The fact that AI models can be so easily distilled and replicated is such a stroke of luck.
10 or 15 years ago if one had asked me to envision a future where a private company invents artificial intelligence, I'd have thought for sure they'd have a massive moat, be very difficult to catch, and it would create an almost instant monopoly.
Rather, it seems that selling intelligence might end up as a race to the bottom.
Who woulda thought that just having access to enough textual inputs and outputs and a vaugely similar transformer architecture would be enough to copy-cat rather useful intelligence.
> The fact that AI models can be so easily distilled and replicated is such a stroke of luck.
Sort of. It means the country on the verge of monopolizing all aspects of hardware production (China) doesn't need to rely on outsiders for the software. So while that weakens one monopoly it strengthens another.
It's a mistake to think only OpenAI and Anthropic are actually spending the big bucks on pretrain, and the others just distill that.
The Chinese models are pretrained on large clusters just like OpenAI ones are. Yes, they use outputs of the frontier models to further improve the final model, but even without those outputs they'd still have very strong models.
It's not like in a world without distillation things would be much different as you claim.
They'd still have strong models without distillation, but strong enough to challenge frontier models and to claim the meaningful market share that they have? Probably not.
For what it’s worth, “race to the bottom” typically refers to a scenario that we absolutely do not want as a consumer. We do want a highly competitive market that drives prices down, but “race to the bottom” specifically refers to a scenario where firms compete by minimizing quality, regulatory oversight, consumer/labor/environmental protection, etc.
Im sure that's well on its way.
Intelligence ended up being an equalising force. Kurzweil kind of predicted this, but SV was too obsessed with total world domination.
It reminds me conceptually of the idea of using a ST:TNG replicator to just give you another replicator of your own, or asking a stereotypical genie for "infinite wishes". The genie is indeed out of the bottle in many ways.
I guess it’s more like asking the paid genie to give you a new cheaper genie.
And for a lot of non-frontier purposes these days, you can bootstrap via LLM-as-judge so your hyperspecific wakeword model or whatever can be trained with little to no human input, that aspect of it is fully terrific.
The frontier models are a replicator that can give you another replicator which specifically produces tea, earl grey, hot, when you push the single button, and does nothing else.
Even before LLMs, ML folks were already aware that you can use a model to teach another model. I doubt this is something AI companies put at the top of their investor materials, but it's been nice to see it play out.
That said, there are other moat factors like, a US company needing to use a US AI provider, sticky customers due to corporate onboarding friction, and others. Not nothing, but not as large a moat as some imagined.
Yes, but 10 or 15 years ago, I would have thought that there'd be more to it than just a slight modification on the ideas behind a CNN to get this level of AI.
There were somewhat good reasons to think it needed more than just this data-driven ML approach.
There's something startling about how (relatively) simple these networks are and yet how powerful they are. The main ingredient the AI darlings are using is vast amounts of compute and data. I don't want to take away anything from what the researchers came up with, but I suspect even they are surprised at how capable some of these models have become.
I'm unnerved by how alphazero is more complicated than the "intelligent llms"; it has at least multiple heads and MCTS, a search algorithm. The LLMs seem to just be monolithic (if complicated) architectures where tokens go in the bottom and tokens are spit out at the top.
imagine what would happen if you gave it MCTS and the data that LLMs were trained on
early on there was a lot of talk about "emergent behaviors" in the models where they were good at things that were unexpected or did not align to the training data. IIRC doing arithmetic is one example from early on. I think this is where the AGI craze took off, the labs were throwing more and more data in the training to see what other behaviors would emerge. The thought was with enough data and enough parameters AGI would surface on its own.
Then i think tool use became a priority or at lest a sibling priority to more data/more params. Along with multiple specialized models communicating with each other which is sort of a special case of tool use. That pretty much brings us to today.
this comment is delusional. LLMs are awful at arithmetic and AGI is still very sci-fi, otherwise Claude would have told Anthropic how to cheaply generate energy for it to justify its existence by now. As long as the energy use debate persists you can be assured AGI has not arrived.
I feel you're conflating AGI and ASI here.
> The fact that AI models can be so easily distilled
They're not, right? If it's really easy why there are no counterparts of DeepSeek from the Europe or Japan?
Because it breaks the TOS.
