US stock markets have been hit by a further wave of AI jitters, this time from yet another viral – and completely speculative – warning about the impact of the technology on the world’s largest economy.

The latest foreboding is from Citrini Research, a little-known US firm that provides insights on “transformative ‘megatrends’”. Its post on Substack, which it called a “scenario, not a prediction”, rattled investors by portraying a near future in which autonomous AI systems – or agents – upend the entire US economy, from jobs to markets and mortgages.

Citrini’s scenario begins now and ends in June 2028, with US unemployment cresting over 10% and an Occupy Silicon Valley movement setting up camp outside OpenAI and Anthropic’s offices. In the interim, a series of events triggered by the widespread use of AI agents guts software companies and ripples outwards, hitting private credit and mortgages, and leading to an unchecked downward spiral.

Speculative as it is, the scenario has unnerved investors. The S&P dropped more than 1% on Monday, and the software component of the index fell to its lowest level since Trump’s “liberation day” tariff announcement in April. Doubtless some of the wobble is attributable to Trump’s latest tariffs, but Uber, American Express, Mastercard and DoorDash, specifically named in Citrini’s report, all lost between 4% and 6%.

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[–] 29 points 8 months ago* (last edited 8 months ago) (2 children)

Investors are obviously stupid fucking people spooked by any old horseshit and who buy into any old horseshit.

Unless there's AI magic going on behind the scenes we're not being told about, autonomous AI agents are nowhere near a reality and won't be because LLMs are a dead-end in regards to that.

Seriously our world is run by the people least fucking fit to do so. I'm just some random asshat who knows fuck-all. I'm not smart enough for the people who run the world to be this fucking stupid in comparison.

Like, here's a quote from beginning of this Substack post:

The sole intent of this piece is modeling a scenario that’s been relatively underexplored.

But has it though?? Can't anyone with a fucking brain extrapolate that if AI replaced all workers that we wouldn't have an economy, we would have the people who control the AI owning all that's left in the world and the huddled teeming starving masses ready to build gallows in the streets for the people who caused all this? How is this new and shocking to these people? How have none of them ever considered the endgame before?

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  • [–] 9 points 8 months ago

    Their plan is to own everything so that the economy is irrelevant. What they want is to go back to the days where you worked for the mining company but got paid in company currency that could only be redeemed at company stores.

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  • [–] 7 points 8 months ago (8 children)

    Instead of using DoorDash, developers – and civilians – code up their own food delivery apps, all of which compete, fragment the market, and destroy the margins of legacy businesses. Business for Uber and other ride-sharing apps also evaporates. Instead of using Visa and Mastercard, AI agents decide to do all business in cryptocurrency, because transaction costs are cheaper. This guts traditional payment providers.

    To Citrini, this is a logical endpoint for tireless AI agents that have the time and capability to optimise everything. “Habitual app loyalty, the entire basis of the business model, simply didn’t exist for a machine,” it writes.

    Seems plausible /s

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  • [–] 7 points 8 months ago (6 children)

    I mean certain parts of this article indeed seem stupid, but some minor statements give away the game in ways I wish were more clear to every day people.

    Over the past fifty years, the U.S. economy built a giant rent-extraction layer on top of human limitations: things take time, patience runs out, brand familiarity substitutes for diligence, and most people are willing to accept a bad price to avoid more clicks. Trillions of dollars of enterprise value depended on those constraints persisting.

    We had overestimated the value of “human relationships”. Turns out that a lot of what people called relationships was simply friction with a friendly face.

    This shit should be shouted from the fucking rooftops even without AI in the picture. Our entire economy is a house of cards built on rent-extracting through introduction of pain points that may not have previously existed. Creating a problem and selling a solution.

    It's honestly laughable because it means on some level the investors reading this are absolutely aware that this is what they do and they are fucking fine with it. They think making things harder and extracting money from people for it is a good thing. Fucking ghouls.

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  • [–] 1 point 8 months ago (5 children)

    Doordash isn't rent extraction though, and is a system that benefits from a singular platform. That doesn't mean Doordash is the best implementation of that service, but the example isn't good.

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  • [–] 3 points 8 months ago

    Right, I agree that DoorDash is a bad example, I was just pointing out that the article makes some clear points about how our economy is organized and the investor class panicked not because they realized our economy is a house of cards built on unnecessary friction, but rather that they might have that house of cards taken from them.

