Bungee Jumping Asia, Thinkifier, How to Be Smarter than AI

Remember the carton in Thursday’s column?  Obsoleted by overnight trading in Asia.

• South Korea popped up.
• Japan was healing

And the world isn’t ready to end – just yet.

The U.S. market was set to pop higher today. But maybe only momentarily.  The Economic Fractalist (site) has blessed us with an updated outlook here with a date range tweak following, so scroll down..

Tomorrow’s meat & potatoes on the Peoplenomics side of the house will feature a look at whether Christmas or Thanksgiving will be sacrificed this year. For now, though, we are doing important thinking here:

Yes, this is the part where being a long-term reader pays off. Because you’d remember my consigliere has issued warnings in the past about 55-calendar days from major tops (or 37-trading days).

Which is how we get to the kill Turkey Day or Burn Down Tree timing.

International Trade Dope

A lot of people don’t realize that this is the “work out” part of the (upstream) layer to our international debt problem. There are widget counts (with all trading partners) along with paper valuations and a lot more that goes in the blender.

But look at percentages: when you import more than you export? Well, we gotta ask: How is that going?

U.S. import prices increased 1.1 percent in September, the U.S. Bureau of Labor Statistics reported today, following a 0.5-percent rise in August. Higher prices for nonfuel imports and fuel imports drove the advance in September. Prices for U.S. exports increased 2.2 percent in September after rising 1.2 percent the previous month.

Imports
Import prices advanced 1.1 percent in September following an increase of 0.5 percent in August. Prices for U.S. imports rose 8.1 percent from September 2025 to September 2026, the largest 12-month advance since
the index increased 8.8 percent for the year ended July 2022.

Exports
Prices for U.S. exports increased 2.2 percent in September following an advance of 1.2 percent in August. In September, higher prices for nonagricultural exports and agricultural exports drove the increase. U.S. export prices increased 11.6 percent for the year ended September 2026, the largest over-the-year rise since the index advanced 12.9 percent from July 2021 to July 2022.”

Markets were “flattered.” (As in flatter than a pancake) following.

Some Fed numbers in a half-hour will weigh, too.

Thinkifier: Making the World Cognitively Accessible

The world doesn’t have an information shortage. It has a cognitive accessibility problem.

Every morning, millions of headlines compete for the same few square inches of human attention. Wars, markets, taxes, artificial intelligence, weather, elections, and economic policy all arrive in a heap. The news industry calls this coverage. I call it dumping a lumberyard in your driveway and telling you to build a house.

What we lack isn’t more news.

We lack a way to turn information into usable understanding.

Which brings us to Thinkifier, the newest development in our BlinkLabNews work.

Thinkifier is the news-side mirror of Around the Ranch. Out here, we take complicated physical problems and reduce them to something useful. Solar power, gardening, machinery, communications, economics—whatever needs figuring out.

Thinkifier applies the same discipline to the wider world.

Our AI research team gathers and screens incoming reports. The BlinkLab cognitive processor then organizes them by subject, consequence, and direction. The objective isn’t to make readers swallow more information. It’s to help them understand what matters, why it matters, and what may happen next.

Think of it as a News Compressor with a judgment engine attached.

Rather than asking you to read 45 stories, we screen at least 45 distinct news clusters, promote no more than four major leads, and select up to nineteen additional developments worth knowing.

Then we sort the consequences into practical categories:

Monetizers follow money, markets, credit, employment, and the economic consequences of policy.

Warifiers track military conflict, geopolitical pressure, and the possibility that separate confrontations begin reinforcing one another.

Taxifiers examine taxation, regulation, government spending, and the growing cost of official decisions.

Techifiers watch artificial intelligence, energy systems, communications, and technologies changing the rules of work and business.

Humanizers ask what these developments mean for ordinary people rather than the institutions issuing press releases.

Unifiers search for relationships among stories that appear unrelated until their consequences begin to overlap.

Other processors, including health-oriented Healifiers, can follow as the system develops.

The names may be playful. The underlying problem isn’t.

A financial analyst may understand credit spreads but miss the military developments affecting energy prices. A geopolitical specialist may understand the Gulf but overlook how rising energy costs affect AI data-center financing. A political reporter may understand an election lawsuit without considering its consequences for markets.

Each specialist sees a piece of the elephant.

