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!

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).