The internet created lots of monopolies with network effects and economies of scale.a low margin commoditized business that still attracted a trillion dollars of investment to get off the ground was not how I envisioned it happening either.
Where is the actual evidence of distillation? I keep seeing this repeated ad nauseam but I must have somehow missed the evidence.
Distillation a pretty well documented technique that actually pre-dates LLMs https://arxiv.org/pdf/1503.02531
Here is a project that guides you through it if you want to prove to yourself that it works https://github.com/arcee-ai/DistillKit
I took GP as asking for evidence that the reduced-cost Sol is actually a distillation of the previous-cost Sol. AFAIK, providers distilling or quantising models and offering them as the same model have not been proven.
I doubt he was claiming that. He's probably saying that the ability of Chinese companies to be able to distill frontier US models has put downwards pressure on the price of all models.
I'm saying that it is unclear that without distillation this wouldn't still be happening. There is a massive narrative that no one but OpenAI, Anthropic, and Google can make a model without distilling. But there's basically no evidence of that.
Alternatively, modern AI is good enough at optimizing its own kernels that it just keeps pushing costs down. Unlike the semi-decentralized inference provider community, OpenAI has both the talent and the compute to throw at the problem of making their models much more efficient to run.
GPU kernel optimization is just the kind of well-bounded problem with clear success criteria that AI loves.
That distillation exists isn't the question.
It's about evidence this is an active force in competition in LLMs.
I think the biggest actual piece of evidence is how hard the major players are trying to stop it
To be precise, the distillation mentioned in this paper is not the distillation used by other model companies. In the one mentioned in the paper, your teacher and student model typically have similar architectures - and you typically need access to the full logits. What happens here instead is motivated by the fact that these companies don't have access to the training data and compute that anthropic/openai have. The distillation they do basically amounts to using traces from ant/oai models trained on much more data with a lot more compute (in many cases including the hidden intermediate tokens! turns out there were many ways to coax it out) and then either directly training on it or using it in many ways in post training pipelines. It falls under imitation learning, IMO.
It turns out you can train a 1b model at almost 1000 tokens/s on a m5 max laptop. As a personal experiment, I've been asking Sol for synthetic training data and synthetic agentic training data (model distillation in it's purest form), plus modified opencode, codex transcripts etc for training data, and nobody's even paying me to do it. If I'm doing it has a hobby, you can bet industrial users are doing it.
Been using a lot of Kimi K3 lately and the answers have been… „load-bearing“ to the point of hilariousness. It‘s obvious from where they distilled, even if sceptics rightly point out it can‘t have been the only source of their secret sauce, as it‘s been better than the current Opus 4.x at the time of release.
Qwen 3.8 27b also enjoys their genuinely load-bearing seams.
The evidence is Anthropic's own reporting [1]. You may doubt that they're telling the truth, but that's what they're reporting.
[1] https://www.anthropic.com/news/detecting-and-preventing-dist...
I'm supposed to believe that DeepSeek distilled a 300B-1T param model with 150,000 requests? Lol.
"Distillation attacks", and it's just the exact same thing they did to the rest of the web and all human works.
Musk confirmed in federal court that xAI does it: https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...
It's also how providers build their smaller models out of their larger ones; they publicly talk about the process.
Why does Kimi insist its name is Claude?
Please elaborate the mechanisms by which a LLM would know what model it is.
By being trained on text containing "I am X" in the model response section.
ask it what type of model it is or what it's name is... it's weird that Kimi will say it's claude...
The training data likely references Claude significantly more often than Kimi, given the popularity of the models. There will simply be more examples of “Claude” being the response to that question.
Doesn't Claude say its Deepseek when asked in Chinese? I remember there being posts about that a while ago.
Okay, but I would also ask “why does Claude say that its name is Claude?”
Because it's in the system prompt
Here's an example: https://github.com/microsoft/Build25-LAB329
[flagged]
there is no evidence. it shortcuts post training by a huge margin this is true. but that is all.
https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...
Make sure to stay updated!
Nothing against what I said in there. Distillation is far from enough to get to the frontier. Its at best a ramp (the most efficient one used by everyone ) that shortcut and saves millions of rl runs before a model moves.
"No evidence" -> Large lab saying that they use distillation when training their models
And probably saves a lot of energy which would be good for the planet
You said there isn't evidence of distillation. Yes there is
How much of this ability to replicate is down to the openness of the science community and the paper on transformers being accessible by anyone?
