The real yardstick for judging a team of AI-equipped offshore devs is no longer the daily rate. It's their capacity for technical judgment. Two engineers with the same access to Claude Code can produce a working MVP and an invisible pile of technical debt that will cost three times as much to fix.
- 📉 Traditional offshore is sliding, layoffs in India and the slow death of ABAP code show where the low-cost model breaks.
- ⚡ Productivity doubles, it doesn't work miracles, AI-augmented teams ship 30 to 55% faster, not ten times faster.
- 🧠 Judgment is still scarce, AI writes code, it doesn't decide on architecture and it doesn't own the consequences.
- 🌍 Vietnam is gaining ground, controlled costs, a solid engineering culture, productivity boosted by AI.
That difference doesn't show up on a quote. It shows up six months later, when the first production bug lands and someone has to work out why nobody thought about the edge case.
Why traditional offshore is breaking down
In a video that has been widely shared, a former software architect recounts a conversation with an ex-colleague in India, a senior architect at an offshore competence centre. The colleague is looking for work again and can't find any. Nothing to do with his skills: an entire segment of the industry is contracting, project after project.
The SAP ABAP case is even starker. 35,000 customers still run on SAP ECC, whose standard support ends at the close of 2027. At the end of 2024, Gartner reported that only 39% had purchased an S/4HANA licence. The migration ahead doesn't swap ABAP developers for AI: it scraps 60 to 75% of the custom code deemed unnecessary, and with it most of the work that tens of thousands of offshore developers were billing by the hour.
What these two stories have in common isn't AI. It's a model built on volume of repetitive code, produced with no real accountability for the end result. That model does not survive automation, whether it comes from an AI tool or a platform change.
Why do the layoffs hit junior and standardised profiles hardest?
In both cases, it isn't the top 10% who take the hit. It's the developers trained on a single language, with no architecture skills or business understanding, whose value lay in the number of lines they wrote. A young developer who has been churning out Z reports since graduation has never had to weigh two architectural options against each other. That is exactly the skill AI doesn't replace, because AI never had it either.
What AI really changes about delivery time
A case study from an Indian agency, RG Infotech, shows clearly where the real gain lies. The agency claims to deliver three times faster while cutting costs by 60%, by layering agents like Claude Code or Cursor on top of standard offshore rates. Setting up the base architecture, which used to take months, drops to a few weeks. The headline result: a working MVP in 5 weeks.
Other figures, less spectacular but easier to verify, point the same way. According to etixio.com, an offshore team of 5 developers equipped with AI tools can deliver the equivalent of what 7 developers used to produce, with a 30 to 55% gain on repetitive tasks. That's a long way from the "ten times faster" promises you read everywhere, but it's real and it lasts.
Eric, a solo developer who documents his journey on YouTube, describes replacing a good chunk of his long-standing offshore team (Philippines, India, Vietnam) with AI agents. Seven months on, his assessment is sober: the tools have improved considerably, but he tests every new model before trusting it. The speed has changed, the vigilance it demands has not gone away.
How does a human developer work differently with AI?
The real shift isn't that developers write less code, it's that they read and correct a great deal more of it. RG Infotech puts it well: human developers no longer write raw code, they analyse a considerable volume of AI-generated code. That production speed calls for more rigorous architectural oversight than before, not less.
Technical judgment, the thing AI still won't do for you
I see it regularly in the briefs that land on my desk: someone generated a first version with Claude Code or Cursor and is now looking for a team to make it solid. Vibe coding is great for prototyping an idea over a weekend. It becomes dangerous the moment you build a serious product without technical oversight.
A non-engineer can generate snippets of code that run. What they generally can't handle is the architecture, the security, or the edge cases that only surface in production. It's not a question of prompting, it's a question of field experience: having already watched a product break, and knowing why.
On r/auscorp, a recent thread on how exposed Australian jobs are to offshoring points to an interesting mechanism: AI doesn't wipe out whole professions at once, it makes it easier to standardise work and slice it into tasks that can be sent offshore. That is precisely why judgment is becoming scarce and expensive, while execution is becoming abundant.
What does a team with technical judgment actually look like?
It's a team that can explain why it chose one architecture over another, not just deliver an endpoint that responds. It's also a team that knows how to say no to a poorly scoped feature before it turns into a production problem. An article I published on AI-generated code in offshore development goes into who carries the responsibility when that kind of code breaks in production, and the answer is never "the AI".
Why Vietnam's advantage grows stronger with AI
Full disclosure: I run an offshore software services company in Vietnam, so I have an obvious bias. That is also why I can spell out its real limitations, not just the sales pitch.
