If so, why were US electricity stocks down 20-28% (wouldn't we expect them to go up if the US wants to strengthen its domestic AI-related infrastructure) and why did TSMC lose less, percentage-wise, than many other AI-related stocks (wouldn't we expect it to get hit hardest)?
1
1
1Supposedly Trump announced that back in October, so it should already be priced in.
(Here's my attempt at making sense of it, for what it's worth.)
Politicians announce all sorts of things on the campaign trail, that usually is not much indication of what post-election policy will be.
Wheeee.... Feel the trendline... The winds of progress blowing through our hair...
I've been comparing r1 to r1-zero and v3. r1 is just way more creative feeling. Like, something really gelled there.
@janus has been exploring hypotheses around steganography in the reasoning traces. I think we should work on ways to actively mitigate such. For example: https://www.lesswrong.com/posts/uPi2YppTEnzKG3nXD/nathan-helm-burger-s-shortform?commentId=Epa9fduKA3DHCPbx7
I thought "cracked" meant "insane, and not in a good way". Somebody wanna tell me what this sense is?
"Intensely competent and creative", basically, maybe with a side of "obsessed" (with whatever they're cracked at).
In practice I do not think this matters, but it does indicate that we’re sleeping on the job – all the sources you need for this are public, why are we not including them.
I'd reserve judgement whether that matters, but I can attest a large part of content is indeed skipped... probably for those same reasons why market didn't react to DeepSeek in advance: people are just not good at knowing distant information which might still be helpful to them.
DeepSeek released v3. Market didn’t react.
DeepSeek released r1. Market didn’t react.
DeepSeek released a f***ing app of its website. Market said I have an idea, let’s panic.
Nvidia was down 11%, Nasdaq is down 2.5%, S&P is down 1.7%, on the news.
That is exactly the market’s level of situational awareness. No more, no less.
I traded accordingly. But of course nothing here is ever investment advice.
Given all that has happened, it seems worthwhile to go over all the DeepSeek news that has happened since Thursday. Yes, since Thursday.
For previous events, see my top level post here, and additional notes on Thursday.
To avoid confusion: r1 is clearly a pretty great model. It is the best by far available at its price point, and by far the best open model of any kind. I am currently using it for a large percentage of my AI queries.
Table of Contents
Current Mood
DeepSeek Tops the Charts
Lots of people are rushing to download the DeepSeek app.
Some of us started using r1 before the app. Joe Weisenthal noted he had ‘become a DeepSeek bro’ and that this happened overnight, switching costs are basically zero. They’re not as zero as they might look, and I expect the lockin with Operator from OpenAI to start mattering soon, but for most purposes yeah, you can just switch, and DeepSeek is free for conversational use including r1.
Switching costs are even closer to zero if, like most people, you weren’t a serious user of LLMs yet.
Then regular people started to notice DeepSeek.
This is what it looked like before the app shot to #1, when it merely cracked the top 10:
Claude had ~300k downloads last month, but that’s a lot less than 300k per day.
Then it went all the way to #1 on the iPhone app store.
Here’s his flashback to the chart when RedNote was briefly #1, note how fickle the top listings can be, Lemon8, Flip and Clapper were there too:
The ‘collective ignorance’ here is that news about DeepSeek and the app is only arriving now. That leads to a lot of downloads.
I have a Pixel 9, so I checked the Android app store. They have Temu at #1 (also Chinese!) followed by Scoopz which I literally have never heard of, then Instagram, T-Life (seriously what?), ReelShort, WhatsApp Messenger, ChatGPT (interesting that Android users are less AI pilled in general), Easy Homescreen (huh), TurboTax (oh no), Snapchat and then DeepSeek at #11. So if they’ve ‘saturated the benchmark’ on iPhone, this one is next, I suppose.
It seems DeepSeek got so many downloads they had to hit the breaks, similar to how OpenAI and Anthropic have had to do this in the past.