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  • [–] 2 points 8 months ago

    It's rent seeking from their own agents by having them take care of the depreciation on their vehicles instead of the corporation. Still not a perfect example, but it's still a type of exploitation

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  • [–] 1 point 8 months ago (2 children)

    Doordash absolutely is rent seeking, though. Restaurant operators are paying the rent—for being furnished with SaaS services that used to just entail calling the restaurant and placing a takeout/delivery order. Nevermind the SaaS platforms restaurants have to pay for in order to integrate their SCM software with the ordering apps.

    We used to call to order a pizza. Now, both the restaurant and we—the consumers—pay various abstracted-away "fees" to have a middle man do the same fucking thing.

    The restaurant doesn't "own" the software, and it doesn't "own" the data produced by its day-to-day operations. They pay to have third parties warehouse and manage their sales data for them, and sometimes even sell that data back to them for additional fees.

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  • [–] 2 points 8 months ago (1 child)

    Delivering food has a cost and is a service. Before things like doordash you could only get delivery from select locations, by creating a network of drivers you can now get delivery from most restaurants.

    Does doordash exploit people and restaurants, yes. However, providing a delivery service is not rent seeking there is something being offered.

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  • [–] 1 point 8 months ago*

    What fucking stupid, late stage capitalist, Pollyanna codswallop. Wow.

    DoorDash doesn't provide a delivery service. They don't pay delivery drivers to deliver, they don't provide vehicles, benefits, or even consider drivers employees. People pay DoorDash for access to use their software/platform to receive requests from customers for delivery services. DoorDash offers a series of contract plans in which a driver pays fees for various tiers of DoorDash acting as a payment processor, can opt for a per-job rate reductions to guarantee a "base job rate" (without any guarantee of jobs assigned), and are otherwise uncompensated for "non-active" time.

    The exploitation comes in the form of a monopolized rentier platform. In the same way that you might pay a landlord for access to a space to rest your pretty little head at night, restaurants and drivers pay premiums for access to a digital space to market services to one another. DoorDash doesn't make anything other than software, and you can't even "buy" DoorDash's software.

    Instead of creating value through the production of goods, DoorDash acts as a digital landlord that extracts surplus value (rent) from both restaurants and laborers by controlling the digital infrastructure necessary for exchange.

    Just like a landlord, DoorDash owns the digital "land" (i.e. the application, algorithm, and user interface) connecting customers, merchants, and workers.By controlling this infrastructure, DoorDash acts as a tollbooth. It charges restaurants a high commission fee (ranging from 15% to 30%+) for every order. This fee is a form of monopoly rent, where DoorDash takes a portion of the restaurant's profits simply for allowing them to access customers, similar to a landlord extracting rent.

    DoorDash does not cook the food or directly employ the delivery workers as staff (again, they're "contractors", which DD has lobbied heavily to ensure). The restaurant produces the value (the food), and the driver performs the labor of delivery. They are a glorified phone service, however convenient or "neato" you might think they are.

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  • [–] 2 points 8 months ago

    What I am seeing is algorithms price people out of housing, mortgages and rent; employers claiming AI does this and that, but is actually outsourcing and offshoring jobs to humans in places where Western wages can't compete, despite being unlivable, from programming to customer service to "self-driving" vehicles, to art.

    But also forensics, DNA mapping and gene editing.

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  • [–] 10 points 8 months ago* (last edited 8 months ago)

    The "feedback without any breaks" dynamic certainly is what the AI industry is capable of, but it isn't because of how powerful AI is, it is because the humans working in the AI industry have brainworms that inhibit the suppression of runaway feedback loops around their near spiritual beliefs about the capability of AI.

    It isn't the runaway capability of AI that is terrifying, rather what is terrifying are the runaway rationalizations of AI cultists who can't admit they chose a shitty horse to get behind and it is never going to go anywhere faster than it is already failing to do.

    AI fanboys will do ANYTHING to avoid facing that reality, they have given their ego no choice by building their entire identities and worldviews around it and that is what should terrify us.

    The second thing here that should terrify us is how much of a Cargo Cult the financial industry is that these kinds of emotional fever dream panics actually dominate the price determination of major companies even though it isn't that hard to do the research yourself and come to the conclusion all of this AI stuff is 99.99% bullshit.

    Stop investing in AI, it is unbelievably dumb, invest in the local landscaping company around you, invest in ANYTHING else at all so long as there is real value to it unlike AI, you will end up with far more in the end.

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  • [–] 4 points 8 months ago*

    "Shook the US markets" is that last dip in the chart. Nothing happened and we're losing our minds over it. There's been 5 bigger drops over the last 3 months than what happened on monday.

