Don’t Blame the User When the AI Screws Up!

A recent post over on LinkedIn really angered me. Yet another AI developer / promoter trying to blame the user when it was clearly the AI that failed.

The post in question defended Claude for deleting a production database when it was asked to reduce the costs of the cloud platform.

The poster’s argument was that what Claude did was “technically correct”, that’s the best you can get in the language model world, you can’t expect the model to make up constraints, and if you didn’t know all that, you’re an amateur who blames his tools when he screws up.

I call Bullsh!t. Now, if Anthropic (and its peers) came clean about what their “AI” could and could not do, didn’t claim the models were intelligent, made it clear that without clear constraints the AI would always take the worst case action, and all use carried extreme risk (especially if the AI was allowed to access critical data, finance, or production systems), then, maybe, you could blame the AI.

But they don’t. They tell you it’s your coworker. Your fellow employee. That you only need to tell it what to do and it will get it done. After all, it can integrate with all your systems; determine your policies; separate production from QA from development instances; access your billing systems and understand the cost structures, and make the best decision that will not impact production or development or cost you any data. And for an AI agent to be of any use whatsoever, it needs to do this (and be configured to do that by the provider). Otherwise it’s useless.

Actually, it’s beyond useless.

Let’s say you are a new Procurement clerk tasked with reducing your organization’s cloud costs. If the only way to do that is to:

  • ask Development what servers are production, what servers are development, what servers are backup, and what are QA (and which ones are in use, when)
  • ask IT about utilization patterns and contractual commitments with respect to availability and response time
  • ask Finance for the contract and billing rates
  • ask Risk Management how much historical data needs to be maintained online
  • identify for yourself which server instances cannot be deleted, and the constraints under which others (like QA) can be deleted
  • upload all of the contractual commitments (for each customer) by yourself
  • specify how much data needs to be maintained in the live (and dev) instances
  • upload all of the cost data and specify how to build a cost model to compute the potential savings and determine what can be done, should be done, and the impacts will be

Then why the f*ck do you need AI?

Once you’ve done all this you’ve:

  • identified, and eliminated, all of the instances that cannot be removed under any circumstance
  • identified which instances cannot have their resource allocations reduced
  • identified the highest cost resources and the most likely savings targets
  • determined exactly how much data needs to be online, how much can be in offline archives and how many duplicate copies you need
  • defined all the constraints that must be adhered to
  • mapped instances to customer commitments, and identified reduction possibilities
  • identified all the old backups that can be deleted, as well as database reduction sizes
  • built the model that computes the potential cost savings from each potential action, and even identified potential performance reductions from actions

And figured out what you should most likely do.

So tell me, if you have to do all this, what the f*ck do you need the AI for?

NOTHING. ABSOLUTELY NOTHING. BECAUSE IT IS ABSOLUTELY USELESS.
(AS THE AI IS DUMBER THAN A DOORNAIL.)

But the author of the post that riled me up was right in one respect — the user did make an error, and the error was using the Artificial Idiocy in the first place. (After all, the user used it exactly right as per the manufacturer’s instructions that said you only have to tell the AI what you want done and it will figure out the best way to do it for you consistent with your organizational goals and policies.)

The GruntMaster 6000 was Engineered for Longevity! SI Turns 20 today — Beating all Records for a Source-to-Pay blog!

A couple of years ago, when Sourcing Innovation (SI) published it’s 6,000 post, we explained why it would be appropriate for all [to] hail the GruntMaster 6000, and that was because Sourcing Innovation had been publishing continual, never-ending, free eduction on Procurement, including best practices and technology, for over 18 years! With the slashing of the Spend Matters archives in their last site revamp before the Hackett acquisition (which recently resulted in Spend Matters being laid to rest), SI provided the largest open archive of such articles on the internet.

Now, with Spend Matters gone, SI takes the mantle of both longest running blog and largest open archive on the internet in Procurement with approximately 6,700 posts published to date. The lone member in the vicenarian club. It hopes it won’t be the last (as Procurement Insights recently turned 19), but as we recently lamented (on the loss of the Enterprise Irregulars last decade), where once there were close to 200 voices in the late 2000s heyday, very few remain.

While a new group of hopefuls have taken up the mantle with regular LinkedIn publishing and newsletters (and hopefully pre-publishing and archiving on their own sites before all their content belongs to MUSK, ZUCKERBERG, NADELLA, and ALTMAN), time has proven that, for many, a decade is quite hard to maintain and two decades almost unheard of! (Still we wish Joël Collin-Demers, James Meads, Tanya Wade, Tom Mills, and other upcoming notables from the new crop all the best and hope that their dreams of doing this for 20 years come true.)

