2023–2025: Organizational Evolution Through the Lens of Large Models

At first, I didn’t really see this as an “organizational-level” issue.

Back in 2023, when large models first entered the company, our discussions were still centered on efficiency. Who could use them faster, who could write more accurately, who could save a bit more on labor costs. The consensus back then was simple: this was a tool problem, not a structural one. Whether a tool was good or not, and whether it was used, depended more on individual ability and attitude. The organization just needed to “keep up,” not “restructure.”

Later, I realized how naive that judgment was.

When a handful of people began using models to compress tasks that once took days into just a few hours, a subtle imbalance emerged in the organization for the first time. It wasn’t a gap in capability—it was a gap in pace. The existing processes, reviews, and decision-making rhythms started to hold back those who had already “gotten faster.” That was the moment I understood: the problem wasn’t the tool, but the structure—the structure set the ceiling on speed.

So we started “adding systems.”

In 2024, the organization began introducing large models more systematically—building platforms, creating middle layers, setting standards. On the surface, everything seemed to be progressing. But new problems quickly surfaced: the more complex the system, the slower the organization became. Middle managers grew busier, but not because they were making decisions—they were busy explaining systems, coordinating processes, and patching exceptions. Technology was advancing, but the organization was getting heavier.

That was when I first felt a strong sense of discomfort: were we using “intelligence” to reinforce a structure that should have been broken?

The real turning point came after a failure.

A highly anticipated AI project didn’t die on the technical side—it died on collaboration. The model was fine, the data was fine, even the output was fine, but it just couldn’t be implemented. In the post-mortem, everyone had “done things right by the process,” yet no one took responsibility for the outcome. That was when I realized a harsh truth: when intelligence enters an organization, traditional accountability and collaboration mechanisms become sources of risk.

From that point on, I began to truly understand “organizational evolution.”

Organizations aren’t just slow to adapt—their logic of evolution is fundamentally different from that of technology. Technology pursues capability leaps, while organizations pursue stability and control. When large models start to possess “judgment-like” abilities, the hierarchical structures, process controls, and approval systems originally designed to ensure stability begin to systematically suppress efficiency and creativity.

And so, a new organizational form began to emerge.

It no longer emphasizes “who reports to whom,” but rather how capabilities can be rapidly mobilized. It no longer requires middle managers to “watch over people,” but instead asks them to design workflows and validation mechanisms. It no longer treats knowledge as static documents to be archived, but as a cyclical system that models can continuously learn from and feed back into the business.

This wasn’t a reform—it was an adjustment forced by reality.

By 2025, I finally understood one thing: large models haven’t changed the essence of organizations, but they have massively amplified their existing problems. A good structure gets amplified by intelligence into a lever; a bad structure gets amplified into a disaster.

It was at that moment that I developed a genuine sense of awe for “organizational evolution.”

Evolution has never been a choice. When the environment changes, an organization either restructures itself or gets restructured. The only difference is whether it happens proactively or reactively.

Looking back at those “just a tool” judgments from 2023, I don’t find them laughable. That’s the first reaction every organization has when facing a paradigm shift. The difference is that some organizations stop there, while others—pushed by reality—keep moving forward a few more steps.

And those few steps often determine where they’ll stand in the next three to five years.

This may not be an article that offers answers, but it comes from a perspective that has made mistakes, moved too slowly, and been schooled by reality.

In the age of intelligence, what truly determines an organization’s fate is never the model itself—it’s whether you are willing to pay the price of structural restructuring for cognitive upgrade.

That’s a lesson I learned the hard way.