Isn’t this the guy who tried to slip his wife STD medicine because he had sex and caught something from (allegedly underage) Russian prostitutes on Epstein Island?
That doesn’t mean he’s wrong about AI though.
But, they are also controlling the narrative by playing good cop bad cop on this subject. So he’s not telling the truth, either.
“allegedly underage” oh sure, ole Willy Gates fucked Epstein’s old lady slaves. Which he was known for.
That’s right! Bill Gates fucked a kid.
I don’t understand what people have against Al. I thought we all loved Al. Al is the coolest. A little weird, but I mean, that’s Al’s appeal.
Transcript:
Interviewer: So tell me about why you think AI really will take jobs away from human beings at a significant scale.
Bill Gates: Yeah, so there’s no doubt to date, AI has created more jobs than it’s destroyed. The demand for the skill sets even just to build the data centers is very, very high. We have a, you know, a reasonably low unemployment rate.
The superiority of these systems is subject to a threshold where you have to believe that it’s incredibly reliable. And if you’re going to have your tele-sales or tele-support capability be AI-driven, you want to make sure that its accuracy is better than humans. And so the only profession we’ve truly crossed over that threshold is coding. And even there, I know a lot of people who are, you know, kind of stuck in the past and, you know, don’t want to use the AI for coding. But managers of coders get that most applications can be developed very inexpensively.
We will, in the next few years, cross over those thresholds for accounting, legal work, tele-sales, tele-support where 24 hours a day in every language with infinite trivia capability and no urgency, you know, so you look at the AI nurse and their several companies. They have perfect memory.
[Interviewer briefly clarifies something here, I removed this part because the autotranscription didn’t handle it well]
You know, you go to San Francisco and ask people, do you rather ride in a Waymo or rather ride with the human driver? Go ask people in the UK who use limbic for mental health support. You know, so the notion that just the market demand preference for driving or the nurse, you know, or even the person on the phone will favor humans. That’s a quality threshold which will be passed through, you know, if say insurance companies are still doing human claims, medical claims, which is a very AI-capable task, then a competitor who has very few human employees will come in and change the pricing model for that industry.
I think robot customer support will always suck because whatever it is you need from the company, they don’t actually want to bother with you. Don’t think I’ve ever made a phone call because I needed “trivia capability”. It won’t be good because they won’t give it access or authority to actually help anyone.
He repeats the “better than humans at coding” which is super situational. Executives got so used to “code is code” mentality they can’t seem to understand that just because they can prompt up straightforward apps and other folks have done stunts like “make a knock off of this compiler” does not mean it’s just better than people.
Also very bizarre to say they have perfect memory, they are pretty spotty at that.
On the matter of support, it will generally always such because it’s rigged for the company in all matters where helping the customer would go against the company’s interests. Has been a long time. Why pay warranty when you can make it so painful they will give up?
They don’t give the average call center worker the ability to fix anything already. The gig mostly became being there for customers to abuse at some point with a bit of passing a handful of people up the chain one step closer to a person with the authorization to maybe offer you appeasement for a problem they won’t actually fix.
My idiotic not-even-briefly-thought-through prediction:
- LLM systems can’t do anything reliably, but they can do it cheaply, so ‘decision makers’ contract out LLM based systems to handle customer support/service and slap a disclaimer on it. You can pay for premium support by a person. Your PA still has to wait on hold to talk to this person. If you can’t afford a PA, you can’t afford the premium service.
- It’s so much cheaper than having real people, not because it’s cheap, but because now you have infinitely scalable team size with no downtime, no managers, no benefits, or other ancillary costs. Quality goes down. Unit economics improve.
- Companies that don’t jump on the LLM train will be more expensive because they have to have real people, which are better on almost every metric, but expensive to maintainne. No one will be able to afford the premium service of having someone who actually has the ability to do anything, so they will lose customer base, so they’ll get more expensive, so they’ll lose customer base, so they… Repeat until they are bought out by their competitors or are a luxury brand charging fifty billionaires whatever they feel like because the check is blank.
- The market polarizes. Everything is either barely affordable crap or expensive luxury service. No one is happy, but ‘profit’ is maximised. The American health insurance company becomes the model for everything.
I can only speak to my field, which is legal. Incomprehensible but prolific drivel has always been something the court system has struggled with getting through. That same drivel is now pretty good at pleading generalities but it’s awful at detailing facts.
