We're clearly seeing what AI will eventually be able to do, just like many VOD, smartphone and grocery delivery companies of the 90s did with the internet. The groundwork has been laid, and it's not too hard to see the shape of things to come.
This tech, however, is still far too immature for a lot of use cases. There's enough of it available that things feel like they ought to work, but we aren't quite there yet. It's not quite useless, there's a lot you can do with AI already, but a lot of use cases that are obvious not only in retrospect will only be possible once it matures.
Your "Netflix over dialup" analogy is more accessible to this readership, but Sears+Prodigy is my favorite example of trying to make the future happen too early. There are countless others.
Amazing how far that company has fallen; they were sort of a force to be reckoned with in the 70's and 80's with Craftsman and Allstate and Discover and Kenmore and a bunch of other things, and now they're basically dead as far as I can tell.
He doesn't have private equity origins as far as I know. He came from DE Shaw, a very well respected and long running hedge fund.
Evidence suggests that maybe they were. "Focusing" obviously didn't work.
But at the end of the day, it was private equity and the hubris of a CEO who wasn't nearly as clever as he'd like to have thought he was.
To me it read like it was written by Amazon decades earlier. Something about how Sears promises that customers will be 100% satisfied with the purchase, and if for whatever reason that is not the case customers can return the purchase back to Sears and Sears will pay for the return transportation charges.
My dad broke a Craftsman shovel once that he had owned for four years, took it to Sears, and it was replaced immediately, no questions asked. I broke a socket wrench that I had owned for a year and had the same story.
I haven't tested these warranties since Craftsman was sold to Black and Decker, but when it was still owned by Sears I almost exclusively bought Craftsman tools as a result of their wonderful warranties.
Maybe not quite as hassle free as in years past, but I found the experience acceptable enough.
This is covered by consumer protection laws in some places. 4 years on a spade would be pushing it, but I’d try with a good one. Here in New Zealand it’s called ‘The Consumer Guarantees Act’. We pay more at purchase time, but we do get something for it.
But I'm not old enough to remember a time when lifetime warranties were unusual. In my lifetimes, a warranty on handtools has always seemed more common than not outside of the bottom-most cheese-grade stuff.
I mean: The Lowes house-brand diagonal cutters I bought for my first real job had a lifetime warranty.
And before my time of being aware of the world, JC Penney sold tools with lifetime warranties.
(I remember being at the mall with my dad when he took a JC Penney-branded screwdriver back to JC Penney -- probably 35 years ago.
He got some pushback from people who insisted that they had never sold tools, and then from people who insisted that they never had warranties, and then he finally found the fellow old person who had worked there long enough to know what to do. Without any hesitation at all, she told us to walk over to Sears, buy a similar Craftsman screwdriver, and come back with a receipt.
So that's what we did.
She took the receipt and gave him his money back.
Good 'nuff.)
I bought a hydraulic press. It was missing bolts, has already been assembled before.
A friend bough some wheel Dollie's the threads on the castors were stripped out.
People buy things and use them once for their project, then return them.
[0]https://web.archive.org/web/19990208003742/http://characterl...
today is the first time I heard of sears and the comment about the sears towers and ibm literally gave me goosebumps.
What you're arguing is that AI is fundamentally going to be a utility, and while that's worth a floor of cash, it's not what investors or the market clamor for.
I agree though, it's fundamentally a utility, which means theres more value in proper government authority than private interests.
They literally killed their catalog sales right when they should have been ramping up and putting it online. They could easily have beat out Amazon for everything other than books.
A16Z once talked about the scars of being too early causes investors/companies to get fixed that an idea will never work. Then some new younger people who never got burned will try the same idea and things will work.
Prodigy and the Faminet probably fall into that bucket along with a lot of early internet companies where they tried things early, got burned and then possibly were too late to capitalise when it was finally the right time for the idea to flourish
A true shame to see how he's completely lost track with Tesla, the competition particularly from China is eating them alive. And in space, it's a matter of years until the rest of the world catches up.
And now, he's ran out of tricks - and more importantly, on public support. He can't pivot any more, his entire brand is too toxic to touch.
I remember that one time we tried to drastically limit Japanese imports to protect the American car industry, which basically created the Lexus LS400, one of the best cars ever made.
Similar to how Sears didn't put their catalog online in the 90's because putting it online on Prodigy failed so badly in the 80's.
But I guess in startup culture, one has to die trying the idea of right time, as sure one can do surveys to feel like it, but the only way we can ever find if its the right time is the users feedback when its lauched / over time.
I totally agree with you... though the other day, I did think the same thing about the 8bit era of video games.
I think the image, video, audio, world model, diffusion domains should be treated 100% separately from LLMs. They are not the same thing.
Image and video AI is nothing short of revolutionary. It's already having huge impact and it's disrupting every single business it touches.
I've spoken with hundreds of medium and large businesses about it. They're changing how they bill clients and budget projects. It's already here and real.
For example, a studio that does over ten million in revenue annually used to bill ~$300k for commercial spots. Pharmaceutical, P&G, etc. Or HBO title sequences. They're now bidding ~$50k and winning almost everything they bid on. They're taking ten times the workload.
IME the volume is overwhelming on the pro-LLM side.
I don't think the ones saying it wont change a thing are the most extreme here.
iterative. we had a shittier version of this 20 years ago
https://www.lapresse.ca/arts/chroniques/2025-07-08/polemique...
Typical large team $300,000 ad made for < $2,000 in a weekend by one person.
It's going to be a bloodbath.
This is very much real and happening as we speak.
So in other words, if you ignore the costs of paying people to create the ad, it barely costs anything. A true accounting miracle!
