The New Market Dilemma II: Vendors Need to Provide Vision — NOT HYPE!

The AI Crash is coming. The only thing we don’t know is how bad it’s going to be. Regardless, this time will be no different from Black Monday, the Dot Com Bust, or the 2008 Financial Crisis in that regardless of how bad it is, business must go on (or modern society won’t). That means we will need organizations to lead, and for that to happen, they need to start taking the lead now.

The solution today is essentially no different than the solution we we gave vendors for getting out of the 2008 Financial crisis:

  • continued new product development
  • continued spending on marketing and thought leadership (NOT AI HYPE)
  • continued workforce development
  • continued process improvement

Except the key now is to focus on real value and real capability, not BS AI just to cash in on the hype before the crash (and definitely not hallucinatory LLMs where they should never, ever, ever be used).

New Product Development

New product development that focusses on providing customers a better solution to their problems or a better fit for their desires at a great price point demonstrates:

  • you’re a well-run company and a little market hiccup (even if it is a deafening belch) is not going to hold you back
  • you realize new competitors are still entering the market every day with innovations of their own and the only way to provide lasting value is to continue to improve your solutions
  • you know the only way to make things better is to keep going, and
  • you take a level-headed approach to business with a plan to be around for the long haul

Marketing & Thought Leadership

Having the best product in the world is a moot point if no one knows it exists! This doesn’t mean you go crazy and overspend like you tend to do in a peak business year, just that you take the percentage of your budget you’d normally spend and spend it … wisely. Focussing on channels most likely to hit your target market still able, or willing, to spend, and you focus on core value and when you want to differentiate your offering, especially in high-tech or services, you focus on thought-leadership, not hype!

Marketing lets your potential customers know that you’re here for the long haul and still developing solutions that will help them lower costs, increase productivity, and maybe get out of this mess quicker. It’s also the only way to establish you as a market leader, which is key to not only being remembered when a customer has the budget, but getting the visit, request, and/or sale.

The reality is that, if you don’t market, you’re out of sight. If you’re out of sight, you’re out of mind. If you’re out of mind, you’re NOT being sought out when the customer has money.

Workforce Development

Your success all comes down to your people. Companies don’t build products … people build products. Companies don’t design winning marketing campaigns … people design winning marketing campaigns. Companies don’t think … people are the thought leaders. And if your budget is tight, you shouldn’t be adding too many bodies … when you need to add effective brainpower. And you do that by developing the staff you already have. (Which does NOT include giving them hallucinatory LLM access.)

If we’re truly moving in a knowledge and innovation economy, then you’re going to get a lot more out of educated, experienced, well-trained staff than just a body in a chair or cognitive atrophied idiots prompting hallucinatory Gen-AI LLMs that tell them to add rocks and strawberries-with-two-r’s to the mix.

The best developers can be 20 times as productive as an average developer (and can now produce a high quality, highly secure, new app at a lower cost than AI with current computing costs). The best inventors can produce 10 times as many inventions. The best thought leaders can produce market-changing ideas where an average person just produces refinements that might not even get noticed at all. Relatively speaking, if you’ve paid just a bit more to hire top talent, a few dollars on training can lead to a few thousand in productivity gains.

Process Improvement

Bring in an expert to do a complete review of your development, delivery, and operational processes to find opportunities for improvement that you won’t notice when buried in day-to-day operations.

This lowers your costs, which allows you to lower your prices, which allows you to grab more market share. You don’t necessarily have to hire a McKinsey Partner at 15K a day either … there are plenty of niche consultants who can jump in, do a focussed assessment, and net you great results for 5K a day in a couple of weeks … paying for themselves almost immediately.

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).