The Fate and Future of Developers in the AI Era: From Road Building to Building Civilizations

In the past year, AI capabilities have advanced by leaps and bounds, far exceeding many expectations. Developers have begun to re-examine their positions: Which skills will be automated? Which jobs will disappear? Which directions are still worth investing in?

These questions may seem new, but they are actually quite familiar. Historically, every technological revolution has left a clear trail. If we place the software industry on a longer timeline, we will find that the history of mechanization in road building, construction, and factories is precisely the mirror of the software industry's future.

This article attempts to extract structural trends in the software industry from the evolution of these traditional industries and provide a realistic, actionable, and not overly anxious path forward.

I. The Revelation of Road Building: Automation Patterns in 2D Industries

Road building is a typical 2D project. It takes place on flat ground, with fixed processes, single materials, and quantifiable quality; the entire process is highly repetitive. Because of this, it is one of the easiest industries to mechanize.

In the era of pure manual labor, building a road several dozen kilometers long often required hundreds of workers for several months. With the popularization of machinery like bulldozers, rollers, and pavers, the same scale of project now only needs dozens of people, and the construction period is shortened to weeks or months.

The essence of 2D software development is strikingly similar to road building. Tasks such as page layout, CRUD, and API glue code are highly standardized, highly repetitive, logically clear, and have automatically detectable quality. AI's role in this field is just like the bulldozer in road building: fast, tireless, never forgetting, and able to automatically generate most code, test, fix, and deploy.

A 2023 McKinsey report pointed out that generative AI can automate 30%–45% of coding work. Official data from GitHub Copilot also shows that developers have 55%–70% of their code generated by AI in certain tasks. Although these numbers vary by project type, the trend is clear: 2D development is the area AI can most easily consume.

However, this does not mean that developers will be rapidly phased out in the short term. Technological progress is often much faster than the pace of industrial adoption. Corporate processes, toolchains, organizational structures, and risk controls all need time to adapt. Legacy systems, old architectures, and existing businesses still require a lot of manpower to maintain.

The automation of 2D development is a structural trend, but the pace is gradual, not instantaneous.

II. Why "Upgrading to Architect" is Not Absolutely Safe

Faced with the impact of AI, many developers believe that "upgrading to architect" will help them avoid being replaced. However, history tells us that when 70% of jobs in an industry disappear, the remaining jobs will become extremely crowded.

When road construction workers lose their jobs, many go to get excavator licenses, but the number of machines is limited, the jobs are limited, and the competition becomes even more intense.

The software industry will go through a similar process.

More importantly, AI is eroding the core capabilities of architects. As context windows continue to expand, AI can understand an entire codebase at once, automatically generating architectural solutions, reviewing designs, and analyzing dependencies. Systems like AutoDev, Claude MCP, and GitHub Copilot Workspace can already automatically generate and review architectural documentation.

This does not mean architects will disappear, but rather that the role of the architect will change. Future architects will need deeper business understanding, stronger system integration capabilities, and an engineering mindset closer to the real world, rather than just drawing diagrams, layering, and writing documentation.

In other words:

Being an architect is not the destination, but a transition phase. What is truly safe in the long term is system-level engineering capability.

III. The Future of 3D Development: More Like Building a Skyscraper Than Building a Road

If 2D development is like road building, then 3D development is more like building a skyscraper, especially a high-rise building.

Construction is a 3D project. It involves structure, electromechanical, piping, fire protection, material science, construction sequence, safety regulations, urban planning, and many other specialties. Every building has unique structural and environmental constraints and cannot be fully standardized like road building. Even as the level of mechanization continues to increase, the construction industry still requires a large number of engineers, foremen, quality inspectors, and safety managers. Even today, in a highly mechanized environment, a 30-story skyscraper still requires over 200 engineers of various types to participate in design and construction.

More importantly, the fault tolerance of construction is extremely low. If a road is built poorly, it can be repaved, but a structural error in a skyscraper can lead to catastrophic consequences. This type of high-risk, high-complexity system cannot be completely turned over to automation.

The complexity structure of 3D development is almost identical to that of building a skyscraper. It includes multiple dimensions such as geometric structure, materials, lighting, animation, physics, interaction logic, performance optimization, toolchains, digital twins, and robotic simulation. Each dimension requires specialized knowledge and systemic thinking.

IV. The True Meaning of 3D Development: Not "3D Graphics," but "Multi-dimensional Complex Systems"

To avoid misunderstanding, it is necessary to clarify in advance:

"3D development" does not mean "going to learn Unity/Unreal."

It truly refers to moving from "planar logic" toward "multi-dimensional complex systems." For example:

• High-concurrency financial systems
• Distributed disaster recovery architectures
• Multimodal AI systems
• Industrial automation
• Robotic control systems
• Digital twins and physical simulation

Although these systems do not have 3D visuals, they are "multi-dimensional" in logical topology, with low fault tolerance, high coupling, and a complexity far exceeding CRUD. They all belong to "building skyscrapers," not "building roads."

V. The Optimal Path for Developers: From Road Worker to Chief Building Engineer

In the wave of AI, the most dangerous choice for a developer is to remain in the field of 2D development, as this part of the work will be quickly automated by AI.

A safer and more promising direction is to gradually enter system-level, engineered, and higher-complexity fields. But this does not mean you need to make a leap-style transition immediately. Going directly from React to robotic simulation is indeed too big a leap.

A more realistic way is to migrate gradually through an "intermediate path."

