One of the most interesting questions right now is not whether AI helps us learn faster — but how far that acceleration actually goes. If someone starts close to zero today, AI can: - explain concepts on demand - adapt explanations to your level - generate examples, exercises, and feedback - help you practice more consistently This applies to many domains: - coding - languages - music - professional skills - analytical or creative work In many cases, AI seems to compress the early and middle stages of learning dramatically. But there are also limits: - intuition still takes time - taste and judgment aren’t instant - real-world constraints push back - some skills only solidify through repetition and exposure So the interesting question isn’t “Can AI make you an expert overnight?” It’s something more nuanced. How far can AI realistically take someone — and where does the acceleration slow down? And from your own experience: - Where did AI help you most? - Where did it stop being enough on its own? - What still required time, effort, or human feedback? Curious to hear how people here see the real ceiling of AI-accelerated learning — across different skills and professions.

A lot of the conversation around AI at work starts with fear: Will this replace me? Will my role still matter? What I’m seeing in practice is something more nuanced. AI doesn’t replace professionals directly. It amplifies how they already work. When AI is used mainly to: - generate generic output - follow templates without thinking - skip judgment and context the work becomes easier to replace. But when AI is used to: - clarify decisions earlier - explore trade-offs before committing - surface blind spots and assumptions - connect ideas across domains it actually strengthens the parts of the job that matter most. In my own work, AI hasn’t reduced my role. It has made the thinking layer more visible — and more valuable. The professionals who benefit most aren’t the ones chasing every new tool. They’re the ones who use AI to: - ask better questions - narrow scope instead of expanding it - make clearer decisions sooner AI doesn’t decide who’s replaceable. It rewards clarity, judgment, and context. I’m curious to hear your perspective: How do you think professionals can use AI to become harder to replace — not by doing more, but by strengthening what only they can provide?

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Imagine you had the time (or permission) to explore one professional project with AI. Not necessarily something you’re launching tomorrow. Just an idea you’d like to build at some point — as: - a side project - a professional tool - an internal solution - or even a future side gig It could be: - a workflow or automation - a small internal product - a client-facing tool - a teaching or documentation system - something that solves a recurring pain in your work No need for a polished plan. Rough ideas are welcome. I’m curious: If AI made it easier to build, what professional product would you want to explore — and why?

Hi, I'm Max, an IBM-certified AI Developer with 20 years in technology -- from open-source C development to VP and CTO roles. I also hold a BsC in Comp Sci. In June 2024, I pivoted fully into AI full-stack dev and automation and haven't looked back. Since then, I've served 40+ clients, organized a week-long Voice AI Summit with 400+ attendees, won $4,500+ in hackathon prizes, and launched 6 production AI applications for Clients that are running today. In the past 6 months I contributed to 18+ Repos with 2000+ Commits. Yes with AI, no not fully autonomous :) My superpower is translating bleeding-edge AI capabilities (voice agents, RAG, custom apps) into apps and practical automation workflows -- and mentoring teams to do the same.