[gordonhart]

Whenever I get worried about this I comb through our ticket tracker and see that ~0% of them can be implemented by AI as it exists today. Once somebody cracks the memory problem and ships an agent that progressively understands the business and the codebase, then I'll start worrying. But context limitation is fundamental to the technology in its current form and the value of SWEs is to turn the bigger picture into a functioning product.

[nemo1618]

"The steamroller is still many inches away. I'll make a plan once it actually starts crushing my toes."

You are in danger. Unless you estimate the odds of a breakthrough at <5%, or you already have enough money to retire, or you expect that AI will usher in enough prosperity that your job will be irrelevant, it is straight-up irresponsible to forgo making a contingency plan.

[overgard]

What contingency plan is there exactly? At best you're just going from an automated-already job to a soon-to-be-automated job. Yay?

I'm baffled that so many people think that only developers are going to be hit and that we especially deserve it. If AI gets so good that you don't need people to understand code anymore, I don't know why you'd need a project manager anymore either, or a CFO, or a graphic designer, etc etc. Even the people that seem to think they're irreplaceable because they have some soft power probably aren't. Like, do VC funds really need humans making decisions in that context..?

Anyway, the practical reason why I'm not screaming in terror right now is because I think the hype machine is entirely off the rails and these things can't be trusted with real jobs. And honestly, I'm starting to wonder how much of tech and social media is just being spammed by bots and sock puppets at this point, because otherwise I don't understand why people are so excited about this hypothetical future. Yay, bots are going to do your job for you while a small handful of business owners profit. And I guess you can use moltbot to manage your not-particularly-busy life of unemployment. Well, until you stop being able to afford the frontier models anyway, which is probably going to dash your dream of vibe coding a startup. Maybe there's a handful of winners, until there's not, because nobody can afford to buy services on a wage of zero dollars. And anyone claiming that the abundance will go to everyone needs to get their head checked.

[Gigachad]

My contingency plan is that if AI leaves me unable to get a job, we are all fucked and society as a whole will have to fix the situation and if it doesn’t, there is nothing I could have done about it anyway.

[nitwit005]

> You are in danger. Unless you estimate the odds of a breakthrough at <5%

It's not the odds of the breakthrough, but the timeline. A factory worker could have correctly seen that one day automation would replace him, and yet worked his entire career in that role.

There have been a ton of predictions about software engineers, radiologists, and some other roles getting replaced in months. Those predictions have clearly been not so great.

At this point the greater risk to my career seems to be the economy tanking, as that seems to be happening and ongoing. Unfortunately, switching careers can't save you from that.

[adamkittelson]

I'm not worried about the danger of losing my job to an AI capable of performing it. I'm worried about the danger of losing my job because an executive wanted to be able to claim that AI has enhanced productivity to such a degree that they were able to eliminate redundancies with no regard for whether there was any truth to that statement or not.

[themafia]

> Unless you estimate the odds of a breakthrough at <5%

I do. Show me any evidence that it is imminent.

> or you expect that AI will usher in enough prosperity that your job will be irrelevant

Not in my lifetime.

> it is straight-up irresponsible to forgo making a contingency plan.

No, I'm actually measuring the risk, you're acting as if the sky is falling. What's your contingency plan? Buy a subscription to the revolution?

[zozbot234]

So AI is going to steamroll all feasible jobs, all at once, with no alternatives developing over time? That's just a fantasy.

[jopsen]

> it is straight-up irresponsible to forgo making a contingency plan.

What contingencies can you really make?

Start training a physical trade, maybe.

If this the end of SWE jobs, you better ride the wave. Odds are you're estimate on when AI takes over are off by half a career, anyways.

[snowwrestler]

Isn’t it a bit silly to say AI is going to eat the entire economy, but you have a contingency plan?

It seems kind of like saying “I’m smarter than all the AIs in this one particular way.” If someone posted that, you would probably jump in to say they’re fooling themselves.

[watt]

Unless I misunderstand your metaphor, there is nothing you can do about the steamroller, it is going to roll, no matter what.

[rockbruno]

While true, my personal fear is that the higher-ups will overlook this fact and just assume that AI can do everything because of some cherry-pick simple examples, leading to one of those situations where a bunch of people get fired for no reason and then re-hired again after some time.

[palmotea]

> leading to one of those situations where a bunch of people get fired for no reason and then re-hired again after some time.

More likely they get fired for no reason, never rehired, and the people left get burned out trying to hold it all together.

[themafia]

As if "higher-ups" is an assigned position.

If you fail as a "higher up" you're no longer higher up. Then someone else can take your place. To the extent this does not naturally happen is evidence of petty or major corruptions within the system.

[sensanaty]

I look through the backlog for my team consisting of 9 trillion ill-defined (if defined at all) tickets that tells you basically nothing.

The large, overwhelming majority of my team's time is spent on combing through these tickets and making sense of them. Once we know what the ticket is even trying to say, we're usually out with the solution in a few days at most, so implementation isn't the bottleneck, nowhere near.

This scenario has been the same everywhere I've ever worked, at large, old institutions as well as fresh startups.

The day I'll start worrying is when the AI is capable of following the web of people involved to translate what the vaguely phrased ticket that's been backlogged for God knows how long actually means

[arcologies1985]

At my workplace we now use Claude Code to parse written specs and source code, search through JIRA, and draft, refine and organize tickets (using the JIRA API via a CLI tool). Way faster than through the UI.

However as you point out we have no program-accessible source of data on who stakeholders, contributors, managers, etc. are and have to write a lot of that ourselves. For a smaller business perhaps one could write all of that down in an accessible way to improve this but for a large dynamic business it seems very difficult.

[UncleOxidant]

The memory problem is already being addressed in various ways - antigravity seems to keep a series of status/progress files describing what's been done, what needs doing, etc. A bit clunky, but it seems to work - I can open it up on a repo that I was working in a few days back and it seems to pick up this context such that I don't have to completely bring it up to speed every time like I used to have to do. I've heard that claude code has similar mechanisms.

I've been doing stuff with recent models (gemini 3, claude 4.5/6, even smaller, open models like GLM5 and Qwen3-coder-next) that was just unthinkable a few months back. Compiler stuff, including implementing optimizations, generating code to target a new, custom processor, etc. I can ask for a significant new optimization feature in our compiler before going to lunch and come back to find it implemented and tested. This is a compiler that targets a custom processor so there is also verilog code involved. We're having the AI make improvements on both the hardware and software sides - this is deep-in-the-weeds complex stuff and AI is starting to handle it with ease. There are getting to be fewer and fewer things in the ticket tracker that AI can't implement.

A few months ago I would've completely agreed with you, but the game is changing very rapidly now.

[taysco]

this works fine for like 2-3 small instruction sets. once you start getting to scale of a real enterprise system, the AI falls down and can't handle that amount of context. It will start ignoring critical pieces or not remember them. And without constant review AI will start priotizing things that are not your business priority.

I don't agree they have solved this problem, at all, or really in any way that's actually usable.

[e_i_pi_2]

A lot of this can be provided or built up by better documentation in the codebase, or functional requirements that can also be created, reviewed, and then used for additional context. In our current codebase it's definitely an issue to get an AI "onboarded", but I've seen a lot less hand-holding needed in projects where you have the AI building from the beginning and leaving notes for itself to read later

[gordonhart]

Curious to hear if you've seen this work with 100k+ LoC codebases (i.e. what you could expect at a job). I've had some good experiences with high autonomy agents in smaller codebases and simpler systems but the coherency starts to fizzle out when the system gets complicated enough that thinking it through is the hard part as opposed to hammering out the code.

[tharkun__]

We have this in some of our projects too but I always wonder how long it's going to take until it just fails. Nobody reads all those memory files for accuracy. And knowing what kind of BS the AI spews regularly in day to day use I bet this simply doesn't scale.

[deet]

Just keep in mind that there are many highly motivated people directly working on this problem.

It's hard to predict how quickly it will be solved and by whom first, but this appears to be a software engineering problem solvable through effort and resources and time, not a fundamental physical law that must be circumvented like a physical sciences problem. Betting it won't be solved enough to have an impact on the work of today relatively quickly is betting against substantial resources and investment.

[slopinthebag]

Why do you think it's not a physical sciences problem? It could be the case that current technologies simply cannot scale due to fundamental physical issues. It could even be a fundamental rule of intelligent life, that one cannot create intelligence that surpasses its own.

Plenty of things get substantial resources and investment and go nowhere.

Of course I could be totally wrong and it's solved in the next couple years, it's almost impossible to make these predictions either way. But I get the feeling people are underestimating what it takes to be truly intelligent, especially when efficiency is important.

[ThrowawayR2]

Many highly motivated people with substantial resources and investment have worked on a lot of things and then failed at them with nothing to show for it.

[datsci_est_2015]

The implication of your assertion is pretty much a digital singularity. You’re implying that there will be no need for humans to interact with the digital world at all, because any work in the digital world will be achievable by AI.

Wonder what that means for meatspace.

Edit: Would also disagree this isn’t a physics problem. Pretty sure power required scales according to problem complexity. At a certain level of problem complexity we’re pretty much required to put enough carbon in the atmosphere to cook everyone to a crisp.

Edit 2: illustrative example, an Epic in Jira: “Design fusion reactor”

[matt_heimer]

It's not binary. Jobs will be lost because management will expect the fewer developers to accomplish more by leveraging AI.

[louiereederson]

Big tech might ahead of the rest of the economy in this experiment. Microsoft grew headcount by ~3% from June 2022 to June 2025 while revenue grew by >40%. This is admittedly weak anecdata but my subjective experience is their products seem to be crumbling (GitHub problems around the Azure migration for instance), and worse than they even were before. We'll see how they handle hiring over the next few years and if that reveals anything.

[datsci_est_2015]

Already built in. We haven’t hired recently and our developers are engaged in a Cold War to set the new standard of productivity.

[krackers]

>progressively understands the business

This is no different than onboarding a new member of the team, and I think openAI was working on that "frontier"

>We started by looking at how enterprises already scale people. They create onboarding processes. They teach institutional knowledge and internal language. They allow learning through experience and improve performance through feedback. They grant access to the right systems and set boundaries. AI coworkers need the same things.

And tribal knowledge will not be a moat once execs realize that all they need to do is prioritize documentation instead of "code velocity" as a metric (sure any metric gets goodhearted, but LLMs are great at sifting through garbage to find the high perplexity tokens).

>But context limitation is fundamental to the technology in its current form

This may not be the case, large enough context-windows plus external scratchpads would mostly obviate the need for true in context learning. The main issue today is that "agent harnesses" suck. The fact that claude code is considered good is more an indication of how bad everything else is. Tool traces read like a drunken newb brute-forcing his way through tasks. LLMs can mostly "one-shot" individual functions, but orchestrating everything is the blocker. (Yes there's progress in metr or whatever but I don't trust any of that, else we'd actually see the results in real-world open source projects).

LLMs don't really know how to interact with subagents. They're generally sort of myopic even with tool calls. They'll spend 20 minutes trying to fix build issues going down a rabbit hole without stepping back to think. I think some sort of self-play might end up solving all of these things, they need to develop a "theory of mind" in the same way that humans do, to understand how to delegate and interact with the subagents they spawn. (Today a failure case is agents often don't realize subagents don't share the same context.)

Some of this is certainly in the base model and pretraining, but it needs to be brought out in the same way RL was needed for tool use.

[malyk]

Can you give an example to help us understand?

I look at my ticket tracker and I see basically 100% of it that can be done by AI. Some with assistance because business logic is more complex/not well factored than it should be, but most of the work that is done AI is perfectly capable of doing with a well defined prompt.

[gordonhart]

Here's an example ticket that I'll probably work on next week:

    Live stream validation results as they come in

The body doesn't give much other than the high-level motivation from the person who filed the ticket. In order to implement this, you need to have a lot of context, some of which can be discovered by grepping through the code base and some of which can't:

- What is the validation system and how does it work today?

- What sort of UX do we want? What are the specific deficiencies in the current UX that we're trying to fix?

- What prior art exists on the backend and frontend, and how much of that can/should be reused?

- Are there any scaling or load considerations that need to be accounted for?

I'll probably implement this as 2-3 PRs in a chain touching different parts of the codebase. GPT via Codex will write 80% of the code, and I'll cover the last 20% of polish. Throughout the process I'll prompt it in the right direction when it runs up against questions it can't answer, and check its assumptions about the right way to push this out. I'll make sure that the tests cover what we need them to and that the resultant UX feels good. I'll own the responsibility for covering load considerations and be on the line if anything falls over.

Does it look like software engineering from 3 years ago? Absolutely not. But it's software engineering all the same even if I'm not writing most of the code anymore.

[lbrito]

Then why isn't it? Just offload it to the clankers and go enjoy a margarita at the beach or something.

[contagiousflow]

Why do you have a backlog then? If a current AI can do 100% of it then just run it over the weekend and close everything

[rockbruno]

I think the "well defined prompt" is precisely what the person you responded to is alluring to. They are saying they don't get worried because AI doesn't get the job done without someone behind it that knows exactly what to prompt.

[dwa3592]

>>I look at my ticket tracker and I see basically 100% of it that can be done by AI.

That's a sign that you have spurious problems under those tickets or you have a PM problem.

Also, a job is a not a task- if your company has jobs which is a single task then those jobs would definitely be gone.

[yodsanklai]

> ~0% of them can be implemented by AI as it exists today

I think it's more nuanced than that. I'd say that - 0% can't be implemented by AI - but a lot of them can be implemented much faster thanks to AI - a lot of them can be implemented slower when using AI (because author has to fix hallucinations, revert changes that caused bugs)

As we learn to use these tools, even in their current state, they will increase productivity by some factor and reduce needs for programmers.

[sarchertech]

What factor of increased productivity will lead to reduced need for programmers?

I have seen numerous 25-50% productivity boosts over my career. Not a single one of them reduced the overall need for programmers.

I can’t even think of one that reduced the absolute number of programmers in a specific field.

[vincent_s]

Ha, this triggered me. I'm building exactly this.

It's a coding agent that takes a ticket from your tracker, does the work asynchronously, and replies with a pull request. It does progressively understand the codebase. There's a pre-warming step so it's already useful on the first ticket, but it gets better with each one it completes.

The agent itself is done and working well. Right now I'm building out the infrastructure to offer it as a SaaS.

If anyone wants to try it, hit me up. Email is in my profile. Website isn't live yet, but I'm putting together a waitlist.

[danesparza]

Apparently you haven't seen ChatGPT enterprise and codex. I have bad news for you ...

[gordonhart]

Codex with their flagship model (currently GPT-5.3-Codex) is my daily driver. I still end up doing a lot of steering!

[audience_mem]

0%? This is as wrong as people who say it can do 100% of tasks.

[pupppet]

We're all slowly but surely lowering our standards as AI bombards us with low-quality slop. AI doesn't need to get better, we all just need to keep collectively lowering our expectations until they finally meet what AI can currently do, and then pink-slips away.

[tines]

Exactly. This happens in every aspect of life. Something convenient comes along and people will accommodate it despite it being worse, because people don’t care.

[zozbot234]

> Once somebody cracks the memory problem and ships an agent that progressively understands the business and the codebase, then I'll start worrying.

Um, you do realize that "the memory" is just a text file (or a bunch of interlinked text files) written in plain English. You can write these things out yourself. This is how you use AI effectively, by playing to its strengths and not expecting it to have a crystal ball.