2/13/2026 at 8:00:08 PM
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.by gordonhart
2/13/2026 at 8:20:58 PM
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.by rockbruno
2/13/2026 at 9:43:40 PM
> 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.
by palmotea
2/13/2026 at 8:19:23 PM
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 laterby e_i_pi_2
2/13/2026 at 8:32:44 PM
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.by gordonhart
2/13/2026 at 10:39:26 PM
Our codebase is well over 250k and we have a hierarchy of notes for the modules so we read as much as we need for the job with a base memory that explains how the notes workby christkv
2/13/2026 at 8:29:45 PM
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.by tharkun__
2/13/2026 at 8:15:18 PM
It's not binary. Jobs will be lost because management will expect the fewer developers to accomplish more by leveraging AI.by matt_heimer
2/13/2026 at 9:07:55 PM
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.by louiereederson
2/13/2026 at 10:33:32 PM
Well, Google just raised prices by 30% on the GSuite "due to AI value delivered", but you can't even opt out, so even revenue is a bullshit metric.by JetSpiegel
2/13/2026 at 10:32:20 PM
> 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.
by zozbot234
2/13/2026 at 8:05:58 PM
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.
by malyk
2/13/2026 at 8:29:21 PM
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.
by gordonhart
2/13/2026 at 8:50:24 PM
This right here is my view on the future as well. Will the AI write the entire feature in one go? No. Will the AI be involved in writing a large proportion of the code that will be carefully studied and adjusted by a human before being used? Absolutely yes.This cyborg process is exactly how we're using AI in our organisation as well. The human in the loop understands the full context of what the feature is and what we're trying to achieve.
by Rodeoclash
2/13/2026 at 9:39:10 PM
But planning like this is absolutely something AI can do. In fact, this is exactly the kind of thing we start with on our team when it comes to using AI agents. We have a ticket with just a simple title that somebody threw in there, and we asked the AI to spin up a bunch of research agents to understand and plan and ask itself those questions.Funny enough, all the questions that you posed are things that come up right away that the agent asks itself, and then goes and tries to understand and validate an answer, sometimes with input from the user. But I think this planning mechanism is really critical to being able to have an AI generate an understanding, then have it be validated by a human before beginning implementation.
And by planning I don't necessarily mean plan mode in your agent harness of choice. We use a custom /plan skill in Claude Code that orchestrates all of this using multiple agents, validation loops, and specific prompts to weed out ambiguities by asking clarifying questions using the ask user question tool.
This results in taking really fuzzy requirements and making them clear, and we automate all of this through linear but you could use your ticket tracker of choice.
by codegangsta
2/13/2026 at 8:53:30 PM
I mean, what is the validation system? Either it exists in code, and thus can be discovered if you point the AI at repo, or... what, it doesn't exist?For the UX, have it explore your existing repos and copy prior art from there and industry standards to come up with something workable.
Web scale issues can be inferred by the rest of the codebase. If your terraform repo has one RDS server, vs a fleet of them, multi-region, then the AI, just as well as a human, can figure out if it needs Google Spanner level engineering or not. (probably not)
Bigger picture though, what's the process of a human logs an under specified ticket and someone else picks it up and has no clue what to do with it? They're gonna go ask the person who logged the bug for their thoughts and some details beyond "hurr Durr something something validation". If we're at the point where AI is able to make a public blog post shaming the open source developer for not accepting a patch, throwing questions back to you in JIRA about the details of the streaming validation system is well within its capabilities, given the right set of tools.
by fragmede
2/13/2026 at 9:05:30 PM
Honestly curious, have you seen agents succeed at this sort of long-trajectory wide breadth task, or is it theoretical? Because I haven't seen them come close (and not for lack of trying)by gordonhart
2/13/2026 at 9:42:20 PM
Yeah I absolutely see it every day. I think it’s useful to separate the research/planning phase from the building/validadation/review phase.Ticket trackers are perfect for this. Just start with asking AI to take this unclear, ambiguous ticket and come up with a real plan for how to accomplish it. Review the plan, update your ticket system with the plan, have coworkers review it if you want.
Then when ready, kick off a session for that first phase, first PR, or the whole thing if you want.
by codegangsta
2/13/2026 at 8:13:57 PM
Then why isn't it? Just offload it to the clankers and go enjoy a margarita at the beach or something.by lbrito
2/13/2026 at 8:53:38 PM
There are plenty of people who are enjoying margarita by the beach while you, the laborer, are working for them.by Gud
2/13/2026 at 8:55:13 PM
Preach. That's always been the case though, AI just makes it slightly worse.by lbrito
2/13/2026 at 8:11:36 PM
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 everythingby contagiousflow
2/13/2026 at 8:12:29 PM
As always, the limit is human bandwidth. But that's basically what AI-forward companies are doing now. I would be curious which tasks OP commenter has that couldn't be done by an agent (assuming they're a SWE)by fishpham
2/13/2026 at 8:14:29 PM
This sounds bogus to me: if AI really could close 100% of your backlog with just a couple more humans in the loop, you’d hire a bunch of temps/contractors to do that, then declare the product done and lay off everybody. How come that isn’t happening?by Analemma_
2/13/2026 at 9:31:36 PM
Because there's an unlimited amount of work to do. This is the same reason you are not fired once completing a feature :-) The point of hiring a FTE is to continue to create work that provides business value. For your analogy, FTEs often do that by hiring temp, and you can think of the agent as the new temp in this case - the human drives an infinite amount of themby fishpham
2/13/2026 at 8:16:07 PM
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.by rockbruno
2/13/2026 at 8:12:55 PM
>>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.
by dwa3592
2/13/2026 at 8:54:01 PM
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.by pupppet
2/13/2026 at 8:21:30 PM
Apparently you haven't seen ChatGPT enterprise and codex. I have bad news for you ...by danesparza
2/13/2026 at 8:35:04 PM
Codex with their flagship model (currently GPT-5.3-Codex) is my daily driver. I still end up doing a lot of steering!by gordonhart
2/13/2026 at 8:13:49 PM
[dead]by ninetyninenine