What 10 Solo AI Builders Taught Me About Building Something Worth Building

I surveyed 10 solo builders about their AI builds to help new builders know what to build with AI.

TLDR: I surveyed 10 solo builders who’ve created AI-powered tools and systems. Five patterns emerged: every build started with a problem the founder lived through, AI consistently pulled toward generic output, the founder’s real edge showed up in how they structured the build, the strongest constraint decisions protected the end user, and some builders deliberately kept friction in the process. If you’re a founder who wants to build something from their mission, let’s figure out where your biggest opportunities are together.

I kept losing people.

Specific people, ones I wanted to build real relationships with. They’d show up in my feed one morning and I’d interact. Then the algorithm would bury them under trending posts and strangers I never asked to follow. By the time I remembered to go back, the moment was gone.

This kept happening. Week after week. I’d find someone worth connecting with, engage once, and lose them to the scroll.

So I built something. A Chrome extension called Daily 10 that saves ten profiles, prompts me to visit them daily, and logs my interactions. Simple. Manual by design. It solved a problem I’d been sitting with for months.

And it changed how I think about what’s worth building with AI.

I decided to write this article to see if other builders had the same moment. And, if so, if there’s something there that founders and creators new to building with AI can work with.

After reading this, I hope you’ll know what exactly to build with AI, not just how.

New here? I’m James — I help founders and creators build AI systems that make them impossible to replace.

My mission is to help you use AI to become irreplaceable at what you do by turning your expertise into systems no one else can replicate. This piece draws from a survey of 10 solo AI builders sharing what they built, where AI helped or failed, and what they left out.

For the philosophy underneath all of it, start with How I Use AI To Become Irreplaceable.

Your build starts with a problem you care about

Last August, I wrote an article called Stop Looking for a Niche. Start Looking for a Problem You Can’t Ignore. The argument: your mission starts with friction you’ve lived through, and the work worth doing is the work that solves it.

That article was the philosophy.

This one is what the philosophy looks like in practice.

Daily 10 was the first time I felt the thesis as a builder. I didn’t start with a market analysis or a competitive landscape. I started with irritation. I was losing track of the people who mattered to my mission because the platform I was using was engineered to scatter my attention. I looked for a solution, but nothing out there was simple enough, light enough, or cheap enough for what I needed.

So I built one.

AI handled the technical side — the code, the scaffolding, the interface. I handled the direction, the design, and one decision that shaped the whole product: when beta testers asked me to integrate AI for auto-logging interactions, I said no. Harvesting feed data would mean potentially violating platform TOS and turning a simple tool into a bloated one.

Plus, the friction of manual logging helped the mission. It kept things more intentional, more human.

I wanted to know if other builders experienced the same thing. So I ran a survey. Three open-ended questions:

• What problem did you solve?

• Where did AI help or fall short?

• What did you leave out?

Ten builders responded. They work in different industries, at different stages, building wildly different products. The same patterns kept showing up.

5 insights from solo builders creating mission-aligned builds

A mission-aligned build is an AI-powered tool or system designed to solve a problem the founder personally observed and cared enough to fix. The build carries the founder’s expertise, judgment, and context into the product itself — which is what separates it from generic AI output that anyone could generate.

These five insights came from pulling on that thread across all ten responses.

1. Start with a problem you couldn’t ignore

“I picked up Claude Code not to write code. I picked it up to build the infrastructure my content business was missing.” — Dheeraj Sharma

Dheeraj is a software engineer with 20 years of IT experience who hadn’t written a line of code in seven years. He’d been running a content business — newsletter, YouTube channel, courses — and the bottleneck was throughput. Research alone ate 20+ hours a month. He had 17 years of content creation experience, so he knew what good output looked like. He just couldn’t produce it fast enough.

He didn’t set out to build a product. He built a research agent that cut those 20 hours down to a single session. Then a content pipeline that compressed 2+ hours of article production to 15 minutes. Then a tool for Substack creators that he built in a 3-day weekend sprint for $135 total.

The product came from the problem. The problem came from his own workflow.

Every single builder in this survey followed the same arc. Nick Quick was losing a war against his own Substack feed and built a CRM because nobody else had. Anfernee kept drowning in scattered thinking across tools and built a Notion AI agent to hold his weekly reflection process. Elena Calvillo couldn’t manage collaboration requests flooding in after her AI Advent Challenge gained traction, so she built DraftKit. Dallas Payne had prompts scattered everywhere with no way to organize them, so she built her own Prompt Vault in Lovable.

