Almost Timely News: 🗞️ What Would A Purely AI-Generated Newsletter Look Like? (2026-08-30)

Almost Timely News: 🗞️ What Would A Purely AI-Generated Newsletter Look Like? (2026-08-30) :: View in Browser

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Content Authenticity Statement

100% of this week’s newsletter was made by me, the human, BUT you will see an issue of a newsletter made entirely with AI. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

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What’s On My Mind: What Would A Purely AI-Generated Newsletter Look Like?

In this week’s newsletter, I want to pull on a thread from last week. I write this newsletter by hand, either by physically putting fingers on keyboard or by voice dictation. Either way, what you get - for good or ill - is almost completely me. For example, last week, I forgot two really important sentences that were in the video but not in the text. Oops.

But one of the questions I’ve gotten from MANY a marketer is how to create a purely AI newsletter that was still useful. It would lack that spark of humanity, sure, but for folks (again, for good or ill) who don’t have the resources but have been given the mandate to publish, how COULD we do it? So I figured, as part of my ongoing promotion of my new course, AI for Writers, I’d take a shot at making an issue of a purely-AI newsletter.

Part 1: What’s the Big Idea?

Before we write a word (or AI writes a word), we need an idea. We need an angle that no one else is likely to have. One of the biggest meta-challenges everyone has right now is that today’s AI models all pretty much know the same things. They all know content strategy, they all know how to write (even if their writing is very, very canned), and they all have the same basic facts, the stuff published on the public internet.

In other words, everyone’s got the same kitchen setup, the same skills, and the same chefs. In that scenario, what differentiator can you offer to get people to dine with you?

The answer: you either differentiate on ingredients or recipes. If everyone has the same people and platforms - and presumably the same purpose and performance, if we’re following The 5P Framework by Trust Insights™ then your only differentiator is process, how to direct the chefs, what they cook, and what ingredients you give them.

So you have two choices: better ingredients or better methods (or both).

Which you choose guides the big idea behind your AI-generated newsletter. Do you have unique data that no one else has, that no one else’s AI has seen, that would allow you create different results than everyone else is getting? That’s the ingredients-led approach.

Do you have unique ways of manipulating data that no one else has or knows, that take advantage of AI blindspots? That’s the process-led approach.

Here’s a simple example. We all have access to basic trends data from places like our favorite SEO tools or Google Trends. That’s not new or news. But if we have our own data from inside our company we can blend with it, like customer feedback, qualitative data, or proprietary data, then like a good hot sauce, we can add that into the same basic ingredients and get a very different tasting dish.

OR, if we have that same data but we know methods like SARIMAX, neural forecasting, or additive regression, we can take data everyone has access to but do different things with it like forecasting.

And if we have both? Unique insights into data plus unique ways of manipulating it? That’s REALLY going to set us apart.

For this experiment, let’s go with a data source everyone has access to - whether you know it or not - and a unique way of manipulating it.

Part 2: AI’s Blind Spot

Because generative AI is trained not only in absolute probabilities, but in probabilities with nearness, it has a huge blind spot, and that blind spot is the same one you and I suffer from: tunnel vision. Not the literal medical phenomenon, but being blind to the unfamiliar.

Here’s an example. If I say we’re talking about eggs in the context of food we eat. What comes to mind? Fried eggs, maybe. Scrambled eggs. Omelets. Poached eggs. If you want to get fancy, eggs Benedict with a nice Hollandaise sauce. We think of eggs principally in the context of breakfast, and those foods I just listed were probably on your top mental list.

Not as many of you thought about shakshuka, or a bowl of ramen with a quail egg, or egg and herb piroshki (a Ukrainian dish with a dough filled with chopped hard boiled eggs, farmer’s cheese, and tons of herbs). Why? These are very different contexts, cultures, and applications. If you know of them, you might have eventually gotten to them, but they were probably not top of mind.

Likewise, suppose I talk about marketing analytics. What comes to mind? Probably either Google Analytics or Adobe Analytics, then maybe your CRM’s analytics, or maybe your social media management software’s analytics. Maybe you thought of analytics techniques like attribution (broadly) but you probably didn’t immediately land on Markov chain Monte Carlo simulation (MCMC), even though that’s a reasonably well known analysis technique outside of marketing.

The association with high probability concepts is a blind spot of both ours and AI, AI even more so because if we don’t know to ask it to think outside the box, it never will. Thus, if we want to create content that’s truly different, we have to think outside the box first before we type even a single character of a prompt.

In last year’s book Almost Timeless: 48 Foundation Principles of Generative AI, one of the concepts was called “Add a banana” - forcing different probability distributions in an AI response by making it use words outside of the domain you’re writing it. Asking AI to write a report about marketing analytics but requiring it to use the word “banana” creates different probability distributions than not requiring it, because bananas are not typically in marketing analytics.

We can extend that concept significantly by feeding large, out of domain ideas to AI and forcing it to reconcile what’s out of domain with what’s in domain - and that’s how you’ll create an AI-generated newsletter that’s worthwhile. What’s out of domain but still valuable that you can use as starting materials?

Once we’ve figured out how to feed AI out of the box thinking and ideas, we can start creating our machine-generated newsletter.

Part 3: Mise En Place

To create our newsletter, we need the following items, based on The 5P Framework by Trust Insights™: