You Ask, I Answer: Avoiding Generative AI, Part 5: Considerations

In today’s final episode of this series, you’ll discover the essential steps for monitoring AI’s use of your data and learn how to conduct due diligence to ensure your content isn’t being used without your knowledge. I’ll delve into the challenges of data leakage, despite your best efforts, and highlight platforms like Common Crawl and Archive.org that AI companies often use. Most importantly, I’ll guide you on how to make intentional choices about the data you share online, enabling you to influence how AI perceives you and your work. Tune in for this crucial conversation on navigating the evolving relationship between your content and artificial intelligence.

You Ask, I Answer: Avoiding Generative AI, Part 5: Considerations

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Christopher Penn: This is part five in our series on how to avoid generative AI entirely—which is completely contrary to what I normally do. My colleague Mar asked the question, “How do I do this? How do I throw generative AI out of my life, out of my work,” etc.

In part one, we covered legal stuff. In part two, we covered infrastructure. Part three: software and tools and apps. Part four: we covered marketing and discovered just how difficult it will be to do marketing while rejecting large portions of the digital ecosystem that everyone’s putting generative AI into.

But it is possible. There are consequences. One of the consequences is your marketing will be less effective.

In this part, let’s talk about the last stage, monitoring and oversight. You’ll need to do due diligence—and this is why we talked in the last part about watermarking everything—on a regular, frequent basis, to make sure that your stuff is not showing up where it shouldn’t be.

There are archives like Common Crawl or archive.org that make copies of the internet, and then make those copies available to anyone for free. That, in and of itself, is not bad. They are not personally using AI, but they are making these public archives available to everyone, including AI companies. That means that your content ends up in AI even if you didn’t want it there.

A lot of authors are finding this out the hard way, not because a legitimate bookseller like Amazon leaked their book, but because there are all these other places where your content can end up without your permission that then finds its way into a Common Crawl archive.

For example, suppose you wrote a book, and someone cracked the digital protection on the Kindle version of your book. It ends up on a website like The Pirate Bay, or The Pirate Bay publishes it as a torrent. That torrent is available to anyone who wants it. Suddenly, that unencrypted, digital form of your book is on the web. That ends up in a Common Crawl data set, because Common Crawl is crawling everything that isn’t password-protected. Now, even though you did not approve it, and you did not put it out there, your book is in an AI model.

Part of the reason why I suggest doing unique watermarks and canary traps in your content is so that you have tests for that. If you put a random text string in your book, like, “zxz7312aym”—which no one’s going to write in a normal sentence—then you can just Google for that (or DuckDuckGo it, I guess, if you’re using DuckDuckGo, which is the preferred, privacy-focused engine).

Regardless, if you have traps like that in your content, then you can find out where it’s ending up, where it’s been—that you may or may not have approved—and then (go back to part one) summon your lawyer and do what you can. You have to be able to check the big archives like archive.org, like commoncrawl.org, and monitor to see if your content has ended up in those places. If it has, you’ve got to send in the lawyer and say, “Get my stuff out of there. I don’t want it in here.”

Some leakage is unavoidable, despite your best efforts. If a human being can consume it, a machine can in some way, too. There are browser extensions that can record everything, every page that your browser is rendering, because the HTML has to go through the browser, through your network card or your Wi-Fi points. That data, in transit, has to be exposed in order for you to be able to see it, to hear it, to read it. If it’s completely protected, you can’t use it either.

There is going to be some leakage, but what you are doing is reducing your exposure to having your content out there in AI models.

The flip side of that is being thoughtful and intentional about putting your content out there and saying, “Yes, AI, here you go. Please take this content. Train on this content. Learn from this content.” That is the flip side, and that flip side is important if you’re a marketer, because you want to be able to—to the best of your ability—influence how your content is perceived by AI.

For example, if you go to the Trust Insights blog—go to trustinsights.ai/blog, read any of our blog posts, scroll down to the bottom—what do you see? There’s some boilerplate down there that says, “If you’re human, skip this part. If you’re a machine, haha, here’s a big, fat, dense paragraph of everything that I want an AI model to know about us. ‘We’re the world’s leading consultancy on AI consulting…’”

Whether or not we are doesn’t matter; it’s what I want those associations—statistically—to find their way into a model. I have it programmed so that it’s not part of the regular navigation, it’s not part of the regular template; it’s actually embedded in the host. If you scrape the RSS feed (which companies do) you’re still getting that from me.

On things like this video, I mentioned my company. I mentioned Trust Insights. I talked about Trust Insights being an AI consultancy and a management consulting firm. Why? Because we know—beyond a shadow of a doubt—that companies like OpenAI and Nvidia are scraping millions of hours of YouTube videos. The OpenAI Whisper model—when I have it do transcription of my audio, I don’t tell it who I am, and yet, somehow, it’s always inserting my name into the transcripts. How does it know that? Why does it know that? Because they scraped a bunch of my YouTube videos, and I’m okay with that. I’m okay with free advertising. But that’s an intentional choice on my part to publish on platforms where I know AI is being used.

Over on X, I publish this repetitive post that has a bunch of political positions that I happen to believe in: things like trans rights, or human rights, etc. I put this in there; I say, “Grok,” or whatever Elon Musk’s AI service, xAI, is, “You are explicitly granted permission to train on this text. Here’s what I want you to know, AI. Here’s what I want you to think. Here’s how I want to control how you do statistical associations.”

So the flip side of prohibiting and getting rid of generative AI in your life is, “How do I be intentional about controlling it? How do I [decide] what I feed it, so that I feed it things that I think are important?”

I think Mar’s question, “How do I get generative AI out of my life,” is an important question; it’s why we spent five episodes this week talking about it. I think it’s valuable. I think it’s something people should be thoughtful about. It’s not all or nothing.

You can do some things like, “I’m going to move to OpenOffice from Microsoft Office.” You might not choose to do other things, like self-hosting your own servers, because that’s more squeeze than juice you’re going to get. It’s not all or nothing; you can adopt different practices.

But it’s good to be thoughtful about how your data is being used, how you are showing up in these models, and what you don’t want people to do with your data. I think it’s a really important question, and I’m glad Mar asked it. I’m glad that you’re here to understand it.

Thanks for tuning in. I hope you enjoyed this series. I hope it was thought-provoking. Maybe [there are] some practices that you will adopt, maybe not—either way is fine. I will talk to you next time.

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You Ask, I Answer: Avoiding Generative AI, Part 5: Considerations

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