Part I

The October Diaries is a supernatural drama centred on the interaction of the protagonist, Jon W. Hansen, a distinguished analyst with a 40 year career in tech and, in computing years, an AI RAM Model 5 based on centuries of development. His work becomes increasingly complicated as other models continually challenge his and self-proclaimed AI Experts continually threaten our space from the shadows. The book chronicles the complex relationships as Jon tries to find new ways to preserve the truth and protect …

Oh wait, that’s the plot archetype for the Vampire Diaries. Did I read the right book?

Yes I did. But I just made you think, and that’s one of the primary goals of Jon’s book and one of the key points I have to make.

Every Influencer, Consultant, and Analyst needs to read this book, but 99% won’t learn anything if they don’t think and question everything they read. (And that’s one of the unwritten reasons Jon says you’ll have to read the book two and even three times.) If they don’t come to suspect the truths on their own before Jon exposes most of them in later chapters. If they don’t understand that this is not a guide or manual for success or the answer to all their problems (as there is none) …

It’s a book designed to make you do what we don’t do enough of in the age of AI: think, and, most importantly think in a way that will, in time (may not today, tomorrow, or even next year) allow you to actually use modern AI tools productively and extract value in real time.

Gen-AI efforts are failing across all the board, from large scale corporate projects down to small scale individual efforts to extract useful content for reasons that include:

• lack of focus
• lack of verified data & reinforcement training
• lack of knowledge
• lack of skill

You see, for success, you need to have

• focussed domain models
• deep context
• deep domain knowledge to know when the output is good, ok, and bad
• appropriate skills to utilize the models effectively

Jon gets at this with his six skills of conversational fluency, which is his name for the methodology he uses to train the models to do what computers do best (identify patterns, surface them, and draw correlations) while he does the strategic thinking humans do best.

As well as his five common mistakes that are one of the reasons the vast amount of human prompted content generated is AI slop.

But he also goes deeper into what is truly required for long-term success. Which may shock many of you who aren’t from the old-school we are, but, like Billy Idol, you have to deal with the shock to the system it will give you and push forward.

Discuss Part I on LinkedIn

Part II

Every Influencer, Consultant, and Analyst needs to read this book, but not for the reasons they think. It’s because they need to think deeply about AI, and that’s what this book forces them to do. It may be framed as a step by step guide to take you from zero hero, but that’s just to psychologically convince you that this is the guide for you — and if you want to understand AI, it is!

Most people are using AI wrong. More specifically, they are using the A.S.S.H.O.L.E. to sh!t out plagiarized slop that is turning the internet into massive sewer that is likely making Jon Oliver rethink his Facebook is a Toilet rant (from 2018) (because now the entire Internet is a sewer).

While that is one of the few things that LLMs can actually do, that’s NOT what they should do. They might be lying, hallucinating, soulless algorithms that will happily tell you to commit suicide, suppress life saving alarms while you’re locked in a server room on fire, or even ignore your shadow and have the self-driving car run you over, but they have their uses.

While they can’t do 94%/95% of what the firms selling them advertise (or we wouldn’t have 94%/95% failure rates, as per McKinsey and MIT), they can do four things very well, with reasonably high reliability when appropriately trained and deployed, that we can’t. The first two, as I keep promoting, are:

1) large corpus search & summarization
2) natural language processing

The third, as Jon makes clear in this book is

3) deep pattern detection and surfacing

But only if you know how to get the algorithm to do it!

You see, all these systems are trained to deliver direct responses to direct requests. As a result, when you give them a typical direct request in your “carefully calibrated prompt“, they give you what they think you are asking for, and that’s it. But that doesn’t help you, or me, or anyone, especially if they weren’t trained on the right data or it’s not available and the only way they can give you what you want is to make sh!t up.

Sure it might spit out 2997 characters for your Linkedin post 10 times faster while addressing the seven points you wanted, but is that really helping you when you have to read it, edit it, and copy and paste and verify it? That takes time — and even worse, it’s not productive time. If you’re not thinking about the 5 Ws, not only are you not sharing anything valuable, but you’re not advancing your thinking. (Right now, the only edge we have over machines is our ability to think critically and strategically — so what happens if we lose that?)

But if you can learn how to work with the technology, instead of getting bland plagiaristic derivations, you can get it to surface patterns across related bodies of work, document progressions over time, and use that to more quickly validate your instincts and formalize your ideas, allowing you to advance your own abilities while ensuring you can serve your customers faster and better by speeding up research and delivery efforts by multiplicative factors.

