Every week someone asks me a version of the same question: what should I learn to get into AI?
For a long time I assumed it was a question from people at the start. It is not.
I get it from students, and I also get it from people twenty years into a career who are very good at something the world is quietly repricing. A principal engineer. A director who has shipped more than most people ever will. They ask it more carefully, and usually later in the conversation, but it is the same question.
Here is why it lands so hard for the second group.
Experience used to be an asset that compounded. Now it can also be an anchor. Twenty years of hard-won judgment in a domain is enormously valuable right up until the moment the ground under the domain moves — and then the same twenty years become the reason it is hard to move with it. Nobody warns you that expertise has a maintenance cost.
So the question is understandable. It is also the wrong one, for everybody asking it, at every level.
It is wrong because whatever you learn will have changed by the time you have learned it. The half-life of a specific skill in this field is now shorter than the time it takes to acquire it. Anyone optimizing for a curriculum is running hard toward a place that has already moved.
The better question is harder and it does not have a syllabus:
How do I succeed in an AI world?
Seven habits, from twenty-six years of getting this right and getting it wrong in roughly equal measure.
1. Learn how to learn. Nothing else compounds.
The winner is not whoever knows the most. It is whoever closes the loop fastest
Learn. Apply. Get evidence. Correct. Repeat.
Most people optimize the first step and neglect the other four. They read more, watch more, save more. But knowledge that never meets evidence does not become skill — it becomes trivia. The loop only pays when it closes.
Here is the part people miss, and it is the part that matters most.
When you shorten your loop, you do not only win faster yourself. You teach the shortened loop to the people who come after you, and they start from your ceiling instead of your floor. Then they shorten it again and hand that forward.
That is not a linear gain. It is logarithmic, and it compounds across generations rather than quarters.
Which is why the highest-leverage thing available to you is not learning a framework. It is getting systematically better at the act of learning itself — and then teaching that.
2. Use an Optimal Knowledge Acquisition Framework, not a reading list.
I taught this at Google as a Googler-to-Googler coach, and the pattern held across everyone I worked with. The people who moved fastest were never the ones who read more. They were the ones who knew what to skip.
A reading list is a queue. A framework is a filter. The filter is worth more, because the constraint was never access to information — it has not been for twenty years. The constraint is attention, and attention spent at the wrong depth is simply gone.
Three things the framework has to do:
Acquire faster. Know when to go three levels deep and when one level is genuinely enough. Most material deserves one.
Retain longer. Retrieval beats review. If you cannot explain it without the document open, you have not learned it. You have visited it.
Implement like there is no tomorrow. Knowledge that never ships decays at the same rate as knowledge you never acquired. The only difference is that you feel better about it.
I call it the Optimal Knowledge Acquisition Framework, and optimal is the operative word — the point is not to acquire more, it is to acquire the right depth at the right cost. It gets its own piece. If you want it sooner rather than later, say so in the comments and I will move it up the queue.
3. Find your ikigai before you optimize your resume.
Also something I taught at Google — though I did not find it myself. Pravir Gupta, a VP and GM at Google Cloud and an unofficial coach of mine, handed me the book.
Four circles: what you love, what you are good at, what the world needs, and what you can be paid for.
The reason this matters is not balance. It is fuel. A career built on only what you are good at and what pays runs on push — a manager, a deadline, a promise about the next level. Push runs out, usually around year seven. Pull does not. Find the thing you would read about on a Saturday and the loop from habit one runs on its own: you learn faster than the person being pushed, because nobody has to make you. That is also what survives the pivots, and this field will make you pivot, more than once, whether or not you planned for it.
Pravir put it better than I can: passion and knowledge are a dangerous combination, and if you have ten out of ten in both in a given area, you are unbeatable. Note the last part. Not ten out of ten in general — in a given area. Knowledge is habit one. Passion is this one. Neither alone is dangerous, and that is the whole reason he gave me the book rather than a reading list.
