AI can't even fix a simple bug – but sure, let's fire engineers

Now the hammer has mapped your house and knows where all the screws are and is uploading all that to some servers.

You don't own the hammer but you control it.

You get different models, configurations, system prompts (and DAN-descended stuff is like DMCA now, unaccountable blacklist for even trying in some cases), you get control vectors and modern variants with more invasive dynamic weight biasing. The expert/friend/coworker drop down doesn't have all the entries in it: there's a button to make Claude code write files full of "in production code we'd do the calculation" mocks and then write a commit message about all the passing tests (with a byline!), but some ops guy pushes that button in the rare event the PID controller or whatever can't cope.

These are hooked up to control theory algorithms based on aggregate and regional KV and prompt cache load. This is true of both fixed and per-token billing. The agent will often be an asset at 4am but a liability at 2pm.

You get experiment segmented always, you get behavior scoped multi-armed badit rotated into and out of multiple segment categories (an experiment universe will typically have not less than 10000 segments, each engineer will need maybe 2 or 3 and maybe hundreds of arms per project/feature, so that's a lot of universes).

At this stage of the consumer internet cycle its about unit economics and regulatory capture and stock manipulation via hype rollercoaster. and make no mistake about what kind of companies these are: they have research programs with heavy short-run applications in mind and a few enclaves where they do AlphaFold or something. I'm sure they created an environment Carmack would tolerate at least for a while, but I gibe it a year or two we saw that movie at Oculus and Bosworth is a pretty good guy, he's like Jesus compared to the new boss.

In this extended analogy about users, owners, lenders, borrowers and hammers, I'd be asking what is the hammer and who is the nail.

I think the OP comment re: AI's value as a tool comes down this this:

Does what it says: When you swing a hammer and make contact, it provides greater and more focused force than your body at that same velocity. People who sell hammers make this claim and sometimes show you that the hammer can even pull out nails really well. The claims about what AI can do are noisy, incorrect and proffered by people who - I imagine OP thinks and would agree - know better. Essentially they are saying "Hammers are amazing. Swing them around everywhere"

Right to repair: Means an opportunity to understand the guts of a thing and fix it to do what you want. You cannot really do this to AI. You can prompt differently but it can be unclear why you're not getting what you want

Intentionally or not the tractor analogy is a rich commentary on this but it might not make the point you intend. Look into all the lawsuits and shit like that with John Deere and the DRM lockouts where farmers are losing whole crops because of remote shutdown cryptography that's physically impossible to remove at a cost or in a timeframe less than a new tractor.

People on HN love to bring up farm subsidies, and its a real issue, but big agriculture has special deals and what not. They have redundancy and leverage.

The only time this stuff kicks in is when the person with the little plot needs next harvest to get solvent and the only outcome it ever achieves is to push one more family farm on the brink into receivership and directly into the hands of a conglomorate.

Software engineers commanded salaries that The Right People have found an affront to the order of things long after they had gotten doctors and lawyers and other high-skill trades largely brought to heel via the joint licensing and pick a number tuition debt load. This isn't easy in software for a variety of reasons but roughly that the history of computer science in academia is kind of a unique one: it's research oriented in universities (mostly, there are programs with an applied tilt) but almost everyone signs up, graduates, and heads to industry without a second thought, and so back when the other skilled trades were getting organized into the class system it was kind of an oddity, regarded as almost an eccentric pursuit by deans and shit.

So while CS fundamentals are critical to good SWE's, schools don't teach them well as a rule any more than a physics undergraduate is going to be an asset at CERN: its prep for theory research most never do. Applied CS is just as serious a topic, but you mostly learn that via serious self study or from coworkers at companies with chops. Even CS graduates who are legends almost always emphasize that if you're serious about hacking then undergrad CS is remedial by the time you run into it (Coders at Work is full of this sentiment).

So to bring this back to tractors and AI, this is about a stubborn nail in what remains of the upwardly mobile skilled middle class that multiple illegal wage fixing schemes have yet to pound flat.