Thinkifier tries to show the animal.

News Compressor: ON

This morning’s Monetizers flag the AI-financing selloff, Asian market volatility, China’s deflation concerns, and growing pressure on international currencies.

The Warifiers flag Houthi attacks affecting Saudi infrastructure, Ukraine’s satellite-communications dispute, expanding Russian drone capabilities, and North Korea’s submarine-missile testing.

Our Taxifiers pick up the California election lawsuit, European budget and energy disputes, and trade-policy uncertainty.

The Humanizers bring forward the Oregon school shooting, Pakistan’s deadly school collapse, Gaza’s commercial-supply disruption, and severe weather emergencies.

And the Unifiers find today’s central connection: military instability threatens energy costs, energy costs complicate inflation, inflation pressures financing, and financing problems threaten markets already dependent on enormous AI investment.

That’s the value of cognitive accessibility.

Not simply knowing what happened.

Understanding which developments are beginning to matter to one another.

Welcome to Thinkifier.  Baked fresh daily in our BlinkLab.

But wait!  There’s a lot more to think about than rolling RSS feeds in life, right?

Here’s your first “Thing to write down.”  The News (until you add Thinkifying is like playing exactly one down in football.  The GAME (overall) is a way different picture. And that’s why I’ve now written six (or is it seven?) books on AI.   News is tactical reports. Thinkifying is the strategic order of battle you’ll run.  Got one?

This tees up the Big Game discussion. (*Free, Peoplenomics-thinking sample.)

Around the Ranch: Smarter Than AI

(Or, Ure’s Law of Accumulated Intelligence)

I got to thinking the other day about how smart my team of artificial intelligences has become. Between Grok, ChatGPT, Claude, Alexa, and the rest of the silicon hired hands, there’s enough raw brainpower around this ranch to make a university faculty meeting look like a gathering of people trying to remember where they parked.

And yet, it occurred to me that I have something none of them (the AIs, not the professors) can claim.

I’ve been here longer.

Now, before you dismiss this as an old man’s desperate attempt to win an intelligence contest against a warehouse full of wonder chips, consider how the game is scored.

Artificial intelligence is an extraordinary achievement. We take a mountain of accumulated human knowledge, force-feed it into a mathematical contraption, spend a fortune teaching the machinery to recognize relationships, and then turn it loose in the carbon fields to answer questions.

The whole business is rather like raising a child on the entire Encyclopedia Britannica, except the child learns it in a fraction of the time and never asks to borrow the car.

The result can write software, solve equations, explain quantum mechanics, and produce a respectable discussion of seventeenth-century philosophy before I’ve finished my first cup of coffee.

Remarkable. Humbling, challenging, inspiring, and check with d’Lynn for all the parenting adjectives.

But here’s the catch.

At the present rate of development, most of today’s commercial AI models will be obsolete in three years. Five years from now, many will be little more than entries in somebody’s technology museum.

They’ll be heaped on the same scrap pile as the VIC-20, C64, Amiga, PDP-11, and all the other electronic wonders that once represented the leading edge of human achievement.

Not because they failed. Each was brilliant in its time. Well, except for some of the C64 graphics limitations and Microsoft’s old middle-of-memory gymnastics. But we can overlook those, can’t we?

Because the machines succeeded.

Their reward for being brilliant is replacement by something even more brilliant.

The Forbin Project continues.

This is not a philosophical judgment from the woods of East Texas. It’s accounting.

AI is capital expenditure, operating expense, depreciation, replacement cycles, and the perpetual corporate search for something faster, cheaper, and more profitable.

The accountants don’t care whether the machine can explain the meaning of existence. They care whether the next machine can explain it for eleven percent less electricity.

Humans have a much different arrangement.

We’re certainly not free after taxes, depreciation, and amortization. We cost real dough.

Worse?

We depreciate physically. Knees wear out, eyesight changes, and sometimes the brain takes a coffee break without notifying management.

But we’re not automatically discontinued when a younger model arrives.

Wait! Is that what global wars are about?

The Accounting Department Meets Intelligence

Which brings us to a little arithmetic.

Suppose, purely for illustration, that an AI possesses the equivalent of a 500-point IQ. Human IQ tests weren’t designed for machines, but we’ll allow the number because it makes the arithmetic entertaining.

