Lets go back in time: So one of the predictions from uh Geoffrey Hinton(Godfather of AI) who who started the whole deep learning phenomenon. Hinton, An incredible researcher, professor at the University of Toronto, and he invented the idea of back propagation.
Backpropagation allows the neural networks to learn. Hold on to that idea and let us circle back to how people used to write software. Historically, software was written when humans applied first principles thinking. They described an algorithm, a step-by-step approach to do a functionality, and then they codified the whole algorithm or steps or procedures, and the package of the code was called software, simple like a recipe book. Now, getting back to Geoff Hinton. If we think about AI, this invention of artificial intelligence and deep learning, we have to talk about neural networks. Neural networks have a lot of neurons, also simply called math units. You can think of them like a switchboard, and we connect all of these math units together. These math units take in some input, let’s say the image of a cat, and produce or guess what the output might be. So these mathematical units that we described as neurons fire and wire together to produce or guess the output as a cat. All the other signals, let’s say, there is a signal that also guesses a dog, and there is one more signal that guesses an elephant and maybe a tiger. All of these other signals tune or switch to zero when it shows the image as a cat.

Now, this mathematical unit switchboard is very gigantic. The more information you give it, the bigger the switchboard has to be to process the information and guess the output and to be accurate.

The first time when this mathematical unit-based switchboard sees the image of a cat, it produces garbage output. You need to keep on showing it a lot of examples of cats so that there comes a stage where it produces meaningful output, which is called a cat. What Geoff Hinton discovered is the idea of back propagation. While you keep on showing all of these examples of a cat, the output transitions from garbage to guessing a cat nicely. At the back of the envelope, these mathematical units are tuning the signals up and down in order to guess the cat correctly, and they are turning down the signals for dog, for elephant, for tiger.

That is the foundation of artificial intelligence, a piece of software that can learn from given examples. That is basically what we call machine learning, a machine that has the capability to learn. Geoff Hinton discovered this process of learning, which is back propagation. Deep learning is when we move on to the complex switchboards, which is nothing but neural networks.
So one of the first big commercialized applications was image recognition, and the most important image recognition application is radiology. He predicted that about five years ago that in four to five years the world will not need any radiologist.
Because AI would have swept the whole field, but it turns out that AI has swept the whole field and every radiologist is using AI in some way. What is ironic, though, is that the number of radiologists has actually grown. Now the question is why? It’s a very interesting question. Why have the radiologists grown?

The prediction stated that the entire profession of radiologists will be wiped out, but why did we need more.


And the reason for that, if you think deeply, is because the purpose of a radiologist is to diagnose disease, not only to study the image as a task. The image studying is simply one such task in service of diagnosing the disease. Now that we can study more images quickly and more precisely without ever making a mistake, you don’t get tired easily and you can study more images. You can study it in its 3D form instead of 2D, because the AI doesn’t care whether you have given it 3D pixels or 2D pixels. You could also study it in 4D so you can study images better, which a single radiologist could not easily do.

And so the number of tests that people are able to do has increased because they are able to serve more patients. The hospital has just gotten better with more clients, with better radiologists, with more patients. As a result, the overall productivity of the system has increased and produced better economics. When the hospital has better economics, they also tend to hire more radiologists. Because the sole purpose of the job of a radiologist is not only to study images, their purpose is to diagnose a disease.

And so the overarching question that we are leading up to is: what is the purpose of a job? What is the purpose of a doctor? What is the purpose of a lawyer? What is the purpose of a radiologist? Has the purpose really changed?

There is one more question that I would like to introspect. What if my car becomes self-driving? Will all chauffeurs be out of jobs? The answer probably is no, because for some people chauffeurs are a service. For some people, chauffeurs are a part of the hospitality experience. For some people, chauffeurs are there to protect the car owner.
So the Chauffeur that would lose their jobs would be a few, and many Chauffeur would also change their jobs. Now, the ones who are losing it, their sole purpose as a chauffeur was to drive the car for the car owner.

If our job is plain, simple automation, if our job is the task, if our job is only a precise, specific task, then the possibility of losing the job is very high. But if our job is more than a task, just like the shuffle, then we might not get replaced by AI.

We are also seeing what Elon Musk is working on. He is working on machines that make machines and robots. When that happens and when everything goes into production, a whole new industry of technicians and people who have to manufacture these robots will rise. The productivity of Elon’s system will increase, and the number of jobs that never existed will rise up. We are going to have a whole new industry of people taking care of these robots with respect to manufacturing and with respect to maintenance.

