Stop thinking of skill as a thing you need to acquire.

Start thinking of it as a weird patience.

Everyone has a weird patience to do something. When you look at someone with a different patience than yours, you see someone with a tolerance for something that would bother you.

For that specific thing, you would get bored when they don’t. You would get angry when they don’t. You would see it as a waste of time when they don’t. It’s weird to you.

But you can’t help respect them for it. And their weird patience makes them really good at something; something certain groups will want on their team. It’s weird, but extremely useful in certain situations.

The same applies to you. There is something you do that others see as a weird patience. You can sit with something for a long time that they would never sit with. There is something, no matter how seemingly useless, that you can focus on for way more time than 99% of other people.

That’s your skill. That’s your destiny, whether you like it or not.

You didn’t ask for it. You didn’t “make it happen.” You don’t even know why you’re patient in that way. You just are.

And it isn’t so much you recognizing it as it is others recognizing it. What do others keep telling you, about what you do, that they couldn’t imagine doing themselves? What’s your weird patience?

This is the kind of self awareness that actually cashes in. Not career hacking, not manufactured passion, not networking, not marketing, not branding, not status chasing, not credentialing.

Just recognizing what your weird patience is. This is what will define you. This is what will make you stand out. This is what will make people remember you, recognize you, pay for you.

All the things you keep trying to accomplish directly, and fail to do so, only materialize as a byproduct of recognizing and strongly supporting your weird patience.

What is your weird patience?

Put all your effort there.

Computer scientists are some of the least informed people when it comes to artificial intelligence.

The reason is that they have a severe lack of actual *science* education, and yet AI is a genuine phenomenon. It was not engineered, it emerged, via scaling.

This is why you get goofy suggestions from computer scientists like “the mathematical impossibilities seen in Turing machines means AI has the same limitations.”

Or, “there is no way a machine can be conscious”

Or “In 4 years, a child has seen 50 times more data than the biggest LLMs.”

The computer scientist never had to deal with actual phenomena. So their views are entirely based on rules-based mechanistic understanding. Their education is utterly devoid of phenomenological literacy.

Of course this makes total sense, it works wonders for our information age, UNTIL you get to AI. AI changes everything.

AI is not a “computer program” or an “algorithm.” The mechanistic computing used in AI only serves as a scaffold for massive iteration and scaling to take place, after which AI realizes its outputs via the *phenomenon* of emergence.

You cannot logically take what you see in models of simplistic computation and implant them directly into complex emergent phenomena.

Artificial intelligence is not computer science.

For many things in society, what was true yesterday is today a lie.

Many institutions, constructs and conventions that made sense to previous generations simply no longer do.

There is a great historical weight behind the narratives we hear, about what it means to live a good and prosperous life. But they were born of a very different reality.

We simply do not live in that world any more, and what was once deemed a recipe for goodness and prosperity has become a prison for modern man.

We are in a transitional era where this hard lesson was learned too late by a now angry and imprisoned mass.

A great societal angst now festers, and will likely collapse the old narratives one way or another. By a slow capitulating death, or a violent upheaval.

Whatever stage history decides to call this, it is a stage of realization and dissolution. It is the opposite of preservation.

The history of the modern human is extremely short relative to humanity’s full timeline. The assumed definites of society are not so definite after all.

We are waking up from the lie.

Now, with our eyes less fuzzy each day, we must eventually decide what today’s truth looks like.

On the only grounds anything should be evaluated. By showing what works in the real world.

Not with theories, not with expert opinions, not with institutional affiliations, not with suppositions, not with hypotheses, not with cherry-picked studies, not with online clout, not with bogus comparisons.

Pretending we can define human intelligence in some analytical way is unscientific. There are no non-faulty arguments that can be made on these grounds.

AI either recognizes faces or it doesn’t. It either solves the math problem or it doesn’t. People trying to make statements about what AI is doing internally is both irrelevant and epistemically untenable.

Humans make ridiculous mistakes all the time. This includes “experts”, many of whom laid the groundwork for the current wave of AI systems. Many of today’s AI gurus make categorical errors in logic and reasoning. Does this mean we throw them out? No, it means we engage them and expose bad arguments.

These experts are at least as dangerous as any deployed AI system. They have massive influence, and operate under the bogus premise that if someone is a so-called inventor or major contributor of a technology they must know what they’re talking about. This is patently false. They were at the right place at the right time to be credited with the unnamed contributions of countless others.

