Which solo businesses ideas will work in 2027?

Why are solo businesses suddenly becoming much more viable?

Solo businesses are becoming much more viable going into 2027 because one person can now build and operate far more of a company without hiring, and the shift is already visible in startup formation data.

Carta found that about 36% of startups created on its platform in 2025 had a solo founder, up from 31% one year earlier. A decade ago, the proportion was roughly half that level. Stripe Atlas saw an even sharper pattern among the companies it incorporates: solo founders represented 63% of C corporations formed during the second quarter of 2026, the highest share Stripe had recorded.

AI is clearly part of the explanation. A founder can now write production code, build interfaces, analyze customer feedback, create support documentation, translate a product, produce marketing assets and automate routine operations with much less outside help.

The technical improvement has been unusually fast. Stanford's 2026 AI Index found that performance on SWE-bench Verified, a benchmark for real software-engineering tasks, rose from about 60% to close to 100% in a single year. AI agents on OSWorld, which measures real computer-use tasks, jumped from roughly 12% success to around 66%.

That still leaves plenty of work that AI handles badly. For solo founders, though, the important change is that more of the repetitive work around building and operating a small company can now be delegated to software.

Is it easier to build a solo business but harder to make money from one?

Yes. Building a solo business is getting dramatically easier while making one stand out is getting harder, and Stripe's revenue data shows the split unusually clearly.

Among Stripe Atlas solo companies incorporated in 2025, median revenue during the first six months fell 23% compared with the previous cohort. Revenue among the top 10%, meanwhile, increased 19%.

The gap has become enormous. Four years earlier, top-decile solo founders generated about 34 times the early revenue of the median founder. That ratio has since widened to 61 times.

Cheaper creation explains why this can happen. When building an acceptable website, app or automation once required meaningful technical skill, the product itself acted as a filter. Today thousands of founders can build a credible first version within days.

Stanford's AI Index gives us a good sense of how quickly the cost floor has fallen. The price of running a model at roughly GPT-3.5-level performance dropped more than 280-fold between late 2022 and late 2024, from around $20 per million tokens to seven cents. Smaller models have continued improving since then.

That pushes the bottleneck toward everything around the product: choosing a problem people care enough about, reaching those people, earning trust, fitting into an existing workflow and giving customers a reason to stay.

For 2027, “I can build this alone” is a prerequisite rather than a business advantage.

Has AI made software too easy to copy?

Generic AI software is already becoming dangerously easy to copy, so the better 2027 opportunities will usually own something beyond the model itself.

Sensor Tower's latest AI-app research counted more than 200,000 apps mentioning AI in their descriptions. Those apps were on track for roughly 10 billion downloads during the first half of 2026 alone.

At the same time, the underlying models are converging. Stanford found that the gap between the best closed and open-weight models had narrowed from about eight percentage points to 1.7 percentage points on one major benchmark by early 2025. Its newer AI Index shows U.S. and Chinese frontier models trading the lead as well.

That makes “we use a powerful model” a weak differentiator.

Consider two products that both use the same foundation model. The first lets a customer upload an invoice and ask questions about it. The second connects directly to an accounting system, recognizes the supplier, checks the invoice against purchase records, flags inconsistencies, sends missing-document requests and remembers how the customer previously handled exceptions.

A competitor can reproduce the first product quickly. Copying the second requires integrations, workflow knowledge, edge cases, historical data and a much deeper understanding of the customer.

We would rather own a small but annoying workflow inside a business than a clever AI feature that looks impressive in a demo.

Is distribution now the hardest part of building a solo business?

Distribution is becoming the main bottleneck for solo businesses because producing another competent product has become much easier than getting sustained attention for it.

Search is a good example. Ahrefs re-ran its AI Overview study on 300,000 keywords and found that when an AI Overview appeared, the click-through rate of the top organic result was about 58% lower than expected. Its earlier study had measured a 34.5% reduction.

The direction is getting worse for publishers that depend on informational clicks.

AI assistants are opening another discovery channel, but that channel currently behaves very differently. Similarweb's latest analysis estimates that AI platforms sent an average of 770.7 million referral visits per month worldwide between mid-2025 and mid-2026, up 117.4% year over year. Despite that growth, AI still represents only a low-single-digit share of traffic for most sites.

The interesting change is where those clicks go. Marketplaces alone received an average 46.8 million AI-referred visits per month in Similarweb's data and grew 237% year over year. Large databases, marketplaces and authoritative sources fit naturally into the way AI assistants research answers.

