The New Market Dilemma I: The Key to Avoiding the Worst of the Coming AI Induced Recession

I’d hoped I wouldn’t have to state the obvious, but every day we’re getting closer to doom and gloom as a result of continued over-valuation of AI companies that are losing billions of dollars a year with no plan for profitability by the end of the decade. Once they go public, a stock market crash is inevitable, and the only question is how bad will the AI crash be?. (If the trend line across the last 3 — Black Monday, Dot Com Bust, 2008 Financial Crisis — continues, the crash will be catastrophic and might trigger a simultaneous global default that ends modern civilization; but if we managed to learn anything from the past, it will just be Black Monday 2.0, which we’ll recover from in a couple of years.)

However, for the time being, the AI hype-induced economic inflation as a result of ridiculous explanations pulled out of a depth so dark that even a proctologist with a flashlight would have trouble finding the source, is getting worse by the day. It’s to the point where the only way that the current reality can be summed up is that we are living in a world of hallucinations (which is, of course, what Gen-AI is famous for).

So, when the crash happens and the fallout brings the next recession, depression, or global catastrophe, how are you going to get through it? And, more importantly, how can you prepare for it in a manner that will allow you to get through it? The answer now is almost the same as it was 20 years ago when we first discussed The Market Dilemma.

FAITH

But not faith in God, a God, Gods, what you perceive thy God or Gods to be, deities, supernatural beings, faith healers, shamans, or any other religious entity you might care to believe in. And definitely not faith in AI. (Although many of the tech bros are treating it like a God and, in the case of Christian religions, actively violating the second commandment.)

Faith in Humanity.

Twenty years ago, the answer was faith in human created systems designed to prevent, deal with, and correct problems that only work (and catch on) if people believe in them and have faith that things will get better with time. However, now that most systems are being redesigned to put AI at the core (where reliability is coin-flip), systems are not the answer — people are. People who employ good, old fashioned, Human Intelligence (HI!) while it still exists. (After all, if the majority of people in developed economies become dependent on Gen-AI, the cognitive atrophy will degrade our ability to the point that solving even simple problems will be nigh impossible.)

Systems, religion, and societies as a whole all fall apart when people lose faith. Just like the influence of a religion will decline until it eventually disappears if people stop believing in it and cease to make it part of their daily life, the strength of the system will degrade when people stop buying, selling, and participating in the system on a daily basis, and the strength of society as a whole will degrade when people stop believing in each other. Since we know a market crash is coming as a result of too much faith in the AI Hype which has led to overzealous buying, selling, evaluations, and run-ups that are unsustainable and can only lead to a crash, we need to return to our societal roots if we want to get through what is coming.

Not only do we need to return to business-as-usual pre-AI Mania, but we need to double down on Human Intelligence, Human Ingenuity, and Human Empathy. We have built, and rebuilt, societies from before recorded history without advanced tech (and definitely without BS AI — which is the Fastest Freeway to Financial Failure), and if this society is to continue, we must continue to build the foundations without unreliable probabilistic AI that has done more harm than good and use our intelligence to design the right systems and tech to allow for continued progress.

Progress that, as LEO XIV wrote in his Magnifica Humanitas, will require an updated human-centric social doctrine that safeguards humanity that focuses on truth, work, freedom, dignity, and shared responsibility. Respect for, and faith in, each other as we strive for peaceful progress and real justice. (Even though it was written by the Pope, it’s not a religious doctrine. It’s a human doctrine. The first real, significant, human doctrine since Pope Leo XIII published his Encyclical Rerum Novarum in 1891.)

This progress will come from those who don’t have, or promote, unrealistic expectations in what they can deliver, in the capability or value of the products and services their organizations offer, or their ability to deliver faster than is reasonable. Slow and steady still wins the race, and those who forget the mistakes of the past are doomed to repeat them (and run in circles). So take it day-by-day, make the best, human-led, decisions you can based on all the information available, and progress in a steadily forward fashion.

There’s a Reason that AI Talk at (Procure)Tech Events is All Talk and No Substance

A recent rant on LinkedIn noted that, at DPW (and other events), there was “Tons of talk about using AI, not so much about how to procure it, scope it, contract it, measure it, commercial agreements, which suppliers are best for which usage.”

Well, there’s a simple reason for that. And it goes as follows.

It’s the tech-du-jour. More specifically, the hype-du-jour. As a result you can forget any advice on how to:

–> Procure It

No one really knows who has what, or what it actually does, what it’s really worth, how to properly cost it, how to compare offerings and offers. So how can they tell you how to procure it?

–> Scope It

According to the hype it’s your new employee that does everything for every one, despite the fact that it hallucinates more times per second than an LSD junkie does in a lifetime. Without knowing what it does or where it goes, they can’t tell you how to scope it!

–> Contract It / Commercial Agreements

Even the vendors wrapping someone else’s model don’t really know what it does, what they can guarantee, what they can’t, or who is really liable (although courts are starting to make those decisions). So no one knows how to contract it.

–> Measure It

Whereas we have tried-and-true mathematically sound measures for traditional deep neural networks where you can get accuracy ranges with confidence ranges, when it comes to LLMs, no one has a f*ck1ng clue how to measure them. Random tests by random humans judged by random people with random definitions of accuracy is not a measure. And I’d have more confidence in a decision made by a Koala. At least it’s cute!

