[cherryteastain]

What's the 'new normality' in the fifth stage? Do you think you'll start to believe it actually works 100%? Or that you won't change your assessment that it works only sometimes, but maybe pulling the lever on the slot machine repeatedly is better/more efficient than doing it yourself?

[opensandwich]

Business will start accepting bad uptime to be the norm. Following the lead of Github: https://mrshu.github.io/github-statuses/

[orangecoffee]

No this is still the "bargaining/negotiating" phase thinking. After this is when depression hits when for your usecases you see that the code quality and security audit is very good.

[bluefirebrand]

Is this a new "delusional" phase?

[Manchitsanan]

hahaha

[sothatsit]

People will accept it as a way to build good software.

Many are still in denial that you can do work that is as good as before, quicker, using coding agents. A lot of people think there has to be some catch, but there really doesn’t have to be. If you continue to put effort in, reviewing results, caring about testing and architecture, working to understand your codebase, then you can do better work. You can think through more edge cases, run more experiments, and iterate faster to a better end result.

[sdenton4]

When you resolve bottlenecks, new bottlenecks become apparent. Right now, it's looking like assessment and evaluation are massive bottlenecks.

[8n4vidtmkvmk]

I'm kind of excited about that though. What I've come to realize is that automated testing and linting and good review tools are more important than ever, so we'll probably see some good developments in these areas. This helps both humans and AIs so it's a win win. I hope.

[qsera]

> it's looking like assessment and evaluation are massive bottlenecks.

So I think LLMs have moved the effort that used to be spent on fun part (coding) into the boring part (assessment and evaluation) that is also now a lot bigger..

[jochem9]

You could build (code, if you really want) tools to ease the review. Of course we already have many tools to do this, but with LLMs you can use their stochastic behavior to discover unexpected problems (something a deterministic solution never can). The author also talks about this when talking about the security review (something I rarely did in the past, but also do now and it has really improved the security posture of my systems).

You can also setup way more elaborate verification systems. Don't just do a static analyis of the code, but actually deploy it and let the LLM hammer at it with all kinds of creative paths. Then let it debug why it's broken. It's relentless at debugging - I've found issues in external tools I normally would've let go (maybe created an issue for), that I can now debug and even propose a fix for, without much effort from my side.

So yeah, I agree that the boring part has become the more important part right now (speccing well and letting it build what you want is pretty much solved), but let's then automate that. Because if anything, that's what I love about this job: I get to automate work, so that my users (often myself) can be lazy and focus on stuff that's more valuable/enjoyable/satisfying.

[qsera]

Fuzz testing has existed long before LLMs...

[hellojimbo]

When writing banal code, you can just ask it to write unit tests for certain conditions and it'll do a pretty good job. The cutting edge tools will correctly automatically run and iterate on the unit tests when they dont pass. You can even ask the agent to setup TDD.

[Ferret7446]

Cars removed the fun part (raising and riding horses) and automatic transmissions removed the fun part (manual shifting), but for most people it's just a way to get from point A to B.

[riffraff]

I'm not sure, but I think it boils down to accepting that some things we were attached to are no longer important or normal (not just software building).

But specifically to your examples, the latter: I think the "brute force the program" approach will be more common that doing things manually in many cases (not all! I'm still a believer in people!).

Edit: Well, I wrote a bad blog post on this some time ago, I might as well share it: I think the accepting means engaging with the change rather than ignoring it.

https://riffraff.info/2026/03/my-2c-on-the-ai-genai-llm-bubb...

[dcre]

It doesn't have to work 100% of the time to be ubiquitous! This is just the strangest point of view. People don't work 100% of the time either, and they wrote all the code we had until a couple of years ago. How did we deal with that? Many different kinds of checks and mitigations. And sometimes we get bugs in prod and we fix them.

[fodkodrasz]

The new normal will be: Everything will get worse and far more unstable (both in terms of UI/UX and reliability), and many of us will loose their jobs. Also the next generation of the programmers will have shallower understanding of the tools they use.

[bitwize]

AI doesn't need to outrun the bear; it only needs to outrun you.

Once the tools outperform humans at the tasks to which they were applied (and they will), you don't need to be involved at all, except to give direction and final acceptance. The tools will write, and verify, the code at each step.

[neonstatic]

> Once the tools outperform humans at the tasks to which they were applied (and they will)

I don't get why some people are so convinced that this is inevitable. It's possible, yes, but it very well might be the case, that models cannot be stopped from randomly doing stupid things, cannot be made more trustworthy, cannot be made more verifiable, and will have to be relegated to the role of brainstorming aids.

[qsera]

>I don't get why some people are so convinced that this is inevitable.

Someone once said that It is hard to make a man understand things if their profit depends on them not understanding it...

[neonstatic]

I don't make money coding, so it doesn't apply to me in this case.

[NateEag]

I think they meant that people insisting total genAI takeover of coding is inevitable are likely people who stand to profit greatly by everyone giving up and using the unmind machines for everything.

[orangecoffee]

the original post is an example of how. Every programmer is discovering slowly, for their own usecases, that the agent can actually do it. This happens to an individual when they give it a shot without reservation..

[sciencejerk]

Large scale AI datacenters require a very expensive physical supply chain that includes cheap land, water, and electricity, political leverage, human architects and builders to build datacenters, and massive capital investments. Yes, AI will outperform humans, but at some point it may become cheaper to hire a human programmer.

[Ferret7446]

Wait till you hear about the resources required to sustain an equivalent number of humans.