That makes products with built-in distribution more attractive. A Shopify app can be found inside Shopify. A product used by accountants can spread from one accounting practice to multiple clients. A monitoring product can publish part of its proprietary dataset. A tool producing reports for customers can expose itself to everyone receiving those reports.

Should a solo founder choose B2B or B2C in 2027?

B2B is currently the safer choice for a solo founder, and the revenue difference in Stripe's data is large enough that we would choose B2C only when we have a real consumer-distribution advantage.

After 24 months, the median solo B2B company in Stripe's analysis was generating more than four times the revenue of the median solo B2C company. Among top-decile founders, B2B companies still generated almost twice as much revenue as their B2C peers.

Funding does not explain the result. Stripe found the same pattern among bootstrapped companies.

Retention also separates the winners. Nearly 30% of customers acquired by top-decile solo startups came back the following month, versus only 8% for middle-decile companies. In solo B2B specifically, top performers retained their earliest customers at roughly six times the rate of median founders.

Recurring billing was also much more common among the better businesses. Top-decile B2B founders were 26 percentage points more likely than middle-decile founders to use it.

A business that saves five employee hours, catches a costly error or brings in another customer can justify $100, $300 or $1,000 each month. Five hundred businesses paying $200 per month already create a $1.2 million annual business.

Will small businesses pay for vertical AI workflow tools?

Small businesses are clearly willing to spend on AI, and the best opening now is turning generic AI into a narrow workflow that actually completes useful work.

JPMorgan Chase Institute analyzed actual payments from 4.6 million small businesses, which gives us something more useful than another survey about intentions. Businesses entering its sample in 2019 took about 77 months before 10% had purchased an AI service. The 2025 cohort reached the same level in six months.

That is almost 13 times faster.

The breadth of purchasing is changing too. In 2019, 89% of AI-paying small businesses bought only one AI service. By 2025 that share had dropped to 72%, while the proportion paying for three or more AI products had reached 9%.

A newer Small Firms Association survey of 404 small companies makes the remaining gap clearer. It found that 92% were already using generic products such as ChatGPT or Microsoft Copilot, but only 30% used customized AI tailored to their business. That customized share had risen from 22% a year earlier.

Among businesses using tailored systems, nearly two-thirds reported improved efficiency and almost half reported faster customer service. The biggest barriers among the others included a lack of technical expertise and simply not having time to figure everything out.

Upwork's 2026 hiring data points in the same direction. Demand for AI integration skills grew 178% year over year. AI chatbot development grew 71%, while the whole group of skills explicitly tied to applying AI inside existing work increased 109%.

A separate Upwork study asked SMB leaders where they were actually piloting AI agents. Customer service led at 40%, followed by scheduling and administrative work at 38% and data analytics at 37%.

The pattern is pretty practical. Companies are starting with work that comes in repeatedly, follows recognizable rules and has an output somebody can verify.

That gives a solo founder plenty of narrow problems to attack. A property-management system could triage tenant messages, request missing information and assign contractors. A quoting system for a specific trade could read drawings or emails and prepare estimates. An ecommerce workflow could handle routine returns and escalate unusual cases. A freight tool could extract documents, update the transport-management system and chase missing paperwork.

We should be more cautious around workflows where every request is different or a single mistake creates major legal, medical or financial consequences. Even the latest Stanford AI Index shows how uneven agents remain: performance has improved very quickly, yet agents still fail roughly one-third of tasks on OSWorld.

For now, the best products automate the predictable work and make the messy exceptions easier for a person to handle.

Workflow	Current adoption evidence	2027 opportunity	Example solo product
Customer support	40% of surveyed SMBs piloting agents	Very high	Support automation for one industry
Scheduling and admin	38% piloting	Very high	Intake, booking and follow-up for one trade
Data analytics	37% piloting; 27% already scaling	High	Automatic recurring client reports
Inventory management	24% already scaling	High	Reorder and stock-exception system
Generic content generation	26% already scaling	Medium	Better embedded inside another workflow
High-stakes autonomous decisions	Reliability remains uneven	Low	Keep a human review step

Can a productized AI service work better than SaaS?

A productized AI service can be an even better starting point than SaaS in 2027 because customers currently need implementation help and AI lets one founder automate a large part of the delivery.

Upwork's latest hiring reports keep showing this move from experimentation toward implementation. Businesses are hiring people to connect AI with products, internal tools and operational systems. Upwork even found AI agents trying to access its marketplace on behalf of users, which led the company to launch an MCP integration allowing people to initiate hiring from AI tools.

This is probably where automation is heading. Companies may increasingly buy a result that combines software, agents and occasional human intervention without caring much about which category the vendor belongs to.

