I spend a lot of time inside AI systems.

Not casually.

For the last several years, I’ve worked at the edge of AI product development, ethical AI, and AI-assisted building.

I manage AI products alongside brilliant researchers, engineers, and compliance leads, so I see the full product chain up close: research decisions, engineering tradeoffs, ethical tradeoffs, and security risks. The product choices that tell you what a company thinks people are for.

I build indie products on the side.

I’ve read every frontier model release note since 2022. Four years of it. OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, xAI, Perplexity, Qwen, the long tail.

And I also have two children.

Some nights I kiss them good night and I don’t know whether the world I’m helping build will let them have a job, a craft, a sense of slow attention, or the kind of boredom that turned me into an ambitious adult.

I open the laptop and I keep building anyway. I haven’t figured out how to stop doing both of those things in the same evening.

The Builder-Parent Paradox

Inside access gives me responsibility, not certainty.

There isn’t a name for the thing I live inside, so I’m going to give it one.

The Builder-Parent Paradox: the realisation that I’m one of the people best positioned to protect my kids from AI precisely because I help build it, and I still don’t know enough to confidently say what the future will look like. The paradox is that inside access gives me responsibility, not certainty.

It’s not the Parents’ Paradox. Not Gopnik’s gardener.

It brings a specific kind of guilt: the guilt of participating in something I also have to help my children question, resist, and live with.

I don’t know how to put the guilt down and I don’t want to. It turns out to be a useful instrument. It keeps me from numbing myself into a cleaner, easier, less honest version.

The people who left the industry to protest from outside don’t get to shape what their kids inherit. The people inside, building carefully, do.

That’s the Builder-Parent Paradox, and it applies to anyone shipping AI with skin in the next generation’s game. Founders, PMs, engineers, teachers using the tools in classrooms. Regulators with kids in those classrooms.

If you have professional authority over a thing whose long-term effects on the people you love are unclear, you live in this paradox.

AI Is the Biggest, Most Ambiguous, and Unevenly Distributed Technology Shift of Our Time

Many writers focus on the size of the shift.

Of course it is big. You don’t need a newsletter to tell you that.

The two parts I keep thinking about are the ambiguity and uneven distribution.

Ambiguity

I don’t see AI as something that neatly fits into one moral category.

• It’s not simply good.

• It’s not simply bad.

We can use it to learn faster, build faster, design faster, research faster, and test ideas that would have stayed stuck in a notebook for years.

But I can also see how the same systems concentrate power, weaken craft and judgment, slop the internet, and remove the kind of entry-level work that used to teach people what thinking even is.

S&P Global’s 2026 labour analysis now describes AI’s employment impact as modestly negative, after a more neutral-to-positive picture in 2025.

Challenger’s May 2026 report found that AI was cited in 40% of US job cuts announced that month, up from 7% in January.

Uneven Distribution

The rural pharmacist who can use AI to double-check a prescription against medical references in seconds is gaining something real.

So is the founder outside the usual tech hubs who can build and ship software without needing an expensive team.

So is the child learning calculus, English, or computer science from a model running on her family’s only smartphone.

But the American copywriter losing their job to a model that produces 70% of the same output for 1% of the cost is unambiguously losing.

So is the junior designer whose entry-level path just got automated.

Both are happening at the same time. The technology is the same technology. What changes is who pays the cost, and who gets to turn it into power.

Things I Will Not Write in This Essay, and Why

This is the list of sentences the AI commentariat keeps writing that I’m explicitly refusing. If you have written any of these, no judgment. I have probably written most of them too. I’m trying to stop.

• I will not write "AI is just a tool." AI is part of a larger system of model choices, product decisions, business incentives, policy constraints, ethical tradeoffs, and human consequences.

• I will not write “the technology is neutral.” Because the technology was built by a small number of people with very specific incentives.

• I will not write “we are still early.” “Early” is the word this industry uses when it wants applause for the future and immunity for the thing it already sells.

• I will not write “the future is uncertain but exciting.” Because it’s only exciting for people who don’t have to live with the downside.

• I will not write “humans will adapt.” Because some humans, yes. Mine, I don’t know yet. People gaining access to capability they never had before, probably. The copywriter whose work just became cheaper to replace, possibly not.

• I will not write “AI will free us to focus on what matters.” Because the people saying this are usually not the ones losing their jobs.

These are the claims I can confidently refuse as someone inside the Builder-Parent Paradox.

Studying the Machine While Using the Machine

Studying, in my case, is not a metaphor. It’s a workflow.

