“Let’s go another way where AI helps us to do things, to learn things, to create things, but in ways that we enhance and advance with our distinct perspectives and unique experiences!”
AI can’t do that. And, even if it could be made to look like it is, no one you’re paying is going to bother.
Fucking idiots.
"I can’t be an idiot, look at all the money I have. "
Stupid is as stupid does. Idiot.
Yeah, they’re kind of slow. They’re throwing money at this, and they’re just realizing it?!?!?! Typical. The only ones with common sense is the common man.
I mean, it’s somewhat possible, depending on context. However even if it helps someone with vision realize their goals 10 percent faster, it helps the person with zero vision pump out slop 100x faster than someone with good content to make.
Now just imagine putting that shit into the code for your flagship operating system without any kind of human oversight.
Lmao my immediate thought was AI when a .NET update broke the Cambria font recently
Then don’t invent the fucking torment nexus!
I keep shoving puppies into the shredder but all i get are shredded puppies!!
The problem is that they see people hating the torment nexus and then they assume it must be because they didn’t market it correctly.
Like "oh I guess they don’t like it because they can’t share it, let’s add a suffering multiplexer so they can enjoy it with friends and family*
*⠀for⠀a⠀small**⠀fee
**⠀might⠀not⠀be⠀small
You don’t get to complain about the slop when you built the slop factory.
He’s probably only complaining because it’s affecting him personally. If it was only destroying everyone else’s lives he would be happy to be an executioner. After all, he’s just following orders.
…And mandated their employees use it.
Now he’s crying they did what they were told
aparently these tech ceo shields thier own children from the brainrot/slop, they know its wrong its doing harm by placing them in expensive private schools away from the “brainrot”
Remember, though: Microsoft invented the slop factory when they created Microsoft Office.
Sending useless documents back and forth via Outlook predates AI by decades.
I thought they admitted defeat, embracing the uselessness and futility of nearly all office work when they released SharePoint, but I guess they’re still just getting started 🤷
Ahh the good old days when no mid size corp bothered to hire an onprem Sharepoint dev so the install just went unused
Or worse, used!
And demanded everyone use the factory or lose their jobs.
*Microslop
One of the hilarious consequences of Microsoft Copilot, ChatGPT, Gemini, and other content-theft platforms is that they’re trained entirely stolen human-made content. AI data sets post 2023 are doomed to rely on cannibalizing AI-generated content, creating an incestuous data “doom loop” Roslansky spoke of here
Haha, I didn’t know about “doom loops.” Ah, that’s why they burning land, water, etc., more assets for the Balance Sheet. If AI flops, they’ll just flip to cryptocurrency and leave us out of the markets entirely.
You need to leave!!!
I really think you should.
Congratulations on making me snort wine out of my nose!
Love it
I’d give him the benefit of the doubt, because surely not everyone at microslop wants copilot everywhere, but he heads the department that put copilot into excel, so all my sympathies evaporated.
[https://futurism.com/artificial-intelligence/microsoft-satya-nadella-ai-slop](Didn’t Nadella beg people not to call it slop?)
Put your descriptor in the brackets then url in the parentheses.
Yes, and that is mentioned very early in the article in the OP…
The phrase “no shit” comes to mind.
They don’t like when people use the AI trash they baked in their apps? Maybe they should get rid of it 🤔
Actions = Consequences
Oh, he’s the one running Linkedin, now it makes sense why that site, of all places, has anti-AI features now. I hope the shareholders will have mercy with his soul.
LinkedIn also has AI features. They ruined the job search a few weeks ago. It’s now natural language, missing a bunch of filters, and doesn’t even return the full text of a job post without additional clicks.
It also takes forever to load a post because it’s trying to decide whether you’re a match or not. It’s not very good at figuring that out. It keeps saying I’m a great match for jobs that require languages I don’t speak, or for jobs that require a master’s.
I hope this is a sign they’ll revert it, but I’m doubtful. LinkedIn was the least bad job search I’ve found.
Microsoft (and maybe all the AI companies) made at least few mistakes in the marketing of this technology. AI isn’t a replacement for human accountability. It still requires oversight and expertise. It is great for a first draft. It is terrible for a complete work product. Agents (with the right loops to verify accuracy) are reasonably complex - and most companies don’t have the people development infrastructure to train their teams with the right skills to build them. Honestly, AI is shining a light on how terribly businesses have been run over the last 30 years, and how lazy business leaders are.
