TL;DR: Software development is undergoing a structural shift. AI-based tools, above all OpenAI Codex, have crossed a tipping point over the past few months. If you have not yet integrated them into your workflow, you are most likely underestimating how far behind you are.
Contents
- What happened since December 2025
- The renaissance of development: not a metaphor
- What Codex can do today
- The cultural shift matters more than the technical one
- Practical steps for teams
- FAQ
What happened since December 2025
In February 2026, Greg Brockman, President of OpenAI, published a post that spread across the entire tech community: “Software development is experiencing a renaissance before our eyes. If you have not used these tools lately, you are most likely underestimating what you are missing.”
This is not marketing rhetoric. It is an acknowledgment that since December 2025, AI-assisted coding has undergone a step-function improvement — a step change that cannot be described as “slightly better.” This is a qualitative leap.
To understand the scale: according to GitHub, by early 2026 more than 40% of code in new repositories has been written with the involvement of AI tools. These are not experiments — this is the workflow.
The renaissance of development: not a metaphor
Brockman chose the word “renaissance” deliberately. A renaissance is not just “better.” It is a rethinking of what is possible at all.
Historically, leaps in software development happened once per decade:
- 1990s: the shift from assembly to high-level languages
- 2000s: web frameworks and cloud infrastructures
- 2010s: containerization, DevOps, open-source ecosystems
- 2020s: AI as a co-developer
The difference is that the current transition is accelerating faster than previous ones. Tools that a year ago were a “fun toy” are now handling tasks that used to take days.
What Codex can do today
OpenAI Codex is not code autocompletion. In 2026 it is:
Routine tasks that used to consume time:
- Resolving merge conflicts in large repositories
- Fixing CI/CD until it turns green
- Refactoring and rewriting code across languages (Python → Go, JS → TypeScript)
- Generating tests for existing code
Higher-complexity tasks:
- Architectural proposals that account for the codebase
- PR review with explanations of potential issues
- Documentation at the module and system level
One OpenAI engineer put it precisely: “Codex handles routine work so well that it raises ambitions — you start thinking about tasks you would not have taken on before.”
This is an important effect: AI not only speeds up existing work, but also expands the horizon of what seems realistic.
The cultural shift matters more than the technical one
This is where the most underestimated consequence lies. Brockman wrote directly that adopting tools like Codex is not only a technical shift, but a deep cultural shift with many consequences that still need to be understood.
What this means in practice:
For developers:
- The very idea of what it means to “know how to program” is changing. Prompt engineering, the ability to give AI a task and critically evaluate the result, are new professional skills.
- Value is shifting from “writing code” to “understanding the task and formulating it correctly.”
For team leads:
- Task estimation needs to be rethought: what used to take 3 days may now take 3 hours.
- Review questions arise: who is responsible for AI-generated code?
For CTOs and managers:
- Companies that do not adopt AI tools today are losing their competitive edge not gradually, but in leaps.
- This is a talent issue: developers who know how to work with AI are orders of magnitude more productive.
According to McKinsey (2025), teams actively using AI-assisted coding show productivity gains of 25% to 75% depending on the type of task.
Practical steps for teams
Brockman explicitly called on managers to personally lead this change in their teams. Here is a concrete plan:
1. Start small, but start now
Choose one type of task — for example, writing tests or fixing CI — and move it entirely to Codex for 2 weeks. Evaluate the result.
2. Train the team to work with prompts
The quality of AI output depends directly on the quality of the task. Invest 2–3 hours in a team workshop on prompt engineering.
3. Rework the review process
Set a rule: AI-generated code is reviewed the same way as human-written code. Not stricter, but not softer either.
4. Measure
Record time before and after. Collect data by task type. This will help you make decisions about investments in tools.
5. Think about culture, not just tools
Encourage experimentation. Remove the fear that “AI will replace me” — it hinders adaptation. Explain to the team: AI replaces routine, not people.
FAQ
Which AI tools for development are relevant in 2026?
OpenAI Codex, GitHub Copilot, Cursor, Claude Code (Anthropic) — the main players. Each has its own niche: Cursor is strong when working with large codebases, while Codex excels at autonomous task execution.
Will AI tools really replace developers?
Not in the usual sense. They replace routine tasks and enhance developers' capabilities. Demand for engineers who can work with AI is growing — not falling.
Where should you start implementing Codex in a team?
Start with a pilot on one project or one type of task. Set a specific success metric (time, number of iterations). In 2 weeks, you'll have data to make a decision.
Is it safe to use AI-generated code in production?
With review in place — yes. AI makes mistakes, just like people do. Standard code review and testing processes remain mandatory.
How did Codex capabilities change since December 2025?
According to the OpenAI team, there has been a leap in task execution autonomy: the agent better understands repository context, requires clarification less often, and handles multi-step tasks more accurately.
Conclusion
Software development with AI in 2026 is not a trend for early adopters. It is already the standard for competitive teams. As Brockman said, if you haven't used the tools recently, you are underestimating what you're missing.
The leap has happened. The question is not “whether to adapt,” but “how quickly.”
Try Codex on one task today — and you'll understand what this is about.