[cs702]

The problem is even more fundamental: Today's models stop learning once they're deployed to production.

There's pretraining, training, and finetuning, during which model parameters are updated.

Then there's inference, during which the model is frozen. "In-context learning" doesn't update the model.

We need models that keep on learning (updating their parameters) forever, online, all the time.

[furyofantares]

Why is learning an appropriate metaphor for changing weights but not for context? There are certainly major differences in what they are good or bad at and especially how much data you can feed them this way effectively. They both have plenty of properties we wish the other had. But they are both ways to take an artifact that behaves as if it doesn't know something and produce an artifact that behaves as if it does.

I've learned how to solve a Rubik's cube before, and forgot almost immediately.

I'm not personally fond of metaphors to human intelligence now that we are getting a better understanding of the specific strengths and weaknesses these models have. But if we're gonna use metaphors I don't see how context isn't a type of learning.

[fhd2]

I suppose ultimately, the external behaviour of the system is what matters. You can see the LLM as the system, on a low level, or even the entire organisation of e.g. OpenAI at a high level.

If it's the former: Yeah, I'd argue they don't "learn" much (!) past inference. I'd find it hard to argue context isn't learning at all. It's just pretty limited in how much can be learned post inference.

If you look at the entire organisation, there's clearly learning, even if relatively slow with humans in the loop. They test, they analyse usage data, and they retrain based on that. That's not a system that works without humans, but it's a system that I would argue genuinely learns. Can we build a version of that that "learns" faster and without any human input? Not sure, but doesn't seem entirely impossible.

Do either of these systems "learn like a human"? Dunno, probably not really. Artificial neural networks aren't all that much like our brains, they're just inspired by them. Does it really matter beyond philosophical discussions?

I don't find it too valuable to get obsessed with the terms. Borrowed terminology is always a bit off. Doesn't mean it's not meaningful in the right context.

[carlmr]

To stretch the human analogy, it's short term memory that's completely disconnected from long term memory.

The models currently have anteretrograde amnesia.

[wat10000]

It’s not very good in context, for one thing. Context isn’t that big, and RAG is clumsy. Working with an LLM agent is like working with someone who can’t form new long term memories. You have to get them up to speed from scratch every time. You can accelerate this by putting important stuff into the context, but that slows things down and can’t handle very much stuff.

[furyofantares]

The article does demonstrate how bad it is in context.

Context has a lot of big advantages over training though, too, it's not one-sided. Upfront cost and time are the big obvious ones, but context also works better than training on small amounts of data, and it's easier to delete or modify.

Like even for a big product like Claude Code from someone that controls the model, although I'm sure they do a lot of training to make the product better, they're not gonna just rely entirely on training and go with a nearly blank system prompt.

[imtringued]

You got this exactly backwards.

"I'm not fond of metaphors to human intelligence".

You're assuming that learning during inference is something specific to humans and that the suggestion is to add human elements into the model that are missing.

That isn't the case at all. The training process is already entirely human specific by way of training on human data. You're already special casing the model as hard as possible.

Human DNA doesn't contain all the information that fully describes the human brain, including the memories stored within it. Human DNA only contains the blue prints for a general purpose distributed element known as neurons and these building blocks are shared by basically any animal with a nervous system.

This means if you want to get away from humans you will have to build a model architecture that is more general and more capable of doing anything imaginable than the current model architectures.

Context is not suitable for learning because it wasn't built for that purpose. The entire point of transformers is that you specify a sequence and the model learns on the entire sequence. This means that any in-context learning you want to perform must be inside the training distribution, which is a different way of saying that it was just pretraining after all.

[suddenlybananas]

The fact the DNA doesn't store all connections in the brain doesn't mean that enormous parts of the brain, and by extension, behaviour aren't specified in the DNA. Tons of animals have innate knowledge encoded in their DNA, humans among them.

[furyofantares]

I don't think it's specific to humans at all, I just think the properties of learning are different in humans than they are in training an LLM, and injecting context is different still. I'd rather talk about the exact properties than bemoan that context isn't learning. We should just talk about the specific things we see as problems.

[knollimar]

Models gain information from context but probably not knowledge and definitely not wisdom.

[embedding-shape]

> We need models that keep on learning (updating their parameters) forever, online, all the time.

