程式碼愈容易生成，軟件團隊持續掌握產品演進愈重要

Are we witnessing the end of traditional software factories, or the beginning of a more interesting kind of engineering organization?

For decades, software companies have relied on a familiar formula: need more features, hire more developers; need faster delivery, add another team; need lower costs, outsource to a lower-cost market.

That formula built an enormous global industry. But in 2026, AI assistants can generate implementations, tests, documentation, and increasingly complex changes. This raises an uncomfortable question for traditional software factories:

If writing code becomes cheaper, what exactly are we paying external development providers for?

I don't think outsourcing will disappear. I think the ability to produce code is becoming less distinctive than the ability to understand, operate, and evolve the product behind it.

The software factory was built around scarcity

The traditional factory emerged from legitimate needs: scarce local engineering talent, temporary specialist requirements, and a desire for flexible delivery capacity. External providers can offer talent, speed, and predictable commercial arrangements.

But there has always been a tension: the people producing software aren't necessarily the people who own its consequences.

A project can pass acceptance tests and be delivered on schedule. Six months later, nobody at the customer may remember why an integration is asynchronous, why a database constraint was omitted, or why an unusual validation exists.

The repository explains what the system does. It seldom preserves the full history of why.

A company can own every line of source code and still lack the capability to evolve its own product.

This is not unique to outsourcing. Internal teams with high turnover or weak documentation can lose knowledge too. But project-based handovers make the problem particularly visible.

AI changes the economics of implementation

In a 2025 field study involving 4,867 developers at three companies, researchers reported a 26.08% increase in completed tasks among developers with AI coding assistance. The gains differed across developers and contexts.

Source: Cui et al., The Effects of Generative AI on High-Skilled Work (2025)

Google's 2025 DORA research similarly suggests widespread AI adoption, while emphasizing that AI amplifies the strengths and weaknesses of the surrounding organization.

But faster code generation is not always faster delivery. In a randomized study of 16 experienced open-source developers working on 246 tasks, METR found that access to early-2025 AI tools increased completion time by 19% in the studied setting.

Source: METR, Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

METR's February 2026 update discussed newer evidence of potential speedups but also methodological limitations that prevented a reliable estimate.

The lesson isn't that AI is always faster or slower. It is that code production and successful software delivery are different things. Review, integration, operational risk, and long-term maintenance still matter.

The first cracks are visible in outsourcing data

According to the ISG Americas Q2 2026 Index, combined cloud and managed-services contract value rose 39% year over year, while traditional IT outsourcing contract value declined 23%. Engineering services, including AI integration work, increased 9%.

Technology services spending can grow while some traditional delivery models shrink.

This is not proof that AI caused the decline. ISG measures particular contract categories and sizes, and a quarter does not establish a permanent trend. But it challenges the assumption that every form of software outsourcing benefits equally from technological growth.

What exactly does ISG measure?

ISG's outsourcing index tracks commercial contracts meeting its reporting thresholds, including annual contract value. Its combined market includes cloud-based as-a-service offerings and managed services. Cloud infrastructure spending is not equivalent to custom software development. A decline in one outsourcing category does not prove that software engineering demand as a whole has declined.

Four regions, four different stories

🇺🇸 North America: Pressure on transactional delivery

North American companies are major buyers of global software services. The Americas ISG data shows weaker traditional application development and maintenance contracting alongside strong cloud and AI-related demand.

One plausible explanation is a changing build-versus-buy calculation: if a capable internal team can implement more with AI, outsourcing routine implementation solely for labor capacity may become less compelling.

But reducing coding contracts is not reducing the need for engineering accountability. Architecture, security, integrations, and production operations still need owners.

🇨🇱 South America: Beyond cheaper nearshore developers

Latin America has developed a substantial nearshore engineering ecosystem based on time-zone alignment, technical talent, and competitive costs.

A 2026 report from the IDB, World Bank, and WTO found that Latin American and Caribbean exports of digitally delivered services grew from $18.5 billion in 2005 to $87.7 billion in 2024, yet represented only about 2% of the global total. These figures cover more than software outsourcing.

If customers can use AI to increase internal capacity, the strongest regional opportunities may move toward differentiated expertise: financial infrastructure, healthcare interoperability, logistics, cloud modernization, distributed systems, and regulated integrations.

The opportunity is not merely to provide cheaper programmers. It is to become a more valuable engineering partner.

🇪🇺 Europe: Continuity and specialization

The ISG EMEA Q2 2026 Index reported 21% year-over-year growth in managed-services contract value, contrasting with the Americas.

Eurostat reported 10.45 million ICT specialists employed in the EU in 2025.

Europe's long-lived enterprise systems, regulated environments, and multinational integrations can reward stable providers with deep institutional knowledge. The distinction is not external versus internal: it is continuous responsibility versus interchangeable project staffing.

🇮🇳 Asia: Scale is not disappearing

The ISG Asia Pacific Q2 2026 Index reported 50.5% year-over-year growth in combined technology-services contract value, including strong growth in cloud services. Individual categories performed differently.

