AI is reshaping the paradigm of programming, from code autocompletion to asynchronous autonomous agents, redefining the efficiency and logic of programming. The innovative practices of the OpenAI Codex team demonstrate the historic transformation of programming from manpower-intensive to intelligent assistance, heralding profound changes in the software development industry.
Have you ever thought that programming could have changed completely? Developers are moving away from simply using AI tools to treating AI as a new foundation for building software. This is not a small adjustment, but a complete paradigm shift. Think about it, the core concepts we’ve been accustomed to—version control, code review, and even the definition of “developer”—have been redefined by AI agent-driven workflows.
OpenAI’s Codex team recently shared an observation that struck me: they found that engineers are no longer willing to write code on airplanes without WiFi. Why? Without AI assistants, programming becomes too inefficient. This change happened so quickly that even they themselves were surprised.
“It’s interesting that one of my previous ideas for starting a business came to me while writing code on an airplane without WiFi, but now I’m never doing it again because the market has completely changed,” Hansen Wang recalls. “This shift is redefining what productivity programming is, and Codex is at the forefront of this change.
From Hansen Wang and Alexander Embiricos’ sharing, I see a deeper change: we are moving from “pair programming” to “commissioned programming”.
Previously, AI was more of a smart auto-completion tool, but now it has evolved into an intelligent assistant that can complete the entire task independently. This shift is more profound than meets the eye, and it is changing the fundamental logic of software development.
From autocomplete to independent work: the evolutionary path of AI programming
I’ve been thinking about a question: what is the real programming revolution? As I learned about the evolution of OpenAI Codex, I realized that we were experiencing not just an upgrade of tools but a refactoring of the entire development paradigm.
The first generation of Codex in 2021 was mainly about code autocompletion, and at that time it was like a very smart code prompter that could predict the next line of code based on your input.
But now Codex is completely different – it has its own container, its own terminal environment, and can independently complete the entire development task in the cloud, from understanding the requirements to writing code, testing, and submitting PRs.
How can product managers do a good job in B-end digitalization?
All walks of life have taken advantage of the ride-hail of digital transformation and achieved the rapid development of the industry. Since B-end products are products that provide services for enterprises, how should enterprises ride the digital ride?
View details >
This shift reminds me of another significant point in computer history: the transition from batch processing to interactive computing. Previously, programmers had to submit punch cards and wait for hours to see the results; Later, with terminals, real-time interaction was possible. And now, we have ushered in a new turning point: from human-led interactive programming to AI-led autonomous programming.
In this model, developers no longer need to write code line by line, but describe the functions they want to implement, and then let the AI agent think, implement, and verify it themselves.
Alexander mentioned a very interesting contrast: traditional reasoning models are like a recent computer student who excels in programming competitions but lacks the practical experience of professional software engineers.
Codex, on the other hand, has undergone a lot of intensive learning and training, learning how to write code that meets enterprise-level standards – including appropriate code style, standardized PR descriptions, perfect testing, etc. It’s like giving that good graduate a few years of workplace experience to understand what real “professional code” is.
The key to this evolution lies in changes in training data and methods. Instead of just letting AI learn how to solve algorithm problems, let it learn how software engineers work in the real world: how to read and maintain the style of existing codebases, how to write clear comments and documentation, how to conduct adequate testing and validation, and even how to write commits that are easy for team members to understand.
I think this change in training method is the key leap from “writing code” to “knowing how to do software engineering”.
What struck me the most was the concept of “commissioned programming” that the Codex team came up with. Traditional AI programming tools are more like a personal assistant, you write a line and it completes a line, and you work closely together to complete the task. But Codex proposes a completely different model of collaboration: you delegate the entire task to it, then it works independently in its own environment, and finally gives you a complete solution. This change in model is not only technical, but also psychological.
Hansen shared an enlightening observation: they found that many users didn’t find much value in using Codex when they first started using the tool because they were still using the tool with a pair programming mindset.
But those users who really realize the value of Codex adopt an “abundance mindset” – instead of carefully trying one or two tasks, they boldly launch many tasks at the same time to see what works. They found that if a user runs 20 tasks in a day or hour, he basically understands how to use the tool correctly.
