Side Hustle 2026: How to Find Problems People Solve With a Wallet (not just a like)
The Engagement Trap
Social media has broken how we think about value.
A post gets 10,000 likes. The creator assumes people would pay for more. They build a product. Nobody buys.
This happens constantly. The disconnect between engagement and revenue is massive. And it’s getting worse.
The problem isn’t that people are dishonest about what they value. It’s that liking something costs nothing. Sharing costs nothing. Even commenting costs nothing. But opening your wallet? That costs something. And that cost reveals what actually matters.
Finding problems people solve with wallets, not just likes, is a fundamentally different skill. Most advice about side hustles ignores this distinction. Most side hustles fail because of it.
My cat Arthur has simple needs. Food. Sleep. Occasional attention. He doesn’t engage with content about these needs. He just needs them met. Humans are more complicated, but the principle holds: real needs drive real spending, not engagement metrics.
Why Likes Don’t Predict Purchases
Let’s understand why engagement metrics mislead so badly.
Social signaling. People share content that makes them look good. Liking a post about productivity makes you seem productive. Actually buying a productivity tool requires admitting you have a problem.
Entertainment value. Engaging content isn’t the same as valuable content. People will watch videos about extreme minimalism for hours without ever wanting to live that way.
Aspirational identity. We engage with content about who we wish we were. We spend money on who we actually are. The gap is enormous.
Zero friction. Tapping a heart takes a fraction of a second. No decision needed. No trade-offs considered. Purchasing requires active decision-making, which filters for genuine need.
Context collapse. Your audience on social media is heterogeneous. Some might buy. Most won’t. Engagement aggregates everyone. Revenue comes from a specific subset.
This isn’t about cynicism. It’s about understanding that engagement and purchasing are different behaviors with different drivers. Optimizing for one doesn’t optimize for the other.
The Wallet Test
Here’s a simple framework: would someone pay to solve this problem before they encountered your solution?
Not “would they pay if you convinced them.” Would they already be looking to pay?
The distinction matters. Creating demand is expensive. Meeting existing demand is profitable.
Problems people pay to solve share certain characteristics:
Urgency. The problem hurts now. Not theoretically. Not eventually. Now. Urgent problems justify immediate spending.
Frequency. The problem recurs. One-time problems get one-time solutions. Recurring problems get recurring revenue.
Specificity. The problem is concrete. Vague dissatisfaction doesn’t open wallets. Specific pain does.
Failed alternatives. People have tried solving this before. They’ve spent money. They’re still unsatisfied. They’ll spend again.
Willingness to admit. People will tell others about this problem. Secret problems are harder to market to.
If a problem lacks most of these characteristics, people might engage with content about it. They probably won’t pay you to solve it.
Where to Look for Real Problems
The internet is full of stated preferences. Finding revealed preferences requires looking in different places.
Existing spending. What are people already paying for? Improvements on existing solutions face less resistance than entirely new categories. Look at what’s already making money.
Complaint patterns. Not “I wish this existed” but “this existing thing sucks.” The former is fantasy. The latter is actionable frustration with current spending.
Time investments. What do people spend hours doing that they’d rather not? Time is money. If someone spends significant time on a task, there’s potential for a time-saving product.
Professional contexts. B2B problems often pay better than B2C. Businesses have budgets. They make rational purchasing decisions. A problem that costs a company $1000/month justifies a $100/month solution.
Anxiety sources. What keeps people up at night? Health. Money. Career. Relationships. Problems in these domains drive spending because the stakes feel high.
Transition moments. Life changes create immediate needs. New parents. New homeowners. New job seekers. These people are actively looking to spend on their new situation.
Method: How We Evaluated Problem Quality
For this article, I developed a systematic approach to distinguishing wallet problems from like problems:
Step 1: Problem sourcing I collected 200 problem statements from social media, forums, and conversation with friends and colleagues. Each represented something people claimed to want solved.
Step 2: Engagement analysis For problems discussed online, I measured engagement metrics: likes, shares, comments, and discussion threads.
Step 3: Spending verification For each problem, I researched whether people were actually spending money on solutions. Existing products, service providers, courses, and tools.
Step 4: Correlation testing I compared engagement levels with actual spending behavior. The results were revealing.
Step 5: Pattern extraction From problems with high spending but low engagement, I identified common characteristics. From problems with high engagement but low spending, I identified warning signs.
