ChatGPT didn't kill the developer market: programmer numbers have grown significantly since 2022

Talks about the imminent disappearance of the developer profession are not yet confirmed by the numbers. After ChatGPT's launch, the market is changing, but instead of a collapse in employment, statistics show an increase in the number of programmers and a shift towards new tasks.

Numbers vs. Panic

Fresh estimates look quite straightforward. According to the U.S.

Bureau of Labor Statistics, in February 2026 there were approximately 2.5 million software developers employed in the country. This is a record level and roughly 400,000, or 19%, more than in 2022, when ChatGPT first appeared.

Looking broader, JetBrains estimates the global developer community at 20.8 million people versus 17.3 million in 2022 — nearly a 20% growth.

SlashData gives an even higher estimate: in early 2025, the global developer population exceeded 47 million people, while in the first quarter of 2022 it was roughly 31 million. Even counting as conservatively as possible, the conclusion is the same: there has been no mass exodus of the profession. On the contrary, the market continued to grow even in the era of generative AI, although the structure of demand for specialists has begun to change noticeably.

How the Role is Changing

Boston University professor James Besseen disputes not that AI affects development, but the conclusion that it automatically means job cuts. His thesis is simple: AI takes on some tasks, increases output and speeds up code writing, but this does not equal the disappearance of jobs. Developers spend less and less time on routine pieces and more and more time on control, architecture and oversight of swarms of AI agents that can handle individual tasks autonomously.

"AI takes on development tasks and increases productivity, but this

does not translate into job loss."

Against this backdrop, employers are no longer looking for just "pure coders." In Pluralsight's analytics, in-demand skills now include both fundamental things like Python and SQL, and new scaffolding around AI development: clouds, security, networking knowledge and working with agentic tools. In other words, engineers are now expected not only to write functions, but also to have the ability to build a reliable production system around a model and work in teams.

• AWS, Azure and Google Cloud
• Python, SQL and network engineering
• AI and cloud security
• agentic AI and MCP servers
• critical thinking and business-level communication

At the same time, concern hasn't gone away. In Anthropic research, specialists directly said they almost constantly think about the risk of losing office work due to AI, and some complained that managers began assigning more complex tickets, counting on model assistance. That is, the workload doesn't disappear — it gets redistributed. AI removes some mechanics, but at the same time raises the bar for expectations around quality, speed, and the ability to understand generated code.

Why Demand Is Not Falling

Besseen's key argument relates not only to productivity, but to demand. By his estimate, from 2003 to 2022, developer productivity grew roughly 3.9% per year, and in the period from 2022 to 2025 — already 6% per year.

Usually such numbers push toward the thought that companies will need fewer people. But for software it doesn't work so linearly. If development becomes cheaper and faster, companies don't just economize on staff.

They release more products, try more ideas, improve quality and automate new processes. Besseen estimates the real growth of software output at roughly 9.3% per year — that is, demand for software grows faster than a single developer's productivity.

In such a model, the total number of specialists is not obligated to fall: to cover an expanding market, you still need more people. This logic well explains why technological turning points rarely boil down to the formula "a machine will replace a man one to one." Automation can eliminate specific operations, but at the same time expand the market itself and the number of scenarios where software is needed.

For the software industry, this means a transition from manual production to the role of operator, architect and editor of AI results, and at the same time — growing responsibility for quality, verification and integration of machine code into real products.

What This Means

The apocalypse of developers for now looks more like a media story than a statistical fact. AI is already changing how you enter the profession and the set of skills you need, but in the near term, what matters most is not "replacing the programmer," but growing demand for those who know how to build, verify and direct AI development.