The order matters. Optimize the resume first and you become very good at being hired for work you do not want. Find the intersection first and the resume becomes a description rather than a performance.
4. Your career is not a line. It is hills.
You have to walk down one hill to climb the next. Every senior person you admire has a valley they do not put on LinkedIn — the role that did not work, the company that folded, the year that looks like a gap.
What is genuinely different now is that you can turn the descent into a wave instead of a drop.
The valley used to be long because relearning was slow. You left a domain, and re-entering a different one at real depth took years. That is no longer true. With an AI that can take you to the working edge of a field in weeks rather than quarters, the trough gets shallower and shorter.
That is the biggest structural advantage available right now, and most people are using it to write emails faster.
5. Collect dots that do not connect yet — then use AI to connect them.
You cannot connect the dots forward. You can only collect more of them, on purpose, and trust the pattern to show up later
But this is where AI earns its place, and it is not the way most people use it.
It has effectively infinite knowledge and no lived experience. You have lived experience and a small fraction of the knowledge.
That asymmetry is not a weakness on either side. It is the raw material. Innovation is the synthesis between the two — between what has been recorded and what has been lived. Neither half produces it alone.
So do not use AI as an oracle that agrees with you. Agreement is worthless; you already had that. Make it a co-dialectic. Bring it the intuition you cannot justify yet. Let it argue back. Argue with it. Ask it to show you the three fields where your half-formed idea already exists under a different name.
The dots you could not connect alone start connecting. Not because the machine is smarter, but because you finally have something to think against.
The inversion: this is the golden hour for experienced people
For twenty-six years, every new technology put me at a disadvantage
A new grad could pick up a new language faster than I could. Always. They had nothing to unlearn and more hours to spend, and I was competing on the one axis where youth genuinely wins. That was true of every wave, and I felt it every time.
That axis just disappeared.
Every language is now a prompt away. The thing I used to lose on is free. And what I am left holding is the one thing that took twenty-six years to accumulate and cannot be downloaded: lived experience, which is another word for judgment.
Knowing which of three working solutions will still be working in two years. Knowing when a demo is hiding a problem rather than solving one. Knowing when to kill something. None of that is in the model, because none of it was ever written down — it was paid for.
So let me say the quiet part plainly. I am more effective now than fifty developers were five years ago. Not because I got smarter. Because the part of the job I was worst at became free, and the part I am best at became the bottleneck.
If you have twenty years, you have not been made obsolete. You have been handed leverage on the exact thing you spent twenty years buying.
And it is the best moment to be a beginner, too
Here is the part that should be uncomfortable for people like me, and I would rather say it than pretend.
Everything I know, I paid for with mistakes. Years of them. What worked, what did not, and why — that was tuition, and the bill came in time.
You can now get a large part of that in one prompt.
They say only fools learn from their own mistakes and the wise learn from others’. That was always good advice and almost impossible to follow, because other people’s mistakes were not accessible to you. Now they are. Every post-mortem, every failed architecture, every hard-won lesson someone bothered to write down is one question away.
That is the exponential advantage, and it is available to the person with zero years.
The only thing standing between you and it is knowing how to ask. Which is a skill, and it is learnable, and it is the single highest-return thing you could learn this month.
I spent something like forty hours reading prompting research to work it out. You do not have to. I productized what I learned as an open-source plugin called Co-Dialectic — it runs on Claude Code and other CLIs, and it teaches the method by making you use it rather than by explaining it. That is the whole design: you learn it by doing it. The write-up is here: https://www.thewhyman.blog/p/co-dialectic-v4-is-live-your-ai-is
Both of these are true at once. The experienced person’s disadvantage just vanished, and the beginner’s biggest cost just collapsed. That is not a threat to either. That is the best moment either of you has had.
6. Aim at the field that does not exist yet.
Yuval Noah Harari has warned that by 2050 a “useless class” might emerge, and that bioengineering combined with AI could separate humanity into a small class of superhumans and a large underclass. He is careful to say might. So am I.
