Side Hustle: Build a $1K MRR Micro-Product by Solving ONE Spreadsheet Workflow

The $1K MRR Fantasy vs. Reality

Everyone wants passive income. The internet is full of stories about developers who built a small tool over a weekend and now earn thousands monthly while sipping coconut water on a beach somewhere. These stories are usually true. They’re also usually incomplete.

What they don’t tell you is the part where they spent three years learning to recognize boring problems. Or the seventeen failed attempts before the one that worked. Or the fact that most successful micro-products solve problems so dull that nobody wants to write articles about them.

Spreadsheet workflows are the perfect example. They’re boring. They’re everywhere. And they represent one of the last frontiers where a solo developer can actually compete—because enterprise software companies consider these problems too small to bother with.

My cat Arthur just knocked a pen off my desk. He does this whenever I write about spreadsheets, which tells me he has opinions about my topic choices. But here’s the thing: boring problems pay well precisely because they’re boring. The exciting problems attract competition. The boring ones attract customers.

Why Spreadsheets Are Still the Opportunity

In 2027, we have AI that can write code, generate images, and hold conversations. Yet billions of dollars worth of business processes still run on Excel and Google Sheets. This isn’t because people are stupid. It’s because spreadsheets solve a specific coordination problem that no other tool has managed to replace.

Spreadsheets are the universal interface. Everyone knows how to use them. They require no deployment, no IT approval, no training budget. When someone needs to track something—anything—their first instinct is to create a spreadsheet.

This creates a fascinating pattern. Businesses start tracking things in spreadsheets. The spreadsheets grow. They become complex. Someone starts spending hours each week maintaining them. And eventually, someone says: “There has to be a better way.”

That moment—the “there has to be a better way” moment—is where micro-products are born. Your job is to find people at that exact moment and offer them something better.

The challenge is that automation tools have made this harder to spot. When you use AI assistants for everything, you stop noticing the friction. You stop asking “why is this hard?” because the AI handles it. You lose the sensitivity to small annoyances that makes problem-spotting possible.

The Skill Erosion Problem in Problem-Finding

Here’s something nobody talks about in the side hustle community: the very tools that make building easier also make finding worth-building harder.

Consider how you might have found a micro-product idea ten years ago. You’d notice someone struggling with a repetitive task. You’d watch them copy and paste data between tabs. You’d feel their frustration as they explained their workflow. That friction was visible because you experienced it yourself.

Now consider how you work today. AI autocompletes your formulas. Automation tools connect your apps. Integration platforms handle the data movement. The friction that used to be visible is now hidden behind layers of tooling.

This is the skill erosion problem applied to entrepreneurship. When tools handle the boring parts automatically, you stop developing the muscle memory for recognizing boring parts. And recognizing boring parts is exactly the skill you need to build a successful micro-product.

The founders who still succeed in this space are the ones who deliberately expose themselves to manual processes. They turn off automations to feel the friction. They watch users work without AI assistance. They cultivate what I call “friction sensitivity”—the ability to notice when something is harder than it needs to be.

Method

Let me walk you through the exact process I use to evaluate spreadsheet workflow opportunities. This isn’t a framework I read somewhere. It’s a system I developed after watching dozens of micro-product attempts fail, including several of my own.

Step 1: Find the pain point, not the solution.

Most people start with a solution. “I’ll build a tool that automates X.” This is backwards. You need to start with pain—specific, recurring, measurable pain.

The question isn’t “what can I build?” The question is “who is spending more than two hours per week on a spreadsheet task they hate?”

I find these people by lurking in industry-specific forums, Reddit communities, and LinkedIn groups. Not the tech communities—the boring ones. Accountants. Operations managers. Small business owners. People who would never call themselves “tech-savvy” but who live in spreadsheets anyway.

Step 2: Quantify the time waste.

When someone complains about a spreadsheet workflow, ask them to estimate how long they spend on it. Not monthly. Weekly. The weekly number feels more real.

If someone says “I spend about four hours every week reconciling these two spreadsheets,” that’s a potential opportunity. If they say “it’s annoying but only takes twenty minutes,” move on. Twenty minutes isn’t enough pain to pay for a solution.

