Wouldn’t Occam’s razor say that the people speaking are highly motivated to continue being paid? Relying on their fear of future outcomes seems to be misplaced to judge the honesty of their statements.
There are many assumptions that need to be true to link "they say they're terrified of AI" to "they get paid more / longer", in both their own minds and everyone else's. It's a bit of 4D chess that could go their way, but also could go 100 different ways.
In contrast, linking "they say it" to "they believe it" is simple.
Occam's razor is about minimizing the number of assumptions needed for a conclusion. In this case, "they believe it" is the same number of assumptions as "they get paid" - just one.
An assumption is something that we can't know, yet assume is true for the sake of coming up with a theory. What we're assuming here is their intention.
The implication (or as you said, "the links"), that talking about how dangerous their product is leads to more money, is true regardless of their intention. They create hype, they get money, that's not an assumption, that's an observation.
The question is only if their intention was to make money, or are they speaking from the heart. That's the assumption we choose.
> Because we can't observe the counterfactual world, this actually is just an assumption.
If you discard all empirical claims categorically due to "inability to observe the counterfactual world", your own reasoning falls apart. Why would you assume people mean what they say?
> Diving their intention also still requires many assumptions about their intermediate beliefs that I find dubious
You're assuming CEOs of multi-billion dollar companies are honest. I find that dubious.
> If you discard all empirical claims categorically due to "inability to observe the counterfactual world"
I don't.
There are a lot of counterfactuals we can observe empirically.
I'm just saying there is no empirical evidence to support the marketing strategy of "tell people our product is going to kill their families". I believe this is the first time it's been tried out.
No, this is not the first time. Scaremongering by frontier AI CEOs has been going on since ChatGPT came out, every time with a massive investment following.
OpenAI got $10 billion from Microsoft after Sam Altman claimed AI could lead to extinction of humanity.
Anthropic got similar multi-billion dollar funding from Amazon and Google after warning AI could develop biological weapons.
xAI got similarly massive funding after Elon Musk predicted a terminator-style future.
If you don't accept this as "empirical evidence", then there's nothing to discuss.
I think it goes beyond money now. A future where AI does not destroy the world means a world where the people who work at these companies are just like everyone else.
Part of the problem is that some investor somewhere making profits is considered the only real marker for success. It’s an insane perspective that devalues all other (arguably more important) aspects of a work’s success.
Ford's vision wasn't that much better. Old school manager controlled companies only work if the people in charge are great like him with big plans and the skills to realize them. If the CEO is just average it will end badly, and if they good but retire or die in office their successor will probably be worse.
And it's extremely vulnerable to bad ideas that get amplified by wealth and power, like Ford's antisemitic paper that inspired the Nazis. Putting one person in charge of everything is never good. The best outcomes come from distributing power as widely as possible to people who are affected the most, aka worker ownership.
“I am, somehow, less interested in the weight and convolutions of Einstein's brain than in the near certainty that people of equal talent have lived and died in cotton fields and sweatshops."— Stephen Jay Gould
If you’re only running models for frontier capabilities, yeah. For tasks where current models are smart enough, running them 100x faster is the most impactful improvement you can make. Consider all the things you could use a model for, but don’t, because the latency is just a bit too high.
But Claude Opus 4.6 is not really practical. Taalas' process seems targeted for edge models. Their proof of concept model, for example, is a heavily quantized version of Llama 3.1 8B and even then they acknowledge their custom 3-bit/6-bit representation causes model quality degradation.
Taalas is going to have a tough time putting a trillion-parameter model on one conventional die. Their HC1 die is already near the maximum size that conventional lithography can expose. They claim they could partition the model across many chips, but I'm not sure if they have tested this process or what it means for compute. The basic storage arithmetic is unforgiving: for a one trillion parameters model at four bits it will take 50–100 chips. To service a sizable customer base will take thousands of 100-chip fabs.
That all said, I'm bullish on this technology, and look forward to seeing it evolve.
A really fast qwen-3.6-27B type of model could be useful. With a specialized harness and this speed I 'd expect it to find many applications. Implementing a coding plan is the minimum I can think of.
I'm sure life would find a way. I'd love to see what kind of power-harnesses people have to come up with to steer 16k tps QPU's (Qwen Processing Units) productively.