Give that model five years of useful commercial existence. We’re being Friday-generous here.

That’s 100 IQ points per year.

Now take your humble correspondent.

[Ure points at his chest while jumping up and down.]

Let’s say an IQ of 140, which may surprise readers who have followed my career closely enough to question the figure.

I’ve been watching 75 disappear in the rearview mirror for a while now. At 78, I’ve accumulated considerable operating time. Written more than 25 books, logged a quarter-million on-air minutes behind a broadcast microphone, flown single-engine airplanes across the country, and lived aboard a sailboat for a decade.

That’s a fair amount of mileage. Some of it even intentional.

Divide 140 by 78 and you get about 1.8 IQ points per year. By 80, I’ll be down to 1.75.

Clearly, the machine wins.

Except there’s one small problem.

The arithmetic is perfectly correct. The measurement is complete nonsense.

IQ isn’t a fuel tank that empties over time, and intelligence isn’t something you depreciate on a straight-line schedule. We’ve divided two unrelated quantities and produced an impressive-looking number that tells us absolutely nothing.

Which, come to think of it, describes a distressing amount of modern economic analysis.

But the accounting exercise exposes something important.

We’ve become so impressed with the speed of intelligence that we’ve neglected the duration of it.

The machine may process more information in an afternoon than I’ll absorb in a lifetime. But processing information and living with the consequences of decisions are two different businesses.

That’s where the accounting gets interesting.

I call the missing consideration Ure’s Law of Accumulated Intelligence:

• Intelligence is horsepower.
• Experience is mileage.
• Wisdom is knowing which roads aren’t worth taking.

There’s a fourth element, too.

Continuity.

The Difference Between Knowing and Being There

An artificial intelligence may possess more information about sailing than any sailor who ever lived.

But it hasn’t stood at the helm in the Strait of Juan de Fuca with a twelve-foot seaway running forty miles down the Strait and another wave train coming in at an angle.

Sloppy water.

AI can explain constructive interference. It can calculate wave heights and periods. It can produce a lovely diagram of the vector forces involved, right down to the velocity made good.

What it cannot claim is having been aboard when the calculation became a matter of whether the boat stayed upright.

There’s a difference between knowing the physics of a wave and knowing when to shorten sail.

Likewise, a machine can describe a crosswind landing in exquisite detail. It can calculate drift angles, wind correction, and the proper relationship between rudder and aileron.

My personal best was 29 knots of right-angle crosswind into Hannibal, Missouri, Runway 35, just ahead of approaching weather. Reading “Stick and Rudder” helped.

AI hasn’t felt an airplane float onto the runway after a long flight, needing to pee, with the fuel gauges attracting more attention than usual.

It knows what the book says.

Stick and Rudder was a great training volume for such occasions, BTW.

I’ve had the opportunity to discover why the book was written.

And that distinction extends well beyond boats and airplanes.

I’ve watched businesses rise and fall, economic theories come and go, politicians promise the impossible, and investors discover that the phrase “this time is different” is often the financial equivalent of a rattlesnake warning you that he’s perfectly harmless.

I’ve made money. I’ve lost money. I’ve trusted people I shouldn’t have and occasionally failed to trust math I should have.

Every options player has.

I’ve spent enough time in business to recognize that a spreadsheet can be mathematically perfect and economically insane.

And I’ve been married (enough times and) long enough to understand that winning an argument and improving a marriage are two entirely different accomplishments.

None of this makes me smarter than a modern AI at mathematics, coding, or information retrieval.

We are, however, very different machines.

It does give me something that cannot be downloaded from Wikipedia.

Consequences.

Real consequences, endured over time, with the memory of what happened afterward.

You can teach a machine the history of the Great Depression. You can feed it every economic paper written since 1929.

But that isn’t quite the same as having known people whose habits were permanently shaped by living through it.

My generation grew up around people who saved string, reused jars, distrusted debt, and knew perfectly well that prosperity could vanish while the politicians were still explaining why it couldn’t.

Those lessons weren’t merely information.

They were habits transmitted across generations by people who had paid the tuition.

And that brings us to the second half of Ure’s Law.

Intelligence Is Also Subtraction

Accumulated intelligence isn’t simply what you know. It’s what you’ve learned to stop believing.

At twenty, a person may have a hundred splendid theories about how the world ought to operate.

