Let's get straight to the point. Today I want to talk about AI Natives.
My definition of an AI Native is someone who pushes AI to its absolute limit. Statistics show that most people in this era use generative AI like ChatGPT, but mostly as a hobby, or at best for basic workplace efficiency. Meanwhile, the top tier experts inside Big Tech are pushing AI to its absolute extreme as their actual profession.
I cannot claim that I personally use AI at a flawless 100%. But today I want to share exactly how that tiny minority of power users pushes AI to its limits, and how the rest of us can start doing the same thing.
From what I have observed, the people who truly master AI fall into two main categories.
The Infinite Loop of Quantified Goals
The first case involves quantifying a specific goal for your work and setting AI loose on an infinite loop until it hits that exact metric.
What does that actually mean? In the past, we worked with AI to find the right answer together, step by step. Now, we define the final answer up front, and we design AI to endlessly loop on its own, updating its own context, evolving as it goes, and navigating its way to that exact destination.
This is possible because AI models have become extraordinarily capable, capable enough to reach a correct answer with just a high level context to work from.
This approach is actually written directly into Anthropic's own guidelines. Their directive states that they "prompted it to be ambitious about scope and to stay focused on product context and high level technical design rather than detailed technical implementation."
In other words, do not micromanage the direction. Let the AI handle the heavy lifting
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| This is how an AI agent slowly converges on the right answer |
Take a look at the diagram above. Each circle represents one exploration cycle of an AI agent. Inside the first circle, the AI lands on an answer, then measures how far off that answer is from the actual goal sitting at the top right. Based on that gap, it updates its own context. That updated context becomes the starting point for the next circle, where the AI explores a brand new area and lands closer to the target. Repeat this loop enough times, and the AI eventually lands exactly on the right answer, or remarkably close to it.
This is not theory. Andrej Karpathy, the former Lead AI Engineer at Tesla who coined the term vibe coding, proved this with a project he called Auto-Research. He gave an agent a single code file, one evaluation metric, and a time limit, then let it run loose on his own project. Over two days, the AI automatically ran 700 experiments and discovered 20 optimizations. It squeezed out an additional 11% performance gain on code Karpathy himself believed was already fully optimized. It even caught bugs overnight that he had missed after months of manual tuning.
The insight here is significant. As Elon Musk has put it, you need to know the "Platonic idea." If you can clearly articulate the most ideal version of what you want, AI will eventually reach it through relentless experimentation and evolution. This exact methodology is what scientists and top tier researchers are using right now.
Injecting Context into the Infinite Space
So how does this translate to the business world?
Unfortunately, in most business scenarios, there is no single correct answer. Even when something resembling a right answer exists, it keeps shifting. So the people who use AI best in business focus entirely on injecting the highest possible quality of context into the system.
The models themselves are already excessive in capability. And now that context windows, the sheer amount of information AI can process at once, have expanded massively, the real opportunity is leveraging that to the absolute maximum.
How do they actually do this? They dump everything related to the business into a massive knowledge base. Brand guidelines, customer data, past performance, market analysis, internal rules, all of it goes in. Then they set hundreds of autonomous agents loose on top of that foundation.
Here is the critical part though. They do not just throw raw data in and walk away. They deliberately design the relationships between that data.
A veteran entrepreneur and a complete beginner can look at the exact same dataset and interpret it in completely different ways. They carry fundamentally different levels of intuition. Current frontier models cannot fully replicate that human intuition on their own yet. So you have to inject that context manually by explicitly defining how pieces of data connect to each other. This is exactly why concepts like Ontology, Context Graphs, and Graph RAG are getting so much attention right now.
The core insight is this. The AI already knows the answer. It has the raw capability to construct it. But to make it find that specific answer inside a near infinite space, you have to feed it as much context as humanly possible.
If your goal can be quantified, you let AI endlessly update its own context to reach it on its own. But if your answer cannot be quantified, which describes most business situations, you have to push the quality of your context to the absolute extreme. You are essentially digitizing human intuition.
The Era of Agentic Orchestration
I know a lot of this sounds complex. This is genuinely how people at the frontier are pushing AI right now, and it is something we all need to start learning step by step.
The first move toward mastering this is sitting down and physically writing out every single process you currently do. Then you design how to delegate each one of those processes to AI.
This is the defining keyword that will dominate 2026.
As Andrej Karpathy put it, "It is now the era of agentic engineering." We call it agentic because for 99% of your time, you are no longer writing code or doing the task yourself. You are orchestrating the agents that do it for you.
You are not getting help from AI to write a piece of code. You are designing an agentic loop so the AI can execute everything from start to finish entirely on its own.
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| This is what an agentic loop actually looks like in action |
Web based AIs are starting to run basic versions of this loop too. But tools like Claude Code let you customize that loop on a much deeper level.
So how do you actually practice designing this yourself?
Like I said earlier, break down every single thing you do into tiny pieces, write them all down, and plan how to automate them one by one using agents. Slowly expand that scope over time. Eventually you end up designing systems where agents automatically evolve and inch closer to the right answer for everything you do.
Of course, running a one person business means you cannot automate literally everything. A human has to stay inside this loop somewhere. Our actual job now is learning to keep only the essential human judgment for ourselves, and relentlessly handing off everything else to AI.
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