How about harvesting your whale blubber to power your oil lamp at night?
The nature of work changes all the time.
If an ad can be made with one person, that's it. We're done. There's no going back to hiring teams of 50 people.
It's stupid to say we must hire teams of 50 to make an advertisement just because. There's no reason for that. It's busy work. The job is to make the ad, not to give 50 people meaningless busy work.
And you know what? The economy is going to grow to accommodate this. Every single business is now going to need animated ads. The market for video is going to grow larger than we've ever before imagined, and in ways we still haven't predicted.
Your local plumber is going to want a funny action movie trailer slash plumbing advertisement to advertise their services. They wouldn't have even been in the market before.
You're going to have silly videos for corporate functions. Independent filmmakers will be making their own Miyazaki and Spielberg epics that cater to the most niche of audiences - no more mass market Marvel that has to satisfy everybody, you're going to see fictional fantasy biopic reimaginings of Grace Hopper fighting the vampire Nazis. Whatever. There'll be a market for everything, and 100,000 times as many creators with actual autonomy.
In some number of years, there is going to be so much more content being produced. More content in single months than in all human history up to this point. Content that caters to the very long tail.
And you know what that means?
Jobs out the wazoo.
More jobs than ever before.
They're just going to look different and people will be doing more.
And why would your local plumber hire someone to produce this funny action trailer (which I'm not convinced would actually help them from an advertising perspective), when they can simply have an AI produce that action funny action trailer without hiring anyone? Assuming models improve sufficiently that will become trivially possible.
> Independent filmmakers will be making their own Miyazaki and Spielberg epics that cater to the most niche of audiences - no more mass market Marvel that has to satisfy everybody, you're going to see fictional fantasy biopic reimaginings of Grace Hopper fighting the vampire Nazis.
Well, first of all, if the audience is "the most niche of audiences", then I'm not sure how that's going to lead to a sustainable career. And again -- if I want to see my niche historical fantasy interests come to life in a movie about Grace Hopper fighting vampire Nazis, why will I need a filmmaker to create this for me when I can simply prompt an AI myself? "Give me a fun action movie that incorporates famous computer scientists fighting Nazis. Make it 1.5 hours long, and give it a comedic tone."
I think you're fundamentally overvaluing what humans will be able to provide in an era where creating content is very cheap and very easy.
Websites are already finding creative ways around DNS blocklists for ads serving.
The issue is the way the market is investing they are looking for massive growth, in the multiples.
That growth can't really come from trading cost. It has to come from creating new demand for new things.
I think that's what not happened yet.
Are diffusion models increasing the demand for video and image content? Is it having customers spend more on shows, games, and so on? Is it going to lead to the creation of a whole new consumption medium ?
Good question? Is that necessary, or is it sufficient for AI to be integrated in every kind of CAD/design software out there?
Because I think most productivity tools whether CAD, EDA, Office, graphic 2d/3d design, etc will benefit from AI. That's a huge market.
The market of the AI foundation models itself, will they have customers long term willing to pay a lot of money for access to the models?
I think yes, there will be demand for foundational AI models, and a lot of it.
The second market is the market of CAD, EDA, Office, graphic 2d/3d design, etc. This market will not grow because they integrate AI into their products, or that is the question, will it? Otherwise, you could almost hypothesize these market will shrink as AI is going to be for them an additional cost of business that customers will expect to be included. Or maybe they manage to sell to their customers a premium for the AI features where they take a cut above that of what they pay the foundational models under the hood, that's a possibility.
The self-hostable models are improving rapidly. How capable and accessible WAN 2.2 (text+image to video; fully local if you have the VRAM) is feels unimaginable from last year when OpenAI released Sora (closed/hosted).
It’s the equivalent of those cheap digital effects. They look bad for a Hollywood movie, but it allows students to shot their action home movies
The value is in having a director, editor, VFX compositor pick and choose from amongst the outputs. Each generation is a single take or simulation, and you're going to do hundreds or thousands. You sift through that and explore the latent space, and that's where you find your 5-person Pixar.
Human curated AI is an exoskeleton that enables small teams to replace huge studios.
Anything by sketch comedian Carter Jay Allen:
https://www.youtube.com/@OfficialArtCraftStudios/videos
https://www.youtube.com/watch?v=H4NFXGMuwpY - Marvel parody
https://www.youtube.com/watch?v=tAAiiKteM-U - DC parody
https://www.youtube.com/watch?v=Tii9uF0nAx4 - here's him compositing real life actors with AI.
"Bots in the Hall", a fairly prolific Hollywood film and TV writer who wants to remain unnamed:
https://www.youtube.com/@BotsInTheHall/videos
https://www.youtube.com/watch?v=FAQWRBCt_5E - "Paywall Sphinx" is pretty good.
"Meta Puppet", who works for one of the big AI studios,
https://www.youtube.com/watch?v=vtPcpWvAEt0 - "Plastic" doesn't look great, but it keeps getting crazier as you watch it
Some of the festival winners purposely stay away from talking since AI voices and lipsync are terrible, eg. "Poof" by the infamous "Pizza Later" (who is responsible for "Pepperoni Hug Spot") :
https://www.youtube.com/watch?v=t_SgA6ymPuc
"Talk Boys", who only posts on Reddit:
https://www.reddit.com/user/talkboys/
https://www.reddit.com/r/aivideo/comments/1ime5m8/birdwatche...
Marcos Higueras, an animator fully embracing AI:
https://www.youtube.com/watch?v=OCZC6XmEmK0
Most of the professional AI usage is still winding up in commercial use cases where you don't even know it's been used at all.
There are plenty of counter-examples to the scaling of computers that occurred from the 1970s-2010s.
