Last Tuesday, I opened a small Python project I’d been putting off for a few days. Nothing complicated—an API call, some data cleaning, and a script that needed to save the results to a database.
Normally, I’d have spent a good chunk of the afternoon writing the first version.
Instead, I described what I wanted to an AI coding assistant, pasted in the relevant error, and had a working first draft in minutes.
That sounds like the dream version of AI coding.
It isn't.
The interesting part came afterward, when the code looked perfectly reasonable but failed on an edge case I hadn't thought about. I had to slow down, understand what the AI had actually written, and fix it myself.
That experience has pretty much shaped how I think about AI and software development in 2026. AI isn't making programming disappear. It's changing where programmers spend their time.
And honestly, that's a much bigger change than simply having a machine write code.
The First Big Shift
For years, software development had a fairly predictable rhythm.
You had an idea. You broke it into smaller tasks. You searched documentation. You wrote code. You ran it. You got an error. You searched Stack Overflow or GitHub. You fixed it. Then you repeated the whole process.
AI has compressed some of those steps dramatically.
Tools such as GitHub Copilot, OpenAI coding models, Cursor, Claude, and other AI-powered development environments can generate code, explain unfamiliar code, suggest fixes, create tests, refactor functions, and work across multiple files.
The practical difference is huge.
A developer who previously spent an hour writing repetitive code might now spend ten minutes generating an initial version and another thirty minutes reviewing and improving it.
But there's a catch.
The time saved writing code can easily come back as review time.
That's the part people tend to leave out.
AI can produce 200 lines of code much faster than I can. That doesn't mean I can safely understand 200 lines of unfamiliar code in ten seconds.
Writing is becoming cheaper.
Understanding is becoming more valuable.
Coding Gets Faster
The easiest place to see the change is repetitive development work.
Imagine you're building a web application and need a basic REST endpoint.
You could manually create the route, validate input, connect it to your service layer, handle errors, write a database query, and create tests.
Or you can tell an AI assistant something like:
"Create a POST endpoint for adding a customer. Validate the email, reject duplicates, save the record, return a useful error response, and add tests for invalid input."
The AI can produce a surprisingly good starting point.
It's not magic. The model is using patterns it has learned from huge amounts of programming material. That's why it can be particularly useful for common tasks.
Need a regular expression?
Ask.
Need a SQL query?
Ask.
Forgot the syntax for a particular library?
Ask.
Need to convert a JavaScript function to TypeScript?
Ask.
Need a unit test for an existing function?
Ask.
Need to understand why a Python traceback is happening?
Paste the relevant error and ask for an explanation.
That changes the economics of small development tasks.
The annoying ten-minute jobs that used to interrupt your concentration become much easier to handle.
And those little interruptions add up.
The Developer's Job Changes
This is where I think a lot of the AI-coding discussion goes wrong.
People ask whether AI will "replace programmers."
I think that's too simplistic.
The better question is: Which parts of programming are easiest to automate?
Writing boilerplate is easy to automate.
Generating standard CRUD operations is relatively easy.
Creating basic tests is easier than designing a complete testing strategy.
Explaining a compiler error is easier than deciding whether the underlying architecture is wrong.
Converting code between formats is easier than deciding what the software should actually do.
That's the distinction.
A developer's value isn't just typing syntax into an editor.
A developer has to decide what should be built, how it should behave, what assumptions are dangerous, how components should interact, what happens when something fails, and whether the final product actually solves the user's problem.
AI can participate in those decisions.
It shouldn't automatically own them.
That distinction becomes especially important as AI coding tools become more capable.
From Autocomplete To Agents
Early AI coding tools were mostly autocomplete machines.
You typed:
``
python
def calculate_total(
``
and the tool tried to predict the rest.
Useful? Absolutely.
But that's not where things are heading.
Modern AI development tools increasingly work at a higher level. Instead of completing one line, they can inspect a repository, reason about several files, propose changes, run commands, interpret errors, and make additional edits.
That's closer to an AI coding agent than traditional autocomplete.
The workflow starts looking like this:
You: "Add authentication to this application."
AI: Inspects the project.
AI: Identifies the relevant files.
AI: Suggests an implementation.
AI: Writes the changes.
AI: Runs tests.
AI: Finds a failure.
AI: Modifies the implementation.
AI: Runs the tests again.
That sounds impressive because it is.
But it's also where developers need to become more careful.
The more autonomy you give the system, the more important your boundaries become.
If an AI agent can modify 50 files, execute commands, install packages, change configuration, and interact with external services, a bad assumption isn't just producing one wrong line.
It can produce an entire wrong direction.
My Biggest Mistake
The mistake I made early on was trusting code because it looked professional.
