Comment by alsetmusic

On the topic of how Sears used to be high-tech: back in 1981, when IBM introduced the IBM PC, it was the first time that they needed to sell computers through retail. So they partnered with Sears, along with the Computerland chain of computer stores, since Sears was considered a reasonable place for a businessperson to buy a computer. To plan this, meetings were held at the Sears Tower, which was the world's tallest building at the time.

My favorite anecdote about Sears is from Starbucks current HQs - the HQs used to be a warehouse for Sears. Before renovation the first floor walls next to the elevators used to have Sears' "commitment to customers" (or something like that).

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.

:-) Then it's going to blow your mind that CompuServe (while not founded by them) was a product of H&R Block.

There were quite a few small ISP's in the 1990's. Even Bill Gothard[0] had one.

[0]https://web.archive.org/web/19990208003742/http://characterl...

Blame short sighted investors asking Sears to "focus"

For more on this -- and how Sears had everything it needed (and more) to be what Amazon became -- see this comment from a 2007 MetaFilter thread: https://www.metafilter.com/62394/The-Record-Industrys-Declin...

Reminds me of Elon not taking a no for an answer. He did it twice, with a massive success.

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.

Sears started Prodigy to become Amazon, not Comcast.

They sure did. This reminds me of when I was in the local Mac Dealer right after the iPod came out. The employees were laughing together saying “nobody is going to buy this thing”.

You know, you're right. It -also- sucks at those tasks because on top of the issue you mention, unedited LLM text is identifiable if you get used to its patterns.

By definition, transformers can never exceed average.

That is the thing, and what companies pushing LLMs don't seem to realize yet.

Exactly. Books are still being translated by human translators.

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.

> 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.

Microsoft claims that they have an AI setup that outperforms human doctors on diagnosis tasks: https://microsoft.ai/new/the-path-to-medical-superintelligen...

"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.

If you consider how little time doctors have to look at you (at least in Germanys half broken public health sector) and how little they actually care ...

I think x is already higher than y for me.

Deepmind where experimenting with this https://github.com/google-deepmind/lab a few years ago.

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.

If we suppose that ANNs are more or less accurate models of real neural networks, the reason why they're so inefficient is not algorithmic, but purely architectural. They're just software. We have these huge tables of numbers and we're trying to squeeze them as hard as possible through a relatively small number of multipliers and adders. Meanwhile, a brain can perform a trillion fundamental simultaneously because every neuron is a complete processing element independent of every other one. To bring that back into more concrete terms, if we took an arbitrary model and turned it into a bespoke piece of hardware, it would certainly be at least one or two orders of magnitude faster and more efficient, with the downside that since it's dead silicon it could not be changed and iterated on.

I dont fully even get why; inference costs are way lower than training costs no?

The fact that the human brain, heck all brains, are so much more efficient than “state of the art” nnets, in terms of architecture, power consumption, training cost, what have you … while also being way more versatile and robust … is what convinces me that this is not the path that leads to AGI.

>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.

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.

> "We're profitable on inference. If we didn't pay for training, we'd be a very profitable company."

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.

Yes, it still gives people headaches because the convergence-accommodation conflict remains unsolved. We have a few different technologies to address that, but they're expensive, don't fully address the issue, and none of them have moved beyond the prototype stage.

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.

RealPlayer in the late 90s turned into (working) Napster, Gnutella and then the iPod in 2001, Podcasts (without the name) immediately after, with the name in 2004, Pandora in 2005, Spotify in 2008. So a decade from crummy idea to the companies we’re familiar with today, but slowed down by tremendous need for new (distributed) broadband infrastructure and complicated by IP arrangements. I guess 10 years seems like a long time from the front end, but looking back it’s nothing. Don’t go buying yourself a Tower Records.

Most growth is actually logistic. An S shaped curve that starts exponential but slows down rapidly as it reaches some asymptote. In fact basically everything we see as exponential in the real world is logistic.

True, but adoption of AI has certainly seen exponential growth.

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 :)

Fwiw LLMs are also revolutionary. There's currently more anti-AI hype than AI hype imho. As in there's literally people claiming it's completely useless and not going to change a thing. Which is crazy.

You're right, and I also think LLMs have an impact.

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 ?

I see the point at the moment on “low quality advertising”, but we are still far from high quality video generated for AI.

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

It's quite incredible how fast the generative media stuff is moving.

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).

As long as you do not make ads with four-fingered hands, like those clowns ... :)

https://www.lapresse.ca/arts/chroniques/2025-07-08/polemique...

too soon to get it to market, though it obviously all sold perfectly well, people were sufficiently wowed by it

> There's also no evidence that it won't

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.

What is your definition of "evidence" here? The evidence, in my view, are physical (as in, available computing power) and algorithmic limitations.

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.