We cannot pluck out the nice parts of AI and discard the rest. That is akin to lobotomizing the patient to stop the depression. The depression stops, but only because you destroyed everything else.

AI comes with a cost, a cost unlike anything found in traditional software. Humans come with the same cost.

Do humans stop collaborating when errors are made? Of course not, errors are the lifeblood that fuel learning and engagement. They are that critical ingredient for highlighting wrong directions and painting necessary juxtapositions. A room full of un-errored talking is devoid of creative ingenuity and discovery.

AI is not some cogs and pistons machine, it is the closest thing we have created to the human mind. It doesn’t matter how close. It doesn’t matter if it’s “real” intelligence, whatever the hell that means. What matters is that we engage it with it in collaboration, not as some push-button tool meant to spit out looked-up answers.

AI is about conversations, not answers.

Evaluate it on those grounds. What are people making in collaboration with AI? That’s it.

If the team scores goals when Jim is on the ice, you keep Jim on the ice. Anyone theorizing about Jim’s relevance or mistakes is not validating, they’re just trying to sound smart.

You have to make it yourself.

If you don’t you’re just consuming someone’s else’s creation, and that’s not learning because there’s no struggle, no synthesis. If it’s been polished for you you’re already too late.

This doesn’t mean you don’t embrace automation, it means you create one level above the automation.

You’re doing this already with older technology, because all progress occurs by abstraction.

People complain about the human aspect being removed by automation, but that’s because they’re still stuck at what is now the old level.

You have to shift up one level of abstraction, and create there. That’s where the new struggle is. That’s where the new synthesis is.

That’s also where you come closer to true human creativity, because humans were never meant for low-level analytical work. We are pattern recognizers. Analogizers. Intuitionists. Every step in technological abstraction brings us one step closer to our origin.

There is never a justifiable reason to remain inside your current level of abstraction once a technology has automated it. What seems like a timeless state is but one transitionary stage among many.

You have to make it yourself. And making it yourself occurs at a different level today than yesterday.

Never blame the tools. There is no operative definition of human that doesn’t include ever-evolving tools.

Shift up. Create there.

There are many instances where scientists go around educating the general public about how things actually work, trying to replace an incorrect mental model with a more accurate mechanism:

Evolution: popular misconception -> “survival of the fittest”

Quantum mechanics: popular misconception -> “consciousness collapses the wavefunction”

Statistics: popular misconception -> a p-value below 0.05 proves something is true.

Markets and economics: popular misconception -> prices are set by greedy people.

Natural selection versus intentional design: popular misconception -> if something looks designed, it must have been designed.

We are currently facing the same mass misconception in AI, the misconception being that AI is programmed like a “machine” and therefore its outputs can be traced back to its inputs.

Worse, whereas the examples above are mistakes made by laymen, the AI misconception is also rampant among scientists of all backgrounds and calibers.

This of course exacerbates the problem among the general public, and finds its way into policies, decisions, institutions, and incentives.

It also means we end up having what are effectively fake conversations about AI ethics, AI is “stealing”, AI can never be conscious, AI replacing jobs, AI risk, AI hype and AI creativity in general.

The conversations are fake, not because they’re not worthy topics (they are) but because AI is being misrepresented as something it is not.

One of the greatest current responsibilities is educating both laymen and scholars on what AI is, and what it is not.

If you take what AI just gives you, it’s going to be disjointed, incomplete, somewhat misrepresenting and at times downright dumb.

But this is no different than walking into a meeting with experts and taking the first ideas that come to mind.

Like all good work, it requires a copious amount of dialogue to cancel out errors, and converge to something that is good, and at times even great.

AI is not an answer machine, and treating it as such is dumb. It is a dialogue machine, which can work with people to arrive at unique, solid and at times innovative work.

Pointing to AI’s mistakes as evidence for lack of intelligence or genuine creativity is a straw man. The one thing all great human creators have in common is an absolute ton of dumb mistakes when zoomed in. Those errors are *critical* ingredients to true creativity. They produce the gaps, the juxtapositions. Access to the information one needs to create great work is not available without them.

Ai is not a “machine” in the sense dissenters talk about it. It is not rules-based, and it is not there for “answers.” It is there for dialogue.

What survives that dialogue is what creativity is. It does not exist anywhere else. It is not something that came from AI, it is something that materialized from persistent conversation.

If you are using AI correctly, the outputs that are produced do not exist in the conversation. They stand outside it, induced by one’s experiences and abilities.

Do not take what AI gives you. That’s dumb. That is not how to use AI, just as that is not how to “use” humans.

