While the whole world is hyping up the AI concept, some people have given a much more down-to-earth answer—forget about things that might happen a thousand years from now, let’s first focus on getting done what can actually be implemented in the next three to five years.

This approach sounds simple, but the underlying logic is quite clear: sociologists love talking about the distant future, scientists like painting ten-year visions, but real tech practitioners are focused on “how large models and powerful computing can solve practical problems in various industries.”

How does this work in practice? A few examples make it clear.

At the port, Tianjin Port has already achieved full automation—from container loading and unloading, stacking, to customs clearance, AI connects the entire process. Underground in the mines, things get even more impressive: data modeling is used to directly predict the risk of gas explosions, so miners no longer even need to go underground. Hospitals are also changing; Ruijin Hospital has developed a pathological large model that helps doctors improve diagnostic accuracy, which means the experience of top doctors can be replicated in more places.

The most interesting case is in the steel plant—blast furnace ironmaking, a traditional industry, is being optimized by AI large models that dynamically simulate and adjust control parameters. Even a mere 1% increase in efficiency translates into astronomical value.

Simply put, this approach doesn’t seek disruptive innovation, but rather focuses on relentless small improvements in every step. It might not sound sexy, but it’s probably more reliable. At the end of the day, technology should serve people—freeing coal miners from dangerous environments and enabling doctors to make more accurate diagnoses. That’s what AI should really be doing.