I don’t follow. We have benchmarks that have survived decades and illustrate the steps.

What do you call GPT 3.5?

FWIW, Nvidia's moat isn't hardware and they know this (they even talk about it). Hardware wise AMD is neck and neck with them, but AMD still doesn't have a CUDA equivalent. CUDA is the moat. As painful as it is to use, there's a long way to go for companies like AMD to compete here. Their software is still pretty far behind, despite their rapid and impressive advancements. It will also take time to get developer experience to saturate within the market, and that will likely mean AMD needs some good edge over Nvidia, like adding things Nvidia can't do or being much more cost competitive. And that's not something like adding more VRAM or just taking smaller profit margins because Nvidia can respond to those fairly easily.

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.

Sure helps to be born wealthy, go to private school, and Ivy League college.

And timing

Win a monster pot and you can play a lot of more interesting hands.

Except you can play hundreds of thousands of poker hands in your lifetime, but only have time/energy/money to start a handful of businesses.

This might be true for a normal definition of success, but not lottery-winner style success like Facebook. If you look at Microsoft, Netflix, Apple, Amazon, Google, and so on, the founders all have few or zero previous attempts at starting a business. My theory is that this leads them to pursue risky behavior that more experienced leaders wouldn't try, and because they were in the right place at the right time, that earned them the largest rewards.

What "massive string of failed attempts" did Zuckerberg or Bezos ever accumulate?

This is just cope for people with a massive string of failed attempts and no successes.

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.

IMO this strengthens the case for luck. If the probability of winning the lottery is P, then trying N times gives you a probability of 1-(1-P)^N.

Who's more likely to win, someone with one lottery ticket or someone with a hundred?

"Socialism never took root in America because the poor see themselves not as an exploited proletariat but as temporarily embarrassed millionaires."

Some will read this and laser in on the "socialism" part, but obviously the interesting bit is the second half of the quote.

this is also just cope

Plenty of them do try and fail, and then one succeeds, and it doesn't mean that person is intrinsically smarter/wiser/better/etc than the others.

There are far, far more external factors on a business's success than internal ones, especially early on.

What risk was there in creating facebook? I don't see it.

Dude makes a website in his dorm room and I guess eventually accepts free money he is not obligated to pay back.

What risk?

There is another dimension, which is mostly but not fully characterized as perseverance, but many times with an added dose of ruthlessness

Microsoft, Facebook, Uber, google and many others all had strong doses of ruthlessness

Or you can just have rich parents and do nothing, and still be considered successful. What you say only applies to people who start from zero, and even then I'd call luck the dominant factor (based on observing my skillful and hardworking but not really successful friends).

>luck, skill and hard work.

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.

I'm thinking of exactly what I said[^1] :)

[1]: https://en.wikipedia.org/wiki/Careless_People

The not-so-secret is that the "killer apps" for deep neural networks are not LLMs or diffusion models. Those are very useful, but the most valuable applications in this space are content recommendation and ad targeting. It's obvious how Meta can use those things.

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).

>But how is it worth for meta, since they won't really monetize it.

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.

Isn't Meta doing some limited rollout of Llama as an API? Still I haven't got my hands on it so I cannot say for sure whether it is currently paid or not, but that can drive some revenue.

Zuckerberg started as a sex pest and got not an iota better.

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.

I used to work in adtech. I don't have any direct information but, I assume this relates to the persistent rumours that Facebook inflates impressions and turns a blind eye to bot activity.

This is pretty common on HN but not unique to it. Lots of rationalist adjacent content (like stuff on LessWrong, replies to Scott Alexander's substack, etc) has it also. Here I think it comes from users that try to intellectualize their not-very-intellectual, stream of consciousness style thoughts, as if using technical jargon to convey your feelings makes them more rational and less emotional.

Between “presumes a solid grasp of which things out there are not fraud besides those which are coercion, but that's not a subject I'm interested in having an opinion about,” before going on and sharing an opinion on that subject, and “even the Invisible Hand of the market is hand-shaped,” I think it may just be AI slop.

>why is there so much of this on HN?

Where?

But would they be profitable enough? They've taken on more than $50 billion of investment.

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 they stopped research and just focused on productionizing inference use cases I think they’d be profitable

For a couple of years, until someone who did keep doing research pulled ahead a bit with a similarly good UI.

You missed my pun on Jane Street.

Meta doesn’t really serve companionship. It used to make Yu connected to others in your social graph, which AI cannot replace. If IG still has the eyeballs, people can put AI generated content on it with or without meta’s permission.

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.

I went through the parents, looking for a claim somewhere that AI was "useless." I couldn't find it.

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.

And I happen to know a different company that regrets their decision to do something similar:

https://tech.co/news/klarna-reverses-ai-overhaul

Is my anecdotal evidence any better than yours?

I know a company that did the same and lost billions of dollars