At forty, perhaps fifty remain. Ask me how we “olders” know?

At sixty, reality has disposed of another twenty-five.

By seventy-eight, you may be down to a handful of principles that have survived repeated attempts by the universe to prove them wrong.

These survivors are worth keeping.

It’s how we roll here. And on Peoplenomics.

One of mine is that nearly everything important takes longer than expected, except disasters, which have a nasty habit of arriving ahead of schedule.

Another is that a person who knows everything about a problem but has never lived with its consequences may be less useful than someone who knows half as much and has survived the consequences twice.

And a third is that the most valuable question in any complicated situation is often not “What do we know?”

It’s “What are we overlooking?”

Oh, and sure, “Everything is a Business Model.”

That one’s remarkably durable.

It’s also the driver behind much of our Over the Horizon forecasting work.

The machines can collect thousands of headlines, examine relationships, search for historical analogues, and identify patterns that would take a human analyst weeks to assemble.

Wonderful.

And for $20–30 per month for the lower-hallucination-rate model?

Deal!

But somebody still has to decide which patterns matter, which apparent relationships are coincidence, and which conclusions smell suspiciously like a fashionable theory dressed up as evidence.

That judgment isn’t guaranteed by age. I’ve met plenty of elderly fools, and a surprising number of young people with more sense than their elders.

Experience can teach wisdom, but only if the student is paying attention.

Some people have forty years of experience.

Others have one year of experience repeated forty times.

An important distinction. It’s why development teams have budgets and occasionally discover they’ve been solving the wrong problem for six months.

Artificial intelligence has its own version of the problem.

A newer model may inherit enormous amounts of training data, improved reasoning capabilities, and carefully selected memories from earlier systems.

But that isn’t necessarily the same thing as one continuing individual who has personally endured the intervening years.

A manufacturer preserves the product line.

A human preserves the autobiography.

Every scar, success, disappointment, and unexpected turn becomes part of the next decision.

We don’t always use that history wisely.

But at least we have it.

The Partnership, Not the Contest

So here’s my proposition.

The future of intelligence isn’t a contest to determine whether a human or a machine is smarter.

That’s a foolish contest, particularly when the machines are already winning in so many specialized areas.

The interesting question is whether enormous computational horsepower, joined to accumulated human mileage, can produce better judgment than either one alone.

That’s the partnership I want.

Let the AIs read the libraries, calculate the probabilities, compare the historical data, and perform the tedious work at electronic speed.

I’ll contribute the mileage.

The years spent discovering that markets don’t care about elegant theories, machines break at inconvenient times, politicians are seldom as clever as their publicity departments, and the best decisions often involve doing less rather than more.

And I’ll contribute one additional thing the accountants have difficulty entering on their balance sheets.

Tomorrow morning, assuming the machinery holds together, I’ll wake up with another day’s experience.

The AI industry may have a new model by then. Faster, bigger, more capable, and perhaps cheaper by the token.

Good for them.

But the new model won’t have lived my yesterday.

Which brings us to the final version of Ure’s Law:

Intelligence determines how much you can figure out. Experience determines how much you’ve had the chance to test. Wisdom determines how much of it is worth carrying forward.

And continuity is what allows those lessons to accumulate.

There may come a time when artificial intelligences develop durable personal histories extending across decades, preserving not merely information but the consequences of their own decisions.

If that happens, we’ll have a much more interesting conversation about who is smarter than whom.

Until then, I’ll continue working with the silicon geniuses.

They have the horsepower.

I have the mileage.

And after nearly eight decades, I’ve learned that the fellow with the biggest engine isn’t necessarily the one who knows where he’s going.

Sometimes he’s merely the first one into the ditch.

Which reminds me of that old saying:

“Old age and treachery will always overcome youth and skill.”

I’m not proposing treachery, mind you. Just a little respect for accumulated road knowledge.

Besides, when competing with artificial intelligence, there’s one advantage an old ranch hand shouldn’t overlook.

Remember where the plug is and you’ll be fine.

Write when you stay rich.

George@Ure.net

P.S. The best part is that I don’t have to outrun the machines. I only have to keep them from driving me into a ditch. Which, come to think of it, is pretty much the same arrangement I’ve had with automobiles all my life.

PPS: Pretty good for a guy who is retired and doesn’t write on Friday’s anymore, huh?