Is it too late to start vibe coding apps?

Why does it suddenly feel too late to start vibe coding apps?

No, it is not too late to start vibe coding apps, but the period when simply shipping an AI-built app felt unusual is already over.

The change has been brutally fast. Lovable says people have created more than 50 million projects on its platform, with roughly one million new projects being started every week. RevenueCat's latest subscription-app study, covering more than 115,000 apps and over $16 billion in revenue, found that monthly subscription-app launches rose from roughly 2,000 in early 2022 to more than 14,700 four years later.

That is roughly a sevenfold increase in monthly launches.

The interesting part is what happened to revenue at the same time. Apps launched in 2025 or later account for only around 3% of subscription revenue in RevenueCat's dataset. Apps launched before 2020 still collect 69%.

So we have far more people capable of putting software into the market, while successful new products remain rare. The crowded part is creation itself.

A polished interface, login screen, database, Stripe checkout and a few AI features can now be assembled remarkably quickly. Two years ago, that could make a solo founder look technically exceptional. Today it mostly gets the founder to the starting line.

What changed	Latest evidence	What we can conclude
Subscription-app supply	About 2,000 monthly launches became 14,700+	Launching has become dramatically easier
Lovable usage	About 1M new projects a week	AI-built software supply is still rising fast
Revenue from 2025+ apps	About 3% of subscription revenue	New supply is far larger than new commercial success
Revenue from pre-2020 apps	69%	Distribution and accumulated customer relationships still compound

What would actually make someone “too late” to vibe coding?

We would be too late if our entire advantage were being able to build a decent-looking app quickly.

Vibe coding has crushed the cost of implementation much more aggressively than it has reduced the difficulty of finding customers, understanding a niche, earning trust or keeping users around.

That gap explains much of what we see today.

Stripe Atlas studied roughly 23,000 companies incorporated through its platform in 2025. Twenty percent reached their first paying customer within 30 days, up from 8% in 2020. Among startups that monetized during their first three months, median time to the first payment fell from 38 days to 34.

Founders are clearly reaching the market faster.

Yet RevenueCat's data shows an increasingly violent separation between winners and everyone else. Median subscription-app MRR grew only around 5% year over year, while the top 10% grew more than 300%. Shrinking by more than 33% was enough to place an app in the bottom quarter.

Cheap building gives more founders a chance to play. It does very little to guarantee that anybody wins.

“Too late” starts to become a real problem when we enter a mature category with no unusual customer access, no deeper workflow knowledge, no distribution advantage and nothing that will become harder to copy over time.

Has vibe coding already flooded the app market?

Yes, the app market is currently being flooded with new software, although most of that new supply will never become a serious business.

RevenueCat calls what is happening in subscription apps a supply shock. Its dataset shows launches increasing about sevenfold in four years, with the sharpest acceleration on iOS beginning around early 2025 as AI-assisted development became much more accessible.

Lovable gives us another view of the same phenomenon. One platform says its users are generating roughly one million new projects every week. Even a tiny conversion rate from project to commercial app would create a remarkable amount of new competition.

Still, project counts can exaggerate how crowded the commercial market really is. Many projects are prototypes, internal tools, abandoned experiments, class projects, personal utilities or near-duplicates. They never compete meaningfully for recurring customer spending.

The useful distinction is between crowded app ideas and crowded customer problems.

Generic AI writing apps, basic trackers, simple dashboards and one-function productivity tools can now attract dozens of near-identical entrants incredibly quickly. A specialized workflow for a niche industry may still have only two mediocre products serving it.

So yes, there are too many apps in plenty of categories. We cannot jump from that fact to “there are no problems left worth solving.”

Are new apps still making money today?

Yes, new apps and startups are still reaching revenue surprisingly fast today, and the latest startup data gives us very little evidence of a closed market.

Stripe Atlas found that the median first six months of revenue for companies formed through its platform in 2025 rose 39% from the previous cohort. The number of companies reaching $100,000 of revenue within their first six months increased 56%.

Those companies also reached $100,000 faster: a median 108 days compared with 121 days for the prior cohort. The average startup acquired 242 customers during its first six months, more than 50% higher than a year earlier.