With thousands of token per second output it would be an enormous waste of resources. Such chips are clearly made to process thousands of conversations simultaneously. Not necessarily in parallel. All LLM workflows are turn based right now, there are often seconds between turns until tool calls finish or users type the next message.
If the LLM response only takes a few milliseconds, the chip can process hundreds of other requests until the first conversation becomes active again.
It costs something like $300,000 for the hardware to run a model of that size. You'd pay that for a single model for 1-2 years? Not even the AI companies can justify that kind of spend which is why they keep extending the expected lifespan on their hardware in the accounting.
I'm expecting the Taalas MSIC version to cost a fraction of that. Then probably have some kind of cheap subscription to Anthropic for updates (yes, Taalas chips can receive a certain kind of updates: they have a small SRAM).
It's the cost of the current nvidia hardware used to run these models. Of course all bets are off if you are accounting for some future chip that doesn't exist yet which could cost less.
Given the fact that Taalas was claiming 1/10th the hardware cost and 1/10th the power consumption, yeah, companies would absolutely jump at that. Now, whether they can achieve that in practice is yet to be seen.
Not so long ago, I was good enough for many coding tasks. But I found that things can change in a hurry.
Yes, a cheap and fast Opus4.6 can drive a lot of value in current context. But if we continue to craft bigger-and-bigger balls of mud, Opus 4.6 may end up hitting its conceptual ceiling and unable to contribute.
Winding the clock back on your statement gives:
> I'd gladly pay for a Claude Sonnet 3.5 in silicon and use it for 1-2 years.
Assuming moore's law like progress, which I'm 100% sure isn't going to happen - I think we're at the top of the S curve already. But assuming dramatically increased intelligence every year this is still the exact same position as anyone who bought a computer in the last 5 decades. Yet, people did very much buy computers.
Depends on how much it costs the consumer. If I could buy a "cartridge" of Kimi K3 for 300 bucks I 100% would buy that shit asap. Even if it's "no good" after lets say 4 months still would be worth it IMO.
Do think about b2b. Companies are already paying much more for AI. a new K3 (or similar model) every 6 months for a monthly rate of ~100$ per month is something MANY businesses would pay for. Then they could even sell them at half the price to consumers.
This will be considered very cheap within the year IMO. The value you get from AI is exponentially increasing and like all tech just takes some time to ramp up. Cell phones, internet and many other amenities when they came out many people were not willing to pay for but that all changed and considering how important AI tech is this will also be the case especially considering if its 100% private such as for that cartridge.
> The value you get from AI is exponentially increasing.
Perhaps in some cases, but the value I personally and professionally got out of LLMs reached a limit a while ago and has since kind of fluctuated between that limit and a bit less.
If the best model was instant, like the demo here, it could certainly provide more value, I guess, but I think the limit I'd quickly hit is the same one as now, which is how much of it do I want to produce, for what reasons?
That's because the super-enthusiast will upgrade in 4 months when a better model is released. The casual user would keep it for years. A year of claude at the lowest plan is almost $300
"seems like baking models into silicon is speed-running obsolescence"
Now maybe. When models are flying passenger aircraft, other prerogatives will assert themselves. When a 50TB ROM means you can impulse purchase a ChatGPT 6.3 xhigh that runs on batteries, yet more use cases will be apparent.
Yes, I know. This view is how these problems are always perceived, decade after decade, as our predecessors filled rooms with iron and silicon, unable to fathom that the equivalent capacity and power would be a portable device 20 years later. We're not at some end point in this process: the devices we have now will appear just a primitive in the years to come as a 10MB 5.25" Winchester drive appears to us now.
One of the underappreciated effects of the AI boom and associated money is that it has strongly reinvigorated R&D in hardware: it is clear that there is a real application for far greater density and lower power demand, and people are now pursuing this much harder than they had been. That will yield what it has always yielded; orders of magnitude jumps in capacity and performance.
That can only work when there is physical capacity for improvement though.
> underappreciated effects of the AI boom and associated money is that it has strongly reinvigorated R&D in hardware
Yes, absolutely: but the point at this stage is more about finding new possibilities in hardware architecture than the improvement of what we had. So
> * That will yield what it has always yielded[:] orders of magnitude jumps in capacity and performance*
That will yield new and renewed hardware technologies.
(Already the distinction between SRAM and DRAM was overly specialistic before this boom - now it's on our mind as we know we need to "expand", "make cheap", "integrate" or find alternatives.)
> That can only work when there is physical capacity for improvement though.
