This bothers me. I completely understand the conversational aspect - "what approach might work for this?", "how could we reduce the crud in this function?" - it worked a lot for me last year when I tried learning C.
But the vast majority of AI use that I see is...not that. It's just glorified, very expensive search. We are willing to burn far, far more fuel than necessary because we've decided we can't be bothered with traditional search.
A lot of enterprise software is poorly cobbled together using stackoverflow gathered code as it is. It's part of the reason why MS Teams makes your laptop run so hot. We've decided that power-inefficient software is the best approach. Now we want to amplify that effect by burning more fuel to get the same answers, but from an LLM.
It's frustrating. It should be snowing where I am now, but it's not. Because we want to frivolously chase false convenience and burn gallons and gallons of fuel to do it. LLM usage is a part of that.
In other words, it's a skill issue. LLMs can only make this worse. Hiring unskilled programmers and giving them a machine for generating garbage isn't the way. Instead, train them, and reject low quality work.
I don't think finding such programmers is really difficult. What is difficult is finding such people if you expect them to be docile to incompetent managers and other incompetent people involved in the project who, for example, got their position not by merit and competence, but by playing political games.
In my opinion the reason we get enterprise spaghetti is largely due to requirement issues and scope creep. It's nearly impossible to create a streamlined system without knowing what it should look like. And once the system gets to a certain size, it's impossible to get business buy-in to rearchitect or refactor to the degree that is necessary. Plus the full requirements are usually poorly documented and long forgotten by that time.
> it's impossible to get business buy-in to rearchitect or refactor to the degree that is necessary
That's a choice. There are some other options:
- Simply don't get business buy-in. Do without. Form a terrorist cell within your organization. You'll likely outpace them. Or you'll get fired, which means you'll get severance, unemployment, a vacation, and the opportunity to apply to a job at a better company.
- Fight viciously for engineering independence. You business people can do the businessing, but us engineers are going to do the engineering. We'll tell you how we'll do it, not the other way.
- Build companies around a culture of doing good, consistent work instead of taking expedient shortcuts. They're rare, but they exist!
Without redoing their work or finding a way to have deep trust (which is possible, but uncommon at a bigcorp) it's hard enough to tell who is earnest and who is faking it (or buying their own baloney) when it comes to propositions like "investing in this piece of tech debt will pay off big time"
As a result, if managers tend to believe such plans, bad ideas drive out good and you end up investing in a tech debt proposal that just wastes time. Burned managers therefore cope by undervaluing any such proposals and preferring the crappy car that at least you know is crappy over the car that allegedly has a brand new 0 mile motor on it but you have no way of distinguishing from a car with a rolled back odometer. They take the locally optimal path because it's the best they can do.
It's taken me 15 years of working in the field and thinking about this to figure it out.
The only way out is an organization where everyone is trusted and competent and is worthy of trust, which again, hard to do at most random bigcorps.
This is my current theory anyway. It's sad, but I think it kind of makes sense.
Being good at the NATO style of management means focusing on the big picture--what, when, why--and leaving how to the people actually doing it.
The way I explain this to managers is that software development is unlike most work. If I'm making widgets and I fuck up, that widget goes out the door never to be seen again. But in software, today's outputs are tomorrow's raw materials. You can trade quality for speed in the very short term at the cost of future productivity, so you're really trading speed for speed.
I should add, though, that one can do the rigorous thinking before or after the doing, and ideally one should do both. That was the key insight behind Martin Fowler's "Refactoring: Improving the Design of Existing Code". Think up front if you can, but the best designs are based on the most information, and there's a lot of information that is not available until later in a project. So you'll want to think as information comes in and adjust designs as you go.
That's something an LLM absolutely can't do, because it doesn't have access to that flow of information and it can't think about where the system should be going.
This is an important point. I don't remember where I read it, but someone said something similar about taking a loss on your first few customers as an early stage startup--basically, the idea is you're buying information about how well or poorly your product meets a need.
Where it goes wrong is if you choose not to act on that information.
> ...
> train them, and reject low quality work.
I agree very strongly with both of these points.
But I've observed a truth about each of them over the last decade-plus of building software.
1) very few people approach the field of software engineering with anything remotely resembling rigor, and
2) there is often little incentive to train juniors and reject subpar output (move fast and break things, etc.)
I don't know where this takes us as an industry? But I feel your comment on a deep level.
