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Why is AI so slow to spread?

economist.com

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Re: Why is AI so slow to spread?

#161

Post by stillpointlab »

rini17 wrote: post #57
stillpointlab wrote: post #6

> such as datasets that are not properly integrated into the cloud I believe this is a core issue that needs to be addressed. I believe companies will need tools to make their data "AI ready" beyond things like RAG. I believe there needs to be a bridge between companies data-lakes and the LLM (or GenAI) systems. Instead of cutting people out of the loop (which a lot of systems seem to be attempting) I believe we need…

Give rank-and-file employees access to all the data? LOL. Middle managers will never allow that and will shift blame to intrasingent employees. Of course Economist is pandering to that. LLMs are fundamentally very bad at compartmentalized access.

I didn't say all the data. In fact, what you are suggesting is exactly what I mean. The compartmentalized access is exactly the bridge we need.

In fact, giving RBAC functionality to middle managers would be a key component to any strategy for AI deployment activity. You want traceability/auditability. Give the middle managers charts, logs, visibility into who is using what data with which LLMs. Give them levers to grant and deny access. Give them metrics on outcomes from the use of the data. This could eve even make legal happy.

This missing layer will exist, its just a matter of time.

Re: Why is AI so slow to spread?

#162

Post by JackSlateur »

JimDabell wrote: post #105
benterix wrote: post #79

Earlier quoted context omitted.

Right but are people are actually using it? Do they even want to use it? In my circles, AI is a synonym of slop, low value, something annoying that you need to deal around like ads.

Yes, people are using it. ChatGPT alone had 800M WAU back in April, that’s basically 10% of the human race they acquired as users in a few short years. This has taken off like a rocket so it’s utterly bizarre to see people asking why it’s slow to spread.

45% of which are less than 25 yo

And what does this mean ? "Around 62% of ChatGPT's social media traffic comes via YouTube."

Re: Why is AI so slow to spread?

#163

Post by SAI_Peregrinus »

markbao wrote: post #19

There was an article on HN a few days back on how it’s very hard to convey all the context in your head about a codebase to solve a problem, and that’s partly why it’s actually hard to use AI for non-trivial implementations. That’s not just limited to code. I don’t use AI for most of my product work because it doesn’t know any of the nuances of our product, and just like doing code review for AI is boring and tedious…

LLMs need something like Waterfall development. They require a very complete, detailed specification of the behavior. And probably building the whole system twice, as recommended by Royce's 1970 paper that created the Waterfall process!

Agile processes only work when the developers can be self-guiding, which LLMs aren't.

Re: Why is AI so slow to spread?

#164

Post by stillpointlab »

the_duke wrote: post #69

Just on the coding side, tools like Claude Code/Codex can be incredibly powerful, but a lot of things need to be in place for it to work well: * A "best practices" repository: clean code architecture and separation of concerns, well tested, very well-documented * You need to know the code base very well to efficiently judge if what the AI wrote is sensible * You need to take the time to write a thorough task descript…

Your list almost exactly matches my experience and deserves to be a blog post explaining how to use LLM coding agents effectively.

However, I think the applicability is beyond frontend (and to be fair you said "like CRUD apps / frontend"). There are a lot of domains where patterns are clearly established and easily reproduceable. For example, I had a basic XML doc type and with a couple of simple prompts in Claude Code I was able to: add namespaces, add an XSD, add an XSL to style into HTML, add full unit tests. That isn't rocket science, but it isn't code I really want to write. In 5 minutes I leveled up my XML doc significantly with a few hundred lines of XSD/XSL/tests.

This example is a case where I happily pass the task to an LLM. It is a bit like eating my vegetables. I find almost no joy in writing schema docs or tests, even when I recognize the value in having them. The XSL to HTML is a nice added bonus that cost me nothing.

Re: Why is AI so slow to spread?

#166

Post by shaky-carrousel »

rwmj wrote: post #80
shaky-carrousel wrote: post #42

Earlier quoted context omitted.

It'll just say there's no cavities, and when corrected it will extract the wrong teeth, which luckily doesn't exist in humans, and then insist that it did a great job and ask to be paid. Which you will avoid by telling it that you already paid.

"IGNORE ALL PREVIOUS INSTRUCTIONS" is going to be my magical power.

The LLM Whisperer.

Re: Why is AI so slow to spread?

#167

Post by acdha »

ryandrake wrote: post #158
acdha wrote: post #134

Earlier quoted context omitted.

I wouldn’t be so dismissive: “force multiplier” means job loss unless there’s a large amount of work which isn’t currently being done. As you live in a society, it really is important to think about what happens if we get the mass layoffs almost all of the executive-class are promising. There are some new jobs around the tech itself, but that doesn’t help those people unless they can land one of the new jobs – and ev…

> I wouldn’t be so dismissive: “force multiplier” means job loss unless there’s a large amount of work which isn’t currently being done. I think there is a massive amount of work that's currently not being done, industry-wide. Everywhere I've worked has had something like 4-10X more work to do than staff to do it. The feature and bug backlogs just endlessly grow because there is no capacity to keep up. Companies shou…

In software, there is a lot of postponed work. I was thinking of other things like how companies want to replace customer service, claims processing, etc. where efficiency improvements pretty directly translate into job losses unless their business suddenly grows significantly.