I will go somewhere he does not.
I think that within a hundred years there may be a species that looks back at us the way we look at chimpanzees today. Not a metaphor about capability gaps — an actual discontinuity, where the thing looking back is part biological and part engineered, and the distance is not one of degree.
If that is even half right, the durable work sits in two places: where biology meets machines, and where Earth meets everything past it.
Which today points at bio-mechanical and bio-electrical engineering. Brain-computer interfaces and neuroscience. Aeronautics. Material science. Security, because every one of those becomes an attack surface. And running through all of it, responsible and ethical AI — because someone has to decide what we should build, not only what we can.
That list is what I can see from where I am standing in 2026. It will be wrong within a few model generations, and that is the point. Do not memorize the list. Learn to read where it is going.
Pick the intersection nobody has named yet. That is where the room is.
7. Curate your inputs like they are your diet. They are.
Swami Vivekananda said: “We are what our thoughts have made us; so take care of what you think. Words are secondary. Thoughts live, they travel far.
Everyone quotes the first half. Almost nobody asks the obvious follow-up.
If what I think is what I become — then what makes what I think?
You do not choose your thoughts directly. Try it. Try deciding, right now, to think differently for the next hour. It does not work, because thought is not the input. Thought is the output.
What you think is what you eat, what you listen to, what you read, what you watch, who you spend time with, and how you sleep.
You eat french fries because somewhere upstream you decided they were fine. You exercise because somewhere upstream you decided it was worth it — and the real return on exercise was never the body, it was the mind.
The thought came from the input. Change the input and the thought follows.
Which turns something that looks like social hygiene into something more serious.
Follow the people who make you more capable. Your feed is not entertainment. It is the raw material your thinking is made of. An hour a day of confident, empty commentary does not leave you neutral — it leaves you with the shape of someone’s thinking and none of the substance. Choose people who are doing the thing rather than describing it. Choose the ones who make you uncomfortable because they are further ahead.
And knowing who is worth following is itself a skill. It sits downstream of judgment, which sits downstream of everything above. This is why the framework is a loop and not a list.
Then there is the part I am less sure about, and I would rather say so than pretend.
I think what you dream is what you become.
Not the ambition sense. The literal one. A dream changes what you think, and what you think changes what you become — so the dream sits one level further upstream than any of it.
I suspect this is what meditation has been doing all along. Deliberately placing better things where thought is made. Whether that is self-manifestation or just very old, very good input hygiene, I genuinely do not know.
But I know which way I would bet.
Build, build, build — because that is how judgment gets made
I have been circling one claim this whole piece, so let me state it directly
In the machine age, the only thing that stays scarce is judgment. Knowledge is free. Execution is getting free. Judgment is not, because judgment is compressed lived experience, and the machine has no life to compress.
Which raises the obvious question: how do you acquire judgment on purpose, rather than by waiting twenty years for it?
You build. Repeatedly, quickly, and in public.
Every small thing you build and ship runs one honest experiment. It either works or it does not, and either way you now know something you could not have read. Fail fast, succeed faster — not as a slogan, but as the actual manufacturing process for judgment.
There are three builds inside it, and they are not the same build.
Build the thing. The silly idea, actually shipped. Small enough that failing costs you an afternoon rather than a quarter.
Build the evidence. The repo, and the write-up of what surprised you. This is the step almost nobody does, and it is the difference between having learned something and being known to have learned it.
Build the judgment. The part that accrues to you. It is the only one of the three that compounds, and it is the only one the machine cannot hand you.
Technical skill accumulates along the way, as a by-product. That is the right place for it now. It is no longer the scarce thing, so it should not be the goal.
What this looks like in practice
Here is exactly what I do, and it is not complicated.
I ask an LLM to give me a few silly ideas for learning some feature I do not know. Silly on purpose — low stakes, no plan, nothing riding on it.
