I just don’t see it coming. I was full on that camp 3 months ago, but I just realize every step makes more mistakes. It leads into a deadlock and when no human has the mental model anymore.

Don’t you guys have hard business problems where AI just cant solve it or just very slowly and it’s presenting you 17 ideas till it found the right one. I’m using the most expensive models.

I think the nature of AI might block that progress and I think some companies woke up and other will wake up later.

The mistake rate is just too high. And every system you implement to reduce that rate has a mistake rate as well and increases complexity and the necessary exploration time.

I think a big bulk of people is of where the early adaptors where in December. AI can implement functional functionality on a good maintained codebase.

But it can’t write maintable code itself. It actually makes you slower, compared to assisted-writing the code, because assisted you are way more on the loop and you can stop a lot of small issues right away. And you fast iterate everything•

I’ve not opened my idea for 1 months and it became hell at a point. I’ve now deleted 30k lines and the amount of issues I’m seeing has been an eye-opening experience.

Unscalable performance issues, verbosity, straight up bugs, escape hatches against my verification layers, quindrupled types.

Now I could monitor the ai output closer, but then again I’m faster writing it myself. Because it’s one task. Ai-assisted typing isn’t slower than my brain is.

Also thinking more about it FAANG pays 300$ per line in production, so what do we really trying to achieve here, speed was never the issue.A great coder writes 10 production lines per day.

Accuracy, architecture etc is the issue. You do that by building good solid fundamental blocks that make features additions easier over time and not slower

I love everything about this direction except for the insane inference costs. I don’t mind the training costs, since models are commoditized as soon as they’re released. Although I do worry that if inference costs drop, the companies training the models will have no incentive to publish their weights because inference revenue is where they recuperate the training cost.

Either way… we badly need more innovation in inference price per performance, on both the software and hardware side. It would be great if software innovation unlocked inference on commodity hardware. That’s unlikely to happen, but today’s bleeding edge hardware is tomorrow’s commodity hardware so maybe it will happen in some sense.

If Taalas can pull off burning models into hardware with a two month lead time, that will be huge progress, but still wasteful because then we’ve just shifted the problem to a hardware bottleneck. I expect we’ll see something akin to gameboy cartridges that are cheap to produce and can plug into base models to augment specialization.

But I also wonder if anyone is pursuing some more insanely radical ideas, like reverting back to analog computing and leveraging voltage differentials in clever ways. It’s too big brain for me, but intuitively it feels like wasting entropy to reduce a voltage spike to 0 or 1.

I think that as a user I'm so far removed from the actual (human) creation of software that if I think about it, I don't really care either way. Take for example this article on Hacker News: I am reading it in a custom app someone programmed, which pulls articles hosted on Hacker News which themselves are on some server somewhere and everything gets transported across wires according to a specification. For me, this isn't some impressionist painting or heartbreaking poem - the entity that created those things is so far removed from me that it might be artificial already. And that's coming from a kid of the 90s with some knowledge in cyber security, so potentially I could look up the documentation and maybe even the source code for the things I mentioned; if I were interested.

We haven’t been inching closer to users writing a half-decent ticket in decades though.

Feedback loops like that would be an exercise in raising garbage-in->garbage-out to exponential terms.

It's the "robots will just build/repair themselves" trope but the robots are agents

Tusted user like Jia Tan.

I think Anthropic will launch backend hosting off the back of their Bun acquisition very soon. It makes sense to basically run your entire business out of Claude, and share bespoke apps built by Claude code for whatever your software needs are.

Ha I just SPECed out a version of this. I have a simple static website that I want a few people to be able to update.

So, we will give these 3 or 4 trusted users access to an on-site chat interface to request updates.

Next, a dev environment is spun up, agent makes the changes, creates PR and sends branch preview link back to user.

Sort of an agent driven CMS for non-technical stakeholders.

Let’s see if it works.

Users are often incorrect about what the software should actually be doing and don’t see the bigger picture.

I think some type of tickets can be done like this but your trusted user assumption does a lot of work here. Now I don't see this getting better than that with the current architecture of LLMs, you can do all sorts of feedback mechanisms which helps but since LLMs are not conscious drift is unavoidable unless there is a human in the loop that understands and steers what's going on.

But I do think even now with certain types of crud apps, things can be largely automated. And that's a fairly large part of our profession.

In the past three weeks a couple of projects I follow have implemented AI tools with their own github accounts which have been doing exactly this. And they appear to be doing good work! Dozens of open issues iterated, tested and closed. At one point i had almost 50 notification for one projects backlog being eradicated in 24 hours. The maintainer reviewed all of it and some were not merged.

What kind of software are people building where AI can just one shot tickets? Opus 4.6 and GPT 5.4 regularly fail when dealing with complicated issues for me.

I don't know if this is the future, but if it is, why bother building one version of the software for everyone? We can have agents build the website for each user exactly the way they want. That would be the most exciting possibility to come out of AI-generated software.

The missing piece for me is post-hoc review.

A PR tells me what changed, but not how an AI coding session got there: which prompts changed direction, which files churned repeatedly, where context started bloating, what tools were used, and where the human intervened.

I ended up building a local replay/inspection tool for Claude Code / Cursor sessions mostly because I wanted something more reviewable than screenshots or raw logs.

I know a company already operating like this in the fintech space. I foresee a front page headline about their demise in their future.

I dont mean this as a shade but ppl who are not coders now seem to think "coding is now solved" and seem to be pushing absurd ideas like shipping software with slack messages. These ppl are often high up in the chain and have never done serious coding.

Stripe is apparently pushing gazzaliion prs now from slack but their feature velocity has not changed. so what gives?

how is that number of pr is now the primary metric of productivity and no one cares about what is being shipped or if we are shipping product faster. Its total madness right now. Everyone has lost their collective minds.

Or perhaps we end up where all software is self evolving via agents… adjusting dynamically to meet the users needs.

Instead of having a trusted user, you can also do statistics on many users.

(That's basically what A/B testing is about.)

I am already there with a project/startup with a friend. He writes up an issue in GitHub and there is a job that automatically triggers Claude to take a crack at it and throw up a PR. He can see the change in an ephemeral environment. He hasn't merged one yet, but it will get there one day for smaller items.

I am already at the point where because it is just the two of us, the limiting factor is his own needs, not my ability to ship features.

"Trusted user" also can be an Agent.

What you're describing is absolutely where we're headed.

But the entire SWE apparatus can be handled.

Automated A/B testing of the feature. Progressive exposure deployment of changes, you name it.

Haha sure, let's just let every user add their feedback to the software.

I think the Ai agent will directly make a PR - tickets are for humans with limited mental capacity.

At least in my company we are close to that flywheel.

We do feedback to ticket automatically

We dont have product managers or technical ticket writers of any sort

But us devs are still choosing how to tackle the ticket, we def don't have to as I’m solving the tickets with AI. I could automate my job away if I wanted, but I wouldn't trust the result as I give a degree of input and steering, and there’s bigger picture considerations its not good at juggling, for now

Then sets up telemetry and experiments with the change. Then if data looks good an agent ramps it up to more users or removes it.

> I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default.

There's a lots of experimentation right now, but one thing that's guaranteed is that the data gatekeepers will slam the door shut[1] - or install a toll-booth when there's less money sloshing about, and the winners and losers are clear. At some point in the future, Atlassian and Github may not grant Anthropic access to your tickets unless you're on the relevant tier with the appropriate "NIH AI" surcharge.

1. AI does not suspend or supplant good old capitalism and the cult of profit maximization.

[dead]

Um, we are already there...