In this episode of the Leaders & Missions podcast with Carolina Marques, I explain the micro-problems philosophy: the biggest market opportunity right now isn’t building the next billion-dollar AI platform, it’s using AI to solve millions of tiny, specific problems that were never worth solving before. We discuss how AI has fundamentally changed the economics of problem-solving and what that means for leaders, creators, and entrepreneurs.
Carolina invited me on to talk about my journey from architecture to AI, why prompt engineering is a real and underestimated skill, and how the people who will thrive in the AI era aren’t the ones building massive platforms — they’re the ones solving small problems at scale.
“If I was able to do this, many people can. AI is not replacing us, it’s enabling us to solve the problems that nobody else is solving.”
Listen to the Full Episode
Key Takeaways from the Conversation
| Theme | Key Insight |
|---|---|
| Micro-problems | The biggest opportunity isn’t building the next ChatGPT — it’s solving millions of small, specific problems that were never economically viable to solve before |
| AI as enabler | AI doesn’t replace humans — it removes the economic barrier that previously made small-scale solutions impossible to build |
| Prompt engineering | More than typing into ChatGPT — it’s a real skill of structuring intent, context, and constraints to get reliable, repeatable AI outputs |
| Domain expertise | Your unique professional knowledge is the competitive advantage — AI is just the amplifier for what you already know |
| Community over courses | Static courses go stale in weeks; dynamic communities adapt in real time and carry people through change |
What We Talked About
From Architecture to AI
I started in architecture — designing buildings, solving spatial problems, thinking in systems. That systems thinking turned out to be the exact skill set that translates into AI. I didn’t plan the pivot. I followed curiosity, started a YouTube channel, and the rest snowballed.
What’s interesting about this path is that it wasn’t a straight line. I went from studying architecture in Lithuania, to a Danish university working with robotic arms and parametric design, to big data analytics in the travel industry, to AI education. Each step felt like a detour at the time. Looking back, every step built on the previous one. The design thinking from architecture, the data obsession from analytics, the communication skills from teaching — they all converge in what I do now.
That’s something I try to communicate to every person I work with: your past isn’t irrelevant just because it’s not in tech. It’s foundational. The accountant who understands financial patterns has an advantage in AI-powered forecasting. The teacher who understands learning has an advantage in building educational AI tools. Your domain expertise is your unfair advantage.
Prompt Engineering Is More Than a Hype
People hear “prompt engineering” and think it’s just typing better questions into ChatGPT. It’s not. It’s about understanding how to communicate intent to AI systems — structuring context, defining constraints, building repeatable workflows. It’s a real skill with real leverage.
Let me give you a concrete example. A basic prompt says: “Write me a marketing email.” A well-engineered prompt says: “Write a follow-up email for a B2B SaaS prospect who attended our webinar on data analytics. They’re a mid-level marketing manager at a company with 50-200 employees. The tone should be professional but warm. Include one specific insight from the webinar about customer segmentation. End with a clear CTA to book a 15-minute call. Keep it under 200 words.”
Same task, wildly different results. The first produces generic content. The second produces something you might actually send. That gap between those two prompts is what prompt engineering is about — and it’s a gap that gets bigger as AI systems become more capable. More capability means more potential, but only if you know how to direct it.
In the podcast, I explained how prompt engineering connects directly to building AI Second Brain. When you develop repeatable prompts and workflows, you’re not just having one-off conversations with AI. You’re building systems that produce consistent, high-quality outputs every time. That’s infrastructure, not just tool usage.
The Micro-Problems Philosophy
Here’s what I genuinely believe: the biggest opportunity right now isn’t building the next billion-dollar AI platform. It’s solving millions of tiny, specific problems that affect small groups of people. Problems that were never worth solving before because the cost of building a solution was too high. AI changes that equation completely.
A niche problem that affects 500 people? You can now build a solution for that in a weekend. Multiply that by millions of micro-problems, and you’ve got an entirely new market.
Let me explain why this matters economically. Before AI, building any software solution required significant upfront investment — hiring developers, designing interfaces, testing, deploying, maintaining. That cost meant only problems that affected millions of people justified the investment. The threshold was high, and everything below that threshold was just… unsolved. People worked around it. They used spreadsheets, manual processes, or just lived with the friction.
AI has collapsed that cost structure. A single person with domain expertise and decent prompt engineering skills can now build solutions that would have required a team of five just three years ago. That means the threshold for “worth solving” has dropped dramatically. Problems that affect hundreds of people instead of millions are now viable targets.
Examples of Micro-Problems Worth Solving
In the podcast, I talked about this concept at a high level. Let me give some more concrete examples of what micro-problems look like in practice:
- A veterinary clinic that needs to generate discharge instructions for pet owners — tailored to each animal’s specific condition, medication, and care requirements. No software company is going to build a product for this niche, but a vet with an AI workflow can solve it in an afternoon.
- A local government office that processes permit applications and needs to check each one against a complex, ever-changing set of zoning rules. An AI system can flag potential issues before a human reviewer touches the file.
- A ceramics teacher who wants to convert their workshop notes into structured lesson plans with safety warnings, material lists, and timing suggestions. Too small for a product company to care about, perfect for an AI-powered workflow.
- A property manager handling 50 units who needs to draft, review, and customize lease agreements for different tenant situations. Not enough volume for enterprise software, but enough to justify a smart template system.
None of these are billion-dollar markets. That’s exactly the point. They’re hundred-dollar, thousand-dollar, ten-thousand-dollar problems. But there are millions of them. And the people best positioned to solve them aren’t AI engineers — they’re the domain experts who already understand the problem intimately.
