> The average person now is far wealthier in terms of actual purchasing power than the average person 100 years ago
access to housing is incredibly expensive. Measuring their purchasing power for how many scented candles they can buy is pretty meaningless when they are much closer to homelessness than ever before.
Land is one thing you can't automate the production of, and construction still consists mostly of manual skilled labor. But I think despite that you'll find housing today still compares very favorably to housing 100 years ago (in terms of median square footage, safety, amenities, etc).
That is a game thoery approach but it completely fails in the face of reality.
The reality is that the floor to become "useful" is relatively low, which means the few billioanires have a large pool of potentially useful people of which they only employ some, leading to no greater salaries due to labour competition.
The other potentially useful workers cannot pool together and compete as the barrier of entry in the sector is prohibitely high.
So a natural moat emerges over cost of setting up a company, workers beg for a job of which they will take for a small wage and a few billioanires control the market.
This is a much closer approximation to the market we currently see
> If WB was any good, would they have been snatched up by Netflix?
Yes because the situation of WB has nothing to do with their performance.
In 1990s they merged with TIME publishing right before the internet killed all magazines. In 2000s with AOL right before th dot com bubble. In 2010s with AT&T who realised they needed a shit ton of money to roll out 5G so they took a massive loan and charged it to Warner debt.
So WARNER keeps performing and the business side keeps adding debt from horrible decisions
>His limbs were in proportion, and I had selected his features as beautiful. Beautiful! Great God! His yellow skin scarcely covered the work of muscles and arteries beneath; his hair was of a lustrous black, and flowing; his teeth of a pearly whiteness; but these luxuriances only formed a more horrid contrast with his watery eyes, that seemed almost of the same colour as the dun-white sockets in which they were set, his shrivelled complexion and straight black lips.
As I said, the contrast between "pretty" or "human" traits vs "monster" just wasn't there.
> the sentence "LLMs don't think because they predict the next token" is logically as wrong
it isn't, depending on the deifinition of "THINK".
If you believe that thought is the process for where an agent with a world model, takes in input, analysies the circumstances and predicts an outcome and models their beaviour due to that prediction. Then the sentence of "LLMs dont think because they predict a token" is entirely correct.
They cannot have a world model, they could in some way be said to receive a sensory input through the prompt. But they are neither analysing that prompt against its own subjectivity, nor predicting outcomes, coming up with a plan or changing its action/response/behaviour due to it.
Any definition of "Think" that requieres agency or a world model (which as far as I know are all of them) would exclude an LLM by definition.
I think Anthropic has established that LLMs have at least a rudimentary world model (regions of tensors that represent concepts and relationships between them) and that they modify behavior due to a prediction (putting a word at the end of the second line of a poem based on the rhyme they need for the last). Maybe they come up short on 'analyzing the circumstances'; not really sure how to define that in a way that is not trivial.
This may not be enough to convince you that they do think. It hasn't convinced me either. But I don't think your confident assertions that they don't are borne out by any evidence. We really don't know how these things tick (otherwise we could reimplement their matrices in code and save $$$).
If you put a person in charge of predicting which direction a fish will be facing in 5 minutes, they'll need to produce a mental model of how the fish thinks in order to be any good at it. Even though their output will just be N/E/S/W, they'll need to keep track internally of how hungry or tired the fish is. Or maybe they just memorize a daily routine and repeat it. The open question is what needs to be internalized in order to predict ~all human text with a low error rate. The fact that the task is 'predict next token' doesn't tell us very much at all about the internals. The resulting weights are uninterpretable. We really don't know what they're doing, and there's no fundamental reason it can't be 'thinking', for any definition.
> I think Anthropic has established that LLMs have at least a rudimentary world model
its unsurprising that a company heavily invested in LLMs would describe clustered information as a world model, but it isnt. Transformer models, for video or text LLMs dont have the kind of stuff you would need to have a world model. They can mimic some level of consistency as long as the context window holds, but that disappears the second the information leaves that space.
In terms of human cognition it would be like the difference between short term memory, long term memory and being able to see the stuff in front of you. A human can instinctively know the relative weight, direction and size of objects and if a ball rolls behind a chair you still know its there 3 days later. A transformer model cannot do any of those things and at best can remember the ball behind the chair until enough information comes in to push it out of the context window at which point it can not reapper.
> putting a word at the end of the second line of a poem based on the rhyme they need for the last)
that is the kind of work that exists inside its conext window. Feed it a 400 page book, which any human could easily read, digest, parse and understand and make it do a single read and ask questions about different chapters. You will quickly see it make shit up that fits the information given previously and not the original text.