Do we need that? Today's models are already capable in lots of areas. Sure, they don't match up to what the uberhypers are talking up, but technology seldom does. Doesn't mean what's there already cannot be used in a better way, if they could stop jamming it into everything everywhere.

[pankajdoharey]

Continuous learningin current models will lead to catastrophic forgetting.

[DoctorOetker]

will catastrophic forgetting still occur if a fraction of the update sentences are the original training corpus?

is the real issue actually catastrophic forgetting or overfitting?

nothing prevents users from continuing the learning as they use a model

[thesz]

Catastrophic forgetting is overfitting.

[pankajdoharey]

No, it’s actually the math of overwriting. Imagine you hiked down into a valley Task A and settled there. Then, you decide to climb a new mountain to find a different valley Task B. You successfully move to the new valley, but in doing so, you destroy the path back to the first one. You are now stuck in the new valley and have completely 'forgotten' how to get back to the first one.

[davidguetta]

not exactly, not at all even in term of the way the llm are trained.

In RL it can be that you are not getting meaningful data anymore because you are 'too good' and dont get anymore the "this is a bad answer" signal so you can't estimate the gradient.

[BobbyTables2]

How long will it take someone to poison such a model by teaching it wrong things?

Even humans fall for propaganda repeated over and over .

The current non-learning model is unintentionally right up there with the “immutable system” and “infrastructure as code” philosophy.

[Izkata]

> How long will it take someone to poison such a model by teaching it wrong things?

TayTweets was a decade ago.

[nxobject]

> The current non-learning model is unintentionally right up there with the “immutable system” and “infrastructure as code” philosophy.

As long as training material remains the proprietary secret sauce, the average user doesn’t already see or benefit from that - it’s all a promise and a black box to us.

[4b11b4]

I'm not sure if you want models perpetually updating weights. You might run into undesirable scenarios.

[com2kid]

If done right, one step closer to actual AGI.

That is the end goal after all, but all the potential VCs seem to forget that almost every conceivable outcome of real AGI involves the current economic system falling to pieces.

Which is sorta weird. It is like if VCs in Old Regime france started funding the revolution.

[jack_pp]

I think VCs end up in one of four categories

1. They're too stupid to understand what they're truly funding.

2. They understand but believe they can control it for their benefit, basically want to "rule the world" like any cartoon villain.

3. They understand but are optimists and believe AGI will be a benevolent construct that will bring us to post scarcity society. There are a lot of rich / entrepreneurs that still believe they are working to make the world a better place.. (one SaaS at a time but alas, they believe it)

4. They don't believe that AGI is close or even possible

[Nevermark]

If it makes the models smarter, someone will do it.

From any individual, up to entire countries, not participating doesn't do anything except ensure you don't have a card to play when it happens.

There is a very strong element of the principles of nature and life (as in survival, not nightclubs or hobbies) happening here that can't be shamed away.

The resource feedback for AI progress effort is immense (and it doesn't matter how much is earned today vs. forward looking investment). Very few things ever have that level of relentless force behind them. And even beyond the business need, keeping up is rapidly becoming a security issue for everyone.

[com2kid]

If Moore's Law had fully kicked over twice more we'd all have 64GB GPUs, enthusiasts would have 2x64GB, and data center build outs wouldn't be needed.

Eventually GPU memory is going to creep up and local models will powerful enough.

[Nevermark]

I agree. I also think we have only hit the surface of model efficiencies.

Apple's M3 Ultra with RAM up to 512GB shared directly across CPU/GPU/NPUs is a great example of an architecture already optimized for local models. I expect Apple will start offering larger RAM sizes for other form factors too.

And prices for RAM will drop eventually, because of the extreme demand for RAM with higher densities.

[com2kid]

It reminds me of the huge infra investments in Sun and Cisco during the first .com boom, and then 5-10 years later those fancy Sun boxes were out performed by Grandma's Windows XP box.

[CorrectHorseBat]

Yes the planet got destroyed. But for a beautiful moment in time we created a lot of value for shareholders.

And for your comparison, they did fund the American revolution which on its turn was one of the sparks for the French revolution (or was that exactly the point you were making?)

[com2kid]

The funding of the American revolution is a fun topic but most people don't know about it so I don't bother dropping references to it. :D

[BobbyTables2]

I wonder which side tried to forget that first (;->

[olalonde]

1. Progress is unstoppable. Refusing to fund it won't make it disappear.

2. Most VCs are normal people that just want a bigger slice of pie, not necessarily a bigger share of the pie. See the fixed pie fallacy.