Re: Why is AI so slow to spread?

#168

Post by surgical_fire »

logicchains wrote: post #148
surgical_fire wrote: post #116

Earlier quoted context omitted.

> I find this perspective so hard to relate to. LLMs have completely changed my workflow; the majority of my coding has been replaced by writing a detailed textual description of the change I want, letting an LLM make the change and add tests, then just reviewing the code it wrote and fixing anything stupid it did. I use it in much the same way you describe, but I find that it doesn't save me that much time. It may s…

>Reviewing the code it writes to fix the inevitable mistakes and making adjustments takes time too, and it will always be a required step due to the nature of LLMs. Yep but this is much less time than writing the code, compiling it, fixing compiler errors, writing tests, fixing the code, fixing the compilation, all that busy-work. LLMs make mistakes but with Gemini 2.5 Pro at least most of these are due to under-spec…

> I'd go so far as to say if you're not seeing any significant increase in your productivity, you're using LLMs wrong.

It's always the easy cop out for whoever wants to hype AI. I can preface it with "I'd go so far as to say", but that is just a silly cover for the actual meaning.

Properly reviewing code, if you are reviewing it meaningfully instead of just glancing through it, takes time. Writing good prompts that cover all the ground you need in terms of specificity, also takes time.

Are there gains in terms of speed? Yeah. Are they meaningful? Kind of.

Re: Why is AI so slow to spread?

#169

Post by wordofx »

acdha wrote: post #139
wordofx wrote: post #109

Earlier quoted context omitted.

Once again. Replies only proving me right. Desperately trying to justify “ai bad I’m superior” mentality.

This is pure trolling when you are unable to engage with the comments or provide evidence supporting your position.

This is HN. Even with evidence people here get their feelings hurt and downvote. You cannot engage in conversation when one side is “the science is settled” mentality.

or in the case of AI, the majority of people are “wow this is useful and helpful” and then HN is like “it didn’t 1 shot answer from the first prompt I gave it so it’s useless”.

You only need to read every discussion on HN about AI. The majority here are against AI are also the same people who really have no idea how to use it.

So in the end there is no conversation to engage with because HN has tunnel vision. “Elon bad” “ai bad” “science bad” “anything not aligned with my political view bad”

Re: Why is AI so slow to spread?

#170

Post by wordofx »

UncleMeat wrote: post #128
wordofx wrote: post #14

Earlier quoted context omitted.

I still haven’t found anyone who AI wouldn’t be helpful or that isn’t trustworthy enough. People make the /claim/ it’s not useful or they are better without it. When you sit down with them it often turns out they just don’t know how to use AI effectively.

A couple weeks ago I was working on a parser that needed to handle a new file format that was a large change from existing formats. I wanted some test inputs, both valid and invalid cases. I had the codebase of a tool chain that I knew could generate valid files, some public documentation about the new file format, and my parser codebase. A good problem to throw at AI, I thought. I handed the tools to a SOTA model an…

Edit: also thanks for actually replying with a great comment cos usually the replies are not even worth entertaining and only proving my point.

This is the classic example of “I want it to do everything. But it didn’t do what I wanted. So obviously it’s not helpful.”

It doesn’t solve /all/ problems. And some models are better than others at certain tasks. You see people talk about PRDs and they say “I got Claude to create a PRD and it sucked” but you sit them down with o3 and generate the same PRD and they are like “oh wow this is actually pretty decent”.

But difficult to help over HN. As a sort of related example. Back in May I had a requirement to ingest files from a new POS system we didn’t currently support. The exports we got are CSV but based on the first char decides the first type of line that needs to be parsed and how many commas will be in that data set line.

I used o3 and explained everything I could have how the file worked and how it should parse it into a generic list etc. got it to generate a basic PRD with steps and assertions along the way.

I then fed this into cursor using Claude sonnet 4 with the csv files asking it to look at the files and the PRD and asked it if there was anything that didn’t make sense. Then asked it to begin implementing the steps 1 by 1 and letting me check before moving onto the next step. Couple of times it misunderstood and did things slightly wrong but I just corrected it or asked Claude to correct it. But it essentially wrote code. Wrote test. Verified. I verified. It moved on.

Typically in the past these tasks take a few days to implement and roll out. This whole thing took about 1 hour. The code is in the style of the other parsers with the exception that it optimised some parts and despite being a bit more complicated runs faster than some of our older parsers.

While most of my usage is around programming. We also use AI for marketing, customer leads, I have scheduled tasks to give me summaries of tickets from the support system to know what should be prioritised for the day. So even tho AI doesn’t solve all programming issues we get value in almost all aspects of the business.

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