A traditional automation consultant has a scalability problem because every client can require discovery calls, a custom proposal, unique implementation work and ongoing support. Narrowing the offer aggressively fixes much of that.

Imagine selling “we process incoming quote requests for commercial cleaning businesses.” Every customer goes through roughly the same intake, connects roughly the same systems and receives roughly the same output. AI reads the request, collects missing details, prepares the draft and routes unusual cases back to the founder or customer.

The business behaves more like software with a human exception layer.

Competitor monitoring for ecommerce companies, recurring reporting for agencies, catalog cleanup for merchants, tender monitoring for contractors and review operations for multi-location businesses can all follow the same pattern.

After serving a few dozen customers, the repeated parts of the service also become obvious enough to automate further.

Is cybersecurity and compliance a good solo business in 2027?

Cybersecurity and compliance look very attractive for solo founders when the product handles a narrow recurring task rather than trying to become an entire security department.

An IDC study commissioned by Sage surveyed 2,210 SMBs worldwide and found that 52% ranked cybersecurity and data protection among their main priorities for the coming year. Sixty percent expected their cybersecurity spending to increase.

The demand is easy to understand. Smaller companies increasingly need to prove their security posture to customers, insurers, platforms and regulators, but many cannot justify a dedicated security team.

The scale of that gap is striking in the United States. NIST pointed out this year that the country has roughly 34.8 million small businesses and that 81.9% have no paid employees beyond the owner or owners.

A one-person software company should stay well away from pretending it can secure all of those businesses. Narrow compliance chores are much more realistic.

Security questionnaires are one example. A supplier may repeatedly receive long questionnaires from larger customers asking about policies, encryption, backups, access controls and incident procedures. A product can maintain previous answers, collect supporting evidence and highlight anything that has changed.

Other possibilities include tracking expiring certifications, maintaining audit evidence, monitoring basic SaaS security settings, keeping vendor documents current or showing a company exactly what evidence is missing before an audit.

The recurring nature makes the category especially appealing. Requirements change, evidence expires and new questionnaires arrive. Customers keep encountering the problem after the first month.

Are Shopify and other ecosystem apps still good solo businesses?

Platform apps are still good solo businesses today because an ecosystem can solve part of the distribution problem, although mature platforms reward narrow products much more than generic ones.

Shopify paid more than $1.3 billion to developers across its ecosystem in 2025. Active app installations rose nearly 20% during the same period, and its App Store now contains more than 21,000 apps.

So merchant demand is clearly real, along with plenty of competition.

Some developer businesses also show just how far a narrow problem can go. Shopify recently highlighted Kaching, whose annual revenue increased from roughly $900,000 to $4.3 million in a year by focusing on upselling and bundling. Locksmith has survived for more than 15 years solving store-access controls. Mechanic focuses on customizable merchant workflows.

Those are useful examples because none depends on discovering an entirely new software category. The opportunity sits inside an existing ecosystem where millions of merchants already spend money.

We can apply the same logic beyond Shopify. HubSpot, Salesforce, QuickBooks, Xero, WordPress and industry-specific systems all create markets for software that fixes problems their core platforms do not solve deeply.

The obvious risk is dependence. A platform can change an API, release a competing native feature, alter its ranking algorithm or change its economics.

Still, distribution has become valuable enough that this trade can make sense. A founder giving up some independence in exchange for access to a concentrated pool of paying customers may have a much easier business than someone launching an isolated SaaS product into the open internet.

Can paid newsletters and niche data products still work in 2027?

Niche information businesses can still work very well, but we would increasingly build the paid product around proprietary data, monitoring or decisions rather than ordinary commentary.

Beehiiv's latest first-party data shows that people certainly have not stopped paying for newsletters. Paid-subscription revenue on the platform reached $19 million in 2025, up 138% in a year. The share of revenue-generating publishers earning from subscriptions doubled from 15% in early 2024 to 30% two years later.

The median economics are much less glamorous.

Across thousands of beehiiv publications, only 0.62% of free subscribers convert to paid at the median. That means a 1,000-person list produces roughly six paying customers. Results vary enormously by niche: investing newsletters charge a median $27 per month, finance $20 and business $15, while travel sits around $7.

AI newsletters illustrate the vulnerability of replaceable information. Their median monthly churn was 13.33%, one of the highest categories in beehiiv's dataset. Free alternatives appear constantly, so yesterday's premium insight can become today's commodity.

We prefer information that changes continuously and affects a decision. A database of tenders for one profession, competitor-pricing alerts for ecommerce sellers, regulation tracking, procurement opportunities, planning applications, supplier changes or grants matched against company attributes all fit that description.

Writing can still be the acquisition engine. The customer ultimately pays because the underlying information saves research time or triggers an action.

