📜 AISW #100: Karo Zieminski, Denmark-based product leader and builder

Written interview with Denmark-based product leader and builder Karo Zieminski ("Product with Attitude") on her stories of using AI and how she feels about AI using people's data and works

Introduction - Karo (Product with Attitude)

This edition of AI, Software, and Wetware features a written interview with Karolina Zieminski, a 🇩🇰 Denmark-based product leader and builder. Karo is the founder and author of the Substack bestseller Product with Attitude and the creator of StackShelf.app.

This post celebrates Karo’s 1-year anniversary on Substack, my 2-year anniversary on Substack, and the 100th interview in the AI, Software, & Wetware series. We discuss:

• Studying AI obsessively and learning about AI by doing, not in the abstract

• The prompt she uses to get review feedback on drafts of her articles from three perspectives

• How she and her husband are guiding their kids on using AI tools responsibly

• Creating a new legal framework to handle machine vision data sourcing responsibly

• Why she started her 2026 “Builders With Attitude” initiative

and more. Check it out, and let us know what you think!

This post is part of our AI6P interview series on “AI, Software, and Wetware”. Our guests share their experiences with using AI, and how they feel about AI using their data and works. (This is a written interview; read-aloud is available in Substack. If it doesn’t fit in your email client, click HERE to read the whole post online.)

Note: In this article series, “AI” means artificial intelligence and spans classical statistical methods, data analytics, machine learning, generative AI, and other non-generative AI. See this Glossary and “AI Fundamentals #01: What is Artificial Intelligence?” for reference.

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Interview - Karo (Product with Attitude)

Karen: I am delighted to welcome Karolina Zieminski from Denmark as my guest on AI, Software, and Wetware! Karo, thank you so much for joining me on this interview. Please tell us about yourself, who you are, and what you do.

Karo: Thank you for having me! It feels a bit surreal to be on the other side of this, especially with you. You were one of the first people I met on Substack, one of my earliest subscriptions, and honestly a big part of how I figured out what this place is about.

By trade, I’m an AI product manager. By inclination, I’m a builder and a Substack writer. I spend most of my time where AI systems, product thinking, and human judgment bump into each other. Both in corporate environments and in public. That’s what I end up writing about in Product with Attitude. Sometimes very explicitly, and sometimes by sneaking product thinking into things like vibe coding guides.

I’m also a wife, a mom, and a dog-mom.

Karen: Karo, I remember when you first started on Substack in February 2025. Your writing and your drawings stood out immediately. I could tell you were going to succeed at leading and building a newsletter and community here, and you have! In less than a year, Product with Attitude became a Substack Bestseller with hundreds of paid subscribers, and I love seeing you on the ‘Rising’ leaderboard for Technology. So well deserved! 🙂

Karen: What is your level of experience with AI, ML, and analytics? I’d like to hear about how you have used it professionally or personally, or studied the technology, or built tools using the technology.

Karo: I’ve worked with AI professionally for over eight years, across product strategy for computer vision and physical AI, and through daily collaboration with ML engineers and researchers. I’ve also spent a lot of time implementing AI tools into real team processes. The unglamorous part where things either work or don’t.

I use AI every day. It would honestly be easier to list the things I don’t use it for than the things I do. Every part of my workflow, from research and brainstorming to productivity and coding, is supported by AI. I even drive a car with self-driving capabilities, so AI shows up in my life well beyond work.

That’s also why responsible tech matters so much to me. I’m not just commenting on it — I’m one of the people building it, and I want to do it right.

And yes, I study the technology, almost obsessively at this point. I don’t think any of us are immune to the career shifts AI is creating, not really. This is just how I deal with that reality.

Karen: Do you have any tips you can share with people who are just getting started on how they can learn and study AI? What worked for you, and how do you learn nowadays?

Karo: Love this question.

I learned more about AI by doing than by studying it in the abstract. My biggest tip for beginners: pick one real task you already care about, commit to using one AI tool on it every day for a week, and see what works. Then ask your favourite AI to help you turn your results into a repeatable workflow.

That will be your first automation. And once you see what’s possible, new ideas will follow, I promise. I’d also encourage everyone to join communities like Product with Attitude and document your journey, so others can support you along the way. Plus, it’s more fun this way.