You can get past the first hurdle in the legal world and unlock a massive and expensive cascade which AI can also effectively navigate for you.
In essence, AI can, very effectively, take an incoherent unreasonable and often mentally unwell litigant and magnify the expense of getting rid of their frivolous claim by over 100.
It’s almost assuredly going to 1. Chock the system. 2. Force massive reallocation of resources in the judiciary 3. Thereby vastly increasing not decreasing legal work. And 4. End up with valid claims being killed in a massive change in judicial attitude toward “clear enough” pleadings as they struggle with an AI influx.
We didn’t need Bill Gates to tell us this.
Fuck this pedophile pirate trying to stay relevant and respectable. Who the fuck is saying that AI will create more jobs??
I still can’t decide if these people are delusional or I am.
Every single time I use an LLM it fucking “lies” to me or otherwise completely fails at the task. The people talking like this seem to me like they’ve never actually used it, or haven’t actually vetted the accuracy (like most AI users).
Maybe I’m just not using the “good stuff”. Or I’m not imaginative enough to foresee a near future where these problems are actually corrected and it becomes trustworthy.
I’ve never been so torn by a technological prediction.
Generally I’ve found that:
If the facts are painfully obvious from a simple web search, then GenAI has a decent chance of getting it right. This can be useful if you can’t recall any “key” words well and the GenAI can craft several searches and get there.
However, if it does mess up the facts, the result looks superficially the same as “correct”. So while it can give accurate data, you always have to double check. This can still be useful, as finding the right search terms can be a decent help.
In coding, sometimes in some situations, you can have requirements that are absolutely testable, and thus you can have the models retry and retry until it works. This isn’t always feasible. Even when it seems feasible, you may screw up the criteria, or the GenAI when enough freedom disables a probablematic test rather than solve it, and it likely will generate code that’s not really fit to modify. There are a lot of situations where this is useful, but it is infuriating that non technical people and even some low skill technical people assume this is always the case.
Then when you get away from facts mattering, it gets “better”. Example, someone jokingly asked for one to “make gta6”. After a while it came back with a GTA 1 clone. Lots of people were impressed, because whatever it did could be considered a success even as it obviously didn’t match the expectation. The operators also like to GenAI some webcomic, where it is a fiction. They almost always didn’t have any interesting thought going in so they tend to be crap, but “correctness” didn’t matter.
Of course, also making fakes. Supreme case where looking correct matters but being factually correct does not matter at all. GenAI above all else “seems” correct.
from my point of view it feels like most people are willing to trade their cognitive function for being lazy, which is a boundary i never want to cross.
AI will produce a lot of code but most of it is pretty poor quality as most training data is going to be poor quality code, there’s just always going to be more bad code than good to begin with just due to how difficult quality code really is to produce. I’ll give a hint, good code is usually small and succinct.
since my work started pushing AI live site issues have increased dramatically. turns out the person using AI looks like they have a ton more productivity but in reality that just shifts to whoever is reviewing the code. and to those who will say its the developer’s responsibility to review the code, yeah no shit, but if you ever work corporate you realize most don’t care as long as their managers think they are productive and just blame others for being bottlenecks.
Overall it just enlightened me to how bad the average developer really is, but i guess obtaining mediocrity is the sacrifice to make in the name of “productivity”.
what if it trained on openbsd and its ports
I think a lot of people just have low standards.
I’m curious what you are using. The free versions of chatgpt have been like that for me, but even Gemini flash with extended thinking, also free for a while longer, is giving me pretty reliable results as long as there training data out there to derive an answer from. The higher (paid) Claude models will one shot most coding tasks.
Claude can one shot tasks until you get a larger system then it completely shits itself. These models are nothing more than autocomplete, and they can’t hold large systems in their heads. Anthropic literally tried to rewrite all of bun using Claude, they said they did it and yet it still hasn’t released six months later.
until you get to a larger system then it completely shits itself
This has gotten a lot better for me by having a “send out scouts” skill that has a lower tier model agent search through the codebase before it starts to plan. Has handled my companies giant monolith pretty well and even can handle cross repo features as well.
