By David Stephen
Why can’t everyone become a mathematician? Or, why does everyone not like advanced math? Why do some people really find math tough? Or, why do some people encounter mathematics and a lot of energy or enthusiasm follows?
These are questions for how human intelligence works in the brain. This is not simply about resources, location, age or whatever.
How does human intelligence work in the brain, amid the rise of artificial intelligence? Simply, what makes humans more advanced than other organisms, giving that there are several similarities across species?
This would have been the best objective that Senator Bernie Sanders [I-VT] would have pursued, against AI, to keep human intelligence competitive with the emergence of artificial intelligence.
AI can do a lot of productive work as well as fulfill social objectives. AI is actually excellent at some skills that are difficult for several humans to learn or master. AI is available on digital, a now dominant medium for important economic human processes. AI is also great at tasks that come with post-academic value.
AI may have a lot of flaws, but there are things that are common [or easy] among humans that are not economically rewarding. So even if AI is weak at those, it does not matter as much, in labor value hierarchy.
The current capabilities of large language models [LLMs] include solving complex math, rigorous analysis, writing, software engineering, domain knowledge across expertise, social communication, teaching and so forth.
AI has learned a lot of what humans train — at school — to become. So, while it appears that the education system seems intact, the availability of an alternative to take on tasks will be a factor in hiring and retention considerations, gradually.
Also, even among humans, intelligence capabilities are varied, especially for types, needs, and so forth. There is already a lot of competition that some people have a little chance against some others. For example, in choice industries like building AI, space, biotech, and so on.
Still, there are ways to try, given that it is humans-against-humans. Now, with machines, there could be a cutoff for what a minimum should be, for what is accepted, at a performance level.
This means that what should be an urgent project to answer is what human intelligence is, in the brain, how it works, how to improve it for problem-solving and so forth. Also, if human intelligence intensity built AI, space science, biotech, if human intelligence improves, would there not be hope for more breakthroughs in more places?
No matter how safe, spread, shared or supportive artificial intelligence becomes, it is not human intelligence, in the brain. It will be used with a connected device — for the most part. AI will still be owned or controlled by a few entities, and AI will work within the capability of individual human intelligence, for its ability to help. For example, learning a new language for an adult is still dependent on human intelligence, even if AI can teach and ease translation.
What Bernie Sanders should be saying now is to have the first human intelligence research lab, or an American Academy of Human Intelligence, or an international day for human intelligence and so forth. The goal is to at least find ways to ensure there is a large effort into human intelligence, comparable to how AI is getting fed and improving, to avoid neglecting human intelligence and assuming it is dead, with the only hope [just] to share AI profits.
Bernie Sanders
There is a recent [June 1, 2026] guest essay in The New York Times, Bernie Sanders: A.I. Is a Public Resource. You Should Own Half of It., stating that, “The question, then, is not whether A.I. will change the world. It will. The question is: Who will own and control that future? Who will benefit from it, and who will be hurt by it? Will A.I. be used to make life better for working families? Will it enrich our quality of life? Will it help us eliminate poverty, extend life expectancies and solve the climate crisis? Or will the future of humanity be determined by a handful of billionaires who have promoted and developed A.I., with virtually no democratic input, who stand to become even richer and more powerful than they are today?”
“Dozens of sovereign wealth funds exist all over the world to ensure that ordinary people benefit from national wealth. Norway’s sovereign wealth fund, one of the largest in the world, was funded from the country’s oil wealth and is now worth more than $2 trillion.”
“Fifty years ago, Alaska created a sovereign wealth fund from the state’s oil revenues. For decades, it has paid annual dividends directly to Alaskans. Moreover, public pension funds in states across the country already hold hundreds of billions of dollars in the stock of companies throughout America.”
“But the principle is simple: When a public resource generates wealth, the public should share in that wealth.”
AI Stake
Even though people own shares in companies, there are those with controlling stake, management teams, as well as those with more voting power. The same with sovereign wealth funds. This means a few will retain power even if they are representatives.
Nothing can compensate for cognitive slump, if AI replaces jobs, and does the most valuable common, white-collar work, and there are few alternatives.