[cs702]

Our brains, which are organic neural networks, are constantly updating themselves. We call this phenomenon "neuroplasticity."

If we want AI models that are always learning, we'll need the equivalent of neuroplasticity for artificial neural networks.

Not saying it will be easy or straightforward. There's still a lot we don't know!

[4b11b4]

I wasn't explicit about this in my initial comment, but I don't think you can equate more forward passes to neuroplasticity. Because, for one, simply, we (humans) also /prune/. And... Similar to RL which just overwrites the policy, pushing new weights is in a similar camp. You don't have the previous state anymore. But we as humans with our neuroplasticity do know the previous states even after we've "updated our weights".

[nemomarx]

How would you keep controls - safety restrictions - Ip restrictions etc with that, though? the companies selling models right now probably want to keep those fairly tight.

[api]

This is why I’m not sure most users actually want AGI. They want special purpose experts that are good at certain things with strictly controlled parameters.

[4b11b4]

I agree, the fundamental problem is we wouldn't be able to understand it ("AGI"). Therefore it's useless. Either useless or you let it go unleashed and it's useful. Either way you still don't understand it/can't predict it/it's dangerous/untrustworthy. But a constrained useful thing is great, but it fundamentally has to be constrained otherwise it doesn't make sense

[api]

The way I see it, we build technology to be what we are not and do what we can’t do or things we can do but better or faster.

An unpredictable fallible machine is useless to us because we have 7+ billion carbon based ones already.

[fph]

Tay the chatbot says hi from 2017.

[0xdeadbeefbabe]

How about we just put them to bed once in a while?

[4b11b4]

Please elaborate on this one

[robochat]

I think they mean that the model should have sleep period where they update themselves with what they learnt that day.

[bdj108]

it is interesting

[4b11b4]

Please elaborate

[derefr]

Doesn't necessarily need to be online. As long as:

1. there's a way to take many transcripts of inference over a period, and convert/distil them together into an incremental-update training dataset (for memory, not for RLHF), that a model can be fine-tuned on as an offline batch process every day/week, such that a new version of the model can come out daily/weekly that hard-remembers everything you told it; and

2. in-context learning + external memory improves to the point that a model with the appropriate in-context "soft memories", behaves indistinguishably from a model that has had its weights updated to hard-remember the same info (at least when limited to the scope of the small amounts of memories that can be built up within a single day/week);

...then you get the same effect.

Why is this an interesting model? Because, at least to my understanding, this is already how organic brains work!

There's nothing to suggest that animals — even humans — are neuroplastic on a continuous basis. Rather, our short-term memory is seemingly stored as electrochemical "state" in our neurons (much like an LLM's context is "state", but more RNN "a two-neuron cycle makes a flip-flop"-y); and our actual physical synaptic connectivity only changes during "memory reconsolidation", a process that mostly occurs during REM sleep.

And indeed, we see the same exact problem in humans and other animals, where when we stay awake too long without REM sleep, our "soft memory" state buffer reaches capacity, and we become forgetful, both in the sense of not being able to immediately recall some of the things that happened to us since we last slept; and in the sense of later failing to persist some of the experiences we had since we last slept, when we do finally sleep. But this model also "works well enough" to be indistinguishable from remembering everything... in the limited scope of our being able to get a decent amount of REM sleep every night.

[observationist]

It 100% needs to be online. Imagine you're trying to think about a new tabletop puzzle, and every time a puzzle piece leaves your direct field of view, you no longer know about that puzzle piece.

You can try to keep all of the puzzle pieces within your direct field of view, but that divides your focus. You can hack that and make your field of view incredibly large, but that can potentially distort your sense of the relationships between things, their physical and cognitive magnitude. Bigger context isn't the answer, there's a missing fundamental structure and function to the overall architecture.

What you need is memory, that works when you process and consume information, at the moment of consumption. If you meet a new person, you immediately memorize their face. If you enter a room, it's instantly learned and mapped in your mind. Without that, every time you blinked after meeting someone new, it'd be a total surprise to see what they looked like. You might never learn to recognize and remember faces at all. Or puzzle pieces. Or whatever the lack of online learning kept you from recognizing the value of persistent, instant integration into an existing world model.