That's a dangerous habit.
AI-generated code often has excellent formatting. Variable names look sensible. Functions are neatly organized. Comments are convincing. The whole thing can have the visual appearance of experienced engineering.
Then you test an unusual input.
And everything falls apart.
One example is error handling. An AI might generate a function that works perfectly when an API returns the expected response but behaves badly when the service times out, returns incomplete data, or changes a field.
The code isn't necessarily stupid.
The assumption was wrong.
And that's the deeper problem with AI coding.
The model doesn't automatically know which assumptions matter to your particular application.
I eventually changed my approach.
Instead of asking AI to "build this feature," I started giving it smaller responsibilities.
First, ask it to inspect the existing code.
Then ask for a proposed approach.
Then have it modify one part.
Then run tests.
Then review the changes.
That extra structure takes longer than blindly accepting the first answer, but it saves a lot of time when the project becomes complicated.
The Review Problem
There's an uncomfortable irony here.
AI makes it easier to create code, which means teams can create more code.
More code means more things to maintain.
More dependencies.
More tests.
More potential bugs.
More security concerns.
More documentation.
More code that somebody eventually has to understand.
If a developer uses AI to generate 5,000 lines of code for a project that could have been 2,000 lines, they haven't necessarily become more productive.
They may have simply created a larger maintenance problem.
This is why I don't buy the idea that "AI makes programmers ten times faster" as a universal statement.
Sometimes it absolutely can make a task dramatically faster.
Sometimes it barely helps.
And sometimes it makes the task slower because you spend your saved coding time debugging generated code.
The outcome depends heavily on the type of work.
Where AI Works Best
AI coding is particularly useful when the problem is well-defined.
For example, suppose you already have a working application and need to add a small feature.
The repository gives the AI context.
The requirements are clear.
The technology stack is known.
Existing patterns provide examples.
Tests can verify the result.
That's a good environment for AI assistance.
It's also excellent for learning.
A beginner can ask:
"Explain this function line by line."
Then:
"Why is this approach better than the alternative?"
Then:
"Show me a simpler implementation."
Then:
"Give me three exercises that use the same concept."
That's a very different learning experience from staring at documentation for an hour without knowing what you're looking for.
AI can act like an always-available programming tutor.
But there's a trap for beginners.
If you let AI solve every problem before you've struggled with it yourself, you can create the illusion of competence.
You can produce working applications without understanding why they work.
That becomes painful the first time something breaks in production.
Where It Gets Risky
The more important the software, the less comfortable I'd be with blindly accepting AI-generated code.
Security is an obvious example.
An AI can generate authentication code.
That doesn't mean the authentication system is secure.
It can write database queries.
That doesn't mean every query is safe.
It can create an authorization check.
That doesn't mean the permissions model is correct.
The same applies to financial systems, infrastructure, healthcare software, privacy-sensitive applications, and anything where a small mistake has serious consequences.
The solution isn't "never use AI."
That's unrealistic.
The solution is to match AI autonomy to the consequences of failure.
For a throwaway script, I'm comfortable experimenting.
For production code handling sensitive information, I'd want substantially more review, testing, monitoring, and human ownership.
The tool should make you faster.
It shouldn't make you careless.
AI Changes Debugging
One of the most useful changes I've noticed isn't code generation at all.
It's debugging.
A good AI assistant can take an error message and help turn it into a conversation.
Instead of searching through ten forum posts, you can provide:
* the error
* the relevant function
* what you expected
* what actually happened
* the environment you're using
Then ask the model to identify likely causes.
That doesn't guarantee the answer is right.
But it gives you a much faster starting point.
The same approach works for unfamiliar codebases.
You can ask:
"What does this module do?"
"Where is this function called?"
"What happens if this database request fails?"
"Which files are responsible for authentication?"
"Explain the data flow from this API endpoint to the database."
That's extremely useful when joining an existing project.
The first few hours inside an unfamiliar codebase can be brutal.
AI can reduce that friction.
What Happens To Junior Developers?
This is probably the hardest question.
If AI can generate basic code, what happens to junior programmers who traditionally learned by doing basic coding tasks?
I don't think the answer is obvious yet.
There is a real risk that entry-level developers get fewer opportunities to do simple implementation work.
And that matters because many senior developers became good by spending years doing exactly that supposedly "boring" work.
At the same time, juniors now have access to something previous generations didn't: an interactive technical assistant that can explain almost anything they're looking at.
A beginner can build something much more ambitious.
The danger is skipping the learning process.
A junior developer who uses AI to generate code and then studies, tests, questions, and modifies that code can become stronger.
A junior developer who copies everything and says "it works on my machine" hasn't really learned much.