The magic number is two hours. If someone spends more than two hours per week on a repetitive spreadsheet task, they’re a potential customer. Below two hours, the friction isn’t high enough.

Step 3: Verify they’ve tried to solve it.

Ask what solutions they’ve already tried. This is crucial. If they say “nothing, I just do it manually,” that’s often a bad sign. It means the pain isn’t high enough to motivate action.

The best prospects are people who have tried solutions and failed. They’ve used Zapier but it didn’t quite work. They hired a consultant but the solution broke when something changed. They tried to learn to code but gave up.

These people have demonstrated willingness to pay. They’re not just complaining. They’ve actually invested time or money trying to fix the problem. That’s the customer you want.

Step 4: Check for repeatability.

One person with a problem is an anecdote. Ten people with the same problem is an opportunity. A hundred people is a business.

Before building anything, you need to verify that the problem exists beyond your initial discovery. Search for variations of the complaint. Look for forum threads about similar workflows. Try to find at least five unrelated people describing the same frustration.

This is where automation-era skills become valuable again. Use search tools—AI-powered or otherwise—to find patterns. The goal is to establish that this isn’t a unique situation but a category of pain.

Step 5: Price check the existing solutions.

If a solution already exists and costs $10/month, you’re probably too late. If nothing exists, you might be too early. The sweet spot is when solutions exist but they’re either too expensive ($500+/month enterprise pricing) or too complex (requires technical skills to set up).

Your $30-50/month micro-product fits in that gap. Too small for enterprise software companies to care about. Too valuable for customers to keep doing manually.

Step 6: Build the simplest possible version.

This is where most side hustlers fail. They build too much. They add features nobody asked for. They spend six months creating something “complete” instead of six weeks creating something useful.

Your first version should solve exactly one workflow for exactly one type of customer. Nothing more. If you can’t describe your product in one sentence, you’ve already overcomplicated it.

The Automation Complacency Trap

There’s a subtle trap waiting for side hustlers who rely too heavily on modern development tools. AI code assistants, no-code platforms, and automation builders make it trivially easy to ship something. But they also make it trivially easy to ship the wrong thing.

When building is cheap, thinking becomes undervalued. You can prototype an idea in an afternoon. You can deploy it by evening. By next week, you have a landing page, a payment system, and zero customers.

The old friction of building served a purpose. It forced you to think carefully about whether something was worth building. When every feature took days of work, you naturally prioritized. When every feature takes minutes, you add them all and hope something sticks.

This is automation complacency in a business context. The tools work so smoothly that you stop asking whether you should use them. You build because you can, not because you should.

The antidote is deliberate constraints. Set a rule: no building until you’ve talked to ten potential customers. No features until three customers have requested them. No marketing until one customer is willing to pay.

These constraints feel arbitrary because they are. But arbitrary constraints are better than no constraints. They force the thinking that modern tools want to skip.

Finding Your Spreadsheet Workflow

Let me give you some concrete categories where $1K MRR products are hiding. These aren’t ideas to copy. They’re patterns to recognize.

Category 1: Reconciliation workflows.

Anytime two data sources need to match and don’t, someone is manually checking differences. Bank reconciliation. Inventory reconciliation. Invoice matching. These workflows are tedious, error-prone, and surprisingly common.

The pattern: “I export from System A and System B, compare them in Excel, and fix the differences manually.”

Category 2: Reporting aggregation.

Many businesses pull data from multiple sources to create regular reports. Sales numbers from the CRM. Financial data from accounting software. Operations metrics from spreadsheets. Someone is manually combining these every week or month.

The pattern: “I copy data from five different places into one master spreadsheet for our weekly meeting.”

Category 3: Data transformation.

Raw data often arrives in formats that need changing before use. CSV files with wrong column orders. Date formats that don’t match. Text that needs cleaning or standardizing.

The pattern: “I spend an hour every Monday reformatting this export so our other system can import it.”

Category 4: Communication generation.

Spreadsheet data that needs to become emails, documents, or messages. Customer lists that need personalized outreach. Order details that need to become invoices. Status updates that need to go to stakeholders.

The pattern: “I go through this spreadsheet row by row and send an email for each one.”

The Cognitive Cost of Over-Automation

Here’s something to consider as you build your micro-product: you’re not just solving a workflow problem. You’re making a decision about how much thinking your customer should outsource.