Part of the speed increase came from Stripe allowing some founders to accept payments sooner, so we should not pretend that every improvement reflects stronger products. The revenue figures go well beyond that infrastructure effect, though. Founders are clearly shipping and charging customers earlier.

The winners are also pulling further away. Revenue for the 90th-percentile Atlas startup increased 52% compared with the previous cohort, while the improvement at the 10th percentile was 18%.

That pattern keeps showing up: starting has become easier, early monetization has become faster, and the gap between products people genuinely want and the enormous pile of mediocre launches is getting wider.

Stripe Atlas metric	Earlier comparison	2025 cohort
Paying customer within 30 days	8% in 2020	20%
Median time to first payment among fast monetizers	38 days	34 days
First-six-month median revenue	Previous cohort baseline	+39%
Companies reaching $100K within six months	Previous cohort baseline	+56%
Median time to $100K	121 days	108 days

Is vibe coding already becoming a commodity?

Yes, the ability to build software through AI is becoming normal incredibly quickly.

JetBrains surveyed more than 15,000 professional developers during May through July 2026. Ninety percent said they were already using some form of AI coding agent at work at least weekly, and 68% were using one daily.

That adoption level changes the competitive value of vibe coding itself. When most professional developers have AI agents in their normal workflow, “we use AI to build faster” stops sounding like a meaningful edge.

Large platforms are moving in the same direction.

Microsoft's current Power Apps preview lets someone describe an application in conversational language and generate the app, its data model and a plan around it. Users can attach Word files, Excel sheets, emails and chats to give the system business context before generation.

GitHub recently shut down new creation in its standalone Spark experience and directed builders toward Copilot-based development environments instead. Google's Firebase Studio is also being sunset, with users pushed toward Google AI Studio and Antigravity.

These moves look less like vibe coding disappearing and more like vibe coding being absorbed into ordinary software development.

For founders, that pushes the interesting advantage elsewhere: knowing which problem deserves to be built, reaching its users and learning faster from what those users actually do.

Does coding skill still give vibe-coding founders an advantage?

Yes, technical skill still helps a lot, although its value now shows up later in the process.

A non-programmer can currently get much further than before. Authentication, databases, APIs, payments, responsive interfaces and fairly complex business logic can all be generated with limited traditional coding.

The difficult part arrives when the product accumulates real users and real history.

Production software eventually has old data, permission rules, migrations, billing edge cases, concurrency problems, strange user behavior, third-party failures and bugs that only appear under specific conditions. At that stage, endlessly prompting an agent to “fix it” can create another layer of problems.

The trust gap among developers remains obvious. Stack Overflow's large developer survey found that 46% of respondents distrusted the accuracy of AI tools, compared with 33% who trusted them. Experienced developers were even more cautious.

At the same time, the JetBrains data shows professional developers adopting coding agents at huge scale. Put those findings together and the direction is fairly clear: skilled developers increasingly use AI heavily without assuming that AI output is automatically correct.

For a founder, the practical advantage of technical knowledge has shifted toward reviewing architecture, spotting dangerous shortcuts, debugging unusual failures and knowing when an apparently working solution is fragile.

Vibe coding has made it possible for many more people to start. Experience still becomes valuable when the prototype has to survive contact with reality.

Can vibe-coded apps actually be trusted with real users?

Some can, but production quality remains one of the weakest parts of vibe coding today and one of the clearest ways a serious product can separate itself from throwaway competitors.

Recent academic work on real-world vibe-coded applications found recurring security problems such as exposed secrets, weak input handling and placeholder logic that survived into deployed software. The researchers traced these failures partly to characteristics of coding agents themselves, including memory loss, narrow optimization and incomplete security knowledge.

The worrying part is that better models and stronger prompting reduced these problems without eliminating them.

That fits what developers have been saying for a while. Stack Overflow found that “almost right” AI output was one of the most common frustrations around AI-assisted development. Code that works in the happy path can still behave badly around authentication, permissions, unusual input or failure recovery.

For low-risk personal utilities, some of those shortcomings are manageable. The standard changes completely when an application handles customer payments, medical information, financial records, private company data or important operational workflows.

This is one area where the flood of cheap software can actually help the better builders. Users eventually notice which products are stable, fast, secure and well supported.

