Can You Make Money With AI Apps? Why Some SaaS Startups Still Fail

When almost anyone can build an app, the advantage is no longer the speed of writing code. It is understanding the problem, reaching customers and proving value.

From "can we build it?" to "will anyone buy it?"

For years, the hardest question facing a technical founder was who could build the software. An idea needed a development team, a budget and months of work before a single customer could touch a usable version. Today, AI-assisted coding tools let a small team, and sometimes a single founder, put a working prototype in front of people in a short time.

But a product that works is not the same as a business that lasts. An app can collect enthusiastic demos and plenty of praise, then never receive a single purchase order. The argument of this article is simple: building has become cheaper, but demand, trust, distribution, retention and margin have not solved themselves.

Why building software got easier

AI coding assistants now help founders generate prototypes, build interfaces, wire up basic integrations and change the product quickly in response to user feedback. That lowers the cost of the first experiment and shortens the path from idea to something worth showing. We described that path step by step in our practical 30-day guide to building an MVP with AI.

There is still a large gap between a prototype that works in a demo and a production system a paying customer depends on. Production software needs security, privacy protection, stable integrations with the customer's systems, support, monitoring, reliability and maintenance that continues long after launch. Those are the parts that turn an app from an experiment into a service.

The Supabase State of Startups 2026 survey reflects the shape of this new reality. It gathered responses from roughly 2,000 startup builders, and 61% of respondents described themselves as solo founders. That figure does not prove AI caused the pattern, and it describes the people who chose to answer, not every startup. It does describe a large group of builders working with very small teams.

The numbers show the gap between building and selling

In the same Supabase survey, 32% of respondents named customer acquisition as their greatest business challenge, and 56% said their first paying customers came through their personal or professional network. In other words, many builders report that selling, not building, is the obstacle. The survey relies on self-selected participants, so it should be read as a signal about its respondents rather than a measure of the whole market.

On the buyer's side, McKinsey's 2026 State of AI research reported that 32% of responding organizations had not purchased one or more software products or features because they built them in-house with AI coding. That share applies to the organizations surveyed, not to every company in the world, and it refers to at least one build-versus-buy decision, not a wholesale move away from purchased software.

The practical meaning is that a lower technical barrier does not only help new founders. It also widens the alternatives a customer can consider, including building the solution themselves. That is not the end of SaaS. It means a product needs a stronger reason to be bought.

Why an app can succeed technically and fail commercially

First, a definition. Profit is revenue minus costs. It is not installs, and it is not registered accounts. Many apps attract users and then stumble on that simple definition. Five traps appear again and again.

1. The problem is not urgent

Interest in a demo is not a budget. A customer can say "great idea" and still find no reason to change how they work or to allocate money to it.

2. The customer is undefined

"All businesses" is not a segment. A product that speaks to everyone struggles to convince anyone in particular, and it is hard to price or market.

3. Switching costs are ignored

The customer has to connect the app to an existing workflow, train a team, configure permissions and sometimes win internal approvals. All of that is a cost paid before any value appears.

4. Distribution is weak

Users rarely discover an app on their own. Without a clear channel to customers, even a strong product stays unknown.

5. Retention and margin are thin

If customers do not renew, if cancellations rise, or if the cost of hosting and AI model usage is high relative to the price, revenue can grow while profit erodes.

If building is easy, where is the advantage?

When code becomes less scarce, advantage moves to what is hard to copy quickly. Possible sources include:

• Real domain knowledge: understanding procedures, vocabulary and pain points as the customer lives them. Test it by asking whether customers describe the problem the way your product addresses it.
• Access to customers: a channel, relationships or partnerships a competitor cannot easily repeat. Test it by counting how many qualified prospects you can reach at a reasonable cost.
• Workflow integration: a product tied to the customer's daily systems and processes. Test it by asking whether leaving would be costly after a few months of use.
• Dependable service and data governance: stability, security, permissions and support a customer can rely on. Test it by asking whether the customer's IT or compliance team would approve it.
• Measured outcomes: proof that the product moves a metric the customer cares about. Test it by asking whether customers renew because they see a result, not out of habit.

These advantages are relative, not guaranteed. A well-funded competitor can catch up, and markets change. They remain harder to copy than an interface or a feature list.

A Saudi illustration: managing maintenance requests

Illustration only: the following scenario is hypothetical. It is not a published company case, and its numbers are targets for discussion, not achieved results.

Imagine two apps competing to serve facility management companies in Riyadh or Jeddah. Both log maintenance requests. The first offers an entry form and a list. The second ties every request to a clear path: the issue, the person responsible, the response time, the resolution and the customer's rating, with defined permissions for each role and a service-level alert when a request is close to running late.