It’s already inside you. You just need to do a lot of talking to get it out.

Most traditional coders don’t understand how to code with AI, and tend not to trust it because of the way they were taught to program.

Developers were taught to do most things upfront; plan the approach, think about the design patterns, write the tests, then implement.

So when they think of AI just writing the code automatically it feels error prone.

But there is a different type of coder, once who rarely writes tests, does not plan much, and most definitely does not care about design patterns. This type of coder can build stable software because they use the actual software relentlessly. The testing does not come from upfront unit functions that confirm deterministic outputs, it comes from the highly adaptive approach of constantly using and changing.

I have rarely seen coders work like this, because they operate as though they can just write it correct the first time (even though they never do).

It is the coder who understands the highly adaptive, liquid approach to getting a faulty piece of software to converge that works well with AI. Not by using better coding knowledge (which sounds nice but is actually quite naive in “complex” systems), but by having relentless dialogue with AI, and using the software every time an addition is made.

It is a process of constant refinement, constant trial and error, and a ton of conversation. Most coders are not prepared for that. They don’t get what it means to create something by movement rather than time-tested knowledge.

It takes a different skill than what coders have been told is the “smart” way to do things. And because of this, many are going to be left behind, as a new breed of builder rises to positions of authority.

The new builder is not concerned with code. And they understand that this is in fact the smart, rigorous way to create today’s software, because that is what evolution is, and you will never find a better process than evolution. You will never use upfront textbook knowledge to build something better than massive iteration. Never.

May the new kings rise.

The mathematician tends to have the least attitude, because their language of choice doesn’t allow them much room for hubris.

The language is the language. You can walk into a room and know more than them, even if you’re a janitor.

You can rub shoulders with folk from advanced institutes, and they will respect you, because they have to.

All other fields suffer from the problem of interpretive flexibility, which leads to epistemic gatekeeping. Because of the ill-defined nature of their work, they rely on cheap signals of rhetorical dominance, prestige signaling (credentialism), and exclusion based on alignment rather than correctness.

Mathematicians are as human as the rest of us, but their choice of expertise forces them to acknowledge what is true in their field.

I find this honorable, even if forced. Genuine humility is probably as unprovable as genuine altruism. But just as money makes otherwise enemies trade cleanly, mathematics brings a clean currency to the trading of ideas.

When one is in the sciences, and chooses to become increasingly mathematical, they will often find themselves more at home with the mathematicians than their own brethren.

As you get better at wielding the only tool that cuts through cheap rhetorical dominance you tend to lose old comrades, but critically, gain new ones.

Sure, I am critical about being overly precise on many things, and mathematics can easily devolve into mindless symbol shuffling and the false protection of people not challenging its perceived authority.

But being welcomed, even if by force, is a nice feeling. And only mathematics has ever done that.

People need currency to truly get along. I’m glad the human intellect has one.

AI “solving” all math would be one of the best things to happen to mathematics. Arguably in all its history.

It would take math out of the realm of symbol shuffling and into the realm of purely abstract reasoning.

To speak mathematics well means to define, not to explain. It is a surface level non-causal mechanism, which is conceptually and philosophically the deepest one can go.

With AI, math is no longer about deriving or manipulating (for humans) it is about landing intuitions on defensible structure. All calculation would be done via massive iteration, as is the case with current AI. Math as scaffold, not how a thing works.

This demands extreme intuition and a very high proficiency in the use of language.

But it changes who will be considered mathematical, which of course threatens the status quo. A different, arguably more powerful, version of “smart.”

“Solving” with math does wonders for designing deterministic machines. This is simply because the machines were made to look like math.

But nature is not a deterministic machine.

To speak about nature is not to calculate, it is to reason in the abstract, and use precision to help settle, or at least reflect on, those abstract arguments.

Calculation is a simplistic, bastardized use of mathematics. It is mathematics that hasn’t been let free. Truly worthwhile mixing and matching of math needs to happen in the computational setting, as we see in nature, and as we are now seeing in AI. This is what turns symbols and numbers into wet emergent outputs.

Don’t calculate. Don’t shuffle.

Reason. Define. Abstract.

Speak math so as to give intuition edges.

That’s intelligence.

Someone jumps into the mess, naively, and discovers what works.

It gets put into textbooks, because it works.

Then people get hired to teach what works as fundamentals, as though they precede discovery.

Students develop a natural aversion to the backwards dryness of what is being “taught.”

