Three Times AI Completely Overwhelmed Me

Three scenes where AI completely overwhelmed me

Anyone who has worked with technology for a long time has their own "moment of shock."

I don't mean the singularity as some grand discourse, but that moment when, with an actual tool in your hands, a chill runs down your spine. The sense that "something has now fundamentally changed." Not an exaggeration—the kind of scene that leaves you certain the old frame of reference can no longer explain the world.

For me, there were three such moments.

Looking back, it was not a matter of mere performance improvements. Nor was it a story of simply getting faster, smarter, and cheaper. Each of those three scenes was changing the way AI interacts with humans, the way it solves problems, and the way an entire industry is being reshaped.

Put differently, it was less a process of AI simply becoming a useful tool, and more a process of changing the very coordinate system through which we see the world.

Today I want to record those three moments of astonishment. And I want to examine, a little more densely, what that astonishment demands of us.

1. The First Shock: ChatGPT Pushes the Age of Search Aside

The first shock came in late 2022, when I first encountered ChatGPT.

Back then, most reactions were similar. "It's just a chatbot that talks plausibly." "Isn't it just stitching together search results?" "You can't use it for real work."

But anyone who actually used it could tell right away. This was not simply a slight improvement in the quality of answers. The very structure of the act of searching was being shaken.

The essence of the traditional search engine was a "technology that finds things for you." The user enters keywords, and the system provides a list of related documents. From there on, it's entirely up to the human. You have to choose a title, open a link, compare several documents, and infer the right answer for yourself from within them.

ChatGPT, by contrast, moved differently. Instead of listing documents, it first interpreted the intent of the question, internally assembled the necessary context, and then generated an organized result in the form of a response.

This difference is far bigger than it seems.

Search solves the problem of accessing information. But generation begins to replace the problem of understanding and organizing it.

And this was exactly where my astonishment lay. It wasn't that the machine spoke well. It was the fact that the machine was inferring even what I hadn't said precisely.

Throw an incomplete question at it, and it filled in the context; put in a clumsy phrase, and it read the intent. Even when the user didn't design the question perfectly, the AI filled in the blanks and carried the conversation to completion. Here, the interface between human and machine began to leave behind the world of keyboards and mice, menus and buttons. The interface was no longer a component on a screen—it became language itself.

This was not a mere UX innovation. It was an event in which the point of contact between technology and humans changed.

In the past, humans had to learn commands in a way the machine could understand.

Now, when a human speaks the way a human speaks, the machine interprets the meaning.

In other words, the direction of learning had been reversed.

The essence of the first shock was a revolution in accessibility. AI didn't make technology more powerful first—it made it easier first. By lowering the barriers of complex commands and specialized interfaces, it moved technology out of the hands of a few experts and into the hands of the many who simply use language. That was the real beginning.

2. The Second Shock: Claude Opus Shows "Emergence" in Coding

The second shock came from coding work.

The sight of AI writing code was already familiar in itself. Fixing syntax errors, generating repetitive boilerplate, quickly replicating familiar patterns—that much was well within the predictable. Up until then, I saw AI coding tools as excellent aids. A capable assistant that saved time, and nothing more.

That perception changed when I put Claude 3 Opus to work on a complex refactoring task.

I had merely asked it to clean up the existing code and improve the structure.

But what came back was not a revised version. It was a proposal for a new architecture that overturned the very design approach I had taken for granted.

The shock I felt then was quite fundamental.

Usually, when a human looks at AI output, they judge between two possibilities.

One is "a result of nicely stitching together patterns seen somewhere," and the other is "a result of actually understanding the context and constructing something new."

In that scene, Opus was closer to the latter.

It pinpointed the bottlenecks, reorganized the data flow, and proposed not function-level fixes but a system-level structural redesign. Where the approach I'd had in mind was loop-centered processing, the AI put forward a completely different approach that combined recursive structures, caching, and asynchronous flows. The astonishing part was that this was not a plausible-sounding alternative in words only—it actually worked better and performed better.

From this moment on, AI coding stopped being a "tool that types fast for you." It began to become something that proposed, ahead of you, the design possibilities a human engineer had missed.

What matters here is not that AI "knows more" than humans. What matters more is that it explores in a different way than humans do.

Human developers build solutions by relying on experience, habit, and familiar patterns. This is a powerful strength, but it is at the same time a limitation—the inertia of thought. AI, by contrast, is relatively less bound by that inertia. So sometimes it lightly skips over a premise humans accept as too obvious, and pulls out a structure that is unfamiliar but better.