It is terrible for a first draft if you’re aiming for something that reads like a human being’s actual thoughts. Don’t let an LLM think for you and create the first draft of “your” thoughts. At most, if you absolutely must, use it as a spell checker. But write your words yourself.
No one wants to know what ChatGPT thinks. We might want to know what you think… But only if it’s actually you doing the thinking.
Sorry but I don’t need my thoughts to write a cover letter lol
Nor do you need an LLM - someone makes a cover letter once and then you use it as a template.
It is great for a first draft
Only if the aim is towards mediocrity.
Do you aim for excellence in your first drafts? You may want to consider better effort allocation.
You are undervaluing the importance of planning the structure and content of what you are writing. The first draft is where future excellence is established. No amount of editing can fix a bad foundation - you can’t polish a turd.
If you are using a generator that combines everything (both bad and good) on the net, you will end right on the middle. If everyone that’s lazy uses the same method, your work becomes the definition of mediocre.
Nothing wrong with mediocre, I mean the corporate world is full of mediocrity and that’s what is expected in assignments.
That assumes that there is no effective way to filter good from bad. But there is - both automated heuristics and manual training does this.
LLMs absolutely produce mediocre output in some ways, but it’s not an inherent limitation caused by them “averaging” the internet. If that were the case there’d be a lot more typos, emojis and internet lingo by default. The fact that LLMs have these instantly recognisable stock ways of writing and stock phrases is a simple way of seeing that they don’t simply produce “average” output in that very naive sense.
The fact that they “average” their inputs is why there are comparatively few typos (different sources have different typos, so they average out), not too many emojis or internet lingo (again, different sources use different ones in different places, so they average away), and why they produce such tedious stock output (it’s an average of the inputs, so all the little quirks and idioms that make human communucation more vibrant have been blended away).
I’m sure there is some filtering on the inputs to try to remove the worst of it, but ultimately it’s still just taking the rest and building it’s probability tables from that, which leads to the homogenised outputs we see.
Mind you, having said there are fewer typos, the last time I bothered trying to get one to write some code, it managed to misspell a popular library name in multiple places, which gives some indication of how bad the inputs are, how bad the tokeniser is, or possibly both.
If “different typos” averaged out to “nearly no typos” the same logic would have different words average out to nearly no words. What actually happens is the model learns context, and can produce output which contains emojis in one context and not others. These contexts can be very far from the average context.
I’m afraid the upshot is you don’t understand how the models work. There is extensive filtering before training - they do not get “the entire internet” and average it. If you want to understand properly, there are a lot of resources that will let you, but I’m not going to try to do it here, so you’ll either have to believe me or be wrong, I’m afraid.
If “different typos” averaged out to “nearly no typos” the same logic would have different words average out to nearly no words.
No, because typos are irregular, so combining multiple sources does not reinforce them, whereas “words” (tokens would be a better term, because they’re not always full words) tend to be used in similar ways, reinforcing those patterns. As you say, context is relevant, an LLM isn’t just looking at the last token to decide the next, but at a much larger window. That does allow it to adjust to tone, as the probabilities of certain tokens, and so words, will depend on that tone, and the type of words used, and thus context, of a conversation. If emojis are used a lot in certain contexts, those patterns will tend to be reinforced in their training, and so produced more in their output.
As to filtering their input, at no point did I say they ingest “the entire internet”, so quoting it seems rather disingenuous. They scrape as much text as they can get, both online, and by OCRing books, as we’ve seen with the recent upset about the number they destroy. What the commercial models do with this afterwards is uncertain, as anything they say is likely to be misleading for commercial purposes. I think it’s a fair assumption that they want good quality data, however they define that, but filtering it all manually is obviously much too vast a project to do entirely manually, so it’s done heuristically, which has the obvious problem that it’ll let through low quality sources some of the time, lowering the quality of the overall data set. You only need to read the anodyne screed they produce to see how all of the little quirks and nuance that marks human communication tends to get left out, leaving LLM prose feeling rather vacuous and repetitive.
Do you even know what the word heuristic means? A heuristic is something that is just good enough. Not great, not perfect, just good enough to get the job done.
Heuristic algorithms were always going to result in LLMs that were only just good enough.
Heuristics are by definition imperfect, but they are not, generally, “just good enough”. In fact, heuristics may not be good enough for a given purpose.
Am I right that you’re not actually disagreeing with my comment?
Heuristics that aren’t good enough aren’t used for that purpose… what kind of rebuttal is this even?
As my violin teacher used to say, “practice doesn’t make perfect, perfect practice makes perfect.”
If you’re not aiming for excellence every time you do something you’re practicing imperfection.