Karen: Can you share a specific story on how you have used a tool that included AI or ML features? What are your thoughts on how the AI features [of those tools] worked for you, or didn’t? What went well and what didn’t go so well?
(Reference for this question: “But I don’t use AI”: 8 Sets of Examples of Everyday AI, Everywhere)

Karo: There are a lot of examples. One very concrete one is my daily use of ChatGPT as a thinking partner. I’ve trained it to push back on my assumptions and understand my thinking patterns better. To ask questions I was hoping to avoid. It’s not always comfortable and it definitely slows me down, but that’s usually where the breakthroughs happen - right around the moment I realize I was wrong.

Karen: Can you share an example of something specific you asked ChatGPT about, and what assumptions it helped you understand that you were making?

Karo: Not from the top of my head, but I have a prompt I use after I draft my articles:

Read this text as someone wearing three different hats: an expert, a beginner, and a skeptic. For each perspective, tell me what’s unclear, what’s missing, and what feels wrong.

It’s a simple trick, but it catches blind spots I’d never find on my own.

Karen: Do you have any examples you can share of a time you used an AI tool and it did NOT work well for your needs? For instance, has your self-driving car ever made any mistakes? Or has a chat tool ever given you bad advice on medical or veterinary questions?

Karo: Oh yes, so many times! It broke my code, deleted my files, gave me hallucinated answers. That’s why developing critical AI literacy matters so much. I wrote more about this in a recent article.

You Can't Critique AI You Haven't Built With

(4) Karen: If you have avoided using AI-based tools for some things (or anything), can you share an example of when, and why you chose not to use AI?

Karo: Yes, I have.

If a product makes it onto my Absurd Product Decision list, that’s a hard no. I won’t use it, recommend it, or accept a sponsorship request.

2025’s Most Absurd Product Decisions

The same goes for social platforms with a reputation for exploitative data practices. I won’t join it, no matter how popular it is.

And I also stop using tools the moment I notice dark patterns during testing. If the ethical baseline isn’t there, I’m not interested.

Karen: So that’s a good view of which tools you choose to use or not use. How about use cases? 🙂 Is there any purpose that you would never use any AI tool for? (For instance, writing a personal email to a family member or friend, or creating music or images)

Karo: I’d never use AI for anything I wouldn’t do in real life. It’s that simple. If I wouldn’t plagiarize someone’s work, generate a fake review, impersonate someone, or manipulate people’s emotions. AI is just a tool, it doesn’t change the ethics.

Karen: You mentioned being a mom. How are you guiding your kids on using and learning AI? Is AI being used in their schools?

Karo: This is one of the most complex questions we face as parents. First of all, I’d never claim I have all the answers. I don’t think anyone does, no matter how confidently they post about it online. We simply can’t predict what AI will look like in 20 years, which means we can’t predict the full impact on the kids growing up with it right now. That’s a humbling thing to sit with.

Our approach at home is controlled exposure. My kids are 14 and 11, and they’re naturally AI-curious. My husband and I both work in tech, so they overhear the conversations, they see us experimenting, they ask questions.

When one of them wants to learn something, I’ll show them how AI can help with that, but I also explain the risks. Every single time. There’s a lot of repeating myself heheh. But I know from experience that understanding doesn’t come from one perfect conversation, it comes from hearing the same message in different contexts, over and over, until it clicks.

That’s the best I’ve got, and I think being honest about that uncertainty is more useful than pretending we have a parenting playbook for something none of us have seen before.

Karen: A common and growing concern nowadays is where AI and ML systems get the data and content they train on. They often use data that users put into online systems or publish online. And companies are not always transparent about how they intend to use our data when we sign up.

How do you feel about companies using data and content for training their AI/ML systems and tools? Should ethical AI tool companies be required to get Consent from (and Credit & Compensate) people whose data they want to use for training? (the “3Cs Rule”)

Karo: This is a very real concern. From my professional work, I know these practices are far more common across AI systems than most people realize, and they tend to live in broad grey zones.

Getting valuable data with full, informed consent is HARD. Enabling others to do it right is even harder.