Yet it still hasn’t released six months later
Claude code has been using the new rust bun for ~5 months now and has been working fine, and bun 1.4 that released in August is using the rust rewrite
Yeah I certainly wouldn’t advocate building a whole business around code it wrote. But for small personal tasks it hasn’t let me down. Building custom server applications, desktop applications, Firefox add-ons, upgrading my homeassistant 10 versions over a couple weeks without letting anything break. These sort of things it handles pretty easily and are all things I wouldnt get done without it.
I’ve not yet fucked with Claude. I don’t want to pay for it, and I really don’t like the surveillance aspect of these centralized systems. Mostly I’m using Gemini, whatever DDG had in their search results, and local models I’ve been fiddling with (like Qwen3.8 right now).
All more or less garbage once I get into the details of anything on the edge of my expertise.
Are you using Gemini in flash extended thinking? (Not flash light) . I haven’t had many hallucinations other than cases where the training data it needs just doesn’t exist (cases where I can’t find the answers by googling either)
I got a free year of perplexity.ai which gives me limited access to claude sonnet and yeah, its honestly pretty good.
I’ve been using OpenCode with whateverthefuck free models they have listed on there and they all seem to do fine with agentic tasks like building me scripts or executables to make my work tasks easier.
I used Gemini at the start with “Frontier Knowledge” and it seemed to do worse than the ones listed on OpenCode, but maybe that’s because i could only do like three prompts a week since I refuse to pay into an AI.
end of the day, its just LLMs writing code for me, but I cannot see how this would be useful for a large scale code base, but also #NotAProgrammer.
How do you verify that everything it tells you is correct?
You check the links/references it gives you.
Gemini does a pretty good job of this because it doesn’t seem to have much built-in knowledge. Instead, it just searches the Internet on your behalf and returns summarized results with links to where it got that specific information.
I use it to search for scientific research all the time and the summaries often aren’t detailed enough so I actually click on those links. I’ve yet to encounter a situation where it fucked that up (invented links that don’t exist) but I have heard about it happening.
So far, the summaries have seemed to be pretty spot-on when it comes to biology papers 🤷
By writing instructions to insist that it double verifies every (non obvious) claim with a minimum of two independent sources. I also told mine to always assume that the initial prompt is missing crucial context, and to ask as many follow-up questions as necessary until it has enough information to provide the answer to the question I’m really asking. (For speed and efficiency you can even make it give you multiple choice options to click on.) Because sometimes the problem isn’t with the LLM, but with the user asking the wrong questions.
Using those two instructions alone, I’ve encountered considerably fewer hallucinations, and when I’m still not certain, I can simply click on the sources linked next to every single claim the AI makes.
Right, but that sounds like you implicitly trust the LLM and only verify statements when they seem off. So your intuition is the final arbiter if truth?
It definitely has an element of garbage in, garbage out.
I’ve been able to find a workflow where it’s mostly correct and can handle most of my gamedev related coding without making too many mistakes. I still have to actually read through the code and pay some attention to what it’s doing, and if it misses something and goes on a wild goose chase, it’s unusable and I have to start over (so someone who didn’t know what they are doing would be cooked), but whatever.
Sure, it does require a lot of looping adversarial reviews, and my average token cost is around 3000$ (4B tokens) a month (we have unlimited budgets and a pretty accurate tracking, and also probably cheaper than consumer prices per token with how large company it is), which is actually more than my monthly salary, but it’s just a job, for a company and on a product I don’t really care about, and I can 1) keep slacking in my job while doing my own coding stuff and projects, and keep seeing how absolutely unreasonable the prices are if you want to get at least semi-submitable results.
You could ask if it’s necessary to spend so much tokens - but so far most adversary review rounds do find serious blocker bugs the first implementation had, most of them being some hidden stuff that would be difficult to reasonably find by hand unless you really understand everything around the code, which you should, but the point of AI is that you shouldn’t have to.
Is it worth it? Lol, no. The whole team is loosing codebase knowledge, we’re getting bottle-necked by pending PR reviews that are just stacking up and no one wants to do, so we’re not even more effective, the cost is absolutely absurd and in no way near sustainable or worth it. And the longer it goes on, the less I can realize it’s spewing bullshit and I should stop it and nudge it into a different direction in our codebase, because I’m slowly loosing touch with it, while for now I’m still relying on what I remember from before we went all-in on AI.