Even if the transformer paper wasnt published, the info would have diffused out eventually.
Its not that wild of an idea. It's not like e.g. chipmaking where even just knowing how things are done doesnt mean you can copy it.
It reminds me of the seo antics out there. The search results page is the engine, much like how distilling is the "intelligence" for your chinese room machine
Funny you mention Chinese Room and LLMs in the same response, I would say LLMs proved Searle wrong, agents now make cutting edge discoveries and meaningful problem solving. They not lookup tables though and you need to pay for inference, so the intuition of syntax doing the work of semantics without understanding was wrong.
Hmm, don't people think that if the frontier labs really put enough engineering effort into preventing distillation that they would be able to do that, or at least diminish it significantly? I'm sure there are variety of additional techniques they could use on top of what they already do, but I suspect it just hasn't been at the top of their priorities yet. Maybe that will change soon. Worst case they could add additional hurdles to account creation ("know your customer" type of thing).
Even if it were possible it wouldn't change the outcome. China is capable of training frontier models even without distillation. Distillation is only an accelerant.
The primary resource you need to train LLMs is money and China has plenty of that.
The frontier labs have competing goals in mind. They want high growth (which means little friction for account creation), API access (because enterprise money is the best money to have), and distillation protection.
Besides, identity verification that actually works at scale is a much harder problem than identity verification which is good enough to satisfy your compliance people and regulators. Especially if the fraudsters have a major world government standing behind them, and if their aim is to be identified as a real customer, not one customer in particular.
In particular, harvesting identities for online fraud is an industrial market for various criminal organizations.
> Hmm, don't people think that if the frontier labs really put enough engineering effort into preventing distillation that they would be able to do that, or at least diminish it significantly?
Distillation was big news a year or even 6 months ago, but as far as we can tell it's not really a moat anymore. Now that multiple players have trillion+ parameter models and the capacity to post-train them, there's no putting the genie back in the lamp.
The nature of LLMs is that you give them input, they give you output. That allows for distillation. They do try to make it harder by hiding the chain of thought stuff, but fundamentally, if your model is publicly available, its distillable.
At the end of the day, while you can do your best to obfuscate your reasoning tokens, it's a losing battle to hide actual user-visible output tokens. The very nature of API offerings is that you can't do KYC on where that API's output is going - there's a rich secondary market that's not going away.
And with the sheer volume of data created from that, coupled with benign-seeming prompts like "plan out your reasoning in a document before implementing" that could never be patched without breaking existing customer workflows... there's more than enough for someone to distill on. Even if that only gets them to not-quite-frontier, if you're pushing the frontier every few months, they're only ever a few months behind you.
Only the Chinese authorities can stop Chinese labs from distilling from western labs. And they won’t do that, for obvious reasons.
i trained another AI on all my codex logs... it's pretty good actually
OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name. Google won because search became a habit-forming product people grew accustomed to using. Bing was once effectively indistinguishable from Google Search, yet still failed to achieve mass adoption because users had already become accustomed to “Googling” things. The same could be said for people "ChatGPT-ing" things. If OpenAI and Anthropic are smart, they will maintain similar pricing rather than aggressively undercutting each other, allowing the market to resemble Home Depot and Lowe’s, or cloud computing, where AWS, Google Cloud, and Azure coexist as highly profitable competitors. Unfortunately, I doubt OpenAI or Anthropic will pursue this strategy, as both companies appear to be acting as though the race to AGI is winner-take-all even if the market may ultimately support several highly profitable competitors.
> OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name.
ChatGPT is AI for the average non-techie the world over, but the average non-techie isn't eager to pay for it. The more progress that's made, the less incentive to pay - most people are happy with the total garbage spewed by google AI overview. They'd be happy with google's 30b MoE gemma, whose performance will likely be squeezed down to something that can run on a phone in 2-3 years. Why would they pay $20 a month?
It's why OpenAI is pushing a variety of things such as ads and offer a more polished ui/ux than the competition, I think. The models are already good enough for people who just want to know how much sugar to add to their cake or when's the next basketball match their team plays - it's OpenAI's game to lose those people, by annoying UX and whatnot. If they can make a few bucks off of every one of their non-paying users it'll stretch their runway immensely. Those users will never go to Antrophic or some cheap Chinese model, but they might defect to Google because a popup on Android / in Chrome told them to.
