Category Archives: AI

Primary ProcureTech Concern: (Gen-)AI Integration/Impact

The non-stop hype coming straight from the A.S.S.H.O.L.E. is continuing to cause market confusion and utter chaos.

Why?

Gen-AI is on the concerns list because it’s the tech-du-jour. Five years ago it was (advanced) (predictive) analytics. Ten years ago it was the fluffy magic cloud. Fifteen years ago it was SaaS. Twenty years ago it was the World Wide Web. And so on.

But not one of these technologies, all sold as the panacea that would solve all your woes, solved your problems because all of the promised capabilities were just silicon snake oil, and Gen-AI is no different. The hype cycle may be slowly coming to an end, but it will quickly be replaced by Some-BS-World-Model-Adjacent-Agentic-AGI that will be sold as the AI that finally solves all your problems but, in reality, still won’t be anything close (but, if narrowly applied in the right domains where the client has sufficient data might actually work quite well … but won’t do anything reliably in general and the failure rate will still be 80%+, which is the average tech failure rate for the last 25 years … and SI knows, because the doctor has been following tech failure for over 25 years).

Not only is Gen-AI no different than the previously over-hyped tech-du-jour offerings of the last two decades, but with a failure rate of 94%+ (McKinsey, and 95%, MIT), it’s arguably the worst yet! And, as per our predictions, it’s not going to get much better. If the failure rate gets as low as 90% this year, it will be the closest thing to a tech miracle that we can conceivably get. Like every other tech before, Gen-AI will only solve a relatively small set of problems.

Just like

  • The Web only solves remote connectivity
  • SaaS only allows solutions to be built in the cloud
  • Analytics only provides insight where you have the right, sufficient, data and the right algorithms to get useful insights
  • Gen-AI is just a next-gen probabilistic deep neural net that often does
    • better semantic processing
    • better search
    • better summarization
    • better potential pattern identification (but only if you can learn how to prompt it to do so and only if you have it trained on the right data subsets, not the entire web which is now more than half AI slop)

    but does so at the additional expense of

    • hallucinations
    • intentional falsehoods
    • thoughtless reinforcement
    • cognitive atrophy
    • etc. etc. etc.

As a result of this, as far as I’m concerned, the AI bubble can’t burst fast enough! It’s all hype, buzzwords, and hallucinatory bullcr@p. And, frankly, any (claims of) agentic AI built on it are fraudulent. (After all, we’ve already seen what happens when you let AI run your vending machine. The last thing you want is it buying for you!)

Especially when, on top of hallucinations, we have plenty of examples of:

We’ve said many times that LLMs are not helpful and ChatGPT (in particular) is not your friend, that if you have a headache you definitely shouldn’t take an aspirin or query an LLM, and that, frankly, you’d be better off with a drunken plagiarist intern because that’s the best case result from an LLM. Most are worse.

Frankly, it’s time to stop falling for the artificial intimidation, fight back against AI Slop, and remember cutting edge tech is NOT defined by the C-Suite or the incessant marketing from the A.S.S.H.O.L.E. that is targeting the C-Suite on a daily basis!

Impact Potential

Huge! Companies will continue to waste millions individually and collectively hundreds of billions on the next generation tech that, with a probability of 90%+, will generate a (huge) loss.

Major Challenges/Risks

The major challenge is not with the tech, it’s helping companies realize that they’re probably not ready for the tech. The reason that tech failure rate has averaged 80%+ over the last twenty years is that consultancies keep promoting, vendors keep selling, and companies keep buying advanced leading edge tech they are not ready for. The reality is that unless you are in the top 10% of buyers of tech, already on the latest tech, and have sufficiently mastered that tech, you are not ready for Gen-AI (which should not have left the research lab when it did and, in all honesty, should still be in the research lab since it still only works in a small number of well defined scenarios and is so bad that every year a couple of AI founders turn away from AI because of it — with Yann Lecun walking away from Meta and LLMs and reverting to world models, that can be thought of as next generation (Semantic) Web 3.0 models augmented with [deterministic and dependable] automated reasoning and, hopefully, very little dependence on hallucinatory probabilistic models [beyond what’s needed to semantically parse an input].)