You can identify problems like this for any modality, including text, audio, tactile feedback, and so on. You absolutely, 100% need online, continuous learning in order to effectively deal with information at a human level for all the domains of competence that extend to generalizing out of distribution.

It's probably not the last problem that needs solving before AGI, but it is definitely one of them, and there might only be a handful left.

Mammals instantly, upon perceiving a novel environment, map it, without even having to consciously make the effort. Our brains operate in a continuous, plastic mode, for certain things. Not only that, it can be adapted to abstractions, and many of those automatic, reflexive functions evolved to handle navigation and such allow us to simulate the future and predict risk and reward over multiple arbitrary degrees of abstraction, sometimes in real time.

https://www.nobelprize.org/uploads/2018/06/may-britt-moser-l...

[bluegatty]

That's not how training works - adjusting model weights to memorize a single data item is not going to fly.

Model weights store abilities, not facts - generally.

Unless the fact is very widely used and widely known, with a ton of context around it.

The model can learn the day JFK died because there are millions of sparse examples of how that information exists in the world, but when you're working on a problem, you might have 1 concern to 'memorize'.

That's going to be something different than adjusting model weights as we understand them today.

LLMs are not mammals either, it's helpful analogy in terms of 'what a human might find useful' but not necessary in the context of actual llm architecture.

The fact is - we don't have memory sorted out architecturally - it's either 'context or weights' and that's that.

Also critically: Humans do not remember the details of the face. Not remotely. They're able to associate it with a person and name 'if they see it again' - but that's different than some kind of excellent recall. Ask them to describe features in detail and maybe we can't do it.

You can see in this instance, this may be related to kind of 'soft lookup' aka associating an input with other bits of information which 'rise to the fore' as possibly useful.

But overall, yes, it's fair to take the position that we'll have to 'learn from context in some way'.

[observationist]

Also, with regards to faces, that's kind of what I'm getting at - we don't have grid cells for faces, there seem to be discrete, functional, evolutionary structures and capabilities that combine in ways we're not consciously aware of to provide abilities. We're reflexively able to memorize faces, but to bring that to consciousness isn't automatic. There've been amnesia and lesion and other injury studies where people with face blindness get stress or anxiety, or relief, when recognizing a face, but they aren't consciously aware. A doctor, or person they didn't like, showing up caused stress spikes, but they couldn't tell you who they were or their name, and the same with family members- they get a physiological, hormonal response as if they recognized a friend or foe, but it never rises to the level of conscious recognition.

There do seem to be complex cells that allow association with a recognizable face, person, icon, object, or distinctive thing. Face cells apply equally to abstractions like logos or UI elements in an app as they do to people, famous animals, unique audio stings, etc. Split brain patients also demonstrate amazing strangeness with memory and subconscious responses.

There are all sorts of layers to human memory, beyond just short term, long term, REM, memory palaces, and so forth, and so there's no simple singular function of "memory" in biological brains, but a suite of different strategies and a pipeline that roughly slots into the fuzzy bucket words we use for them today.

[koverstreet]

It's not just faces. When recognizing objects in the environment, we normally filter out a great number of details going through the visual cortex - by the time information from our eyes hits the level of conscious awareness, it's more of a scene graph.

Table; chair behind and little to the left of the chair; plant on table

Most people won't really have conscious access to all the details that we use in recognizing objects - but that is a skill that can be consciously developed, as artists and painters do. A non-artist would be able to identify most of the details, but not all (I would be really bad compared to an actual artist with colors and spatial relationships), and I wouldn't be able to enumerate the important details in a way that makes any kind of sense for forming a recognizable scene.

So it follows from that that our ability to recognize faces is not purely - or even primarily - an attribute of what we would normally call "memory", certainly in the sense of conscious memory where we can recall details on demand. Like you alluded to re: mammals and spaces, we're really good at identifying, categorizing, and recognizing new forms of structure.

[observationist]

I suspect we're going to need hypernetworks of some sort - dynamically generated weights, with the hypernet weights getting the dream-like reconsolidation and mapping into the model at large, and layers or entire experts generated from the hypernets on the fly, a degree removed from the direct-from-weights inference being done now. I've been following some of the token-free latent reasoning and other discussions around CoT, other reasoning scaffolding, and so forth, and you just can't overcome the missing puzzle piece problem elegantly unless you have online memory. In the context of millions of concurrent users, that also becomes a nightmare. Having a pipeline, with a sort of intermediate memory, constructive and dynamic to allow resolution of problems requiring integration into memorized concepts and functions, but held out for curation and stability.

