HumanPrint: How to Use AI Without Losing Yourself

That conversation you’re having with AI? It’s also data going somewhere.

In this episode of HumanPrint, I talk with Apostolos Stamenos, an award-winning former federal technologist and founder of ouroborOS Consulting, about what responsible AI use looks like when privacy, accuracy, and data governance actually have consequences.

During the COVID-19 response, Apostolos worked with public-facing hospital data at HHS, where bad numbers could eventually reach Congress, the media, and the public. That experience taught him to think about AI systems the same way he thinks about any other data infrastructure: What can fail? What can fail silently? Where is the information going? And who will notice if something goes wrong?

We talk about why a conversational interface can make people forget they’re still submitting data to a company’s servers, the difference between local and proprietary AI models, what organizations may be giving away when employees paste information into ChatGPT or Claude, and why privacy is about much more than removing somebody’s name from a document.

We also get into AI-generated fraud and manipulation, the difference between what a chatbot “remembers” and what the company behind it may retain, and why supposedly harmless platform features can also double as data annotation systems.

Apostolos’ starting question for organizations considering AI is refreshingly simple: What are the human stakes? The reputational stakes? The financial stakes?

Because sometimes the responsible answer really is: don’t paste that into the chatbot.

Learn more and connect with Apostolos: https://substack.com/@astamenos

Jon Mick spent 40 years trying to fix his brain. Then he built AI infrastructure around the way it actually works.

In this episode of HumanPrint, I talk with Jon, a twice-exceptional product leader and founder of AIs & Shine, about the personal AI system he built to better understand and support the way he thinks.

We get into what changes when AI has meaningful context instead of just a single chat window, why Jon created rules, a personal constitution, and even a “treaty” governing how his AI interacts with him, and how he uses gates, evidence, and explicit labels for inference to keep the system from becoming the authority on who he is.

We also talk about the relationship between AI and self-understanding, why validation matters when a model surfaces something about you, and what Jon learned when he tried to turn his own system into something other people could use. One of the biggest lessons: insight can arrive faster than a person is ready to integrate it.

AI can help you see patterns, build context, and understand yourself differently. The human operator still has to decide what those patterns mean.

Connect with Jon: https://substack.com/@jonmick

What if the most interesting use of AI isn’t doing the same work faster, but making entirely different things possible?

In this episode of HumanPrint, I talk with designer and writer Emma Klint about AI as augmentation, using models to think out loud, wrestle with ideas, and expand what she’s capable of creating without handing over the thinking itself.

We talk about why she sometimes asks AI to prompt her, what happens when a model rushes too quickly toward a finished deliverable, how her background in human-computer interaction shapes the way she sees AI interfaces, and why the blank chat box itself may be influencing how people understand and use these tools.

We also get into learning, creativity, authorship, and the difference between generating more content and creating more opportunities for people to express experiences and ideas that might otherwise never make it out into the world.

Learn more & connect with Emma: https://substack.com/@emmaklint

What happens when you stop asking AI to work like everyone else’s tool and start building it around the way your own brain actually works?

In this episode of HumanPrint, I talk with Tracy Albinson about the Portal, a custom system she built around Claude and Obsidian to support memory, executive function, writing, research, and the way she makes connections as a neurodivergent thinker.

We get into AI as scaffolding rather than a substitute, why she deliberately limits what the system is allowed to do, the difference between memory and context, and how she uses AI heavily without handing over her writing, judgment, or autonomy.

Tracy’s approach is deeply personal by design: build what helps, leave out what doesn’t, and keep yourself in charge of the process.

For the first guest interview on HumanPrint, I’m talking with Cookie Young of The Neon Vegas — marketing heretic, synthetic era image provocateur, and boutique AI filmmaker — about what happens when you stop debating AI long enough to actually make stuff with it.

Cookie takes us inside her filmmaking process, from writing the script and developing storyboards with ChatGPT to directing AI-generated video, editing, music, sound, and figuring out when the technology needs another round of direction…and when it’s time to say, okay, bro, whatever, this works.

We talk about the parallels between AI filmmaking and traditional filmmaking, why “right” matters more than perfect, keeping your own vision at the center when you're working with generative tools, and the difference between showcasing what an AI model can do and using it to tell a story that actually belongs to you.

And then there’s the idea that kept coming up throughout our conversation: fun.

What changes when you approach these tools with curiosity instead of fear? What becomes possible when AI expands what you can create instead of becoming the subject of everything you create?

Cookie has a pretty simple answer to all the endless talking about AI: Don’t talk about it. Be about it.

So we did. And had a ridiculous amount of fun in the process.

What are you waiting to create while the rest of the world argues about AI? In this episode, you’ll examine how uncertainty, criticism, and shifting standards can quietly delay your work, then use your own WHY, creative history, and controllable inputs to decide what you’re ready to make now instead of waiting for consensus.

Has AI actually improved your writing process, or just changed it? This episode gives you a practical way to compare how you worked before generative AI with how you work now, so you can identify what genuinely helps, what may be weakening your skills or voice, and which parts of your process are worth protecting.

How do you protect your creative headspace when everybody seems to have an opinion about how you should write? In this episode, you’ll learn how to separate valuable criticism from noise, spot when imagined judgment is starting to steer your work, and decide who actually deserves influence over your creative process.

What this episode is about

You can disagree with everything a platform is doing and still have your subscribers, income, archive, and professional network sitting on it. Leaving is an option. Staying is also an option. But staying doesn't mean accepting every feature or treating a tech company's business strategy as a moral instruction for your creative life.

In this episode

Why we need to stop reacting to each new feature as if it came from a literary friend who knows us personally. Karen Hao's Empire of AI and the competitive pressure driving companies to push costs onto workers, artists, and anyone whose data helps build the systems. My theory, labeled clearly as a theory, that distinctively human content is becoming more valuable precisely as it becomes harder to identify. Six boundaries worth setting: your process, scanning, your archive, direct access to your audience, unpaid platform labor, and knowing in advance what would make you leave. Plus recent research showing that AI accusations track social gatekeeping more than they track actual AI use, which means writing badly on purpose will not save you.

HumanPrint homework

Write your platform terms. Pick the platform where you publish or build professional relationships and answer five questions: What does it genuinely give me? What do I give back in work, attention, data, and revenue? Which parts of my process stay private? What do I need to export or protect somewhere I control? And what specific change would make me leave? Then take one action this week. Download your archive, export your contacts, update your site, or write your private AI-use boundaries.

What this episode is about

Spotting AI writing has become an internet sport. Someone sees an em dash or a tidy structure and starts a forensic investigation from the comments section, and now the platforms have handed everyone official tools to do it. This episode is about why "I can always tell" is almost never true, and why turning readers into unpaid AI investigators is a bad trade for everyone including the people who think they're winning.

In this episode

Why "seems like AI slop" is a process accusation, not a quality judgment, and what happens to actual criticism when we stop asking whether the argument held up. Three possible motives behind these features: money, engagement, and data. Substack's Pangram partnership sitting alongside its MCP connection that lets publication owners pipe their data into ChatGPT and Claude. Why human fingers on a keyboard are not an ethical standard. And the movement from statistical estimate to moral verdict, which is where the real damage happens.

HumanPrint homework

For one week, refuse to play AI detective. Don't scan anybody's work, don't accuse a writer, and don't use "sounds like AI" as your explanation for why something didn't land. Name the actual issue instead. Was the language generic? Was the argument unsupported? Was the story missing? Be specific. And notice what the platform is asking you to report, label, and classify on its behalf. You don't work for the detection department.