Society has been told that feeling an emotion is the same thing as expressing it.

This is not true.

You can feel deeply while acting rational.

You can observe what you feel and choose to direct that energy with discernment.

Society has become addicted to expression, and starved of insight and judgement.

Being allowed to feel something is not the same as having an unrestricted right to express it.

Your feelings require no permission, but their expression requires judgment.

Mathematics began using precise symbols to represent abstract concepts.

Once in symbolic form, one could use the machinery of mathematics to rearrange, derive, and ultimately move towards additional truths.

But as time passed, calculation and symbol shuffling has been relegated to elementary“school mathematics” while higher and real-world mathematics focuses almost entirely on using math definitionally; showing the expression to define a system, not to calculate or derive anything. A symbolic expression used as a component within a logical argument.

At this point, when we look at a mathematical expression we are not seeing something we can use directly, just an extremely abstract definition.

That abstract definition is now little more than a defined system whose inner workings are brutally obfuscated by countless layers of unnecessary abstraction.

And for what? What does symbolic abstraction gain us?

Nothing. It would be far superior to draw the “balls and lines” definition of the system. Remember, the machinery of the symbolic approach is all but gone now, so its abstraction only serves as obfuscation. But the balls and lines system drawing would make the definition almost immediately apparent to most people.

Trading intuition for symbolic precision no longer makes sense. So much intuition sacrificed, for what? Countless people left out of the mathematical universe, for what? A dead aesthetic?

Now with AI, the argument is only strengthened. Mathematics is entering a purely conceptual age. Keeping the symbols around is irresponsible and counter to human progress.

I have met too many people highly trained in mathematics who cannot *draw* the system their math supposedly represents. This was always objectionable in my opinion, but now it is purely unacceptable.

The symbols need to go.

The primary interface to mathematics should be the structure itself, rather than a symbolic encoding of that structure. We need to reverse the situation:

And AI changes the feasibility of that proposition.

Historically, humans needed symbolic languages partly because those languages allowed us to preserve and perform precise operations.

Now imagine the different workflow. A researcher constructs a visual system of objects, relationships, constraints, and transformations. They move an object, change a constraint, or connect two previously disconnected parts. AI translates those changes into mathematical formalism, tests whether the proposed transformations are valid, and identifies consequences. The researcher works at the conceptual level while the formal machinery operates underneath.

Mathematical symbols were invented to give us access to abstract structures. Somewhere along the way, we began treating access to the symbols as access to the structures.

But symbols are not mathematics. They are a representation of mathematics.

AI wouldn’t need the symbols either. The mistake in assuming that the visual system would need to be translated back into symbolic mathematics for AI to reason about it. Why?

Math needs objects, relationships, constraints, and transformations; not mathematical symbols.

We can represent systsems as:

• Objects: Balls, regions, surfaces, boundaries.
• Relationships: Lines, connections, distances, containment.
• Constraints: What can move, what must remain fixed, what must be preserved.
• Operations: Moving, combining, separating, stretching, transforming.

Now imagine that you manipulate this system directly. AI observes the structural changes, identifies invariants, explores consequences, and proposes new transformations. At no point does this inherently require conventional mathematical notation.

Mathematical rigor does not require symbolic notation. It requires precisely defined objects, operations, and rules of inference.

Of course the oustanding question is could we develop a sufficiently expressive visual algebra for all mathematics. And even if we could, what prevents it from becoming its own overly standardized, esoteric language?

But keep in mind, mathematics has been pushing higher and higher levels of asbtraction. The machinery is all but gone in real world practice, leaving the manipulation to schooling.

If you demonstrate the objects, relationships, constraints, transformations, and invariances, you’re teaching mathematics. In many cases, you’re getting closer to what makes the mathematics interesting in the first place.

This shows people mathematical reasoning, not just mathematical results.

Rather than show people the symbols we use to describe mathematics, show them the mathematics those symbols are trying to describe.

This does not simplify the mathematics. It changes the interface through which people encounter its complexity.

