data is locked behind a door. I know what some of you are thinking. Is fake data really as good as real data? Great question. Google answered it. They ran the tests. The results are really interesting. On a math reasoning dataset called GSM8K, they compared low complexity and high complexity Simula data. 64,000 data points, the high complexity version gave a 10% accuracy gain over the low version. 10%. That's massive in AI terms. Here's the catch. It only works if the teacher model is strong enough.

[00:05:54]On the legal dataset, the teacher model was only 57% accurate. When they pushed higher complexity data through a weak teacher, performance actually dropped.

[00:06:02]So the lesson is simple. Complex fake data only helps when your base model is smart enough to label it right.

[00:06:08]Otherwise, you're teaching your student AI from bad notes. That's a really honest finding from Google because they're showing you the limits as well as the wins. Another cool finding. Real reference datasets covered less of a topic than Simula-built ones in many tests. Let that sink in. Real-world data missed whole parts of the subject, while the synthetic dataset covered more of the full map because Simula was designed to cover on purpose. Real data just shows up in random patterns based on what people happen to write online. So sometimes synthetic is not just as good, it's actually more complete. Let's zoom out and talk about what this means for the future because this isn't just about one research paper. This is about a whole new way of building AI. Till now, most of AI has been about getting more data. Scrape more, crawl more, buy more, steal more in some cases. And that works for general-purpose stuff. But it hits a wall fast when you go into a niche, medical, legal, financial, kid safety, fraud, robotics, self-driving cars. Each of these is blocked by real-world data problems, privacy, risk, cost, lack of examples. Simula offers a different path. Build the data on purpose, from logic, from domain rules, from reasoning. This means any smart specialist can design training data without needing a giant pile of real-world samples. And that flips the whole game for small teams, solo builders, and small business owners. You don't need to be a giant company with a data warehouse. You need a clear map of your problem space and a smart AI to help you generate the data for it.

[00:07:30]That's it. And this is exactly why I keep saying the future of business is not about who has the biggest data. It's about who has the sharpest thinking, who can break down their domain, who can design their own training sets, who can plug everything into automation. That's the edge. And tools like Simula are proving it works. Simula also tells us something else. The critic step matters a lot. The AI system you build from now on needs a quality filter, second pair of eyes, reviewer AI. Whether you're generating content, emails, reports, product ideas, or training data, always put a critic in the loop. That's one of the biggest practical lessons from this paper. Build with a reviewer baked in.

[00:08:06]Your output quality goes through the roof. Okay, let me sum it up really quick. Simula is Google's new reasoning-first framework for making training data from scratch. No seed data, scraping, copying. It treats the whole dataset like a product you design on purpose. It uses three stages: mapping the space, filling it with diverse examples, and filtering out the weak stuff with critics. It scales huge.

[00:08:28]It works in tough areas like cybersecurity and law. It even powers real features like Android scam detection and Google Messages spam filters. And it gives users control over quality, diversity, and complexity as three separate levers. This is a giant step for specialist AI. And the ideas are going to trickle down to every business tool soon. If you want the full process, SOPs, and over 100 AI use cases like this one, join the AI Profit Boardroom and how to automate your workflows with tools like Simula and scale your business with AI automation.

[00:08:56]Link is in the comments and description.

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