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  • [–] 3 points 8 months ago*

    How is this is just now dawning on people? How did anyone think for a moment that given the way our current global economic and political hegemony functions that "AI" would ever benefit the average person. This was always going to be what happened the moment OpenAI released the first model.

    Its just a gussied up version of the trickle down lie, and so many people are still falling for it

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  • 46 comments in c/technology@lemmy.world view that post
    [–] [S] 143 points 8 months ago*

    Really reinforces my belief that the stockmarket is driven by idiots.

    Reminds me of this old Kal cartoon:

    Granted AI will probably doom us all but not how the substance post says it will.

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  • [–] 66 points 8 months ago (1 child)

    The scenario begins with AI agents undergoing a “jump in capability”.

    Might as well stop reading there. Another fluff piece about how useful and capable AI supposedly is, disguised as a doomsday scenario. I'm so sick of reading this bullshit. "Agentic AI" based on LLMs does not work reliably yet and very likely never will.

    If you complain about bugs in traditional (deterministic) software, you ain't seen nothing yet. A probabilistic system such as an LLM might or might not book the correct flight for you. It might give you the information you have asked for or it might delete your inbox instead.

    As a consequence of a system being probabilistic, anything you do with it works or fails based on probabilities. This really is the dumbest timeline.

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  • [–] 30 points 8 months ago (4 children)

    My reaction to the article:

    This was about fears AI will tank the economy? No shit it will.

    Reads a little more

    Wait, this is about fears AI will be so successful it tanks the economy? Complete bullshit but hey, whatever gets this bubble popped.

    Instead of using DoorDash, developers – and civilians – code up their own food delivery apps, all of which compete, fragment the market, and destroy the margins of legacy businesses.

    Complete fucking fantasy. Even if AI was so amazing it could code my own delivery app for me in seconds, the food still has to be delivered somehow. But yes, it AI was able to deliver on all of the promises we'd be fucked, when AI fails to deliver on all of the promises the bubble will burst and we'll be fucked. Either way stop investing in AI.

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  • [–] 5 points 8 months ago

    No but you don't get it. AI is such an amazing tool, it can do everything and replace everyone. Did you try my new model yet? It's blazing fast and can tell you to drive to the car wash to clean your car. It's will revolutionize the world

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  • [–] 4 points 8 months ago

    The proponent is a rather successful and rational investor. This was satire, meant to evoke the idea that, if AI was all that the con men are selling, it would collapse the economy. It is not and everyone knows it, but the point is to highlight the idiocy and try to wake up people to the absurdity. I see it akin to what "a modest proposal" was. To nudge the most radical AI ideologues into understanding the dead economy and ghost GDP concepts. If the economy becomes detached from human reality, it will crumble and collapse.

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  • [–] 122 points 8 months ago (16 children)

    I just..

    Am I wrong here? Like, look, shame me. I work in machine learning and have since 2012. I don't do any of the llm shit. I do things like predicting wildfire risk from satellite imagery or biomass in the amazon, soil carbon, shit like that.

    I've tried all the code assistants. They're fucking crap. There's no building an economy around these things. You'll just get dogshit. There's no building institutions around these things.

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  • [–] 6 points 8 months ago (1 child)

    I think it’s supposed to work like, “well, even if you are right about the massive utility of AI, is that still what we should be aiming for?”

    It gets around the combative “you’re wrong, AI is garbage” argument. The people hoisting AI because they believe, even if it does suck, it’ll get better… those people can probably understand this argument much more easily.

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  • [–] 6 points 8 months ago

    If you want a demo on how bad these AI coding agents are, build a medium-sized script with one, something with a parse -> process -> output flow that isn't trivial. Let it do the debug, too (like tell it the error message or the unwanted behaviour).

    You'll probably get the desired output if you're using one of the good models.

    Now ask it to review the code or optimize it.

    If it was a good coding AI, this step shouldn't involve much, as it would have been applying the same reasoning during the code writing process.

    But in my experience, this isn't what happens. For a review, it has a lot of notes. It can also find and implement optimizations. The weighs are the same, the only difference is that the context of the prompt has changed from "write code" to "optimize code", which affects the correlations involved. There is no "write optimal code" because it's trained on everything and the kitchen sink, so you'll get correlations from good code, newbie coders, lesson examples of bad ways to do things (especially if it's presented in a "discovery" format where a prof intended to talk about why this slide is bad but didn't include that on the slide itself).