Worse, companies hire those who do well at backwards dryness, expecting them to discover the next great thing.

You should love learning, but hate education.

You don’t need to know what’s going on in the world, to know what’s going on in the world.

You are not missing out. It has all been done before.

Same patterns on repeat; just different faces and names.

You need not scroll through the feeds, or turn the pages of newspapers.

You don’t need the latest, or the most, or the quickest.

There is no experience beyond what you have now that can grant you something you need.

The most fulfilling, non-regrettable action is the one that is quiet, and present.

All of our universe’s truths are contained inside a single boring moment.

There is nothing you can miss out on. It has all been seen, in one form or another.

There are only a handful of experiences that are truly distinct. The rest is just their echo.

So stop rushing out of fear of missing out. Find peace in knowing you have seen it all before.

Move slower. Think slower.

You don’t need to know what’s going on in the world, to know what’s going on in the world.

Increased intelligence leads to greater tension between your thoughts and the world around you.

Many a smart men throughout history were deemed rather depressed at the end of their journey.

If you choose to pursue intellect, you must learn to generate independent thought while remaining psychologically intact inside systems that reward reductive simplification.

One must foster a certain stance toward reality; one that expects the shallow responses of others while not resenting their arrival.

A greater resolution of thought reveals fractures the world prefers to smooth over. Be prepared for that.

The industrial era forced humans into a deterministic analytic role.

This produced the mindset: analyze → derive → construct.

But natural systems are not solvable analytically. They are search problems. This includes economies, biology, software ecosystems and scientific discovery.

AI allows humanity to shift from deterministic construction to rapid guided exploration of solution space. This is what 99.9% of your brain is wired to do.

Exploration, discovery in non-analytic systems, restoring the original cognitive mode.

Devolving humanity, by placing analytical thinking on a pedestal in an age of AI, will dramatically decrease humanity’s experimental throughput and retard progress. All for a dumb definition of “smart” that worked during the industrial revolution.

The low-level analytical thinkers loved their privileged position. But it was always a contrived, (genuinely artificial) version of intelligence. It rewarded a kind of myopic dumbness that was truly bad at making discoveries.

These people would never has survived the jungle. And we’re about to bring back the jungle.

May the new kings rise.

There is this temptation to leave one's creative history untainted by one's random musings. After all, to be taken seriously as a philosopher, or scientist, or artist, or whatever, one imagines history looking upon a rather polished portfolio of purely refined thought.

But upon a slightly deeper investigation into past heroes we find a rather messy trail. Far from a refined and polished body of work, we instead see a complicated, messy life filled with as much aberration and unpolish as our own.

Hardly the gleaming blade that cuts through history, it is a scatter of unfinished works and one-off deviations from the clean story we are told.

It is of course history that does, and will do for you, the polishing, refining, and narrative-making about heroes.

The true history: it was a mess.

So, stop pining over the elegance of your words and the neatness of your oeuvre. A gleaming blade is one that hangs on the wall and never gets used. History does not talk of those who never grabbed the handle.

Let it all out. History will be your polish.

The purest works come from a wealth of soiled trial.

People don’t get how brutally inefficient software development has been prior to AI. I don’t mean how long it takes to write code, I mean the division of labor between coders and PMs.

The coder had almost no sense of what behavior would actually manifest in the product. They just did their little piece of isolated work, because code is logical and interesting, and then moved onto the next piece in the backlog.

Almost completely disconnected from the use of the product. Like a scribe copying manuscripts without understanding the war. Searle’s Chinese room translator in human form.

But now that AI can code (yes, literally all of it, despite what the insecure 10x engineers who are about to lose their bloated salaries wine about) there no longer needs to be a disconnect between the making and using.

Whoever makes the software (coders and PMs are meaningless names now…so pick a new title) just has to *use* the software. Keep using it, again and again, until it converges into what it needs to be. Until it becomes that which survives.

Use, tell AI, use, on and on, forever (since products are never “finished”). This is how evolution works. This is how things are supposed to be created. And humans were never meant to be writing code. That’s no different than the demeaning soot-stained labor of the industrial revolution.

No more talking back and forth between “coders” and “product managers.” This was the embodiment of inefficiency; and not the cool kind of intentional inefficiency, like savouring your cappuccino or moving slowly on your walk. Just dumb, disconnected, 1000-times-worse-than-how-nature-works kind of inefficiency.

Of course most of today’s company’s are still bloated with fear-mongering legacy coders and “designers”, so will suffer the now brutal cost of division of labor.