> We really don't know how these things tick
I don't know enough about the universe either. But if you told me that there are particles smaller than plank length and others that went faster than the speed of light then I would tell you that it cannot happen due to the basic laws of the universe. (I know there are studies on FTL neutrinos and dark matter but in general terms, if you said you saw carbon going FTL I wouldnt believe you).
Similarly, Transformer models are cool, emergent properties are super interesting to study in larger data sets. Adding tools to the side for deterministic work helps a lot, agenctic multi modal use is fun. But a transformer does not and cannot have a world model as we understand it, Yann Lecunn left facebook because he wants to work on world model AIs rather than transformer models.
> If you put a person in charge of predicting which direction a fish will be facing in 5 minutes,
what that human will never do is think the fish is gone because he went inside the castle and he lost sight of it. Something a transformer would.
Anthropic may or may not have claimed this was evidence of a world model; I'm not sure. I say this is a world model because it is a objectively a model of the world. If your concept of a world model requires something else, the answer is that we don't know whether they're doing that.
Long-term memory and object permanence don't seem necessary for thought. A 1-year-old can think, as can a late-stage Alzheimers patient. Neither could get through a 400-page book, but that's irrelevant.
Listing human capabilities that LLMs don't have doesn't help unless you demonstrate these are prerequisites for thought. Helen Keller couldn't tell you the weight, direction, or size of a rolling ball, but this is not relevant to the question of whether she could think.
Can you point to the speed-of-light analogy laws that constrain how LLMs work in a way that excludes the possibility of thought?
> I say this is a world model because it is a objectively a model of the world.
a world model in AI has specific definition, which is an internal representation that the AI can use to understand and simulate its environment.
> Long-term memory and object permanence don't seem necessary for thought. A 1-year-old can think, as can a late-stage Alzheimers patient
Both those cases have long term memory and object permanence, they also have a developing memory or memory issues. But the issues are not constrained by their context window. Children develop object permance in the first 8 months, and similar to distinguishing between their own body and their mothers that is them developing a world model. Toddlers are not really thinking, they are responding to stimulus, they feel huger they cry. They hear a loud sound they cry. Its not really them coming up with a plan to get fed or attention
> Listing human capabilities that LLMs don't have doesn't help unless you demonstrate these are prerequisites for thought. Helen Keller couldn't tell you the weight, direction, or size of a rolling ball
Helen Keller had understanding in her mind of what different objects were, she started communicating because she understood the word water with her teacher running her finger through her palm.
Most humans have multiple sensory inputs (sight, smell, hearing, touch) she only had one which is perhaps closer to an LLM. But conditions she had that LLMs dont have are agency, planning, long term memory etc.
> Can you point to the speed-of-light analogy laws that constrain how LLMs work in a way that excludes the possibility of thought?
Sure, let me switch the analogy if you dont mind. In the chinese room thought experiment we have a man who gets a message and opens a chinese dictionary and translates it perfectly word by word and the person on the other side receives and read a perfect chinese message.
The argument usually goes along the idea of whether the person inside the room "understands" chinese if he is capable of creating 1:1 perfect chinese messages out.
But an LLM is that man, what you cannot argue is that the man is THINKING. He is mechanically going to the dictionary and returning a message that can pass as human written because the book is accurate (if the vectors and weights are well tuned). He is neither an agent, he simply does, and he is not crating a plan or doing anything beyond transcribing the message as the book demands.
He doesnt have a mental model of the chinese language, he cannot formulate his own ideas or execute a plan based on predicted outcomes, he cannot do but perform the job perfectly and boringly as per the book.
I am not someone working on AGI but I think a lot of people work backwards from the expected outcome.
Expected outcome is usually something like a Post-Scarcity society, this is a society where basic needs are all covered.
If we could all live in a future with a free house and a robot that does our chores and food is never scarce we should works towards that, they believe.
The intermiddiete steps aren't thought out, in the same way that for example the communist manifesto does little to explain the transition from capitalism to communism. It simply says there will be the need for things like forcing the bourgiese to join the common workers and there will be a transition phase but no clear steps between either system.
Similarly many AGI proponents think in terms of "wouldnt it be cool if there was an AI that did all the bits of life we dont like doing", without systemic analysis that many people do those bits because they need money to eat for example.
> they’re using their equity to buy compute that is critical to improving their core technology
But we know that growth in the models is not exponential, its much closer to logarithmic. So they spend =equity to get >results.
The ad spend was a merry go round, this is a flywheel where the turning grinds its gears until its a smooth burr. The math of the rising stock prices only begins to make sense if there is a possible breakthrough that changes the flywheel into a rocket, but as it stands its running a lemonade stand where you reinvest profits into lemons that give out less juice
There is something about an argument made almost entirely out of metaphors that amuses me to the point of not being able to take it seriously, even if I actually agree with it.
