Most software teams now use AI coding assistants in some form. That changes the hiring question. It is no longer "can this person write code quickly?" It is "can this person tell whether the code in front of them is right?"
If you are a controller approving headcount, an owner of a mid-sized firm, or a sales or operations leader who depends on internal software, this affects what you pay for and what you should expect from your developers.
Speed moves the bottleneck
Assistants take the repetitive work off a developer's plate: boilerplate, simple tests, first-draft documentation. A working prototype shows up sooner. That part is real.
But the time saved does not vanish. It moves to review. Code that is close but not quite correct is harder to catch than code that is plainly broken, and someone has to read it carefully. If nobody on the team owns that job, defects reach production faster than before.
So the constraint is no longer how fast code gets written. It is how fast a team can check it.
What to look for in a developer now
When we screen for software roles, the weight has shifted toward a different set of strengths:
- Code review judgment. Can the candidate spot a subtle logic error, an unsafe dependency or a query that will not scale?
- Systems thinking. Can they design how pieces fit together, not just fill in a function?
- Testing habits. Do they write tests that would catch a wrong answer from a tool, or only tests that pass?
- Clear specification. Can they describe the behaviour they want precisely enough for a person or a tool to build it?
- Security awareness. Do they treat generated code as untrusted until checked?
Ask candidates to walk through a pull request that contains a flaw. How they read it tells you more than a timed coding test.
Junior hiring needs a rethink
A lot of entry-level work used to be the repetitive tasks that tools now handle. That does not mean you stop hiring juniors. It means you plan how they learn.
If juniors only accept tool output, they never build the instinct to question it. Pair them with a senior who reviews their reasoning, not just their results. Employers who skip this end up with a thin bench in three years and nobody who can supervise the tools.
Your existing process shows up faster
An assistant speeds up whatever a team already does. A team with clear standards, good test coverage and disciplined releases gets faster and stays stable. A team with unclear requirements, patchy testing and informal releases gets faster at producing problems.
That makes AI adoption a useful diagnostic. Before you add tools or headcount, check a few basics:
- Are requirements written down before work starts?
- Does every change get a second pair of eyes?
- Is there automated testing that runs on each release?
- Is there a written rule on what data may be pasted into outside tools?
If the answer to any of these is no, fix that first. A new hire will not solve it.
Governance is a staffing issue too
Security guidance from bodies such as the US National Institute of Standards and Technology now covers secure development of AI-enabled systems across the whole lifecycle. In Canada, privacy obligations under PIPEDA and provincial laws apply when customer data touches these tools. Someone has to own that.
In a smaller company, that is often a senior developer or IT lead who was never given the mandate. Name the person. Put the policy in writing. If you cannot cover it internally, a contract security or QA specialist for a defined stretch is a sensible way to set up the controls without adding permanent headcount.
Where contract help fits
Three situations come up often:
- You want to add AI features to an existing product and have no one who has done it before.
- Your team ships faster but your testing and review have not caught up.
- You need a data or AI engineer for a single project, not a full-time seat.
In each case, a contractor with a clear scope and a handover plan beats a rushed permanent hire. Define the deliverable, the review standard and what documentation you expect at the end.
What to do next
Before your next software requisition, rewrite the job description around review, design and testing rather than output volume. Then audit your own process against the four questions above, so a new hire lands in a team that can use them well.
If you are hiring developers, AI and data specialists or QA contractors in Canada or the US, a Traverse recruiter can help you define the role and find people who fit it.