There is a narrative gaining traction in tech circles, on social media, and in breathless conference keynotes that goes something like this: AI will soon let anyone build their own software. Need a budgeting app? Just describe it to an AI and it will create one for you. Want a custom CRM for your small business? A few prompts and you are up and running. Personal software, built on demand, by anyone, for anything.
It sounds revolutionary. It also fundamentally misunderstands what software actually is.
I have spent more than twenty years building, deploying, and maintaining production software across fintech, blockchain, and high-traffic platforms. What I have learned in that time is that writing code has never been the hardest part of software development. It is everything else. And that everything else is precisely what this “everyone can build software” narrative conveniently ignores.
The Seductive Simplicity of the Demo
If you have watched any AI coding demonstration in the last two years, you have seen something genuinely impressive. Someone types a natural language prompt, and within seconds, working code appears. A to-do app. A landing page. A simple game. The audience gasps. Twitter threads go viral. The conclusion seems obvious: if AI can build a working app in sixty seconds, why would anyone pay a developer?
But this is the demo trap. Demos are designed to show the best possible outcome in the most controlled possible environment. They work because the problem is well-defined, the scope is tiny, the requirements are clear, and nobody has to use the result in production for more than five minutes. It is the software equivalent of assembling IKEA furniture in a showroom and concluding that anyone can build a house.
The gap between a working demo and a working product is not a small one. It is an ocean. And most of the challenges that make that ocean so vast have nothing to do with writing lines of code.
Software Is Not Code
This is the most important distinction that the “anyone can build software” narrative misses entirely. Code is a component of software, but software is vastly more than code. Software is a living system that includes product design, architecture decisions, data modeling, security considerations, error handling, performance optimization, deployment infrastructure, monitoring, user feedback loops, and ongoing maintenance. Code is the bricks. Software is the building, the plumbing, the electrical, the foundation, the zoning permits, and the decades of upkeep.
When someone asks an AI to “build me a budgeting app,” what they are really asking for involves questions they have not thought to ask yet. Where does the data live? How do you handle authentication? What happens when the app crashes at 2 AM and all their financial data is in limbo? How do you back up the database? How do you handle a user who enters invalid data? What about privacy regulations? What happens when the AI model that generated the code gets updated and the code no longer works the same way?
These are not edge cases. These are the actual substance of software development. And they require precisely the kind of thinking that average users are not equipped, trained, or interested in doing. Not because they are incapable, but because they have their own expertise, their own careers, their own priorities. They should not have to become software engineers just to use a custom tool.
Product Thinking Is the First Barrier
Before a single line of code gets written, someone has to decide what the software actually needs to do. Not in vague, aspirational terms, but in specific, concrete, sometimes painfully detailed terms. This is product thinking, and it is a skill that takes years to develop.
What features are essential versus nice to have? What is the user flow for the most common task? What happens when a user tries to do something unexpected? How should the system behave when it has no data versus when it has ten years of data? What are the performance requirements? Who are the actual users, and what do they actually need versus what they say they need?
Most people, when they imagine a software tool they want, are picturing the happy path. They see the ideal scenario where everything works perfectly, the data is clean, the user does exactly the right thing, and the output is exactly what they expected. Professional software engineers spend roughly eighty percent of their time on everything that happens off the happy path. Error states. Edge cases. Concurrent access. Data corruption. User confusion. Graceful degradation.
AI cannot do this product thinking for you, because product thinking is about understanding a problem deeply enough to anticipate what you have not yet imagined. It requires context about the business, the users, the constraints, and the tradeoffs. It requires judgment. It requires saying “no” to features that seem good in isolation but create complexity that will cripple the system later. This is not a prompting problem. It is a thinking problem. And it is one of the reasons professional software development exists as a discipline.
Debugging Is Where Reality Sets In
Every experienced developer has a version of this story: you ask AI to generate some code, it looks right, it runs, and then three days later something breaks in a way that makes no sense until you spend four hours tracing through logic you did not write and do not fully understand. Debugging AI-generated code is often harder than debugging code you wrote yourself, precisely because you lack the mental model of how it was constructed.
For a professional developer, this is manageable. You understand the language, the frameworks, the common failure patterns. You know what stack traces mean. You know how to isolate a problem, form a hypothesis, and test it. You know that the bug is probably not where you think it is, and you have the experience to navigate that uncertainty.
For an average person who just wanted a budgeting app? A cryptic error message is a dead end. A silent data corruption bug is invisible until the damage is done. A race condition that only manifests under certain timing is effectively unsolvable without deep technical knowledge. The AI might help you generate the initial code, but when that code breaks, and it will break, you are standing in unfamiliar territory with no map.