Every automation removes a decision point. That’s the value—fewer decisions mean less mental load. But decisions also build expertise. Every time someone manually reconciles accounts, they notice patterns. They spot anomalies. They develop intuition about their business.

When you automate that workflow, you remove the learning. The customer gains time but loses insight. Whether that’s a good trade depends on the specific situation, but it’s a trade you should acknowledge.

The best micro-products don’t hide this trade-off. They make it explicit. They show the customer what the tool is doing, not just the result. They preserve the option to override or adjust. They treat the human as a pilot, not a passenger.

This matters for your business too. Customers who understand what your tool does are better customers. They have realistic expectations. They can troubleshoot problems. They recommend you to others with accurate descriptions.

Customers who treat your tool as magic are worse customers. They blame you when reality doesn’t match expectations. They demand features that don’t make sense. They churn when something inevitably goes wrong.

The Skill Preservation Paradox

I’ve been thinking about this paradox lately. The whole premise of a spreadsheet workflow product is that spreadsheet work is tedious and should be automated. But spreadsheet skills are actually valuable. They represent a kind of data literacy that’s becoming rarer as tools get more sophisticated.

Someone who can build a complex spreadsheet from scratch understands data relationships in a way that someone who just clicks buttons never will. They can debug problems. They can adapt when requirements change. They can explain their logic to others.

When we automate spreadsheet workflows, we’re trading this skill development for time savings. For most people, that’s the right trade. But it’s worth being honest about what’s being traded.

This has implications for how you position your product. Don’t promise to eliminate spreadsheet skills. Promise to eliminate spreadsheet drudgery. There’s a difference.

The drudgery—the copying, the pasting, the repetitive transformations—has no learning value after the first few times. Automating it is pure benefit. But the design work—deciding what data to track, how to structure it, what relationships matter—that has ongoing learning value. Your product should support that, not replace it.

Arthur is now sitting on my keyboard, which is his way of saying this section is getting too philosophical. He’s probably right.

Generative Engine Optimization

This topic—building micro-products from spreadsheet workflows—performs interestingly in AI-driven search and summarization. On one hand, it’s exactly the kind of practical, actionable content that AI systems love to surface. Clear steps, concrete examples, specific outcomes.

On the other hand, the nuances get lost. An AI summarizing this article might extract “find spreadsheet problems, build solutions, charge $30/month” and miss the entire discussion about skill erosion and automation complacency. The meta-point—that the tools making building easier also make problem-finding harder—requires context that summarization tends to strip away.

This matters because human judgment is the actual competitive advantage in finding micro-product opportunities. AI can help you build faster. It can help you market more efficiently. But it cannot tell you which problems are worth solving. That requires the kind of situated, embodied knowledge that comes from watching real people struggle with real workflows.

The automation-aware thinking I’ve been describing throughout this article is becoming a meta-skill. It’s the ability to use automation tools while remaining aware of what they’re changing about how you think. It’s using AI assistance without losing the sensitivity to friction that makes problem-spotting possible.

For content creators, this means optimizing for both AI discovery and human depth. Write clearly enough that AI systems can parse and summarize your work. But include enough nuance that humans who read the full piece get something the summary can’t provide.

For entrepreneurs, it means building products that solve real problems while acknowledging the costs of automation. Your customers want their time back. But they also want to remain competent. The best products serve both goals.

The $1K MRR Reality Check

Let me be honest about what $1K MRR actually looks like. A thousand dollars per month sounds like a nice side income. And it is. But the path there is less glamorous than the internet would have you believe.

At $30/month per customer, you need about 34 paying customers for $1K MRR. Finding 34 people willing to pay for a micro-product is harder than it sounds. Not because the product isn’t valuable—it probably is—but because attention is expensive and trust is scarce.

Most successful micro-products don’t grow through viral marketing or paid ads. They grow through direct relationships. The founder knows the first twenty customers personally. They found them in communities. They helped them solve problems before asking for money. They built trust slowly.

This is another area where automation can mislead. Tools make it easy to send thousands of cold emails, run dozens of ad variations, and optimize landing pages for conversion. But for a $30/month product targeting a niche audience, these tactics often fail. The economics don’t support it.