Information business	2027 outlook	What the customer is really paying for
Generic AI newsletter	Weak	Easily replaceable commentary
Broad news summary	Weak	Information AI can summarize elsewhere
Expert niche publication	Moderate	Judgment and reputation
Proprietary professional database	Strong	Harder-to-reproduce information
Continually updated alerts	Very strong	Knowing about a change quickly
Data plus workflow subscription	Very strong	Information that immediately produces an action

Are directories and SEO-only websites dying?

Static directories and SEO-only information sites are becoming much weaker solo-business ideas, although search can still be an excellent acquisition channel for a product with deeper value.

The click problem is now measurable. Ahrefs found that AI Overviews were associated with a 58% reduction in clicks to the number-one organic result. Similar research from Pew and other SEO datasets has found the same direction.

AI discovery complicates the picture rather than simply destroying website traffic. Similarweb currently sees AI referral traffic growing by more than 100% year over year, and every industry in its latest comparison at least doubled.

Those AI referrals increasingly favor resources that contain something useful to retrieve: databases, marketplaces, original information, products and authoritative sources.

That makes a static “500 best AI tools” directory a weak proposition. Generating another version is trivial.

Add continuously changing information and the economics improve. A grant directory becomes much more interesting when a business enters its profile and sees which programs it actually qualifies for. A supplier directory gains value when we monitor certification changes, pricing or availability. A property database becomes useful when we track inventory, launches and price movements.

Those pages can still rank on Google and appear as sources inside AI answers. Our stress test is simple: if search traffic dropped 50%, would customers still have a reason to pay?

If the answer is no, we would be uncomfortable building that business for 2027.

Are consumer AI and mobile apps still worth building?

Consumer AI apps can still become enormous businesses, but for a typical solo founder they remain a much harder bet than a narrow B2B product.

Demand itself looks excellent. Sensor Tower expects AI apps to generate more than $4 billion of in-app purchase revenue during the first half of 2026, 36% above the previous six-month period. Global time spent inside generative-AI apps is projected to reach 36 billion hours, more than double the equivalent period a year earlier.

The problem is concentration.

More than 200,000 apps already mention AI. ChatGPT became the first AI mobile app to pass one billion monthly active users, according to Sensor Tower, while Gemini and Claude have also been gaining share.

A new general-purpose AI assistant is therefore walking straight into some of the world's strongest distribution networks.

A niche consumer app has a better chance when it has another reason to exist: repeated habit, entertainment, identity, social sharing, specialized hardware access, a camera workflow, a hobby or a community.

The distribution question should come unusually early. Can the app generate shareable output? Does App Store search already contain high-intent demand? Is there a TikTok or creator format that naturally demonstrates it? Does the founder already control an audience?

Without one of those answers, we would usually prefer B2B.

The upside in consumer remains much larger. The probability distribution is simply much harsher.

Does ecommerce still make sense for a solo founder?

Physical ecommerce can work in 2027, but it is less naturally suited to a one-person operation than software, data or an automated digital service.

AI is actually making online shopping more interesting. Sensor Tower found generative-AI referrals increasing across every major retail category it tracked, and Similarweb saw marketplaces receive the largest AI referral volume of any industry.

Similarweb's marketplace data is especially striking: average monthly AI referrals grew from 13.9 million to 46.8 million year over year. By the final month of its measurement period, the category had reached almost 90 million referrals.

So there is no obvious demand collapse coming from AI. Discovery is changing.

The difficulty for a solo founder comes after the purchase. Physical products still involve inventory, suppliers, fulfillment, damaged packages, returns and customer support. Software can add its 1,000th customer without sending another box anywhere.

Outsourcing solves much of this, but then margins and operational dependencies become important.

We would therefore be selective. High margins, few SKUs, low return rates, simple fulfillment and repeat purchases make a physical business much more compatible with staying solo.

Otherwise, digital products have a structural advantage. A $100-per-month workflow product with 200 business customers creates $240,000 in annual recurring revenue without moving 20,000 parcels.

That is hard for physical ecommerce to beat on operational simplicity.

Should a solo business sell globally from day one?

Digital solo businesses should usually sell globally from the beginning because the best solo founders already show how powerful a narrow niche becomes when geography stops limiting the market.

Stripe found major differences between its top-performing and median solo companies. In their first month, top-decile founders sold into an average of ten countries, compared with three for median founders.

After two years, top performers were selling into about 40 countries outside the United States. The median company was selling into six.

The revenue difference was even bigger. International customers represented 51% of revenue for top-decile solo companies and only 2% for the median group.