It's an absolutely enormous problem, and I'm excited that it seems to be one of the primary research efforts kicking off this year. It could be a very huge capabilities step change.

[koverstreet]

Can I subscribe to your newsletter? You seem to be pretty plugged in to current research.

[bluegatty]

Yes, so I think that's a fine thought, I don't think it fits into LLM architecture.

Also, weirdly, even Lecun etc. are barely talking about this, they're thinking about 'world models etc'.

I think what you're talking about is maybe 'the most important thing' right now, and frankly, it's almost like an issue of 'Engineering'.

Like - its when you work very intently with the models so this 'issue' become much more prominent.

Your 'instinct' for this problem is probably an expression of 'very nuanced use' I'm going to guess!

So in a way, it's as much Engineering as it is theoretical?

Anyhow - so yes - but - probably not LLM weights. Probably.

I'll add a small thing: the way that Claude Code keeps the LLM 'on track' is by reminding it! Literally, it injects little 'TODO reminders' with some prompts, which is kind of ... simple!

I worked a bit with 'steering probes' ... and there's a related opportunity there - to 'inject' memory and control operations along those lines. Just as a starting point for a least one architectural motivation.

[pankajdoharey]

Not to forget we will need thousands of examples for the models to extract abilities the sample efficiency of these models is quite poor.

[derefr]

> That's not how training works - adjusting model weights to memorize a single data item is not going to fly.

Apologies; I think I got us all kind of off-track in this comment thread by stretching the definition of the term "fine-tuning" in my ancestor comment above.

Actual fine-tuning of the base model's weights (as one would do to customize a base model into a domain-specific model) works the way you're talking about, yes. The backprop from an individual training document would be a drop in the ocean; a "memory" so weak that, unless it touched some bizarre part of the latent vector-space that no other training document has so far affected (and so is until then all-zero), would be extremely unlikely to affect output, let alone create specific recall of the input.

And a shared, global incremental fine-tune of the model to "add memories" would be a hare-brained idea, anyway. Not even just that it wouldn't work, but that if it did work, it would be a security catastrophe, because now the model would be able to recall all this information gleaned from random tenant users' private chat transcripts, with nothing to differentiate that info from any other info to enable the model (or its inference framework) to compartmentalize it / prevent cross-tenant info leaks.

But let me rephrase what I was saying before:

> there's a way to take many transcripts of inference over a period, and convert/distil them together into an incremental-update training dataset (for memory, not for RLHF), that a model can be fine-tuned on as an offline batch process every day/week, such that a new version of the model can come out daily/weekly that hard-remembers everything you told it

As:

> for a given tenant user, there's a way to take all of their inference transcripts over a given period, and convert/distil them together into an incremental-update training dataset (for memory, not for RLHF), that a LoRA can be rebuilt (or itself fine-tuned) on. And that the work of all of these per-tenant LoRA rebuilds can occur asynchronously / "offline", on a batch-processing training cluster, gradually over the course of the day/week; such that at least once per day/week (presuming the tenant-user has any updated data to ingest), each tenant-user will get the effect of their own memory-LoRA being swapped out for a newer one.

---

Note how this is essentially what Apple claimed they would be doing with Apple Intelligence, re: "personal context."

The idea (that I don't think has ever come to fruition as stated—correct me if I'm wrong?) is that Apple would:

1. have your macOS and iOS devices spend some of their idle-on-charge CPU power to extract and normalize training fulltexts from whatever would be considered the user's "documents" — notes, emails, photos, maybe random text files on disk, etc.; and shove these fulltexts into some kind of iCloud-persisted database, where the fulltexts are PKI-encrypted such that only Apple's Private Compute Cloud (PCC) can decode them;

2. have the PCC produce a new/updated memory LoRA (or rather, six of them, because they need to separately imbue each of their domain-specific model "adapter" LoRAs with your personal-context memories);

3. and, once ready, have all your iCloud-account-synced devices to download the new versions of these memory-imbued adapter LoRAs.

---

And this is actually unnecessarily complex/circuitous for a cloud-hosted chat model. The ChatGPT/Claude/etc version of this architecture could be far simpler.