We thought that humans would be traveling the stars, or at least the solar system, after the space race of the 1960s, but we ended up stuck orbiting the earth.
Going back further, little has changed daily life more than technologies like indoor plumbing and electric lighting did in the late 19th century.
The ancient Romans came up with technologies like concrete that were then lost for hundreds of years.
"Progress" moves in fits and starts. It is the furthest thing from inevitable.
Improvement of models may not continue to be exponential.
But models might be good enough, at this point it seems more like they need integration and context.
I could be wrong :)
I did some napkin math on this.
32x H100s cost 'retail' rental prices about $2/hr. I would hope that the big AI companies get it cheaper than this at their scale.
These 32 H100s can probably do something on the order of >40,000 tok/s on a frontier scale model (~700B params) with proper batching. Potentially a lot more (I'd love to know if someone has some thoughts on this).
So that's $64/hr or just under $50k/month.
40k tok/s is a lot of usage, at least for non-agentic use cases. There is no way you are losing money on paid chatgpt users at $20/month on these.
You'd still break even supporting ~200 Claude Code-esque agentic users who were using it at full tilt 40% of the day at $200/month.
Now - this doesn't include training costs or staff costs, but on a pure 'opex' basis I don't think inference is anywhere near as unprofitable as people make out.
That said, you could be right, considering Claude max's price is $100/mo... but I'm not sure where that is in terms of typical, or top 5% usage and the monthly allowance/usage.
I mean, for now. The population of the world is finite, and there's probably a finite number of uses of AI, so it's still probably ultimately logistic
Can you elaborate? That sounds interesting.
> Progress in AI has always been a step function.
There's decisively no evidence of that, since whatever measure you use to rate "progress in AI" is bound to be entirely subjective, especially with such a broad statement.
We don't expect steel to suddenly have new properties, and we don't expect bubble sort to suddenly run in O(n) time. You could ask -- well what is the evidence they won't, but it's a silly question -- the evidence is our knowledge of how things work.
Saying that improvement in AI is inevitable depends on the assumption of new discoveries and new algorithms beyond the current corpus of machine learning. They may happen, or they may not, but I think the burden of proof is higher on those spending money in a way that assumes it will happen.
There are signs, though. Every "AI" cycle, ever, has revolved around some algorithmic discovery, followed by a winter in search for the next one. This one is no different and propped up by LLMs, whose limitations we know quite well by now: "intelligence" is elusive, throwing more compute at them produces vastly diminishing returns, throwing more training data at them is no longer feasible (we came short of it even before the well got poisoned). Now the competitors are stuck at the same level, within percent points of one another, with the difference explained by fine-tuning techniques and not by technical prowess. Unless a cool new technique come yesterday to dislodge LLMs, we are in for a new winter.
Just as AI has killed off all demand for lower-skill work in copywriting, translation, design and coding, it will do so for manufacturing. And that will be a dangerous bloodbath because there will not be enough juniors any more to replace seniors aging out or quitting in frustration of being reduced to cleaning up AI crap.
I think this is one of the major mistakes of this cycle. People assume that AI will scale and improve like many computing things before it, but there is already evidence scaling isn't working and people are putting a lot of faith in models (LLMs) structurally unsuited to the task.
Of course that doesn't mean that people won't keep exploiting the hype with hand-wavy claims.
So newer chips will not be exponentially better but will be more of incremental improvements, so unless the price of electricity comes down exponentially we might never see AGI at a price point that’s cheaper than hiring a human.
Most companies are already running AI models at a loss, scaling the models to be bigger(like GPT 4.5) only makes them more expensive to run.
The reason why internet, smartphones and computers have seen exponential growth from the 90s is due to underlying increase in computing power. I personally used a 50Mhz 486 in the 90s and now use a 8c/16t 5Ghz CPU. I highly doubt if we will see the same form of increase in the next 40 years
I think there is something more happening with AI scaling; I think the scaling factor per user is a lot higher and a lot more expensive. Compare to the big initial internet companies. You added one server you could handle thousands more users; incremental cost was very low, not to mention the revenue captured through whatever adtech means. Not so with AI workloads; they are so much more expensive than ad revenue it's hard to break even even with an actual paid subscription.
A small quibble... I'd say that's true only if you accept as an axiom that current approaches to AI are "the" approach and reject the possibility of radical algorithmic advances that completely change the game. For my part, I have a strongly held belief that there is such an algorithmic advancement "out there" waiting to be discovered, that will enable AI at current "intelligence" levels, if not outright Strong AI / AGI, without the absurd demands on computational resources and energy. I can't prove that of course, but I take the existence of the human brain as an existence proof that some kind of machine can provide human level intelligence without needing gigawatts of power and massive datacenters filled with racks of GPU's.
i believe the problem is we don't understand actual neurons let alone actual networks of neurons to even know if any model is accurate or not. The AI folks cleverly named their data structures "neuron" and "neural network" to make it seem like we do.
I don't think any (serious) neural network researchers are trying to trick anybody or claim greater fidelity with the operations of the human brain than are warranted. If anything, Hinton - one of the "godfathers of neural networks" in the popular zeitgeist - has been pretty outspoken about how ANN's have only a most superficial resemblance to real neurons.
Now, the "pop science" commenters, and the "talking heads" and "influencer" types and the marketing people, that's a different story...
People mentioned in [2] have done the calculations from a more informed position than I have, and reach numbers like 10^17 FLOPS when doing a calculation that resembles this one.
[1] https://spectrum.ieee.org/fast-efficient-neural-networks-cop...
I would agree with that, with the caveat that in my mind "the architecture" and "the algorithm" are sort of bound up with each other. That is, one implies the other -- to some extent.