As vibe-coded interfaces become easier to imitate, boring things such as tests, backups, monitoring, access controls and reliable migrations become more valuable.

Is distribution harder than building a vibe-coded app now?

Yes, distribution is currently a much bigger constraint than building for most new vibe-coded apps.

The clearest evidence comes from the extraordinary amount of software being produced alongside the small share of revenue captured by recent entrants. Getting an application onto the internet has become cheap enough that we should assume competitors can reach roughly the same technical starting point.

Customer attention has not multiplied sevenfold alongside app launches.

Older products also arrive with assets that cannot be recreated through a prompt. They may have years of App Store reviews, search rankings, backlinks, customer lists, integrations, community mentions and word of mouth.

A newcomer can clone their visible interface in a weekend and still be years behind commercially.

That changes one of the most useful questions we can ask before building. Instead of spending days wondering whether the app is technically feasible, we should know where the first 20 customers are likely to come from.

A founder who already works in an industry, runs a newsletter, participates in a niche community, sells another product to the same buyers or can reach customers directly starts with something much harder to generate than code.

These days, distribution often becomes the first real moat before the product has had time to develop any other one.

Do people still pay for AI-powered apps?

Yes, people currently pay more readily for AI-powered apps than for conventional subscription apps, but they also leave them faster.

RevenueCat classified roughly 27% of the apps in its latest dataset as AI-powered. Median trial-start rates were 8.5% for AI apps compared with 5.6% for non-AI products.

AI apps also converted downloads into paying users somewhat better, with a median 2.4% download-to-paid rate against 2.0%. After one year, median realized value per payer reached $30.16 for AI apps versus $21.37 for non-AI apps.

So willingness to pay has clearly survived the enormous increase in AI-app supply.

Retention tells a less comfortable story. Twelve-month retention on monthly subscriptions was 6.1% for AI apps and 9.5% for non-AI apps. On annual subscriptions, the figures were 21.1% and 30.7%.

Refund rates were also higher for AI products.

RevenueCat summarizes the gap neatly: AI apps generate about 41% more revenue per payer while churning roughly 30% faster.

That tells us where many weak AI products fail. Getting somebody excited enough to pay once is quite achievable. Giving that person a reason to keep returning every week or every month remains much harder.

The strongest vibe-coded ideas therefore tend to sit around recurring work rather than one spectacular generation: repeated business processes, ongoing monitoring, collaboration, operations, records, communication and workflows that keep coming back.

Which vibe-coded app ideas already look overcrowded?

Generic AI utilities with weak retention and no unusual route to customers are becoming very difficult bets.

RevenueCat's category data gives us a useful clue about where the density is highest. AI already represents 61.4% of subscription apps in Photo & Video and 41.1% in Productivity. Those are dramatically higher than categories such as Travel, Gaming and Business.

That does not make every productivity or photo app doomed. It does mean we should demand a much stronger reason for entering one.

Consider a generic AI writing assistant. The core capability can be reproduced using several model providers. The interface can be copied quickly. Existing platforms increasingly include writing assistance themselves. Search ads and app-store attention are competitive. And users can switch tools without moving much data.

The same problem affects generic résumé generators, broad “AI assistant” products, simple meeting summarizers and basic chat-with-your-files apps.

A narrow version can still work. A résumé product distributed through a university network has a route to customers. A document assistant built into a specific legal workflow has specialized context. A meeting product that owns a team's downstream CRM process has more staying power than a transcript generator.

The category label alone tells us very little. The dangerous idea is the one where a customer can look at five alternatives and struggle to explain why any of them is different.

Where can new vibe-coded apps still find real room?

There is still a lot of room in narrow business workflows where existing software feels too generic, too expensive or simply annoying to use.

Microsoft's own direction with Power Apps gives us a useful glimpse of the underlying demand. Its new conversational builder is designed around business users describing a workflow and attaching things such as existing spreadsheets, emails, documents or screenshots.

Those are exactly the places where huge amounts of work still live.

A wholesaler may calculate quotes using a peculiar mix of supplier spreadsheets. A recruiting agency may copy the same candidate information through email, documents and a CRM. A small property manager may reconcile payments manually because the mainstream software was designed around a larger operator. A laboratory may have an intake process that existing SaaS handles badly.