It requires robust legal frameworks and a clear exchange of value for the people sharing their data. That’s exactly what we’ve been building in computer vision, and it was really, really hard. But if you want to build things the right way, you do hard things.

Karen: It’s great to hear that you helped to drive respect for data dignity in your work!

As a user of AI-based tools, do you feel like the tool providers have been transparent about sharing where the data used for the AI models came from, and whether the original creators of the data consented to its use?

Karo: I honestly doubt that any of the tools I use have 100% provable data lineage, especially when it comes to data collected in the past, before regulations really tightened. BUT! I do think companies are getting better at understanding that transparency = trust. And more of them are starting to act on that.

Karen: As you pointed out, it’s often hard to know where companies get their data, because they’re often not transparent about it. As an AI tool user, does how a company acquires their data, and whether they respect these principles, factor into how you choose a tool? Or, if a company was transparent or if their process was proven in a way that we could actually know, would that factor into your tool choices in some way? Those decisions are always tradeoffs.

Karo: Yes, absolutely. No company is perfect on this front, so for me it comes down to transparency. If a company is open about how they source and handle data, I’ll invest in their tools. If they’re not, I move on. That’s the most practical stance I can take right now.

Karen: Can you share about where the data came from and how it was obtained when you’ve built an AI-based tool or system?

Karo: As I mentioned, it was really hard. To give some context, we had to build an entirely new legal framework, because there wasn’t one to rely on.

That process alone took over two years and involved multiple legal firms, ethical and regulatory bodies, customers, engineers. We wanted to go beyond just satisfying legal requirements and make sure that the value exchange was fair.

Karen: I love hearing about companies that are trying to handle data sourcing responsibly! Can you elaborate a bit on what kind of data was involved, and how the value exchange was eventually handled?

Karo: Yes, I could talk about this for hours! We’ve built a library of responsible data for computer vision.

By responsible, I mean the data is collected with informed consent, meets the highest GDPR standards, complies with human rights principles, the EU AI Act, and local regulations, is fully anonymized, and is never directly accessible to humans during model training. And all data is collected in exchange for access to better models.

Everyone wins:
- People who share data do so because they want to benefit from more accurate models. And they can do it securely, knowing their data is protected.
- And developers get access to data they wouldn’t be able to obtain otherwise, but they can never download it, and no private or sensitive information is ever exposed to them.

Nothing like this existed for computer vision before, not at this scale. And building it was really, really hard.

Karen: As consumers and members of the public, our personal data or content has probably been used by an AI-based tool or system. Do you know of any cases that you could share (without disclosing sensitive personal information, of course)?

Karo: Yes, almost certainly. Like most people, my data has flowed through social media platforms, newsletter platforms, and analytics tools: signup information, posts, photos, probably behavioral data as well.

None of this is unusual, which is exactly the problem. Much of our data participation in AI systems happens quietly, almost by default, and through everyday interactions rather than dramatic moments of consent.

Karen: I agree - concepts of informed consent are problematic in the way terms and conditions are presented, and we often don’t have any real choice. But as a US resident, I tend to assume that people in Denmark have somewhat better protection. Do you find that regulations like GDPR and the Digital Markets Act are having an impact on making companies respect your personal data? Or are they not as helpful as people outside the EU might assume they are?

Karo: Absolutely. In Denmark, GDPR, the EU AI Act and other regulations are a part of training for everyone and we have corporate mechanisms to act if we see something wrong. It’s business as usual, a constraint everyone respects. To the point that when someone in our neighbourhood photographed a person stealing a bike and shared it in a local Facebook group rather than giving it to the police, they faced a backlash.

Karen: Are there any tips you can share with people on steps they could take to protect their data?

Karo: Yes, keep it close to you. Don’t share anything you wouldn’t want strangers to know about you, ever.

Karen: Do you know of any company you gave your data or content to that made you aware that they might use your info for training AI/ML? Or have you ever been surprised by finding out that a company was using your info for AI? It’s often buried in the license terms and conditions (T&Cs), and sometimes those are changed after the fact. If so, did you feel like you had a real choice about opting out, or about declining the changed T&Cs?

Karo: Meta and Linkedin are good examples, where changes to terms around AI training were announced long after people had already shared years of content.