There are great opportunities for advancement. Both in the physical hardware and in how and where it's deployed and powered.
Consider this, as only one point: there hasn't really been a demand for advancements in ROM. RAM has been scaling at approximately Moore's law rate, and nonvolatile R/W storage has been sedately scaling, but there hasn't been a use case for really dense, high performance ROM. Now there is. ROM used to be a big deal in computing and media (cartridges, optical disks, etc.,) but that tapered off long ago; volatile and R/W storage was sufficient and convenient for the time, and the inference model use case, where dense, high speed ROM can have extremely high value, didn't exist.
Now there is a use case, and industry is thinking about something they haven't cared about in a long time. Current fabrication nodes, stacked in the third dimension à la NAND flash, could produce staggeringly dense, fast and low power ROM. That's why AMD snatched up Taalas: they're thinking about an aspect of the future that has been (reasonably) neglected.
Sure, but actually, our current need is not really for "ROM": it is for "CiM", compute-in-memory - we want to minimize the data movement bottlenecks. That some implementations could be read-only is actually a disadvantage.
Clearly there are possibilities, some of them proven (proof-of-concept, in-production etc.) - but taking for granted "Moore's law" like spaces for them may not be founded on what we know at this stage.
A fast, low power ROM is the key ingredient to near term local inference with large models at low power. If I could offer you a $500 ROM that provided the model data for frontier inference on power similar to a desktop GPU, you would buy it, and consider it a bargain, even when it came time to pay another $500 for the upgrade.
Surely it is clear to you that Read-Only /Memory/ does not /compute/, and our need is to compute through the data in the memory... That is CiM - a technology not that similar to ROM... Because a plain ROM does not solve problems in this area...
In other words,
> If I could offer you a $500 ROM that provided the model data
Then I would have a physical token containing what I already had as a file, and the problem of running that file into something efficient would remain... Because the ROM does not "run" its contents...
> Surely it is clear to you that Read-Only /Memory/ does not /compute/
> Because the ROM does not "run" its contents...
Conventional GDDR/HBM don't compute either, yet inference is implemented using these.
Compute isn't the inference bottleneck. Inference requires high bandwidth, high capacity memory. The compute resources necessary are fungible, comparatively cheap and already available, at least for a small number of concurrent loads, such as in most local inference use cases.
> Then I would have a physical token containing what I already had as a file
I suspect you are not grasping what I mean by ROM. Dense, high performance ROM would not be the hardware equivalent of a "file", with performance bottlenecked by low bandwidth, high latency storage media, serialized for RW coherence reasons. It would have extremely high bandwidth, on par with GDDR, low latency due to a dedicated high performance bus, high concurrency due to a lack of any RW coherence obligations, and operate at low power (no gate leakage, no dynamic refresh,) and low cost compared to equivalent GDDR/HBM capacity.
Essentially what high performance ROM would provide is high capacity, low power HBM, albeit read-only. At that point all you need is sufficient TOPS to run the inference algorithm. The compute part is already available, affordable and readily scales up and down as per performance/cost/power budgets.
But ROM has a massive disadvantage being static. So, either it is cheap and practical "like a CD", or decision making will be forced to do its evaluations.
We have a von-Neumann architecture RAM<->CPU, which is really suboptimal for running current relevant Neural Networks ("RAM<----...---->CPU"). Advantage: flexible.
We have a CiM with Taalas HC1 which has the massive and enabling advantages of running NNs very fast and very energy efficiently.
What could high-speed ROM bring? It must be a good combination of "fast" and "cheap" to to be "interesting" for the market, between those two contenders.
I believe that "practical" as in "replaceable" is also a fundamental property of what we desire in this field: the Processing units are not all there is, also the side-RAM (for context, kv-cache etc.) is a necessary part of the system, so the NN-container is just a piece (which needs expensive co-parts). Whether the NN-container is CiM or not, it will be critical if it can be replaced (like a cartridge, disk, etc.) so that the other parts will not need replacement with it.
> I believe that "practical" as in "replaceable" is also a fundamental property of what we desire in this field
I suspect that there is a important frequency factor in in the "replaceable" calculus. Already I see people dragging their feet about adopting newer models once they've found familiarity with some older model: "good enough" is a thing. I know there are industries where "validated" is a concept, and they do not ride wave crests. So, if we imagine that as all this eventually shakes out and we're not replacing models every few months, but instead with about the same frequency as our cell phones or similar, the ROM model works. If the performance and price make this pattern highly appealing, then that's what will win, certainly for local inference. If some datacenter operator could, today, adopt a ROM approach that cut their power budget by a large factor, but had to suffer 2-3x longer model update cycles, they'd likely consider it.
