# From AlphaGo to now
> **What you will learn**
> Why your daily work did not change when AlphaGo won, and why it is different
> now. Once that question has an answer, everything else gets easier.
## March 2016, Seoul
Lee Sedol, a 9-dan Go player, played five games against AlphaGo in Seoul. From
March 9th to the 15th, for a full week, an entire country watched.
People who had never played Go watched too. Not because they were curious about
Go, but because they were curious about **whether a machine could beat a human**.
The result was **AlphaGo 4, Lee Sedol 1**.
### That one game
After three straight losses, Lee Sedol won game four. Move 78, cutting into the
middle of the board. Commentators called it **"God's Touch."**
By AlphaGo's own calculation, the chance a human would play that move was **one
in ten thousand**. AlphaGo blundered on move 79 and did not realise it was
losing until move 87.
That game remains **the only official record of AlphaGo losing to a human**.
### What people felt at the time
Shock. The conventional wisdom was that Go had far too many possible positions
for a machine to win any time soon. Headlines about "which jobs disappear now"
were everywhere.
## And then, for six years, what changed?
Let us be honest for a moment.
Since March 2016, **has the way you work changed because of AlphaGo?**
Almost certainly not. The news was hot, but the office stayed the same. You
still wrote your own email, built your own reports, and read your own long
documents.
**Why?**
### Because AlphaGo was a machine that only played Go
AlphaGo played Go better than any human. And that was all it did.
- You cannot ask AlphaGo to write an email
- You cannot ask AlphaGo to clean up meeting notes
- You cannot ask AlphaGo to review a contract
**Off the board, it could do nothing.** To anyone who did not play Go, AlphaGo
was "impressive, but not my problem." So when the news cooled off, daily life
went back to normal.
```mermaid
graph TD
A["AlphaGo · 2016"] --> B["Extremely good at Go"]
B --> C["Can do nothing off the board"]
C --> D["No use to anyone
who does not play Go"]
```
## November 2022, something else happened
Something called ChatGPT appeared. This time the reaction was different.
It passed **100 million users in two months**. For comparison:
| Service | Time to 100 million |
|---|---|
| Instagram | about 2.5 years |
| TikTok | about 9 months |
| **ChatGPT** | **about 2 months** |
Analysts who had covered the field for years said they had never seen a curve
like it.
### Why it was different this time
Not because the technology was more impressive. Because **the set of people who
could use it was different.**
| | AlphaGo | Today's AI |
|---|---|---|
| Good at | Go | **language** |
| Who can use it | people who play Go | **anyone who works with words** |
| Preparation needed | you must know Go | you must be able to talk |
| Relationship to you | a spectacle | **a tool you can use today** |
Think about what you actually do at work. Writing email, producing reports,
organising material, capturing what was said in a meeting — **nearly all of it
is language.**
What AlphaGo was good at (Go) was irrelevant to most people. What today's AI is
good at (language) is relevant to **almost every desk job.** That single
difference explains the difference in reaction.
## The two AIs also have different temperaments
This part matters. AlphaGo and today's AI do not merely play different games —
**they fail differently.**
| | AlphaGo | Today's AI |
|---|---|---|
| Problem type | rules are exact (the rules of Go) | no rules (what is "good writing"?) |
| Right answer | exists (you win or you lose) | none (only better answers) |
| How it succeeds | calculates moves to the end | imitates what people have written |
| How it fails | a miscalculation | **it invents something plausible** |
Remember that last row. Today's AI does not fail the way AlphaGo failed. It
fails in a confident tone of voice. We give that its own chapter later —
[things to watch out for](/guide/ai-cautions).
> **The most common misconception starts right here**
> If you think "AlphaGo beat Lee Sedol, so AI must be smarter than people," you
> will use today's AI badly. Today's AI is less a genius and **more a very
> tireless assistant** — fast, never bored, and never to be trusted without a
> check.
## 2024: two Nobel Prizes
So far this has been an office story. In the same period, something happened on
the research side too.
| 2024 | Awarded to | For |
|---|---|---|
| Nobel Prize in **Physics** | John Hopfield, Geoffrey Hinton | foundational discoveries and inventions that enable machine learning with artificial neural networks |
| Nobel Prize in **Chemistry** | Demis Hassabis, John Jumper (Google DeepMind), David Baker | DeepMind's share was for protein structure prediction with **AlphaFold** |
In a single year, **both Physics and Chemistry** went to AI-related work. One
prize could be called a coincidence; two is something else. It is a symbolic
moment — AI moved past being a tool that assists research and into **the place
where the discovery happens.**
Two things worth adding:
- The protein structures AlphaFold predicted were released to researchers
worldwide, and became a **starting point** for drug and enzyme work. The
result did not end as one paper; it became where other people's research
begins.
- AlphaGo solved **a game with a fixed right answer**. The AI that followed
moved toward **problems where the answer is open** — how a protein folds, how
these meeting notes should be organised. Neither comes with an answer key. If
"right answer: none" looked like a weakness in the table above, here it is the
scope.
> **Which is why this product exists**
> Taking AI that is already proven in world-class research and making it usable
> in an organisation's everyday work is what
> [HyperTeams Connect](/guide/cn-what-is-it) does. Predicting a protein
> structure and tidying up meeting notes differ in scale, but the technology
> underneath is the same family.
## Where we are now
A little over three years have passed since 2022. We are past the novelty stage
and into the **actually-used-at-work** stage. Published surveys sketch roughly
this picture:
- In McKinsey's 2025 survey, a substantial share of responding organisations
reported **measurable results on at least one AI initiative**.
- People using it at work report saving something like **40–60 minutes a day**.
- In customer service, cases where AI handles a large share of inbound requests
directly are growing quickly.
> The numbers vary a lot by survey and by date. Take the direction (going up,
> results starting to be measured) rather than the figures, and check the
> original source if you need a specific number.
The important part is this: **unlike the AlphaGo era, this one is your problem.**
## So what this guide does
This part (New to AI) exists to get you **using it today**. Unlike Go, it takes
no special talent and no prior knowledge.
The next chapter explains what this thing actually is, by comparing it to
search.
---
## Check yourself
**1. Why did ordinary work barely change in the six years after AlphaGo?**
Answer
Because AlphaGo was **a machine that only played Go**. Off the board it could
not write an email or read a document. To anyone who did not play Go it was
"impressive, but not my problem."
**2. Explain ChatGPT reaching 100 million users in two months — without
referring to the technology.**
Answer
**The range of people who could use it** was different. Go, which AlphaGo was
good at, matters to a small group. Language, which today's AI is good at, is
nearly all of desk work. And there is nothing to prepare — if you can talk, you
can start.
**3. Why is "AlphaGo beat Lee Sedol, so AI is smarter than people" a dangerous
thought?**
Answer
The two are different in kind. AlphaGo solved a problem with exact rules and a
right answer by calculation; today's AI handles problems with no right answer by
imitating human writing. So **they fail differently** — today's AI invents
things in a confident tone. Treat it as a genius and hand it work without
checking, and that is exactly where the accident happens.
---
Next we look at what this tool actually is, and **how it differs from search** →
[What AI is](/guide/ai-what-is-it)