AI "On-Shoring"

For decades, offshoring software development was a matter of cost efficiency. Companies moved labor to regions with lower wages, creating large-scale delivery centers in India, Eastern Europe, and Southeast Asia. U.S.-based firms retained strategy and design, while the heavy lifting took place offshore.

That structure worked when human labor was the critical resource and the competitive landscape permitted longer cycle times. It optimized for hourly cost, delivery scale, and global coverage. Now, that model is collapsing. Not because talent has diminished. Not because domestic costs have dropped. It is collapsing because the relationship between what can be automated and what humans can contribute to the overall process has fundamentally shifted.

AI systems are not just support tools. They are production engines. Large language models and other foundational systems are now generating documentation, writing test cases, performing static analysis, suggesting architecture, and building interfaces. They do not replace every function, but they compress the time and human effort required to deliver working software.

These AI systems are not tied to labor markets. They are tied to physical infrastructure. The portion of software “work” that has been automated is now being relocated based on the availability of compute capacity, stable energy supply, high-bandwidth fiber, and jurisdictional clarity. This shift does not bring back jobs in the traditional sense. It brings back production capacity, driven by machines, not people.

At the same time, the portion of software work that still requires human involvement is also being pulled closer to home, but for different reasons. In an AI-first development environment, where models can generate output in near real time, the limiting factor becomes the human response cycle. Reviewing, validating, and integrating AI-generated output must happen quickly to maintain the pace. For this reason, organizations need to relocate human software orchestration to the same time zones as their business and product leadership, as well as colocate them with their compute infrastructure to minimize latency and maximize throughput.

As Trend Micro CEO Eva Chen noted before Jensen Huang’s keynote, if modern data centers are the new factories, then software engineers are already in the role of traditional manufacturing labor. The comment was not speculative. It was descriptive of what has already happened.

Offshoring was built on labor arbitrage. It made sense when code had to be written manually, line by line, and the cost of each hour mattered. The global delivery model succeeded by reducing those hourly costs while maintaining throughput.

That model no longer applies. AI does not relocate. It does not wait. It does not negotiate. It executes in place, at speed, within the bounds of available infrastructure.

With machines doing the majority of the work, the comparative advantage of offshore labor diminishes. There is little point in hiring for price when the most critical variable is now optimizing AI Factory Cycle Time.

Offshoring is not being reversed by policy. It is being bypassed by architecture.

This shift is not isolated to software. It is reshaping the physical economy around digital needs. The new supply chain starts with electricity and ends with fiber.

Energy

AI workloads are energy-intensive and continuous. Inference clusters and training pipelines require reliable, high-output electricity. Cloud providers are already securing long-term contracts with nuclear and renewable energy suppliers. Power is not a cost center. It is a strategic resource.

Land

Land near power infrastructure, fiber routes, and water sources is now more valuable than space in coastal innovation hubs. Areas previously known for agriculture or warehousing are becoming key digital infrastructure zones.

Connectivity

Modern infrastructure priorities include low-latency routing, high-bandwidth access, cooling systems, and direct fiber interconnects. These capabilities now define the competitive edge in digital production and are more important than proximity to urban centers or workforce size.

Software development now depends more on physical infrastructure than on human labor.

Even as AI systems take on more tasks, a thin layer of human work remains. That layer includes interpreting model output, validating edge cases, refining requirements, aligning results with business context, and overseeing integration. It is high-leverage and dynamic. And it needs to move fast.

In an AI-first development environment, the primary constraint is no longer writing the code. It is approving, deploying, and iterating on what AI systems produce. If a model generates a working feature in minutes, but that feature cannot be reviewed for twelve hours due to a time zone gap, then the advantage is lost.

This is where traditional offshoring becomes counterproductive. Communication latency becomes the bottleneck. The solution is not just moving the compute closer to the customer. It is also moving the human decision-makers into the same time frame.

While it is technically possible to offshore this orchestration work, doing so effectively requires that those workers operate in the customer’s time zone. Without time alignment, cycle times stretch out. The machine may be instant, but the organization is not.

Companies that recognize this will not only shift their hiring models for reasons of locality and iteration speed but also for a different focus in terms of skills. They are not optimizing for geographic spread or cost savings alone. They are optimizing for synchronous velocity. Developers, analysts, and leads who operate in real time with their AI systems will enable continuous iteration.

In the end, this is not about location. It is about throughput. With so much of the “labor” being shifted to machine, the labor cost that remains will not be optimized on hourly rate but instead on iteration speed. This trend will only continue as the percentage of hourly labor in the total process decreases over time.

The current wave of onshoring is not bringing jobs home in the traditional sense. It is bringing infrastructure home. The work is still being done, but not by human hands. It is being done by machines operating inside domestic data centers, powered by local energy, and governed by local law.

The engineers who remain are few. They work close to the models, the hardware, and the orchestration layer, but most importantly close to the customer and product leadership. They are not building by hand. They are directing by signal, and they are iterating dozens if not hundreds of times per day.

The competitive battlefield is no longer wage cost. It is cycle time. The winners are not those who have the most people. The winners are those with the fastest path from idea to deployment. This is not the future. This is the present. The AI-first operating model is already in motion. The only question that matters now is whether your systems, human and machine, are aligned to move at the speed that modern software demands.

Every disruptive shift creates a window of advantage for early adopters. AI-first development and infrastructure-localized software production is no exception. Companies that embrace these models will move faster, operate leaner, and outpace those still tied to slower, human-centric pipelines. But this lead is temporary.

Over time, all competitive markets converge on operational best practices. Just as cloud computing, continuous integration, and agile development were once differentiators before becoming standard, AI-first workflows will become the default. Once that happens, simply having the tools and infrastructure will no longer be a competitive edge. They will be the cost of staying relevant.

At that point, the difference will come from execution. The companies that outperform will be those that manage rapid iteration cycles with discipline and precision. The orchestration of AI output, not the existence of it, will become the new battleground.

Customers do not only want lower-cost features. They want more features. They want richer, more responsive products that can adapt to complex needs in real time. Delivering that experience requires people, not as builders, but as guides, validators, and stewards of a faster process.

In domains where engagement speed matters, the need for time-aligned human orchestration will return. Companies will need staff working in the customer’s time zone, not because AI failed, but because success demands context and responsiveness that AI alone cannot deliver.

The long-term effect of this shift is not labor elimination. It is labor repositioning. As machine velocity increases, human attention must become sharper, more available, and better coordinated. Demand for labor will grow again, not in volume, but in proximity and precision.

The end result is not all that complicated. The world requires more software, that is better, cheaper, and delivered much faster than it is today. AI is creating a new equilibrium between lower costs and demand. At the same time the software community must adapt to this new reality. Software development will look much more like factory operation. People will reorient the way they work to operate most efficiently within that context.

Yes, AI will help traditional software development in the same way that a carpenter’s power tools are often superior to hand tools. That said, the bulk of the work will not reside in the human centric bespoke software development model we see today. The shift to AI based factories producing significant percentages of the software we require is here.