–> Assign It

If we can’t even measure what it does, do you think we can measure how well the vendor pushing it really understands it? Definitely NOT!

And that, in a nutshell, is why there’s a lot of hot air and nothing of actual substance in all the AI discussions. Which you should avoid anyway. Because you don’t want tech with a 6% success rate [McKinsey, MIT], which is half the general success rate of new tech installations (now that tech failure rates have reached an all time of 88% [Bain]).

You cannot export-control math!

A truly brilliant observation by Mr. Stephen Klein in a recent post on how China May Be The Only One With Mythos because they may have saved its responses (and thus figured out how to replicate it).

(Gen-) AI LLMs are just mega math models. Really big mega math models with probabilities being computed on top of probabilities being computed on top of probabilities in force-feedback loops that reinforce its computations (which could be brilliant deductions or hallucinations that equal the acid high of the most LSD addicted junkie on the planet).

And since the outputs are dependent on the equations that define the model and the training data, if someone can recreate the training data they can reverse engineer the equations from the output if they are sufficiently adept at mathematics, or at least a close proximity.

Which means that blocking off access to those who can evaluate and improve the model will not help if those you don’t want to have the model already have it.

But this isn’t a post about the blocking of AI models (because I personally think that’s great), but a post about what happens if you try to hide your capabilities behind “proprietary algorithms based on math” assuming that no on else can recreate it if you don’t talk about it and that the IP alone justifies an unreasonable price for your product or valuation for your company.

Every country has mathematical geniuses, and more than one can come up with the next iteration of a mathematical theory at about the same time. Maybe only one gets remembered (Newton vs. Leibniz), but it doesn’t mean they didn’t both invent the same concepts at about the same time (in calculus).

And the more you trump it up, the more it entices someone to tear it down. (And figure out how you built it.)

On its own, the math alone is not the advantage you think it is. There are a lot of free scientific papers with great math. In fact, more than enough to create your own Claude, DeepSeek, Gemini, Grok, etc. But most people can’t because it’s not just the equations, it’s the parameters, the training data, and the implementation.

With regards to the implementation, just because you have server racks that can do trillions of calculations per second, that doesn’t mean you can code inefficiently. For example, an average token output by Claude requires billions of operations, and an average question posed to Claude will require 1,000 to 10,000 tokens to answer, for 1 trillion to 1 quadrillion calculations for an output. A poorly designed model could require 10, 100, or 10,000 times that.

The same goes for classical machine learning or optimization algorithms. Good implementations will require billions of calculations. Bad, trillions to quadrillions to quintillions. Responses go from real-time to hours to days to the computations never end.

Then there is the training data. You can’t judge a model implementation unless you have good training sets that are representative of real-world problems. Optimizing for theoretical problems that don’t exist in the real world isn’t helpful and may, in fact, lead to a worse solution that not even testing it at all!

The real differentiator is the expertise both in the implementation of solutions based on math and deep knowledge of the domain the solution is for. That can’t be recreated by mathematical ability alone and requires experience. And that can be export controlled (and represents the real value).

What Data Do You Need For Successful Procurement?

In a comment to a recent post over on Linked in, Mr. Buckingham asked Do you think that data driven decisions are clearly the correct thing to do, but that they tend to maintain the status quo and can be restrictive to innovation?

I couldn’t leave this one alone and responded that:

“They only maintain the status quo IF the data collected and constraints created are limited to those that support the status quo … which, sadly, they usually are …

As the Sourcing Optimization Grand Master Paul Martyn recently pointed out — the real context never gets mentioned, stakeholders just add “necessary” constraints, costs, and weightings that they know will heavily favour the incumbent

And as the Sourcing Simplifier Garry Mansell regularly points out, organizations fail because they only include the best lagging indicators in board presentations to ensure they can keep doing the same old, same old

But if you instead collect [only] external data on the market, and not internal data that supports the status quo, the tendency will be to focus heavily on innovation and change.

Data is the way to go, but it has to be evenly distributed across internal and external so you can get the full picture.”

Seemed like this is something that should be elaborated on.

For example, let’s say Procurement is trying to gauge the effectiveness of their category sourcing in a key category. They could demonstrate this through internal or external metrics. Internally they could show the average price per unit decreased year-over-year by a significant percentage. Externally, they could retrieve the average market price for the primary products and show they are paying less. If they only ever use one of these metrics, and it stays good, as Mr. Buckingham notes, it maintains the status quo, even if it shouldn’t. In order to truly gauge effectiveness it has to, at the very least, measure both. Just reducing costs doesn’t mean you are getting the best price, and just beating the market doesn’t either. In some categories, market quotes / GPO rates / etc. are never the best price, which is dependent on what you actually need, who, and where, you are getting it from.

Digging deeper, if the specs are over engineered, you restrict to a suppliers in a certain geography, or only deal with incumbents, you’re not getting the best price. So you not only need to take internal and external measures and benchmarks, but ensure you are taking the right internal and external measures and benchmarks.

And then, if there are hidden costs (from increased risk, revised shipping / order lead times, quality reductions, etc.), take this into account as well. (Which is why you need strategic sourcing decision optimization with what-if scenarios, as we’ve been telling you for the past 20 years.)

Only when you identify the right data and collect the right data can you make the right decision, which might reinforce the status quo, might tell you do do something completely different, or tell you to split your bets and do both!

The reality is, you should always make data-driven decisions, but you can only do so if you are collecting the right data for your needs.