[crystal_revenge]

> The new skill is mastering the craft of directing cheap inputs toward valuable outcomes.

Strongly agree with this. It took me awhile to realize that "agentic engineering" wasn't about writing software it was about being able to very quickly iterate on bespoke tools for solving a very specific problem you have.

However, as soon as you start unblocking yourself from the real problem you want to solve, the agentic engineering part is no longer interesting. It's great to be solving a problem and then realize you could improve it very quickly with a quick request to an agent, but you should largely be focused on solving the problem.

Yet I see so many people talking about running multiple agents and just building something without much effort spent using that thing, as though the agentic code itself is where the value lies. I suspect this is a hangover from decades where software was valuable (we still have plenty of highly valued, unprofitable software companies as a testament to this).

I'm reminded a bit of Alan Watts' famous quote in regards to psychedelics:

> If you get the message, hang up the phone.

If you're really leveraging AI to do something unique and potentially quite disruptive, very quickly the "AI" part should become fairly uninteresting and not the focus of your attention.

[dw_arthur]

It's funny that so many people are using AI and still hasn't really shown up in productivity numbers or product quality yet. I'm going to be really confused if this is still the case at the end of the year. A whole year of access to these latest agentic models has to produce visible economic changes or something is wrong.

[sillyfluke]

>funny that so many people are using AI and still hasn't really shown up in productivity numbers or product quality yet.

That's because the threat is now not other businesses, but your own users who decide to vibe-code their own "Claw" product instead of using your company's vibeslop, so there are no buyers for your single-week product. All these new harness developers are engaging in resume-driven development to save their own asses. The only ones that are not naked when the tide recedes are the ones that are able to jump to the next layer of abstraction on the infinite staircase, until the next tide comes five seconds later.

[crystal_revenge]

I used to think this was a sign that AI code isn't really useful, but I've changed my tune (also I believe these numbers have changed in the last few months).

As an example: One of my most promising projects I was discussing with a friend and we realized together we could potentially use these tools to build a two person agency with no need to hire anyone ever. If this were to work, could theoretically make nice revenue and it shouldn't show up in any metric anywhere.

Additionally I've heard of countless teams cancelling their contracts with outsourced engineers because cheap but bad coders in India are worse that an LLM and still cost more. I'm not sure if there's a number around this activity, but again, these type of changes don't show up in the usual places.

My current belief is not that AI will replace traditional software engineering it will replace a good chunk of the entire model of software.

[ehnto]

My intuition from talking to people across different parts of the industry, is that adoption at bigger companies is really limited or slow, or totally banned. Additionally some developers are not seeing it help their specific roles all that much anyway. This is hard to level with success other people are having, but software is a super broad discipline which I think explains a lot of the mixed success stories.

It seems to depend a lot on the industry and niche you're in, working at an agency I get experience across many different projects and industries and sometimes you are just at the edge of AIs training and it can get very unhelpful. Noting many if not most companies are working on proprietary code in donain specific problems, that isn't all that surprising either.

[fragmede]

I wouldn't say it hasn't shown up. The number of ShowHN's per weekend has definitely gone up, and while that isn't rigorous scientific proof, I'd consider is a leading edge indicator of something. Unfortunately, we as an industry have yet to agree on anything approaching a scientific measure of productivity, other than to collectively agree that Lines of Code is universally agree that LoC is terrible. Thus even if someone was able to quantify that, say, they're having days where they generate 5000 LoC when previously they were getting O(500) LoC, that's not something we could agree upon as improved productivity.

So then the question is, lis there anything other than feels to say productive has or has not gone up? What would we accept as actual evidence one way or another? Commits-per-day is similarly not a good measure either. Jira tickets and tshirts sizes? We don't have a good measure, so while ShowHN's per weekend is equally dumb, it's also equally good in the bag of lies, damn lies, and statistics.

[LtWorf]

I think if you're doing front-end development AI is good. If you are reading a db and sending a json to said webpage AI is decent, if you are doing literally anything else AI is next to useless.

At least, in my own experience.

[kakapo5672]

This is actually an old syndrome with technology. It takes a longt ime for the effect to be reliably measured. Famously, it took many years for the internet itself to show up in significant productivity gains (if the internet is actually useful why don't the numbers show that? - a common comment in the 1990s and 2000s). So it seems to me we're just the usual dynamic here. Productivity in trillion-dollar economies do not turn on a dime

[ehnto]

That's a great insight about iterating on bespoke tools. I have seen the most speed up when diving into new tools, or making new tools as AI can make the initial jump quite painless, and I can get straight to the problem solving. But I get barely any speedup using it on legacy projects in tools I know well. Often enough it slows me down so net benefit is nil or worse.

Another commentor said it makes the easy part easy, and the hard part harder, which I resonate with at the moment.

I am pretty excited by being able to jump deep into real problems without code being the biggest bottleneck. I love coding but I love solving problems more, and coding for fun is very different to coding for outcomes.

[mjevans]

That's my observation / fear as well. It makes delivering something that sort of works easy. It makes doing that well more difficult by obscuring the problem domain from the humans and expanding the standard library of tools into patterns of using said standard library. Hope they're correct for your use case.

There's also the question of the true cost of all the hardware, electricity, and potential output that's being tossed onto the pyres. We aren't getting the real Cortana from the books / games; we're getting GIR trained on the corpus of fallible human code, prompted by fallible humans.