“The advances in machine learning have taught us more about the essence of intelligence than anything that neuroscience has discovered in the past hundred years.” — Doris Tsao, neuroscience professor at the University of California, Berkeley.
The world has been unable to solve most of the major global problems in the last few years. This is not about things that went bad but needed to be solved to return to the preexisting status, but major problems that would have exponentially advanced the world, including in economics.
Nations, mostly, are not solving their national problems either. There are projects, budgets, and much else, but there is mostly stagnation in most nations across the globe.
Though it is true that there are lots of differences from centuries ago, progress, at this time, has become incremental. Sometimes, negligible. The lack of solutions to [most of] the world’s problems is an indication that human intelligence may have peaked.
People complain about brain rot, AI slop, doom scrolling, and much else. There are layoffs, hiring freezes, illegal migration and deportations, automation, and so forth. The number of universities, institutes, centers, labs, startups, and so forth has ballooned, yet fewer major problems are getting solved because human intelligence is having efficiency issues. Humans are sticking with intelligence with little economic value.
Several problems that would not even require huge facilities or funding, where there is no excuse for not doing something excellent, yet nothing of major usefulness gets done.
Criticisms are often partitioned. Oh, it’s this political party, or that group, or that device or app, or whatever. Maybe some have had an effect, but the core is that major problems are no longer within reach of solutions.
Human intelligence seems to be at sunset, after rapid progress in the last 200 years. So, for now, there is often something to utilize to manage most problems or find a way around [minor] problems [that were major, years ago].
And when big problems cannot be solved, which is a purpose in itself, then small or new problems take the stage, and non-problems as well.
Most importantly, human intelligence became adapted to finding ease, as the world became easier. AI is not suddenly making people lazy or unwilling to think, work, try, or whatever else; human intelligence had already dwindled, unable to action solutions, then AI came along.
LLMs Capex
AI is the only thing propping the economy because AI is the only thing worth investing in, it seems, and it is the only direction where growth in intelligence appears possible. Hence, the prospect for solutions, so the stock market gives AI all its mitochondria.
The capital expenditures on large language models by four of the big tech, were reported to be $360 billion in the last year.
It was shown that job openings, since 2022, after ChatGPT was launched, had plummeted, compared to the surge in the stock market. All these are signals that hiring en masse for the expectation that problems would get solved is now anachronistic. Managing or operating things is the outline of most tasks, while improvements that are expected are marginal or spread over a long, unknown time.
The United Nations does not have a World Day for human intelligence. There should be a World Day for human intelligence every month, starting from this November 10, 2025, and then December 20, 2025, and some date per month.
There is no human intelligence research lab on earth. Neither the World Health Organization nor the United Nations has it.
Has Human Intelligence Peaked?
People are hating on AI. They are critical of data centers in investments, energy consumption, and water. However, there is something more to these. In recent years, several investments have been made to processes involving human intelligence — while some things are complex and take a lot of time — most of those have not paid off.
Whether AI pays off or not may not be certain, but it, at least, holds promise for intelligence, for problem-solving, for now, more than human intelligence that has seemed to hit a weak stretch or retirement, given all that is now possible or available, but little is the outcome for necessary change. There is chatter about an AI bubble. Maybe AI is worth the risk. Human intelligence is now for debates, arguments, pettiness, distractions, and disagreements, even with no way to find paths forward against complicated problems.
There is a new [November 4, 2025] report on Reuters, Amazon’s $38 billion OpenAI deal shows it is no longer an AI laggard, stating that, “Amazon’s $38 billion cloud deal with OpenAI marks a major endorsement for the e-commerce giant’s cloud business after recent setbacks, including ceding market share to rivals and an outage that disrupted large parts of the internet.”
“Recently, though, the company has ramped up spending on its AI efforts, and last month opened an $11 billion AI data center in Indiana called Project Rainier, where startup Anthropic’s models are being trained using Amazon’s own Trainium chips.”
There is a recent [November 3, 2025] feature in The New Yorker, The Case That A.I. Is Thinking, stating that “Models sometimes make amateur mistakes or get caught in inane loops, but, as I’ve learned to use them effectively, they have allowed me to accomplish in an evening what used to take a month. Not too long ago, I made two iOS apps without knowing how to make an iOS app.”
“If simple training techniques can enable a program to behave like a human, maybe humans aren’t as special as we thought. Could it also mean that A.I. will surpass us not only in knowledge but also in judgment, ingenuity, cunning—and, as a result, power? To my surprise, Hasson told me that he is “worried these days that we might succeed in understanding how the brain works. Pursuing this question may have been a colossal mistake for humanity.”
This article was written for WHN by David Stephen, who currently does research in conceptual brain science with a focus on the electrical and chemical signals for how they mechanize the human mind, with implications for mental health, disorders, neurotechnology, consciousness, learning, artificial intelligence, and nurture. He was a visiting scholar in medical entomology at the University of Illinois at Urbana-Champaign, IL. He did computer vision research at Rovira i Virgili University, Tarragona.
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