A founder in Kampala opens an AI coding tool on Friday evening. She types a few sentences describing a booking app for her salon chain. By Sunday night, she has a working product: customer sign-up, appointment slots, a staff dashboard and even a payment screen. She shows it to her team on Monday morning, and everyone is impressed.
Two months later, the same app is leaking customer phone numbers through an unprotected API, double-booking stylists whenever two people tap "confirm" at the same moment, and marking payments as successful that never actually went through.
Both parts of that story are true of AI-built software in 2026. AI can turn an idea into a working product faster than ever before. It can also produce code that looks finished long before it is safe to depend on.
That is the real conversation behind "vibe coding vs traditional coding". It is not a fight between old and new. It is a question every business building software now has to answer: what can we safely let AI build, and where do we still need engineers in charge?
At a glance
- Vibe coding means describing what you want in plain language and letting AI write most of the code.
- It is excellent for prototypes, demos, internal tools, and testing ideas quickly.
- Its weaknesses are hidden: security gaps, fragile logic, and code nobody fully understands.
- Engineering discipline (architecture, review, testing, security) is what turns a working demo into a product people can trust.
- The strongest teams combine both: AI for speed, engineers for judgement and accountability.
What vibe coding actually is
The phrase was popularised in early 2025 by AI researcher Andrej Karpathy, who described a style of programming where you stop reading the code closely and simply give in to the vibes: describe what you want, accept what the AI produces, and keep prompting until it works.
In practice, vibe coding today looks like this:
- You describe a feature in plain language: "add a login page with Google sign-in" or "build a dashboard showing sales by branch".
- An AI tool such as Claude Code, Cursor, Replit, Lovable or Bolt generates the code, often across many files at once.
- You run it, see what breaks, paste the error back in, and let the AI fix it.
- You repeat until the product does what you want on screen.
The appeal is obvious. Someone with no programming background can build a working app. An experienced developer can produce in an afternoon what used to take a week. The distance between an idea and something you can click on has collapsed.
The catch is in the second half of Karpathy's idea: you are not really reading the code. You are judging the software by how it behaves in front of you, not by how it is built underneath.
What engineering adds
Traditional software development is sometimes described as "writing code by hand", but that misses the point. Typing was never the hard part. The real work of engineering is everything around the code:
- Architecture. Deciding how the system is structured so it can grow, change, and integrate with other systems.
- Business logic. Making sure the software handles real situations correctly, including the awkward ones: refunds, cancellations, failed payments, two users acting at once.
- Security. Controlling who can see and change what, protecting data and secrets, and anticipating how someone might abuse the system.
- Testing. Proving the software works, and keeps working as it changes.
- Maintainability. Writing code that the next developer, or the same developer six months later, can understand and safely change.
- Accountability. Someone who knows why the system works the way it does, and can fix it when it breaks.
AI can help with every one of these. What it cannot do on its own is take responsibility for them.
How the two approaches compare
Where you start. Vibe coding starts with a prompt describing the result you want. Engineering-led development starts with a plan for how the system is structured, how data flows, and where the risks are.
How fast you get a first version. With vibe coding, a working version can appear in hours or days. Engineering-led work usually takes days or weeks to reach the same point, because more is decided up front.
Who can do it. Almost anyone can vibe-code a simple app. Engineering-led development needs experienced developers.
How well the code is understood. Vibe-coded software is often understood only on the surface: it works, but few people know why. In an engineering-led project, the team understands the code deeply and shares that knowledge.
Security. In vibe coding, security is only as strong as what someone remembered to ask the AI for. In engineering-led work, it is designed in from the start and reviewed before release.
Edge cases. Vibe-coded apps frequently miss the unusual situations nobody described in a prompt. Engineering teams plan for those situations and test them deliberately.
The cost of changes later. As a vibe-coded codebase grows, every change gets slower and riskier. Well-engineered software stays manageable as it evolves.
What each is best for. Vibe coding suits prototypes, demos, internal tools, and experiments. Engineering-led development suits production systems, payments, sensitive data and products that need to last.
Neither approach is "better" in every situation. A prototype built with full engineering ceremony wastes time and money. A payments platform built purely on vibes is a risk to its owners and its customers.
Where vibe-coded software breaks
The dangerous thing about AI-generated code is not that it fails. All code fails sometimes. It is that it fails quietly, in ways a demo will never reveal.
The demo works, the edge cases don't
AI is very good at the "happy path": the version of events where everything goes as expected. It is much weaker at the situations nobody described in the prompt. What happens when a customer's mobile money payment times out? When two staff members edit the same record? When a user enters a phone number in a format the app has never seen? Unless someone asked, the AI probably did not plan for it.
Security is not a default setting
AI tools optimise for making the feature work, and the quickest way to make something work is often the least secure. Common problems in AI-built apps include API keys and passwords written directly into the code, databases left open to the internet, admin pages without proper access checks, and user input passed straight into database queries.
For businesses in Uganda, this is not only a technical risk. Under the Data Protection and Privacy Act, an organization that exposes customers' personal data is responsible for that breach, regardless of whether a person or a machine wrote the vulnerable code.
