Category Archives: Market Intelligence

America: Please Get a Plan and Sign Your Trade Deals! FAST!

the doctor stopped reading the daily tariff news about a month ago, because it was too depressing. (Especially since he had already told you that, since you didn’t start preparing years ago, your only real solution was BTCHaaS.) But now it’s unavoidable with the 90 days expiring, few deals done, and “letters” supposed to replace deals. Moreover, the news hasn’t improved any since the rumours in May that the Big Three Automakers were going to scale back and shift global production outside the US. (EEEK!)

These trade wars aren’t helping America. They’re hurting America. Every day more and more American small businesses close their doors. Every day an average lower class or working class American pays more and more taxes on basic necessities that cannot be sourced from within America’s borders. And every time an American Government representative attacks Canada with false claims of hostility, 400% tariffs on US imports, huge trade deficits (which don’t exist, as per yesterday’s post), and so on, more and more Canadians go elbows up and forget about the pain an average American is experiencing and how important it is for Canada and the USA to work together to combat global threats and maintain a strong North America.

Anyway, back to the point, you need to get a plan and sign your trade deals fast because if

  • small businesses continue to fail,
  • the 12% lower class and 31% blue collar working class have to continue to pay 10% to 30% more on food and necessities they need just to survive, then your poverty rate (which is already 11% and quite high for the richest country in the world) is going to explode, and
  • trade partners continue to look elsewhere to trade their products and services

then America is losing out!

It’s important to remember that there are two, and only two, good reasons for tariffs:

  1. Tax Rates in a Consumption-Based Tax Regime. (America, like Canada and most first world countries are Income-Based Tax Regimes.)
  2. Protection of core/critical industries by ensuring third parties can’t dump massive amount of cheaper (and usually inferior) products and services into your country and damage your industries.

In other words, in America, and Canada,

  1. there should ONLY be significant tariffs for products and services that the country is capable of meeting it’s total domestic need for,
  2. there should ONLY be moderate tariffs for products and services where the country is close to, but not yet capable of meeting, the domestic need (so that the remaining need can be met, but outside products and services will only be chosen to meet the gaps)
  3. there should ONLY be low tariffs for products and services that the country can not (come close to) meet(ing) the domestic need for, but where the government has to ensure safety, quality, compliance with laws etc. (e.g. outside food needs to be regularly inspected by the FDA, for example)
  4. there should be essentially no tariffs (beyond minimal inspection/processing fees) for products/services the country cannot produce domestically

Anything else hurts the populace. Also, since American economists didn’t do the math, a Canadian economist did. And the outlook for (sustained) tariffs above 10% is NOT Good! See this article. Or, if you don’t like economics and math, note that it more-or-less reinforces what the doctor said above. Low tariffs (on the majority of products and services) are actually good. They reduce trade deficits (presumably by discouraging dumping) and encourage real GDP growth (as current factories have the chance to maximize production and local markets with some protection), but only to a point! Somewhere between a 5% and 10% tariff rate, any and all benefits from tariffs cease.

So get those deals, and get the tariffs down to the right rate for the category of good or service (and country of origin) in question. Next to nothing for basic foods (like mangos) you don’t produce locally. The 5% to 10% range for raw materials (like aluminum and steel) you can produce of lot of domestically, but not totally meet your need for. 10% for industries that are strong and you need to protect (and grow). But please remember that you can’t build a new factory overnight, and in most modern manufacturing industries, and hi-tech electronics in particular, it takes 5 to 10 years to build and get a factory up and running. In the interim, you have to buy those products elsewhere.

In other words, you need a detailed plan, not just broad goals, reactionary policies, or a belief that if you will it hard enough, it will happen. Just because you want to play baseball, that doesn’t mean the world does. And, unfortunately, the nature of trade is you have to work with your partners (while, and this is key, making sure they work with you — don’t just get agreements for reciprocal trade, encode penalties into those agreements where if they don’t increase their purchasing, the tariff will go up every time the trade deficit fails to decrease by a pre-determined amount. Remember that some countries, like China, like to make broad promises, like they did in your President’s first time, but then fail to follow through).

