Category Archives: rants

Remember When The Worst We Had To Worry About Was The Patent Pirates?


Those were the good old days
Those were the good old days
The years go by, but the memory stays
And those were the good old days

Good Old Days, Weird Al Yankovic, 1988

Twenty years ago, echoing the great Dave Stephens of Procurement Central fame, we cried about the software patent pirates plundering away your hard earned revenue as they scooped up patents from failing enterprises (or enterprises not willing to enforce them) for pennies on the filing dollar, and then sued any decently sized company that was offering software that sounded like it was covered by one of the patents in their hold, threatening to bankrupt the company with an expensive lawsuit that would be dragged out endlessly if the threatened company didn’t pay a patent licensing fee for a totally bogus patent claim. It worked well, until they got greedy and went after bigger fish who fought back and made it costly for them to make bogus claims.

When that was the worst corporate theft we had to worry about, in hindsight, it really wasn’t that bad.

Considering that today the Big AI players are stealing all of your copyrighted and corporate data and using it to train their systems in the best case, then using those trained systems to output similar derivative works for their profit in the average case, and allowing shareholders and foreign governments to access it in the worst case, the patent pirates don’t sound so bad — you had to have similar software for them to even consider targeting you!

The posts on your private sites, the published articles in major publications, and the books that took you years to write are being sucked into these LLMs without a penny of royalty to you or your publisher. If you’re an artist, they’re stealing your entire catalog from Youtube, Spotify, etc. to train their music generator app to output music that sounds like you (just with worse lyrics, off tones, and no heart or soul), and if you’re a corporate enterprise — everything on your website, in your emails, and in your private meeting notes for meetings you send your AI assistants to for note-taking purposes. Once they speech-to-text those meetings, all of the output is fed into the LLM training archive for “future improvement” before it is summarized and fed back to you.

Now, if that AI platform is owned by a company based in the USA, it doesn’t matter if the instance you’re using in the EU is hosted in the EU — US law gives them the right to access all your data at any time. And if you’re an American accessing DeepSeek … all that data is passing though Chinese government servers!

So not only are they using your information without your consent and without compensation, since the majority of the big players are in the US, they are using it without any repercussions as the US Federal Government put a 10-year moratorium on AI legislation, basically allowing these companies to steal all your data without consequence for the next decade! Because, even if they say it’s “just for training”, we all know that training data leaks out of LLMs with the right prompts in the right circumstance. There is no safe “just for training” instance, so if the data is your copyright, they’re giving it away for free. And if the data is your trade secret, its a trade secret no more!

And there’s nothing we can do! Our only hope is for every major publishing house and media company that does business in every country outside of the USA and China with copyright and IP protection legislation to launch lawsuits in those countries against these global AI companies that are stealing their IP and copyright and serving it up outside the US. Force them to defend hundreds of suits across the global stage, while lobbying other governments to create stronger protections and mandate consent before using content, and penalties for violating the law equal to at least 10X what the fair market value of the content is. Since the market won’t bankrupt these companies that shouldn’t exist based on the fact they lose more every year than 99.9% of businesses generate in revenue, let the media industry and legal systems do it.

Today’s “Social” and “Professional” Sites are Bad Netizens! So What Do We Do?

Two decades ago, I asked: Are You Being a Good Netizen because odds were if you were reading Sourcing Innovation (or, at the time, e-Sourcing Form or Spend Matters or one of the other sites offering you best practice advice and free insights on a daily basis) you were a consultant, service, or software provider. And while that wasn’t a bad thing, it also wasn’t a good thing.

The reason: the people who needed to be reading these independent sites the most were the actual procurement and sourcing professionals that these sites were created to serve. People who didn’t have regular access to high-priced best practice consultants, training course, or local professional groups to learn from. People who were thrust into the Island of Misfit Toys with little or no experience. People who needed real help, especially when it came to understanding the root of their problem, what processes and knowledge were needed to address them, and how to identify the right technology, and then solution provider, to help them.

People who, once they understood what they needed, would happily invite the consultancy or solution provider for a briefing or demo once they understood what that provider had to offer — yet few, (and sometimes) if any, consultancies or solution providers would tell their customers about the great resources that would not only help the customer, but the consultancy or solution provider. They weren’t good netizens, even when it would cost them nothing and only help them in the end. (And, frankly, helping a customer understand you’re not the right provider for them and saving you months of sales cycle effort only for the customer to walk away when the light-bulb turned on is a good thing — you want to spend your time with potential customers whom your solution is right for, because, once those customers get a taste of that solution, they’ll never let it go. While it’s true that organizations never want to change tech because of the time, effort, and cost required, it’s ten times true for tech that actually works that users like — they will fight tooth-and-nail to keep it, and when it’s a department/low-enough cost solution, even if corporate mandates something new, that department or user will still renew a license on a P-Card to keep it.)

Back before the social and professional networks were a big thing, the average buyer had no source of information beyond the country’s professional organization, the highly redacted analyst and consultancy sites, and whatever Google would serve them. That was not nearly enough.

But then the social and professional sites came along, and, for a while, things started to get better. Peers could inform each other of third party and independent sites that had free knowledge for the taking. And for a while, that’s what happened.

But then things changed, especially with the two most popular and commonly used sites (and, more or less, the only two sites that remain), when their focus shifted from enabling people to connect and learn to extracting money from their users any way they could, tweaking the algorithms to only advertise paid content or content from their most popular posters and favouring the few that regurgitate the mainstream hype over the independent thinkers trying to move knowledge, or at least the conversation, forward.

They’re not good netizens, and it’s hard for people who need good content to find the content they want.

Used to be you could Google, but now that “AI summary” is injected by default, the sources, and real information gets buried.