The equation behind Vietnamese offshore's success hasn't changed in principle: trained engineers, a solid engineering culture, reasonable cost compared with an equivalent French team. What has changed is that AI widens the gap rather than closing it. A team that is already rigorous about architecture becomes markedly more productive with Claude Code. A team that made up for a lack of rigour with volume loses its reason to exist, whether it's based in Hanoi or anywhere else.
An article on breedj.com sums up the dilemma many executives face: bet on AI-driven employees or on offshore professionals. Its conclusion, a hybrid balance, matches what I've been seeing on the ground for two years: the teams that win don't choose between AI and humans, they combine the two with discipline. On ai-dev.team, the positioning we built starts from the same observation: senior devs who code with AI in production, not around it.
How to tell a genuinely augmented team from a prompt factory
Before signing with an offshore provider selling "AI-native development", ask three questions: who reviews the generated code, who decides on the architecture, and what happens when an edge case breaks in production. If the answer boils down to "the AI takes care of it", walk away.
The table below sums up the three models you most often come across on the market in 2026.
| Criterion | Traditional volume offshore | Low-cost AI factory | Augmented senior team |
|---|---|---|---|
| Daily rate | €150-350 | €100-250 | €250-450 |
| MVP delivery speed | 3-6 months | 5-8 weeks claimed | 6-10 weeks actual |
| Who reviews generated code | Rarely formalised | Little or none | Systematic, code review |
| Accountability for production bugs | Diluted, high turnover | Vague, often the client | Owned by the team |
| Technical debt at 12 months | High | Very high | Under control |
SOURCE: cited transcripts, etixio.com, GoLive Software field observations · UPDATED 08/2026
Turnover remains a blind spot that few providers volunteer. I cover it in more detail in the article on offshore IT services firms to avoid, with figures on what a 20% turnover mid-project really costs. If you want to see how these same AI tools are run from the SME side rather than the provider side, the AI First blog covers the day-to-day operational use cases.
"The future belongs to augmented developers, not replaced ones. A developer using Claude Code is still an engineer, not a prompt operator."
Vincent Roye, August 2026
The verdict: judge the team, not the tool
No, AI isn't replacing offshore devs. It's replacing the teams that had nothing to sell but a low rate and a volume of standardised code. That's what the layoffs in India and the announced end of ABAP code show: not a sudden switch, but the acceleration of a sorting process that had already begun.
For a French startup or SME looking to strengthen its tech team, the question to ask is no longer "how much does a developer cost" but "who, on this team, takes responsibility for what breaks". Over the past few months I've seen a string of briefs already built on an AI-generated first version, and the real work always started in the same place: reworking the architecture before it turned into a production problem.
Technical judgment can't be prompted. It gets hired, trained, and proven project after project. It's the only criterion that matters when you evaluate an offshore development team in 2026, AI or no AI.
Frequently asked questions
Is AI really going to replace offshore developers?
No, not as a whole. It mainly eliminates standardised profiles whose value rested on volume of repetitive code, as the layoffs in India or the scheduled end of ABAP code at SAP show. Senior developers who can make architectural decisions are, if anything, in higher demand than before.
How much time do you actually save with an AI-augmented offshore team?
Measured gains sit around 30 to 55% productivity on repetitive tasks. The "three times faster" promises exist, but they mostly rely on the initial build of an application, not on maintenance or fixing bugs in production.
Is vibe coding reliable for building a real product?
Vibe coding is useful for prototyping an idea quickly, but it becomes risky as soon as you build a product meant for real users. Without technical oversight, architecture and edge cases are rarely handled properly, and that costs more to fix than building it cleanly from the start.
Why choose an offshore team in Vietnam rather than elsewhere?
Vietnam combines still-reasonable costs, a solid engineering culture, and fast adoption of AI development tools. The advantage grows with AI: a team already rigorous about architecture becomes more productive, while a team that made up for a lack of rigour with volume loses its reason to exist.
How do you check that an offshore provider has real technical judgment, not just access to AI?
Ask who systematically reviews the AI-generated code, who decides on the architecture, and what happens when a bug hits production. If the answers stay vague or push responsibility onto the AI tool, that's a warning sign before you sign.
Vidéos YouTube
- AI Is Destroying India's Outsourcing Industry? — Asian Dad Energy
- 50,000 SAP ABAP Programmers About to Lose Everything | Here's Why — Noel DCosta | ERP & AI Strategy
- The Strategic Showdown: Evaluating AI-Native Offshore Development | RG INSYS — RG INSYS
- I Replaced My Dev Team with AI Agents (7 Months Later) — Overpass Apps