India's established outsourcing ecosystem and global capability centers (GCCs) show that globally distributed engineering can remain important. GCCs also illustrate an alternative: permanent engineering organizations abroad, with closer strategic alignment and knowledge continuity than a succession of temporary projects.

What is a core engineering team?

A core team is not defined simply by who signs its paychecks. It is defined by who remains accountable after a feature ships.

That team understands the domain, architectural trade-offs, operational failure modes, product priorities, and consequences of change.

Consider two delivery arrangements:

Project delivery	Continuous product ownership
Receive requirements	Clarify and challenge requirements
Implement accepted scope	Consider effects on the whole system
Pass tests and hand over	Monitor behavior in production
Close the engagement	Remain accountable for evolution

Neither model guarantees good engineering. But continuous responsibility creates stronger incentives to consider long-term costs.

A feature that takes two days to generate can still create years of maintenance obligations.

What does research say about ownership?

The 2024 study Examining Ownership Models in Software Teams: A Systematic Literature Review and a Replication Study reviewed 79 papers and revisited relationships between ownership and software quality. Not all previously reported correlations with defects replicated.

That matters: a smaller number of contributors does not automatically mean fewer bugs.

We should distinguish:

Code ownership: Who changes particular files or components?
Knowledge ownership: Who understands the decisions and constraints?
Product ownership: Who is accountable for behavior, outcomes, and evolution?

These overlap, but they are not identical. The third is the organizational capability I believe becomes increasingly valuable in the AI era.

AI doesn't eliminate engineering responsibility

Imagine asking an AI agent to add a customer field and propagate it across several services. It may update models, APIs, migrations, events, and tests rapidly.

An engineer familiar with the system still needs to ask: Are old consumers compatible? Is the field sensitive? What happens during replay? Is the migration safe? What happens if downstream processing fails? Does the change contradict an earlier business decision?

AI can help investigate these questions. But someone must establish which questions matter, validate the answers, and accept responsibility.

DORA's central organizational insight is relevant here: stronger testing, internal platforms, feedback loops, and team practices help turn AI assistance into actual delivery value. Fragile organizations may merely generate changes faster than they can safely absorb them.

A small core team with a large capability surface

The alternative is not necessarily full insourcing.

              PRODUCT STRATEGY
                     |
              CORE PRODUCT TEAM
                     |
       +-------------+-------------+
       |             |             |
     Domain      Architecture   Operations
    knowledge    & decisions    & reliability
       |             |             |
       +-------------+-------------+
                     |
            CONTINUOUS OWNERSHIP
                     |
       +-------------+-------------+
       |             |             |
   AI agents      External      Specialized
   & tooling      engineers     providers

The core team retains domain knowledge, architectural authority, release decisions, and operational accountability. External specialists provide expertise or capacity. AI assists with implementation, analysis, documentation, and tests.

This is a proposed operating model, not an empirically established optimal staffing ratio.

The question shifts from “How many developers do we need for these tickets?” to “Which capabilities must we retain to own this product, and which can we obtain externally?”

Three futures for the software factory

1. Commodity factories face pressure

Providers competing mainly on available developers and hourly rates may find it harder to differentiate when customers and competitors share powerful AI tools.

2. Specialized providers become more valuable

Domain expertise, complex integrations, security, regulated systems, and modernization may remain defensible areas of external engineering value. These providers sell judgment and outcomes, not just hours.

3. External teams become genuine product teams

A long-term external team can have stable membership, operate production, participate in architecture, and retain institutional knowledge. External employment is not incompatible with ownership. The key is whether responsibility survives delivery milestones.

Measure ownership, not output volume

Counting developers, tickets, story points, or generated lines of code cannot establish whether a product is becoming more sustainable.

Dimension	Better measure
Delivery	Lead time from validated need to production
Reliability	Change failure rate and recovery time
Maintainability	Cost and risk of subsequent changes
Knowledge continuity	Time for a new engineer to make a safe change
Product impact	Adoption, retention, revenue, or operational outcomes
Vendor dependence	Ability to operate and change without a particular supplier
Economics	Total lifecycle cost, including rework and incidents

I'd like to see longitudinal studies comparing AI-enabled permanent product teams with outsourced project teams, controlling for product maturity, complexity, and engineering practices. The existing evidence does not yet justify a universal claim that one model wins.

The question we should be asking in 2026

For years, software organizations were designed around the scarcity of people who could write code. AI changes that constraint without removing the need for engineering expertise.

As implementation becomes more accessible, domain understanding, architectural judgment, operational reliability, and accountability may become even more valuable.

That doesn't mean every company should build everything internally. It means companies should avoid accidentally outsourcing the knowledge and decision-making capabilities required to sustain their products.

The question is no longer just who can build the software. It's who will understand it, maintain it, and take responsibility for what happens next.

Perhaps that's what AI is really forcing us to reconsider: not the value of developers, but the value of ownership.

Sources and further reading

This article presents an interpretation of multiple studies and market reports, not a causal demonstration that AI is replacing outsourcing. The underlying sources measure different populations and outcomes.

What do you think? Has AI changed how your organization thinks about external engineering capacity? And who really owns the product after the project ends?