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Ask HN: Will AI LLMs for coding get smarter and cheaper in the future?

A user on Hacker News asks whether AI LLMs for coding will become both smarter and cheaper in the future, sparking discussion on the trajectory of AI coding tools, expected improvements in model capabilities, and potential cost reductions.

Background

- This is a discussion thread on Hacker News (HN), a tech-focused social news site where engineers and founders debate technology trends. "Ask HN" means a user is posing a question to the community. - The user is asking whether large language models (LLMs) used for coding — like GitHub Copilot, Claude, or GPT-based code tools — will continue to improve in capability ("smarter") while also becoming less expensive ("cheaper"). - This touches on a central debate in AI: scaling laws (bigger models + more data = better performance) vs. the efficiency turn (smaller, specialized models and inference optimizations that cut costs). The answer affects which tools developers adopt and how software companies budget for AI. - Recent history: coding LLMs have rapidly progressed from autocomplete helpers to agents that can scaffold entire projects, but costs per query remain significant for advanced models. Competition between OpenAI, Anthropic, Google, and open-source alternatives is driving prices down.

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Meta's engineering culture is deteriorating under Mark Zuckerberg and Scale AI CEO Alexandr Wang, who have introduced keyboard tracking, reassignments to data labeling, and AI-centric performance metrics. Critics argue this incentivizes performative AI use, drives away experienced engineers, and contributed to a major Instagram hijacking incident caused by AI-written and AI-reviewed code.

Ask HN: Will AI LLMs for coding get smarter and cheaper in the future?

Background

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