Written by: Kamil Ponicki, Director of Talent Acquisition, Digital Colliers
The prevailing wisdom says AI coding assistants make contractor capacity more attractive. They ramp faster now. They ship faster. The knowledge gap closes because the tools do more of the heavy lifting. That logic sounds right until you look at what's actually happening to teams that lean hard into contract capacity for their AI work.
The institutional context tax just got steeper
More than 80% of AI projects fail. That's roughly twice the failure rate of conventional IT projects. The issue isn't that teams lack people who can write code. The issue is that most AI initiatives die because nobody quite understood what needed to be built or why it mattered to the business.
Contractors don't accumulate that context. They can't. They ship a thing and move to the next engagement. Six months later when the system needs changes, the people who know why it was built a certain way are gone. The decisions that seemed obvious at the time are now mysteries.
With AI tools in the mix, that problem compounds. Experienced developers using AI coding assistants show a 19% drop in their own original code productivity. They're reviewing more code but originating less. When the person reviewing that AI-assisted code is a contractor who'll be gone in three months, you're building on sand.
What the contractor playbook used to solve
The old logic made sense. You needed burst capacity for a big push. You needed a specialized skill your core team didn't have. You wanted flexibility to scale down after launch. Contractors solved all of that.
That worked when the constraint was execution speed or access to rare technical skills. You could hire someone who knew Kafka or Kubernetes, they'd build the thing, they'd document it, you'd be fine.
Now the constraint is different. It's not "can we build this." It's "do we actually know what success looks like" and "can we maintain this after it ships." Those are context problems, not execution problems.
The maintenance cliff nobody's pricing
Here's the pattern I keep seeing. A company spins up a POC with a contract team. The POC ships. It looks good in the demo. Then in month four or five, someone needs to change how it handles a specific edge case. Nobody on the core team knows why the architecture looks the way it does. The decisions aren't documented because they seemed obvious when the contractor team made them.
The numbers on AI-generated code make this worse. More than 15% of commits from AI coding assistants introduce at least one issue. Unresolved technical debt from AI-generated code climbed from a few hundred surviving issues in early 2025 to over 100,000 by February 2026.
Contractors optimize for shipping, not for the next person six months later. That's not a criticism. That's the job. But when your codebase is accumulating debt faster than ever and the people who wrote it are already on their next engagement, you've got a problem that compounds.
Where contract capacity still works
This isn't an argument for never hiring contractors. It's an argument that the default has flipped.
Contractors still make sense when the scope is genuinely bounded and the end state is crystal clear. If you're migrating a known system to a known platform and the requirements won't change, contract capacity works fine.
They work when you're augmenting an existing team that holds the institutional context. One contractor embedded with three full-time engineers who know the domain cold. That can work.
They work when you're building something genuinely new where there's no context to preserve yet. If you're launching an entirely separate product line with its own team that'll own it long-term, starting with contractors to prove the concept makes sense.
But the old pattern of spinning up a contract team to ship an AI initiative, then handing it off to a skeleton crew to maintain, that's where teams are getting burned. The maintenance costs swamp the savings. The knowledge loss kills the second and third iterations. The thing ships but never quite delivers.
The teams that win in 2026 are the ones who realize context is the new scarcity. Execution speed is table stakes now. Knowing what to build and why, knowing how to maintain it, knowing how the pieces fit together, that's what separates the 20% of AI projects that survive from the 80% that don't.