Ten out of ten. Every build started with personal friction.

If you’ve read AI Made Building Easy. So Why Can’t You Start?, you know the mental shift that needs to happen before you open a tool. This is the step before that shift. You don’t need a business plan. You need a problem that’s been costing you time, energy, or sanity — one you know well enough to describe in plain language.

Your move: Write down the friction you experience every week in your own business. What do you keep solving manually? What problem would you fix even if nobody paid you for it? That’s your build.

2. AI pulled every builder toward generic — pull away

“AI is phenomenal at helping you build things that already exist. Ask it to build something that doesn’t, and it will gently, confidently, relentlessly walk you back toward the thing it’s seen ten thousand times before.” — Nick Quick

The pattern was universal. Every builder in the survey hit the same wall.

Nick fought it on every spec rewrite. There is no Substack CRM — nobody had built one. So every LLM he worked with had no frame of reference, and a model with no frame of reference doesn’t say “I don’t know.” It guesses. It kept trying to turn Stackwise into a generic contacts database or an email tool because those things already exist.

Chris Tottman calls AI’s default output “90% vanilla.” Anfernee had to override AI’s tone because early outputs were too clinical for solopreneurs still figuring things out. Manas Moon had to constantly simplify because AI overcomplicated explanations his beginner audience didn’t need. Dheeraj caught AI generating 600 words of setup before getting to the point in a 2,500-word script draft. Joel Salinas scrapped his AI-drafted outreach emails entirely because they sounded like someone else wrote them.

The mechanism is consistent, and we all know this. AI produces the most statistically likely output — the average of everything it’s trained on. That average is usable. It’s also forgettable.

The builder’s job is to recognize when the output matches their specific context versus when it matches what already exists elsewhere.

Nick put it well: the “why” was the one input AI could not generate for him. He rewrote his spec so many times he started to suspect AI’s real job was to make him defend his own idea until there wasn’t a soft spot left.

Your move: Next time AI gives you output that feels “right,” pause. Check whether it feels right because it matches your context, or because it matches what already exists. If you can’t tell the difference, that’s the gap your judgment needs to close.

3. Show your edge in the structure, not the features

“AI would have built one generic path. I split it into three because I knew from experience that a digital product founder and a consultant have fundamentally different validation problems.” — Anfernee

The builder’s expertise didn’t show up in feature lists. It showed up in architecture decisions — the way the tool was organized, the workflows it enforced, the logic underneath the interface.

Anfernee built the Solopreneur Idea Validator with three separate paths: one for Digital Product/SaaS founders, one for Service/Consulting, one for Education/Content. AI would have built a single generic validation flow. Anfernee split it because he’d watched enough solopreneurs fail to know that a SaaS idea and a coaching offer require fundamentally different questions. That knowledge came from experience, and it shaped the skeleton of the entire tool.

Elena built DraftKit around a discovery she made after her first version shipped. She thought the core problem was scheduling — so she built a calendar-first tool. Writers told her otherwise. “If I can’t write with someone in here, I’ll just use Google Docs.” The real gap was collaboration, and she had to rebuild around it. That redesign — calendar demoted to supporting feature, writing workspace promoted to core — came from listening to her users and having enough product instinct to act on what they said.

Nick built Stackwise around a constraint nobody else had to work with: Substack has no public API. He had to construct the entire CRM from the outside, working around the platform rather than through it. That architecture decision came from understanding how Substack works at a level that only someone deeply embedded in the platform would.

Dheeraj structured his content pipeline around 17 years of knowing what “good” looks like. AI can draft a script. It can’t tell you that the opening has a hedge in it that undercuts every confident claim that follows. Dheeraj caught that — at minute 7 of a recorded video, after the audio was already done. Expensive lesson. But it proved where the real edge lives in an AI-powered system: in the human who knows what to keep and what to cut.

Your move: Before you add features, map the structure. What workflow splits, categories, or decision trees come from knowledge you earned through experience? AI can build the features. The structure is yours.

4. Protect the end user with strong constraints

“AI is great at generating content. It has no idea when generating content is the wrong move.” — Elena Calvillo

The most telling decisions in the dataset were about what builders removed — and why the removal made the product better for the person using it.

Anfernee cut the validation score from his Idea Validator. The first instinct was to give founders a grade at the end. It felt satisfying to build. He killed it because scores create false confidence. A founder can answer every question well and still be chasing a problem nobody will pay to solve. The tool needed to surface thinking. Manufacturing certainty would have undermined the entire purpose.