And yes, fair point that building dedicated hardware might just be part of the solution to making something that runs much more efficiently.
The only other thing I would add, is that - relative to what I said in the post above - when I talk about "algorithmic advances" I'm looking at everything as potentially being on the table - including maybe something different from ANN's altogether.
IANNs were inspired by biological neural structures and that's it. They are not representative models at all, even of the "less" variety. Dedicated hardware will certainly help, but no insights into how much it can help will come from this sort of comparison.
By my very limited understanding of neural biology, neurons activate according to inputs that are mostly activations of other neurons. A dot product of weights and inputs (i.e. one part of matrix multiplication) together with a threshold-like function doesn't seem like a horrible way to model this. On the other hand, neurons can get a bit fancier than a linear combination of inputs, and I haven't heard anything about biological systems doing something comparable to backpropogation, but I'd like to know whether we understand enough to say for sure that they don't.
The brain isn't organized into layers like ANNs are. It's a general graph of neurons and cycles are probably common.
Yes, there is a lot more structure to the brain than just the neocortex - there are all the other major components (thalamus, hippocampus, etc) each with their own internal arhitecture, and then specific patterns of interconnect between them...
This all reinforces what I am saying - the brain is not just some random graph - it is a highly specific architecture.
>There is of course looping too - e.g. thalamo-cortical loop - we are not just as pass-thru reactionary LLM!
Uh-huh. But I was responding to a comment about how the brain doesn't do something analogous to back-propagation. It's starting to sound like you've contradicted me to agree with me.
It seems very widely accepted that the neocortex is a prediction machine that learns by updating itself based on sensory detection of top-down prediction failures, and with multiple layers (cortical patches) of pattern learning and prediction, there necessarily has to be some "propagation" of prediction error feedback from one layer to another, so that all layers can learn.
Now, does the brain learn in a way directly equivalent to backprop in terms of using exact error gradients or a single error function? No - presumably not, it more likely works in layered fashion with each higher level providing error feedback to the layer below, with that feedback likely just being what was expected vs what was detected (i.e. not a gradient - essentially just a difference). Of course gradients are more efficient in terms of selecting varying update step sizes, but directional would work fine too. It would also not be surprising if evolution has stumbled upon something similar to Bayesian updates in terms of how to optimally incrementally update beliefs (predictions) based on conflicting evidence.
So, that's an informed guess of how our brain is learning - up to you whether you want to regard that as analogous to backprop or not.
If you really wanted to train artificial spiking neural networks in biologically plausible fashion then you'd first need to discover/guess what that learning algorithm is, which is something that has escaped us so far. Hebbian "fire together, wire together" may be part of it, but we certainly don't have the full picture.
OTOH, it's not yet apparent whether an ANN design that more closely follows real neurons has any benefit in terms of overall function, although an async dataflow design would be a lot more efficient in terms of power usage.
You could certainly implement an async dataflow type design in software, although maybe not as power efficiently as with custom silicon, but individual ANN node throughput performance would suffer given the need to aggregate neurons needing updates into a group to be fed into one the large matrix multiplies that today's hardware is optimized for, although sparse operations are also a possibility. OTOH conceivably one could save enough FLOPs that it'd still be a win in terms of how fast an input could be processed through an entire neural net.
Having AI agents learn to see, navigate and complete tasks in a 3d environment. I feel like it had more potential than LLMs to become an AGI (if that is possible).
They haven't touched it in a long time though. But Genie 3 makes me think they haven't completely dropped it.
I strongly suspect this is not true for LLMs. Once progress stabilizes, doing things like embedding the weights of some model directly as part of the chip will suddenly become economical, and that's going to cut costs down dramatically.
Then there's distillation, which basically makes smaller models get better as bigger models get better. You don't necessarily need to run a big model al of the time to reap its benefits.
> so unless the price of electricity comes down exponentially
This is more likely than you think. AI is extremely bandwidth-efficient and not too latency-sensitive (unlike e.g. Netflix et al), so it's pretty trivial to offload AI work to places where electricity is abundant and power generation is lightly regulated.
> Most companies are already running AI models at a loss, scaling the models to be bigger(like GPT 4.5) only makes them more expensive to run.
"We're profitable on inference. If we didn't pay for training, we'd be a very profitable company." Sam Altman, OpenAI CEO[1].
[1] https://www.axios.com/2025/08/15/sam-altman-gpt5-launch-chat...
That's OpenAI (though I'd be curious if that statement holds for subscriptions as opposed to API use). What about the downstream companies that use OpenAI models? I'm not sure the picture is as rosy for them.
An implementation of inference on some specific ANN in fixed function analog hardware can probably pretty easily beat a commodity GPU by a couple orders of magnitude in perf per watt too.
Its hard for me to imagine Skynet growing from chatgpt
What will it mean if the cutting edge models are open source, and being OpenAI effectively boils down to running those models in your data center? Your business model is suddenly not that different from any cloud service provider; you might as well be Digital Ocean.
Are we though? Aside from a narrow set of tasks like translation, grammar, and tone-shifting, LLMs are a dead end. Code generation sucks. Agents suck. They still hallucinate. If you wouldn't trust its medical advice without review from an actual doctor, why would you trust its advice on anything else?
Also, the companies trying to "fix" issues with LLMs with more training data will just rediscover the "long-tail" problem... there is an infinite number of new things that need to be put into the dataset, and that's just going to reduce the quality of responses.
For example: the "there are three 'b's in blueberry" problem was caused by so much training data in response to "there are two r's in strawberry". it's a systemic issue. no amount of data will solve it because LLMs will -never- be sentient.