The last thing Canada wants to see is this come crashing down, which would result in millions of layoffs (outside the tech industry), big manufacturers relocating production to the global market outside of the US, or global partners dumping American holdings or the American dollar as the default currency. It’s important to look at history and remember that while America was globally one of the richest countries the last time tariffs were high in the Gilded Age, the average American was quite poor. Furthermore, the short-lived Progressive Era that followed ended in the Great Depression, and that’s something we never want to see again! Short term trade wars can be a good thing if it leads to a re-stabilization of a drifting global economy, but long term trade wars aren’t good for anyone — and the country that started it in particular.

So please, get your deals, establish a new operating norm, and let everyone get back to work. Thank you!

Apple Demonstrates AI Collapse

Not long ago, Apple released the results of its study of Large Reasoning Models (LRMs) that found that this form of AI faced a “complete accuracy collapse” when presented with highly complex models. See the summary in The Guardian.

We want to bring your attention to the following key statement:

Standard AI models outperformed LRMs in low-complexity tasks while both types of model suffered “complete collapse” with high-complexity tasks.

This point needs to be made crystal clear! As we keep saying, LLMs WERE NOT ready for prime time when they were released (they should never have escaped the basement lab) and they ARE NOT ready for the tasks they are being sold for. Basic reasoning would thus dictate that LRMS, built on this technology, are definitely not ready either. And this study proves it!

It’s always taken us about two decades to get to the point where we have enough understanding of a new type of AI technology, enough experience, enough data, and enough confidence to understand where it is not only commercially viable BUT commercially dependable. And then we need to figure out how to train the appropriate (experts) users on how to spot any false positives, false negatives, and improve the technology as needed.

Just like nine (9) women can’t have a baby in 1 month, billions of dollars can’t speed this up. Like
wisdom, it takes time to develop. Typically, decades!

Moreover, while not saying it, the study is implying a key point that no one is getting: “our models of intelligence are fundamentally wrong“. First of all, we still don’t fully understand how the brain works. Secondly, if you map the compute of any XNN model we’ve devised and map the compute of a human brain in response to a question task, completely different subsets light up, and those will change as tasks become more complex or you’ll see some back and forth. We can understand data, meta-data, meta-meta-data and thus chaos. We can use clues that computers don’t, and can’t, know exist to know context and which of the 7 possible meanings of a word is the intended one. We can learn on shallow data. In contrast, these models stole ALL the data on the internet and still tell us to eat rocks!

This means what this site keep leaning towards — if you want “autonomous agents“, go back to the rules-based RPA we have today, use classic AI tech that works for discrete tasks we understand, link or “orchestrate” them together for more complex tasks, and, if you really think natural language makes software easier and faster to use (for most complex tasks, it doesn’t, but we’ve also reached the point where no one can do design engineering any more it seems), then use LLMs for one of the two things they are good for — faster, usually more accurate, semantic input processing and then system translation of output to natural language — instead of pouring billions upon billions into fundamentally flawed tech to try and fix problems from hallucinations that result from fundamental attributes that can’t be trained out, as this is an utter waste of time, money and resources.

Vendors Have Lured Big Analyst Firms Astray Because Buyers Don’t Understand They Get What They Pay For!

About the same time we asked Why Aren’t ProcureTech Analysts Doing Their Jobs Anymore, THE REVELATOR asked, in a comment stream, how did … the analyst consulting and ProcureTech solution providers lose their way by championing technology-led, equation-based modelling?”.

Which is a fair question as this ties into why we believe many ProcureTech analysts aren’t doing their job anymore. As per our previous post, we believe the firm is the problem (even if the firm doesn’t know it, but in most cases, the firm should), and, more specifically, the primary reason is bad direction.

But let’s get back to THE REVELATOR‘s question. The answer is this:

At one point, the successors to the founders and/or the sales team took the easy way out and switched to vendor sponsorship.

