After AI Native, the World No Longer Rewards Those Who Only Do Things Themselves

Many people still understand AI purely as a tool:
writing code is just using AI to write code, when encountering a problem just ask AI for an answer, when doing research just ask ChatGPT to summarize a few articles. In reality, this only unleashes about 40% of AI’s capability.
Honestly, this is useful, but it doesn’t change our original workflow. At best, this is called AI-assisted.

This article is almost entirely handwritten, with a small amount of AI. For those who still haven’t grasped “AI Native” or “AI First,” it’s no exaggeration to say this mindset can restructure your work and even change half your life path.

Also, this article is a bit long, so feel free to bookmark it. You’re welcome to follow me @Pluvio9yte and read at your own pace.

AI Native is not shoving AI into old workflows; it’s using AI to restructure the entire workflow.

Let me give a simple example to lead into the theme I want to express:

Here’s the thing: for Markdown editors, I’ve been using Typora. But with the rise of Claude Code and Codex, Obsidian — thanks to its file system advantages, which make it easier for AI to read and operate — is gradually becoming the champion among many MD editors.

However, as anyone who has used Obsidian’s editing features knows, they are terrible… So I wanted to figure out how to replicate the Typora experience in Obsidian.

I assigned this research task to an intern. The intern did get the job done, but the document they gave me was a mess… Let me show you.

Obviously, this was purely summarized by AI. For practical content (especially niche needs like this), due to software version updates and some misleading experience blogs written by humans, AI’s directly summarized practical content has a certain probability of containing errors, so timeliness and feasibility are most important.

My purpose in giving it to the intern was to have them go through the process themselves and deliver me a final, workable solution. Then I could just follow their lead and configure it, saving a lot of detours.

Think about it: if this were your intern, how would you handle it?

Although I could tell it was AI at a glance, based on my experience, even if the original plan details in the reference articles are outdated and prevent a perfect replication, as long as there’s a general idea in the right direction, there’s a high chance of finding a solution on macOS.

So the intern’s article was better than nothing. The remaining task was to figure out how to make use of this verbose article that only AI could tolerate. I simply threw the whole article into Codex and had it use the Computer Use feature to directly configure it for me, to see if it would work.

The result was surprisingly good. Some operations in the article were indeed outdated, but the overall direction was correct. Codex corrected some wrong solutions and eventually configured it for me, perfectly replicating the Typora experience in Obsidian.

In the past, if I had to configure it myself or send each step to AI for guidance, the whole process would have taken at least one to two hours. Moreover, besides the time cost, it would have drained a lot of my energy.

But now with AI, this imperfect plan was optimized and executed directly by Codex.

Of course, looking at it now, the above process could still be further optimized. For example, letting Manus do the research instead of the intern might be even more “AI Native.”

However, I still managed to compress a configuration task that originally took at least 2 hours down to 16 minutes. I went to make a cup of coffee, and when I came back, Codex had already configured my computer for me.

That’s the end of this example. Let’s return to the discussion about “AI Native” and “AI First.”

True AI Native doesn’t just shove AI into old workflows to save humans a bit of time;
it redesigns the entire process from the ground up, making AI the default participant.

Let me give the simplest example:

In the past, when we encountered a problem, our first reaction was to search or ask someone. Searching solved “where is the information,” and asking solved “who knows the answer.”

But with the advent of AI, the entry point has changed. For many problems, the first step is no longer to search for materials or ask people, but to ask AI for an answer. (Note: for multi-step, long-context problems, asking an experienced person is still the best option — that’s a different kind of context problem.)

If you still have the above habits, your thinking needs to start shifting.

In the AI era, the value standards for many things are changing. That document from the intern was terrible for a human, but for an Agent, it was already good enough as contextual fuel. It wasn’t suitable as a final deliverable, but it could serve as input for the next round of execution.

This is the difference between AI-assisted and AI Native.

AI-assisted means letting AI generate a document, and then humans continue to painfully read, filter, and execute it.
AI Native means handing that document to an Agent, letting it execute, verify, and correct with a goal until the result is achieved.

Let me talk about my current way of working:

The Agent handles execution; automated tests and LLM eval handle the first layer of checks;
canary releases and user behavior data handle the second layer of validation;
I handle the final judgment and architectural evolution.

As a human, I’m only responsible for the harness and direction decisions — nothing else.

On an individual level, AI Native means when you encounter a problem, you let AI decompose it first, rather than blindly searching yourself.
On an Agent level, AI Native means letting AI not just answer questions, but directly participate in execution and verification.
On an organizational level, AI Native means reconstructing engineering, product, growth, feedback, and knowledge retention into a system where AI can continuously participate — every log point and user feedback becomes fuel for the system’s continuous iteration.

This is why I increasingly believe that AI Native will be the most important dividing line in the coming years.

Search-first people are still looking for materials.
AI-assisted people are using AI to write summaries for efficiency.
AI Native people are letting Agents get things done — ultimately, this is result-oriented.
AI-first organizations turn this ability into a continuously iterating SOP — a flywheel of capability.

Finally, let me outline the levels of AI Native as I understand them. You can check where you stand. Based on my experience, I believe this rough classification is fairly accurate.

Level 0: Search-first
Search first when encountering a problem, filter information yourself.

Level 1: AI-assisted
Ask AI when encountering a problem, let AI summarize, polish, and draft.

Level 2: AI-native individual
Let AI decompose the problem, generate a solution, and retain context, then the human makes the judgment.

Level 3: Agent-executed workflow
AI not only answers but directly executes tasks, corrects errors, and delivers results.

Level 4: AI-first organization
The organization/company restructures engineering, product, GTM, evaluation, and data feedback all around AI.

Regarding Level 4, I was deeply inspired by this article and have started practicing it.

Peter Pang @intuitiveml · Apr 13
Article
Why Your “AI-First” Strategy Is Probably Wrong
99% of our production code is written by AI. Last Tuesday, we shipped a new feature at 10 AM, A/B tested it by noon, and killed it by 3 PM because the data said no. We shipped a better version at 5…
[Link]

I recommend reading this article — it gave me a lot of insight.

The gap will only grow. You shouldn’t just know how to use AI.

Let’s return to our title:

After AI Native, the World No Longer Rewards Those Who Only Do Things Themselves