I will talk about the correct way to combine "AI x Offshore"
"If AI advances, won't offshore development become unnecessary?"
I get this question almost every week lately. The answer is "the opposite".
The more AI becomes widespread, the more collaboration with high-quality engineers becomes important. Why? I will explain step by step.
Who reviews the "code written by AI"?
With the spread of AI coding tools like GitHub Copilot and Cursor, the cost of "writing" code has dropped dramatically. According to one estimate, development speed when utilizing AI tools improves by up to 40-50%.
However, a problem has arisen here.
The amount of code written by AI has increased. The speed has also increased. But there are not enough humans to review it.
Code generated by AI may look correct at first glance, but it sometimes implements things without understanding the context. Error handling is weak. Consideration for performance is missing. It contains security issues.
To discover these kinds of problems, reviews by real engineers are necessary. Moreover, as the amount of code written by AI increases, the review workload increases proportionally.
Far from "engineers becoming unnecessary if you introduce AI," the reality is that "more engineers who can master AI are needed."
The trap of "AI overconfidence" that startups fall into
Since around 2024, the number of managers who have the illusion that "development costs can be reduced to zero with AI" has increased.
"I asked ChatGPT and it gave me working code."
It certainly does. It works for simple scripts. But actual product development is not that simple.
A CTO of a startup told me, "I left it to AI to build an MVP, but when the number of users increased, problems with the DB design were exposed, and in the end, we had to rebuild everything. The code that AI outputs guarantees that it 'works now,' but it doesn't guarantee that it will 'work in the future.'"
This is the essence. AI writes 'code that works now.' Engineers think about 'design that will work in the future.'
Both are necessary, and one cannot be substituted for the other.
The "correct combination" of AI x Offshore
So, what is the correct way to combine AI and offshore?
I will share what we are actually doing at the Last Partner site.
1. Create a draft with AI, and have engineers refine it
Have AI output basic API designs and DB structures from requirements definitions, and use that as a base for Nepalese engineers to implement and optimize. It is 30-40% faster than writing from scratch, and humans control the design quality.
2. Leave document generation to AI
By having AI generate comment descriptions, READMEs, and API specification drafts, engineers have more time to focus on "implementation." Documentation costs have been reduced by approximately 60% compared to before.
3. Automatic generation of test code + addition of cases by humans
Have AI write a draft of unit tests, and have humans add edge cases and test cases specific to business logic. Test man-hours are reduced, and coverage increases.
4. Use AI to assist with code reviews
AI performs a primary review on PRs to identify obvious issues. Afterward, a senior engineer performs a secondary review to look at design and performance perspectives. Review time has been shortened by approximately 35% .
Compatibility between Nepalese engineers and AI
This is an important point.
Nepalese engineers have a high ability to read English documentation. University curricula are English-based, and they are accustomed to acquiring the latest technical information in English.
In other words, their learning speed for utilizing AI tools is fast.
GitHub Copilot, Cursor, Claude (Anthropic), ChatGPT—there are no linguistic hurdles to incorporating these tools into their work. There are several members on our team who proactively catch up every time a new AI tool is released.
Last month, one member voluntarily brought a proposal saying, "If I set the custom rules for Cursor like this, review feedback decreased by 30%." There is a state where they are not being "made to use" AI, but are "mastering it."
Cost structure changes
The combination of AI x Offshore compresses the cost structure in two stages.
Stage 1: Cost reduction through offshore (approx. 60%) Compared to domestic development, engineer labor costs are reduced by 60%.
Stage 2: Productivity improvement through AI utilization (approx. 30-40%) The amount of tasks that can be handled with the same man-hours increases.
Combining these, it is calculated that approximately 2.5 to 3 times the output can be produced with the same budget.
Of course, this changes depending on the nature of the project and the complexity of the design. But these are numbers that only emerge by combining them, not by "AI alone" or "offshore alone."
Summary: AI is not a substitute for offshore, but a multiplier
The arrival of AI does not make offshore development "unnecessary," but rather "makes it more powerful".
If good engineers use AI, their output more than doubles. If cost-effective offshore engineers use AI, the cost performance skyrockets even further.
It is not that "offshore is over because AI is here," but rather "offshore has become even more important precisely because AI is here"—we are convinced that this is the reality of 2026.
About Last Partner Inc.
Last Partner is a company that provides offshore development utilizing Nepalese engineers.
✅ Owns in-house development centers and Japanese language schools in Nepal
✅ Japanese PM manages and bridges the entire project
✅ Achieves approximately 60% cost reduction compared to domestic development
✅ Specializes in business systems and Web development
📩 Start with a free consultation 🌐 https://lastpartner.jp/
✉️ info@lastpartner.jp Please feel free to contact us!