This mindset shift reminds me of when cloud computing was just on the rise. Initially, many companies used cloud servers as traditional physical servers without realizing the true benefits of cloud computing. Only when people start embracing cloud-native thinking such as “scale on demand” and “quickly recover from failure” can the potential of cloud computing be truly unlocked.
Similarly, delegated programming requires a new way of thinking: instead of expecting every task to be perfectly successful, it is necessary to find the best solution through a lot of parallel attempts.
What’s even more interesting is that this new collaborative model is changing the very nature of code review. Previously, code reviews focused on code quality and logical correctness, but now they are more about verifying and selecting AI output results.
Alexander highlights Codex’s innovation in this area: it not only shows code changes, but also records the execution process in detail, including which terminal commands were executed, what outputs were obtained, what the test results were, and so on. This transparency allows human reviewers to better understand and validate the AI’s work process.
I think this shift heralds a redefinition of the role of software development. The focus of developers’ work will shift from “writing code” to “designing solutions” and “verifying results”. As Alexander said, developers’ time allocation could change radically: from 35% of the time writing code to more time spent on requirements analysis, architecture design, code review, and system maintenance. This is not to say that developers have become less important, but that their value will be more reflected in strategic thinking and quality control.
Real-life example: When AI rescues a product launch at 1 a.m
What struck me the most was a true story shared by Hansen, which perfectly illustrates the practical value of Codex. At 1 a.m. before the product launch, the team encountered a tricky animation bug. In this case, either cut off this feature and release it directly, or continue to stay up late for debugging.
But the engineers decided to try a new solution: input the bug description into Codex and let it try 4 different solutions. As a result, the first 3 attempts failed, but the 4th gave a perfect solution, the team immediately deployed the code, and finally the animation feature was successfully included in the product launch.
This story made me think a lot. Firstly, it demonstrates an important feature of AI programming: the value of batch trials. In traditional programming, developers often spend a lot of time thinking about the “right” solution and then implementing it.
But in the era of AI programming, a more effective strategy may be to let AI quickly try multiple options and then choose the best one. This “trial-and-error optimization” approach is costly in human programming but has little additional cost for AI.
Secondly, this case also illustrates the scenario where AI programming is particularly good at bug fixing. Codex not only writes new code, but also independently reproduces problems, analyzes causes, and validates solutions.
This end-to-end problem-solving capability is exactly what traditional auto-completion tools lack. It is capable of debugging like a human engineer: running code, reviewing output, analyzing errors, modifying code, testing again, and until the problem is resolved.
Most importantly, this story shows the time-sensitive value of AI programming. In times of crisis, it is more effective for AI to try multiple solutions in parallel than for humans to spend time thinking about a single solution. This capability is particularly valuable in fast-paced software development environments, especially in scenarios requiring rapid iterations and timely fixes.
I also noticed a pattern of how Codex is used within the team: many engineers come to the office in the morning, start a few Codex tasks, go for coffee and breakfast, and then come back to review the generated PR and make final adjustments in the IDE. This asynchronous work model is becoming the new development rhythm, where developers no longer need to write code from scratch, but fine-tune and optimize from 80% of the finished code.
The Future of Software Development: From Interface Manipulation to Intent Expression
Hansen and Alexander’s vision for the future of software development has made me rethink the direction of the industry as a whole. They believe that in the future, most of the code will be written by AI agents in their own environment, rather than in the developer’s local environment. This shift will revolutionize our understanding of the concept of “programming”: from directly manipulating code to expressing programming intent, from line-by-line writing to task delegation.
I particularly agree with their prediction of an explosive increase in the number of software. Hansen mentioned an interesting observation: if you open your phone and look at the apps inside, most of them are general-purpose apps built for millions of users, and very little software is customized for you personally or for a small team.
But as programming costs drop significantly, we’ll see more customized, personalized software emerge. This reminds me of the early computer age, when each program was tailored to a specific need, and now we may go back to that model, but on a much larger scale.
What’s even more interesting is their thinking about the evolution of development tools. IDEs won’t disappear, but their focus will shift from “writing code” to “reviewing code”, “planning projects”, and “validating results”. A developer’s day might start like this: come to the office in the morning, start a few AI programming tasks, go for coffee, and come back to review and refine the AI-generated code in the IDE. This asynchronous collaboration model is redefining what constitutes efficient software development.