Elena learned this the hard way. DraftKit’s SMART Draft feature used AI to generate a starting point for a collaboration. Early on, it auto-applied the draft to the workspace — and writers pushed back hard. “Did you write this or did the AI write this?” became real friction. She redesigned the flow so AI only stages a suggestion. The human always pulls the trigger. That was her product sense, and AI had no mechanism to figure it out on its own.

Nick cut every integration that pointed outside Substack. The first version in his head tracked subscribers flowing in from YouTube, Instagram, SEO — full attribution, very dashboard-with-a-corner-office. Then he asked the only question that mattered: does growing on Substack require any of that? The answer was no. So he cut it all. What remained was a tool that does one thing and refuses to blur it.

Dheeraj killed an entire product category. He’d been building and selling n8n workflow templates — individual automations, specific use cases. Each one made sense in isolation. Collectively, they were scattered. Worse: n8n was already his biggest free asset. 42 lessons, 31 hours of YouTube content, completely free. Trying to monetize a category he’d already given away was, in his words, “offer sprawl with extra steps.” He consolidated what remained into a single $27 bundle and moved on.

If you’ve been thinking about what to build, your constraint decisions will matter as much as your build decisions. The AI prompts and workflows you’re already using probably contain the same kind of judgment — features you instinctively left out because you knew they’d hurt more than help.

Your move: For every feature in your build, ask one question: does this serve the user’s outcome, or does it just make the product look more complete? If you can’t answer clearly, cut it.

5. Learn when to keep friction on purpose

“I purposely have not connected it with any LLM because this is also where the friction needs to live for me.” — Dallas Payne

This was the most counterintuitive pattern. Three builders chose to keep manual steps in their products — and the friction was the point.

Dallas built her Prompt Vault entirely for herself. It lets her tag prompts, sort them into categories, link back to original sources, record when a prompt is used and any tweaks she made. She can combine prompts in a play environment and merge them into something new. She loves it. And she deliberately did not connect it to any language model, because the manual engagement with prompts — the act of reading, evaluating, combining — was where the thinking happened. Automating that step would have removed the value.

I made the same call with Daily 10. Some beta testers wanted AI to auto-log interactions by harvesting data from the feed. I said no. The extension needed to stay platform-agnostic, light, and simple. More than that — the act of manually visiting a profile and manually logging the interaction was an added mechanism that made the relationship-building intentional. Remove the friction and you remove the intent.

Joel landed in the same place through trial and error. He asked AI to draft outreach emails to potential sponsors. The emails came back generic. They didn’t sound like him, and he didn’t want that to be his first impression. So he flipped the workflow: AI researched each contact and gave him background. He wrote the emails himself. The act of writing — by hand, or dictated into his microphone — was the signal that made the outreach real.

Everyone tells you to automate everything. These three builders found the places where automation would have destroyed the thing that made the build work.

Your move: Before you automate a step in your build, ask whether the friction is where your judgment or relationship equity lives. If it is, protect it.

Mission-aligned building is the way forward for solo builders

Every response in this survey traced the same arc.

• A problem the builder couldn’t walk past.

• A build shaped by their expertise.

• A set of constraint decisions that protected what mattered.

And at every stage, AI was useful — but it pulled toward the middle. The builder’s job was to pull it back toward something specific.

That’s what makes a build mission-aligned. The founder’s judgment is in the product itself. Their experience shaped the structure. Their convictions determined what got cut.

AI handled the bricks, but the blueprint came from someone who cared enough about the problem to get the architecture right.

I experienced this with Daily 10. The build started because I cared about a specific problem — losing track of people who mattered to my mission. AI handled the code. I handled the direction, the design, and the decision to keep it simple when the easy thing would have been to make it complex. That pattern held across all ten builders in this survey. And I think it holds for you, too.

You have friction in your business right now. Something you keep solving manually, something that costs you time or focus every week. You have expertise — years of knowing what good looks like in your domain, what questions to ask, what to cut. And you have access to tools that can turn that expertise into something that compounds.

The build worth building starts with a problem you couldn’t ignore. Start there.

P.S. If you’re a passionate, mission-driven founder or creator figuring out where to start with a clear vision of what to build, but not the time and expertise to make it a reality, we should talk. We can give you a clear picture of where your biggest opportunities are.