Programming is still a good career, and it is a different career from the one described in guides written five years ago. AI assistants have changed what a working day looks like, the entry-level market has tightened, and the skills that get you hired have shifted towards judgement and away from typing. If you want to build things and are willing to keep learning, it remains one of the better-paid, more flexible, more portable careers available. If you were drawn by stories of easy money for writing simple code, that version has gone.
What has changed#
AI writes a large share of the code. Most professional developers now use an assistant daily, and for routine code — boilerplate, tests, glue, small functions — the assistant produces the first draft. The job has moved towards specifying, reviewing, integrating and debugging what the assistant produced, and towards the parts it does badly: understanding what the customer actually needs, designing systems, and knowing when the output is wrong.
Entry-level hiring has tightened. The tasks that used to be given to junior developers as training — small tickets, simple features — are exactly the tasks assistants do well. Companies have posted fewer junior roles and ask more of the ones they post. This is real, and it is the hardest part of the answer.
The work has broadened. “Programmer” increasingly means someone who can take a problem from vague request to running, monitored software, using whatever tools help. Narrow specialists in one language, doing one kind of task, are the most exposed.
What has not changed#
- Software still has to be built, and there is far more that people want built than gets built.
- Someone has to be responsible for it working. An assistant cannot be paged at 3am, cannot be held accountable, and does not know your business.
- Understanding how systems fit together, why a design will not scale, or what a customer meant, remains scarce and valuable.
- Pay, remote-friendliness and the ability to work in any industry are still well above the average for skilled work.
The demand for people who can produce working software has not fallen. The demand for people who can only produce code has.
The entry-level problem, honestly#
Getting the first job is harder than it was, and no article should pretend otherwise. What helps, in the current market:
- Finished, deployed projects that show judgement: choices explained, trade-offs acknowledged, something a user could actually use.
- Fluency with AI tools used well — as an amplifier, with the output reviewed and understood. Employers now expect this and can tell the difference between someone who uses the tools and someone the tools use.
- A domain. Programming plus healthcare, finance, logistics, education or any field you know from the inside is a far stronger position than programming alone.
- Adjacent entry points. Support engineering, QA automation, data analysis, internal tools at a non-tech company. Less competitive, and they lead into development roles.
What has stopped helping: a certificate from a bootcamp, a portfolio of tutorial projects, and a resume that lists languages without evidence of having built anything with them.
Who it is still a good career for#
- People who enjoy solving problems and are not bothered that the tools for solving them keep changing.
- People who like building things and seeing them used.
- People with knowledge of another field who can combine it with software.
- People who can explain, write and work with others; the communication half of the job has grown, not shrunk.
Who should think twice#
- Anyone who wants to learn one thing once and be set. The half-life of specific tools is short and getting shorter.
- Anyone who dislikes reading and debugging other people’s code, including code an assistant wrote, because that is now a large part of the day.
- Anyone whose main attraction is the pay. It is good, but the people who last are the ones who would do some of this for free.
What to learn now#
- Fundamentals that do not change: how programs run, data structures, networking basics, how databases work, how to read an error.
- One language properly, to the point of building and deploying something real.
- AI tools, used critically. Learn to review, test and reject their output. That skill is the new differentiator.
- Systems thinking: how pieces connect, where things fail, what happens under load. This is what assistants are worst at and what senior people are paid for.
- Something outside programming you can apply it to.
Questions people ask#
Will AI replace programmers?
It has replaced some of the tasks and none of the responsibility. The job is changing shape rather than disappearing. Predictions beyond that are guesses, including the confident ones.
Is it too late to start learning to code?
No, but the bar for the first job is higher than it was, and the path through it runs through real projects and a domain, not through certificates.
Which area is safest?
Nothing is safe; some things are more durable. Work that requires understanding a specific business, integrating unreliable systems, or owning production is more durable than work that is purely writing code to a specification.
Should I learn to code or learn to use AI?
Both, in that order. Using AI well for software requires knowing what good software looks like, which means knowing how to code.
Where to go next#
- How to become a junior developer — the first job, in today’s market.
- How to learn to code without wasting time — what to focus on.
- Using AI to learn programming — the tools as a teacher, not a crutch.
