Will AI Coding Agents Replace Offshore Developers?

Every buyer asks it now, usually near the end of the first call: if AI writes the code, why am I hiring a team at all? It is a fair question, and the honest answer is more interesting than yes or no. AI did replace something real. It just did not replace the thing most people think.

This piece is written by E-Cybertech Solution, which sells developer time and therefore has an obvious incentive to tell you AI changes nothing. We are not going to do that. Below is what the 2026 research actually shows — including two findings that argue against our own business model — and what it means if you are deciding whether to hire developers this year.

The headline numbers are real. Sonar’s survey published in January 2026 put AI at roughly 42% of all committed code. Satya Nadella said in 2025 that 20–30% of code in Microsoft repositories was AI-generated, and Google has reported a similar and rising share of new code written by AI and approved by engineers. GitHub’s own research found that in files where Copilot was enabled, up to 46% of accepted code came from suggestions.

So the volume claim is not marketing. Machines write a large and growing fraction of what lands in repositories. If code volume were the constraint on shipping software, we would be living through the fastest delivery boom in the industry’s history.

We are not. That gap — between how much code gets generated and how much value gets shipped — is the whole story.

In 2025, METR ran the study most vendors would rather you had not read. It was a randomised controlled trial: 16 experienced open-source developers, 246 real tasks, in mature repositories where they averaged five years of familiarity. Half the tasks allowed AI tools, half did not.

Developers using AI took about 19% longer. They had expected to be 24% faster. Even after finishing, they still believed AI had made them roughly 20% faster. The perception gap is arguably the more important result: teams cannot feel this effect, which means they cannot manage it by intuition.

METR has since identified selection effects in the design and is revising its methodology, so this is one careful data point rather than settled science. But it lines up with what larger datasets show. The 2025 DORA research found roughly 90% of technology professionals using AI at work and over 80% believing it made them more productive — while AI adoption correlated with higher delivery instability. Deployment frequency and lead time improve; change failure rate gets worse. Time saved in creation gets spent on auditing and verification instead.

DORA’s own framing is the useful one: AI is an amplifier. Teams with strong review, testing and deployment practices get faster. Teams without them get faster at shipping problems.

GitClear analysed 211 million changed lines of code and found the pattern that worries engineering leads more than any benchmark. Between 2023 and 2024, as AI assistance became normal:

None of this means AI writes broken code. It means AI writes plausible code, quickly, and the path of least resistance is to accept it rather than fold it into what already exists. Duplication is cheap today and expensive in eighteen months, which is precisely the kind of cost that never appears in a quarterly productivity chart.

The developer experience matches the data. In Stack Overflow’s 2025 survey, the single biggest frustration — cited by 66% of developers — was “AI solutions that are almost right, but not quite”. And 46% actively distrust the accuracy of AI output against 33% who trust it, with just 3% highly trusting it. Almost-right code is the expensive kind: it passes a skim, fails in an edge case, and costs more to debug than code that never worked at all.

Being honest about the losses matters more than defending a business model. Work that used to justify a paid developer hour and now mostly does not:

• Boilerplate and scaffolding. CRUD controllers, form validation, API clients, config files. This is gone as billable work, and it should be.
• First-draft tests. Generating unit tests from an implementation is a solved problem. Deciding what to test is not.
• Mechanical translation. Framework version bumps, syntax migrations, converting between formats — supervised, these are now hours instead of days.
• Documentation drafts and commit messages. Straightforwardly better and faster with AI.
• Simple, well-specified tickets. The classic junior task — a clearly described change to a known file — is the work AI handles best.

If a vendor’s pitch is “we have lots of developers who can type quickly”, that pitch died. We would not defend it either. The stack-specific work we do on Laravel and React projects looks materially different from how it looked in 2022, and a team that bills the same hours for scaffolding today is charging you for a machine’s work.

The work that survived has one thing in common: it requires being responsible for an outcome, not producing an artefact.

This is why the companies generating the most AI code have not cut engineering headcount. They redeployed it. The same shift shows up in how AI agents are actually deployed in production, which we covered in agentic AI in 2026: the systems that work have tight specifications and a human gate on anything irreversible.

Here is the part of the story with a genuine cost attached, and it is not being handled well by anyone.

Because AI absorbed entry-level tasks, entry-level hiring collapsed. Employment for software developers aged 22–25 has fallen roughly 20% since late 2022, while employment for older developers grew. Forrester expects computer science enrolments to drop about 20% as students read the signal.

The problem is arithmetic. Section 05 says verification is the new bottleneck and only experienced engineers can do it. Seniors are grown, not found — every one of them learned judgement by writing bad code and being corrected. An industry that stops hiring juniors today is choosing a senior shortage in five to ten years, at exactly the moment senior review capacity becomes the constraint on everything.

If AI were replacing outsourced development, the outsourcing market would be shrinking. It is not. Industry estimates put global IT outsourcing at roughly $588 billion in 2025 and above $630 billion in 2026, with offshore delivery the fastest-growing segment. Around 83% of buyers report AI already being used within the services they outsource.

But the reason people buy changed, and this is the number worth remembering: the share of executives naming cost reduction as their primary outsourcing driver fell from about 70% in 2020 to roughly a third today. Talent access and speed now rank as high or higher. That matches what we see on enquiries from European companies: the constraint is not budget, it is that the roles cannot be filled locally at all. Spain alone carried up to 200,000 unfilled ICT positions in 2025.

So the market did not die. The pitch died. “Cheaper hours” is a weak argument in 2026; “capacity you cannot hire locally, with people who can verify AI output” is the real one. The same logic is pulling global capability centres into tier-2 Indian cities.

If AI changed what developers do, it should change what you ask before hiring one. Five questions, and you should ask them of us too.

1. “What is your written policy on AI-assisted code?” Not whether they use it — everyone does. Whether the rules exist on paper.
2. “Who reviews AI output before it merges, and what does that review check?” The answer should mention duplication and edge cases, not just “it works”.
3. “How do you handle provenance and licensing of generated code?” You are taking ownership of this code. Silence here is a real risk.
4. “What is your test coverage requirement on AI-assisted work?” Generated code needs more test discipline, not less.
5. “Can you turn AI off for a sensitive module?” Some contracts, regulators and clients require it. A team that cannot say yes has no control over its own process.

Then judge the answers the way you would judge any vendor: evidence over adjectives. Our 30-point vetting checklist covers the rest — contracts, IP transfer, replacement terms and the red flags worth walking away from. We would rather you ran it on us properly than signed on trust.