OpenAI invests heavily into integration with other products. If model development stalls they just need to be not worse than other stalled models while taking advantage of brand recognition and momentum to stay ahead in other areas.
In that sense it makes sense to keep spending billions even f model development is nearing diminishing return - it forces competition to do the same and in that game victory belongs to the guy with deeper pockets.
Investors know that, too. A lot of startup business is a popularity contents - number one is more attractive for the sheer fact of being number one. If you’re a very rational investor and don’t believe in the product you still have to play this game because others are playing it, making it true. The vortex will not stop unless limited partners start pushing back.
But, if model development stalls, and everyone else is stalled as well, then what happens to turn the current wildly-unprofitable industry into something that "it makes sense to keep spending billions" on?
I suspect if model development stalls we may start to see more incremental releases to models, perhaps with specific fixes or improvements, updates to a certain cutoff date, etc. So less fanfare, but still some progress. Worth spending billions on? Probably not, but the next best avenue would be to continue developing deeper and deeper LLM integrations to stay relevant and in the news.
The new OpenAI browser integration would be an example. Mostly the same model, but with a whole new channel of potential customers and lock in.
Because they’re not that wildly unprofitable. Yes, obviously the companies spend a ton of money on training, but several have said that each model is independently “profitable” - the income from selling access to the model has overcome the costs of training it. It’s just that revenues haven’t overcome the cost of training the next one, which gets bigger every time.
> the income from selling access to the model has overcome the costs of training it.
Citation needed. This is completely untrue AFAIK. They've claimed that inference is profitable, but not that they are making a profit when training costs are included.
The bigger threat is if their models "stall", while a new up-start discovers an even better model/training method.
What _could_ prevent this from happening is the lack of available data today - everybody and their dog is trying to keep crawlers off, or make sure their data is no longer "safe"/"easy" to be used to train with.
Well, the thing is that that kind of hardware chips quickly decrease in value. It's not like the billions spend in past bubbles like the 2000s where internet infrastructure was build (copper, fibre) or even during 1950s where transport infrastructure (roads) were build.
Data centers are massive infrastructural investments similar to roads and rails. They are not just a bunch of chips duct taped together, but large buildings with huge power and networking requirements.
Power companies are even constructing or recommissioning power plants specifically to meet the needs of these data centers.
All of these investments have significant benefits over a long period of time. You can keep on upgrading GPUs as needed once you have the data center built.
They are clearly quite profitable as well, even if the chips inside are quickly depreciating assets. AWS and Azure make massive profits for Amazon and Microsoft.
> Do you think a non-programmer could realistically build a full app using vibe coding?
For personal or professional use?
If you want to make it public I would say 0% realistic. The bugs, security concerns, performance problems etc you would be unable to fix are impossible to enumerate.
But even if you had a simple loging and kept people's email and password, you can very easily have insecure dbs, insecure protections against simple things like mysqliinjections etc.
You would not want to be the face of "vibe coder gives away data of 10k users"
Ideally, I want this to grow into a proper startup. I’m starting solo for now, but as things progress, I’d like to bring in more people. I’m not a tech, product or design person, but AI gives me hope that I can at least get an MVP out and onboard a few early users.
For auth, I’ll be using Supabase, and for the MVP stage I think Lovable should be good enough to build and test with maybe a few hundred users. If there’s traction and things start working, that’s when I’d plan to harden the stack and get proper security and code reviews in place.
One of the issues AI coding has, is that its in some ways very inhuman. The bugs that are introduced are very hard to pick up because humans wouldnt write it that way, hence they wouldnt make those mistakes.
If you then introduce other devs you have 2 paths, they either build on top of vibe coding, which is going to leave you vulnerable to those bugs and honestly make their life a misery as they are working on top of work that missed basic decisions that will help it grow. (Imagine a non architect built your house, the walls might be straight but he didnt know to level the floor, or to add the right concrete to support the weight of a second floor)
Or the other path is they rebuild your entire app correctly. With the only advantage of the MVP and the users showing some viability for the idea. But the time it will take to rewrite it means in a fast moving space like start ups someone can quickly overtake you.
Its a risky proposition that means you are not going to create a very adequate base for the people you might hire.
I would still recommend against it, thinking that AI is more like WebMD, it can help someone who is already a doctor but it will confuse, and potentially hurt those without enough training to know what to look for.
access to housing is incredibly expensive. Measuring their purchasing power for how many scented candles they can buy is pretty meaningless when they are much closer to homelessness than ever before.
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