Discuss Part II on LinkedIn

Part III

Today we continue with our review of the supernatural drama that chronicles the interaction of the protagonist, Jon W. Hansen, and the RAM Model 5 that we’re sure you’ll find more thrilling than the pages of the Vampire Diaries we thought we were reviewing (due to the similarities in plot archetypes). You might not have the love triangle, but I’m sure the dollar signs will be more than enough to get your attention. (What dollar signs? Well, you’ll have to read it.)

In part one we said that you need to read the book because it will make you think (if you’re reading it right).

In part two we said you need to read the book because it helps you understand the power of LLMs is not its ability to create watered down plagiarized slop 10 times faster than the drunken plagiarist intern ever could but uncover patterns that you might never uncover on your own due to lack of time.

Today we’re giving you a third reason — and that reason is that it helps you understand why you are invaluable in the age of AI. While it has been true since the introduction of computers that monkeys could do all back office jobs if they knew what buttons to push, the reality is that AI, which should be called Artificial Idiocy, still doesn’t know what buttons to push, it’s just able, in many situations, to compute what button to push with high probability. But it DOES NOT know. Only YOU know! (You see, what AI really stands for is Algorithmic Improvement, as it is the label that is consistently applied to any algorithm that is an advancement over a previous algorithm, and that has nothing to do with intelligence.)

Now, it does mean that if your job is simply tactical data processing then you’re out of work, and it does mean some of your peers who aren’t as good and efficient as you are also out of work since the tech will make those who know how to use it up to 10 times as efficient at some tasks, but if you’re a skilled expert, then you are more desperately needed than ever because, as per our last post, only you will be able to detect the very convincing inaccuracies, lies, and hallucinations it returns.

But understanding is not enough, you need to be able to explain it, and when pressed, demonstrate it. That is what the book, after a few reads, will help you do. Use AI in a way that demonstrates you are what’s needed to make AI effective and make sure the organization isn’t part of the 95% failure statistic.

Part IV

In Part I of our review of Jon W. Hansen’s October Diaries, his take on the modern thriller, I told every Analyst, Consultant, and Influencer (ACI) that they need to read it because it will force them to finally think — deep — about AI.

In Part II of our review I told the ACI they need to read it because it will help them use LLMs properly and surface patterns they might not ever find on their own due to time constraints.

In Part III of our review I told the ACI that it will help them defend their positions in the “Age of AI” purge that is coming. (Since it’s a new excuse to fire people so the organizational shareholders can [temporarily] get richer!)

Now, in Part IV, I tell most of the ACI that I’m sorry. You shouldn’t read it. You want a quick fix and an easy solution to your relevance problem and this isn’t it. In fact, for some of you, it won’t even be worth the cost of the minuscule amount of storage it takes up on your hard drive.

Because it makes a few assumptions.

1) You have, or are willing to build (with your own hands), a deep archive of unique, human authored content to augment the models with.

2) You are willing to take the time to not only ensure the models are trained on this, and only this, archive but to learn how to both use the models appropriately and get them to retain and access relevant context across multiple sessions over days, weeks, and months, which is a skill that goes beyond creating executable ChatGPT prompts.

3) You have, or are willing to develop, the expertise necessary to know when the model is 100%, 95%, 90%, 50%, and 0% right, no matter how convincing the words are that it returns, and how to correct it and guide it to 95% every time (so you can make the corrections faster than doing the work from scratch), which could take minutes, hours, or days for any particular request you throw at it.

But let’s face it.

1) Most of you don’t have the archive, unless you work for a consultancy that has been delivering projects for at least five years, and preferably 10. Jon and I remember the early days with hundreds of blogs, and the 3/3/3/3 rule. Up to 90% of wanna-bees would quit after 3 posts/3 days, then the next batch by 9 posts/3 weeks, then the next batch by 27 posts/3 months, and the majority by 3 years would say “hey bloggie, I’m packing you in“. The hundreds of blogs I chronicled on the now-defunct SI resource site were down to a few dozen by the 2010s.

2) You won’t put in the months necessary to get the model and your skills to the point you are getting close to what you want every time. And it will be months!

3) Not only do you have to keep learning tech, you have to be constantly seeking out experts to learn your trade. That’s also a lot of work. When you’re Bowling for Soup, you know that High School Never Ends!

In an age where founders want to vibe code and flip companies within 3 years, you want instant gratification, but you’re not going to get that!

All it will give those of you starting out is a way to build a skill that is sustainable for life. But the vast majority of you will have to wait for the good things to come. And I don’t think you will. Sorry.

But if you want to prove me wrong, get the book!