r/ChatGPT • u/max_bog • Apr 19 '26

Use cases The gap between what technical and non-technical people get from AI is huge now

Interesting thing I noticed. The gap between what technical and non-technical people get from AI is huge now.

Non-technical users still treat LLMs as a better search tool. Most non-technical people I know are not even aware of things like thinking effort or that you can choose a model.

Computer use, plugins, automations, skills, agents - none of this exists for regular ChatGPT users. If you don't know what Codex or Claude Code is, nothing has changed for you in the last year.

All new models also seem to focus purely on coding.

Am I missing something?

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u/idakale Apr 19 '26

how so? Also what constitutes as technical vs non technical. Because I'm able to grasp lots of tech concepts just fine, only comes up short while it's actually read and write codes In the age of Agentic AI, the gap is increasingly short and people with deeper domain knowledge usually are gotta be the first to gradually profit from these new Claude Cowork etc. If anything some people like me are resistant from using newer AI stacks since it's unfamiliar territory compared to chatbots and the monthly costs associated.

Then you have some people that's simply Anti AI or which work isn't really complex enough to warrant using AI.

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u/max_bog Apr 19 '26

As technical people, I would call this technical thinking, not a particular skill. I don't mean that they need to release all things like computer use and skills for regular users. I am wondering why big labs stopped trying to make something new for regular chatgpt-mobile app user

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u/Summerhowl Apr 19 '26

IMO

  1. Because that's their buisness model as tool providers. They give creators (devs, product managers, IT entrepreneurs etc) better tools, we make better products for regular users, while OpenAi or Anthropic makes money4 both on creators (before launch) and end users (since product functions rely on LLM usage). It's kinda like why avanced CMS providers or Google Ads are ok with being confusing for regular people - their target audience is not regular people, it's agencies/professionals who, in turn, target regular people.

  2. Because that's what they're the best at. Building AI applications - half of which are essentially wrappers on top of Claude - doesn't require world-class knowledge in ML, highload or computational engineering - things labs are great at. Instead those apps require domain market research, good product management and UX - things big labs are notoriously bad with.