The future of software development up to 2034 will be less radical than many expect: programming languages, core principles and testing skills stay central. AI tools will mainly speed up the production of legacy code and lower its quality. Those who can describe things precisely, test well and ask the right questions will have the decisive advantage.
Key Takeaways
- Developers who accept generated code without checking it turn into maintenance programmers: they lose the creative part of their work and produce worse code faster.
- Code quality on GitHub has measurably declined since AI assistants became widespread: according to a GitClear study from January 2024, code churn, duplication and copy-paste patterns are already rising.
- The world’s most widely used programming languages were created in the 20th century, and that mix will change little by 2034.
- Precision, testing skills and asking the right questions about requirements are the core competencies developers cannot hand over to AI.
- Spreadsheets have shown that a tool which makes programming accessible to everyone does not destroy jobs. It creates new maintenance and quality work.
Programming Languages Change More Slowly Than the Hype Suggests
To understand the future of software development, look at the constants first, not the headlines. The most widely used programming languages mostly come from the 20th century. Of the five most used languages, only one was created in the 21st century.
That inertia runs through the whole ranking. The top 20 and top 50 are also full of languages from the last century. The languages themselves keep evolving, but the mix stays remarkably stable.
That matters for any forecast. No language created in the 2020s is currently among the 20 most used. If you do native programming in Rust today, you will very likely still be doing so in 2034. Rust is already more than ten years old.
Kevlin Henney recommends using this stability as a guide. The language you will be working in ten years from now probably already exists. It may not be in today’s top 20, but it is very likely in the top 50.
Why the AI Hype Misses the Real Trends
A sober look back helps against inflated expectations. Anyone living in 2024 can remember 2014, 2004 and 1994. Those cycles brought real change, but also a lot that barely moved.
Several technologies sold as the future have failed that test. The metaverse is fragmented and, in Kevlin’s view, will not become a ubiquitous platform. More likely are further AR and VR applications in niches like industry and gaming, and gaming already exists.
Web3 shows the pattern especially clearly. The term has been around for more than a decade without real impact. By comparison, Web 2.0 was already dominant within the decade in which the term was coined. As for cryptocurrencies, Kevlin only considers central bank digital currencies plausible.
Agile development is one of the slow movements, too. The label has become the norm, but the number of teams that actually work in an agile way remains low. That is unlikely to change much by 2034.
AI Raises Legal and Ethical Questions That Technology Can’t Answer
How far AI spreads depends on open questions that lie outside technology. The relationship between AI and big data is unresolved: where does the training data come from, and what are the copyright consequences? Those questions are being fought out in court right now.
When automated systems make decisions about people’s lives, the issue isn’t technical capability. Anyone who frames it as a purely technical matter hasn’t understood how people work. It is a matter of choice: what do we want, and how do we shape it?
The reason lies in the method itself. Statistics-based AI reproduces the most probable past. That means it systematically leaves out edge cases and minorities. If you don’t make that choice consciously, you put a group of people at a disadvantage, because that is the nature of a statistical approach.
“Anyone who presents this as a question of technology hasn’t understood how people work. We have a choice here.”
(Kevlin Henney)
The effect of regulation is still open. The EU AI Act is one of the first laws of its kind. Whether it will shape the world the way GDPR did is too early to say, especially since it was watered down. When we spoke in 2024, Kevlin expected noticeable practical effects the following year at the earliest, possibly not until 2026.
The Spreadsheet Shows Why Developers Will Still Be Needed
The most widespread programming paradigm in the world is the spreadsheet, and it is the best template for the future with AI. Most people who build spreadsheets have no background in software development.
The result is well known. Most spreadsheets are unmaintainable, hard to understand and full of bugs. Yet they haven’t cost anyone their job. They have created opportunities. If more and more non-specialists start producing software, developers’ jobs stay safe, because that code has to be understood and fixed.
Programming is more than stringing syntax together. It is the pursuit of precision and the answer to the question of what you actually want to achieve. An imprecise specification in natural language often turns out worse than the same imprecision in code.
That is exactly where the value lies. Software development has always bridged the soft, flexible space of human needs and a precise, fixed notation. The same input has to produce the same result every time, verifiable empirically through tests and runtime data.
Generative AI Turns Many Developers Into Maintenance Programmers
The sober forecast: most developers will use AI to produce legacy code, just faster than before. That isn’t speculation about the technology. It’s an observation about how people use tools.
The irony is in the details. If you rely entirely on generative tools, whether Copilot or ChatGPT, you end up working on code someone else wrote, without knowing why it was written that way. The fun part of the work disappears. What’s left is maintenance.
Early data backs this up. A GitClear study published in January 2024 already shows pressure on code quality on GitHub. Code churn is rising, and there is more duplicated code and more copy-and-pasted code. More often than before, code that was just written gets rewritten because it was wrong.
Producing the wrong thing faster is not the goal. More commits don’t mean more productivity. The obvious lesson would be to focus on quality, but many organizations won’t learn it.
The Profession Isn’t Disappearing, It’s Splitting
Software development will still exist in 2034, because the world runs on software and demand isn’t shrinking. But the profession will split, and the gap between the two groups will be wide.