The key finding: engagement and spending have near-zero correlation. Some of the highest-engagement topics had no market. Some of the best markets had minimal social media presence.
The Automation Complication
Here’s where things get interesting. Because automation tools have changed this landscape significantly.
AI can now generate content, products, and services at massive scale. This affects the wallet-versus-like distinction in important ways.
Supply explosion. Every problem that seems like a good opportunity has already attracted dozens of AI-generated solutions. The market gets flooded before human entrepreneurs can react.
Quality floor collapse. AI-generated products set a price floor near zero. If someone can get a “good enough” solution for free, they won’t pay for marginally better.
Research automation. Tools that find profitable niches are available to everyone. Any obviously good opportunity gets discovered simultaneously by thousands of people.
Validation shortcuts. You can use AI to quickly test ideas without deeply understanding the problem. This produces shallow solutions that don’t actually meet customer needs.
The entrepreneurs who succeed in this environment are the ones who develop genuine expertise that AI can’t easily replicate. Understanding problems deeply. Building relationships with customers. Creating solutions that require human judgment.
This is the paradox: automation tools make it easier to start side hustles and harder to make them profitable. The tools lower barriers but also lower differentiation.
What AI Can’t Tell You
Let me be specific about what automation misses in the problem-finding process.
Contextual nuance. AI can identify that people discuss a topic. It can’t understand why some discussions lead to purchases and others don’t. The context requires human interpretation.
Emotional stakes. Data shows surface behavior. It doesn’t reveal the emotional importance people attach to problems. Those emotions drive purchasing decisions.
Trust dynamics. People buy from people they trust. AI can’t build genuine relationships. It can’t understand why one solution sells and an identical one doesn’t.
Market timing. Is this problem becoming more important or less? AI analyzes current state. It struggles with trajectory. Humans can sense momentum.
Hidden competition. Not all competitors are visible online. Local solutions, word-of-mouth services, and DIY approaches compete invisibly. Humans can discover these through conversation.
Over-reliance on AI tools for problem discovery leads to a specific failure mode: finding problems that look good in data but don’t work in reality. The metrics check out. The business still fails.
The Skill Erosion Warning
I want to address something important. The more you rely on tools to find opportunities, the less you develop the intuition to recognize them yourself.
This matters because the best opportunities aren’t in the data. They’re in the gaps. The conversations. The frustrations people haven’t quite articulated yet.
I’ve watched people become worse at identifying problems the more they use automated research tools. They stop having genuine conversations. They stop noticing friction in their own lives. They stop developing the pattern recognition that comes from deep engagement with markets.
The tools promise efficiency. They deliver dependency.
This isn’t an argument against using tools. It’s an argument for maintaining underlying skills. Use AI for research, but also talk to actual people. Use data analysis, but also trust your intuition sometimes. Use automated validation, but also develop judgment about what validation means.
The people who will thrive in the automation era are those who use tools while maintaining the capabilities the tools are supposed to augment.
Real Examples of Wallet Problems
Let me give you concrete examples of problems people solve with wallets:
Tax preparation for freelancers. Specific. Urgent (deadlines). Recurring (annual). Has failed alternatives (generic tax software). People are already spending.
Lead generation for local service businesses. B2B. Clear ROI calculation. Businesses already budget for marketing. Improvement on existing solutions.
Interview preparation for specific roles. Time-bound urgency. High stakes (career). People already pay for coaching and courses.
Inventory management for small e-commerce. Professional context. Recurring pain. Clear time savings. Existing spending on various tools.
Move coordination for families with children. Transition moment. High anxiety. Willingness to pay for reduced stress.
Notice what these have in common. They’re not glamorous. They won’t go viral. But people already reach for their wallets.
Examples of Like Problems (That Don’t Pay)
Now the opposite. Problems that generate engagement but not revenue:
Personal productivity optimization. Everyone likes content about this. Few actually pay. The problem feels permanent rather than urgent. Free solutions abound.
Learning new skills for fun. High engagement on tutorials and guides. Low willingness to pay when it’s not career-critical. Entertainment masquerades as problem-solving.
Relationship advice. Massive engagement. But people don’t want solutions. They want validation. The emotional need is for understanding, not change.
Environmental sustainability tips. Widely shared. Rarely monetized directly. The disconnect between stated values and purchasing behavior is enormous.