Sure man, but the vast majority of us do not work in the making beautiful sounds with violing business.
If some middle manager boss says to some minion to make a quick draft of a keynote of some random info that tangentially may make sense in one of his meetings, and the minion then goes on working on it on full attention for a day to make the beautifullest keynote of the word he did not do a draft and the boss is pissed because other shit did not get done. Having an llm quarter ass a keynote based on a prompt and two refinements is precisely the effort required for that job, any more than that is wasting time.
the vast majority of us do not work in the making beautiful sounds with violing business.
And you should be. This… complacency you have is near literally rotting the world away. If you want to see love and beauty in the world around you, you have to put it there.
Having an llm quarter ass a keynote based on a prompt and two refinements is precisely the effort required for that job,
I’ll permit you this, I don’t want to work on that either, but you ought to consider how much of your life is spent doing this… corporate bullshit you obviously hate.
Find some dignity within you, man. Chase a life that’s worth living. Yes, even in a Cyberpunk dystopia this is still possible.
If the quick draft doesn’t justify the time and energy, the keynote certainly doesn’t. If the keynote is a waste of time, then the meeting is pointless. The best place for the LLM is to replace the jackass who called the pointless meeting in the first place.
This thread of comments reads like you just now discovered bullshit corporate jobs exists. Welcome to 1985. Some books to help you navigate are: Dealing with people you can not stand, bullshit jobs, almost any work stress self help book, and arguably American Psycho - specifically the movie version but the book is fine. Embrace the 80/20 rule, don’t let perfect be the enemy of good, show solidarity with your fellow workers, and just play the game until you can reach a point to enact change.
I agree with you. Yet those jackasses will not be replaced any faster by us agreeing here.
It still requires oversight and expertise
Perhaps some people who don’t mind the ethics of gen AI work very hard to produce good work. But I believe that when you give people an easy, low-effort path, a great many of them will take it (regardless of the quality of the results). Training notwithstanding, a vast number of people cannot resist the lure of doing as little as possible.
Yeah I get the feeling lazy people love it, and people who work hard know it doesn’t produce very good results in most circumstances.
Unfortunately it seems a lot of managers are in the lazy group. So they assume that when employees tell them it doesn’t work very well, they think it’s because they have a “bad attitude” or something like that.
First draft of what? The AI* doesn’t know what you’re trying to make, so to use it, you first need to write a prompt to convey to it what you’re trying to convey. If what you’re trying to make takes the form of prose, there’s your first draft already; there’s nothing the AI can add other than padding and replacing your voice with a psychopath’s. But to begin with, if you have the skill to turn a first draft into a final product, it’s almost always going to be faster to just use that skill to create the first draft yourself instead of trying to convey to an AI what it’s supposed to be like. If it isn’t, it’s something that’s so easy to describe that there’s either no value in making it or those few words are already the perfect way to convey it.
Also, this just completely undermines the whole thing:
It still requires oversight and expertise.
The sole virtue AI arguably has, is its accessibility; anyone who can read and write a supported natural language, can make use of it. But the moment it starts requiring expertise, that accessibility becomes worthless. To anyone who is even somewhat serious about what they’re trying to do, that natural language interface just offers far too little control for their purposes.
*) For the purposes of this comment, “AI” refers to the kinds of natural language-driven generative AI heavily marketed by companies like Microsoft, not the general concept of artificial intelligence or even the underlying technology of those products.
This is an incredibly stupid take, can’t believe people upvoted this shit, lol
Have you not used AI agents anytime in the past 6 months?
They can pull in information that you don’t write down, it can create Powerpoint slides, it can create mermaid diagrams from your description.
Those are not things you just do manually because you can? At that point why not code in Notepad instead of relying on the stupid machine assisted IDEs?
You may want to have your AI re-summarize that one for you. It seems to have royally fucked up somewhere.
You asked for it:
This is an incredibly stupid take, and I genuinely can’t believe people are upvoting it.
Have you actually used an AI agent at any point in the last six months?
Your entire argument seems to rest on the bizarre assumption that describing what you want is roughly equivalent in effort to producing it yourself. It isn’t. That’s literally why abstractions and tools exist.
I can describe the architecture I want in a few paragraphs and have an agent generate a Mermaid diagram. I can give it a pile of documents and have it pull together information I didn’t manually write into the prompt. I can describe the structure and content of a presentation and have it generate the actual PowerPoint. I can give it a repetitive refactoring task that I fully understand how to perform myself and have it apply that change across a codebase.