How Leaders Should Think About Micro-Problems
Carolina asked me what leaders specifically should take away from this perspective. My answer was threefold:
- Stop looking up, start looking around. Leaders obsess over what OpenAI, Google, and Meta are building. But the real opportunities are in the problems sitting right in front of them — inside their own organizations, in their own industries, in the complaints their customers voice every week.
- Empower your team to solve their own problems. The most effective AI implementations I’ve seen aren’t top-down mandates. They’re bottom-up experiments. Give your team access to AI tools and permission to experiment. The person doing the repetitive task every day knows better than anyone where AI could help.
- Build a portfolio of small solutions, not one big bet. Instead of betting the company on one massive AI initiative, encourage dozens of small experiments. Most will fail or produce marginal value. A few will transform entire workflows. But you won’t know which is which until you try.
AI as an Enabler, Not a Replacement
This is the core of everything I do with Autonomee. AI isn’t here to replace anyone. It’s infrastructure that amplifies what you can already do. The people who understand this — who treat AI as a thinking partner rather than a magic button — are the ones building real things.
I make this distinction constantly because the dominant narrative is so destructive. “AI will take your job” is not only inaccurate — it’s actively harmful. It makes people defensive, resistant, and afraid. The truth is more nuanced and more empowering: AI changes the nature of work, not the need for humans. It handles the repetitive, the data-heavy, the time-consuming. It frees humans for the creative, the strategic, the relational.
Think of AI the way we think of electricity. Electricity didn’t replace factory workers — it changed what factories could do. It made entirely new industries possible. AI is doing the same thing for knowledge work. And just like with electricity, the people who figure out how to use it first — and help others use it — will shape the next era. That’s essentially what I argued in my TEDx talk about being a better early adopter.
The Red Line: How It All Started
It all started with a YouTube channel. I began sharing what I was learning about AI tools, prompt engineering, and productivity systems. That turned into a community. The community turned into courses. And now we’re building infrastructure for autonomous humans — people who own their AI stack instead of renting it.
The progression from content to community to infrastructure is something I think about a lot. Most creators stop at content. Some build communities. Very few build the actual systems and tools their audience needs. But that’s where the real value lives. Content educates. Community supports. Infrastructure empowers. All three matter, but infrastructure is where autonomy happens — where people stop depending on you and start building their own things.
That’s the vision behind Autonomee: not just teaching people about AI, but giving them the tools, templates, and systems to build their own AI Second Brain. A Telegram bot that handles their specific workflows. A Claude Code setup that matches their exact needs. Systems that work for them, not just tools they use occasionally.
The Micro-Problems Framework: A Practical Guide
If the micro-problems concept resonated with you, here’s a practical framework for identifying and solving them:
Step 1: Notice the friction. For one week, pay attention to every small frustration in your work. Every time you think “there should be a better way to do this” or “I wish someone would fix this” — write it down. Don’t filter. Just collect.
Step 2: Validate the problem. Take your list and ask: does anyone else have this problem? A quick search in forums, Reddit communities, or industry groups will tell you. If other people are complaining about the same thing, you’ve found a micro-problem worth solving.
Step 3: Prototype with AI. Don’t build an app. Don’t write code (yet). Use AI to create a working prototype of the solution. A well-structured prompt, a simple automation, a no-code workflow. Can AI solve 80% of this problem? If yes, you’re onto something.
Step 4: Share and iterate. Put your solution in front of the people who have the problem. Get their feedback. What’s missing? What’s wrong? What’s surprisingly useful? Iterate based on real usage, not assumptions.
Step 5: Systematize or move on. If the solution works and people value it, turn it into a repeatable system — a product, a service, a template. If it doesn’t work, learn what you can and move to the next micro-problem on your list. The cost of experimenting is low. That’s the whole point.
About the Podcast
Leaders & Missions is hosted by Carolina Marques — a podcast for everyone who actively shapes the economy, society, and innovation. Carolina speaks with inspiring doers, creatives, and leaders who drive change through their visions and missions. New episodes every two weeks.
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Frequently Asked Questions
What are micro-problems and why are they a market opportunity?
Micro-problems are small, specific issues that affect a limited number of people — too few to justify traditional software development costs. AI has made it economically viable to solve these problems because the cost of building a solution has dropped from tens of thousands of dollars to nearly zero. Multiply thousands of these small solutions together, and you have an entirely new market category.
How is AI an enabler rather than a replacement?
AI handles the repetitive, data-heavy, time-consuming parts of work, freeing humans for creative, strategic, and relational tasks. Rather than making people obsolete, AI amplifies what they can already do. A consultant with AI can serve more clients. A small business owner with AI can automate tasks that previously required hiring additional staff. The tool enhances capacity without replacing judgment.
What is prompt engineering and why does it matter?
Prompt engineering is the skill of communicating intent to AI systems — structuring context, defining constraints, and building repeatable workflows that produce consistent, high-quality outputs. It goes far beyond “typing better questions into ChatGPT.” Well-engineered prompts can turn a vague AI response into a polished, actionable output. As AI systems become more capable, the gap between a basic prompt and an engineered prompt grows wider.
How can I find micro-problems worth solving?
Start by paying attention to friction in your own work and industry. Every recurring frustration, every “I wish someone would fix this” moment is a potential micro-problem. Validate by checking if others share the frustration (forums, communities, social media). Then prototype a solution using AI before investing significant time or money. The best micro-problem solvers are domain experts who already understand the problem deeply.
What does “AI Second Brain” mean?
AI Second Brain means building AI systems that you own and control — customized to your specific workflows, connected to your data, and running on your terms. Instead of using generic AI tools for one-off tasks, you build repeatable systems: bots that handle your specific communication patterns, automation that processes your specific data, and workflows that integrate AI into your daily operations. It’s the difference between renting a tool and owning a system.