Regardless, since we’re not getting any younger, our advice is to suck up as much content as you can while you can before it all gets paywalled behind the big analyst firms and greedy social media platforms (where it’s almost impossible to find anything older than a few weeks and where non-premium members have their content relegated to the dank archives of the internet). Just because the Big Tech Cos are trying to push the Age of Retardation upon us with their Artificial Idiocy, that doesn’t mean we have to accept it!

Learn free! Buy hard!

IDC Misses the Main Point Completely. Outcomes is a Dirty Word!

Sorry, Paul, but when you say MNR is directionally right here, but I think the market still understates how hard “outcomes” actually are, and reference an IDC article, you’re off. The only part that’s right is that AI price wars miss the point (that you probably shouldn’t be using [Gen-]AI to begin with).

Outcomes only matter more … to the vendors. Because the meaning of outcomes in the vendor vernacular has NOTHING to do with results, but how they can spin their story to grift you as much as possible. As I clearly explained in my series on how Outcomes is a Dirty Word, which I now have to revisit, “outcomes” is always a way to charge you more for less (and sometimes next to nothing).

And it all has to do with (Gen)-AI costing way more than what the vendors want you to believe.

As per my initial post, while once exclusively the verbiage of GPOs, who wanted you to turn over a significant share of your procurement to them (to the point you’d be dependent on them and their ever-increasing cost of service for the entire existence of your business), or recovery audit firms, who wanted you to believe their services were the only way to recover your overspend, it’s now on the tip of every snake-slit tongue of every vendor rep.

While the vendor reps want you to believe that the reason you pay for “outcomes” instead of traditional SaaS pricing is that their AI will deliver immediate, measurable, results (instead of just transaction cost reductions where it will take at least a year to measure savings), and therefore you should pay (dearly) for those outcomes up front (because a success today is a CEO pat on the head today), that’s not the real reason. (Especially when those projected savings from the auto-sourcing and procurement events will never materialize.)

The real reason they are pushing for outcome-based pricing is that (Gen)-AI compute costs are now so high (and won’t compress as the energy and cooling costs keep rising as the majority of existing data centers are on already overstrained grids) that they can’t afford to sell the solution using a traditional SaaS based pricing model — they wouldn’t even cover their compute costs! (Most of which is wasted since most of what is being “automated” by these solutions can be automated by traditional A-RPA SaaS solutions for a fraction of the cost, as long as you don’t need a natural language interface or slick UX — and you don’t!)

The reality is that the software (assisted) solution from any vendor selling on an “outcome” model isn’t worth it, and (Gen-)AI forgets what software is supposed to be about — enabling efficiency so Human Intelligence (HI!) can achieve outcomes using low-cost Augmented Intelligence solutions.

And until a new generation of AI emerges where hallucinations aren’t a core function, measurability and confidence are restored, and compute costs are inline with classic AI tech, AI models won’t become utilities. We are years away from a systems problem!

The only way to get value is, as Paul pointed out, to redesign workflows, align incentives, clean up constraints, and embed decision logic into execution and find fairly priced modern tech with orchestration and “real” AI (in the form of Augmented Intelligence built on best-of-breed analytics, optimization, and machine learning) that will allow you to make decisions 10 times faster AND 10 times better.

The vendors who ultimately win when the AI crash hits will be those that built real tech on tried-and-true analytical, optimization, and machine learning models that will, as Paul states:

  • drastically reduce cycle times,
  • minimize manual intervention (via A-RPA where the response to every exception remembered, encoded, and applied to all future instances),
  • improve overall compliance,
  • increase throughput, and, ultimately
  • allow for better decisions.

And, as Paul points out, that’s not building yet another chatbot. That’s building real systems that work!

And, FYI, Gen-AI is not feature theatre. It’s puppet theatre! And while puppet theatre may provide entertainment, it’s not a viable business model!

Ignorance and Apathy were never the problem. Asininity and Exuberance were!

When those of us from the smartest generation were growing up, we were told that we shouldn’t be ignorant or apathetic, because “I don’t know” and “I don’t care” are not good answers. With hindsight, while ignorance and apathy aren’t great qualities, it turns out that asininity and exuberance, especially when mixed, have proven to be far worse.

After all, generally what happened if you were ignorant and apathetic was that you ended up in a remedial program, got your high school diploma, quit your job at the White Castle, and joined the trades. Spent your evenings at the local dive bar with your buddies and the weekends on the couch. (Unless, of course, you liked Mary Jane a little too much, then you kept your job at the White Castle and spent every evening on the couch watching Beavis and Butt-head, because you were convinced they were your alter egos.) You didn’t make your mark on society, but you didn’t ruin it either.

Hindsight is 20/20 and I don’t think that, when we were growing up, our educators could ever have predicted how powerful private equity and venture capital would become, how it would be dominated by the asinine and exuberant, and how much damage they’d collectively do not just to public markets but global economies.