What I felt then was not simple admiration.

It was a slight sense of crisis, and a clear thrill and awe.

For a long time we have regarded "creativity" and "problem-solving ability" as domains unique to humans. But in the actual field, that boundary is already blurring. AI didn't copy the right answer—it began to generate solutions that fit the conditions. And sometimes in a way that pushes in further than a human would.

The essence of the second shock was an incursion into creativity. AI is no longer an automation tool that carries out instructions. It is now becoming a collaborator that designs alongside you, offers alternatives, and stimulates human thinking in reverse. Problem-solving is no longer a domain monopolized by humans alone.

3. The Third Shock: The Speed at Which Monopolies Crumble—Qwen, DeepSeek, and Others

The third shock came not from the performance of a single model, but from the way the board of an entire industry was being flipped.

For a while, the AI market used to be explained with a relatively simple narrative.

The prospect that U.S. Big Tech, with more capital, more data, and more GPUs, would ultimately win. It looked as though a structure would set in where a handful of companies like OpenAI, Google, and Anthropic build the top-tier models, and everyone else consumes or chases after the results.

But watching Qwen and DeepSeek, that assumption was quickly shaken.

The shock did not lie simply in "Chinese models turn out to be good too."

The more fundamental point was the fact that performance, cost, and openness strategy were all being redefined at once.

While America's leading models largely moved around closed strategies, enormous infrastructure costs, and high usage prices, Qwen and DeepSeek pushed in an entirely different direction. A release strategy close to open weights, an extremely optimized structure, an efficiency-centered design, and competitive performance. This combination changed the market's very question.

What mattered now was no longer "who built the biggest model."

Rather, "who can deploy sufficiently powerful intelligence at the lowest cost" became the crux.

The emergence of DeepSeek-like players, in particular, exposed a very uncomfortable truth for the AI industry.

Namely, that intelligence keeps improving, but the process doesn't necessarily have to be more expensive and more closed. In other words, the AI competition was moving from a simple contest of technological superiority toward a contest of efficiency, a contest of deployment strategy, a contest of ecosystems.

This change sends a large ripple through the entire industry.

The smaller the performance gap among top-tier models becomes, the faster the technology is leveled.

The lower the cost falls, the more the barriers to entry collapse. The more accessible models become, the more value shifts away from the model itself and toward the product and execution, the user experience, and domain-specific capability.

This means monopolies weaken, but it also means a far fiercer era is opening. The moment everyone can use a strong model, the question is no longer "which model do you use." It becomes "what will you build with that intelligence," and **"how fast can you connect it to real-world problems."**

The essence of the third shock was the acceleration of competition and the democratization of intelligence.

AI is no longer the exclusive property of a few companies. Performance is leveling upward, and costs are falling sharply. In the end, the contest will be decided not by model choice, but by productization and execution.

Epilogue: We Are Living Through an "Inflation of Intelligence"

If you lay out the three scenes in chronological order, the direction of AI's evolution emerges clearly.

ChatGPT changed the interface between human and machine. Claude Opus shook the boundaries of problem-solving. Qwen, DeepSeek, and others reshaped the ownership structure of that intelligence.

In other words, AI became able to converse, began to appear able to think, and is rapidly descending into a resource anyone can use.

If I summarize this trend in one sentence: we are living through an age of the inflation of intelligence.

What was marvelous yesterday becomes the default today, and today's innovation becomes tomorrow's commodity feature. The half-life of astonishment keeps getting shorter.

So what attitude do humans need in this age?

Vaguely fearing AI is not the answer, and on the contrary, blindly trusting it uncritically is not the answer either. What we need now is two things.

First, the judgment to verify the quality of the solutions AI proposes. Second, the interpretive power to translate the efficiency AI creates into human value.

Stronger models will keep coming. They will be faster, cheaper, and spread more widely.

That trend itself is now hard to reverse.

In the end, what matters is not how far AI goes. It is, in the face of that change, by what criteria humans choose, what they build, and what they protect.

The three shocks I experienced are not an ending but closer to a prelude.

The real question is this.

When the next overwhelming moment arrives, will we remain spectators of that change, or will we become people who convert it into our own language, our own product, our own world?

AI is now becoming less a mere tool and more a partner that stimulates thought and shakes up design.

When was the moment you were most overwhelmed while using AI?