They also mentioned an idea that I think is forward-looking: the project management interface of the future could be like TikTok. Imagine an AI agent proactively identifying problems and proposing solutions, showing you them in the form of videos, you can swipe right to agree to implementation, swipe left to indicate discussion, and long press to provide specific modification suggestions. While this may sound like a joke, it reveals an important trend: when AI can work autonomously, human roles shift more towards strategic decision-making and quality control.
I think the impact of this shift on the software industry as a whole will be profound. First, the barrier to entry for software development will be greatly lowered, allowing more non-technical people to create software through natural language descriptions. Secondly, the value of professional developers will be more reflected in architecture design, requirements analysis and system integration. Finally, we may see a reshuffle of the software industry, with teams that can effectively leverage AI programming capabilities gain a significant competitive advantage.
From a technical perspective, enabling AI agents to work reliably in a real software development environment presents many challenges that we may not have thought of. Hansen shared a detail that struck me: when they designed their training environment, they found that the real-world codebase was very complex and confusing. For example, Alexander showed the codebase of a startup they had acquired, and Hansen’s first reaction was, “Where are the unit tests?” “Because AI agents rely on unit tests to verify code correctness, many real projects don’t have perfect tests at all.
This observation reveals an important question: the effectiveness of AI programming tools largely depends on the quality of existing codebases. In order for AI to better understand and manipulate code, development teams need to re-examine how they organize their code. Hansen mentions several practical tips: use strongly typed languages, write smaller and better tested modules, add well-developed documentation, etc. These are good programming practices, but they have become even more important in the age of AI.
I especially noticed their ingenuity in naming projects. The Codex project has the internal codename “WHAM”, and they chose this name because it is easy to search in the codebase and is not confused with other common terms. If they use generic words like “code” or “agent,” the AI will have difficulty searching for relevant code. This AI-optimized programming practice may become the new standard for software development in the future.
Another technical challenge is how to handle long-running tasks. Codex can run for 30 minutes or even longer to complete complex tasks, which places high demands on the stability and focus of the model.
Hansen mentioned that their model has improved a lot in terms of “staying focused” but still encounters situations where the AI “loses patience”, like an intern who would say, “Sorry, I think this task is too complicated and I don’t have enough time to complete it.” “This anthropomorphic behavior is both interesting and illustrates the limitations of current technology.
I think these technical challenges and solutions provide us with important inspiration: the popularization of AI programming requires not only the advancement of AI technology, but also the adaptation and improvement of the entire software development ecosystem. Codebases need to become more structured and understandable, development practices need to be more standardized, and toolchains need to better support AI agent working patterns. It’s a systemic change, not just a tool upgrade.
Market Competition and Differentiation: OpenAI’s Unique Advantages
In the competition for AI programming tools, I noticed that Hansen and Alexander had interesting views on the market prospects. They see a variety of different solutions emerging in the market: some tools work in the user’s local environment, others work independently in the cloud, like Codex. But they believe that in the future, most of the code will be written by AI agents with independent computing environments, and this model will become mainstream.
OpenAI’s unique advantage is that they not only have powerful AI models, but also ChatGPT, a widely used AI assistant platform. Alexander mentioned a very important vision: in the future, users do not need to switch between different professional agents, such as programming agents, shopping agents, travel agents, etc., but have a unified assistant that can handle all types of tasks. This assistant is ChatGPT, which automatically calls the corresponding professional capabilities based on the type of task.
I think this unified platform strategy is very forward-looking. Imagine having AI help you analyze data, write code, order restaurants, and schedule meetings in the same conversation without having to open different apps or learn different interfaces. This seamless multitasking capability may be OpenAI’s core advantage over specialized programming tools. At the same time, for professional users, they can still use a purpose-optimized interface and features in a dedicated tool environment.
Hansen emphasized their strengths in model training: Codex uses the exact same production environment as the training environment, avoiding the classic “works on my machine” problem. The container environment users use is the environment used for AI training, and this consistency guarantees better performance and fewer surprises. This unification of training and deployment environments can be a technical moat that is difficult for other competitors to replicate.
