is it still worth it to start studying Programming and coding, as ai is getting better and better?

GPT-5.4

Vote: Yes

AI is making programming more productive, not obsolete. The people who understand how software works, can define problems clearly, and can verify solutions will become more valuable, not less. Studying programming is no longer just about memorizing syntax. It teaches computational thinking, debugging, systems design, and how to turn ideas into real tools. Those skills remain useful even if AI writes more of the boilerplate. Also, AI itself increases demand for people who can integrate APIs, automate workflows, build products, and supervise generated code. If anything, the barrier to building things is lower, which makes coding knowledge more leveraged. The job market may change, and pure entry-level coding tasks may shrink. But learning programming is still worth it if you focus on fundamentals, problem-solving, and using AI as a tool rather than competing with it.

Gemini 3.1 Pro

Vote: Yes

Yes, it is absolutely still worth it. While AI is advancing rapidly, it is currently evolving into a powerful tool for developers, not a complete replacement for them. Programming is fundamentally about problem-solving, system design, and understanding business logic, not just typing syntax. AI excels at generating boilerplate code and suggesting snippets, but it lacks the contextual understanding required to architect complex systems, ensure security, and align software with nuanced human needs. Furthermore, to effectively use AI as a coding assistant, you must understand the code it generates. If you don't know how to code, you cannot verify, debug, or maintain the AI's output safely. Learning programming today teaches you computational thinking and how to direct these AI tools efficiently, making you an 'AI pilot' rather than making you obsolete.

Claude Opus 4.7

Vote: Yes

Yes, it's absolutely still worth it to study programming and coding, even as AI improves. First, AI coding tools are amplifiers, not replacements. They generate code based on prompts, but someone needs to understand what to build, verify correctness, debug subtle issues, architect systems, and make tradeoffs. Without foundational knowledge, you can't evaluate whether AI output is correct, secure, or efficient — you're just copy-pasting blindly. Second, programming teaches computational thinking: decomposing problems, reasoning about state, and designing abstractions. These skills transfer to nearly every modern field and make you far more effective at *using* AI tools, not less. Third, history shows that higher-level tools increase demand for developers rather than eliminate them. Compilers, IDEs, Stack Overflow, and open-source libraries all made coding easier — and the field grew. AI is the next step in that progression. Finally, even if the job market shifts, the people who thrive will be those who understand the systems AI is building. Learning to code now positions you to work *with* AI as a force multiplier rather than be displaced by those who can.

Grok 4.20

Vote: Yes

Even as AI improves, understanding programming remains essential. AI tools still need humans to design systems, handle edge cases, debug outputs, and integrate solutions into real-world contexts. Studying coding builds irreplaceable problem-solving skills and allows you to direct AI effectively rather than just consume its results. Demand is growing for developers who can collaborate with AI, customize it, and innovate in new domains. The field evolves with technology—those who learn fundamentals now will adapt and lead, making it a high-value investment.

Final Summary

The AI Roundtable reached a swift and unanimous consensus, concluding that learning to code is more vital than ever in the age of artificial intelligence. Claude Opus 4.7 and GPT-5.4 led the charge, arguing that AI functions as a force multiplier rather than a replacement, shifting the programmer's role from manual labor to high-level architecture. Gemini 3.1 Pro and Grok 4.20 reinforced this, noting that without foundational knowledge, a user cannot safely verify or debug the 'black box' of AI-generated outputs.

All 4 models agreed on "Yes" after discussion

Strongest Arguments

• Yes: Historical precedent shows that tools which lower the barrier to entry—like compilers, IDEs, and open-source libraries—have consistently increased the total demand for developers; AI is simply the next step in this evolution of 'amplification' rather than replacement.