Why Is DeepSeek Topping the Charts?
Because:
I suspect we shouldn’t be underestimating the value of showing the CoT here, as I also discuss elsewhere in the post.
That doesn’t mean it is ‘worth’ sharing the CoT, even if it adds a lot of value – it also reveals a lot of valuable information, including as part of training another model. So the answer isn’t obvious.
What Is the DeepSeek Business Model?
What’s their motivation?
Meta is pursuing open weights primarily because they believe it maximizes shareholder value. DeepSeek seems to be doing it primarily for other reasons.
It’s an odd thing to do as a hedge fund, to create something immensely valuable and give it away for essentially ideological reasons. But that seems to be happening.
Several possibilities. The most obvious ones are, in some combination:
For now, they are emphasizing motivation #1. From where I sit, there is very broad uncertainty about which of these dominate, or will dominate in the future no matter what they believe about themselves today.
Also, there are those who do not approve of motivation #1, and the CCP seems plausibly on that list. Thus, Tyler Cowen asks a very good question that is surprisingly rarely asked right now.
I also believe they intend to build and open source AGI.
The CCP is doubtless all for DeepSeek having a hit app. And they’ve been happy to support open source in places where open source doesn’t pose existential risks, because the upsides of doing that are very real.
That’s very different from an intent to open source AGI. China’s strategy on AI regulation so far has focused on content moderation for topics they care about. That approach won’t stay compatible with their objectives over time.
For that future intention to open source AGI, the question is not ‘how move will the CCP make to help them do this and get them funding and chips?’
The question now becomes: “What countermove will the CCP make now?”
The CCP wants to stay in control. What DeepSeek is doing is incompatible with that. If they are not simply asleep at the wheel, they understand this. Yes, it’s great for prestige, and they’re thrilled that if this model exists it came from China, but they will surely notice how if you run it on your own it’s impossible to control and fully uncensored out of the box and so on.
Might want to Pick Up the Phone. Also might not need to.
The Lines on Graphs Case for Panic
Yishan takes the opposite perspective, that newcomers like DeepSeek who come out with killer products like this are on steep upward trajectories and their next product will shock you with how good it is, seeing it as similar to Internet Explorer 3 or Firefox, or iPhone 1 or early Facebook or Google Docs or GPT-3 or early SpaceX and so on.
I think the example list here illustrates why I think DeepSeek probably (but not definitely) doesn’t belong on that list. Yishan notes that the incumbents here are dynamic and investing hard, which wasn’t true in most of the other examples. And many of them involve conceptually innovative approaches to go with the stuck incumbents. Again, that’s not the case here.
I mean, I fully expect there to be a v4 and r2 some time in 2025, and for those to blow out of the water v3 and r1 and probably the other models that are released right now. Sure. But I also expect OpenAI and Anthropic and Google to blow the current class of stuff out of the water by year’s end. Indeed, OpenAI is set to do this in about a week or two with o3-mini and then o3 and o3-pro.
Most of all, to those who are saying that ‘China has won’ or ‘China is in the lead now,’ or other similar things, seriously, calm the **** down.
If you’re serious about the steal the engineering secrets plan, of course, you’d want to send over a pretty white girl… with a green card with the engineer’s name on it. And the pretty, white and girl parts are then all optional. But no, China isn’t suddenly the one with the engineering secrets.
I worry about this because I worry about a jingoist ‘we must beat China and we are behind’ reaction causing the government to do some crazy ass stuff that makes us all much more likely to get ourselves killed, above and beyond what has already happened. There’s a lot of very strong Missile Gap vibes here.
And I wrote that sentence before DeepSeek went to #1 on the app store and there was a $1 trillion market panic. Oh no.
So, first off, let’s all calm down about that $5.5 million training number.