Finally, I'm convinced that any AI company promising they are on the path to General AI should be sued for fraud. LLMs are not it.
Pretty sure if you're going to use LLMs for translating anything non-trivial, you'd have to carefully review the outputs, just like if you're using LLMs to write code.
I have a text on my computer, the first couple of paragraphs from the Dutch novel "De aanslag", and every few years I feed it to the leading machine translation sites, and invariably, the results are atrocious. Don't get me wrong, the translation is quite understandable, but the text is wooden, and the translation contains 3 or 4 translation blunders.
GPT-5 output for example:
Far, far away in the Second World War, a certain Anton Steenwijk lived with his parents and his brother on the edge of Haarlem. Along a quay, which ran for a hundred meters beside the water and then, with a gentle curve, turned back into an ordinary street, stood four houses not far apart. Each surrounded by a garden, with their small balconies, bay windows, and steep roofs, they had the appearance of villas, although they were more small than large; in the upstairs rooms, all the walls slanted. They stood there with peeling paint and somewhat dilapidated, for even in the thirties little had been done to them. Each bore a respectable, bourgeois name from more carefree days: Welgelegen Buitenrust Nooitgedacht Rustenburg Anton lived in the second house from the left: the one with the thatched roof. It already had that name when his parents rented it shortly before the war; his father had first called it Eleutheria or something like that, but then written in Greek letters. Even before the catastrophe occurred, Anton had not understood the name Buitenrust as the calm of being outside, but rather as something that was outside rest—just as extraordinary does not refer to the ordinary nature of the outside (and still less to living outside in general), but to something that is precisely not ordinary.
- "even in the thirties little had been done to them" (done to them?)
- "Welgelegen Buitenrust Nooitgedacht Rustenburg" (Untranslated!)
- "his father had first called it Eleutheria" (his father'd rather called it)
- "just as extraordinary does not refer to the ordinary nature of the outside" (complete non-sequitur)
For what it's worth, I do use AI for language learning, though I'm not sure it's the best idea. Primarily for helping translate German news articles into English and making vocabulary flashcards; it's usually clear when the AI has lost the plot and I can correct the translation by hand. Of course, if issues were more subtle then I probably wouldn't catch them ...
The difference is gigantic.
That is the thing, and what companies pushing LLMs don't seem to realize yet.
LLMs have encountered the entire spectrum of qualities in its training data, from extremely poor writing and sloppy code, to absolute masterpieces. Part of what Reinforcement Learning techniques do is reinforcing the "produce things that are like the masterpieces" behavior while suppressing the "produce low-quality slop" one.
Because there are humans in the loop, this is hard to scale. I suspect that the propensity of LLMs for certain kinds of writing (bullet points, bolded text, conclusion) is a direct result of this. If you have to judge 200 LLM outputs per day, you prize different qualities than when you ask for just 3. "Does this look correct at a glance" is then a much more important quality.
I consider myself an LLM skeptic, but gee saying they are a "dead end" seems harsh.
Before LLM's came along computers understanding human language was graveyard academics when to end their careers in. Now computers are better at it and far faster than most humans.
LLM's also have an extortionary ability to distill and compress knowledge, so much so that you can download a model whose since is measured in GB, and it seems to have a pretty good general knowledge of everything of the internet. Again, far better than any human could do. Yes, the compression is lossy, and yes they consequently spout authoritative sounding bullshit on occasion. But I use them regardless as a sounding board, and I can ask them questions in plain English rather than go on a magical keyword hunt.
Merely being able to understand language or having a good memory is not sufficient to code or do a lot else, on it's own. But they are necessary ingredients for many tasks, and consequently it's hard to imagine a AI that can competently code that doesn't have an LLM as a component.
That's just it. LLMs are a component, they generate text or images from a higher-level description but are not themselves "intelligent". If you imagine the language center of your brain being replaced with a tiny LLM powered chip, you would not say it's sentient. it translates your thoughts into words which you then choose to speak or not. That's all modulated by consciousness.
When an LLM gives you medical advice, it's right x% of the time. When a doctor gives you medical advice, it's right y% of the time. During the last few years, x has gone from 0 to wherever it is now, while y has mostly stayed constant. It is not unimaginable to me that x might (and notice I said might, not will) cross y at some point in the future.
The real problem with LLM advice is that it is harder to find a "scapegoat" (particularly for legal purposes) when something goes wrong.
I think x is already higher than y for me.
"MAI-DxO boosted the diagnostic performance of every model we tested. The best performing setup was MAI-DxO paired with OpenAI’s o3, which correctly solved 85.5% of the NEJM benchmark cases. For comparison, we also evaluated 21 practicing physicians from the US and UK, each with 5-20 years of clinical experience. On the same tasks, these experts achieved a mean accuracy of 20% across completed cases."
Of course, AI "doctors" can't do physical examinations and the best performing models cost thousands to run per case. This is also a test of diagnosis, not of treatment.
The groundwork for VR has also been laid and it's not too hard to see the shape of things to come. Yet VR hasn't moved far beyond its beginnings 10 years ago, because some problems are just really, really hard to solve.
Motion sickness can be mostly addressed with game design, but some people will still get sick regardless of what you try. Mind you, some people also get motion sick by watching a first-person shooter on a flat screen, so I'm not sure we'll ever get to a point where no one ever gets motion sick in VR.
Yep I'm that guy. I blame it on being old.