VI. Realistic and Feasible Intermediate Path: From Web 3D to Engineering 3D

• For front-end developers, 3D technology in the browser is the most natural entry point. Technologies like Three.js, Babylon.js, and WebGPU allow you to enter the 3D world at a low cost based on your existing JS/TS skills. You can first understand rendering, cameras, and lighting, and then gradually come into contact with physics engines, scene management, and toolchains, finally entering more engineered 3D systems.
• For back-end developers, the entry point for system engineering usually comes from distributed systems, high-reliability architectures, event-driven systems, or real-time data processing. These systems possess "multi-dimensional complexity" by nature and are a natural bridge to future engineering.
• For data engineers, directions such as multimodal data pipelines, robotic data processing, and data synchronization for digital twins are all foundational infrastructure for the future robotic ecosystem.

You don't need to transition immediately; instead, you can gradually expand your dimensions based on your existing skills.

VII. Clear Trends, Gentle Pace: Counter-intuitive Phenomena May Occur in the Short Term

While the direction discussed in this article is a long-term trend, it must be emphasized that technological trends and industrial pace are not always synchronized.

In the next one to two years, we could very well see situations like this:

1. Demand for 2D development increases instead of decreasing.
2. Companies continue to hire front-end, back-end, and full-stack developers.
3. AI toolchains are not yet fully mature.
4. Legacy systems still require a lot of maintenance.
5. 3D development, digital twins, and robotic simulation are still in the early stages of exploration.
6. Those who transition may not immediately find a corresponding position in the short term.

These phenomena do not contradict the long-term trend; they are part of the normal pace of industrial evolution. Short-term counter-intuitive phenomena will not change the long-term structural direction.

For developers, this means you don't need to transition immediately, nor do you need to panic. You have time, space, and a path to prepare. You can continue to do 2D development in the short term and gradually move toward system engineering and the 3D world in the long term.

VIII. AGI Boundary Note: Even if it Appears, it Won't Overturn the Logic of This Article

Some might ask: If an "AGI that can independently complete most software development tasks" appears in the next few years, will these judgments still hold?

My view is: If AGI can fully handle 2D development, then the compression of 2D positions will only happen faster, not slower.

Even so, 3D engineering and system engineering involving the physical world, safety responsibilities, and legal sign-offs will still not be fully automated. In other words, AGI will change the pace, but it will not change the direction. AGI is a variable, but not a variable that reverses the trend. Even if future multimodal models (such as Claude, Gemini) can understand images and 3D scenes, they still cannot replace human judgment and responsibility in physical systems.

IX. Accountability: The Moat AI Can Never Replace

Another crucial point often overlooked is: AI can generate a plan, but it cannot "sign off."

In systems involving personal safety, huge assets, or physical risks—such as construction, aircraft, medical equipment, autonomous driving, and future robotic systems—signing off means legal responsibility, ethical responsibility, and accident responsibility. No matter how powerful AI is, it cannot bear legal responsibility.

This means: The closer to the real world, the closer to physical systems, and the closer to high-risk engineering, the higher the value of human engineers. This is also the long-term moat for 3D engineering, system engineering, and the robotic ecosystem.

X. Robot Simulation and Robot Ecosystem: The Next Step for Human Civilization

The 3D world is not just about games or visualization; it is the infrastructure for robot training, the core of digital twin factories, the environment for autonomous driving simulation, and the underlying ecosystem for future robot operating systems.

Tesla's Optimus, Boston Dynamics' Atlas, and Figure AI's robots all rely on digital twin environments for training. Real-world data is expensive, dangerous, and scarce, while 3D simulation can provide infinite, safe, and low-cost training data. Of course, there is still a non-negligible gap between current digital twins and reality (the Sim-to-Real Gap), but this precisely means that the field needs more deep system engineers to bridge this gap.

The future path is very clear: 3D Simulation → Robot Development → Robot Ecosystem → Robot Civilization.

Just as the App Store was to smartphones, future Robot OSs will also need independent task stores, skill pack ecosystems, and human-machine interface layers.

Why is this inevitable? Because humans intend to build lunar bases, Mars bases, and deep-sea bases, collect resources from asteroids, and explore worlds beyond the solar system. These tasks cannot be accomplished by humans in person and must rely on large-scale robotic systems.

This means that the skills accumulated today in the field of 3D simulation will become the infrastructure builders of the robot civilization in the future.

Robots are the "second leg" of human civilization, and 3D simulation is the "womb" of robot civilization.

XI. Three Traps of Transition

To keep this article balanced, I also need to point out a few common pitfalls:

1. Leap-style Transition: Jumping directly from React to robotic simulation is extremely difficult and easy to abandon halfway.
2. Learning Tools instead of Engineering: Three.js, ROS, and Unity are all just tools; the true core is systemic thinking.
3. Treating the Trend as an Urgent Task: A trend is for 5–10 years, not 5–10 weeks. Anxiety will lead you to make wrong decisions.

XII. Conclusion: The Future of Developers is Not Writing Code, but Building Civilizations

AI will write code, plan systems, and automate most 2D development. But AI will not decide for you where human civilization will go.

The future belongs to those who can build systems, build worlds, and build civilizations. Perhaps, the true developers are not those who write code, but those who can enable AI, machines, and humans to jointly build the future world.

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Three Things You Can Do Starting Today

1. Evaluate Your Work: Determine if it belongs to "road-building" type tasks. Repetitive, automatable, and low-fault-tolerant tasks are the most vulnerable to being consumed by AI.
2. Start a Two-week Project: For example, a simple Web 3D scene, an event-driven system demo, or a real-time data processing pipeline.
3. Build a Solid System Engineering Foundation: Focus on complexity theory, physical world constraints, and responsibility boundaries, rather than just learning new APIs.

You don't need to transition immediately, but you can start preparing for the future today.