The fact that I need enough expertise to verify the result doesn’t somehow eliminate the time saved producing it.
I know how to write Java without an IDE. That doesn’t mean IntelliJ becomes useless because I need to understand Java to verify whether the code it generates, refactors, or autocompletes is correct.
And the claim that requiring expertise somehow eliminates AI’s usefulness is particularly strange. Most useful tools require expertise. IDEs require expertise. CAD software requires expertise. Excel requires expertise if you’re doing anything remotely complicated with it. Their purpose isn’t necessarily to let an unskilled person impersonate an expert; it’s to make an expert substantially more productive.
Natural language also isn’t the only interface. Modern agents operate on files, repositories, documents, databases, APIs, tool outputs, search results, and existing context. The prompt can literally be “turn this into a presentation” or “diagram this architecture.” You don’t have to painstakingly reconstruct the entire source material in prose first.
There are plenty of legitimate criticisms of generative AI: hallucinations, unreliable output, loss of control in certain workflows, mediocre prose, inappropriate use cases, people blindly trusting the output, etc.
But “if you know enough to check its work, you might as well have done the work yourself” is basically an argument for coding in Notepad because a competent programmer shouldn’t need a stupid machine-assisted IDE.
Knowing how to do something and wanting to spend your time manually doing every part of it are not the same thing.
is basically an argument for coding in Notepad because a competent programmer shouldn’t need a stupid machine-assisted IDE.
I actually don’t need to know how my IDE works to use it. That is one benefit to not using dice as your main construction technique.
the bizarre assumption that describing what you want is roughly equivalent in effort to producing it yourself.
With increasing degrees of specificity, it kind of is, yeah. It only saves time in places where you don’t care about shit-quality work.
I actually don’t need to know how my IDE works to use it.
Neither do I need to know how a transformer works to use an AI agent. What does that have to do with anything?
I need to know Java to recognize whether IntelliJ’s refactoring produced sensible Java, just as I need domain expertise to recognize whether an AI agent produced sensible output. “Requires oversight and expertise” does not mean “requires understanding the internal implementation of the tool.”
With increasing degrees of specificity, it kind of is, yeah.
No, it really isn’t, and this is probably the strangest part of your argument.
Specificity of requirements and effort of implementation are two completely different things.
“Take these 30 classes, rename this field, update its usages, add null checks at these boundaries, update the affected tests, and run the test suite” is a reasonably specific description of a task. It is quite obviously not equivalent in effort to manually performing every edit.
“Create a sequence diagram showing the interaction between these five services for this request flow” can take seconds to describe and considerably longer to manually construct.
“Take these documents, extract these specific metrics, compare them by quarter, and put the results into a PowerPoint using this existing deck as the visual template” is a perfectly comprehensible specification. Actually reading the documents, extracting the data, calculating the comparisons, creating the charts and assembling the slides is where the work is.
This distinction is the entire reason programming exists. A specification describes what computation you want performed. We don’t conclude that because SQL lets me precisely specify which data I want from a database, I might as well manually inspect every row.
And “it only saves time where you don’t care about shit-quality work” is just begging the question. You’ve defined AI output as shit and then concluded that anyone accepting AI output must therefore not care about quality.
The useful workflow isn’t “ask AI for something and blindly ship whatever comes out.” It’s “specify the task, let the machine perform the expensive mechanical portion, inspect the result, and correct or reject it where necessary.”
Sometimes that is slower than doing it yourself. Sometimes the output is shit. Sometimes AI is simply the wrong tool.
But the idea that describing a task with sufficient precision inevitably approaches the effort required to execute that task is just demonstrably false.
If that were true, half of software engineering wouldn’t exist.
There is like a very basic topological fact here that you are just failing to grasp.
Specificity of requirements and effort of implementation are two completely different things.
No, they’re not. Implementations are just requirements with very high specificity. When you roll your eyes over the horribly inept dogshit your AI produces and then correct it, you are adding specificity to the system. This can proceed ad infinitum until you end up just doing the whole thing yourself—which I have done with Suno, because Suno is garbage.
You’ve defined AI output as shit and then concluded that anyone accepting AI output must therefore not care about quality.
It’s a rule that’s served me well. I think I’ll keep doing it.
“Create a sequence diagram showing the interaction between these five services for this request flow” can take seconds to describe and considerably longer to manually construct.
It is extremely funny you would put this up as an example.
I will have to manually construct it anyway because that is literally the only way I’ll know if the AI-that-fucks-up has fucked it up or not.