Now there would also be some people who would come up with a company that manufactures Apple for robots, because people would want their robot to look different than other people’s robot. This phenomenon will give rise to a whole new industry that never existed.

A mental model for modeling human life using Transformers

One of the most powerful realizations in the last decade of AI is the idea that almost anything can be represented as a sequence of tokens — language, images, code, music, and even protein structures. Once you tokenize something into a sequence, you can train a Transformer to predict the next element of that sequence. That is the foundation of models like GPT.

Today, GPT predicts the next token in a sentence.

Tomorrow, could a model predict the next decision in a human life?

🌍 All of human knowledge is text

The internet holds the collective intelligence of our species — papers, books, conversations, code, opinions, history, news, even emotional expression. We tokenize this massive corpus and train models to learn patterns of how thoughts unfold.

A Transformer receives tokens → embeds them into vectors → and through self-attention learns structure, causality, dependency, and intent. When it predicts the next token, it is essentially predicting human thought continuation.

So here’s the jump:

If text is predictable, and text captures human decisions, then decisions should also be predictable.

🧠 The core idea

What if you take all decisions a single human has ever made:

• Where did I study?

• Which job did I choose?

• Who did I meet?

• What did I buy?

• When did I change habits?

• Which books did I read?

• What content did I consume?

• What goals did I set?

And represent them as ordered tokens in the timeline of life. Now treat this timeline exactly like a sequence of text tokens.

Every decision = a token

Every token has an embedding representing:

• context (why/when)

• emotions

• constraints

• environment

• past experiences

• personality traits

Feed that tokenized life sequence to a decoder-only transformer, and ask:

Given the full sequence of this person’s life decisions so far, what is the most likely next decision?

Autoregressively roll it forward:

Decision1 → Decision2 → Decision3 → ... → Decision_N → predict Decision_(N+1)

You just built a generative model of a human life.

Applications

1. Personal Future Simulation

Simulate your tomorrow, next quarter, or entire decade based on the pattern of your choices.

2. Counterfactual Generators

Ask:

• What would my life look like if I accepted that job offer?

• What if I moved cities?
  You modify the starting token and re-generate the future.

3. Coaching & Self-Awareness

A mirror that shows where your internal algorithm is leading you.

4. Behavioral Optimization

Detect loops and biases:

• impulsiveness

• procrastination

• risk aversion

• pattern of relationships

and propose alternative trajectories.

🔮 The philosophical angle

Humans believe we are infinitely complex and unpredictable — yet we are predictable enough that companies infer our behavior through recommendation models.

But LLMs prove a deeper truth:

Anything that is a sequence can be predicted. And a human life is nothing but a sequence of decisions.

So the idea becomes:

Treat life as a language model.

Treat decisions as tokens.

Treat the future as next-token prediction.

👣 Final thought

We may soon reach a point where we can:

• Upload our history

• Generate multiple future trajectories

• Pick the most meaningful one

Like chess engines compute the best move, life engines could compute the best decision.

The future of AI may not just be generating text.

It may be generating lives.

His idea that some systems don’t just survive volatility but actually get better because of it. And right there, in between loss functions and gradient descent, I stumbled on a single word that connects both worlds: convexity.

Calculus in Our Algorithms and in Our Lives

That beautiful U-shaped curve we love in ML, the convex loss function, is our guarantee of stability.

It tells us:

• Small deviations don’t hurt much.

• Moving in the right direction pays off disproportionately.

• Mathematically elegant. Emotionally… familiar?

Because Taleb’s “convex life” is built on the same logic.

When randomness hits you, the average outcome should improve. You either don’t lose much, or you gain a lot.

The Fragile vs. The Convex Life

The Fragile, Concave Life: smooth, predictable, but one big shock can wipe years of progress.

The Antifragile, Convex Life: messy, volatile, but every small stress adds strength.

You take asymmetric risks, the kind that can quietly fail but might change everything if they succeed.

Applying Convex Optimization to Everyday Life

Career: Most jobs can be linear. The side project is convex. Worst case, you learn. Best case, you take off disproportionately.

Learning: Each concept compounds. Calculus didn’t just help me pass exams, it gave me a framework for life & a framework to take asymmetric bets in investing & in life.

Relationships: Smooth ones can be fragile. Convex ones embrace honest stress, emerging stronger after each conflict.

Convexity, it turns out, isn’t just a mathematical property. It’s a way of structuring your existence.

Convex optimization makes algorithms stable.

Convex living makes humans antifragile.

And maybe, just maybe, that’s the most practical use of calculus we’ll ever find outside a classroom.

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