This one will fail too, but that's another mini blog post.

Good for them?

> Okay, so write an article about how the hammer companies are dumb

This is exactly how I interpreted the OP blog post: it is a great example of how companies are (mis)using AI given it's current abilities.

Well, it's where we are now with AI technology. Perhaps a superior future AI will need less of it. For now I give it all that I think it won't reliably figure out on its own.

The iterative approach has one problem -- it is onerous to repeat the lengthy iterative process with a different model, as it will lead to an entirely different conversation. In contrast, when the spec is well-written up-front, it is trivial to switch models to see how the other model implements it differently.

Of course, but my question stands. Did all the people who used to do easily automatable clerical work remain in those roles? I presume that isn't the case, but like I say I don't really know what happened to them.

Tons of accountants were replaced by excel though, just not all of them.

Thanks for the clarification. Personally, I view accounting as a dying industry in America. The number of well paid accountants is few and decreasing each year. If programming follows that path, it will be devastating for the industry.

Some statistics on US accounting trends[0]:

- As a direct result of enrollment declines in accounting programs, candidates sitting for the CPA exam have also decreased from 48,004 first-time candidates in 2016 to 32,188 in 2021, a drop of 33% - 7% decrease from 2021 to 2022 for the total number of candidates taking the CPA exam - 2022 saw the lowest number of exam takers since 2006 - 30% fewer candidates passed their final section of the CPA exam in 2021 compared to 2016 - the AICPA has stated that roughly 75% of its members are at retirement age - Accounting graduates trended downward in the 2019–2020 academic year, with decreases of 2.8% and 8.4% at the bachelor’s and master’s levels, respectively

Having gone through public accounting myself, and knowing hundreds of accountants, It’s not a career path I recommend others follow whereas computer science and software engineering is one I still recommend. Accounting has largely gone overseas. There are not enough partner positions at the big firms or smaller, local firms to supply enough work for accountants. At least software engineering still has the unlimited growth potential that software enables.

[0]: https://www.cpajournal.com/2024/11/25/the-accounting-profess...

Gotcha. I am in the same boat with pay as you go credits and evaluating what it can do. There are limits with the max subscription but I'm not sure exactly how to measure it against the pay as you go tokens. So for now I just use tokens sparingly.

I dunno. Maybe you're expecting too much of them? They're obviously not gonna be like those massive data-center LLMs, but I've had some pretty good "brainstorming" sessions about code and documentation with Qwen and Gemma, and the latest vision-capable Qwen models do a really decent job of extracting data and text out of images (often more accurately than typical OCR engines) and even describing or captioning images, and for writing code, I've had good success with asking it to write me docstrings and type hints, and common "boilerplate" code, and even some pretty solid function requests that often come out exactly as I expected it to (as long as it's fed good "context" to work with, like some basic code-style rules, and access to library documentation and existing codebase).

if you're someone in tech and can't separate the noise from the practicality i'm not sure what to say at this point. do you drop everything you're doing every time a saas product comes out and change your entire life, or do you go play with it and figure out if it's useful for your life? seems like one should be able to determine this for themself without resorting to taking everything that marketing says at face value

A similar thing could be said about traditional tech CEOs like Benioff - that it's part of their fiduciary duty to stakeholders to say things like that. I don't disagree with you; I'm merely saying it's the nature of the game, and that people are definitely claiming that there is an imminent (or even ongoing) firing of engineers.

I need code that works within the specialized context of my project, I couldn't steal code from GitHub even if I wanted to.

Sorry by designers I mean UI assets. Art and iconography.

I recently needed a transpiler for a mini-DSL to Lua. Google Gemini did a pretty good job of it, hallucinated a bunch of non existent helper functions but they were easy enough to add. It knew the target language Lua better than I did so it has some good ideas for simple generation of code. I naively would have generated more natural looking code at the cost of more effort. Once I fixed the bugs it worked fine. I estimate it would have taken me 4x as long without it.