And that’s while the whole industry is in the “Uber pricing” phase, so it will get a lot worse. But yeah, if you can burn 100-200$ per a simple implementation task, then it can have a pretty usable results. And that 3000$ a month does not include our CI review bot, that does additional rounds of multi-agent council reviews. And I’m not even working on anything complex, mostly just various menu screens UI. I can imagine the bill getting a lot higher if you get into more involved systems.
you give me hope
No matter how much I carefully structure a prompt, define specific behaviors in skills, and tweak the agent md files it will still just go do something I don’t tell it to or not do something it’s got really specific instructions for. We have to write all code by LLM now at work and I’m trying to do my due diligence to review code before putting it up for PR. 9 times out of 10 when I tell it to show me a diff before committing it silently runs git diff in the background and prints “that’s the full diff”. That’s with some basic “here’s what I want when I ask for a diff” in the base context.
Tbf I think its the context limit that makes things hard.
1m token context is so stupidly low for all of the input we consider and filter in real time.
He doesn’t take about what is, he talks about what’s coming.
Problem is that Gates isn’t really “in the loop” and doesn’t have especially valuable insight.
His position in tech was always a bit removed from the core technologist work, and now his exposure is a telephone game with people that are as distant from the tech as he was.
It is really going around with no shortage of commentators spewing out guesswork, but Gates is given more credibility by virtue of his role 30 years ago.
That’s a fair point. I just don’t know if the future they see can become a reality.
I said that a year ago, and half a year ago, too, expecting the S curve to hit and the technology to flatten out at some upper limit. But instead we got the agent loop, and models that make really good use of it, the releases only get faster and faster, and real improvements with each one, either faster and cheaper, but just as good, or actually a good deal more capable. I’m seeing signs of behavior that is more than just a good auto complete, and think there is actual intelligence.
Even should the S curve start to turn towards slowing down now ( and it doesn’t look like it), the ceiling that it’s going towards is so high, I am with Bill Gates here. We are not prepared for what is coming.
I’m seeing signs of behavior that is more than just a good auto complete, and think there is actual intelligence.
I suggest you be real careful not to anthropomorphize these systems. To me this sounds almost like AI psychosis.
AI psychosis is what I accused by precious boss of, when he fabulated about replacing all his employees based on gpt-4o. What we have today is something completely different.
Also, I’m not anthropomorphize these things. Intelligence is not a uniquely human quality.
Where I am forced to encounter advanced LLMs is every company rolled out replacements for my “dumb” assistants. Like Alexa on an echo dot or Gemini on Android auto.
What I ask them to do does not take significant processing power. I need you to set a 5min timer. Not talk to me like you’re alive. Don’t overthink things, just do simple things.
All that extra banter they added in eats up processing power. So now it’s slower and works like shit because it’s formulating some complex response to my request to beep in 5 minutes. Just set the timer, my hands are covered in raw chicken juice.
You can disable Android auto’s Gemini by setting the digital assistant on your android phone to “none” under default apps.
If you use these things more generally that might hurt you more than help, but it got rid of the bloviating idiot in the way of the old useful voice that gave me directions.
Thanks I’ll have to dig deeper. I figured out dumbing down Alexa but haven’t dug into Gemini yet.
I just wish their was an alternative besides Apple and Google for car things. Sooner or later the choice of having it disabled is going to quietly go away.
Turning off the assistant entirely isn’t a step I’d expect a lot of people to take (because they use the assistants). I think it’ll stick around if you can tolerate it, because it’s likely provided for certain parties that will be very irritated if that ability is removed.
I not only can tolerate it, but it’s what I prefer. I wish they’d put “hold power button” back to show the restart dialog, I never used the assistant at all, and I even turned off that “google feed screen” that sits to the left of normal android screens. I love to disable junk.
I’m in college and for one of my classes we actually have a prompt we feed to ChatGPT so that ChatGPT creates an excel spreadsheet for us as a step in an assignment process. The whole spreadsheet.
The class is for healthcare and I won’t get more specific, but yes, you can use AI today to make whole ass Excel spreadsheets.
(One of the steps in that particular assignment involves fact-checking the spreadsheet, btw, but about 95% of the time all the answers are word-for-word in the book, 4% they’re taken from the web and have more up-to-date info than the book has, and 1% is blatant lies.)
