My Honest Thoughts on AI and the Job Market in 2026

AI Progress and Trajectory
📌 The exponential growth curve predicted by some AI CEOs is inaccurate for AI model improvement; the actual improvement rate has slowed down since the initial launch of tools like ChatGPT.
⚙️ Improvements in AI functionality are increasingly driven by tooling (e.g., Claude Code, Cursor, agentic coding) which shows a steady, nearly linear trajectory of improvement, rather than massive leaps in the core models.
🔬 Current AI models are beginning to show specialization, being better at specific tasks (like UI or specific code base sizes) because one model cannot be good at everything.

Web Development Job Market Analysis
📉 Job listing data (from Indeed, US only) shows a drop from a 230% spike in 2022 down to around 68% currently, resembling levels seen during the 2014 job market period.
🧑‍💻 The severe job market downturn in 2024/2025 was largely due to companies overhiring during the peak and using AI as an excuse for layoffs to lower costs.
📈 Job listings are trending upward again, showing that companies are realizing AI is a tool, not a replacement for developers, leading to increased hiring, especially for junior developers who were hit hardest initially.

Developer Adaptation and Skill Focus
✈️ AI should be viewed like an airplane's autopilot: it handles the routine 95% of the work, but a skilled pilot (developer) is required for monitoring, critical decision-making, and handling tasks the AI cannot manage (like landing/takeoff).
📚 Developers must master code review, reading code, and oversight of AI output, as these skills are now incredibly useful in an AI-augmented environment.
✨ To stand out, developers should focus on niche, complex skills that AI currently struggles with, such as advanced CSS features like layers, custom properties, or animations, beyond the basic flexbox/grid AI typically generates.

Actionable Learning Strategies
✍️ Junior developers should avoid letting AI write all their code; instead, use AI for asking questions, seeking recommendations, and getting feedback on self-written code to build foundational understanding.
💡 Once fundamentals are strong (around 50% mastery of core concepts), advanced developers should practice agentic workflows—learning prompt engineering, using prompt files, and understanding rules—to build code quicker with AI assistance.
🌟 The ultimate combination for success is proficiency in working with AI AND mastery of advanced, specific skill sets that differentiate you from AI-generated results.

Key Points & Insights
➡️ The current job market dip is a correction from overhiring, not an indication that AI has eliminated the need for developers.
➡️ Future success hinges on developers becoming adept overseers and auditors of AI-generated code, ensuring it is secure and high-quality.
➡️ Focus learning on deep, complex concepts in your domain (e.g., advanced CSS) as these are the areas where AI output remains basic and unspecialized.
➡️ Writing code yourself first is crucial, even when learning, to build the necessary understanding required to effectively use AI tools later.

📸 Video summarized with SummaryTube.com on Feb 11, 2026, 04:43 UTC