Payments need special care
Payment flows are one of the clearest examples. A vibe-coded checkout may show "Payment successful" because the app received a response, without ever confirming with the provider that money actually moved. Properly verifying mobile money callbacks, preventing duplicate charges and handling reversals are exactly the kinds of details AI skips unless an experienced engineer insists on them.
Code that nobody understands
Each new prompt can solve problems in a slightly different way. After a few weeks, the codebase may contain three different approaches to the same task, duplicated logic in several places, and files no one on the team has ever read. Every change becomes slower and riskier. This is technical debt, and AI can create it faster than any human team.
Hidden dependencies
AI tools often install libraries to get things working. Some may be outdated, unnecessary, or carry known vulnerabilities. Occasionally, AI suggests packages that do not exist at all, which attackers have learned to exploit by publishing malicious packages under those invented names.
Nobody owns it
When something breaks at 2 am, who fixes it? If the honest answer is "we'll ask the AI", the business has a problem. Software that matters needs a person or a team who understands it and is accountable for it.
Match the method to the risk
The simplest way to decide how to build is to ask one question: what happens if this software gets it wrong?
Low risk: vibe coding is fine. Mock-ups, pitch demos, hackathon projects and personal tools can be built quickly with AI. If something breaks, little is lost, so move fast and learn.
Medium risk: AI-built, but checked. Internal dashboards, simple business tools and early MVPs with real users can still be built mostly with AI, but they need a code review, basic security checks and tests before launch.
High risk: engineering-led, with AI as an assistant. Payments, health records, financial data, customer accounts and government or NGO systems need proper architecture, security review and testing. AI can speed up the work, but experienced engineers must stay in charge.
A useful habit is to treat every vibe-coded product as a prototype by default. The moment it starts handling real customers, real money or real personal data, it needs to be reviewed, strengthened, or rebuilt by people who understand it.
How strong teams combine both
The best development teams in 2026 are not choosing between AI and engineers. They are designing a workflow where each does what it is good at. A healthy AI-assisted process usually looks like this:
- Engineers set the structure first. Architecture, data models, security rules, and coding standards are decided by people before AI writes anything significant.
- AI writes the first draft. Boilerplate, standard features, tests, documentation, and repetitive code are generated quickly.
- Humans review everything that ships. AI-generated code goes through the same review as code written by a person, with extra attention on security and business logic.
- Automated checks catch the rest. Tests, dependency scanners, and security tools run on every change.
- The team owns the result. Someone can explain how every important part of the system works, and why.
Done well, this gives you most of the speed of vibe coding with the reliability of proper engineering. AI becomes a powerful accelerator, not an unsupervised builder.
What this means for founders and businesses
For startups and SMEs in Uganda and across Africa, AI-assisted building is a genuine opportunity. You can now test an idea with real users for a fraction of what it used to cost. You can show investors a working product instead of a slide. Your internal teams can build the small tools they have always needed.
The smart way to use that opportunity:
- Use vibe coding to learn, not to launch. Prove the idea, gather feedback, and decide what is worth building properly.
- Get a professional review before going live. A few days of expert review costs far less than a data breach, a payment failure or a full rebuild later.
- Be honest about what you are handling. If your app touches money, health information or personal data, treat security as a requirement from day one.
- Keep ownership of your knowledge. Make sure someone, whether in-house or a trusted partner, truly understands the system your business depends on.
What this means for developers
For developers, AI is not a threat to good engineering. It is a threat to engineering that consists only of typing code.
The skills growing in value are the ones AI cannot easily replace: system design, security thinking, debugging, understanding the business problem and judging whether generated code is actually right. Developers who learn to direct AI well, and to review its output critically, are becoming significantly more productive than those who either refuse to use it or trust it blindly.
How NASDAN helps
At NASDAN, AI-assisted development is part of how we build every day, and we apply the same engineering discipline to AI-generated code as to anything our team writes. We help organizations get the speed of AI without inheriting the risks:
- AI code review and audits. We examine apps built with AI tools and identify security gaps, fragile logic and hidden technical debt before they cause damage.
- MVP to production. We take vibe-coded prototypes and strengthen, refactor or rebuild them into secure, scalable products.
- Architecture and security. We design systems that can grow, protect user data and meet requirements such as Uganda's Data Protection and Privacy Act.
- Payments and integrations. We build reliable payment flows, including mobile money and card payments, and connect your software to the systems you already use.
- AI-assisted development teams. We deliver custom software faster by combining experienced engineers with AI tools, inside a process built on review, testing and accountability.
- AI adoption guidance. We help teams set clear rules for how AI tools are used, what they can access and what always needs human approval.
The verdict
Vibe coding is one of the most useful things to happen to software in years. It lets more people build, lets teams test ideas faster and puts working prototypes within reach of almost anyone.
But a prototype is not a product. The moment software carries real customers, real money or real data, it needs real engineering behind it. The winners will not be the teams that use the most AI, or the least. They will be the teams that know exactly where AI belongs, and where human judgement is non-negotiable.
Built something with AI and not sure it's ready for real users? Let's take a look together.