This is a huge problem. I don't know where it comes from, I think maybe sort of learned helplessness? Like, if systems are so complex that you don't believe a single person can understand it then why bother trying anyway? I think it's possible to inspire people to not accept not understanding. That motivation to figure out what's actually happening and how things actually work is the carrot. The stick is thorough, critical (but kind and fair) code--and, crucially, design--review, and demanding things be re-done when they're not up to par. I've been extremely lucky in my career to have had senior engineers apply both of these tools excellently in my general direction.
> 2) there is often little incentive to train juniors and reject subpar output (move fast and break things, etc.)
One problem is our current (well, for years now) corporate culture is this kind of gig-adjacent-economy where you're only expected to stick around for a few years at most and therefore in order to be worth your comp package you need to be productive on your first day. Companies even advertise this as a good thing "you'll push code to prod on your first day!" It reminds me of those scammy books from when I was a kid in the late 90s "Learn C In 10 Days!".
I’ve had the good fortune of getting to write some firmware that will likely work well for a long time to come, but I find most things being written on computers are written with (or very close to) the minimum care possible in order to get the product out. Clean up is intended but rarely occurs.
I think we’d see real benefits from doing a better job, but like many things, we fail to invest early and crave immediate gratification.
I have this one opinion which I would not say at work:
In software development it's easy to feel smart because what you made "works" and you can show "effects".
- Does it wrap every failable condition in `except Exception`? Uhh, but look, it works.
- Does it define a class hierarchy for what should be a dictionary lookup? It works great tho!
- Does it create a cyclic graph of objects calling each other's methods to create more objects holding references to the objects that created them? And for what, to produce a flat dictionary of data at end of the day? But see, it works.
this is getting boring, maybe just skip past the list
- Does it stuff what should be local variables and parameters in self, creating a big stateful blob of an object where every attribute is optional and methods need to be called in the right order, otherwise you get an exception? Yes, but it works.
- Does it embed a browser engine? But it works!
The programmer, positively affirmed, continues spewing out crap, while the senior keep fighting fires to keep things running, while insulating the programmer from the taste of their own medicine.
But more generally, it's hard to expect people to learn how to solve problems simply if they're given gigantic OO languages with all the features and no apparent cost to any of them. People learn how to write classes and then never learn get good at writing code with a clear data flow.
Even very bright people can get fall for this trap because engineering isn't just about being smart but about using intelligence and experience to solve a problem while minmaxing correctly chosen properties. Those properties should generally be: dev time, complexity (state/flow), correctness, test coverage, ease of change, performance (anything else?). Anyway, "Affirming one's opinions about how things should be done" isn't one of them.
Ahh, well, in order to save money, training is done via an online class with multiple choice questions, or, if your company is like mine and really committed to making sure that you know they take your training seriously, they put portions of a generic book on 'tech Z' in pdf spread spread over a drm ridden web pages.
As for code, that is reviewed, commented and rejected by llms as well. It is used to be turtles. Now it truly is llms all the way down.
That said, in a sane world, this is what should be happening for a company that actually wants to get good results over time .
There is no incentive to do it. I worked that way, focused on quality and testing and none of my changes blew up in production. My manager opined that this approach is too slow and that it was ok to have minor breakages as long as they are fixed soon. When things break though, it's blame game all around. Loads of hypocrisy.
Traditional search (at least on the web) is dying. The entire edifice is drowning under a rapidly rising tide of spam and scam sites. No one, including Google, knows what to do about it so we're punting on the whole project and hoping AI will swoop in like deus ex machina and save the day.
Google wasn’t crushed by spam, they decided to stop doing text search and build search bubbles that are user specific, location-specific, decided to surface pages that mention search terms in metadata instead of in text users might read, etc. Oh yeah, and about a decade before LLMs were actually usable, they started to sabotage simple substring searches and kind of force this more conversational interface. That’s when simple search terms stopped working very well, and you had to instead ask yourself “hmm how would a very old person or a small child phrase this question for a magic oracle”
This is how we get stuff like: Did you mean “when did Shakespeare die near my location”? If anyone at google cared more about quality than printing money, that thirsty gambit would at least be at the bottom of the page instead of the top.
By the time I graduated highschool you already couldn't trust that Boolean operators would be treated literally. By the time I graduated college, they basically didn't seem to do anything, at best a weak suggestion.
Nowadays quotes don't even seem to be consistently honored.
Google’s verbatim search option roughly does that for me (plus an ad blocker that removes ads from the results page). I have it activated by default as a search shortcut.
(To activate it, one can add “tbs=li:1” as a query parameter to the Google search URL.)
Even more naive, but my personal preference: just ban all advertising. The fact that people will pay for ChatGPT implies people will also pay for good search if the free alternative goes away.
