my brother uses it to do ocr on his books but, lately, claude has been refusing because of copyright or whatever so i've had to do it for him (using ai, yes, but not anthropic's)

I know firefighters and machinists heavily using it, the only places I see slower adoption is purely because of bad leadership- exclusivity contracts with certain providers or just plain fear.

I think this dynamic is underappreciated. Another thing I see in working with different companies (consulting) and discussing with colleagues at different jobs is that we as humans tend to do a pretty bad job of modeling business outcomes and the dynamics that govern them. There are lots of outcomes where you can identify one contributing role, increase the productivity for that role by a significant factor, and still ultimately not move the needle much, because the constraints span multiple functions.

As an example outside of software engineering, a lot of people in marketing roles have found agents to be incredibly useful for particular tasks. I've talked to multiple people who wouldn't standardly be classified as "technical hires" who are using Claude to manage things like digital advertising. It's valuable here because managing campaigns on a platform like Google or LinkedIn involves a ton of granular tweaking and analysis. Every performance marketer makes tradeoffs around what they're realistically able to spend time doing, and what makes the biggest impact. So if you have 250 campaigns that you manage solo, you likely don't have the time to really manually optimize the bidding and budget for each campaign every couple of days based on new data. You can only try so many new angles, because creating new media is time consuming. You don't have time to construct custom experiences on-page for every particular campaign. There's a whole industry of tools that turn a tidy profit automating parts of this for this reason. And agents are really good at it, because a lot of it is just applying the same analysis over and over again.

But for a lot of startups, increasing traffic from ads 20% while decreasing spend is a good outcome, but it doesn't actually turn into revenue. The product itself might just be limited, and already being discovered by the narrow segment of potential users who find it valuable enough to pay. Or the sales process might be kind of busted, meaning the team will really only close a certain volume of new deals in a given month, no matter how much inbound they have. Or some combination of all of the above.

If it's just one marketer using an agent to optimize their advertising, there's not a lot they can do about the other constraints. And so what you end up with is one employee who feels really productive and sings the praises of Claude, but not a meaningful lift in the higher level outcomes you care about, and the analysis gets messy. In an ideal world, everyone realizes where the other bottlenecks are, but that's a genuinely hard thing to do and people have complicating incentives.

I don't think this is a particularly new problem. It feels identical to any other situation where a department asks for a tool, loves it, and it doesn't really make a difference to the bottom line or make anyone else's life easier in noticeable ways.

We are capped by testing and PR review. The junior devs can spam out a million PRs a day but we keep having production incidents from not carefully scrutinising the changes. So the speed of code generation is limited by how fast we can actually accept those diffs.

Right, this is what astonishes me about the constant rhetoric/hyperbole about how this is going to impact "all the jobs" and make everyone wildly more productive than ever before. It's a fabulous tool in a limited number of areas, and it's otherwise only handy here and there. But just because it's a fabulous tool for the coding that is so prevalent among HN users does not mean that it's a fabulous tool for everything.

A hammer is fantastic for driving nails, but it's not really all that good at washing a dog.

I use classic engineering to refer to the (engineering) disciplines where mother nature messes your day up rather than obscene levels of man made (or machine made) pure complexity ruining your day.

It's easy to understand how LLMs got so good at coding, they can rapidly iterate, self test, and it all happens in their digital universe.

These models now starting to spit grey beard knowledge of precision analog electronics, lightweight fastener design, and chemical surfactant systems is something very very recent. A few years ago I think we were pretty confident that the tech would hit a wall at the digital divide. Well, I can tell you now that it did not. It seems it kinda blasted right past it.

Vibes based analysis is very high volume right now due to upcoming IPOs.

On HN, can you please not be snarky? We're trying for something a bit different than internet mean here.

The idea is: if you have a substantive point, make it thoughtfully; if not, please don't comment until you do.

https://news.ycombinator.com/newsguidelines.html