Yeah, I think most people here don't realize how far most of the population is on the adoption curve with this stuff.
I had a discovery call last week with someone who did not realize he could use ChatGPT for work. It was a revelation that he could drag a PDF into ChatGPT and it could summarize it for him.
FWIW, guy in his late-30s in a pretty senior sales role.
Codex has 20 million users and growing. There is a possibility that they take a huge chunk of the coding agent market share.
Difference is that it was free to google/bing search. Ai prompting costs money.
If I run out of tokens on ChatGPT of course I will try Claude. I never ran out of Google searches so no reason to try Bing
You haven't searched hard enough then.
At some point Google gets suspicious of your persistent searches and makes you solve captchas and puts cooldowns on your searches.
OpenAI has a free tier. I'm guessing a lot of people never need to upgrade. Not the case for me but I can see the average person only needing to run a few prompts every day.
My understanding is that most OpenAI users are on a free tier. Secondary effect of this is that OpenAI free tier model capability (assuming Luna) is what what most users associate with frontier AI capability giving somewhat warped view to many people.
> If I run out of tokens on ChatGPT of course I will try Claude.
More like the other way around - Claude burns tokens faster than any other LLM.
Google also spent heavily setting up deals with other platform owners, invested in Chrome and Android to establish Google search as the default option which most users accepted. If integrations of good enough AI features are made within existing platforms most users will probably accept using those and not think too much about whose model is powering it.
Mass adoption isn’t where the money is, though. It doesn’t matter if ChatGPT has 1 billion users if they won’t pay for it.
> If OpenAI and Anthropic are smart, they will maintain similar pricing rather than aggressively undercutting each other, allowing the market to resemble Home Depot and Lowe’s, or cloud computing, where AWS, Google Cloud, and Azure coexist as highly profitable competitors.
Wouldn't that just be price fixing? If they arrive at their prices independently and they all happen to be similar, fine. But if they're all "smart" and coordinate so none of them undercuts the other, that's probably illegal.
Illegal for sure but rarely enforced.
My understanding is that collusion among competitors is illegal (although I’m not a lawyer). I was referring instead to the prisoner’s dilemma that Bruce Greenwald discusses in Competition Demystified. In theory, competitors, like prisoners who are pitted against one another to rat on one another, are usually better off cooperating rather than turning against each other.
Never in my life have I bought any of the household names when it comes to household equipments. Of course, I use the household names’ names when I speak about wipes, washing powder, glue, etc., but never ever would I pay the 2x to 5x premium. Same will be true for most people with regard to “intelligence”, as long as it’s suffices their needs. Thats the crucial part. Cloud computing, on the other hand, has a massive lock-in. But more importantly, their ideosyncratic ways of doing things leads to tribalism. It’s so much harder to move running processes from one provider to another without interruption than just changing an API key or the URL for your chatbot.
Their current strategy is "make the product affordable in tiny little bursts here and there." That is not a great way to build a moat.
That's just the first mover moat, OP was worried about intelligence itself being monopolized.
Meh distillation doesn't mean you can create an existing model from scratch of similar quality. It's the AI equivalent of making a VHS copy of a video, it doesn't enable you to make your own movies very well and post training is the equivalent of video editing, which again, doesn't let you make your own movies very well.
Your seeing the AI labs respond by never publishing chain of thought now and in the future, I see them not even publishing their top models as a general purpose API and instead using it to drive their own AI apps, which will obscure even more model output. Anthropic Mythos was internal only for many months for example.
> Rather, it seems that selling intelligence might end up as a race to the bottom.
Personally, I came to this conclusion early this year. To acquire the data that AI Companies are using to train their models is low cost and once they have it, they can refine and store it. Creating the LLM takes a bit of money but it is not a serious blocker. Clearly, the Chinese companies can make AI so they will drive down costs. There is a need for good AI (Not just Great AI) and it is not cost prohibitive to make good AI (The same with specialized AI).
My prediction is that AI will spilt into two categories, Great AI (High Cost) and Good Enough AI (Low Cost). Which for the long run of AI and companies that use AI, this is good.
Altman specifically has said in an interview that I listened to once that he envisions AI being as cheap as electricity.
I hope it's a good bit cheaper than that, I pay close to $400/mo for electricity and I'm in no way interested in paying anything like that for AI.