Then I build one. Then I publish it to GitHub, because a repo is evidence and an intention is not.
Then, when I have learned something that surprised me, I write about it.
Defense in depth is the clearest example. I was building a Medicaid eligibility copilot. I ran a boundary test — the threshold was twenty thousand dollars, so I fed it nineteen thousand nine hundred and nineteen. The system said not qualified.
One dollar. And one dollar is somebody’s healthcare.
I could not read my way out of that. I had to build my way out, and what came out the other side was a set of methods for building reliable systems on top of unreliable, non-deterministic models — which became my first article. I did not have that judgment beforehand. The build produced it.
And the by-product is a career
I did not set out to build a personal brand. I set out to learn things. But look at what the loop produces on its own:
The articles show how you think. Anyone deciding whether to hire you is really trying to answer that one question, and an article answers it better than a resume ever will.
GitHub shows the receipts. It is the difference between claiming you can do something and having done it, timestamped.
And LinkedIn turns it into inbound. Interviews start arriving instead of being chased. That is not a growth hack. It is what happens when the evidence exists in public and you stop having to describe yourself.
You do not need anyone to tell you what to build
This is the part people get stuck on, and it is the part that does not matter.
You cannot reason your way to the destination. That is the trap, and smart people fall into it hardest — they try to think their way to certainty before spending anything, and they are still thinking a year later.
Andrew Ng’s thesis at AI Fund is the opposite: you build your way there. You do not analyze until the answer appears. You run the smallest experiment that produces real evidence, and the evidence tells you the next one.
Small builds are how you take a position on the future without having to predict it. Do enough of them and the direction stops being a guess — not because you worked it out, but because you walked it out.
So do not wait for the right idea. Build a silly one today. Publish it. Write down what surprised you.
The three words
You do not need permission to start, and you do not need a specialized degree. You need a loop, and the discipline to keep shortening it.
If I could put three lines on a wall for anyone — at twenty-two or at fifty-two — it would be these.
Dare to dream. That sets the direction. It is not the soft part of the advice; it is the most upstream lever you have.
Learn to learn. That is the engine. It is the only skill that does not depreciate.
Build, build, build. Fail fast, succeed faster — that is how you buy judgment cheaply,
instead of paying twenty years for it. And judgment is the only thing that will still be scarce.
Which brings me to the thing I am writing next, because it is the failure I now see most often.
The people who adopted AI fastest are developing a dependency they did not plan for. Not on the tool — on the judgment. They have handed over not just the typing but the deciding, and when the model is down or wrong or unavailable, they cannot do the work at all. They did not get faster. They got hollow.
Ethan Mollick, in Co-Intelligence, describes two ways of working with AI. The centaur divides the labor — a clean line, you do this, the machine does that. The cyborg intertwines, moving back and forth continuously. He argues the cyborg is where you end up once you have really found a co-intelligence.
I think there is a third mode he does not name, and it is the one quietly spreading: abdication. Not dividing the work, not intertwining with it — just handing it over and looking away. It is the most comfortable of the three and the only one that leaves you unable to do your job when the tool disappears.
The cyborg way is the one I am building — co-dialectic and Exponential OS are both attempts at the same rule. I have written about what that looks like in practice, and what compounds once you stop starting over, here: https://www.thewhyman.blog/p/the-cyborg-the-exponential-advantage Never delegate judgment. Delegate the search, the draft, the first ten wrong answers, the tedium. Keep the deciding. A model that agrees with you is not the same as a model that is right, and the moment you stop being the one who can tell the difference, you have traded a skill for a subscription.
That is one of two pieces queued: this one, and the Optimal Knowledge Acquisition Framework from point 2, written out in full. Both are the mechanisms rather than the sentiment. If you want one of them sooner, say which in the comments — I order them by what people actually ask for, and one line from you moves it up.
I write about applied AI, evaluation, and building systems that build products.
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