Every frontier release note gets logged with the date and my private commentary about what the company is signalling about:

• who they think their user is now

• whose work just got cheaper to automate

• what new aspects of critical AI literacy I should explore next

• what that changes in the “who do you want to become when you grow up” discussion with my kids

• how I plan the next moves of my own career

The first three become topics for my Substack. The last two are why I have a Substack.

That is what studying looks like for me.

What I’m Betting On

These are things I would put real money on.

• I’m betting that critical AI literacy will be a bigger career moat in 2028 than any specific AI tool.

• I’m betting that the builders (big or small) who document publicly will outlast the VC-funded sprinters.

• I’m betting that my kids will need taste and judgment more than they will need to learn to code.

• I’m betting that the writers who refuse the smooth version will become disproportionately citable.

• I’m betting that the next five years of AI value creation will skew heavily to the Global South. More than six billion people are already online, and more than 1 billion people now use AI monthly. The European-American discourse keeps missing this. The product opportunities will not.

If I’m wrong on any of these, I will write the essay called Where I was wrong about AI in 2026. I will publish it, I promise.

The part I can’t bet on is whether I’m raising my kids the right way for the world these bets describe.

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What I’m Doing About It

Here is the unglamorous list of what I’m doing:

• I write with a builder’s hands and a product strategist’s brain: practical workflows, sharper product judgment, and deeper insight into how AI systems are built and positioned.

• I write the harder version. I analyse AI product launches without the hype, through the lens of someone who builds AI for a living.

• I test and systemise pretty much everything, then document what broke, not just what worked.

• I share what I build: code, prompts, workflows, tools, mistakes, and decision points. Other builders can copy it, fork it, or argue with it.

• I let my kids see me struggle with it. The version where I’m pacing the kitchen trying to decide whether a release is worth covering or if it’d only add to hype. They need to see what it looks like when an adult sits with a hard question.

• I keep one foot outside, binge-watch TV shows, garden, cook badly and take my kids to the woods. Keeping a self that AI can’t touch.

Am I on the Right Side of This?

I don’t know if I’m on the right side of this.

I think most people working in tech don't know either, and the ones who say they do are often the ones I would not let near a real decision.

What I know is this: I’m going to keep learning, keep writing about it, and keep my attitude. I’m going to try to leave my kids a world where curiosity, judgment, and the ability to think for themselves still count for something.

If you remember nothing else from this essay, remember this: inside access gives me responsibility, not certainty. That is the Builder-Parent Paradox. If you have professional authority over a thing whose long-term effects on the people you love are unclear, you live in it too. Act accordingly.

FAQ

What does it mean to think about AI as a builder and a parent at the same time?

It means you cannot read a release note as just a release note. Every product decision becomes a question about the world your kids will grow up inside. It adds weight, and it also kills naivety fast.

Is AI good or bad?

Neither, and both. AI is the biggest and most ambiguous technology shift of our time. It can expand human ability and it can quietly remove the conditions that built that ability. The honest answer is to hold both truths at once and act carefully.

What should non-technical people do about AI in 2026?

Build something. Even one small thing. The fastest way to stop being passive about AI is to make something with it, break it, fix it, and notice what it does and does not understand. Literacy follows building, not the other way around.

Why is “ambiguity” the right word for AI right now?

Because the benefits and harms are tangled at the root. You cannot pull one out without the other coming with it. Calling it “ambiguous” is not fence-sitting. It is the most accurate description of the technology in mid-2026.

How do you stay sane working inside AI?

I keep one foot outside. I write the harder version. I let my kids see me struggle with it. And I treat the guilt as an instrument, not a problem to solve.

I’m Karo Zieminski. AI Product Manager and builder. I write Product with Attitude, an AI newsletter for thousands of subscribers developing critical AI literacy the only way it sticks: through practice.

We don’t just use AI. We build workflows, automations, and products with it, while studying how AI itself is built, positioned, and woven into our work. Subscribe here.

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• First: Are you an AI optimist or a pessimist?

• And second: Why are you looking at AI from a product builder perspective instead of just a technical one?

My short answer to the first question: It depends.

My short answer to the second question: Because understanding different AI systems and design philosophies contributes to critical AI literacy. That's the whole answer. Everything else is elaboration.

But the elaboration matters. It changes how you see every AI tool you touch. And it's the reason I started Product with Attitude.