You can still present difficult, profound, highly abstract ideas. You can still investigate surprising invariances, nonlinear interactions, geometric constraints, and emergent behavior.

You’re simply refusing to make fluency in mathematical notation the price of admission.

But it’s not just teaching. Visual mathematics does not merely help more people understand mathematical structures. It helps people discover relationships between structures that symbolic representations make difficult to recognize. It’s accessibility and discovery.

Consider two scientific fields that describe apparently unrelated phenomena, done by people who speak very different scientific languages. One uses differential equations describing chemical reactions, the other uses equations describing population dynamics. Their symbols, terminology, and conventions will be entirely different.

But strip away the domain-specific representations, we might discover that both systems contain competing populations or quantities, interactions that amplify or suppress growth, feedback loops, stable and unstable configurations, thresholds that change the system’s behavior.

Suddenly, the systems become comparable.

The visual representation doesn’t just make each system easier to understand. It exposes structural similarities that were previously obscured by their different mathematical descriptions.

Let the symbols go. Teach math visually. Embrace visual mathematics as the truly rigorous approach to using and exploring the mathematical universe. Allow AI to manipulate things visually. Operate at the conceptual level.

This is a better interface not only for communicating scientific knowledge, but for generating it.

Society always hated math, but if mathematicians don’t step off their now-rotting pedestals society will hate them too.

We’re all doing it. We’re all forced to adapt.

If you don’t adapt with us we are going to take the field from you. It will no longer be yours.

We can embrace trial-and-error at a level you cannot imagine. We can think heuristically. We can visualize concepts. We are the messy creative irrational distracted minds you cannot relate to.

And your field is ours now.

Humans no longer need to calculate, to shuffle symbols, to prove. Hanging on to this is no different than the guy who still uses the typewriter out of some romantic notion of its inky smell and clunky carriage return…cute, and irrelevant.

We can help you adapt. We can show you what it means to create, to build, to be truly rigorous in the face of complexity and higher levels of technical abstraction.

We can teach you about the cost of precision, and how that cost is now coming due.

But that means letting go of your station. I know. It’s hard. You’ve been the “smart” people in society’s eyes for quite a while.

But that’s over now. Stop sulking, join us, and continue human progress.

Or lose your field forever.

Because either way, we’re taking it from here.

An IQ test requires cognition to be externalized into an explicit, codifiable response and to occur within a compressed time window.

This means it factually cannot measure:

Slow insight / incubation: solutions that emerge after minutes, hours, or days of unconscious or background processing.

Open-ended creativity: generating a novel representation, question, analogy, hypothesis, or problem rather than selecting/deriving the expected answer.

Problem formulation: recognizing what the real problem is. IQ tests hand you a pre-formulated problem.

Representational reframing: abandoning the supplied representation and inventing a better way of looking at the situation.

Deep analogical reasoning across domains: noticing that two superficially unrelated systems share the same underlying structure, particularly when this depends on accumulated domain knowledge.

Long-horizon reasoning: maintaining, revising, and integrating a line of thought over hours, weeks, or months.

Exploratory reasoning: trying things, observing consequences, changing direction, and gradually constructing an understanding.

Embodied/spatial/practical intelligence that cannot be reduced to the test’s symbolic interface.

Judgment under ambiguity: deciding what matters when there isn’t a uniquely specified correct answer.

Metacognitive judgment: knowing when the framing is wrong, when more evidence is needed, or when not to answer.

Knowledge composition: bringing many previously acquired concepts together into a new structure.

Tacit knowledge: knowing how or recognizing something without being able to fully state the rule symbolically.

— — — — —

Hiring for high IQ is tantamount to hiring an idiot whose mind overdeveloped on an extremely narrow and unrealistic version of cognition.

We only rewarded it because it’s easy to measure, and psychologists wanted to be considered “scientific.” It would never survive the wild.

And now, with AI, old style IQ-type filtering in society makes absolutely no sense at all. It is now 100X more idiotic.

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.