TetraMesa

Archives for August 2025

Teach Your Children (AI) Well

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This is for parents of school age children, which is generally elementary through middle-school age, but some consider it the full K – 12 period. Some of us may be AI builders. Others users, and some wish it would go away. Regardless, we have to help our kids navigate another technology. It’s another amazing tool, carries some risks, and maybe another distraction. I’ve written about AI GPT Safety & Issues for Kids earlier. This is more about some specific things to consider doing as a parent. This isn’t coming from a professional educator. Just a parent who works with these tools. I couldn’t find a good guideline to use with my own family. Most things I found were about safety or overly generic. So I’ve tried to make one.

What’s next is first some background regarding this area and then the Specific Guidelines. BUT YOU CAN SKIP THE BACKGROUND IF YOU LIKE and head right down to the section: Specific Guidelines for Teaching Kids

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PM Perspective: Is AI a Great Business?

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Introduction

Short Answer? Maybe not so much. (At least when talking about the core tech vs. specialized solutions.) This won’t be as much about AI as about category and startup creation in general. AI just happens to provide us with a great developing case to review. (At least in terms of Large Language Models (LLMs) and Generative Pre-trained Transformer (GPT) products.)

Product people need to zoom in and out. There’s so much noise around new AI features, we may forget to take a wider view. While we’re often responsible for detailed product roadmaps, some of us also do strategy and business planning. In that role, we need to think about offerings in terms of long term strategy. Rapid growth in AI LLMs / GPTs provides a case worth study in terms of new category creation. We should keep in mind that AI / ML has been around for decades. What’s interesting now is the LLM / GPT space is a great example of a Blue Ocean strategy that is perhaps only fulfilling its promise in a general way. That is, the rising tide is lifting many boats, but early pioneers might not be able to take margin advantaged positions, which should be a benefit in a new category. (If you buy the book, be sure to get the latest edition.) Even though OpenAI bust this category open, they didn’t get to a winner-take-all or even winner-take-most position.

Why?

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When LLM Models Get all Forked Up

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Things are going to get forking ridiculous. I think there’s already enough results in that I can make a prediction. And that’s this…

These new tools we have are going to get a lot better and also somewhat worse at the same time. Like a lot of you, I’m having great fun using some of these new tools. Sometimes as a consumer. Sometimes for digital product craft. And sometimes for personal experiments. Insofar as those of us working in Product should be paying attention to trends and adjusting our efforts, we should be watching this emerging area of a patchwork modelverse of LLM dark matter. (Apologies for mixing metaphors so early, but I’m just really an LLM myself with drastically limited training data.)

Why should we care from a Digital Product Management perspective? As we look at some of the To Do items below, it’s not necessarily a product person’s job to handle the specific task, but we should understand the risks and make sure at the very least that table stakes items are part of the work plan. Because we need to ensure what we build is not only performant to its fit-for-purpose build, but also be confident that go-to-market claims are trustworthy. And I think it’s possible a lot of our products may end up sitting on shelves next to dain bramaged garbage. Trust is still going to matter. Actually, more so than ever. But we’re going to have to prove quality and branding will still matter.

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