The only place you should be using Gen-AI is where a non Gen-AI solution doesn’t exist, the task is well defined, and you can build a custom in-house model that works reasonably well in the majority of situations and that can be implemented with guard-rails. But that’s something you can only do if you have a high TQ (Technical Quotient) and have mastered last generation tech. Right now, you should be tripling down on E-MDMA and Advanced Analytics as this tech has improved to the point where it can allow you to optimize processes, spending, schedules, and anything else you can think of with high accuracy and low cost with basic analytics skills as so much comes pre-packaged and the visualizations and drill-downs are much more intuitive than they were a decade ago. Plus, these firms have figured out how to use multiple forms of AI to classify your data with high accuracy and minimize the work required by you to fix errors and reclassify to your preferred schemas. It’s literally drag and drop as compared to the complex rule-building that used to be required. In addition, you should be looking for the mature A-RPA (Advanced Robotic Process Automation) solutions that are highly customizeable and capable of “self-learning” such that the parameters that trigger exceptions will adjust over time based upon user acceptance or rejection of recommended actions and the platform will automatically encode new processing rules based upon the users’ actions on an exception. Much better than Artificial Iiocy that decides everything based on hallucinations.

THE FINAL WORD

If you haven’t mastered all of the tech that existed before Gen-AI, including classical machine learning AI that has been studied, optimized, and proven to work for over a decade, you’re not ready for Gen-AI, should treat it like the drug it is (as it does more damage to your cognitive abilities than many illegal drugs), and JUST SAY NO!

There is NO Infinite Compression – The Latest DeepSeek Paper is BullCr@p!

Every decade or so, some idiots who never studied Huffman coding or Information Theory believe they have cracked the problem of infinite compression, and this linked paper is just the latest example of this lunacy. I really hope this was a joke paper authored by AI because it’s all bullcr@p!

On average, a text token in a LLM should require 20 bits or less (as 17 bits support a 129,000 word vocabulary) while a vision token can be 16,384 bits (based on 1024 dimensional continuous vectors) — because it takes a lot of bits to represent pixelation of a square in a 2-D image! This says you can store about 820 text tokens in the same space it takes to store one vision token. Or, you can store the entire text (lossless) in 48K, versus the 4M it would take to store the 250 vision tokens (using very lossy compression) that are required in the paper. Looks like a LOT of people can’t do basic math if this is being praised as revolutionary!

Moreover, the raw text, which maintains the full context if the tokens are kept in order, is not only fully lossless, but can be compressed using a modified Lempel-Ziv algorithm to take up an average of less than 2 bits per character (and achieve up to an 80% compression rate). Given that the average length of a word in average text is 5 characters, and a space is one character, 2500 words would be 15,000 characters, storable in 30,000 bits or a mere 4K! In other words, this paper is trying to pass off a ONE THOUSAND FOLD increase in space requirements as space saving! Pure lunacy!

In other words, if someone is claiming something too good to be true, it is! Don’t fall for it or the sure to follow claims that DeepSeek OCR is revolutionary because of this. (Since every document is different, you can’t imagine the true loss with a 90% vision token reduction!)

CEOs are hugely expensive. Why not automate them?

As per Will Dunn, as published on The New Statesman

Especially when hiring a CEO who doesn’t understand what makes the business profitable loses Billions:

Starbucks Loses 30 Billion

and doesn’t understand what is critical to the company product to the point costs can never be cut no matter how high those costs may look on the spreadsheet because the net result is not only product failure, but grounding/banning of your product and expensive lawsuits that costs Billions:

Boeing lost 11.8 Billion in 2024

After all, if we’re hiring CEOs without any relevant experience, actual business intelligence, or even logic, then why not use Artificial Idiocy? It’s not like the occasional hallucinations will be any worse that an average CEO’s these days (who believes investing Billions on empty promises is a good idea) … and the actual compute costs, even if in the six figures, will still be a tenth (or [much {much}] less) of what a CEO salary and benefit package actually costs!

So if you insist on creating fictional “AI Employees”, why not kick off 2026 by starting with a job that, sadly, Gen-AI agents can actually do?

Here’s why you DO NOT want Agentic Buying and you DEFINITELY DO NOT want AI Employees

buying for you!

An AI Vending Machine lost hundreds of dollars!

Just imagine what AI is gonna lose on your multi-million dollar categories?!

And when you demand a certain savings that’s unachievable, it’s going to find a loss that equals the savings amount, multiply it by -1, and tell you that’s the savings.

< Stanford, Anthropic, Redwood, Meta, etc. studies on negotiation games, competitive scenarios, and goal-seeking behaviours, etc. >

So unless you’re looking to LOSE money …

Stick with classic automation and point-based AI where the automation runs everything for you, does all the verifications and data checks that can be automated, does all the standard analysis for raking and recommendations, and gets rid of 90%+ of the tactical time-consuming work, freeing you up for the manual review, safety checks, and strategic decisions where you, as a human, can check and find obvious supplier misunderstandings, frauds, and bad decisions for the long term because the system does the grunt work and pre-does all the standard analytics, freeing up 80% of your time to do more sourcing, more relationship management (to prevent problems and loss), and more decision making (when it’s hard to make the right decisions on numbers alone or its impossible to satisfy all the goals and choices must be made).

Who’s Funding Your ProcureTech Vendor?

This question is more important now than ever! Not only is the RCD (Relative Corporate Debt) of many FinTech companies too high right now (See: Calculating RCD), signalling a decline in customer service and potential abandonment, if not outright vendor failure down the road, but the ongoing viability of many VC and PE firms, or at least their ability to support their investments, is also in question.

Many firms are too heavy on AI plays that are still losing as much as $4 (or more) for every $1 of revenue they take in, requiring massive ongoing investments to maintain. Even big PE funds only have so much cash to burn, and the only way they can do this is to liquidate assets and holdings if they can, or, in the worst case, simply write off losses (and associated future costs) of those holdings they can’t liquidate.

Softbank’s end-of-year investment in OpenAI really puts this into perspective, as chronicled by Mr. Klein of Curiouser.AI and Berkley in this LinkedIn post.

As far as I am concerned, this is bad news for any of SoftBank’s FinTech holdings that may require funding in the next few years, and a warning to make sure you don’t select / continue / depend on any of their FinTech holdings where they have a large or majority stake until verifying those holdings are profitable and likely to stay that way! (Now, SoftBank has traditionally had very good investment chops, so it’s likely the majority of holdings are profitable …)

However, they aren’t the only firm making huge over-investments in AI and weighting the portfolio down with companies that might never see a profit. This means that this warning also applies to many other Tech investment funds, starting with Thrive, Dragoneer, Altimeter, and Coatue who also have large stakes in OpenAI. They could all end up in the position where they are going to have to sell off / dump assets to maintain the ridiculous losses OpenAI is seeing, and any holdings not performing well will likely be the first to go / get dropped. (Remember that the average age of the first three of these groups is 15 years, and they are [becoming] modern SaaS/AI heavy, whereas Softbank Capital has been investing for 30 years, and is a lot more diversified. Softbank may be able to weather a complete crash in OpenAI valuation if it occurs. But these other firms may not!)

But, as we noted, the real warning is not for SoftBank or these other mega funds (in the significant 8 and 9 digit range) that have funds to weather a storm. It is for the smaller funds, especially those less than 1 Billion, that are too AI heavy.

As a result, when selecting any FinTech platform, you need look at the portfolio of any investment player with a substantial majority stake. If a large segment of the portfolio of a significant/majority investor is “AI” companies losing money hand over fist, then the vendor of that FinTech platform cannot be considered a stable vendor if it is not profitable. This is because you can’t count on the fund having the resources to support the vendor to profitability, even if vendor is a fund darling. This is the case even if the RCD calculation looks good! A lot of the smaller funds can’t afford an AI crash given the AI-heavy focus of their SaaS portfolio.

(Face it. An AI crash is coming. Too much valuation against too little return, and investors only have so much patience. The only thing we don’t know is how severe the crash is going to end up being. Is it going to be a minor drop across the tech markets or a major crash like the 2008 housing crash or the 1999/2000 dot com crash?)