The class is for healthcare and I won’t get more specific, but yes, you can use AI today to make whole ass Excel spreadsheets.
lol, it amazes me what some people are impressed by
Yeah, so far when someone shows me a concrete example of this amazingly complicated thing that GenAI helped then realize, I think “really… that’s it?”
It’s a decent reminder that a lot of people have more modest needs and a lot of the enthusiasm can come from that, and maybe that’s more understandable.
The bar is not that AI needs to be right. It just needs to be more right than the employee willing to do the job for the price it costs.
People have been bad at their jobs for years. Now computers can be to.
Your experience is pretty unique then.
Yes, LLMs make mistakes, but even small, self-hosted ones are pretty efficient today if you prompt them well. They’re not mind reading software so you need to be able to describe the task and HOW you want it done, not just barf in some basic instructions like “write me a copy of Facebook but better”.
It sounds like you’re saying people still need to be smart enough to use them properly… Which will be a problem as people rely on them more and more, and in turn become more stupid.
Eh idk I still have it make mistakes constantly.
Recent example: I asked AI to help me come up with a search/replace string for editing a file in vim. My prompt was something like “I am editing a text file in vim and I need to replace <description of pattern> with <description of pattern>. How can I do this with :s/ ?” And it spat out something that didn’t work. It was not valid syntax (for vim, I think it was valid standard regex, or close to it), and it didn’t quite follow the pattern I was trying to describe (although ofc that could be my fault to some extent). But it was useful in that it pointed me in the right direction to come up with a correct formula with some follow up googling and experimentation.
That has been very typical of my experiences with AI. Useful sometimes, but absolutely not “does everything to the point you don’t have to think about it” that seems to be a common opinion.
For context I’m using ~20GB local models, so not Claude, which people who pay for LLMs swear by.
I use Claude + local models.
Yes, older models will make those mistakes. The solution is to provide appropriate agent/skill definitions so it doesn’t just spit something out, but rather comes up with the solution, then smoke tests it in a separate scratch environment.
Claude uses this approach for reinforced learning, and it works well. Takes a few more rounds to resolve, but the solution is generally flawless (for that specific purpose).
This is basically the same as asking someone who knows a bit about everything to recite obscure information from memory. I doubt most humans would do better.
What you’re missing here is a feedback loop, validation, skills, etc. For example, if I ask one of my well-configured agents the same question, they go read the help/man page/other docs, start a vim session, quickly test/iterate until the result is correct, then give me a one-page document explaining what I need to know, with cited evidence — far faster than I’d do it, and I can keep working for the few seconds it takes. This rigor is written into my global instructions, not something you get out of the box on most models (Anthropic’s models tend to be good at this without handholding, which is part of why they’re so popular, aside from the fact that they just don’t make as many mistakes).
The same mindset scales to larger software problems, too. As long as you have a well-defined specification and good agent instructions (and/or something like Spec Kit), you can have agents break it down, implement, and then other agents compare the result to the spec, and just keep looping until it is done. The hard part is writing good specs and requirements, but that’s not a new problem.
This is basically the same as asking someone who knows a bit about everything to recite obscure information from memory. I doubt most humans would do better.
Sure, but a good web resource or even a good reference book would provide better help faster. Unfortunately it’s getting harder to harder to find those good web resources as search results get overtaken by AI slop.
iterate until the result is correct, then give me a one-page document explaining what I need to know, with cited evidence — far faster than I’d do it
I have gotten it to do this in certain situations, like the other day I wanted to simplify a math formula, so I gave it a loop with a Python script that checked its solution against the original reference version. This worked pretty well.
But I had to write code specifically for that situation. Even with a good skeleton to start with, it’s a non-negligible amount of work to get that set up. I feel like the scenario in which this is useful is kinda narrow: when I have a very good idea of exactly what I want, but some step along the way is a hassle. General software engineering, like making a whole app, is far too open ended, and most of the sub-problems I encounter in software engineering seem either too open ended or too small to benefit from this approach.
That also doesn’t account for the speed of models. My experience is a ~20GB locally hosted model takes like 1-5 minutes to produce a good length response, and the few times I have used online models they are often slower. A few minutes per iteration, accounting for debugging when it goes off track, is not exactly fast or hassle free.
I just feel like the trade off where using AI vs doing it all myself is pretty limited in when AI offers an advantage.
