For the last few years, one of the biggest changes in programming has been the rise of AI coding assistants.
At first, they helped us complete a line of code.
Then they started writing functions.
Then entire files.
Now, AI coding agents can research a problem, create an implementation plan, modify a codebase, run tests, fix errors, and continue working without needing a person to explain every single step.
That makes me wonder:
What comes next?
I don't think the next big change in programming will simply be a better autocomplete tool. I think the way we build software is going to change around AI.
Here are some trends I expect to become much more important.
1. AI agents will become the normal way of building software
The biggest shift is already happening.
Developers are moving from asking AI for individual pieces of code to giving it entire tasks.
That distinction is important.
There is a big difference between saying:
"Write a Python function that does X."
and saying:
"Add this feature to the project, test it, and let me know if anything breaks."
The second request is much closer to delegating work to another developer.
AI coding agents are already becoming common in professional development, and I expect this trend to continue.
The IDE of the future may not primarily be a place where you type code. It may become a place where you direct, inspect, test, and coordinate AI agents that write the code.
2. Writing code will become less important than describing what should be built
This does not mean programming disappears.
It means the abstraction level changes.
For decades, programmers have already worked with abstractions. We stopped writing machine code directly. We moved to higher-level languages, frameworks, libraries, and APIs.
AI could be the next layer.
Instead of thinking about every implementation detail first, developers may increasingly describe the desired behavior, constraints, architecture, and requirements, while AI handles more of the implementation.
In other words, the ability to specify a problem may become almost as important as the ability to implement it.
And that could completely change what we consider a programming skill.
3. Spec-driven development could become much more important
There is a problem with giving AI a vague instruction:
It can produce a lot of code very quickly, even when the original idea was poorly defined.
This is why I think specifications will become more important as AI gets better.
Instead of immediately asking an agent to build something, developers may first define:
What the system should do
What it should not do
Expected inputs and outputs
Security requirements
Performance constraints
Tests and acceptance criteria
How success should be measured
Then the AI gets to work.
This is almost the opposite of the idea that AI means you no longer need to plan.
If anything, better AI may require better planning.
The easier it becomes to generate code, the easier it becomes to generate the wrong code at a much larger scale.
4. Testing and verification will become a major part of AI programming
This might be one of the most important trends.
If an AI agent can write thousands of lines of code without much human intervention, someone still needs to determine whether those thousands of lines are correct.
That means testing becomes even more valuable.
Automated testing, static analysis, security scanning, benchmarks, simulations, and AI-based code review could become standard parts of AI-assisted development.
The question will increasingly change from:
"Can AI write this?"
to:
"How can we prove that what AI wrote actually works?"
This could also change the skills expected from programmers.
A developer who understands how to evaluate a system may become more valuable than someone who is simply very fast at manually writing code.
5. Multi-agent development will probably become normal
Why use one AI agent when several can work on different parts of the same problem?
One agent could analyze the requirements.
Another could write the backend.
Another could work on the frontend.
Another could generate tests.
Another could review the implementation.
A final agent could run everything together and look for problems.
This sounds excessive today, but agent orchestration is already becoming an important direction in AI development.
The interesting part is that the developer may increasingly become the person coordinating the whole system rather than manually performing every step.
Programming could start looking less like writing every sentence yourself and more like managing a team of extremely fast but imperfect assistants.
6. Context and memory will become a competitive advantage
One of the biggest limitations of AI coding tools is context.
A model can be very capable and still produce a bad solution if it does not understand the project.
It needs to know the architecture, coding conventions, previous decisions, dependencies, documentation, issues, and sometimes even why a strange piece of code exists in the first place.
That makes long-term memory and project context extremely important.
I expect future coding environments to know much more about the project before the developer even asks a question.
Instead of repeatedly explaining:
"This file works this way because..."
the environment may already know.
That could make AI programming much more useful, but it could also make development environments significantly more complex.
7. Vibe coding will survive, but serious software will move toward structure
Vibe coding has become popular because it makes software development much more accessible.
You can describe an idea, generate an application, change things through natural language, and see the result without understanding every part of the implementation.
I don't think that is going away.
In fact, I expect even more people who are not professional programmers to build software this way.
But there will probably be a separation.
For prototypes, personal tools, experiments, and small applications, vibe coding can be enough.
For systems handling money, personal information, healthcare, infrastructure, or millions of users, someone still needs to understand what is happening underneath.
The easier it becomes to create software, the more important the distinction between "it works" and "it is reliable" becomes.
8. More people will become software creators
This might be one of the most positive effects of AI programming tools.
The number of people capable of creating software is no longer limited to people who spent years learning how to code.
A designer can build a prototype.
A researcher can create a data-processing tool.
A business owner can automate an internal process.
A student can build an application for a university project.
This does not mean all of these people will become software engineers.
But they may not need to.
AI could make programming more like spreadsheets or website builders: something that specialists use professionally, but that many other people can use when they need it.
9. Local and open AI will become more interesting
There is another direction I think will receive much more attention: running capable AI closer to the user.
Cloud models are incredibly powerful, but they also introduce costs, latency, privacy concerns, and dependence on external services.
As models become smaller and hardware becomes more capable, running useful AI locally becomes more attractive.
For developers, this could mean having coding assistants that can work with private repositories, sensitive documents, or internal systems without sending everything to an external service.
The most capable model will not always be the most useful one.
Sometimes the best model will be the one that is fast, cheap, private, and available exactly when you need it.
10. Programming education will have to change
This one is particularly interesting to me.
If AI can write code for students, universities will have to reconsider what it means to teach programming.
Simply asking students to write a program may become a poor way of evaluating whether they actually understand programming.
We may see more emphasis on debugging, system design, explaining decisions, reviewing AI-generated code, and solving problems without assistance when necessary.
Students may also need to learn something new:
How to use AI without becoming dependent on it.
That balance could become a skill of its own.
So, what will the programmer of the future look like?
I don't think the answer is "someone who doesn't code anymore."
I think it will be someone who understands software deeply enough to direct machines that can write it.
They will still need programming fundamentals.
They will still need to understand algorithms, systems, databases, networks, security, and architecture.
But they may spend less time manually implementing every detail.
Instead, they could spend more time deciding what should be built, describing the requirements, reviewing the results, testing the system, and deciding whether the solution is actually good.
That sounds very different from the way I learned programming.
And that may be the most interesting trend of all.
AI is not simply becoming another programming tool.
It is changing what the word "programming" might mean.
We spent decades making computers easier for humans to program.
Now we may be entering a period where we are making computers capable of programming themselves — while humans increasingly decide what they should build.
I'm not sure where that ends.
But I think the next few years will tell us whether AI primarily makes programmers more productive, or whether it fundamentally changes who needs to be a programmer in the first place
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