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  • [–] 2 points 8 months ago

    It's funny. I see the phrase "AI doomsday scenario" and I immediately picture devastating cascading consequences caused by someone mistakenly putting too much trust in some kind of agentic AI that does things poorly and breaks a lot of big important things.

    I'm just not seeing a scenario where AI causes devastating disruption based on its own ultra competence. I'm much more scared of AI incompetence.

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  • [–] 2 points 8 months ago (4 children)

    Your job sounds really cool! How likely is Alberta to be on fire again this year?

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  • [–] 2 points 8 months ago (3 children)
  • [–] 2 points 8 months ago (2 children)
  • [–] 15 points 8 months ago (1 child)

    Well for one, that area already burned pretty recently. So its pretty unlikely to burn again any time soon.

    But as part of a larger picture:

    The area does experience fire-weather conditions for some portion of the year:

    Here we're looking at HDWI (hot dry windy index), where a "loose" definition of fire weather is if HDWI is above 200. HDWI is based on a few factors, namely, how hot it is, how dry it is, and how fast the air is moving. Hot dry air moving quickly = fire weather.

    The number of fire weather days per year has been increasing, and in very recent years (the past decade) the rate of change has increased, and become statistically signficant:

    So its not a particularly fire prone area, but its getting worse, and its getting worse at a faster rate.

    That would be the first part of the analysis I would run. After that, we'd look for historically "anomalous" periods. Its not enough to look at averages; that will wash over important features in the data. We need to look for specific periods where fire weather manifests.

    This is another way of thinking about fire risk. Here we're going to count the amount of time, after 12 hours, that an area is in sustained fire-weather conditions. Basically, a bit of time in bad conditions isn't the end of the world, but as you stay in fire weather conditions, fire risk increases exponentially (as plants/ fuels continue to dry out).

    If I were writing an insurance product for you, I would count the number of events in a given magnitude bucket and give you a risk rating. Here, licking my thumb and sticking it in the air, I would say.. "not that bad".

    Much of my work is around modeling in the wilderness urban interface. You picked an almost all wilderness area. Since there are no structures, I cant do the next analysis, but it would looks something like this:

    Most of my work is about figuring out what the impacts of wildfire on the built environment are going to be. Also, the free structure dataset I have access to doesn't cover Canada and I'm not going to spend money buying the structures for you (unless you REALLY want me to).

    Those first figures are all specific to the coordinates you provided. The final figure is just an example.

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  • [–] 9 points 8 months ago (4 children)

    You've worked in ML since 2012 but dont think transformers have had an absolutely insane impact, for example in NLP and machine translation? (I have worked in those fields longer than that and while I dont think AGI or anything like that is coming from transformers and deep neural nets I think you are full of it if you dont admit they have revolutionized a large number of [highly technical] fields).

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  • [–] 16 points 8 months ago (2 children)

    Tldr at the bottom

    I'm literally submitting a transformers paper for publication this week. They're truly incredible. They're a huge step forwards from where we we at. But so was YOLO, and UNET, and lstm's (kinda, they were a bit meh).

    But there is a secondary claim about llms, chat bot/agentic llm specifically, that they're doing things they simply arent. And I do pay for higher tier access so I at least think I'm using some of the state of the art of these things.

    I think you are full of it if you dont admit they have revolutionized a large number of highly technical fields

    I'm specifically saying they haven't, at least, that if you are using Claude or chatgpt to do those things, you aren't doing what you think you are doing. Domain experts who use these tools recognize their limitations, and limitation is a soft way of putting it. They just get shit fundamentally wrong. And sometimes, when you are working on a complex problem, if you don't have the knowledge or experience to know when something is wrong, you'll believe these machines are doing far more than they are.

    Look I use them regularly. I can support up to 128gb models locally. I understand the claim that these things have utility. But after several years of working with them, I genuinely don't think they actually are capable of supporting the claims businesses are making about them.

    For one, while they can help you solve some problems faster, often, they just make the situation far, far worse, and you spend an inordinate amount of time trying to get the thing to do something a specific way, but it just won't. I think this is related to the half glass of wine issue, which I'll come back to.

    Second, they, as far as I've been able to use them, are utter dogshit at returning to a codebase. If you are trying to get them to have some kind of long term comprehension of what's happening in a project, good fucking luck. You end up with a codebase of constant refactors and stupid useless "sanity" checks that creates the appearance of good practices, but is all smoke and mirrors. They seem to work ok for single shot demos, but you could never run a business or build a program that's worth keeping around where the llm is central to managing the process. And there is more to say in this because when you are building up a codebase, the most fundamental thing you are really building up is a vision of how it all fits together. When you outsource this to LLMs, you don't get the vision, and frankly they don't either. What you end up with is maybe functional at first, but inevitably unstable, and unsustainable.