This is not a temporary limitation that will be solved with better AI. Debugging is fundamentally about understanding systems at a level of depth that requires real knowledge. Even when AI gets better at debugging, someone still needs to understand the output well enough to trust it, verify it, and know whether the fix introduced three new problems. That someone, by definition, is a developer.
Architecture Decisions Compound Over Time
Architecture is the set of decisions you make about how parts of a system fit together. It includes how data flows through the application, how different components communicate, how you structure your database, how you handle state, how you manage dependencies, and dozens of other structural choices that shape everything the software can and cannot do in the future.
These decisions are invisible at the beginning. When your app has ten users and one feature, architecture barely matters. Any structure will work. But architecture decisions compound. A bad data model at the start becomes a massive migration problem two years later. A monolithic architecture that was fine for a prototype becomes a deployment bottleneck when you need to scale. A tight coupling between two components that seemed harmless means you cannot change one without breaking the other.
AI, as it stands today, does not do architecture. It generates code in response to prompts, but it does not reason about how today’s decision will affect next year’s requirements. It does not know that the quick solution it suggested will create technical debt that takes months to unwind. It does not understand your business context well enough to know that the database schema it generated will not handle the regulatory requirements you will face in six months.
Average users have no reason to think about architecture because they have no reason to know it exists. They see the surface of software. Professionals see the structure underneath, and they know that the structure is what determines whether the software survives its first encounter with reality.
Deployment Is Its Own Discipline
You have code. It runs on your laptop. Congratulations. Now what? Where does this software actually live? How do users access it? How do you handle DNS, SSL certificates, server configuration, container orchestration, environment variables, CI/CD pipelines, database hosting, backup strategies, logging, monitoring, and alerting?
Deployment is where a shocking number of AI-built projects go to die. It is one thing to generate code in a sandbox. It is another to get that code running reliably on infrastructure that real people depend on. And “reliably” is the key word. Anyone can get something running once. Getting it to stay running, under load, through updates, across failures, is what operations engineering is all about.
I have spent significant portions of my career working with Docker, Kamal, firewall configurations, and deployment automation. These are complex, interconnected systems where a single misconfiguration can take down a production environment. The idea that someone with no technical background is going to navigate this landscape because AI wrote them some Python code is, with respect, not grounded in reality.
Yes, there are platforms that abstract some of this away. Vercel, Heroku, Railway, and similar services handle deployment for certain types of applications. But abstraction is not elimination. The complexity still exists. It is just hidden until something goes wrong, at which point you need someone who understands what is underneath the abstraction to fix it.
Iteration Never Stops
Software is never done. This is one of the hardest things for non-technical people to understand, and it is one of the biggest reasons the “build your own software” dream falls apart. You do not build software once and walk away. You build it, release it, discover it does not quite work the way you expected, gather feedback, fix bugs, add features, adjust to changing requirements, update dependencies, patch security vulnerabilities, and repeat this cycle indefinitely.
Professional software teams spend the vast majority of their time not building new things but maintaining and improving existing things. Studies consistently show that maintenance accounts for sixty to eighty percent of the total cost of software over its lifetime. This is not a failure of process. It is the inherent nature of software. Requirements change. Users behave unexpectedly. External APIs change their interfaces. Operating systems release updates. Security threats evolve. The world changes, and software must change with it.
When proponents of AI-built personal software describe their vision, they almost always describe the creation moment. The prompt, the generation, the first working version. They almost never describe what happens on day thirty, or day three hundred. Who updates the dependencies when a critical security vulnerability is discovered? Who migrates the database when the schema needs to change? Who fixes the integration when the third-party API you depend on releases a breaking change?
The answer, overwhelmingly, is: nobody. Because the average person who generated that code has neither the skills nor the desire to maintain it. And unmaintained software is not just useless. It can be actively dangerous, especially when it handles personal data, financial information, or anything that matters.
Maintenance Is the Silent Killer
Let me put this plainly: most people do not want to maintain software. They want finished tools that work. This is not a character flaw. It is a perfectly rational preference. When you buy a microwave, you do not expect to rebuild the magnetron every six months. When you use Google Sheets, you do not worry about the database engine underneath. You use the tool and get on with your life.
The entire history of consumer technology is a story of taking complex, high-maintenance systems and turning them into reliable, low-maintenance products that normal people can use without thinking about the internals. This is what companies do. They absorb the complexity of maintenance so that users do not have to.