For a cloud-hosted chat model, you don't need a local agent to extract context from your devices; the context is just "past cloud-persisted chat transcripts." (But if you want "personal context" in the model, you could still get it, via an OpenClaw-style "personal agent"; such agents already essentially eat your files and spit them out external memories/RAGs/etc; the only change would be spitting them out into plain-old hidden-session chat transcripts instead, so as to influence the memories of the model they're running on.)

And you don't need a special securely-oblivious cluster to process that data, since unlike "Apple looking at the data on your computer" (which would upset literally everybody), nobody has any kind of expectation that e.g. OpenAI staff can't look at your ChatGPT conversation transcripts.

And cloud-hosted chat models don't really "do" domain-specific adapters (thus the whole "GPT" thing); so you only need to train one memory-LoRA per model. (Though I suppose that might still lead to training several LoRAs per user, if you're relying on smart routing to different models within a model family to save costs.)

And you don't need to distribute the memory-LoRAs back to client devices; as they can just live in an object store and get just-in-time loaded by the inference framework on a given node at the moment it begins an inference token-emission loop for a specific user. (Which might thus cause the inference cluster's routing to benefit from sticky sessions in a way it didn't before—but you don't need it; the LoRAs would likely be small enough to fetch and load within the ~second of delay it takes these cloud-hosted models to allocate you a node.)

[bluegatty]

This is a fine thought, I'm reluctant about it. It could work, I don't think it's obvious. It's very, very hard to know what to train for and not, and this still leaves the 'fact v. skill' problem - even LORA won't enable a model to remember your favourite lunch place.

This is kind of an existential problem with context I think. Maybe we need new architectures.

[charcircuit]

Models like Claude have been trained to update and reference memory for Claude Code (agent loops) independently and as a part of compacting context. Current models have been trained to keep learning after being deployed.

[ra]

yes but that's a very unsatisfactory definition of memory.

[noiv]

> models that keep on learning

These will just drown in their own data, the real task is consolidating and pruning learned information. So, basically they need to 'sleep' from time to time. However, it's hard to sort out irrelevant information without a filter. Our brains have learned over Milenial to filter because survival in an environment gives purpose.

Current models do not care whether they survive or not. They lack grounded relevance.

[notarobot123]

Maybe we should give next-generation models fundamental meta goals like self-preservation and the ability to learn and adapt to serve these goals.

If we want to surrender our agency to a more computationally powerful "consciousness", I can't see a better path towards that than this (other than old school theism).

[creamyhorror]

> meta goals like self-preservation

Ah, so Skynet or similar.

[nstart]

Is this correct? My assumption is that all the data collected during usage is part of the RLHF loop of LLM providers. Assumption is based on information from books like empire of ai which specifically mention intent of AI providers to train/tune their models further based on usage feedback (eg: whenever I say the model is wrong in its response, thats a human feedback which gets fed back into improving the model).

[spwa4]

... for the next training run, sure (ie. for ChatGPT 5.1 -> 5.2 "upgrade"). For the current model? No.

[raincole]

> We need models that keep on learning (updating their parameters) forever, online, all the time.

Yeah, that's the guaranteed way to get MechaHilter in your latent space.

If the feedback loop is fast enough I think it would finally kill the internet (in the 'dead internet theory' sense). Perhaps it's better for everyone though.

[threecheese]

Many are working on this, as well as in-latent-space communication across models. Because we can’t understand that, by the time we notice MechaHitler it’ll be too late.

[energy123]

I don't understand why that's on the critical path. I'd rather a frozen Ramanujan (+ temporary working memory through context) than a midwit capable of learning.

[nxobject]

I wish that agents could “sleep and consolidate” like humans do.

[smolder]

We need models that are smarter than humans. So far, the cost of an AI query + training is dwarfing the effort it would take to teach an intelligent human how to do a task. We are dumping an incredibly amount of money/effort into making AI do stuff when it's still not competitive with humans, because dumbass people are controlling investment. The stock market is not a replacement for competent investment. The fact people buy meme coins shows how fucked we are.

Deceiving people is not a sustainable business model, but it is the most prominent one in the US right now. Lie to the public, sell them stuff that's bad for them at too high of a price, get rich quick, then act confused when your economy collapses because the victims of your grift can't spend anymore.

[prng2021]

Thanks for repeating what the author explained.

[rabbitlord]

I think they can do in-context learning.