Karen: Yeah, the LinkedIn opt-out and Meta opt-outs in summer 2024 are great examples of this. Opt-out requests from European citizens had to be honored while ours did not. I ended up spending hours deleting my Facebook posts and photos before the deadline.

Are there any special steps you are taking to protect the privacy of your kids’ data? Any advice that you could share with other parents?

Karo: I’m in the ‘no social media for kids and teens’ camp, heheh. Not by the kids, not by the parents. Not until their prefrontal cortex is developed at 25 and they can make this decision for themselves. Luckily, where we live everyone agrees, so my kids aren’t the odd ones out.

Karen: Has a company’s use of your personal data and content created any specific issues for you, such as privacy, phishing, or loss of income? If so, can you give an example?

Karo: Luckily, no. I haven’t seen any concrete negative impacts so far, although I’m aware that these issues often surface indirectly or over time.

Karen: You are lucky and I hope that holds up! Most Americans I know have been caught up in at least one data breach, and sometimes many. And we get lots of junk calls and spam and phishing emails from companies that bought our info from a data broker or someone who just stole it. It sucks 🙁

(10) Public distrust of AI and tech companies has been growing. What do you think is THE most important thing that AI companies need to do to earn and keep your trust? Do you have specific ideas on how they can do that?

Karo: The most important thing companies can do is treat trust as a product requirement, not a PR problem. That means designing systems where consent, explainability, and accountability are built in from the start, treated as part of the value proposition, not added later when regulators or users push back. My first Substack article, “Fix your ethics after lunch, not after launch,” focused on exactly that idea.

Fix Your AI Ethics After Lunch, Not After Launch

Karen: I remember that article — it resonated with a lot of people, and “after lunch, not after launch” was a brilliant tagline!

I appreciate you making the time for this interview, Karo! Is there anything else you’d like to share with our audience?

Karo: I’d probably just add this: a lot of the most important conversations about AI aren’t happening in company-sponsored press releases. They’re happening among practitioners, builders, and writers. People like you and me, trying to make sense of it as we go. That’s why I’d encourage anyone who’s curious to spend time on Substack, where those conversations are actually unfolding.

Karen: Absolutely; we have an amazing community around the topics of AI, both in general and in SheWritesAI. And you are one of the clear leaders here on Substack, one of the people whose articles I always look for. In fact, could you say a few words about your 2026 “Builders With Attitude” initiative?

Karo: Yes, and thank you for letting me!

It’s meant as a counter-narrative to the ‘I built an app in two hours and became a millionaire’ stories all over YouTube and X.

I wanted to show how people build real value instead of just extracting money from users. That means taking readers through the build process and the product decisions involved, including skipping features or even shelving products when they know they won’t be able to deliver on their promises to customers.

Build With Attitude ✊ - An Open Invitation To Vibe Coders Who Care About Building Things That Matter

Karen: Karo, thank you so much for joining me on this interview! I can’t wait to see what you do this year and beyond. 🙂

Karo: Thank you sooooo much for having me! Really appreciate you bringing these topics to the rest of us. We need to keep this conversation going.

Interview References and Links

Karo Zieminski on LinkedIn

Karo Zieminski on Bluesky

Karo Zieminski on Substack (Product With Attitude)

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About this interview series and newsletter

This post is part of our AI6P interview series on “AI, Software, and Wetware”. It showcases how real people around the world are using their wetware (brains and human intelligence) with AI-based software tools, or are being affected by AI.

And we’re all being affected by AI nowadays in our daily lives, perhaps more than we realize. For some examples, see post “But I Don’t Use AI”:

"But I don't use AI": 8 Sets of Examples of Everyday AI, Everywhere

We want to hear from a diverse pool of people worldwide in a variety of roles. (No technical experience with AI is required.) If you’re interested in being a featured interview guest, anonymous or with credit, please check out our guest FAQ and get in touch!

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Microphone photo by Michal Czyz on Unsplash (contact Michal Czyz on LinkedIn)

Credit to CIPRI (Cultural Intellectual Property Rights Initiative®) for their “3Cs' Rule: Consent. Credit. Compensation©.”

Credit to Beth Spencer for the “Created With Human Intelligence” badge we use to reflect our commitment that content in these interviews will be human-created:

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