No amount of prompt “engineering” will help when they outright make shit up.
Yes it does. Just need to go beyond prompt. Add reinforcement loops, make it test the solution in a separate environ it can’t screw up in, and have it not just INVENT things (“give me X”), but research the topic and base its solution on the rules created by the research.
This is what basically the Claude harness (not the local but the remote harness you can’t see) adds to the LLM what makes it so powerful and useful. Replicate those processes and even a small 4B mode will be incredibly capable.
I think most people are so disorganized in their thinking, they can’t “prompt” well. There’s a lot of unclarified assumptions and leaps in how many people communicate
And funnily enough, AI is great at helping streamline that process too. People just need to ASK for help (even if they’re asking the AI model) instead of being set in one way of thinking and expecting miracles.
Like back when we had to teach people how to google shit. Tedious.
I use AI all the time to parse log files for errors. Or write up simple scripts to do things that are one-offs or test of concept. Most, if not all succeed. I made a parser that translates the config of one brand of switches to another one, worked perfectly.
In what way are you using an LLM? It sounds like you’re asking it moral questions, to which it of course can’t give you an answer in any sort of objective sense.
Give us an example of some of the prompts you’re using and what LLMs. I’m curious if it’s a use case difference or you’re using the dollar store’s customer service AI to try to help you with your coding homework.
Here’s a very common response to anyone that suggests they had a bad time with LLMs. You just aren’t using the right model. You didn’t ask the right questions. You didn’t give enough context in your prompt.
It’s not the fault of this infallible AI, it’s PEBKAC.
Nonsense.
I mean yeah, it’s not magic, it’s a tool and it does take some skill / know how to use them correctly, and the people making those comments could be trying to teach those skills.
Like if someone said they had a bad time with Linux you’ll get similar questions and suggestions about their setup.
I’ll concede that using LLMs in a useful way may take some skill. However, that’s not how these things are presented to everyone and it doesn’t reflect the reality of how the majority of people use them.
Forgot to tell it ‘make no mistakes’.
Prompting an AI is very much a garbage in, garbage out type thing. Just like with any tool, you need to know how to use it properly to get the results you want.
Hell, some of the things I’ve seen people ask AI would confuse a human too.
Uh… I was just curious because sometimes it’s fun to see how the LLMs screw up. e.g. rocks on pizza.
It’s not the lack of evidence presented, it’s the person that asked the question that’s the problem.
No you weren’t. You literally suggested I was using a “dollar store’s customer service AI to try to help [me] with [my] coding homework.”
Using a dollar store’s AI to help with coding homework would be hilarious! Just like rocks on pizza.
You seem to be placing me in the wrong bucket. I use AI professionally, yes, but it also pisses me off pretty regularly. I’m not some “AI Bro” or whatever TF you’re thinking. Just some guy who finds AI mistakes to be funny and I want to reproduce them.
They think it means more employment opportunities.
It actually means a clone army of Steve Jobses. Nobody will see it coming. Except Bill, I guess.
Nobody will see it coming. Except Bill, I guess.
And, now, us.
This how you know Microsoft is behind on forcing AI into everything.
Probably lost the race, in fact.
Bill didn’t say anything. Prices won’t go down because AI replaced humans. If anything they will rise, as they are.
Prices will definitely rise to cover operating costs and to recoup all the investment costs.
I honestly hope the whole thing crashes once people realize they can run their own LLM on their own hardware.
And what hardware is that, exactly? Speaking as someone with what I’d consider to be a pretty decent homelab, I can’t self host the kind of AI that I use for work, not without literally emptying my savings into the hardware and energy needs. AI is already expected and required in my job, so the only move forward is to give elon money for access to cursor, which my company is already doing. If the company were to self-host, they’d prolly still use one of those shiny new datacenters since there’s real value in offloading hardware ownership to a third party, meaning those never go away and they still dictate the cost of using AI. I have a hard time believing this will happen in most companies simply because they already shelled out tons of money for SAAS software that has been self-hostable for over a decade, so why would you think they’d stop and think “hmm maybe we could cut costs by taking over the hosting and maintenance of the AI ourselves”?
I think we’re at least a decade away from having user-accessible hardware for AI that doesn’t break the bank. I don’t think we have a decade to spare, however.
