Summary of Key Points
This article starts with a humorous reference to the "old-fashioned programming" practices within the developer community to highlight the profound changes that AI is bringing to the software industry. In the past, it was common for programmers to write code by hand; however, today AI can generate code in bulk, transforming code from a scarce and expensive resource into a cheap and readily available commodity. This shift has not only changed the tasks that programmers perform (from writing code to verifying its correctness and the reliability of systems) but also impacted the way software teams are organized (from a model involving multiple people executing tasks with a few making decisions to a model where a few highly skilled individuals work alongside machines). Additionally, it has altered the pricing logic (simple software has become cheaper, while critical, reliable systems are more expensive). The ultimate conclusion is that AI is not intended to replace programmers; rather, it aims to redefine their core capabilities. The future programmers will be those who can make critical decisions about what should be built, what can be compromised, and what must not be sacrificed.
Detailed Analysis
1. Code from a Luxury Good to an Inexpensive Commodity: AI Has Altered Scarcity
Why was writing code expensive in the past? For example, when a business department requested an automated approval process based on customer levels, programmers had to translate this requirement into machine-readable code. This involved analyzing the needs, building data models, designing interfaces, writing front-end and back-end code, and conducting testing and debugging—all of which required manual effort. As a result, code was a scarce resource, and companies had to maintain dedicated teams to develop internal systems. With AI, interfaces that used to take half a day to create can now be generated in minutes. AI can also configure unfamiliar frameworks without the need for manual documentation and handle repetitive and testing tasks. Although the code generated by AI may not always be perfect, it has significantly reduced the cost of code development, making it as accessible as tap water.
2. Writing Code Has Become Easier, but Verifying Its Correctness Is the Biggest Challenge
Many people think that AI has made software development simpler, but in fact, it has reduced the cost of code generation while not decreasing the effort required to verify its accuracy. For instance, while AI can quickly create a payment interface, it cannot consider questions such as when the payment should be authorized, whether a failure can be retried, or who has the authority to adjust the amount. These issues involve business logic and security considerations that AI cannot address. More importantly, AI has greatly increased the speed at which code can be produced; whereas programmers used to write 300 lines of code a day, AI can generate tens of thousands in the same amount of time. The bottleneck in software development is no longer the speed of writing code but the ability to understand and be responsible for the code.
3. Software Teams Need to Become More Efficient: From a Mass-Labor Approach to a Focus on Elite Professionals and Machines
The traditional software development approach relied on the idea that more people meant more power. More requirements meant more developers and testers, and larger systems required more maintenance. Many outsourcing companies even charged based on the number of man-days. However, this model is no longer effective. Reuters has reported that Indian outsourcing giants like Tata Consultancy Services and Infosys are shifting their business models from charging by the hour to charging based on the results. Customers are increasingly using AI to handle some tasks in-house, making the number of employees less of an advantage. In the future, teams will consist of a few core professionals who understand architecture and can make decisions, while AI agents handle the bulk of the work. For example, instead of 10 people writing code, one person will set goals, define boundaries, and review the results, with the rest of the work being handled by machines.
4. The Core Competencies of Programmers Have Changed: From Code Writers to System Judges
In the past, young engineers competed to see who could implement a particular feature. In the future, the focus will be on how a feature should be integrated into the overall system. For example, while knowing how to write a payment interface is still necessary, programmers will also need to understand how it interacts with other systems, where the security boundaries lie, and who is responsible for any issues that arise. This is similar to photography; after the widespread adoption of smartphones, the ability to press the shutter is no longer valuable, and professional photographers rely on composition, storytelling, and aesthetic skills. Similarly, being able to write code will become basic, while the ability to evaluate the correctness of systems and define their boundaries will be the true core competency.
5. Divergent Software Pricing: Cheap Software for Basic Functions, Expensive Software for Critical Systems
In the past, companies bought software because developing it in-house was too costly (for example, a CRM system might require dozens of employees). With AI reducing development costs, companies might wonder why they shouldn't use AI to develop such systems themselves. However, they will quickly realize that simple internal tools and one-time applications can be created cheaply using AI and become more commoditized. On the other hand, critical systems involving finance, healthcare, or manufacturing require stability, reliability, and accountability, which AI cannot provide. Therefore, these systems will remain expensive. In the future, companies will buy software not just to have code written for them but to ensure that their systems run safely and reliably. Reliable software will become the new luxury.
Final Conclusion
AI is not meant to eliminate programmers but to free them from the tedious and repetitive tasks of writing code, allowing them to focus on making crucial system decisions. The future programmers will be those who can determine what needs to be built, what can be compromised, and what must not be sacrificed, effectively becoming the decision-makers of software systems rather than mere code writers.