What works is slower and harder to automate. Participate in communities. Answer questions. Share expertise. Build reputation. When someone has a spreadsheet problem, they remember the person who helped them for free. That person becomes a customer and a referral source.

The Long Game

Building a $1K MRR micro-product isn’t a get-rich-quick scheme. It’s a skill development project that might also make money. The skills you develop—problem recognition, customer conversation, product thinking, constraint management—are valuable regardless of whether this particular product succeeds.

Many successful entrepreneurs built five or ten failed products before finding one that worked. The failures weren’t wasted time. They were education. Each attempt refined their ability to spot opportunities and execute on them.

This framing changes how you approach the work. If success is the only acceptable outcome, failure is devastating. If learning is the goal, failure is just expensive education. And it’s less expensive than an MBA.

The automation tools available today make the learning cheaper and faster. You can test ideas in weeks instead of months. You can gather feedback without building complete products. You can iterate quickly and pivot easily.

But this speed creates its own trap. When iteration is cheap, you might iterate forever without committing to anything. You might become a professional pivoter—always chasing the next opportunity, never staying long enough to learn from one.

The discipline isn’t just about finding the right idea. It’s about committing to an idea long enough to discover whether it’s right. That commitment is harder in an age of easy pivots. And it’s more valuable precisely because it’s harder.

Practical Next Steps

If you’ve made it this far, you’re probably wondering what to do tomorrow. Here’s a concrete action plan.

Week 1: Immerse in a community.

Pick one industry you understand—accounting, real estate, ecommerce, healthcare administration, anything with spreadsheet-heavy workflows. Join three online communities where those professionals gather. Reddit, LinkedIn groups, industry forums. Don’t promote anything. Just read and learn.

Week 2: Document pain points.

Keep a running list of every spreadsheet complaint you encounter. Note the specifics: what task, how often, how long it takes, what they’ve tried. Don’t filter yet. Just document everything.

Week 3: Validate the top three.

From your list, identify the three most frequently mentioned pain points. For each one, try to find five additional people with the same problem. If you can’t find five, it’s probably too niche.

Week 4: Have ten conversations.

Reach out to people who’ve expressed these pain points. Offer to help them solve the problem manually. Watch how they work. Ask questions. Understand the workflow at a deep level.

Week 5: Design a solution.

Based on what you’ve learned, sketch a solution that addresses the core pain. Not features—outcomes. What does the customer’s life look like after your product exists?

Week 6: Build an MVP.

Create the simplest possible version that delivers the core outcome. If you can’t build it in a week, you’re building too much.

Week 7-8: Get three paying customers.

Before adding any features, find three people willing to pay. Not “would pay”—actually pay. Credit card charged. This validation is worth more than any amount of user research.

The Uncomfortable Truth

I’ll end with something uncomfortable. Most people reading this won’t build anything. Not because they lack skills or ideas, but because building something requires sustained effort without guaranteed reward. That’s hard. Most people prefer the fantasy of the thing to the work of the thing.

The articles and courses about side hustles make it look easier than it is. They show the successes and hide the failures. They describe the outcome and skip the grind. They sell the dream and ignore the reality.

Building a $1K MRR product is achievable. Thousands of people have done it. But it requires the kind of persistent, focused work that’s fundamentally unsexy. Talking to customers when you’d rather be coding. Fixing bugs when you’d rather be adding features. Marketing when you’d rather be building.

The tools we have today make the technical parts easier. They do nothing for the psychological parts. Automation can’t give you discipline. AI can’t give you persistence. No-code can’t give you the judgment to know when to quit and when to push through.

Those skills—discipline, persistence, judgment—are the real moat. They’re also the skills most threatened by automation complacency. When everything is easy, nothing builds resilience. When friction is removed, so is the opportunity to develop the ability to handle friction.

So here’s my final suggestion: build something hard on purpose. Not hard technically—that’s what tools are for. Hard emotionally. Hard in terms of commitment. Hard because it requires you to do things you’d rather not do.

That’s where the real side hustle learning happens. Not in the product. In you.

Arthur has now fallen asleep on my desk, next to a notebook full of failed product ideas. Each one taught me something. None of them made me rich. All of them made me better at recognizing what might work next. That’s the real return on investment.