Category Archives: jobs

AI Isn’t all that I

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And it’s unlikely to be so anytime soon.

A human brain contains 100 billion neurons and over 100 trillion synaptic connections. That’s a thousand, or more, connections per neuron. A human brain’s cortex alone contains approximately 20 billion neocortical neurons, with an average of 7,000 synaptic connections each (primary source). The cerebral cortex has about 0.15 quadrillion synapses—or about a trillion synapses per cubic centimeter of cortex. More, the brain uses all of 20 watts of power to function fully. That works out to a vanishingly tiny amount of wattage per synapse (that’s 0 decimal point 12 zeros and a 2 at the end).

Intel’s latest AI-supportive chip suite (as of April 2024, anyway) supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores[.] That’s a bit over 110 “synapses” per “neuron.” The setup uses 2,600 watts at max function. That works out to 0 decimal point 7 zeros and a 2. Which is five orders of magnitude more power drain per “synapse” for the chip than for our brain.

Artificial Intelligence isn’t all that. It may well get there, but not tomorrow.

An Overblown Concern

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Citrini Research wrote a report that’s associated with Monday’s stock market spike down. Its report centered on the risk of heavy white collar job losses from AI’s alleged ability to do white collar work and completely replace those white collars.

For the entirety of modern economic history, human intelligence has been the scarce input. We are now experiencing the unwind of that premium.

And so on.

Not so much, though. It took more mental acumen to run the steam drill than John Henry needed to run his hammer. It takes more mental acumen to work a modern auto production line, with all of that automated equipment, than it did—and does—to work an artisan, unautomated auto production line. The move extends into the white collar milieu, also. It begins with requiring more mental acumen to check AI’s work than it does to work the spreadsheets or do the research oneself. It takes a great deal of mental acumen to ask the right questions and then give AI the tasks of answering them—and then checking AI’s responses. Creativity is something AI cannot do.

AI is good at the artificial part; it’ll be quite some time before AI gets good at the intelligence part. Alan Turing once said that when a computer can answer certain kinds of questions, they’ll be impossible to distinguish from humans. That doesn’t prove computers’—AI’s—superiority, though. Answering questions isn’t the same as asking them.

Who Drove the Settlement?

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Centerview Partners, a niche investment bank, agreed to settle a lawsuit brought by an intern who claimed she was terminated improperly over a disability she said she had. As is usual in many civil suits, the terms are unknown. The settlement came just before the trial was due to start, and

just a few days after the judge seemed to cast doubt on [Kathryn ] Shiber’s ability to claim the millions of dollars in compensation. During a pretrial conference Thursday, the judge said at one point that it would be improper for the jury to consider what she would have earned had she stayed at Centerview beyond the three-year program.

That timing raises questions in my suspicious pea brain, primary of which is who was the motivator for the settlement. Was it Centerview, looking to avoid the potential of an enormous payout to Shiber? Was it Shiber, who was satisfied with the settlement terms, whatever they are? Was it her lawyers, who in a fee-seeking imperative, bailed on Shiber since they no longer would be guaranteed their own enormous payout cut from those millions of dollars in compensation that otherwise would have been available to get access to?

Enquiring minds want to know.

AI and Entry-Level Jobs

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Richard Smith, Johns Hopkins University’s Human Capital Development Lab Professor of Practice, and Arafat Kabir, writer about AI, in their The Wall Street Journal op-ed think that AI is spelling the death knell of entry-level jobs.

When AI automates routine tasks, organizations often find they need experienced employees who can combine AI capabilities with years of business knowledge. What those organizations don’t need is entry-level employees learning the basics. Data shows rising unemployment since 2022 among 22- to 25-year-olds in AI-affected sectors—even while employment for older workers remains stable.

Not so much. The transition from hand-spinning thread from cotton balls—an entry-level job for making cloth—changed with spinning jennies, powered looms, and the like. Entry-level work didn’t disappear, it transitioned to requiring different, and better, skills and the knowledge required to understand the more complex work. Hand spinners and weavers had to upgrade their skill sets and knowledge or go unemployed. New basic employees learned those new skills and gained that new knowledge. Employers who invested in the requisite training prospered, those that didn’t, didn’t.

Similarly, the transition from hand-fabricating and assembling automobiles to the assembly line changed the nature of entry-level work. Henry Ford blew away his competitors when he invested in training his new employees, which along with a small pay raise increased worker retention with its associated reduced labor costs from worker turnover and needing constantly to get new ones trained. OJT of hand crafters no longer could cut it, but the entry-level work, while changed in nature, remained in fact.

So it is with AI when it’s properly put to use. The scut work and grunt work of interns as gophers along with the routine most basic work that will be done by AI applications also does not replace entry-level work; it merely changes the nature of that basic work and, as before, requires a bit more knowledge of how to do it. The existing work force—those older workers—will retire sometime between sooner and later. Their loss will require companies to train their replacements in this new entry-level work, and those that do will move ahead, while those that do not will fall behind.

Smith and Kabir acknowledge as much without, apparently, recognizing so.

[R]ecogniz[e] that AI represents a fundamental shift rather than merely another tool. One example could be focusing on “AI native” tracks in which, instead of starting new employees with routine tasks that AI can handle, they begin with AI oversight and optimization roles. They learn to train, monitor, and improve AI systems while simultaneously building domain expertise—combining technical fluency with business acumen.

Yet, that’s precisely what a tool does. The steam-power was a fundamental shift for industry and industry-related work. It powered mining drills, heavy transportation, forges, and on and on. That fundamental shift, though, was just a means of getting new tools for more efficient work with an associated change in what constituted entry-level work. That basic work ranged from running those new tools to maintaining them to manufacturing them.

As technology evolves, so too does the nature of “entry-level.”

Some Arithmetic Regarding Social Welfare

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This arithmetic centers on the Western canonical welfare State of France, but the lessons apply to us also.

Today there are 39 seniors for every 100 working-age people in France. But by 2070 working-age French will account for only 50% of the population, down from more than 55% in 2023.

That works out to ratios of 1.8 working age persons for every retiree and 1 working age person per retiree, respectively. Each working age person in 2070 will have a retiree on his payroll whether he wants that or not.

That’s the outer bound.

[M]any of France’s working-age ranks aren’t actually working. The French unemployment rate was 7.7% in October 2025….

That reduces today’s ratio to 1.6 actually working person for each retiree. That’s an outer bound on the burden actually laded onto the worker. Those working age unemployed, those 5+ of the 100 who are unemployed, are being supported him, too.

Our demographics are only a couple of generations behind France.