On one side are developers who work on quality and understand what a tool actually does for them. Making that judgment has always been the real challenge. On the other side are people who become puppets of their tools, stuck in poor management processes.
By 2034, some companies will have realized that quality counts and will act on it. They’ll do fine. Most won’t realize it and will wear themselves out on details, which raises the pressure on individual developers. In those companies, work can get unpleasant.
Continuous deployment adds to that pressure. What used to be managed in small steps once a month is now a daily routine. Without an environment of real collaboration, it feels like micromanagement and permanent deadline pressure.
Agility Means Shortening the Distance Between Need and Code
At its core, agility means reducing distance and communication barriers between the people who need software and the people who build it. That principle doesn’t change, even as the organizational setup keeps shifting.
A look back makes this concrete. Software used to be built inside the companies that used it, often as a custom application. The distance between business users and developers was short, because both sat in the same building.
Today both models exist side by side. Outsourcing creates new organizational boundaries that have to be bridged. Small companies have a natural flexibility; large ones often sell what is really just fewer hierarchy levels as agility. Three management levels instead of four don’t turn an organization into an agile team.
What stands out is that the direction has flipped. Software now drives the business instead of just being the product of business decisions. The word agile is now used as a matter of course in general business contexts where it meant nothing a few years ago.
Software Development Talks About People More Than Other Disciplines Do
Contrary to its reputation, software development deals intensively with interpersonal matters, often more than other industries. Problems between people get discussed more here than in many technical fields.
That shapes how difficulties are handled. Instead of making conflicts personal, the field treats them as systemic issues to be understood. This attitude puts the discipline in a better position than its reputation suggests.
How to Prepare for the Future of Software Development
Focus on the fundamentals, because they change the slowest. Principles, deeper skills and the question of what a piece of software is actually supposed to do will outlast any individual tool.
There is a natural cycle between requirements and testing. To understand how to test something, you first have to understand what it is supposed to do. Learn to describe both well, in natural language and in code: what does the right thing look like, and what does the wrong thing look like?
With specific tools, an honest look at the pace of change helps. Some things move surprisingly fast, others barely at all. Ten years ago, many people reacted to Kubernetes with bewilderment, while JUnit dates back to the 20th century and is still in use today.
Three recommendations give you direction:
- Think more broadly about languages. Don’t define yourself by a single language. If you work in Java or C#, you use other languages such as JavaScript anyway. Treat them as first-class too.
- Look beyond your own patch. If you use a top 10 language, look at the next ten and the rest of the top 20. That is often where your next language comes from.
- Keep an eye on open source. Many new ideas start in open source. That’s where you’ll spot early what later goes mainstream.
In the end, it’s about balance. Stick to the fundamentals and ask about the core principles of good software development, while keeping an eye on which innovations really hold up and where to find them. Your ability to be precise, test well and ask the right question has always mattered. In the future, it will be the visible difference between developers, and between companies.
Frequently Asked Questions
Is it worth betting on a brand-new programming language for the next ten years?
Probably not. Of the five most widely used languages in 2024, only one had emerged in the 21st century, and no language developed in the 2020s made it into the top 20. The language someone will be working in ten years from now most likely already exists: perhaps not in the top 20, but very likely in the top 50.
Why haven’t the metaverse and Web3 established themselves as technologies of the future?
Neither has passed the test of time. By 2024, Web3 had been discussed for over a decade without any real impact, whereas Web 2.0 had already become dominant within the same decade the term was coined. The metaverse was fragmented and failed to establish itself as a ubiquitous platform. AR and VR applications remained more plausible in niches such as industry and gaming.
Does the use of AI assistants measurably degrade code quality?
Initial data suggested so. A study by GitClear published in January 2024 pointed to increasing code churn on GitHub, as well as more duplicated code and more code inserted via copy-and-paste. More often than before, code that had just been written was rewritten because it was incorrect. More commits, therefore, do not mean greater productivity.
Why do statistics-based AI systems systematically disadvantage minorities?
Because a statistical approach reflects the most likely past, thereby disregarding edge cases and minorities. For systems that make decisions about people’s lives, this is not a question of technical capability, but a decision. Those who do not make this decision consciously are disadvantaging a group of people, because it is inherent in the nature of the process.
Will developers become obsolete if non-experts use tools to create software themselves?
No. The most widely used programming paradigm in the world is the spreadsheet, created predominantly by people with no experience in software development. The results are usually hard to maintain, difficult to understand, and buggy, but they haven’t cost anyone their job. The more non-experts create software, the more code needs to be understood and fixed.
How can you tell if a company is truly working in an agile way?
By the distance between those who need the software and those who build it, not by the number of hierarchical levels. Three levels of management instead of four do not make an organization an agile team. Small companies possess a natural flexibility, while large ones often pass off flatter structures as agility. The number of teams that actually work in an agile manner has remained low.
How can you assess which new tools should be taken seriously?
By distinguishing the pace of change in individual areas. Some things move quickly, others barely at all: JUnit dates back to the 20th century and is still in use, while Kubernetes was still a source of confusion for many around 2014. Anyone using a top-10 language should look at the next top 10 and the rest of the top 20. New ideas often emerge in the open-source community.