Hey, I’m Karo 🤗
I’m an AI product manager, builder and thinker who spends most of her time inside AI systems to understand what they’re built to do. I write Product with Attitude to help people stop consuming AI passively and start building with it deliberately.

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Things Are Bad (But We Can Build Our Way Out)

From the human cognition perspective, things are bad. And I assume, they will get worse.

A 2025 MIT study found that participants who relied exclusively on AI for writing showed weaker brain connectivity and lower memory retention; effects that lingered even after they stopped using AI.

We could blame everything that’s wrong with our cognition on AI, but AI is just the latest in a long history of automating away the parts that made us sharper. From GPS, autocomplete, spell-check and phone numbers memorized by our mobiles, we’ve been choosing convenience over friction for years.

What comes next is worse. With GPS, we offloaded navigation. With AI, we're offloading judgment. And unlike spell-check or GPS, there's no obvious ceiling on what AI can absorb.

Friction is where learning happens. And we’ve been engineering it out.

Build To Learn

Some people instinctively push back against AI. Others just let it wash over them, accepting the outputs without questioning what produced them. I get both impulses.

But neither one builds the muscle we need.

It's immersion - hands-on, deliberate immersion - that helps us understand why, and how these systems influence our thinking.

That's why I keep saying: build with AI. I don't necessarily mean launch a startup. I mean use AI for more than just a chat box.

Design your own workflows, assemble your own systems, put together micro apps that only you’ll use, test different scenarios.

For example:

• Build a personal knowledge base that an AI can search, then compare its answers against a model without your context. The difference shows you exactly how much of an AI’s usefulness comes from your decisions versus raw capability.

• Run the same image generation prompt across DALL-E, Midjourney, and an open-source model. The aesthetic defaults are wildly different, and those defaults reflect cultural assumptions their training data baked in.

• Build a simple automation that routes different tasks to different models. Use one model for writing, another one for code. The act of deciding which model for which job forces you to understand what each one does well, and what it quietly gets wrong.

Accept the friction that comes with it. Build to learn.

Build To Verify

We all know that AI sounds confident even when it’s wrong. A hallucinated answer and an accurate one arrive in the same calm, well-punctuated tone.

With code, correctness is observable.
Building creates your own verification layer.

Try this: give an AI data you already know well. Say, your company’s last quarter.

• First, the passive consumer way: paste the spreadsheet into a chat and type Analyze this. You’ll get a polished summary that sounds right. Maybe it is. You’d have to check every line to know.

• Then, the builder’s way: collaborate with AI to write a skill.md file or a short script that tells the AI exactly what to look for:

  • which metrics matter

  • what good analysis looks like

  • what to flag.

Now the AI works within your framework, and deviations from reality become obvious.

Same data. Same model. Completely different level of trust in the output.

Do this a few times, and you develop an instinct for the patterns: where models cut corners, where they over-generalize, and - most importantly - how to prevent it.

Consumers trust the output. Builders learn to test it.

Tools Are Never Neutral

Tools are never neutral. They carry the fingerprints of their creators.

You can’t separate a tool from the company that funds it.

Values travel through code.

Some models optimize for speed, some for the ‘‘wow’’ effect, others for safety. Those trade-offs are more than just technical details - they’re value statements.

AI outputs are shaped by alignment decisions we didn’t participate in. Critical AI literacy means reclaiming that context and learning to see the decisions that were made before we ever opened the app.

Once we see the values embedded in tools, we stop reacting to what they show us and start noticing what they don't.

Digital literacy, in the deepest sense, is pattern recognition.

Critical AI Literacy Begins Where Brand Loyalty Ends

When we use AI daily, we stop asking Which one is best? and start asking Best for what? That shift is not small. It’s the difference between being a consumer and being a practitioner.

But most people never make that shift. They pick one tool and settle in. They build habits around its defaults, absorb its tone, accept its boundaries as universal limits.

Over time, the tool’s way of thinking starts to feel like their way of thinking. The model’s alignment becomes their alignment; and they forget that behind each AI response, there’s a business model.

This is why I encourage everyone to use multiple AI systems, as a discipline. Each one exposes blind spots the others protect. Navigating those differences forces decisions that no single tool demands of you.

That’s friction. And we’ve established what friction does.

Abundance Gives Us Power, But Only If We Choose Deliberately

We don’t have to live inside the dominant ecosystem. We can assemble our own.

Abundance of tool choices shifts power from vendors to users, but only if users are willing to choose deliberately.

That “if” carries enormous weight - the power is theoretically there, but most people never claim it.