The “build your own software” narrative essentially proposes reversing this entire trajectory. Instead of professional teams maintaining robust products, individuals would maintain their own custom software. Instead of spreading the cost of maintenance across millions of users, each person would bear the full cost of their own system. This is not progress. It is regression. It is asking people to become their own IT departments, their own QA teams, their own DevOps engineers, their own product managers.
There is a reason companies employ entire teams to keep software running. It is not because they enjoy spending money. It is because software maintenance is genuinely hard, ongoing, and essential. The idea that AI eliminates this requirement is wishful thinking of the highest order.
The Spreadsheet Analogy Is Revealing
Advocates of the “everyone builds software” future often point to spreadsheets as precedent. Excel, they argue, turned millions of non-programmers into builders. Regular people create complex spreadsheets all the time. Is AI-assisted coding not just the next step?
The analogy is more revealing than they intend. Because the world is absolutely full of broken, unmaintainable, business-critical spreadsheets. There is an entire genre of corporate horror stories about multi-million dollar decisions based on spreadsheets with hidden errors, circular references, and formulas that nobody understands anymore because the person who built it left the company three years ago.
JP Morgan lost $6 billion in the “London Whale” incident, and a contributing factor was a flawed Value at Risk model built in Excel with copy-paste errors. The European Spreadsheet Risks Interest Group maintains a catalog of spreadsheet horror stories that would make any CFO lose sleep. This is what happens when non-specialists build critical tools without engineering discipline.
Now imagine that same dynamic, but instead of spreadsheets, it is custom software handling authentication, personal data, financial transactions, and API integrations. The failure modes are not miscalculated totals. They are data breaches, corrupted databases, and security vulnerabilities. The spreadsheet analogy does not support the “everyone builds software” argument. It undermines it.
Security Is Not Optional
Of all the things that the “average person builds software” narrative glosses over, security might be the most dangerous omission. Writing secure software is hard. It is hard for experienced professionals who think about it constantly. For someone who just wanted a budgeting app and asked AI to generate it, security is an afterthought at best and invisible at worst.
SQL injection. Cross-site scripting. Insecure authentication. Improper data storage. Missing encryption. Overly permissive API endpoints. Exposed environment variables. Every one of these is a common vulnerability that AI-generated code can and does produce, and every one of them can lead to real harm when the software handles real data.
The average person building software with AI has no way to evaluate whether the generated code is secure. They do not know what to look for. They do not know what questions to ask. They cannot perform a code review or a penetration test. They cannot assess whether the authentication implementation is sound or whether the data storage approach complies with relevant regulations. And AI, while improving, regularly produces code with security flaws that would make any experienced developer wince.
This is not a problem that gets solved by better AI. Security is adversarial. Attackers actively probe for weaknesses, and the weakest targets will always be unmaintained, unreviewered software built by people who do not understand the threat model. A world where millions of individuals build and deploy their own software is a world where the attack surface expands exponentially.
What AI Actually Does Well
None of this means that AI is useless for software development. It means the value is different from what the hype suggests. AI is an extraordinary tool for people who already understand software. It accelerates professionals. It does not replace them.
AI excels at generating boilerplate code that experienced developers would otherwise write by hand. It is excellent at translating well-specified requirements into initial implementations. It is useful for exploring unfamiliar APIs, generating test cases, and scaffolding projects. It is a powerful assistant for code review, documentation, and refactoring. In the hands of a skilled developer who can evaluate, modify, and maintain the output, AI is a genuine productivity multiplier.
The key phrase in that paragraph is “in the hands of a skilled developer.” AI amplifies capability. It does not create it from nothing. A senior developer with AI tools can do the work that previously required a small team. A junior developer with AI tools can accelerate their learning curve and handle tasks above their experience level with appropriate supervision. But a non-developer with AI tools can create something that looks like software in the same way that a non-pilot with autopilot can keep a plane level in clear skies. The moment turbulence hits, the illusion of competence disappears.
What People Actually Want
When non-technical people say they want to “build their own software,” what they usually mean is one of three things. First, they want an existing tool to be customizable enough to fit their specific workflow. Second, they want someone to build something custom for them without the traditional cost and timeline of software development. Third, they want to automate a specific task that currently requires manual effort.
All three of these are real needs, and AI is genuinely helpful for all three, but not in the way the “build your own software” narrative suggests. Better customization means better products, not personal software. Cheaper custom development means AI-augmented developers building for clients more efficiently, not clients building for themselves. Better automation means smarter integrations and workflows, not custom applications.
The real impact of AI on software for non-technical users is not that they will build their own tools. It is that the tools built for them will be better, more personalized, and more rapidly developed. The value chain does not disappear. It compresses. And the people who benefit most are still the ones who understand the full stack of what software actually requires.