Any information that whatever you’re working on hinges upon, you need to already know and have confirmed before you start working on it. So not only did you need to look it up anyway, only you know what aspects of that information are most important for the goals of your work. Of course, there can also be less vital pieces of information that you may consult as you work, like maybe you want to compare the size of something with football fields, to make it harder to understand. Undoubtedly an AI can do that for you, but in the end you still need to look it up to verify it, so you haven’t really saved any time or effort.
It’s a similar kind of story for things like generating PowerPoint slides. Slides based on what? An article? The script? Slides that just summarise the prose make for a terrible presentation and aren’t any better as a starting point for one. Also, anything that can be described using less time and (cognitive) energy than to just write it in Mermaid code, isn’t worth making a diagram out of.
At that point why not code in Notepad instead of relying on the stupid machine assisted IDEs?
This is comparison makes no sense, because an IDE is the very opposite of AI. There is little value in a person manually performing the same algorithms over and over again, other than maybe for a learning process. So if they can be automated, it just means that people can focus their efforts on doing the work that requires, you know, personhood. AI is the opposite in that it’s useless for procedural automation, and the only thing it can do automatically is the kind of work that can only have value if a person is doing it.
Ok
And it is usually half right, its really a mess, unless you give it a really good rough draft outline.
I find writing the code is the easy part. The difficult part is figuring out what the requirements are, fitting together all of the data relationships, figuring out the security so the correct people and systems have access to the data. To do these things I need very specific knowledge of the systems I’m working on, knowledge the AI doesn’t have.
I could see it could give you a simple interface that’s similar to something it’s been trained on. But it’s capabilities are very limited in terms of doing anything novel. It can’t actually understand a problem, it can only give some code similar to problems that exist in it’s training data. Sometimes that’s useful, since at times I do have to solve problems that’s been solved by others. But most of the time the AI can’t understand what I’m trying to do and it’s easier to write the code than it is to try to explain to an LLM how to write the code I want it to write.
Maybe you are doing some exotic stuff, but for me it’s more than capable of writing code, coding is mostly about applying existing patterns to solve known problems, very few programmers deal with novel problems.
Design Patterns book has been out for what? 2 decades, and it’s not outdated.
For good team interoperability you want to have code that’s standardized, that’s also where LLMs excel at.
It’s a nice productivity tool, just like an IDE is a nice productivity tool
In software engineering, sure there’s patterns, but that’s just guidelines. Eventually you run up against a problem where an something you don’t have control over isn’t 100% reliable so you have to think through how to handle that scenario. Maybe you retry an API call, maybe you notify some people about the issue, it’s all context dependent what you need to do. The AI doesn’t know what to do, because how would it? There are a massive (I won’t say infinite, but in terms of a human lifespan it may as well be) number of combinations of technology X connecting to technology Y. So there is always going to be a great many problems that no one has ever encountered before. LLMs increasing the number of features in the software doesn’t solve those kinds of problems it just increases the number of problem.
I think it’s fine to think of it as a tool, like an IDE. But just like an IDE, it doesn’t solve the problems, the person using the tool solves the problem.
For instance today, the LLM explained to me how to enable SSL certificate negotiation with TLS 1.3 some shitty IIS setup I have to maintain. I used the LLM because I figured that I’m probably not the only one that has seen that issue. Now the problem I have to deal with is whether the software that updates the certs might break the config, that’s a little less likely the LLM will have a reliable solution to. I might just wait until the software updates the cert an manually test if it broke. The day before I had to launch some stuff in the middle of the night so I wouldn’t interfere with business operations. The launch was to change something from single linked list to a doubly linked list which is important for reasons I can’t explain now. I mean I would explain it, but honestly I can’t remember why. I just have in my notes that it needs to be a doubly linked list for the recursion to work properly. Maybe next week when I fix the FE I’ll remember why, but if I was correct many months ago (it’s a low priority issue), the FE should now have the data it needs to solve whatever problem was there.
So yeah, I use LLMs sometimes for problems that I think other people have seen before but sure as hell can’t be driving on what I do. Someone has to make the various pieces of software work together, know the business the software is being used in. More software means more problems that someone has to figure out. If you make n pieces of software with LLMs, you’re make n! problems to solve.
Yes AI or LLM isn’t able to code solution itself, but it can fill in a lot of gaps based on what you tell it, it can copy existing patterns.
And you can direct it, it’s a tool like an IDE, just much better .
Attention driven economy. All enterprises now behave like they had a field week at used cars salesmen retreat. You can’t sell unless the value proposition is grotesquely inflated.