The customer is not paying for the look of the interface. They are paying for fewer backlogged requests or faster response times that can be demonstrated. A serious offer therefore begins by agreeing a baseline, a target and the data to collect:

• Baseline: the current average response time, as the customer measures it in their own records.
• Proposed target: a reduction both sides agree on during a defined pilot period.
• Data required: when each request was opened, first answered and closed, plus the customer's rating.

Some sectors may also carry regulatory or contractual requirements around data and its storage, so these need to be confirmed with the customer before going live.

A seven-step checklist to validate a SaaS idea before you spend on it

This is a practical checklist, not a promise of income within a set period.

1. Choose a narrow segment: a specific sector, role and company size.
2. Hold ten interviews with people who actually live the problem, and listen more than you present.
3. Establish how often the problem occurs and what workaround they use today, even if it is a spreadsheet.
4. Estimate what the problem costs their business, using their numbers rather than yours.
5. Build a simple demo that focuses on the outcome, not the features.
6. Offer a paid pilot with a defined scope, duration and price.
7. Judge real repeat usage and willingness to renew, not expressions of enthusiasm.

If the evidence is encouraging but the launch keeps slipping, our essay on the startup that never launches looks at why founders postpone that first controlled release.

Five numbers that matter more than user count

User count is a comfortable number that says little. These are closer to the health of the business. The formulas are approximate:

• Conversion to paid: paying customers divided by people who tried the product.
• Activation: the share of users who reach a first meaningful result inside the product.
• Retention and churn: customers who renewed versus those who cancelled during the period.
• Customer acquisition cost (CAC): total sales and marketing spend divided by new customers.
• Gross margin: revenue minus the direct cost of serving it, including hosting and AI model usage, divided by revenue.

This is consistent with BCG's October 2026 analysis with Stripe and AWS, which argues that AI-driven competition is putting pressure on pricing and that tying price to the value a customer perceives matters more. The report describes a shift from pure per-seat pricing toward hybrid models, and in Stripe's sample of hypergrowth SaaS companies, 57% used hybrid pricing that combines a subscription with usage or outcome elements. That share describes fast-growing companies in one sample, not all SaaS businesses. Subscriptions remain viable, but they need measurable value to justify them.

The Nawa perspective: start with the outcome, not the app

At Nawa, we work with founders as a venture studio, and the pattern above matches what we look for before any serious build. The first question is not which features to ship. It is whether a specific customer has a costly, recurring problem and whether they will commit something real, time, data or money, to solve it.

That is why we treat validation, the MVP and the first customers as one connected sequence rather than three separate projects. Problem interviews shape what the MVP must prove. The MVP is scoped to test the riskiest assumption, not to impress in a demo. And the first customers are treated as evidence: who they are, how they were reached and whether they keep using the product tell a founder more than any launch announcement.

We are cautious about one thing in particular. Fast AI-assisted building can make a founder feel further along than the evidence supports. Speed is genuinely valuable, but only when it is aimed at a clear, measurable outcome. If you are weighing a venture and want a partner on that sequence, you can explore a venture with Nawa.

Counterarguments and the limits of the evidence

It is worth separating what the 2026 surveys actually say from what we infer from them. The make-up of Supabase's respondents and the maturity of their projects may explain part of the patterns observed, and the results should not be generalized to the Saudi market or to every startup. McKinsey's finding concerns at least one build-versus-buy decision among responding organizations, not a sweeping shift. BCG's hybrid pricing figure applies to a specific sample of hypergrowth companies.

Our expectation that differentiation will become harder for small niche products is an editorial hypothesis, not a proven forecast. The picture is balanced. New entrants can win when they solve a specific problem deeply, and established SaaS companies may keep their edge through accumulated trust, compliance and integrations.

Conclusion: easier building is not easier profit

Almost any founder can now build an app faster than before. But customers do not buy code. They buy an outcome worth what they pay.

Start with the problem, the segment and evidence of willingness to pay, then build. Speed in development is a real advantage, and it pays off only when it is pointed at a clear result you can measure.

Sources

• State of Startups 2026, Supabase, 2026. Survey of roughly 2,000 startup builders; a self-selected sample that does not represent the whole market.
• The state of AI, McKinsey & Company, 2026. Organizational survey; shares apply to responding organizations.
• SaaS Pricing Must Change in the Age of Agentic AI, BCG with Stripe and AWS, October 2026. The hybrid pricing share refers to Stripe's sample of hypergrowth companies.