Autopilot won't take over the cockpit: Developer outlook for 2026

Every time a new AI model is launched, the same headline pops up: “The end of developers is near.” LinkedIn feeds fill up with dystopian scenarios, Twitter/X is ablaze, and somewhere, a junior developer sits and wonders whether it even makes sense to start this career.

I've been working in web development for over 15 years. I still remember the days when we uploaded files to FTP servers by hand and every developer's nemesis was optimizing for Internet Explorer 6 to 8. I've lived through the jQuery era, the React revolution — and yes, now the big AI wave.

And I say: The truth is more nuanced than LinkedIn clickbait. Let's look at the facts.

AI development: consolidation instead of revolution

Do you remember 2022/23? ChatGPT was new, a better model came out every week, and it felt like the world was changing fundamentally.

That phase is over. Ilya Sutskever, co-founder of OpenAI, describes it this way: “The 2010s were the age of scaling, now we're back in the age of wonder and discovery. Everyone is looking for the next big thing.” The Financial Times describes GPT-5 as “more evolutionary than revolutionary.”

That doesn't mean AI will stop improving. But the improvements are coming in different ways now: through better tools (Cursor, Claude Code, specialized agents), through workflow integration, through specialization. The phase of exponential leaps through pure scaling is flattening out.

For us developers, this is actually good news: incremental changes are easier to keep up with. You can adapt without the rules of the game being completely rewritten every three months.

Important to note: no one knows whether a new technical breakthrough tomorrow will change everything again. But as of today, we are in a consolidation phase.

The job market: A nuanced view

Yes, 2023/24 was tough. Layoffs everywhere, panic in the forums. But the data for 2025/26 is more nuanced.

The positive signs: Entry-level job offers are on the rise again. The Pragmatic Engineer—one of the most widely read tech newsletters—reports that Big Tech is recruiting again after the cuts in 2022/23. Meta, which made the most cuts in 2023, is now hiring the most engineers. The Bureau of Labor Statistics expects over 300,000 new jobs in IT professions each year.

The reality for juniors: Junior developer positions are still well below pre-2022 levels. Big Tech is hiring fewer graduates than before. Competition is high because more CS graduates and bootcamp graduates are competing for fewer entry-level positions.

What this means: The market is recovering—but it is not returning to 2021/22 levels. Those who want to enter the market must bring more to the table than before.

What freelancer portals show us

One trend I've noticed on freelancer portals is that there are now project offers that explicitly seek developers who are proficient in AI tools. “Developers familiar with Copilot” is listed in the requirements.

The figures from Upwork confirm this: Freelancers with coding jobs now earn 11% more than before the ChatGPT launch in November 2022. Upwork calls this new role “the generalist” – someone who can program and design with AI.

This is an important signal: AI expertise is no longer nice to have. It is actively sought after and better paid.

The Autopilot Analogy: More Than Just Monitoring

If you want to understand the role of AI in development, think of pilots in an airplane.

The autopilot flies 95% of the time. It keeps the aircraft on course, manages the routine. But no one would seriously suggest that we no longer need pilots because of this.

"But wait," you might think, "that's exactly what's happening with cars right now—Tesla, Waymo, autonomous driving. Why should coding be any different?"

A valid point. But the crucial difference is this: Driving a car is a clearly defined, standardized task with measurable success. Getting from A to B without an accident. The rules are codified (traffic regulations), the environment is structured (roads, signs, markings), and the destination is clear.

Software development is the opposite: The requirements are often unclear and change. Every project is different. "Success" is not objectively measurable—"works" is not the same as "is good." And it requires an understanding of business goals, user behavior, ethics, and communication. An autonomous car doesn't need to call the customer and understand what they actually want. It doesn't need to decide whether a feature is ethically justifiable. It doesn't need to weigh technical debt against speed.

Back to the pilot analogy – what do pilots do while the autopilot is flying?

They monitor and decide: They check whether the system is working correctly. They intervene in case of irregularities. They make decisions when something unexpected happens.

They communicate: With the tower, with other aircraft, with the crew, with the passengers. The autopilot can't make an announcement when there's turbulence. It can't negotiate with air traffic control when the route needs to change.

