Category Archives: Economics

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.

Turst is Real Procurement Currency — And That’s Why AI CANNOT Do Procurement!

A couple of months ago Garry addressed a point made by the Peter Smith, the Bad Buying Bard, which boiled down to an issue more important than anything technical where AI is concerned … and that point is Trust.

In his original post, Gary asked if AI would change Procurement. However, after reading Peter’s comment, he realized the real question is whether Procurement is trusted enough that the organization will accept Procurement setting the rules around how AI is used. As Garry notes, that’s the crux.

When it comes to trust, it’s not whether or not the suppliers trust Procurement that’s the real issue, it’s whether Procurement is trusted internally. If Procurement is not trusted, it will be bypassed, ignored, and even sabotaged. This includes the (mis)use of AI. If Procurement is not trusted, it will not have any authority, and the organization will not heed their warnings (based on logic and the research they are used to doing), charge ahead with AI, and become yet another failure contributing to the 94%+ failure rate (while costing the organization millions upon millions of dollars and wiping out any savings Procurement may generate, especially if the C-Suite dictates an AI-first solution for Procurement).

Furthermore, you can’t use tools that you cannot trust. And you can’t trust any Gen-AI Procurement platforms built on hallucinatory LLMs. Since hallucinations are a core feature, results can’t be guaranteed, and LLMs can’t even be counted on to follow explicit instructions (and will corrupt your documents and data even when explicitly told not to), you can’t use Gen-AI/LLM-based AI.

And, unless your data is clean, categorized, up-to-date, and easily accessible through modern APIs, “classic” AI won’t work either. Good Procurement Pros will remind you that you can’t jump straight to AI. Just like you couldn’t expect a tribesmen from a culture with no written word who never set foot in modern civilization to begin reading lessons on the works of Shakespeare accessible only on a modern tablet, you can’t jump decades of technology. Or process.

Successful Procurement requires:

  1. getting your processes in order
  2. getting the supporting data in order
  3. implementing classic technology with high-degrees of deterministic, dependable, determination

And then, and only then, do you sit down, identify where there are still inefficiencies and/or a lot of tactical bit-pushing work, and try to figure out where AI will actually help. This means that most organizations are still years behind where they need to be to successfully implement any AI. In the classic Hackett journey to best-in-class, which will take an average large multi-national 8 years, it will be at least 4 years before the organization is far enough along on any process to consider advanced AI. (For a mid-size, this journey can be reduced to 6 years, and then it’s 3 years before Procurement is ready for advanced AI. It’s always People, Process, and Data before AI!)

Fastest Freeway to Financial Failure? Gen-AI!

Not joking here.

First of all, AI is getting more expensive for coding.

Input-output token pairs, which used to cost pennies per M tokens, are approaching $100/M for high-end models.

An average enterprise app starts at 100,000 lines. It will require 2M output tokens for initial output. It will take at least 5 iterations to get code good enough for the devs to even begin to work with, or 10M tokens. Then you will have to test and debug, figure another 5 iterations, or 20M tokens. But this doesn’t include the context history or coding samples required to produce a baseline, integrate a security framework, or account for multiple service-based deployments. This will consume an additional 10X to 30X the token count, and you will require 40M to 80M tokens to produce the app along with an experienced team of senior developers who will have to shore, as only 20% of AI-generated code survives unscathed. And then comes the testing, debugging, and QA. This could double the token requirement again.

For coding, which requires about 20 tokens per line, it would, in theory, only require 10,000 tokens to produce 5,000 lines of code, which is the net-new production code you’d expect from a senior developer every year, but given that it will require at least 5 iterations to get something to start with, and then all the updates to get it to testing and then all the testing and debugging, that’s at least 50M tokens as per above — with prices expected to rise (and possibly double) by the time you’re done (at the current rapid rate of token cost increase), or $10,000 to $20,000. Not bad in theory, as a senior Dev costs you 10X to 20X that on the low end, but …

As we said before, only 20% of AI code ends up being usable, so you still need a team of devs to review it and fix the major bugs/issues. With 80K lines needing correction, and a top dev only producing 5,000 lines of net new production code a year, you would still need 16 devs. That’s still expensive. You might realize that you only need to fix the critical issues to get your MVP out the door, and cut the team in half because you can stagger the reviews and fixes to issues. And while you think you saved the cost of 12 devs …

As time goes on, you realize there are fundamental flaws in the code. The security framework it chose was an old framework off of an abandoned Github code branch that used a lot of methods and procedures that were already marked for deprecation in the next framework release, which hit as soon as you released your code. They all have to be redone. The “multilingual” support is clumsy and requires the manual production of very carefully crafted fixed format text files. The workflow is rigid and not malleable. You wanted it AI friendly, but it doesn’t properly support MCP. And so on.

Then, like so many enterprise app startups are finding, you can’t scale the MVP into enterprise quality, have to scrap it, and rewrite if from scratch. Which means the 10K to 20K in LLM cost and the 800K to 1600K + in minimal dev support cost to get the MVP up and running in a production environment was all wasted — most of your seed money went up in smoke, and you have to start from scratch.

Second, its performance is much worse for trying to correct/update existing code where it has to ensure all unit, functional, user journey, workflow, and integration tests still work. This is evidenced by the fact that many companies, like Uber are now blowing through their annual AI budgets in a quarter. Engineers trying to rely heavy on AI are already spending 2,000 a month! Backtracking the math, it’s easy to see that the amount of project code, documentation, and online (GitHub) samples it has to ingest and compute to create an output, that might not even be 20% acceptable on the first few passes, is astronomical!