As us grey beards, who have been around since the beginning of ProcureTech, will recall, there was a time buyers paid for research because they understood the value of unbiased research. But, like Project Assurance, that’s a hard sell when a buyer might spend 10K, 50K, or 100K with no guarantee they’ll identify a single viable solution among those covered in a report. Seasoned, well educated, and thoroughly experienced executives will understand the value of risking 10K to 100K on a report or study before committing to a 100K or 1M+ annual investment, because losing 10K is much better than losing 100K or 1M, and can be chalked up as a cost to doing business. But those executives who are uneducated in management and risk and inexperienced, which are many of today’s executives who were put in place because of their affiliation with investors, or a perceived ability to run a business off of balance sheets alone (even though these MBAs are the reason so many high tech companies are struggling and companies like Boeing are facing disaster after disaster — they don’t realize that you can’t run a business you don’t understand and that’s why, in the first Industrial Revolution [and the Gilded Age the US is so desperately trying to bring back], Engineers ran the show, and not over-glorified accountants and lawyers), don’t understand that or the risk of using vendor funded reports to make a decision.

For these successor and sub-par sales people who just weren’t up to the task of the hard sell, when marketing organizations come along and, out of the blue, threw big money at them to sponsor a study, no sales effort required, they jumped on it. More vendors see the success of the first vendors to adopt this approach, follow suit, the money starts flowing in, and the model shifts. Unbiased researchers have to shift their studies to those aspects where the sponsors do well or leave the firm. Moreover, the search for new hires focus on those with less experience or ethics (who can be easily swayed in the direction the big sponsors want). (So before accepting the results of any study, you should be echoing Mr. Klein and asking Who Paid For That Study?)

This means that, over time, instead of an industry leading analyst firm we get a marketing organization that echoes the “technology-led” approach or puts the product, vs. the solution, first.

Moreover, it’s going to stay this way until some big firms step up and say “enough is enough” and stop vendor sponsorships all together and some big clients step up to fund the research. As Mr. Köse keeps saying, you get what you pay for.

Sourcing Innovation stands by it’s statement that the USA is …

… math stupid that it made in it’s post explaining why the lack of adoption of analytics is NOT complicated. the doctor knows it ruffled a few feathers, but it’s not the doctor claiming that, it’s the OECD data (which is available here).

At least the doctor didn’t point out in that post that the USA is effectively failing across the board as it is below average in literacy, numeracy, and adaptive problem solving (and significantly below average in numeracy, as we pointed out in our last article), that there should be no reason for this when the USA is seventh in the world in nominal GDP per capita (beaten only by Iceland*, Singapore, Norway, Switzerland, Ireland, and Luxembourg*, where the * countries are not in the OECD rankings), and that the USA could afford to have the best educated people in the world if it desired (and it could allocate the budget if it desired, considering the percentage it spends on defence is more than twice the global average, and that’s before all of the foreign military aid).

However, he feels it is now very important that he does point this out because too many Americans are heralding the budget cuts to the Federal Department of Education (on the basis that funding should be tied to performance, which is a justifiable goal, but the best way to do that needs to be carefully considered) without a plan instead of insisting that it be restructured to address the serious educational deficiencies or replaced with more state level agencies (where funding is tied to specific focal points and not allowed to be disbursed on whims).

To nail these points home, here is the relevant data:

Literacy

Country Rank Score
Finland 1st 296
Canada 10th 271
Czechia 14th 260
AVERAGE 260
USA 16th 258

(which is a 12 point drop for the USA since the last OECD ranking!)

