HyperTeamsFrom 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.
graph TD
A["AlphaGo · 2016"] --> B["Extremely good at Go"]
B --> C["Can do nothing off the board"]
C --> D["No use to anyone<br/>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 |
|---|---|
| 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.
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 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