They default to what's popular, what's bundled, what requires the least evaluation.

Deliberate choice means deciding which model handles which task and why. It means noticing when a default setting serves the vendor more than it serves us.

At times, it means building our own stack to practice the kind of thinking that pre-built tools are designed to eliminate.

Cognitive strength is built the same way as muscle: resistance first, results later.

Building is Participation. Participation is Prevention.

The danger isn’t AI alone. It’s disengaged communities.

When we stay silent, systems get designed about us, not with us. The people who show up early - who test, who critique, who build alternatives - are the ones who can influence defaults. And defaults shape everyone else.

This isn’t theoretical.
Every platform you use today was formed during a window when a small number of active participants set the norms that billions now live inside. Facebook’s News Feed, Google’s search ranking, Twitter’s engagement metrics, all designed during periods when most future users hadn’t arrived yet.

AI is in that window right now. The models, the interfaces, the policies, the defaults about what’s helpful and what’s harmful, all of it is being shaped in real time by whoever shows up.

If that’s only corporations, the systems will reflect their priorities. If it includes engaged, critical, building communities, the defaults have a chance to shift.

The problem is collective, the answer isn’t. It’s personal. Pick a tool you've never used. Build something small with it this week. That's it.

The good news is that we don’t need permission to participate. We need a willingness to be early, to learn, and to share what we’ve learned. The tools will evolve. Our edge depends on evolving with them.

The Real Stakes: Intelligence By Design, Not By Accident

Here’s what I think about more than anything else in this space.

Bigger models aren’t automatically better futures.

An unconstrained AI maximizes output. A well-designed one maximizes human growth.

The risk isn’t that humans can’t think, it’s that we won’t need to. If nothing pushes our reasoning, our reasoning stops pushing back. The most advanced era in history could also be the most cognitively complacent.

Human intelligence shouldn’t survive by accident. It should be engineered for.

And this is exactly where product thinking becomes essential. Product thinking asks: Who is this built for? What behavior does it encourage? What does the user lose if the design succeeds?

These are the questions that determine whether AI systems amplify human capability or replace it.

Why The Product Lens Matters

Every section of this essay has been a product question, whether it looked like one or not.

• Which tools carry which values? That’s product thinking.

• Why do defaults capture users? That’s product thinking.

• How do you choose deliberately in an age of abundance? That’s product thinking.

The product lens isn’t one perspective among many - it’s the one perspective that connects all the others.

That’s why it matters.

Critical AI literacy doesn't begin with a course. It begins the moment you stop asking which AI is best and start asking what it was built to do, for whom, and at whose expense.

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What’s Being Built in the PwA Community Right Now

Theory’s over. Here’s what’s actually happening in our community right now:

• is launching CarouselBot tomorrow, and I'll be posting an interview with her where she takes us behind the scenes of the build.

• is working on a new tech art project and she needs your help! It's one anonymous question. That's it.

• built Wiz, a personal agent that, among many other tasks, helps him publish articles on a consistent schedule.

• built an AI Tutor for Claude, that won’t let you fake understanding.

• built an app for long-distance couples to help them stay connected.

• shipped Markdown-to-Branded PDF generator that turns any markdown file into a professionally branded PDF in seconds. Let him know if you’d like to try it! Dheeraj just joined StackShelf; you can see his profile and freebies here.

• built a guide about AI risk before deployment.

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If you’ve read anything I’ve written before, you know I struggle with finding the perfect title.

Like in my previous post:

There is a reason for that.

And if you assume it’s a growth hack to reach more people, you’re only half right.

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Look, I get it: ‘‘Ethics’’ doesn’t exactly scream ‘‘Fun!’’ or ‘‘Oh my god, cancel my weekend plans!’’

It sounds kind of boring and kind of bitter. Like broccoli.

Even if you sprinkle it with something spicy like ‘’disruption’’ or ‘’innovation’’, it still doesn’t exactly set hearts racing.

Now you’ve just got… well, still broccoli. Like when parents hide veggies in muffins while insisting, ‘‘You can't even taste it!’’.

Meanwhile, the "Growth Hack Your Way to Billions" post down the hall is serving cocktails with tiny umbrellas and promising everyone they'll be the next unicorn founder.

Would you choose broccoli over that?

The Question That Changed My Tech Career

But here’s the twist I didn’t see coming when I first started working in tech.

At first, everything feels thrilling and dynamic. You’re trained to ship fast, scale faster, and pick your next project from post-its flying through a wind tunnel.

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