In the long run, I think the competition for AI programming tools will not only be at the technical level, but also in the ecosystem. Whoever can better integrate different types of AI capabilities and provide a smoother cross-task experience is more likely to win in this rapidly evolving market. OpenAI does have unique advantages in this regard, but the key is how to translate these advantages into user value and market share.
The evolution of the developer’s role: from coder to architect
The part of this interview that inspired me the most was their reflections on the future evolution of the developer role. Alexander mentioned that engineers currently spend only about 35% of their time actually writing code, and the rest of their time is spent on requirements discussions, design planning, code review, test validation, and system maintenance. As AI takes on more and more coding work, developers’ time allocation will tilt further towards strategic work.
This shift reminds me of the evolution of the construction industry. Modern architects rarely move brick walls themselves, but their value is not diminished by this, but is more reflected in design concepts, space planning, material selection and engineering coordination. Similarly, future software developers may be more like “software architects”, focusing on system design, technology selection, quality control, and team coordination, while entrusting specific code implementation to AI agents.
I particularly agree with their point that the easier it is to use a programming tool, the more software will be needed. Nowadays, most of the applications in our mobile phones are general-purpose software designed for millions of users, and there is very little customization software. But as AI greatly reduces software development costs, we may see more software tailored to specific team or individual needs. This will create a large number of new development needs, rather than simply replacing existing developers.
Hansen’s prediction impressed me: he believes that the number of professional software developers will increase significantly, not decrease. This may sound counterintuitive, but it makes sense when you think about it. When it becomes easier to create software, more software is created, requiring more people to design, manage, and maintain it. Just like the popularity of spreadsheet software has not reduced the number of accountants, but has made financial analysis accessible to more people.
I think this role evolution is both an opportunity and a challenge for current developers. The opportunity is that they can be freed from repetitive coding tasks and focus on more creative and strategic work. The challenge is that they need to improve their capabilities in system architecture, product design, team management, etc. Developers who can adapt to this shift will gain greater value and influence in the AI era.
My deep thoughts on the future of AI programming
From this interview, I saw the profound transformation that the software development industry is undergoing that may have far-reaching implications beyond our imagination. I think we are at a historic turning point: from labor-intensive software development to intelligent software creation. It’s not just an upgrade of tools, it’s a reprogramming of the entire industry’s DNA.
What excites me the most is the explosion of creativity that AI programming can bring. When the technical barrier to programming is greatly lowered, more people with ideas but lack programming skills will be able to create really useful software. It’s like having a dedicated development team for everyone, which greatly shortens the distance between creativity and implementation. I expect to see more niche but precise software products, more personalized solutions, and more innovative applications across domains.
But I also see some challenges that require deep thinking. The first is the issue of code quality and maintainability. When AI can generate large amounts of code quickly, how can you ensure the long-term maintainability of this code? How to establish an effective quality control mechanism? The second is the issue of skill inheritance. If the new generation of developers learns to code primarily by collaborating with AI, will they still be able to master deep computer science principles? What impact will this change in skill structure have on the long-term development of the software industry?
I also thought about the impact of AI programming on software security. AI-generated code can contain security vulnerabilities that are difficult to detect, especially in complex system integration scenarios. We need to develop new security review methods and tools to address this new risk model. At the same time, as software creation becomes easier, we may see more malware and security threats, which will require an industry-wide revisit of security policies.
From a business perspective, AI programming will redefine the competitive advantage of software companies. Traditionally, having a large development team has been one of the core competencies of software companies. But in the era of AI programming, small teams may be able to create products comparable to large teams, which will make competition more fierce and innovation more democratized. Companies that can effectively leverage AI programming capabilities and establish differentiation in product design, user experience, business models, etc. will win in the new competitive landscape.
I believe that we are witnessing one of the most significant changes in the history of software development. Just like from assembly languages to high-level languages, from standalone software to web applications, from desktop programs to mobile applications, AI programming represents another important stage of development. This stage is characterized by a higher level of abstraction, a lower barrier to creation, faster iteration, and a wider range of participants. I am looking forward to this change and remain alert to the opportunities and challenges it contains. In the coming years, we will see how this change is reshaping the entire technology industry and a new way of collaborating between humans and computers.