For better or worse.
I have no problem with CiM as a concept. If it can reduce power/size/cost then it's another avenue that inference will probably incentivize, where incentive has previously been insufficient. As we both agreed long ago in this thread this new era is motivating things that were previously neglected, and CiM is possibly a part of that. My dream is that all of these get a hard look as people try to figure out how to run all of this without enormous gigawatt sucking datacenters that rival DOD program budgets.
You missed the whole point of Taalas HC1: that it is Compute-in-Memory.
> 2. Merging storage and computation // Modern inference hardware is constrained by an artificial divide: memory on one side, compute on the other, operating at fundamentally different speeds. // This separation arises from a longstanding paradox. DRAM is far denser, and therefore cheaper, than the types of memory compatible with standard chip processes. However, accessing off-chip DRAM is thousands of times slower than on-chip memory. Conversely, compute chips cannot be built using DRAM processes. // This divide underpins much of the complexity in modern inference hardware, creating the need for advanced packaging, HBM stacks, massive I/O bandwidth, soaring per-chip power consumption, and liquid cooling. // Taalas eliminates this boundary. By unifying storage and compute on a single chip, at DRAM-level density, our architecture far surpasses what was previously possible.
Yes but have we considered employing, like, a really big block of ice? Like old-timey surgeries? What if we put a big block of ice on the 2.5 cubic meter CPU what happens then?
2026-08-13:
"Ukraine’s Main Directorate of Intelligence (HUR) said it identified an Nvidia Jetson Orin computer module inside Russia’s new S-71M Monokhrom air-launched cruise missile"
Not "multi TB frontier" by any means, but the direction is clear: weapons will be made to think, for better or worse. Something of the scale of a frontier model will likely be seen in: loyal wingman aircraft, autonomous warships, military satellites, to name a few platforms.
Not sure. You can fix the transistors but leave the connections between them open for flexibility, so you only need to change the manufacturing process for the upper masks for every new model.
I think they already do that, except it's not 1980 so you don't fix the upper mask, you fix the lowest metal layer (the upper layer is very coarse and is only useful for power). But even a single mask is still quite expensive.
Compute the cost of producing n of them devices, imagine a fair price based on that, and see if that local, blazing fast card* can be an asset that could be replaced periodically.
*(It's local: private files managing firm oriented. It's blazing fast: it can be placed into recursive, intensive local workflows.)
It depends on how quickly you can bake new architectures.
Text diffusion might be a disruptor here, but let me just say the most cutting edhe form of image diffusion (JiT and DiT) right now is just a big fat stack of alternating attention and MLP matmulls.
Not theoretically hard to bake
obsolescence is the whole point. apple gets to sell a new phone very 6-12 months because of it.
i have written about this:
"For device makers
Packaging models with laptops and smartphones will let application access near free, low latency inference and potentially offer users a better experience with the option of preserving data on-device. This is viable under the condition that tasks that do require larger expert models that run in the cloud can be routed to external models.
A side-effect of local models and what will let Apple cut upgrade cycles from ~4 years (?) down to 12-18 months is specialized hardware to run them. For almost a decade, smartphones have been trying to compete on better cameras. This coming decade will see them selling better GPUs, NPUs, ASICs and whatever other things they'll be calling the inference chips, to drive re-purchase. Every six months will see a better model on new hardware, which will enable better performance in certain applications."
I do. The point is inference speed and power, making previously impossible local inference possible. A side effect of that hardware optimization is fixed capabilities.
You've confused engineering compromise for malice, and reversed the purpose. For the model capabilities and inference power draw, what alternative do you see to a (at least mostly) fixed hardware model?
What I'm saying is that Apple will use these type of models etched into chips, and they will do it because it drives obsolescence, so they can shorten the upgrade cycle. They will do it because they figure out it's good for them.
You've confused engineering compromise for malice, and reversed the purpose. For the model capabilities and inference power draw, what alternative do you see to a (at least mostly) fixed hardware model?