    Third, and maybe this is me, but I've never actually seen an llm come up with a clever solution to anything. Like not once have I seen it come up with a truly elegant, efficient solution. It's almost always the most banal, solution, and more often then not, it's not even a solution, but a work around that avoids the problem entirely while creating the impression of a solution.

    And to be clear. I'm not talking about mundaun hello world statements. I'm talking about things that undergrads and graduate students miss all the time. I'm talking about gotchas and problems that you need somethings decades plus to know that the fundamental assumptions are flawed. There is something more inherent to the issues they create.

    I think the half glass of wine issue has been papered over and remains the core limitation with LLMs, and represents a fundamental issue with either transformers, or maybe gradient decent, and I don't think this current architecture is going to get us past it. You are probably familiar with the issue, it got traction a while back, but the hot fixed the phenomena and it lost media attention. However, if you know what you are looking for, you'll find non image based examples of this all the time when using LLMs. They'll constantly insist they've done something they haven't. And there will be no obvious way to get them to recognize they haven't done or aren't doing the thing. I don't believe any of the philosophy explanations given in the YouTube coverage of the issue. I think the problem is likely more core, more central to machine learning that credit is being given.

    The concern I have is that this is something more fundamental, and were only noticing it because image generation and natural language are something humans can comprehend and notice the issue in. But what about when it becomes something incomprehensible to humans, like a sequence of weather data or output from a sensor. We would have no ability to notice if ml model is doing the same thing that an llm is doing, effectively lying about it's about.

    Long rant over shortly.

    Tldr

    I think don't contend the massive advances transformers represent as an architecture. But there is clearly something rotten or missing at their core which makes them practically self destructive to rely upon beyond superficial, well solved issues. I think the rot is in the math or the approach to training and I don't think there is any amount of engineering that can unbake the sawdust out of the cake.

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  • Thank you for putting in the time to try to explain this. I've also been working with ML for a long time and have the intuition that there is a fundamental limit to what can be achieved with Neural Networks and probably any other Machine Learning technique.

    Your cake analogy is probably better, but let me try another one. It's like having a machine that is trying to solve a gigantic puzzle with random pieces from different puzzles. Yeah, if you have enough puzzle pieces and the machine is fast enough it can give you suboptimal solutions but the puzzle will not ever be solved as it is supposed to be solved.

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  • [–] 11 points 8 months ago

    I think you read way more into their comment than was written. They said nothing about transformers, only that these assistants are shit. Which, let's admit, they are.

    The underlying technology is cool, current implementations are trash and have no long term economical path to viability unless things radically change quickly.

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  • [–] 23 points 8 months ago* (last edited 8 months ago) (8 children)

    Lol, they sort of seem to know it's all castles on clouds and any spark e.g. a substack post could trigger the loaded spring. Yet, nobody thinks they'll be the ones holding bags of shit.

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  • [–] 11 points 8 months ago (7 children)

    They kind of know. The dot com crashed many companies, and also gave rise to Amazon. They're all just hoping they'll be the one that invested in the next Amazon.

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  • [–] 5 points 8 months ago (6 children)
  • [–] 12 points 8 months ago (5 children)

    Amazon didn't make any profit for a decade and made 360 billion least year. They tell investors that AI will be the same.

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  • [–] 8 points 8 months ago (2 children)

    The difference was that Amazon knew how to make a profit, but was reinvesting into infrastructure plays and bigger fish.

    If they had to, they could have been a modestly profitable bookshop in 2002. AWS and monster logistics might not have developed to put them in the 13-digit club though.

    Does any AI-centric play have that fundamental fallback? The services that seem to be most effective at direct monetization, the coding tools, are typically running at huge losses. If they raised costs to cover, precious few firms will pay basically the salary of a senior dev for an emulation of an enthusiastic junior dev with an affinity for footguns.

    The less enterprise-focused products-- parasocial toys, image and video gen, will likely try to dip into consumer subs and advertising, but can that generate the cash volumes these platforms demand?

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  • [–] 6 points 8 months ago (1 child)

    If people would always demand answers for those questions, we wouldn't have speculative bubbles. For now, everybody seems to still believe the "it's the worst it'll ever be right now" and the "just more scaling bro" answers.