So we’re back to the early 2000s, where the only solution is for providers to stop pushing the hype and start sharing the knowledge again. There aren’t many sites left from when SI started (with Jon W. Hansen‘s Procurement Insights now being the 2nd oldest blog), but others have arisen — but how many people know about them?

(Including those who might follow the authors on LinkedIn and maybe see every third or fifth post. We’re talking about

and others. How many people really know about these sites? The answer: Not enough.)

So, pretend it’s the early noughts and go back to sharing human to human what’s really useful and what really matters. Otherwise, you’ll just end up getting dragged down to the lowest common denominator as a result of all the derivative Gen-AI garbage posts that now clog your social and professional media feeds.

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).

AI That Makes Recommendations Makes You Dumber, NOT Smarter!

Still too many posts about how great (Gen) AI is (in the age of LLMs).

They tell you truths:

AI can process all of your spend in seconds and find anomalies.

AI can collect all of the relevant market data and find opportunities.

AI can review large amounts of text and find risks or unfairly onerous contract clauses.

AI can track your SaaS utilization and ensure you are not being overcharged.

Yada Yada Yada.

All true, all fine.

But then the blasphemy starts. (Where I’m using the word in the context of the profane for the humanity bashing that it is.)

AI is great because it can recommend the spend “opportunities” you should pursue … and even automatically generate sourcing events for you.

AI can find the lowest cost when you need to spot buy and automatically buy/cut and send the PO for you.

AI can tell you what clauses to take out, what clauses to edit, and what clauses to add and automatically suggest the edits and write the new clauses for you.

AI can automatically enable and disable user accounts/seats, compute capability, application instances, etc. and save you money.

Now, theoretically, it can do all this. But practically, when it does so, it costs you money, capability, and you humanity.

When it selects opportunities, generates an event, and selects suppliers, it does so on a cost and historical utilization basis, with vacuum forecasts and whatever specs it can find. It doesn’t look at associated logistics costs, lead times, quality levels, service costs, certifications, safety, or anything else that is critical. You teach it cost, the metrics dictate that the CFO only cares about what shows up in the P&L, and you get the lowest cost piece of cr@p on the market. No big deal until customers get so fed up they start leaving, unless, of course it was the bolt holding the axels together on the bus that regularly drives the cliffs of the local mountain range or the door on the Jet you cram hundreds of passengers into.

When it selects the lowest price, there’s no guarantee the product will arrive on time or meet all of your requirements (because you just specified the cheapest card stock, but didn’t specify white and got pretty pink; 32 GB DDR chips, but didn’t specify they were for laptop upgrades and got server RAM; specified DBA, but didn’t specify Oracle and got someone who’s only ever used SQL Server).

When you tell it to slash your SaaS and Cloud costs, it happily deletes all the C-Suite accounts because they only log in once a month, cancels your vulnerability scanning service, because that costs way too much for something that happens only monthly, and deletes your main production database (because it cost way more than the QA database). And yes, plenty of news stories where it has already done all this.

When it scans the 60 page contract behemoth from the supplier, it overlooks the clause that transfers all liability to you for their AI failures (because you deployed the product) because you trained it on contracts where you transfer all liability of use of the equipment you create to your customers (because they use the product and accept not to use it beyond your specifications). Then when the AI accuses your best supplier of submitting fraudulent invoices, automatically files a report with the bank, which in turn freezes the suppliers accounts, which blocks all automated payments, which results in their energy supply being turned off for non-payment, which brings down their production line, which costs the supplier 2 million dollars, which results in them suing you … guess who’s on the hook when the court says “you can’t say the AI is responsible”?

But that’s just the direct costs.

The indirect cost is that it’s making you stupid.

The problem is this. Most of the time,

  • the opportunities will be real, not the most significant, but real, and the recommendations will be “good enough” that an average human won’t feel it worth the effort to qualify and/or improve
  • 80% of tail is usually well-defined cookie-cutter finished products/basket services and there will be enough description in the catalog/e-Pro system for the AI to get it “good enough” that the org can make it work
  • the security settings and “manual overrides” will usually prevent exec accounts and production instances from being deleted, and its ITs job to deal with the odd glitch, so who really cares
  • the missed risk won’t materialize in 95% of contracts, and usually not in the first 6 months

So people quickly stop questioning, start trusting, and then start blindly depending on it. The systems are allowed to do whatever they want as long as they aren’t creating fires worse than what the humans are already dealing with. And even as performance degrades, requirements change, or system costs escalates, nothing is checked, and as slightly worse decisions are constantly reinforced and bad data piles up, the organization is sitting on a ticking time bomb. Which is going to go off. The only question is how much damage it is going to do.

Which you won’t be able to deal with when it does go off because you won’t have a clue what to do.

Every time you fail to question it, your cognitive skills atrophy.

Every time you fail to use a skill, your expertise dissipates.

Every time you fail to put the effort in, your problem solving stamina degrades.

Every time you fail to think about the situation, your knowledge erodes and you forget.

When they say your mind is a muscle, they’re not exaggerating. Bodybuilders don’t work out every day just to build, they do it because it’s a basic requirement to maintain what they have so their muscles don’t atrophy and their hard work dissipate.

The brain works the same way, when you don’t use it, it atrophies.

Numerous studies have shown what happens when you use AI even for the simplest of tasks. Even typing vs hand writing reduces brain power. Using it to edit reduces more. Using it to write reduces more. (15% or more — you’re effectively shaving 15 points off your IQ!) Using it for strategy gets to the point where you might as well be boarding the short bus and taking the remedial class, because in a matter of months you’ll have trouble keeping up with that!

So, the more AI you use, the dumber you get.

And that’s great?

Maybe if you’re a malevolent billionaire trying to dumb down humanity to the point that they are too stupid to question your true intentions, but otherwise …