No, you still don't understand what I'm saying. Yes, ASICs make inference faster, but also makes the hardware obsolete faster, if it's embedded in a phone. That sounds like a negative, but Apple is going to turn it into a positive for their business and use it to speed up upgrade cycles as cameras are no longer a driving factor and cycles have been getting longer.
The kind of people who don’t want to become police officers because they’re worried about being held accountable for their actions? Those are exactly the kind of people who should never be allowed become police officers. If we were really interested in Justice we’d build the system specifically to eliminate those kinds of people before they got anywhere near badge and a gun.
While I understand the upside, the reality of why it’s being pursued in the United States is that this is intended to drive academia into an advanced version of a trade school.
Do the math: The number of professorial positions is not growing (or not very quickly). Under steady-state, each professor only needs to train one single tenure-track PhD in their entire career. (Or maybe 10 professors need to train 11-12 students, to account for various forms of attrition.)
If you want more PhD's than that, you have to figure out a place for them to work; industry is a good "pressure relief valve".
This is true but it doesn't necessarily mean that academia ought to shift its focus to professional training. Also keep in mind that this has ~always been the case. Sometimes you train with a specific task in mind. Other times you do something open ended and unrelated "for the experience" with the idea that it will benefit you in various ways in the future. Neither of those approaches is always wrong or always right. Sometimes you need to focus but there are other times narrowing your view would materially diminish the benefits of the experience.
I do think it would be good for all levels of academia to be more explicit about the goals of any given program. I think these things are often blurred together to the detriment of both student and staff.
The goal of PhD programs has never been to have every student become a professor. Also, strong STEM PhD students generally don't need much help finding jobs after they graduate. Claiming that PhD students "aren't prepared" for industry careers is simply saying that students lack industry experience (obviously, being students!).
The problem here -- and why universities even do research at all -- is that industry's support of basic science is not very stable. At least when I was in school, industry research generally was more applied and engineering-focused.
i think major federal funding agencies should track department-level stats on "how many phd students did you admit?" versus "how many tenure track positions did you hire?" and use that to implement mechanisms that encourage a healthier balance (e.g., changing funding lines on grants, adjusting their overhead cuts, boosting trainee grants at certain places).
this would hopefully stop departments from over-admitting and encourage less adjunctification.
That sounds like an excellent thing based on that last sentence. Anecdotally, I hear people talk about trade school as being much more practical compared to the majority of traditional 4 year degrees. So I am interpreting a "move towards trade school" as an increase in practicality and therefore utility to the student.
I just wanted to echo your comment. Most people, in my experience, pursue an undergrad program with the intention of being employable. This is my personal experience, but also a learned opinion from being a teaching assistant for some years. Places like waterloo stress co-op, it is built in to the program. IMO the average person seeking an education are served under a model that places them in or near industry related work at some point. While I understand the original post is relating to PHDs, I think most programs (or rather, participants in programs) would love to have this sort of integration despite it being a hard problem.
Interestingly enough, Kitchener, adjacent to Waterloo has a large population of German-descent. I wonder if the OP's spouse's experience in Germany has any relation.
There is no reason to assume it is “more practical” or that it will increase utility for the student. Especially at the graduate level, the deeper you are able to explore a subject and the more you can connect that subject to a broader range of topics the more utility you get from the degree. Less time to finish a degree means you are less able to do that. I would think this will decrease the likelihood of new ideas being developed which will quickly become a problem for society in general. Also no one gets a PhD because it is practical, you get it because you want to explore a topic and push the boundaries of human knowledge.
My impression is that many of the indian engineering schools are some form of trade school and it shows. A lot of people are very good at churning out things, but they vastly prefer to use well defined tools for their trade and do a lot of work impassionately. Taking the creative path or even reflecting on failures isn’t natural.
On the other side of things, you have PhDs coming out of the US with too many ideas and the inability to deal with basics such as sprint planning and CI/CD.
Not sure if this will help, but I would love it for the Colleges of Sciences peoples instead of a postdoc.
The problem is that this makes it friction-free for the student ... and the actual trade? Your wage. It takes away risk, which everybody likes, until the costs of that start accumulating. It's an excellent way to give the student a very strong reason to avoid applying anywhere else (and make applying elsewhere far less successful), and so effectively to pay them (much) less.
Get a PhD with years of experience, who knows your company, for the price of a secretary.
Practicality and utility to the student must be the reason I started getting junior software engineers that are computer illiterates out of whatever Java IDE they were taught in.