Jeeze, what are you doing that uses so much electricity?
I live in Germany where people won't stop whining about electricity prices, and I pay 75€/mo.
Easy to get there without doing anything abnormal, 2 EVs and a large 5 bedroom house that gets air conditioned / heated, in a location that does time of use pricing. My bill is closer to $200/mo but if I didn’t have solar & battery it’d be in the $400 range.
It will be cheaper than electricity - but your electricity will become a lot more expensive to enable that!
If it can automate your time by more than that it's a good trade.
He also wanted to do a non-profit.
He even raised money on that premise.
He is a pathological liar, so is Dario. Don’t rely on the benevolence or truthfulness of these people.
They will say whatever is beneficial to say in the moment.
Telling. Downvotes but actually no arguments. Fitting, because there are none.
Referring to a baseless prediction by Sam Altman that AI will become like electricity without any push-back? Who really thinks Sam is working toward that future?
He already worked to undo every early promise made (non-profit, open source models, strong governing board, strong ethics/alignment/security focus). He's flip-flopped on other things like first characterising Trump "an unprecedented threat to America", then contributing 1M USD to Trump's inaugural fund far exceeding his earlier political contributions. Lately OpenAI, under his supervision, has also been working with Anthropic to lobby regulators in Washington for restrictions on open weights models - why so if not to undermine a free market in favour of an oligopoly?
Beyond that, you have the simple fact that most of his personal wealth and very probably the fate of OpenAI hinges on AI inference NOT becoming an interchangeable commodity.
I mean.. Honestly. The naivete is downright astounding.
Altman of *Open* AI? No idea why I would trust him without very convincing proof.
Yeah, he sure does lie about a variety of things! He doesn't have the name Scam Altman for nothing.
Lol, of course what he left out is this will happen by inflating the cost of electricity rather than driving down the cost of AI.
I wouldn’t quite call it a “race to the bottom” because the costs to produce the models aren’t actually decreasing.
the moat is real. the big expensive base models are like the data collected from huge particle accelerators - there's enough unknown structure to be mining for years. you can extract features with more and more generation loss but access to the raw weights is a real advantage, and literally a moat if the interesting behaviors are fenced off
Was it not obvious that the value and advantage was going to be in AI-adjacent services?
The quality of the harness UX, and random fun crap like Sora, it's a shame that OpenAI killed that so soon, and also Group Chats in ChatGPT.. they risk running a Googlelike reputation at this rate
Maybe ultimately whomever can be the "Apple of AI" will win
OpenAI implemented measures to reduce reverse engineering, following the Anthropic lead.
They disabled the temperature and seed parameters. There's still logprobs, so they aren't as closed up as Anthropic yet.
I might write about an article of the history of LLM APIs, I used to think the ChatGPT was going to be a de facto standard like intel's 80866 mutated into x86, but it seems to be a bit more nuanced and diverse than that, vibecoding introduced so much complexity because the vibecoding product itself became vibecoded so the enshittification was accelerated, many such cases.
I think the only moat in the future will be the scale of hardware deployment. If one company is able to deploy an order of magnitude more silicon, they'll have a firm grip on a SOTA model and massive inference usage.
China or SpaceX seem like the 2 likely candidates in 5 years, but who knows.
"Who knows" is the right answer, I think.
If (a) demand for AI continues to increase, and (b) SpaceX can get to ~$100/kg to orbit, then they will have a ridiculously deep moat. Probably more like 10 years, though.
But as you said, who knows.
They will have moat in the satellite launching business, which is not useful in the AI datacenter market.
You can put AI chips in datacenters in the desert for far less than $100/kg. With lots of solar power available, the option to easily access your hardware and far less radiation issues.
The datacenter in space story really only exists to make it possible for Musk to sell X to SpaceX and make more money from the IPO. That's all. There is no engineering reason.
Yours is the only valid argument. If space datacenters are not competitive with terrestrial (in terms of megatokens/$ or whatever), then they will never work and SpaceX will fail. That's the whole ball game.
But anyone who thinks they can predict those prices in ten years is wildly overconfident.
Space Datacenters eventually get quietly discontinued after finally killing the zombie brand at which point the only reaction is "huh, remember that?"
https://techcrunch.com/2026/08/21/teslas-solar-roof-is-dead-...