Numeracy

Country Rank Score
Finland 1st 294
Canada 12th 271
AVERAGE 263
Croatia 21st 254
USA 25th 249

(which is a 7 point drop for the USA since the last OECD ranking)

Adaptive Problem Solving

Country Rank Score
Finland 1st 276
Canada 10th 259
AVERAGE 250
Slovak Republic 19th 247
USA 19th 247

I’m old enough to remember when the US education system was the envy of the world (even though the US has scored in the lower half, and sometimes the bottom, of the FIMS, FISS, and the IEA — which measured the global performance of the primary and secondary education systems across 12 to 20 countries back in the 1960s through 1980s), because, post Sputnik, the US poured money into public education in an attempt to produce the best students in the world to enter post-secondary STEM programs and become the best engineers in the world … and its Universities took prominence as the Universities you wanted to be admitted to (bypassing centuries old Universities in the UK and Europe in popularity).

Now it’s true that the US should have improved substantially based on this investment (which means that there are fundamental issues that have never been addressed), but just saying “it doesn’t work” and attempting to tear it down without a plan to put something better in place is not only unhelpful but sends a message to the world that the US no longer values having the best education system. I’m afraid this will have ripple effects on the popularity of US institutions, which rely a lot on full tuition foreign students to maintain their top-tier quality programs, and lead to further degradation in adult literacy, numeracy, and problem solving skills (which are now barely on par with countries North Americans grew up believing, partially thanks to propaganda, to be significantly below us).

For those of you who not only want your American-based companies to continue to be the best in the world, but also want America to attract global headquarters (or at least regional headquarters) of more multi-nationals, the sincere hope is that you will fix this. In this increasingly unstable global economy (thanks to natural and man-made disasters), the winners will be those with the best educated people who have the skills to use the best tools at their disposal to make the best decisions fast enough to survive. As a result, companies that want to weather the storms should now be more inclined to choose the Nordics, Japan, or Canada (which top the adaptive problem solving list with high literacy and numeracy scores, and don’t have the energy issues Germany is dealing with or the lack of local population that Estonia is dealing with). Now, while that last option is good for the doctor, let’s face it, for the past eigthy years, the market dynamics worked best when the biggest companies were in America and, through mutual trade agreements (NAFTA or USCMA), Canada supported.*

* Although it must be admitted that maybe the time of American dominance with Canadian and Mexican support has, unfortunately, come to an end. Especially since Canada is still “Open” on the Civicus Human Rights Watchlist and not one of the two countries that recently had their score narrowed significantly in the March 2025 update. While research needs to be done on the subject, when you consider that 17 of the top 31 countries are “open” and 11 are “narrowed” in terms of human rights and civic freedoms on the Civicus rating scale, there does seem to be a high correlation between civic freedom and average educational level as only 2 countries are “obstructed” and only 1 country is “repressed”. And while the repressed country of Singapore comes in high at #13 if you take the average across the 3 scores, the two “obstructed” countries come in low at 22 and 26 respectively.)

Gen-AI is Bad for Consulting Firms … But Even Worse For You When the Consulting Firms Blindly Use It!

A recent post on LinkedIn noted how there’s a wave of AI products flooding the consultancy and advisory space and how they are, frankly mediocre, overpriced wrappers on public models with minimum innovation, if any.

This is sad, but true, and it’s not the worst of it. The worst of it is that some of the Big X firms are training tens of thousands of consultants and f6ckw@ds on these tools to generate hundred page pitch decks and three hundred page strategy and implementation guides of standard generic, meaningless, drivel to deliver to you as “highly tailored guidance and expertise from their leading partners with 20 years experience delivering high-value projects” and charge you tens of thousands of dollars for the privilege.

This is especially egregious when you can use free/cheap (and I’m talking put it on your personal credit card cheap because you won’t notice the fee that is less than your monthly coffee charge from the coffee shop) to build the exact same pitches, strategy, and implementation guides from the thousands of freely available documents on the web in a few hours with a few generic prompts over a Sunday morning coffee. (And then, when the coffee kicks in, realize it’s all a load of cr@p and put in the bit bucket, but at least you will know what a load of cr@p looks like in pitch deck, strategy guide, and implementation plan form and will recognize it the next time an overpriced Big X tries to sell it to you for a ridiculous price tag and will have learned something from the exercise.)