AI is already expected and required in my job
then you should be looking for a new job… unless you happen to be on a very specific niche, any company so committed to current AI that cannot survive without, will implode in short time
Have fun finding a coding job that doesn’t expect you to use AI. My entire circle of contacts on the field are already there, so right off the bat my main way of getting a job would be crippled.
Yeah the IT department of my company had an all hands meeting today and I was a little taken aback by how much AI push there was, how much devs are expected to use it, and how much it’s costing us. They say it saves time, but there was a notable drop in app quality over the last few months, I can’t say it was caused by devs using AI for sure, but it is the most obvious answer considering there hasn’t been a big staff turnover in the same time period…
Speaking as someone with what I’d consider to be a pretty decent homelab, I can’t self host the kind of AI that I use for work, not without literally emptying my savings into the hardware and energy needs.
I think we’re at least a decade away from having user-accessible hardware for AI that doesn’t break the bank.
So, for coding, which is what Gates was specifically talking about being doable now, maybe we could corral up the hardware. Like, maybe one could cover specific fields.
There are still going to be issues like power and cooling and hardware cost and whether people who make competitive models are even interested in providing it for home use (since it makes it harder for them to make a return on their model). But set that aside.
As I’ve said before on here, I would say pretty confidently that we will not have local models running to do all of the stuff that cloud compute is used for or is being built out to for at least something like four to five years, and that’s if we started immediate, massive buildout of memory fabrication to a much greater degree than we have. You cannot build a new memory factory in less than that timeframe, and we will not have that capacity with existing factories. The majority of fabricated memory now is going to cloud AI use, and cloud AI hardware will have considerably higher capacity utilization than hardware at home. You’d have to have many times over as much memory being produced to have the same compute capacity at home.
I’m not opposed to doing LLMs or parallel compute at home at all. I have a 128GB Framework Desktop and an XT 7900 XTX that I got to do just that. I’m just saying that we are not going to realistically be able to move all of the stuff in the cloud to the home for at least something like half a decade, and very probably more, because humanity does not have the memory available and can’t build enough memory fabrication capacity for it in that timeframe. It doesn’t matter how much value is being provided by some home user of that hardware or what their willingness is to spend on it if we don’t have the memory. Like, even if every person in the world could produce, to pull a number out of the air, a real $1M in value every year via use of a home AI rig, even if all that demand suddenly materialized out of thin air, all that would happen is that prices would rise sufficiently to make the hardware unaffordable even at those extreme levels. The constraint is on the supply end, not the demand end.
Understandably you won’t be running meta level Machine models, but running olama and downloading a open LLM model can get users started for simple things they might have already been doing.
I am running a fully local model for my smart home needs, since Google’s killed of Assistant and is now pushing Gemini down my throat, I figured I would just do it my self.
For anyone interested take a look at NetworkChuck on YouTube. His videos are a little to sensational for me, but he does have some interesting topics covered from time to time.
Understanding is a long time coming. It will probably take businesses realizing they are stealing all their trade secrets when using LLMs for any meaningful information to come out.
People are already losing the jobs they have because of Ai
Every answer here saying llms aren’t that bad is basically telling you to stop trying to learn for yourself, and just get better at telling the Lazy Lying Machine to not be so lazy and not lie as much.
Even if you get good at it, you still don’t have an actual skill at the end of the day
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Oh hey both of these guys can get a data center shoved up their asses sideways
Data centers can hum very loudly, I wonder if they vibrate too
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This is uncommonly off the mark for Gates. There’s a sort of lack of nuance in these remarks. Too much confidence.
Frankly, I feel like the media hasn’t really engaged him in tech for the last couple of decades since he stepped back. So he hasn’t been pushed for takes on tech.
There is zero reason to expect Gates to have particular credibility on this front, he hasn’t been part of “the game” in a long time. Even then his perspective was a bit indirect.
If back in his hayday you had asked Gates about the core technologies of the Web infrastructure, he would have confidently assumed Windows NT, IIS, MSSQL, and . net would be the foundation of everything. Not because of deep technical understanding and vision, but because his guys were doing it and surely his guys were the best guys.
I have located the PLUG and I’m ready to PULL.
Just say the word.
I’ll join you just need more ammo
Ooh I’m way ahead of you. I had my pitchfork gold plated, just for this occasion. I’m ready NOW!