Are you saying that if space datacenters fail then SpaceX stock will go up? Then it sounds like you should invest a lot of money in them.
ETA: If you had invested $1,000 in 2016 when Tesla Roof was announced, you'd have $25,000 today. 25x return in 10 years.
It's not an engineering bet.
Musk's bet is dysfunctional politics will make it impossible to build enough data centres and the energy needed to power them. There are many, many reasons that might be wrong. However, if the economics are even close to viable, they could start throwing up data centres quicker than anyone can build them terrestrially (at least in the democratic west).
So to be clear, the prediction is that every single location on earth will be economically infeasible due to politics, and that these same politics will have no impact on the launch or satellite businesses?
GMAFB.
It's stock pump bullshit from a guy who has figured out how to extract the maximum from stock markets.
Meanwhile Nvidia just announced a space-optimized Vera Rubin designed with SpaceX designed for orbital datacenters.
But yeah, I'm sure you people with Elon Derangement Syndrome actually have it all figured out /s
https://x.com/nvidia/status/2091920680317046847
> Meanwhile Nvidia just announced a space-optimized Vera Rubin designed with SpaceX designed for orbital datacenters.
well, no, they announced the concept of a space-optimized Vera Rubin designed with SpaceX:
> NVIDIA and SpaceXAI are working to adapt that foundation to the requirements of orbital computing while preserving a common NVIDIA architecture and software ecosystem.
The press release is really announcing that SpaceX's terrestrial data centres are going to use Vera Rubin.
https://nvidianews.nvidia.com/news/spacexai-adopts-nvidia-ve...
> You can put AI chips in datacenters in the desert for far less than $100/kg
if you can't put them outside of Amarillo Texas without people throwing a fit then you can't put them anywhere. I mean freaking Pantex is there ffs!
https://en.wikipedia.org/wiki/Pantex
Any cooling issues to be resolved?
No.
Cooling is probably the easiest problem to solve, easier than power. And in both cases, the problem is solved by mass to orbit. All you need for cooling is a big f-ing radiator. Solar panels are chips, and not trivial to manufacture. But a radiator is just a hunk of metal with some pipes.
That's why the cost of mass to orbit is the most important thing. You can solve almost any space problem by just throwing more mass at it.
Any hardware that could be launched to orbit could also be put on a container ship in international waters. It makes zero sense to put these things in space.
Yeah, very hard to predict the future at this point. But the Starship + Terrafab combo will be this type of order-of-magnitude-moat IF it works out. Big if.
If it doesn't work out, I think China's exponential terrestrial energy deployment will eventually give them the lead, IF they can get enough chips. Another big if.
well, a stroke of luck until the whole US stock market crashes & everyone's retirement funds get cut 40% I guess when people internalize this. it will have to happen sooner or later though I suppose
I'd take a market crash over a monopoly in the hands of a ghoul like Altman.
The economy he and his ilk want to build is infinitely worse.
In truth it crashes either way.
interestingly also, open weight models are also more effectively run in the cloud, so it creates a weird scenario where the frontier labs crash but the compute providers, not as much
I wouldn't be so sure about that. The popping of a bubble is usually just as irrational as its rise.
If investors start fleeing from senseless businesses in the AI sector, that does not mean that sensible businesses will be spared. These things follow herd mentality, and the primary drivers of the herd are greed and fear, not fundamentals or business logic.
America is pretty close to rhyming with nazi germany circa 1929.
Ok, I'll bite. What's your rationale?
Flock powered surveillance state that sends ICE goons to kill random people in areas where there is any resistance to the regime.
People who say stuff like that are unhinged chronically online trolls. Best not to feed them.
This is funny because the stock Market has been ahistorically high. My portfolio went up over 20 percent in the last 12 months.
A major correction would be a bummer but we were never entitled to these abnormal gains in the first place.
It’s not as bad as dot.com of course since all purely AI companies are private and the ones on the market have pretty decent cash flow outside of AI. But the stock market pattern is not that dissimilar, the largest increases are usually just before the crash.
Yeah the last year has been astonishing, my portfolio is kicking ass. But I'm 10 years out from retirement and I am pretty confident a correction is coming; I hope the correction happens soon.
The market (s&p500) crashing 40% puts us at levels we haven't seen since 2024, well into the creation of LLMs. Probably a worthwhile trade if it was either/or!