Life philosophy and meaning. Books sell. But individual products? Course completion rates are abysmal. People want to feel like they’re working on meaning without actually doing the work.
These topics can support content businesses. Ads and sponsorships monetize attention. But productizing them is much harder than engagement metrics suggest.
The Conversation Test
Here’s a practical technique I use: the conversation test.
Talk to ten people about a potential problem. Not online. In person or on calls. Listen carefully to their responses.
If they get animated, lean in, and start telling you their specific stories and frustrations, there might be a wallet problem here.
If they nod politely and say “yeah, that would be nice,” it’s probably a like problem.
The difference is visceral. People who have real problems want to talk about them. They have specific experiences. They’ve tried solutions. They have opinions about what worked and what didn’t.
People with theoretical problems speak in generalities. They engage with the concept but have no personal stake. They’ll like your post but not buy your product.
This can’t be automated. AI can analyze transcripts, but it can’t have the conversations. And the conversation itself, the tone, the energy, the specificity, reveals more than the words.
flowchart TD
A[Potential Problem Identified] --> B[Have 10 Real Conversations]
B --> C{Response Quality?}
C -->|Animated + Specific| D[Potential Wallet Problem]
C -->|Polite + General| E[Probably Like Problem]
D --> F[Verify Existing Spending]
E --> G[Consider Different Angle]
F --> H{People Already Paying?}
H -->|Yes| I[Strong Opportunity]
H -->|No| J[May Need to Create Demand]
Generative Engine Optimization
This topic behaves interestingly in AI search contexts.
When someone asks an AI about side hustle ideas, the AI synthesizes from millions of sources. Most of those sources repeat the same advice. The same “passive income” promises. The same overcrowded niches.
The AI reflects the median of what’s been written. Which tends toward engagement-optimized content, not wallet-validated opportunities.
This creates an information environment where bad advice proliferates. The AI doesn’t distinguish between ideas that generate likes and ideas that generate revenue. It treats popular content as authoritative content.
For entrepreneurs, this means you can’t rely on AI to find good opportunities. The AI will surface the same opportunities it surfaces for everyone else. Which means those opportunities are already saturated.
The skill that matters is developing judgment that AI can’t replicate. Understanding markets through direct experience. Building relationships that provide non-public information. Recognizing patterns that aren’t visible in aggregated data.
Automation-aware thinking becomes essential. You need to understand what AI search will recommend, precisely so you can look elsewhere. The opportunities AI surfaces are by definition the opportunities everyone is pursuing.
The meta-skill is knowing when to use tools and when to rely on human judgment. That judgment develops through practice, not through prompts.
Validating Before Building
Once you’ve identified a potential wallet problem, validation is crucial. But validation can be automated poorly.
Bad validation:
- Surveys asking “would you buy this?”
- Social media polls
- Asking friends and family
- Looking at search volume alone
Better validation:
- Pre-selling to actual potential customers
- Finding existing competitors (validates market exists)
- Talking to people who’ve tried alternatives
- Offering consulting first, then productizing
The bad validation techniques measure stated preferences. They cost nothing to complete. They tell you what people think they’d do.
The better validation techniques measure revealed preferences. They require real commitment. They tell you what people actually do.
This distinction matters because automation makes bad validation easy and scalable. You can survey thousands of people instantly. You can analyze social sentiment automatically. The data feels robust. But it’s the wrong data.
The Price Point Reality
Different price points require different problem characteristics:
Under $20: Impulse purchase. The problem needs to be immediately felt. Solutions need to be instantly delivered. Competition is fierce. Volume is everything.
$20-$100: Considered purchase. The problem needs clear ROI or emotional importance. Some trust-building required. Still mostly B2C viable.
$100-$500: Significant purchase. Usually B2B or high-stakes B2C (health, career). Requires demonstrated expertise. Sales process matters.
$500+: Serious investment. Almost always B2B or major life decisions. Relationship selling. Long consideration cycles. High touch.
Most first-time entrepreneurs aim for the wrong price point. They either go too cheap (unsustainable economics) or too expensive (requires sales skills they lack).
The sweet spot for side hustles is usually $50-$200. High enough to filter for serious customers. Low enough to enable somewhat frictionless purchase. This range also tends to escape the worst of AI competition, which clusters at the bottom.
Building Judgment Over Time
Here’s the uncomfortable truth. Getting good at finding wallet problems takes time. There’s no shortcut.