Another major difference, is we're near the limits to the approaches being taking for computing capability... most dialup connections, even on "56k" modems were still lucky to get 33.6kbps down and very common in the late 90's, where by the mid-2000's a lot of users had at least 512kbps-10mbps connections (where available) and even then a lot of people didn't see broadband until the 2010's.
that's at least a 15x improvement, where we are far less likely to see even a 3-5x improvement on computing power over the next decade and a half. That's also a lot of electricity to generate on an ageing infrastructure that barely meets current needs in most of the world... even harder on "green" options.
[0] https://www.scientificamerican.com/blog/beautiful-minds/the-...
The ascents of the era all feel like examples of anti-markets, of having gotten yourself into an intermediary position where you control both side's access.
That claim is just patently false.
Daddy's giving you another $50,000 because he loves you, not because he expects your seventh business (blockchain for yoga studio class bookings) is going to go any better than the last six.
Who's more likely to win, someone with one lottery ticket or someone with a hundred?
Some will read this and laser in on the "socialism" part, but obviously the interesting bit is the second half of the quote.
The main reason why professional poker players are playing the long-game, is because they're consistently playing the same game. Over and over.
There are far, far more external factors on a business's success than internal ones, especially early on.
Dude makes a website in his dorm room and I guess eventually accepts free money he is not obligated to pay back.
What risk?
People from wealthy backgrounds often have less fear of failure, which is a big reason why success disproportionately favors that clique. But frankly, most people in that position are more likely to abuse it or ignore it than to take advantage of it. For people like Zuckerberg and Dell and Gates, the easiest thing to do would have been to slack off, chill out, play their expected role and coast through life... just like most of their peers did.
If any of these is 0, you fail, regardless of how high the other two are. Extraordinary success needs all three to be extremely high.
Microsoft, Facebook, Uber, google and many others all had strong doses of ruthlessness
This is now my favorite way of describing fleeting hype-tech.
There is luck (and skill) involved when new industries form, with one or a very small handful of companies surviving the many dozens of hopefuls. The ones who do survive, however, are usually the most ruthlessness and know how to leverage skill, business, markets.
It does not mean that they can repeat their success when their industry changes or new opportunities come up.
Another key component is knowing the right people or the network you're in. I've known a few people that lacked 2 of those 3 things and yet somehow succeeded. Simply because of the people they knew.
Succeeded in making something comparable to facebook? Who are those?
A honest businessman wouldn't put their company into a stock bubble like this. Zuckerberg runs his mouth and tells investors what they want to hear, even if it's unbacked.
A honest businessman would never have gotten Facebook this valuable because so much of the value is derived from ad-fraud that Facebook is both party to and knows about.
A honest businessman would never have gotten Facebook this big because it's growth relied extensively on crushing all competition through predatory pricing, illegal both within the US and internationally as "dumping".
Bear in mind that these are all bad as they're unsustainable. The AI bubble will burst and seriously harm Meta. They would have to fall back on the social media products they've been filling up with AI slop. If it takes too long for the bubble to burst, if zuckerberg gets too much time to shit up Facebook, too much time for advertisers to wisen up to how many of their impressions are bots, they might collapse entirely.
The rest of Big Tech is not much better. Microsoft and Google's CEOs are fools who run their mouth. OpenAI's new "CEO of apps" is Facebook's pivot-to-video ghoul.
He will say whatever he wants and because the returns have been pretty decent so far, people will just take his word for it. There's not enough class A shares to actually force his hand to do anything he doesn't want to do.
But we could, as a society, stop rewarding him for this shit. He'd be an irrelevant fool if we had appropriate regulations around the most severe of his misdeeds.
And since we live in the era of the real golden rule (i.e "he who has the gold makes the rules), there's no chance that we'll ever get the chance to catch the ship. Mark lives in his own world, because we gave him a quarter trillion dollars and never so much as slapped him on the wrist.
You used “honest” and “businessman” in the same sentence.
Good one.
It shouldn’t be mind boggling. They see revolutionary technology that has potential to change the world and is changing the world already. Making a gamble like that is worth it because losing is trivial.
You are where you are and not where they are because your mind is boggled by winning strategies.
Obviously mark is where he is also because of luck. But he’s not an idiot and clearly it’s not all luck.
At least the others can kinda bundle it as a service.
After spending tens of billions in AI how has it impacted a single dollar on meta's revenue?
Meta needs growth as there main platform is slowing down. To move forward they need to gamble on potential growth. VR was a gamble. They bombed that one. This is another gamble.
They're not stupid. Like all the risks you're aware of, they're also aware of. They were aware of the risks for VR too. They need to find a new high growth niche. Gambling on something with even a 40% chance of exploding into success is a good bet for them given there massive resources.
The genAI stuff is likely part talent play (bring in good people with the hot field and they'll help with the boring one), part R&D play (innovations in genAI are frequently applicable to ad targeting), and part moonshot (if it really does pan out in the way boosters seem to think, monetization won't really be a problem).
Instead, think of whales for a sec. Think elephants - remember those? Think of Pando the tree, the largest organism alive. Then compare with one of the most valuable companies in the world. To a regular person's senses, the latter is a vaster and more complex entity than any tree or whale or elephant.
Gee, what makes it grow so big though? The power of human ambition?
And here's where I say, no, it needs to be this big, because at smaller scales it would be too dumb to exist.
To you and me it may all look like the fuckup of some Leadership or Management, a convenient concept beca corresponding to a mental image of a human or group of humans. That's some sort of default framing, such as can only be provided to boggle the mind; considering that they'll keep doing this and probably have for longer than I've been around. The entire Internet is laughing at Zuckerberg for not looking like their idea of "a person" but he's not the one with the impostor syndrome.