Now that there are companies selling overpriced “custom” products to these consultancies, the situation is only getting worse, especially when the “customization” is just a wrapper with some pre-engineered prompts that aren’t well tested, only work at a point in time, don’t really give the consultancies what they need, and sometimes translate mediocre inputs to inputs that are even worse. Moreover, when you consider the price is sometimes a 100X multiple on the products they build on top of, it’s disgusting. Consultancies are paying more for less, and, in return, you are paying even more for even less!

Which makes no sense when the current publicly available LLM tech is being offered cheap (to try and hook you on it, even though, as we’ve repeatedly explained, the tech is not ready for prime time and will never deliver more than a fraction of what they are promising), and new implementations will get a lot cheaper. Just look at how DeepSeek undercuts the cost by a factor of 100 and gets 90% of ChatGPT (as long as you don’t mind exposing all of your secrets to the CCP). LLMs are nothing more than a fancy next-gen “deep learning” Neural Networks that construct responses vs. serving up canned responses (which is why hallucinations and lies are a core function, not an error that can be trained out) which gets us closer (but no cigar) to decent natural language processing (NLP) for the express purpose of the generation of desired outputs from inputs, but not there (and now, in addition to all the false positives and false negatives, we had to deal with, we now get to deal with hallucinations and lies as well). It’s not secret magic, it’s layers and layers of interconnected statistics and probabilities that no human can understand, in rather standard models that any Theoretical CS and Applied Math PhDs can build, and implementations that are better and cheaper are going to keep appearing as time goes on.

This means three things to any consultancy thinking about using these custom “AI” solutions

  • you still have to be even more tech savvy to use them to any degree of effectiveness
  • it’s not “the art of the prompt“, it’s the art of the training (even though they don’t really learn because they are NOT intelligent) because that determines the maximum level of effectiveness you will ever reach with them (and you need to provide them with sufficient correct data, which needs to be in the high gigabytes at a minimum, and, preferably, in the petabytes)
  • you don’t have to worry about when they are right (enough), which will happen between 90% and 95% of the time with proper training and proper prompting, or when they are obviously wrong, which will happen a very low percentage of the time (say 5% to 9%), but when they are oh so wrong but the response is constructed in a way that is oh so convincing that an above average person in intellect and experience wouldn’t know otherwise (that danger zone between obviously wrong and good enough that is likely only 1% to 2% of the time).

Now remember that your consultants aren’t that tech savvy, and you should know right off the bat incorporating and using these is going to be difficult and time consuming. (There’s a reason we are constantly advising you to be very careful about using Big X for tech selection and tech projects, and that’s because, even though they say it is, it’s NOT their forte. They weren’t built on tech, and they don’t have the best talent in tech — that talent goes to the big tech companies who can offer the 500K salaries to leading devs or the wild-west startups that leading devs think are cool.)

You only have so much clean and complete data you can use for training. You can’t just throw in the 1000s of decks you’ve built as you can’t share work you’ve explicitly created and sold to past clients, and the AI won’t anonymize the decks and suggestions (even though you think it will). It won’t know that “Ford” is the name of your client and might think that “Ford Data” is another term for shallow data and copy sections from that custom strategy straight into your pitch deck for General Motors (and chances are your overworked junior consultant won’t catch it when skimming that 200 page deck with only 2 hours to go before the meeting). And we know what happens then … (and it ends with the consultancy not keeping either client).

It will take a lot of analysis to identify those 1% to 2% of cases where it is very, very wrong but so convincingly right that you will miss some. What happens when you do and give your client advice that explodes in their faces? (We’ll let you answer that one.)

And for you as a consumer, if your consultancy is using this Bogus AI tech, it means that:

  • the situation that results from solution delivered might be even worse than the situation you started with (as should be evidenced not just by the tech project failure rate that is approaching 92% but the fact that 42% of projects are being abandoned during implementation!)

A solution designed by Gen-AI is not a solution. A real solution is a solution designed by human intelligence that uses real, augmented intelligence, to research and validate that solution. Remember that if you are going to hire a consultant!