Preface: Co-written with Gemini.

Demis Hassabis was born in North London to a Greek-Chinese-Singaporean family. He described them as “quite bohemian”, I’m guessing that’s the British equivalent of “hipster” here. According to Demis, his dad did a lot of different things, including working as a singer-songwriter with aspirations of being like Bob Dylan, working as a teacher and running a toy shop with his mom. His mom, on the other hand, is Chinese Singaporean and moved to Britain in the early 1970s. She originally trained as a nurse for children with special needs. She later worked as a manager at the department store chain John Lewis, ran the family toy shop, and also worked as a teacher. Hassabis has noted that his parents were not interested in computers and did not particularly like them, which he believes forced him to be self-reliant in his technical pursuits. He credits his mother’s roots for his early exposure to technology, as the family spent summers in Singapore, where he encountered "futuristic" gadgets and Nintendo games that were not yet available in the UK. While Demis became a scientist, his siblings followed their parents' more artistic path: his sister is a composer and pianist, and his brother is a professional poker player who studied creative writing.

At age 13, Hassabis was the second-highest-rated player in the world in the Under-14 category. He was only 35 Elo points behind the top-ranked player, Judit Polgár, who is widely considered the greatest female chess player of all time. He reached an Elo rating of 2300 by age 13, which is the "Master" standard. For perspective, the average casual chess player typically has a rating between 400 and 800. Hassabis often credits chess as the reason he became obsessed with Artificial Intelligence. He has said that playing at such a high level forced him to look "under the hood" of his own mind. He would ask himself questions like: "Why did I make that mistake?" or "How am I visualizing these moves five steps ahead?" He’s so good at thinking, that he became curious about why he’s so good at it: what is it that I’m doing when I think? How does that make me better? Why am I different? Geniuses always realize their difference from a young age, contrary to popular belief though, while it is confusing to watch how a genius does his work from the outside, it is equally confusing being the genius himself and not knowing where this genius-ness is coming from. He knew he was good at thinking, exceptionally good, yet he didn’t know why he had this gift. What is this gift that he clearly has that other people don’t? In this documentary that premiered at the 2024 Tribeca Film Festival called The Thinking Game, Demis Hassabis describes the moment he decided to give up on chess. During one of his final competitions, he started to wonder what he could possibly do with all the brains and computing power in this room he’s competing in, being surrounded by all the best players in the world. Could they have solved cancer? Instead of wasting their brain power on this game called chess? A deep sense of meaninglessness swallowed him, he started to feel sick. And right there, on the spot, he decided to give up on chess. 

Most eight-year-olds have to beg their parents for a computer. Hassabis didn't have to. He used the prize money he won from a major chess tournament to buy his first machine—a ZX Spectrum 48K. That was 1984, when he was just eight years old. 1984 was the height of the home computing revolution in the UK. The ZX Spectrum (developed by Clive Sinclair) was the affordable, rubber-keyed machine that essentially taught an entire generation of British engineers how to code. Because he bought it himself, he didn't have to follow anyone else's rules. He spent his time typing in code from magazines and books, which is how he transitioned from playing chess to thinking about how a machine could be programmed to "think" like him. A few years later, he upgraded to a Commodore Amiga, which was significantly more powerful. It was on the Amiga that he wrote his first "real" AI program—a version of the board game Othello (Reversi) that could beat him. It's fascinating to think that the same brain that was winning chess prizes in 1984 to buy a 48KB computer would, exactly 40 years later in 2024, be awarded the Nobel Prize in Chemistry for using AI to map the "building blocks of life." 

When Demis Hassabis stepped back from professional chess at 13, he didn't stop being competitive; he simply shifted his focus to programming, simulation games, and early AI experiments. He taught himself Assembly language, which allowed him to write code that ran directly on the computer's processor, making his programs much faster and more efficient. He was part of a small group of teenagers in the late 1980s who "hacked around," making their own games and software tools. He bought the Computer Chess Handbook by David Levy to learn about search algorithms and evaluation functions. He used these concepts to write an AI program to play Othello (also known as Reversi). He famously tested the program on his younger brother, George; the program was successful enough to beat him, which Hassabis cites as a major "aha!" moment regarding the potential of AI.

Writing that Othello program on a Commodore Amiga was Demis’s first real "Frankenstein moment"—the first time he built something that could outthink a human. Since he couldn't just download an AI library like developers do today, he had to build the "brain" from scratch using the fundamental logic he had learned from chess. To make the computer "think," Demis used a classic AI concept called Minimax. In simple terms, Minimax is a decision-making algorithm used in two-player games (like Chess, Tic-Tac-Toe, or Othello) to find the optimal move. The name comes from its core goal: Minimizing the maximum possible loss. It assumes your opponent is just as smart as you and will always make the best move for themselves. Imagine a tree where the "trunk" is the current board position, and every "branch" is a possible move. You want to choose the move that leads to the highest score (the "Max"). Your opponent wants to choose the move that results in the lowest score for you (the "Min"). The algorithm works backwards from the future. It looks ahead several turns to the end of the game (or a certain depth). It assigns a score to those future boards (e.g., +10 for a win, -10 for a loss). It then "bubbles" those scores back up to the present. If a future branch leads to a guaranteed loss because your opponent is smart enough to take it, the algorithm "discards" that path and picks a safer one. If you are about to win on the next turn, the Minimax algorithm sees a +10 score on that branch. However, if it sees that by moving there, you leave an opening for your opponent to win first, it sees a -10 on that branch. It will choose the move that minimizes the chance of that -10 happening.