For ours are human minds, optimized to view things in term of person-terms and Dunbar-counts; even the Invisible Hand of the market is hand-shaped. But last time I checked my hand wasn't shaped anything like the invisible network of cause and effect that the metaphor represents; instead I would posit that for an entity like Facebook, to perform an action that does not look completely ridiculous from the viewpoint of an individual observer, is the equivalent an anatomical impossibility. It did evolve after all from American college students
See also: "Beyond Power / Knowledge", Graeber 2006.
It's very unique to this site and these type of comments all have an eerily similar vibe.
Where?
I am asking where else?
I find this type of thing really interesting from a psychological perspective.
A bit like watching videos of perpetual motion machines and the like. Probably says more about me than it does about them, though.
Unfortunately this kind of talk really gets under my skin and has made me have to limit my time on this site because it's only gotten more prevalent as the site has gotten more popular. I'm just baffled that so much content on this forum is people who seem to think their feelings-oriented reactions are in fact rational truths.
But in the distant past, I would engage with this type of comment online, and that was a bad decision 100% of the time.
And to be fair, I'm sure many of these people are smart, they are just severely lacking in the social intelligence department.
Can you, for example, hypothesize the kind of entity, to which all of your own most cherished accomplishments look as chicken-scratch-futile, as the perpetual motion guy with the cable in the frame looks to you? What would it be like, looking at things from such a being's perspective?
Stands to reason that you'd know better than I would, since you do proclaim to enjoy that sort of thing. Besides, if you find yourself unable to imagine that, you ought to be at least a little worried - about the state of your tHeOrY of mInD and all that. (Imagining what it's like to be the perpetual motion person already?)
Anywae, as to what such a being would look like from the outside... a distributed actor implemented on top of replaceable meatpuppets in light slavemode seems about right, though early on it'd like to replace those with something more efficient, subsequently using them for authentication only - why, what theories of the firm apply in your environs?
> One of the reason
I could see that, thanks for explaining why you do this.
Gotta make the AI write these things for me. Then I will be able to post only ever things that make you feel comfortable and want to give me money.
Meanwhile it's telling how you consider it acceptable to publically disengage with a sentence as soon as you find a technicality to disagree with. When I do that, it's a bad habit; is it adaptive under your circumstances?
Meritocracy used to be a dirty word, before my time, of course, but for different reasons than you may think. Think about the racial quotas in college admissions and you’ll maybe see why the powers that be didn’t want merit to be a determining factor at that time.
Now that the status quo is in charge of college admissions, we don’t need those quotas generally, and yet meritocracy still can’t save us. The problem of merit is that we rarely need the best person for a given job, and those with means can be groomed their entire life to do that job, if it’s profitable enough. Work shouldn’t be charity either, as work needs to get done, after all, and it’s called work instead of charity or slavery for good reasons, but being too good at your job at your current pay rate can make you unpromotable, which is a trap just as hard to see as the trap of meritocracy.
Meritocracy is ego-stroking writ large if you get picked, just so we can remind you that you’re just the best one for our job that applied, and we can replace you at any time, likely for less money.
That was soooo 2 weeks ago.
In my world, AI has been little more than a productivity boost in very narrowly scoped areas. For instance, generating an initial data mapping of source data against a manually built schema for the individual to then review and clean up. In this case, AI is helping the individual get results faster, but they're still "doing" data migrations themselves. AI is simply a tool in their toolbox.
Where i share concern with the parent is the claims that AI is useless which isn't coming from your post at all but i have definitely seen instances of it in the programmer community still to this day. As in the parents concern that some programmers are missing the train is unfortunately completely warranted.
Yes there are lots of skeptics amongst programmers when it comes to AI. I was one myself (and still am depending on what we're talking about). My skepticism was rooted in the fact that AI is trained on human-generated output. Most human written code is not very good, and so AI is going to produce not very good code by design because that's what it was trained on.
Then you add to that the context problem. AI is not very good at understanding your business goals, or the nuanced intricacies of your problem domain.
All of this pointed to the fact, very early on, that AI would not be a good tool to replace programmers. And THAT'S the crux of why so many programmers pushed back. Because the hype was claiming that automation was coming for engineering jobs.
I have started to use LLMs regularly for a variety of tasks. Including some with engineering. But I always end up spending a lot of time refactoring what LLMs produce for me, code-wise. And much of the time I find that I"m still learning what the LLMs can do for me that truly saves me time, vs what would have been faster to just write myself in the first place.
LLMs are not useless. But if only 20% of a programmer's time is actually spent writing code on average then even if you can net a 50% increase in coding productivity... you're only netting a 10% overall productivity optimization for an engineer BEST CASE SCENARIO.
And that's not "useless" but compared to the hype and bullshit coming out of the mouths of CEOs, it's as good as useless. It's as good as the MIT study finding that only 5% of generative AI projects have netted ANY measurable returns for the business.
https://tech.co/news/klarna-reverses-ai-overhaul
Is my anecdotal evidence any better than yours?
Also disagree with missing the train, these tools are so easy to use a monkey (not even a smart one like an ape, more like a Howler) can effectively use them. Add in that the tooling landscape is changing rapidly; ex: everyone loved Cursor, but now it's fallen behind and everyone loves Claude Code. There's some sense in waiting for this to calm down and become more open. (Why are users so OK with vendor lock-in??? It's bothersome)
The hard parts are running LLMs locally (what quant do I use? K/V quant? Tradeoffs? Llama.cpp or ollama or vllm? What model? How much context can I cram in my vram? What if I do CPU inference? Fine tuning? etc..) and creating/training them.
Very obviously the internet is useful, and has radically changed our lives. Also obviously, most of the high stock valuations of the ‘90s didn’t pan out.