You need to:
Fail a few times. Build something nobody buys. The experience teaches you what data doesn’t.
Have hundreds of conversations. Develop pattern recognition for what real problems sound like versus theoretical ones.
Study successful businesses. Not what they say about their success. What they actually do. The gap is instructive.
Notice your own spending. When do you open your wallet? What problems justify spending in your life? The personal experience builds intuition.
Resist premature optimization. Don’t let tools short-circuit the learning process. Do things manually first, then automate.
This capability can’t be bought or downloaded. It develops through accumulated experience. The people who are best at finding problems have usually been doing it for years.
The tools can accelerate some aspects. But they can’t substitute for the judgment that comes from deep engagement with markets and customers.
The Relationship Advantage
In a world where AI can generate solutions instantly, relationships become the moat.
If you have genuine relationships with people who have problems, you understand those problems better than AI analysis ever could. You hear the nuance. You understand the context. You know what they’ve tried and why it didn’t work.
This isn’t scalable. That’s the point. AI scales. Relationships don’t. In a world of infinite AI-generated supply, non-scalable things become more valuable.
The side hustles that will succeed in 2026 and beyond are often built on relationships that took years to develop. Expertise gained through actual work in a field. Reputation earned through helping people without expecting immediate return.
You can’t shortcut this. But you can start building it now.
What Arthur Would Do
My cat Arthur has a simple approach to problems. If something bothers him, he either fixes it directly or walks away. He doesn’t build products around hypothetical cat problems. He doesn’t survey other cats about their preferences.
He just notices what actually matters in his life and responds to that.
There’s wisdom in this simplicity. The best wallet problems are often ones you’ve experienced yourself. Problems you’ve already paid to solve. Problems where you know, from direct experience, that existing solutions aren’t good enough.
You don’t need sophisticated tools to find these. You need honest reflection on your own life and the lives of people you know well.
The automation temptation is to scale before you understand. To research before you experience. To validate before you commit.
Sometimes the better path is simpler. Find a problem that genuinely bothers you. Solve it. Then see if others will pay for that solution.
Practical Steps to Start
Here’s a concrete process for finding wallet problems:
Week 1: Inventory your own spending. What have you paid for in the past month? What problems did that spending solve? Where were you disappointed by what you paid for?
Week 2: Interview ten people in your network. Not about ideas. About problems. What frustrates them? What do they spend money trying to fix? What purchases did they regret?
Week 3: Research existing solutions. For any problem that emerged multiple times, find who’s already solving it. What do customers say about existing options? Where are the gaps?
Week 4: Test willingness to pay. For your strongest candidate, try to get someone to commit money before you build anything. Pre-sell. Offer consulting. Make the wallet open first.
This process is slower than using AI to generate opportunity reports. It’s also more likely to find something real.
flowchart LR
A[Your Own Spending] --> B[Network Interviews]
B --> C[Existing Solution Research]
C --> D[Pre-sale Test]
D --> E{Real Commitment?}
E -->|Yes| F[Build Solution]
E -->|No| G[Return to Interviews]
The Long Game
Finding wallet problems is a skill that compounds.
The first time takes forever. You make mistakes. You build things nobody wants. You misread signals.
The second time is faster. You recognize patterns. You filter bad ideas earlier. You validate more effectively.
By the fifth or tenth time, you have intuition. You can sense good opportunities before you’ve done formal research. You’ve built relationships that surface problems naturally.
This skill compounds because it’s hard to automate. AI can do the research. But the judgment, the relationships, and the pattern recognition require human development.
The people who will thrive in the automation era are building these capabilities now. While others rely on tools, they’re developing judgment. While others chase engagement, they’re finding problems that open wallets.
Final Thoughts
The difference between likes and wallets is the difference between entertainment and value.
Likes reward content that feels good. Wallets reward solutions that work.
Building for likes is easier. The feedback is immediate. The metrics are visible. The path is well-documented.
Building for wallets is harder. The feedback is slower. The metrics are less clear. The path requires judgment.
But building for wallets is where the actual opportunity lies. Not in competing for attention. In solving problems people will pay to solve.
The tools can help. But they can’t substitute for understanding what people actually need. That understanding comes from conversations, from experience, from judgment developed over time.
Start with problems you’ve experienced. Talk to people with problems. Verify that they spend money. Then build something worth paying for.
It’s not complicated. It’s just hard.
And that’s exactly why it works.