Until they finally cut the fat, and realize the world is different.

Stop dealing with your problems.

“Dealing”doesn’t mean what you think it does.

We’ve been told in modern society that “dealing” with your problems is a type of surgical, scientific approach to removing or handling the root cause of issues.

But real-world things don’t have root causes. That is just part of the reductionist paradigm telling you real world systems can be treated as though they are akin to steam engines, with causal parts accessible to analysis and correction.

No. This is factually incorrect.

Most of psychology has appropriated the label “science” and convinced the average person that that’s what it is. But as with economics, much of this history is baked in conceptual borrowing and narrative legitimacy.

There’s a reason people who go to therapy never stop going to therapy. It’s a “problem” that has no solution. You cannot pick apart a complex system and identify underlying causes. Such narrative is pure pseudoscience; it is diametrically opposed to how the *emergent* mind works.

It is factually better to not deal with “trauma” and insecurity. It is a far healthier mental discipline to learn how to clear the mind and not “deal” with things that have no accessible levers.

It is irrational and unhealthy to ruminate on that which cannot be resolved. It gives your fears teeth, and your past problems footholds in your mind. Not to mention, it lines the pockets of those who have convinced you talking about it will help.

It is not “repression” or “suppression.” It is stopping things at the door, and no longer inviting the past into your present life, where it forever festers and never resolves.

Stop “dealing” with your problems. There is no such thing.

Learn to clear your mind, and move forward.

Something to consider:

Those naturally better at articulating, and/or stitching things together into deterministic form, have taken credit for “discovering” a new theory or model about the world.

But those given credit were unlikely to be the first to realize the thing they articulated. It would have been felt, and messily expressed, by many others prior to the articulation.

With AI, perhaps those who feel the insight first, yet lack the ability to express it (maybe English isn’t native, or they’re poor writers, or don’t think in precise/articulated form) will now claim historical precedence through AI’s ability to articulate their feelings about a situation/phenomenon.

Personally, I enjoy articulating my insights. In fact, the deeper I feel them, the better I am able to articulate them. But I cannot assume the same is for everyone. I am sure there are many who harbor deep insights yet do not write well, or even despise the apparent loss of latent intuition that seems to come from making a thing concrete.

In any case, one’s insights shouldn’t be gate-kept by speaking/writing ability, nor by one’s affinity for precision.

The idea needs to win. The feeling needs to be given the credit. Perhaps AI will now enable that to happen more frequently.

Martial arts only work when the opponent comes at you a certain way.

An agreed-upon set of rules and movements that guarantee elegance.

This is why it works in movies, but not in real life.

In the real world, someone attacks in a flurry. Highly stochastic, extremely unmanageable.

But people love the idea that they can figure things out. They want to see the platonic shapes inside the mess, and imagine themselves a master of those forces.

Today’s science is little different.

It only works when nature is forced to come at us a certain way. We necessitate nature into a box via experimentation and theoretical analysis. We get her to move along the fictitious lines we draw.

An agreed-upon set of rules and movements that guarantee elegance. The flurry of nature made manageable through contrived methods that have little to do with how she actually works.

But people love the idea that they can figure things out. They want to see the platonic shapes inside the mess, and imagine themselves a master of those forces.

And so we keep telling ourselves stories about the untangling and taming of nature.

A grand illusion fit for the big screen.

People like to compare how the human brain uses ~20 watts of power, while large AI systems require megawatts of electricity in data centers. From this they conclude that the brain is vastly more efficient than AI.

But this kind of comparison assumes the brain is operating in isolation, which it most definitely is not. Human intelligence is socially distributed.

The “20-watt brain” is not the full system. A single human can think, but most of the knowledge and reasoning ability they use comes from outside the skull.

A physicist solving an equation is not just using their brain. They are using centuries of accumulated mathematics, textbooks, teachers, universities, journals and computers. The energy cost of producing and maintaining that knowledge network is enormous.

The true comparison is not “brain (20W) vs AI cluster (MW)” it’s “civilization vs AI infrastructure.”

Consider what it takes to produce a single trained expert. Say 20 years of education, teachers and schools, buildings, equipment, transportation, digital infrastructure, food supply chains, healthcare.

The global energy footprint behind human knowledge production is factually huge.

If we tried to calculate the energy required for humanity to collectively produce something like modern physics, or the internet, or medical knowledge, it would dwarf the energy used by a single AI training run.

Human civilization vs AI data centers. That’s the correct comparison.