Across the globe, for most of the last decade without AI in several countries, there has been high youth unemployment. It is not like human intelligence solved that problem at scale. So, with AI, the problem is compounded and broadened, and it is not like there will just suddenly be things to do, for people, when new sectors are not getting made so easily.
This means that even with novel labor economics models, small business workforce growth research lab, and so forth, it is still necessary to find how to improve human intelligence.
What should be sought is versatility and to ensure that human intelligence stays competitive. Not just let AI win and hope in sharing. That could be ruinous, without simmering human intelligence, providing new options for development and exploration across purposes.
Human Intelligence in the Brain
Human intelligence is defined as the use of memory for expected, desired, or advantageous outcomes. Simply, intelligence is how memory is used. There are two types: operational and improvement intelligence.
Operational intelligence is for regular endeavors, to get things done routinely, while improvement intelligence is to advance stuff.
There are mild, mid and extreme ranges of operational and improvement intelligence.
Extreme improvement intelligence is rare. Even extreme operational intelligence is not as common. LLMs can do some aspects of operational and improvement intelligence, matching several humans in aspects of those.
The elements of human intelligence in the brain are postulated to be electrical and chemical signals of neurons across sets.
Why is it difficult to learn math for many people?
There are two ways to answer, at least from conceptual brain science. First, when some people learn math, they often lack quick overlays and addition into thick sets.
Sets are conceptually collection of electrical and chemical signals in a cluster of neurons. Thin sets retain what is most unique about any information. Thick sets collect what is common. So door, window, chair, desk, are respective thick sets — collecting what is common about those into one.
To learn, most information has to be added to an existing thick set. While it is possible that some information will remain unique and stay in a thin set, most will have to join a thick set.
For some people, having math join some existing thick set takes much longer — so to speak. The same with learning a new language. Aside from thick sets, there are overlays, or temporal states where thick sets layer on each other, to reach understanding or clarity, or connecting the dots and so forth. The thick set of a desk could layer on that of an office.
Simply, even if thick sets already have common elements of information, some thick sets still have things that are similar with other thick sets, like [say] the thick set of square root and the thick set of cube root.
Both can sometimes overlay, or something like that, this overlay can prompt understanding. For some people, overlays in math solutions are more difficult. The quickness of overlays, sometimes, for some people who do intense math, result in high-energy states.
Then there is pleasure. There are people with thick sets that have faster ability for new sequences [or paths] to the emotion of pleasure, when learning math. Simply, for some, learning math has a faster pleasure derivative than others.
While it is true that learning and understanding does this for many, there are people who have it faster.
So, human intelligence mechanism can conceptually be used to explain why some soup complex math and while it remains difficult for some.
This does not mean persistence, consistency, or practice is not necessary [for all], just like learning a new language, but that learning is subject to the relays and destinations of electrical and chemical signals, in sets. This is also expounded in the postulate in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology.
Now, AI can solve math, which is not so direct for humans to learn. AI can also translate several languages, which is also difficult for humans.
How does human intelligence work, what adjustments can be made to increase the chances to learn better, understand and to solve problems?
These should be at the fore of equality. Sharing AI wealth is the easy part. Because, if the problem is sharing then AI owners can let some go. They have investors. They proposed universal basic income [UBI].
But as human intelligence crashes, the world will not be a place that even those that get the handouts would want to inhabit.
There is a recent [June 18, 2026] report on Axios, Bernie Sanders unveils AI “tax” plan, stating that, “Sen. Bernie Sanders (I-Vt.) on Thursday unveiled his plan for the U.S. government to take 50% equity stakes in large AI companies.”
“The one-time “tax” would apply to any company with annual AI revenue of at least $200 million.”
“The stakes would go into a new U.S. sovereign wealth fund, overseen by seven commissioners nominated by the president and confirmed by the senate. It then would pay out a 5% annual dividend directly to Americans.”
“The stakes would go into a new U.S. sovereign wealth fund, overseen by seven commissioners nominated by the president and confirmed by the senate. It then would pay out a 5% annual dividend directly to Americans.”
“The bill also would require such companies to separate their “AI and non-AI businesses,” with the government only taking equity in the former.”
“It would be voting stock — which means the government would have a major say in how such companies operate.”