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.
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.