Plus, as we’ve explained before, when a dev has to correct up to 80% of the code, you’re losing on the efficiency improvement if a dev is spending 20% of their salary to get you that 20% increase in code lines which, as we’ve also explained before, is still of a worse quality than if that senior dev had wrote it by hand, that’s not a savings. That’s, at best, net 0.

However, this isn’t taking into account that it will likely have to be refactored or written out in very short order. You won’t get the median 2.5 to 3 year lifespan for a small app or 5 to 7 years for an enterprise framework, you’ll get 0.5 to 1 year — which means you’ll write and re-write each line of code three times as often with the use of AI. Or, in other words, you’ll inadvertently spend three times as much on that code! And your customers won’t pay 3 times as much for an app just because you spent three times what you need to, so bankruptcy will be just around the corner!

Third, it is getting infinitely more expensive for any document processing with a legal ramification.

Judges are now fed up with AI hallucinations and slop. Include AI hallucinations, and you’re getting fined at a minimum, and probably sanctioned.

Even worse, if it takes out a risk mitigation clause or creates an unforeseen risk you didn’t catch, a failure could cost you (hundreds) of millions of dollars that you would have otherwise been protected against if an experienced lawyer had written the contract for you.

Fourth, it’s making us physically AND mentally sick.

The cognitive atrophy is becoming well documented. People aren’t remembering what they wrote even an hour later when they use Gen-AI. They are being lulled into a false sense of security and accepting its outputs, even when those outputs are false and dangerous to their health (and tells them to effectively commit suicide). (But go ahead, eat that poisonous mushroom. The one rock a day it told you to eat will protect you, right?) Average decline in mental acuity and performance after regular use is 17% (which effectively equates to a loss of 17 IQ points. In comparison, it took us almost 120 years since the Victorian age [before we had industrial revolution technology to make our lives easier or media to dumb us into submission] to lose 14 IQ points). It’s making our society mentally sick!

Moreover, given how much energy and water a modern data centre consumes annually (100MW for a hyperscalar site or an amount of energy that would power at least 10,000 greedy American homes for a year) as well as how much water it consumes for cooling (100M+ G, assuming it recycles efficiently, or easily 200M+ G if it doesn’t, which would meet all the water needs of at least 5,000 of those homes per year, if not all 10,000), when energy and fresh water is becoming in scarce supply in first world countries, we’re jeopardizing the well being of 10,000 people for every unneeded AI data centre that we build. Given that there are now about 11,500 data centers consuming about 2% of planetary energy and likely between 0.1% to 1% of available fresh/drinking water, that’s a lot of energy and water being wasted to produce cr@p code and poor documents that can often be produced better by interns*. Especially when, in energy or water stressed areas, these data centers take systems to the breaking point and risk our health due to lack of necessary heating, cooling, bathing, and/or drinking water.

But, even worse, since this energy often comes from grids powered by dirty coal and oil, and the water extracted from desalination plants also require energy from those same grids powered by dirty coal and oil, they are polluting the environment to a significantly measurable degree as they account for somewhere between 0.5% and 1.0% of global CO2 emissions. With the global slowdown in shipping thanks to all the conflicts in the Red Sea and the Strait of Hormuz as well as the lack of water (due to less rainfall) in the Panama Canal, and the rampant increase in Data Center construction, data centers will soon account for more CO2 production than global (unregulated) shipping, which is the dirtiest industry on the planet. That’s NOT good for our health!

* There’s a reason Builder.ai was successful in its efforts to pass off human-written code as AI for over 7 years. Human produced code actually works! Even hastily written shoddy code works better than AI generated code by orders of magnitude!

The Mythical AI ROI!

A few companies claimed ROI from AI. (About 6% if you believe McKinsey or 5% if you believe MIT.)

And by few, we mean a few. One in twenty (1 / 20) is not a lot. And that’s just some ROI, not amazing ROI. Not necessarily enough to justify the elimination of even a single human (that you had hoped to replace), as that human is still generating more ROI than the BS AI you were sold (and making decisions at a much higher success rate).

There’s only one way to get true AI ROI.

1. Stop believing in Artificial Intelligence, realize all the vendors claiming it are only offering Artificial Idiocy, and that the best you can get is Augmented Intelligence.

Repeat

2. Identify a major problem that is hurting.

3. Use your Human Intelligence (HI) to map the current, and required, workflows end-to-end.

4. Identify all the manual steps that could be automated with the right data.

5. Do the hard work of identifying where all the data is, implementing a data orchestration platform to collect it all, and make it forward deployed everywhere it is needed for task automation.

6. Automate each step with the appropriate (A)RPA tool.

7. Implement a workflow orchestration platform to connect all of the steps together to the extent possible which ensures everything that can be automated with the automation and orchestration tools is once the intelligent human provides the right inputs and makes the right decisions.

8. Analyze where humans are still involved and where human inputs and/or decisions can be further automated through the integration of additional (external) data feeds and encoding of the (business) logic the human always uses to make the decision.

9. Analyze what’s left and determine where “AI”, even with a poor accuracy rate and hallucinations, could be helpful to an intelligent human making decisions and acquire small, focussed, specialized model licenses only for those steps.

10. Ensure Augmented Intelligence, connected to your forward deployed data, is available everywhere Human Intelligence (HI) requires it to make a decision.

until all major problems solved.

One by one. Put the effort in once, do it right, and with modern tech, you’ll never have to do it again.

You only win with AI when you’ve first centralized, validated, and forward deployed your data; implemented deterministic (adaptive) robotic process automation everywhere you can, and identified precise use cases where custom solutions actually provide a benefit (and not just a fairy-tale promise).