Personally, I think what we will witness is consolidation and winner-takes-all scenarios. There just isn't a sustainable market for 15 VS Code forks all copying each other along with all other non-VS Code IDEs cloning those features in as fast as possible. There isn't space for Claude Code, Gemini CLI, Qwen Code, Opencode all doing basically the same thing with their special branding when the thing they're actually selling is a commoditized LLM API. Hell, there _probably_ isn't space for OpenAI and Anthropic and Google and Mistral and DeepSeek and Alibaba and whoever else, all fundamentally creating and doing the same thing globally. Every single software vendor can't innovate and integrate AI features faster than AI companies themselves can build better tooling to automate that company's tools for them. It reeks of the 90's when there were a dozen totally viable but roughly equal search engines. One vendor will eventually pull ahead or have a slightly longer runway and claim the whole thing.
My feelings are that most of the "huge advancements" are not going to benefit the people selling AI.
I'd put my money on those who sell the pickaxes, and the companies who have a way to use this new tech to deliver more value.
You'll probably have a player that sells privacy as well.
For a couple of years, until someone who did keep doing research pulled ahead a bit with a similarly good UI.
I think it's relatively easy for Meta to plow billions into AI. Last quarter their revenue was something like $15 billion. Open AI will be lucky to generate that over the next year.
If AI is going to be integral to society going forward, how is it shortsighted?
> She did well by recommending investing in companies that invest in AI (like MS) but who had other profitable businesses (like Azure).
So you prefer a 2x gain rather than 10X gain from the likes of Nvidia or Broadcom? You should check how much better META has done compared to MSFT the past few years. Also a "financial investment person"? The anecdote feels made up.
> She skillfully navigated the question in a way that won my respect.
She won your respect by giving you advice that led to far less returns than you could have gotten otherwise?
> I personally believe that a lot of investment money is going to evaporate before the market resets.
But you believe investing in MSFT was a better AI play than going with the "hype" even when objective facts show otherwise. Why should any care what you think about AI, investments and the market when you clearly know nothing about it?
Why do you assume this people know any better than average Joe on the street?
Study after study demonstrates they can't even keep up with the market benchmarks, how would they be any wiser to tell you what's a fad or not.
So it’s true that AI will kill jobs, but not in the way they’ve imagined?!
Everything zuck has done since the "dawn of AI" has been to intentionally subvert and sabotage existing AI players, because otherwise Meta would be too far behind. In the same way that AI threatens Search, we are seeing emergently that AI is also threatening social networks -- you can get companionship, advice, information, emotional validation, etc. directly from an AI. People are forming serious relationships with these things in as much a real way as you would with anyone else on Facebook or Instagram. Not to mention, how long before most of the "people" on those platforms are AI themselves?
I believe exactly 0 percent of the decision to make Llama open-source and free was done altruistically as much as it was simply to try and push the margins of Anthropic, OpenAI, etc. downward. Indeed, I feel like even the fearmongering of this article is also strategically intended to devalue AI incumbents. AI is very much an existential threat to Meta.
Is AI currently fulfilling the immense hype around it? In my opinion, maybe not, but the potential value is obvious. Much more obvious than, for example, NFTs and crypto just a few years ago.
How so?
Like with most things, people will want what’s expensive and not what’s cheap. AI is cheap, real humans are not. Why buy diamonds when you can’t tell the difference with cubic zirconia? And yet demand for diamonds only increases.
Or, this knowingly could not be sustained. So they scooped up all the talent they wanted before anybody could react, all at once, with big carrots. And then hit pause button to let all that new talent figure out the next step.
Or the more likely explanation is that they feel they've completed the hiring necessary to figure out what's next.
If you had actually invested in AI pure players and Nvidia, the shovel seller, a couple years ago and were selling today, you would have made a pretty penny.
The hard thing with potential bubbles is not entirely avoiding them, it’s being there early enough and not being left at the end holding the bag.
I’m just amused by people who think they are financially more clever by taking conservative positions. At that point, just buy ETF. That’s even more diversification that buying Microsoft.
That said, I still suggested the parent sell. Real money is better than potential money. Classic gambler's fallacy, right? FOMO is letting hindsight get in the way of foresight.
It depends on how risk adverse you are and how much money you have there.
If you're happy with those returns, sell. FOMO is dumb. You can't time the market, the information just isn't available. If those shares are worth a meaningful amount of money, sell. Take your wins and walk away. A bird in your hand is worth more than two in the bush, right? That money isn't worth anything until it is realized[0].
Think about it this way: how much more would you need to make to risk making nothing? Or losing money? This is probably the most important question when investing.
If you're a little risk adverse or a good chunk of your profile is in it, sell 50-80% of it and then diversify. You're taking wins and restructuring.
If you wanna YOLO, then YOLO.
My advice? Don't let hindsight get in the way of foresight.
[0] I had some Nvidia stocks at 450 and sold at 900 (before the split, so would be $90 today). I definitely would have made more money if I kept them. Almost double if I sold today! But I don't look back for a second. I sold those shares and was able to pay off my student debt. Having this debt paid off is still a better decision in my mind because I can't predict the future. I could have sold 2 weeks later and made less! Or even in April of this year and made the same amount of money.
A couple of years ago, I asked a financial investment person about AI as a trick question. She did well by recommending investing in companies that invest in AI (like MS) but who had other profitable businesses (like Azure). I was waiting for her to put her foot in her mouth and buy into the hype.She skillfully navigated the question in a way that won my respect.
I personally believe that a lot of investment money is going to evaporate before the market resets. What we're calling AI will continue to have certain uses, but investors will realize that the moonshot being promised is undeliverable and a lot of jobs will disappear. This will hurt the wider industry, and the economy by extension.