Google results are polluted with spam because it is more profitable for Google. This is a conscious decision they made five years ago.
Then why are DuckDuckGo results also (arguably even more so) polluted with spam/scam sites? I doubt DDG is making any profit from those sites since Google essentially owns the display ad business.
Google not only has multiple monopolies, but a cut and dry perverse incentive to produce lower quality results to make the whole session longer instead of short and effective.
For example: a beginner developer is possibly better served by some SEO-heavy tutorial blog post; an experienced developer would prefer results weighted towards the official docs, the project’s bug tracker and mailing list, etc. But since less technical and non-technical people vastly outnumber highly technical people, Google and Bing end up focusing on the needs of the former, at the cost of making search worse for the later.
One positive about AI: if an AI is doing the search, it likely wants the more advanced material not the more beginner-focused one. It can take more advanced material and simplify it for the benefit of less experienced users. It is (I suspect) less likely to make mistakes if you ask it to simplify the more advanced material than if you just gave it more beginner-oriented material instead. So if AI starts to replace humans as the main clients of search, that may reverse some of the pressure to “dumb it down”.
I mostly agree with your interesting comment, and I think your analysis basically jives with my sibling comment.
But one thing I take issue with is the idea that this type of thing is a good faith effort, because it’s more like a convenient excuse. Explaining substring search or even include/exclude ops to children and grandparents is actually easy. Setting preferences for tutorials vs API docs would also be easy. But companies don’t really want user-directed behavior as much as they want to herd users to preferred content with algorithms, then convince the user it was their idea or at least the result of relatively static ranking processes.
The push towards more fuzzy semantic search and “related content” everywhere is not to cater to novice users but to blur the line between paid advertisement and organic user-directed discovery.
No need to give megacorp the benefit of the doubt on stuff like this, or make the underlying problems seem harder than they are. All platforms land in this place by convergent evolution wherein the driving forces are money and influence, not insurmountable technical difficulties or good intentions for usability.
Good luck finding those, you end op with SEO spam and clone page spam. These days you have to look for unobvious hidden meanings which only relate to your exact problem to find what you are looking for.
I have the strong feeling search these days is back to the Altavista era. You'd have to use trickery to find what you were looking for back then as well. Too bad + no longer works in google due to their stupid naming of a dead product (no, literal is not the same and no replacement).
That's not my experience at all. While there are scammy sites, using the search engines as an index instead of an oracle still yields useful results. It only requires to learn the keywords which you can do by reading the relevant materials .
The problem with Google search is that it indexes all the web, and there's (as you say) a rising tide of scam and spam sites.
The problem with AI is that it scoops up all the web as training data, and there's a rising tide of scam and spam sites.
Tailoring/retraining the main search AI will be so much more expensive that retraining the spam special purpose AIs.
You make this claim with such confidence, but what is it based on?
There have always been hordes of spam and scam websites. Can you point to anything that actually indicates that the ratio is now getting worse?
No, there haven't always been hordes of spam and scam websites. I remember the web of the 90s. When Google first arrived on the scene every site on the results page was a real site, not a spam/scam site.
I'm sure they can. But they have no incentive. Try to Google an item, and it will show you a perfect match of sponsored ads and some other not-so-relevant non-sponsored results
He couldn't pronounce the name of the extension, apparently not noticing that trgm == trigram, or what that might even be. Copying the output from the LLM and pasting it into a PR didn't result in anything other than him checking off a box, moving a ticket in Jira, and then onto the next thing--not even a pretense of being curious about what any of it all meant. But look at those query times now!
It's been possible for a while to shut off your brain as a programmer and blindly copy-paste from StackOverflow etc., but the level of enablement that LLMs afford is staggering.
In fact, I'd say I use AI more for documentation than I do for code itself, because AI generated documentation is often superior to official documentation.
In the end, these things shouldn't be necessary (or barely necessary) if we had well constructed languages, frameworks, libraries and documentation, but it appears like it's easier to build AI than to make things non-convoluted in the first place.
Traditional search was only Google, and Google figured out that they don't need to improve their tools to make it better, because everyone will continue to use it as a force of habit (google is a verb!). Traditional search is being abandoned because traditional search isn't good enough for the kinds of search we need (also, while google may claim their search is very useful, people rarely search stuff nowadays, instead prefer being passively fed content via recommendations algorithm (that also use AI!))
Since the collapse of Internet search (rose tinted hindsight - was it ever any good?) I have been using a LLM as my syntax advisor. I pay for my own tokens, and I can say it is astonishingly cheap
It is also very good.
That's because traditional search fucking sucks balls.