> I think developers tend to be ahead of the general public by 1-2 years in AI use.

I'm pushing the perennial 'developers (and engineers) think differently' trope here, but I think you're wildly optimistic.

The inability of the average person to write a detailed enough spec for implementation, compared to how trivial it is for an experienced developer, is mind boggling.

Imho, this is going to extend to agentic interactions with app-as-service-for-agent.

You don't magically increase customer experience by multiplying (garbage average user prompt) * (whatever agent they use) * (MCP, etc).

The first two variables are too unbounded.

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aurareturn 2 days ago | parent [-]

I didn't mean that non-developers will be writing apps.

I meant that non-developers will also find that staying within their AI agent to do what they want is better than downloading, logging in, learning different app UIs.

In our company, we recently gave Claude subscriptions to all office workers. They all hooked it up to their favorite tools already and they've been raving about it. It was a revelation for them.

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thepryz 2 days ago | parent | next [-]

My employer has been integrating agentic AI into every tool and updating the UI/UX to make the agent the primary interface. This applies everywhere, including people management tasks. It sucks.

In the past, you could look at a web page, see some structure, understand process, etc. and intuitively see and understand what you're suppose to do. When you remove all of that in favor of a text box, you spend a lot of time asking questions and prompting to get information or accomplish tasks that would otherwise have taken a minute or two and when you're done, you still don't understand how the underlying system, process, or policy is and how it works.

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ethbr1 2 days ago | parent | prev [-]

Went back and edited while you were replying. Sorry! Coffee still incoming.

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aurareturn 2 days ago | parent [-]

You'd be surprised at what people can do. My chief revenue officer never knew what SQL is but once he had access to Claude, he built some amazing dashboards that he always wanted.

LLMs are getting exceptionally good at interpreting vague asks.

Do I trust a non-developer to build a production software system? No.

Do I think they can control Excel spreadsheets, PPTs, and other productivity tools through MCP? Absolutely.

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thepryz 2 days ago | parent [-]

The question that needs to be asked is when everyone can create their own dashboard, are they all looking at the same information and in the same way so that they can be aligned?

One of the biggest issues I've seen in this kind of environment is that there are 50 dashboards, created by different people, and the data presented on them hasn't been validated to ensure that the data is correct and pulling from the same location. It's not clear that the data can be trusted and it's not clear what the authoritative source of truth is.

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aurareturn 2 days ago | parent [-]

I don't think most people are that naive.

IE. They know what they're looking for. They know when the data is right or wrong because they've seen it from other places here and there over time.

When they're not sure and the risk of getting it wrong is costly, they'll find the data analyst to verify.

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thepryz 2 days ago | parent [-]

All I can say is that my experience suggests they are that naive because in a fast paced environment, there are often people pulling data without the context to validate it and the push to move faster and faster means proper validation isn't happening. Often it's only discovered after someone who does understand begins looking, asking questions and the errors become known and by then, it's too late.

I've seen it firsthand, multiple time and while anecdotes aren't data, conversations I've had with peers at other companies suggest they have similar experiences.

	▲	ethbr1 2 days ago \| parent [-]
		I'd second that. My experiences, across multiple companies, are that data validity risks are drastically increased by someone not diligent & knowledgeable about implementation details. Furthermore, that persona is also often incapable of self-checking and recognizing errors in their presented data, because they don't know how it was generated in the first place.