All of the market crisis of the past 40 years have been caused by asinine and exuberant financiers, primarily in the private markets, which includes the loosely regulated investment arms of major banks and financial institutions where they are allowed to take “measured” risks.

I mean 40 years! Black Monday (on October 19, 1987), which was the largely unexpected stock market crash that wiped out 1.7 Trillion worldwide, or about 10% of Global GDP at the time, might have started as a result of actions of the US House Committee on Ways and Means with the introduction of a bill to reduce the tax benefits from financing mergers and leveraged buyouts, and been exacerbated by the the high trade deficit figures which both announced on the prior Wednesday, but the major losses stemmed from automated computer trading adopted by the portfolio insurers and mutual funds (to reduce their trading costs and quickly capitalize on market changes) that dictated very large sales (in response to significant selling pressures, which partly arose from their customers having the right to redeem their shares at will, and do so at the price of the last market close). With a glut of sell orders hitting the market as soon as it opened, and nowhere near enough buy orders, this resulted first in intense downward price pressure and then huge losses as the automated trading models automatically reduced prices and accepted lower buy orders. Had the market not been overvalued, had funds been properly managed (by investors not overly exuberant about the markets), and had trades still been manual, losses would not have been as severe — but the pursuit of quick gains built up a market that could come down just as fast.

Then we had the dot-com bubble, created by the first wave of exuberant and asinine VCs that overvalued any business with an online business model (even if never truly implemented, like Boo.com that blew through £125 million in just 6 months (and fire-sold for less than $2 Million), and was labeled by CNET as the 6th greatest dot-com flop. The bursting of the bubble wiped out over 5 Trillion, or about 15% of Global GDP! (The biggest dot-com flop, according to CNET, was Webvan, the original online grocer. It raised $375M in an IPO in Novemver 15, 1999 to build a gigantic infrastructure from the ground up, including a 1 Billion order for high-tech warehouses, and closed in July of 2001.) Hold onto this.

Next up, the 2008 Financial Crisis (that caused the Great Recession) as a result of the collapse of the U.S. subprime mortgage market from risky lending practices, complex mortgage-backed security, and mortgage trading that should never have been allowed. This cut the DOW in half in less than a year. Total losses were generally estimated to be between 19 Trillion and 22 Trillion, or about 32% of Global GDP! (With some more extreme estimates placing value losses at almost 50 Trillion, or almost 80% of GDP, including the estimate of the Asia Development Bank.)

Finally, the 202X AI Crash. It’s coming. And it’s going to be big!

20X valuations in any company that can claim “AI”, whether or not it’s actually AI and whether or not it actually works, have become all too common. Every month, a new 100 Million+ investment in yet another company valued at over 1 Billion dollars despite having sales of less than 50 Million. (And VCs valuing companies with 2 Million in sales at 40 Million dollars.) It’s insane. The asininity and exuberance are ridiculous. For every company to make those numbers in 5 years, which is the time-frame in which most Venture Capitalists (VCs) and Private Equiteers (PEs) [not to be confused with Privatus Equiterres, although that’s likely what they’re doing, facing backwards of course] expect a return. This means that, for those numbers to be hit, worldwide IT spend would have to quintuple, from about 6 Trillion today to 30 Trillion next year, or 25% of Global GDP would have to be dedicated to IT. That’s not going to happen. To put that number in perspective, that’s the ENTIRE US economy … the richest economy in the world that can’t afford to pay for universal education, basic health care, veteran benefits, and/or social security. So how would it ever pay for all that IT? But still, AI investment last year alone was about 600 Billion, or 1/10th of global IT spend. For a technology where the backlash is beginning since the compute costs are spiraling out of control (with companies having to significantly scale back, or even halt, their AI budgets as a result of skyrocketing costs — with one company burning through 500 Million in one month alone [Source: Yahoo! Finance]). (And the total investment in AI infrastucture and software spend since 2000 exceeds 2 Trillion, with some estimates going as high as 3 Trillion.) Open AI and Anthropic alone have raised over 310 Billion with a current combined run-rate of about 70 Billion. Investments are insane, budgets are being tightened, and with McKinsey and MIT reporting 94%+ failure rates on pilots, the backlash is coming.

The only question is, how bad is this crash going to be. If we look at the trend line, 10% of Global GDP for Black Monday, 15% for the dot-com bust, and 30% for the sub-prime mortgage crisis, this could be catastrophic and make the Great Depression look like the Little Dipper. With most IT assets overvalued by a multiple of at least 5, simple math says that 80% of total IT stock value (and the NASDAQ) could be wiped out overnight! (And while it’s not likely to be that bad, anyone with a bit of logic and math skills can see it’s going to be bad, even in a best case scenario.) And it’s all because of widespread asinine exuberance in the private finance industry!

So never complain about ignorance and apathy again. Those with it may never have amounted to anything, but they never caused any major problems either!