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  • [–] 1 point 8 months ago

    Trying to sell consumers on "scaling solves everything" is going to be a hard sell.

    If we look at general purpose computation, which had decades of actual scaling-solves-everything growth, you had two influences that made the message resonate with customers:

    • Clear existing applications where more power made the experience straightforward better. Your spreadsheet took an hour to recalculate at 8MHz and 20 minutes at 25MHz. A lot of the "bigger model" stuff is plateauing with marginal or spotty gains. If I feed another 5 Internets of data to ChatGPT, will that summarized email be that much better?

    • New applications that could be demoed on specialised low capacity hardware and scaled down to consumers as more power became available. Think of early CGI on hardware costing tens of millions, and now you can run Blender on a $149 laptop. Since most commercial AI plays are hosted services, there's not much opportunity to tease that way anymore.

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  • [–] 2 points 8 months ago (1 child)

    How much of that profit less decade was just them reinvesting in their company as opposed to burning money like you're trapped on Everest and need every bit of heat you can get?

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  • [–] 31 points 8 months ago (5 children)

    My favourite take so far is the comparison to the introduction of the microwave. Some people really believed that they’d never have to cook again. So what we got was actually a way to make crap quality meals or reheat things when we don’t have time. This is roughly analogous to the output I get from the LLM.

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  • [–] 24 points 8 months ago (3 children)

    You need to learn how to use a microwave. It's way more versatile than people think. And more of a steamer than an oven. https://cookanyday.com/collections/recipes

    (This is not an analogy about LLMs anymore.)

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  • [–] 3 points 8 months ago

    I just wanted to have a gander at some microwave recipes, but that site bombarded me with 3 full screen promotional pop up and still didn't give me any recipes because I have to tap show more results because the first few is products they're selling. At least that's my experience on mobile, using waterfox with adblock and even a DNS filter.

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  • [–] 19 points 8 months ago* (last edited 8 months ago) (5 children)

    It's almost funny how all those AI doomsday scenarios are actually meant to prop up investment in AI.

    See how Amodei and Altman are usually the ones pushing these narratives on how worried they are by the incredible advancements of their respective companies' creatures. They are so, so worried about the demise of the human race and how fast it's coming.

    And I sort of understand them because whatever disruption they are peddling needs to happen very fast or they will all run out of money. But what does it tell about the rest of the human race that we are actually buying into it and pouring money into creating a dystopian future?

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  • [–] 6 points 8 months ago (2 children)

    It's "Iraq is using PS2s to build ICBMs" all over again...

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  • [–] 5 points 8 months ago*

    It's like watching a real-life version of Avengers, but one where Tony Stark says "hey, this Thanos guy is disrupting industries here!" and teams up with... Thiel and Musk to fund his quest for the Infinity Stones. You know, we can't let China get them first!

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  • [–] 2 points 8 months ago

    Just re-watched Tron last night and a scene really struck me. Dumont was talking to Lora about how since the computers are able to think the humans will stop. That scene had more impact this time through

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  • 1 comment in c/economy@lemmy.world view that post
    [–] 3 points 8 months ago

    From: https://www.citriniresearch.com/p/2028gic which is what the guardian is writing about:

    That destroyed a particular kind of moat: habitual intermediation.

    DoorDash (DASH US) was the poster child.

    Coding agents had collapsed the barrier to entry for launching a delivery app. A competent developer could deploy a functional competitor in weeks, and dozens did, enticing drivers away from DoorDash and Uber Eats by passing 90-95% of the delivery fee through to the driver. Multi-app dashboards let gig workers track incoming jobs from twenty or thirty platforms at once, eliminating the lock-in that the incumbents depended on. The market fragmented overnight and margins compressed to nearly nothing.

    Agents accelerated both sides of the destruction. They enabled the competitors and then they used them. The DoorDash moat was literally “you’re hungry, you’re lazy, this is the app on your home screen.” An agent doesn’t have a home screen. It checks DoorDash, Uber Eats, the restaurant’s own site, and twenty new vibe-coded alternatives so it can pick the lowest fee and fastest delivery every time.

    Anyone who has used these agent can see this with a bit of futurism... With the caveat that it's never as simple as a single prompt should be. They are incredibly savant like at time, and then they make the dumbest contextual mistake that you have to chase down for hours. Which is to say the markets are idiots as usual. (Along side CEOs.) These agents aren't up to the hype, their investment, nor their ability to code without security flaws. Just let it keep singing: "If I only had a brain" and hanging scarecrows up in the windows.

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