A first look at my new book, “Almost Timeless” (+ a free chapter)

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Almost Timely, AI, Artificial Intelligence

It’s that time again. It’s been 4 years since my last AI book, which may as well have been written in the Jurassic era.

So it’s time.

For the last 2 years, I’ve been hearing one question everywhere I go, from boardrooms to workshops: “How do you keep up with AI?”

The answer is to focus on foundation principles. To know the technology, how it works, and what doesn’t change, no matter what breathless announcement is coming out of any given Big Tech on any given day.

These principles are almost timeless. Almost, because nothing is ever permanent in AI, but this is close.

Hence the book name, Almost Timeless: 48 Foundation Principles of Generative AI.

This isn’t another book of temporary hacks. There aren’t even any prompts in it (okay, maybe a couple, but not many). It’s a collection of durable, almost timeless principles that will help make AI work better for you – no matter the vendor, no matter the system, no matter your level of skill.

To give you a taste of what I mean, I’ve included a sample chapter below.

The full book, with all 48 principles, is available now in two editions over on Gumroad:

Standard Edition: Get the book in PDF, ePub, and MP3 audiobook formats suitable for all audio devices. The audiobook is read by Google’s AI.
Deluxe Edition: Get everything in the Standard Edition PLUS a video walkthrough of how I made the book, along with the transcript, and the original rough, human-made audio recordings from my car (more for your curiosity than anything else).

👉 Grab your copy here.

Thanks, and see you on Sunday for the regular newsletter.

Chris

Principle 16: Add a Banana

I’m about to share one of the most powerful, most effective, and strangest-sounding secrets to getting more creative output from generative AI. It’s a technique that, on the surface, sounds absurd. But once you understand the mechanics of why it works, it will fundamentally change how you prompt for originality. The secret is this: Add a banana.

No, that’s not a typo. This may sound like a very strange thing to read in a book about artificial intelligence, but it is the key to breaking AI out of its rut of predictable, boring, and generic content. It’s the method you use to force the machine to surprise you, to generate language and ideas that are genuinely different.

To understand why this works, we have to revisit how these models generate text. As we’ve discussed, an AI model is a probability engine. When you give it a prompt, it looks at the words you’ve used and calculates the most statistically likely words to come next. This is its “token distribution”—the cloud of probable next words.

The Tyranny of High Probability

Here’s the problem: the most probable words are, by definition, the most common words. And the most common words are almost always the most boring words.

If I ask an AI to write about B2B marketing, what are the high-probability words that will immediately spring to its “mind”? Sales, commerce, pipeline, strategy, value, synergy. These are all the terms we would expect to see in any generic article on the topic. Because everyone expects them, they carry no surprise, no interest, and no creative spark. The AI is simply serving up the most predictable, highest-probability meal possible: plain, unseasoned toast.

This is why so much unguided AI-generated content sounds the same. The AI is functioning perfectly. It is giving you the most mathematically probable—and therefore most boring—output. So, how do you get a model to generate more unique text? How do you force it to access lower-probability words and create more interesting combinations? You add a banana.

How a Banana Breaks the Machine

Here’s what I mean by “add a banana”: you must add words to your prompt that are completely out of context. You must introduce concepts that force the model to think differently and to look outside the normal, predictable domain it’s working in.

Let’s go back to our B2B marketing example. Instead of a simple prompt, we give it this:

“Write a blog post about the importance of account-based marketing. You must also use the words banana, Wankel rotary engine, and green.”

What have we just done to the poor AI? We’ve thrown its predictable process into chaos. The model now has a much harder job. It sees “account-based marketing” and activates its usual token distribution: sales, leads, funnel, ROI. But it also sees “banana,” which activates a completely different distribution: fruit, yellow, tree, peel, tropical. And it sees “Wankel rotary engine,” activating yet another, highly technical distribution: Mazda, rotor, apex seal, eccentric shaft, combustion.

The generative AI tool now has to reconcile these wildly different token distributions. It has to find a logical, or at least a metaphorical, path that connects them. It has to build a bridge between these completely separate word neighborhoods. The creative magic happens in the unexpected connections it is forced to build. It has to find where the word clouds for marketing, bananas, and esoteric car engines intersect to create the content you requested.

That is how you provoke different responses. That is how you generate very different language.

A Better Way Than “Be More Creative”

You might be thinking, “Can’t I just tell the AI to be more creative?” You can try. You can add phrases like “make this more unique” or “use an interesting metaphor.” Sometimes it will work, but it’s a weak and unreliable instruction. Why? Because “creative” is an abstract concept. You are still asking the AI to operate within its default, high-probability space, just with a vague instruction to try a little harder.

Adding a concrete, out-of-context word like “banana” is a much more direct and mechanical instruction. You are giving the AI a tangible anchor point in a different probabilistic space. You are not asking it to feel creative; you are forcing it into a situation where creativity is the only mathematical path forward to solving the puzzle you’ve presented.

When you just give it words that are out of the distribution it’s expecting, out of what would not occur in the normal context and language it has seen around a term, you’re going to get different and better responses. You are hijacking its tendency to be predictable.

Timely Takeaway

Think of your generative AI as a brilliant but lazy chef who, left to their own devices, will always make you toast because it’s the easiest, most probable meal. Your job is to be the host of a reality TV cooking show. Hand the AI a mystery basket of ingredients.

The next time your AI’s output feels bland, repetitive, and uninspired, don’t just ask for more “flavor.” Disrupt its process. Challenge its assumptions. Throw a banana in the prompt. Or a Wankel rotary engine. Or the concept of existential dread. The more unexpected the ingredient, the more inventive the final dish will be.

A first look at my new book, “Almost Timeless” (+ a free chapter)