"Learn what we believe to be the textbook-correct, fundamental, foundational theories for a topic; learn about the frontiers of new research in the topic; find a niche on that frontier where there's something new to try; try something new; write down the results (no matter how boring); share with your friends; rinse, lather, and repeat" is a perfectly respectable trade.
No reason why the good plumbers, electricians, or truck drivers of the world ought to look down on academics-- no matter how stuffy or highfalutin or soft-handed they might seem.
Every once in a while, academics come up with something useful. (And in fact, I think that that might just be the whole point).
There is a non-insignificant segment of the population that would have been better suited and employed as painters or HVAC technicians than for their chosen careers. Some software developers (and 80% of the city of Portland) would also fall into this category. The world has a surplus of PhDs opening cupcake shops.
i think there are a lot of college-educated folks that would love to switch into a trade, but there are cultural barriers to starting such a career. a not-insignificant number of tradespeople have been taught to see college as the enemy and college-educated folks as soft, coddled people with poor work ethic. if you showed up at a job site after getting a four-year liberal arts degree, you'd be met with lots of mocking and "oh, you thnk you're better than me?" defensive posturing.
It already is. Most PhDs in, let's say CS but probably true for a lot of STEM, just get a job at a tech company. I know a guy who did a PhD in meteorology and now he just programs at Google. The promise of contributing to science and being an academic probably doesn't work out for most. There's actually a big cost to this too, this isn't just an alternative path. It's also costly to non-PhDs who suddenly find themselves competing for jobs against them in careers that really don't need them. At least if we're going to continue printing PhDs it would be great to make them shorter and less costly.
Germans are big on trade schools. Despite being one of the richest western european countries, Germany (and Switzerland) have along the lowest college graduation rates, because they prioritize trade schools.
My only question is why you didn’t believe police could/would do this in the first place? My default perspective is that this is exactly what law-enforcement and other government officials would use this technology to do, especially if there are no guardrails or laws preventing them from abusing it.
> While Flock’s audit logs have been successful in bringing to light certain abuses, that very fact is likely to deter many officers from honestly describing the purposes of their searches moving forward. Take the controversial Texas abortion search. If the officer who logged, "had an abortion, search for female" had just left out the word "abortion” that story never would have seen the light of day. And there’s no doubt that the department — and police around the nation — realize that.
> Our ability to identify officers was clearly effective. In a direct attempt to stop us from providing transparency, Flock and police departments have dropped the unique IDs (UUIDs) in the transparency portals entirely. They now simply replace them with the word “redacted” in the public audit logs, effectively preventing oversight and individual accountability.
> Flock Safety updated the system on 12/11/2025 to protect officer safety and active investigations, Network Audit Logs no longer include officer names, license plate, or vehicle fingerprint information.
Audit logs are only useful if they are used to action abuses. The issue is that police departments protect abusers due to the “thin blue line” mentality.
I'm sure there is an audit log. But because, you know, computers, they'll probably charge you a few thousand dollars to process the FOIA request. If you could get the time cards and GPS tracks from police departments, how much detail and overtime fraud do you think you would find?
What makes you think they’d restrict this to logged in accounts? Not so long ago, Meta got caught building “ghost” social media profiles for people who didn’t have accounts on their platforms. They essentially constructed a “missing puzzle piece” based on people this person knew who did have profiles in publicly available information. Building a psychosocial profile on a user who has not set up an account yet using AI would be trivial.
Not the person you asked, but my nephews and mentees (all under 18), very plainly see it as the destruction of their own future careers (regardless of field), assimilation of everything meaningful humans have ever created into a copy machine , massive privacy violations, wrecking the environment and generally run by some of the biggest sociopaths in human history. When looking at it practically over the last few years, I think a lot of younger folk don’t see any upsides.
I’m very pro-AI myself, but think the kids are quite right in their perspective, and that the tech companies designing and pushing this tech are in for a bad time when this wave finally comes crashing down on them.
Personally I don't think the environment is a factor, because young people seem just fine with crypto. I also don't think the privacy issue is a factor because young people don't have much that can be stolen, and if anything they're the ones stealing (movies etc) without guilt.
That's an interesting point regarding crypto. My guess is that most young people have very low exposure to crypto, with it being kind of a niche subcultural thing at this point after its height of cultural relevance in 2021, whereas AI is simply everywhere and is inescapable.
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