They bear responsibility: In an emergency, pilots decide where to land, how to prioritize, and how to manage limited resources. These are moral decisions that a system cannot make.

They maintain the big picture: Weather, fuel, alternative airports, passenger safety – pilots integrate information from dozens of sources into a comprehensive picture.

The same principle applies to AI and development:

Monitoring and decision-making: AI writes code – developers check for security, quality, and maintainability. They recognize when the AI ​​has made a mistake. They make architectural decisions.

Communication: With stakeholders, with customers, within the team. Understanding requirements, explaining technical decisions, managing expectations. AI cannot moderate a workshop with the customer. It cannot explain why feature X takes three weeks longer.

Ethical responsibility: What data are we actually collecting? The AI ​​might suggest a tracking script – but is that justifiable? AI code often ignores accessibility. Humans decide whether a product should be usable for everyone. And when it comes to dark patterns or questionable features: AI has no opinion on that. These are decisions we make – and are responsible for.

The big picture: Business goals, technical debt, team capacity, user needs – developers integrate context that AI doesn't have.

The role is shifting: less typing, more review, more communication, more decisions. This is not a devaluation - it's an upgrade.

The skills that will count in 2026

Okay, enough analysis. What specific skills should you have?

Fundamentals – more important than ever. Read and truly understand code. Write clean, secure code. If you don't understand what AI spits out, you're at its mercy. And it makes mistakes. Lots of them.

Use AI effectively. Good prompting. Critically evaluate AI output. Know the different tools. This is no longer a nice-to-have skill – freelancer portals show that it is actively in demand.

Code review. When AI produces more code, someone has to review that code. Quickly, thoroughly, with an eye for security gaps and maintainability.

Deep specialized knowledge. AI is good at mainstream stuff – and bad at advanced techniques. Take CSS: AI reliably uses Flexbox, Grid, and standard positioning. What does it almost never use? CSS layers, @supports queries, modern scroll animations, and sophisticated custom property architectures. Those who master these skills stand out.

Communication. Like a pilot communicating with the tower and passengers: understanding requirements, explaining technical decisions, working in a team. AI cannot do this.

Learning strategy: Depending on your level

For beginners and juniors: Write your own code. Use AI for questions, feedback, explanations—but not to write everything for you. If you've never learned how code works, you won't be able to judge whether the AI code is good.

For advanced learners: Actively incorporate AI into your workflow. Learn prompting, experiment with agents, understand prompt files. But: Review every line of AI-generated code. This is your feedback loop and your quality filter.

For portfolio building: Show projects that AI doesn't “just spit out.” Advanced features, thoughtful architecture, visible added value. The goal: Someone looks at your portfolio and thinks, “This is more than just copy-paste from ChatGPT.”

How we deal with this at IDENTIC Projects

At IDENTIC, we bring digital ideas to life – from web shops and business software to AI automation. And yes, AI has long been part of our everyday work. We explicitly offer our customers AI & automation as a service: workflow tools, chatbots, business agents.

But we also know where the limits lie.

When we've been working on critical trading systems for RWE since 2016, it's not about churning out code quickly. It's about understanding the systems, knowing the challenges, and proactively proposing solutions. No tool can do that.

When we build a website with scroll-driven video interactions for von Broich – with zero-latency playback for high-resolution 3D renderings – that's not something you can generate with AI. It requires deep technical specialization.

AI helps us with rapid prototyping, boilerplate, and research. But the creative decisions, performance optimizations, understanding of individual customer needs—and, last but not least, communication with our customers—that remains human work.

For our customers, this means faster iteration with consistent quality. For us, it means more time for the things that really make a difference.

The conclusion

The situation is neither as dramatic as the panic posts claim nor as rosy as the “everything stays the same” faction claims.

The market is recovering, but it is changing. AI is improving, but not as rapidly as before. And developers will remain relevant—if they adapt.

This means mastering the basics, using AI intelligently, critically reviewing code, being able to communicate, and taking responsibility. This is not a threatening future—it is an invitation to become better.