Think of the Minimax algorithm as a hyper-cautious chess player who assumes their opponent is a literal genius. It doesn't just look for a way to win; it obsessively looks for ways it might lose. The core of Minimax is the belief that your opponent will always make the best possible move for themselves. If a path has a potential +10 (you win) but also a branch where the opponent can force a -10 (you lose), Minimax assumes the opponent will find that -10. It treats that entire path as a "losing path," even if there’s a trophy at the end of one version of it. Imagine you are playing a game where you have two choices, Move A and Move B. Move A Opponent can let you win (+10) OR they can trap you (-10), which results in -10, assuming the opponent isn't a fool.While for move B, no matter what the opponent does, the game stays tied (0), which is a safer choice. Minimax will choose Move B. Even though Move A could lead to a win, the algorithm "discards" it because it sees a guaranteed way for the opponent to make you lose. It prefers a boring tie over a risky gamble. This is a very interesting strategy, one I always use. I always assume my opponent is just as smart as I am, or even smarter, though in reality that's rarely true, however, thinking this way yields me the best move, though it does take more work and brain power. On top of that, I'm self-aware enough to know I'm quite a lazy person as well. I always try to do least amount of work that yields maximum amount of results. I'm not greedy. I'm a very cautious and safe strategist, contrary to popular belief, I try to avoid having too many rounds of back-and-worths. In real life, when I strategize, I usually go for the move that leaves my opponent with no comeback. This Minimax strategy makes a lot of sense to me, except I never summarized the way I strategize in this way.

The problem with Minimax is the "State Space Explosion." Think of State Space Explosion as the "curse of possibilities." It is the primary reason why computers can easily beat humans at Tic-Tac-Toe, but still struggle to "solve" a game like Go or simulate complex chemical reactions. It’s what happens when the number of possible configurations (states) of a system grows so rapidly that even the world’s fastest supercomputer runs out of memory and time to look at them all. I understand this as a chaos system that comes from the Chao Theory. These chaos systems, are systems that are deterministic, which means given the initial conditions, the output of the system is always the same. However, when the initial conditions change only a little bit, the final results will be drastically different. These are systems that are extremely sensitive to the initial conditions, such as the weather, hence to predict the output, the initial conditions have to be exact with no measuring error. Chaos systems are extremely complex and have too many factors at play at any given time, each in its own complicated way that progresses differently each on their own, at the same time these factors interact with each other in even more complex / exponentially more complex ways that makes future predictions almost impossible, since it has essentially astronomically huge sums of scenarios, that it's impossible to compute. In this sense, earth itself could be a computer, and weather is one of those deterministic chaos systems that requires the scale of earth to lay out all the variables to include the Sun, the Moon, the surrounding planets in this galaxy, but also comets, blackholes, and whatever it is that we do not know that's out there, will all play a part in changing the weather and tides on earth. And since we do not have all the information, and initial conditions change the outcome by a lot, predicting the output / result is essentially impossible.

In Tic-Tac-Toe, there are only 255,168 possible games. A computer can calculate every single one in a fraction of a second. In Chess, there are more possible moves than there are atoms in the observable universe. Because of this, young Demis couldn't just use "pure" Minimax. He had to use Heuristics (shortcuts). Since the computer couldn't see to the end of the game, he wrote a "Scoring Function" to tell the computer: "Even if the game isn't over, having control of the center of the board is worth +5 points." Basically, even though he couldn't train the computer to learn the best strategies on its own, Demis gave the computer his own strategies when he plays chess, such as taking over center of the board as soon as possible. Demis had many strategies like this. While controlling the center is the "Golden Rule" of chess, it’s really just the foundation. Once you have a foothold in the center, you use that leverage to execute more specific strategies. The most common way beginners lose is by leaving their King in the middle of the board. Castling is therefore also important. This moves the King to a corner protected by a "wall" of three pawns. It also brings your Rook toward the center. A piece is only as good as the squares it controls. Knights are short-range. To be effective, they need to be deep in enemy territory. An "outpost" is a square (usually on the 5th or 6th rank) that is protected by one of your pawns and cannot be attacked by an enemy pawn. A Knight sitting there is a nightmare for your opponent. Rooks are like snipers; they hate being stuck behind their own pawns. You want to move your Rooks onto "open files" (columns with no pawns) so they can slide all the way into the enemy's back row. Grandmaster Philidor famously said, "Pawns are the soul of chess." They aren't just fodder; they are the terrain of the battlefield. Diagonal lines of pawns that protect each other. These create "walls" that block enemy Bishops. A pawn with no "friends" on the columns next to it. It’s hard to defend because no other pawn can help it. Two pawns of the same color on the same column. They block each other and are usually a major liability. With all these rules and strategies combined, each given a different weight and priorities as of which one to execute first, Hassabis was able to build a program that allowed a computer to beat his brother at Othello. This is exactly what DeepMind eventually disrupted. In the old days, Demis had to hard-code those "points" himself. With AlphaGo, the AI taught itself what a good position looked like through deep learning. 

He programmed the computer to look at the current board and "branch out" every possible move he could make, then every possible move the opponent could make in response. Even on that limited hardware, he tried to make the computer look 4 or 5 moves ahead. In a game like Othello, where the board can flip entirely in one turn, this required massive computational efficiency. A computer can see the moves, but it doesn't know which board position is "good" or "bad" unless you tell it. Demis wrote a mathematical Evaluation Function—a set of rules that gave points for certain positions (like owning the corners of the board, which is vital in Othello), which he taught his computer to do. He essentially translated his own strategic intuition into a scoring system. Demis sat his brother George down in front of the Amiga and had him play against the code. When the program started beating George, Demis realized he had created a "digital version" of his own strategic mind. It was a profound moment of realizing that intelligence wasn't just a "human" thing—it was a process that could be coded. ☀️