1. AI is hot now — and will become ordinary
AI is everywhere: investors, founders, developers, product people — even street vendors talk about it.
Here is a judgment that may differ from the crowd: this heat will fade within a few years and become ordinary.
Ordinary like software engineering today. Nobody calls a company “high-tech” just because it uses Java or a database — those are infrastructure, baseline skills. AI will follow the same arc — from “magical new thing” to “engineering problem.”
An engineering problem means you know what it can and cannot do, how to assemble it, tune it, and step on the landmines — with methods, practices, and mature toolchains. Then AI stops being mysterious.
2. What an LLM really is: a natural-language reasoning engine
Many people mythologize large models as if they had minds or souls. To me, an LLM is fundamentally a computational reasoning engine.
More precisely: a reasoning engine that understands natural language.
You feed text in; it computes internally; text comes out. That is not fundamentally different from a calculator doing 1+1=2 — input, compute, output. The calculator handles numbers; the model handles language.
Once you see that, many confusions clear. It is not an “artificial brain” or “digital life.” It is an engine — compute stacked high enough to sound human.
3. Why this is a revolution in computer science
Still just an engine — yet for computer science, a massive revolution.
Because computing’s mode expanded from arithmetic to natural-language reasoning.
What could old computers do? Arithmetic, logic, storage, retrieval. Languages, algorithms, databases — all grew from that arithmetic root.
One simple numeric foundation spawned operating systems, compilers, networks, databases, distributed systems, graphics — the whole tree of computer science.
Now the boundary widens: machines can “understand” speech and reason over language. Change the root, and nobody knows how wide the canopy can grow.
Software can reach into far more of the world than before.
4. Software’s frontier expands dramatically
Building software always had a tax: translate human intent into machine instructions. PMs write specs, devs write code, testers write cases — all translation.
Translation leaks. Intent A becomes spec A−, dev reads B, ship looks like C. That is why requirements drift, bugs persist, and delivery hurts.
LLMs lower that barrier. Machines can “hear” human language. You describe what you want; the model helps with the middle translation.
Meaning: software that was too expensive to translate into code can now be built.
And conversational interaction — no CLI, no UI grammar — just speak. That shift is genuinely startling: a machine that answers in human language was unthinkable not long ago.
Humans always gasp at what exceeds intuition. We are seeing unprecedented capability.
5. Still — it is only computation
If this sounds like hype, read the opposite: however magical AI feels, it remains computation.
Input, process, output — deterministic or probabilistic — engineerable, optimizable, replaceable.
LLMs advance computational engineering. They widen and diversify compute; they do not change compute’s nature.
Steam → mechanical engineering; electricity → electrical engineering; the internet → network engineering. Every revolution settles into a discipline — from miracle to routine.
AI is early, fast-moving, unknown — so it feels miraculous. Methods, toolchains, and talent will mature; the wonder will flatten.
One day you will tell a junior, “When AI arrived, people thought it would end humanity,” and they will laugh.
6. AI is tool capability — not business capability
One plain sentence to close.
However loud the hype, however media shouts “AI replaces everything,” we still must ground ourselves in our own business and domain.
Because AI is tool power outside the business — not business craft itself.
Healthcare without clinical paths, insurance rules, or hospital workflows — AI cannot save you. Finance without risk logic, regulation, or customer reality — AI output is scrap paper. Education without learning science, student psychology, or pedagogy — AI slides are junk.
AI can draft code, copy, analysis, translation — but it cannot understand your business for you. Roots live in your industry, your customers, your daily trenches. AI does not stand in your chair or carry your accountability.
Business capability stays ours — we own the work.
AI is a fine wrench. A wrench does not build the house; the craftsperson who knows the site does.
Do not let the wave dizzy you. Learn domain knowledge, accumulate field experience, think product problems. AI does not make those less important — it makes them more important, because stronger tools widen the gap between those who wield them well and those who merely possess them.
Stay grounded. AI will come and fade into the background; your business craft remains yours.