None of these problems sounds as exciting as launching “the next AI productivity platform.” That is partly why they remain interesting.

Historically, some niches were too small to justify months of custom software development. Vibe coding lowers that threshold dramatically. A market that could never support a 30-person SaaS company might support a founder with a much smaller cost base.

Business software also remains much less saturated with AI than some consumer categories. RevenueCat found AI in about 19% of Business subscription apps, compared with more than 40% in Productivity and more than 60% in Photo & Video.

That does not automatically make business apps easy. Sales can be slower, integrations can be messy and customers expect reliability.

But if we want whitespace today, boring workflows deserve far more attention than another generic AI tool.

Are vibe-coded apps automatically cheap to run?

No, some vibe-coded apps can become surprisingly expensive once people actually use them.

Base44 gives us one of the clearest recent examples. Wix said Base44 entered 2026 with non-GAAP gross margin around zero. Later in the year, after launching its own model and gaining more control over inference costs, Wix said it expected Base44's non-GAAP gross margin to reach roughly 60% during the second half.

That is a huge improvement in a short period, and it shows how much AI economics can depend on model costs.

Traditional lightweight SaaS often becomes highly profitable because serving the next customer costs very little. An AI-heavy product may pay every time a customer generates an image, runs an agent, processes a long document or makes a model call.

A $20 subscription with several dollars of monthly infrastructure expense behaves very differently from a $20 subscription whose marginal software cost is almost negligible.

This becomes especially important for solo founders because vibe-coding platforms can hide infrastructure complexity extremely well. An app may feel almost free while it has ten users and become uncomfortable when hundreds of people start hammering the expensive feature.

We should therefore watch gross profit per customer almost as carefully as revenue.

Fast development improves startup economics. Expensive usage can quietly take part of that advantage back.

Can Microsoft, Google or another big platform just copy a vibe-coded app?

Yes, large platforms can absorb thin AI features very quickly now, so a new app needs more than a clever interface around a common capability.

Microsoft already lets Power Apps users generate connected applications and data models from conversational instructions. Google is consolidating its AI-building tools around broader development environments. GitHub has retired its standalone Spark experience while pushing builders toward Copilot inside their normal development workflow.

The direction is clear enough: natural-language software creation is becoming part of larger ecosystems.

That creates real risk for apps whose value could fit neatly inside an existing platform's feature list.

A generic summarizer can appear inside an office suite. A simple database chat interface can become a feature of the database product. A basic AI website builder can be integrated into a hosting platform. A generic coding helper can become part of the editor.

Vertical workflow products have more room because the useful part often extends far beyond the visible AI feature. They may contain industry-specific rules, integrations, historical records, approval processes, reporting requirements and habits accumulated across an organization.

The more of the customer's actual workflow we own, the less threatening a copied feature becomes.

When evaluating an idea today, we should ask one uncomfortable question early: if Microsoft, Google, OpenAI or the main software vendor in this niche added our headline feature next quarter, would customers still need us?

If the answer is no, the idea is fragile.

Is it too late to start vibe coding apps?

No, it is still a good time to start vibe coding apps, provided we stop treating the ability to build them as the business advantage.

The market has clearly become tougher in one sense. Millions of people can create functional software. Professional developers use coding agents every day. App launches have exploded. Big platforms are turning natural-language development into a standard feature.

That makes generic products much easier to copy and much harder to distribute.

At the same time, the commercial evidence is surprisingly healthy. New Stripe Atlas companies are reaching customers and $100,000 of revenue faster. AI apps still convince users to pay at higher rates. Small niches that once could not justify custom development suddenly can.

What has disappeared is the old scarcity of implementation.

Customer access is still scarce. Deep knowledge of an industry's ugly workflow is scarce. Trust is scarce. Products that people keep using are scarce. Reliable software is scarcer than functional demos. Proprietary customer context takes time to accumulate.

Those are better places to build an advantage anyway.

So if the plan is to prompt out another generic AI utility and hope the internet discovers it, we are very late.

If we already know a group of people with an annoying recurring problem, can reach them directly and can use vibe coding to learn from them faster than traditional teams could, the window remains wide open.

The builders who win from here probably will not be the ones most impressed by how fast AI can make an app.

They will be the ones who know exactly which app is worth making.