The difference will be discipline.
The New Valuable Skills
I don't think programmers need to become professional prompt engineers.
That's another piece of advice I wouldn't overthink.
You don't need a collection of magical prompts to use AI well.
You need to become good at specifying problems.
That's a programming skill anyway.
Good developers already break vague requirements into concrete behavior.
They understand constraints.
They identify edge cases.
They know what information is missing.
They test assumptions.
AI makes those skills more important.
You also need to become good at reviewing code.
If AI writes your first draft, you need enough technical knowledge to decide whether that draft is actually good.
Architecture matters more.
Testing matters more.
Debugging matters more.
Security awareness matters more.
Understanding systems matters more.
And communication matters more because developers increasingly describe desired behavior to machines rather than manually implementing every tiny detail.
The Economics Are Interesting
There's also a business side to all of this.
Software has always had a strange constraint: good developers are expensive, and building software takes time.
AI can reduce the amount of human effort required for some tasks.
That creates an obvious temptation for companies.
Instead of asking, "How can our developers use AI to build more?"
Some managers will ask, "How many developers can we eliminate?"
Those aren't the same strategy.
A team that uses AI intelligently might deliver features faster while keeping strong engineering practices.
A company that simply cuts people and expects AI to fill the gap may discover that software development wasn't expensive because people typed slowly.
It was expensive because understanding complex systems is difficult.
You can automate code generation.
You can't automatically eliminate responsibility.
Someone still needs to decide whether the product is correct.
Someone needs to understand the architecture.
Someone needs to respond when the system behaves unexpectedly.
Someone needs to maintain it six months later.
"But AI Makes Mistakes"
Yes.
It does.
That's not an argument against using it. It's an argument against treating it like an unquestionable authority.
I've seen AI produce answers that are almost correct.
Those are often more dangerous than obviously wrong answers.
If the syntax is broken, you'll notice.
If the code runs but quietly mishandles one unusual situation, you might not.
That's why tests matter so much.
If I'm asking AI to modify existing code, I want a way to verify the result.
Tests are one of the best forms of feedback you can give an AI coding workflow.
The loop becomes:
Generate → Test → Inspect → Fix → Test again.
Not:
Generate → Ship.
That difference sounds small.
It isn't.
The Tools Are Becoming Different
Another change worth watching is the disappearance of the boundary between the chatbot and the development environment.
A few years ago, you might have had an AI chat window open beside your code editor.
You'd copy some code.
Paste it into the chatbot.
Ask a question.
Copy the response.
Paste it back.
That workflow already feels clumsy.
Modern AI development environments are moving the assistant closer to the repository itself.
The AI can understand project structure, search files, inspect code, suggest edits, and sometimes execute development tasks.
That context is important.
A model that knows what the rest of your project looks like can make much more useful suggestions than one looking at a single 30-line snippet.
The next step is increasingly agentic development, where developers describe a goal and the AI handles more of the implementation loop.
That's where things get genuinely interesting.
Programming Isn't Going Away
I've heard people say that programming will disappear because AI can write code.
I don't believe that.
Programming languages are abstractions already.
Nobody seriously argues that writing machine code disappeared because higher-level languages became popular.
We keep moving upward.
The job changes.
There may be less value in memorizing every syntax detail.
There may be more value in understanding systems, designing good interfaces, defining requirements, reviewing generated code, and knowing what can go wrong.
That's not the death of programming.
It's another shift in what programming means.
The person who understands the underlying technology will still have a major advantage over someone who simply knows how to ask an AI to build something.
Because when the AI gets stuck, the person who understands the system can change the question.
What I'd Do Today
If I were starting software development today, I wouldn't try to avoid AI.
I'd use it from day one.
But I'd make one rule for myself: never accept code I can't explain.
I'd learn the fundamentals—variables, functions, data structures, databases, networking, APIs, Git, testing, debugging, and basic security.
Then I'd use AI aggressively around those fundamentals.
I'd ask it to explain code.
I'd ask it to generate exercises.
I'd use it to find bugs.
I'd compare different implementations.
I'd let it handle repetitive work.
But I'd keep ownership of the decisions.
That's the part I think matters most.
AI coding isn't really about getting rid of programmers. It's about reducing the amount of time programmers spend fighting syntax and repetitive implementation so they can spend more time thinking about the actual problem.
And yes, that will change the job.
Some coding tasks will become cheaper. Some entry-level work will change. Teams will expect developers to accomplish more. The best developers will probably become even more productive.
But the programmers who understand what they're building will still have an advantage.
If anything, AI makes that understanding more valuable.
Because when everyone has a machine that can write code, knowing what code should be written becomes the scarce skill.