The Historical Pattern Is Clear
We have been here before. Every generation of technology produces a wave of predictions that programming is about to become accessible to everyone and professional developers will become obsolete. COBOL was going to let business managers write their own software. Fourth-generation languages were going to eliminate the need for programmers. Visual Basic was going to democratize application development. No-code and low-code platforms were going to make developers redundant. Every single time, the prediction followed the same arc: initial excitement, genuine productivity improvements for specific use cases, and then the quiet realization that the hard parts of software development have nothing to do with syntax.
Each of these technologies delivered real value. COBOL made business applications more accessible. Visual Basic genuinely expanded who could create Windows applications. No-code platforms are legitimately useful for specific, well-scoped problems. But none of them eliminated the need for professional software developers. If anything, each wave increased demand for developers by making software more prevalent, more ambitious, and more critical to business operations.
AI is the latest iteration of this pattern, and it is a more powerful one. The productivity gains are real and significant. But the fundamental dynamics have not changed. The hard parts are still hard. The boring parts are still essential. And the gap between a demo and a production system is still measured in months of skilled effort, not minutes of prompting.
The Real Question Nobody Is Asking
Instead of asking “will AI let everyone build software?” we should be asking “should everyone build software?” The answer, I believe, is clearly no. Not because people are incapable, but because it is a terrible allocation of human attention. A doctor’s time is better spent treating patients than debugging a custom appointment scheduling app. A teacher’s time is better spent educating students than maintaining a custom grade tracking system. A small business owner’s time is better spent running their business than troubleshooting deployment failures.
Specialization exists because it works. We do not ask doctors to design their own medical equipment or teachers to manufacture their own textbooks. We have professionals who do these things well, at scale, so that others can focus on what they do best. Software is no different. The dream of universal software creation sounds empowering until you realize it is actually asking everyone to take on a second job they did not want and are not trained for.
The more productive vision, and the one I believe will actually materialize, is that AI makes professional software development faster, cheaper, and more responsive. That the cycle time between “I need a tool” and “here is your tool” shrinks dramatically. That bespoke software becomes economically viable for smaller organizations. That customization becomes deeper and more accessible within professionally built products. This is a vision where AI amplifies the value of skilled developers rather than rendering them unnecessary.
Where This Narrative Comes From
It is worth considering why the “everyone builds software” narrative is so popular despite its obvious limitations. Part of it is genuine excitement about a powerful technology. AI coding tools are impressive, and the instinct to extrapolate from impressive demos is natural.
But part of it is also driven by commercial interests. Companies selling AI tools benefit from the narrative that their product eliminates the need for expensive developers. Venture capitalists funding AI startups benefit from the story that the total addressable market includes every human being, not just professional developers. Conference organizers benefit from provocative claims that fill seats. The “everyone can code” narrative is good for business, even if it is not good analysis.
There is also a cultural dimension. In tech culture, there is a persistent belief that any sufficiently complex problem can be abstracted away with the right tool. This belief has produced genuine breakthroughs, but it has also produced a blindspot: the assumption that because something is technically possible, it is practically likely. Yes, it is technically possible for a non-developer to use AI to create a working application. It is also technically possible for me to perform my own dental work. The question is not possibility but probability, practicality, and wisdom.
The Bottom Line
AI is a transformative tool for software development. It is not a replacement for software developers. The skills that make software work in the real world, product thinking, architectural judgment, debugging expertise, deployment knowledge, security awareness, and the patience for ongoing maintenance, are not eliminated by AI. They are made more valuable.
The average person will not build and maintain their own software for the same reason the average person does not build and maintain their own car, plumbing, or electrical system. Not because they are stupid, but because it is a specialized discipline that requires deep knowledge, ongoing attention, and years of experience to do well. AI lowers the barrier to entry for writing code. It does not lower the barrier to entry for building software. And until we stop conflating the two, we will keep having this same conversation every time a new technology makes coding look easy.
The future is not everyone building software. The future is better software, built faster, by people who understand what it takes to make it work. That is the real opportunity, and it is more than exciting enough without the fantasy.
Final Thoughts
If this post resonated with you, I would love to hear your perspective. Are you a developer navigating the AI wave? A non-technical leader trying to separate hype from reality? Either way, the conversation matters.
You can follow me on LinkedIn and Threads where I share regular thoughts on AI, software engineering, and building technology that lasts. If you have questions or want to discuss any of these ideas further, feel free to reach out through my blog’s contact page.
I would genuinely like to know what you think. Drop a comment below or send me a message. Whether you agree, disagree, or see it differently from your own experience, the best insights come from real conversations with real builders.