There is a causality problem here. Selling internationally does not magically make a bad company successful. Strong digital businesses may simply internationalize more easily.

Still, the opportunity is obvious for a solo founder. A very specific problem can be too small in France, Thailand or the United Kingdom while remaining plenty large across 30 countries.

Software for independent vacation-rental managers, for example, can target the same workflow in dozens of markets. An ecommerce tool can serve Shopify stores everywhere. A compliance-monitoring product can expand market by market as it adds relevant rules.

Local focus still makes sense when language, regulation, proprietary local data or relationships create the advantage.

When none of those constraints exists, we would avoid shrinking the market unnecessarily.

Which solo business ideas should we avoid in 2027?

We would avoid solo business ideas where AI has removed most of the barrier to entry without creating a new reason for customers to choose one provider over another.

Generic AI wrappers are the clearest case. A product that sends a prompt to a foundation model and displays the answer can certainly make money, but another founder can now reproduce much of the experience extremely quickly.

Generic content production has a similar problem. Businesses will keep paying for excellent writers, editors and marketers, while bulk “30 AI articles per month” offers become easier to reproduce internally.

Static directories face the same pressure unless the information requires continuous collection or verification. Generic prompt libraries and broad template packs also suffer from almost unlimited supply.

We would be cautious with horizontal productivity software too. Another broad CRM, note-taking app, project manager or general AI assistant has to compete against mature products that can add similar AI features to an existing customer base.

Two-sided marketplaces have a different weakness. They require demand, supply, trust and operational dispute handling at the same time. That can be a lot for one founder before network effects appear.

Fully autonomous products in high-stakes workflows deserve caution as well. Stanford's latest agent benchmarks show dramatic progress alongside persistent failure rates. Human review remains valuable anywhere mistakes are expensive.

The common problem is easy replication combined with difficult distribution.

A 2027 idea becomes more attractive when copying the interface is only a small part of copying the business.

Which solo business ideas will actually work in 2027?

The solo businesses most likely to work in 2027 are narrow B2B products and automated services that remove a recurring piece of work, especially when they own a workflow, proprietary data, an integration or a built-in distribution channel.

AI makes solo companies more feasible, but the evidence consistently favors businesses with stronger retention, higher-value B2B customers and more embedded workflows. Small-business AI spending is accelerating while customized implementation still lags far behind generic ChatGPT-style usage. Upwork is seeing especially fast growth in AI integration work, and adjacent markets such as ecosystem software, cybersecurity and professional data continue to support real spending.

Vertical AI workflow SaaS sits at the top of our list. Productized automation services are almost as attractive and may be easier to launch because we can sell the result before building a complete product. Niche monitoring, proprietary databases, compliance tools and ecosystem apps also fit the economics of a one-person company extremely well.

The weakest categories are the ones that look easiest to launch: generic AI wrappers, static directories, bulk AI content businesses and interchangeable digital products. Ease of creation is exactly what makes those markets uncomfortable.

If we had to reduce the entire 2027 thesis to one practical idea, we would look for a boring process that a particular type of business performs every week using email, PDFs, spreadsheets, browser tabs and manual copying. Then we would automate most of that process and charge for the finished outcome.

Solo business idea	2027 outlook	Example	Why it works for one founder
Vertical AI workflow SaaS	Excellent	Quote processing for one trade	Recurring pain, high automation potential
Productized AI service	Excellent	Automated reporting for one agency niche	Sell the outcome before building full SaaS
Niche monitoring subscription	Excellent	Tender, pricing or regulation alerts	New information creates recurring value
Proprietary professional database	Excellent	Continuously updated industry dataset	Harder to copy than written content
Compliance workflow software	Very strong	Security evidence and questionnaire automation	Recurring requirements and rising budgets
Ecosystem app	Very strong	Narrow Shopify or accounting workflow	Existing platform helps solve distribution
Data-backed professional publication	Strong	Newsletter plus proprietary database	Content acquires users; data drives payment
Specialized prosumer utility	Moderate to strong	Tool for one profession or serious hobby	Narrow need can beat general-purpose software
Consumer mobile app	Moderate	Habitual or shareable niche utility	Huge upside, much harder distribution
Physical ecommerce	Moderate	High-margin, low-SKU niche brand	Works when operations stay unusually simple
Generic AI wrapper	Weak	Basic model front end	Easy to reproduce
Static directory	Weak	Generic list of tools or businesses	Low defensibility and weaker search clicks
Generic AI content service	Weak	Bulk AI articles	Customers can increasingly do it themselves
Two-sided marketplace	Poor for most solo founders	New freelancer marketplace	Too many problems must work simultaneously