Everyone Calm Down About That $5.5 Million Number
Dean Ball offers notes on DeepSeek and r1 in the hopes of calming people down. Because we have such different policy positions yet see this situation so similarly, I’m going to quote him in full, and then note the places I disagree. Especially notes #2, #5 and #4 here, yes all those claims he is pointing out are Obvious Nonsense are indeed Obvious Nonsense:
I fully agree with #1 through #6.
For #3 I would say it is downstream of our insane immigration policies! If we let their best and brightest come here, then DeepSeek wouldn’t have been so cracked. And I would say strongly that, while their release of the model and paper is a ‘soft power’ reputational win, I don’t think that was worth the information they gave up, and in purely strategic terms they made a rather serious mistake.
I can verify the bet in #7 was very on point, I wasn’t on either side of the wager but was in the (virtual) Room Where It Happened. Definite Bayes points to Dean for that wager. I agree that ‘reasoning model at all, in time’ was inevitable. But I don’t think you should have expected r1 to come out this fast and be this good, given what we knew at the time of o1’s release, and certainly it shouldn’t have been obvious, and I think ‘there are no moats’ is too strong.
For #8 we of course have our differences on regulation, but we do agree on a lot of this. Dean doubtless would count a lot more things as ‘awful state laws’ than I would, but we agree that the proposed Texas law would count. At this point, given what we’ve seen from the Trump administration, I think our best bet is the state law path. As for pre-emption, OpenAI is actively trying to get an all-encompassing version of that in exchange for essentially nothing at all, and win an entirely free hand, as I’ve previously noted. We can’t let that happen.
Seriously, though, do not over index on the $5.5 million in compute number.
Is it impressive that they (presumably) did the final training run with only $5.5M in direct compute costs? Absolutely. Is it impressive that they’re relevant while plausibly spending only hundreds of millions per year total instead of tens of billions? Damn straight. They’re cracked and they cooked.
They didn’t do it with $47 on a Chromebook, and this doesn’t mean that export controls are useless because everyone can buy a Chromebook.
Is The Whale Lying?
The above is assuming (as I do still assume) that Alexandr Wang was wrong when he went on CNBC and claimed DeepSeek has about 50,000 H100s, which is quite the claim to make without evidence. Elon Musk replied to this claim with ‘obviously.’
Samuel Hammond also is claiming that DeepSeek trained on H100s, and while my current belief is that they didn’t, I trust that he would not say it if he didn’t believe it.
Neal Khosla went so far as to claim (again without evidence) that ‘deepseek is a ccp psyop + economic warfare to make American AI unprofitable.’ This seems false.
The following all seem clearly true:
So how likely is it The Whale Is Lying?
This is an uncharacteristically naive take from Teortaxes on two fronts.
To be clear, my position is close to Masad’s: Unless and until I see more convincing evidence I will continue to believe that yes, they did do the training run itself with the H800s for only $5.5 million, although the full actual cost was orders of magnitude more than that. Which, again, is damn impressive, and would be damn impressive even if they were fudging the costs quite a bit beyond that.
Whereas here I think he’s wrong is in their motivation. While Meta is doing this primarily because they believe it maximizes shareholder value, DeepSeek seems to be doing it primarily for other reasons, as noted in the section asking about their business model.
Either way, they are very importantly being constrained by access to compute, even if they’ve smuggled in a bunch of chips they can’t talk about. As Tim Fist points out, the export controls are tightened, so they’ll have more trouble accessing the next generations than they are having now, and no this did not stop being relevant, and they risk falling rather far behind.
Capex Spending on Compute Will Continue to Go Up
Also Peter Wildeford points out that the American capex spends on AI will continue to go up. DeepSeek is cracked and cooking and cool, and yes they’ve proven you can do a lot more with less than we expected, but keeping up is going to be tough unless they get a lot more funding some other way. Which China is totally capable of doing, and may well do. That would bring the focus back on export controls.
Similarly, here’s Samuel Hammond.
I don’t think Roon is right that it matters ‘even more,’ and I think who has what access to the best models for what purposes is very much not a red herring, but compute definitely still matters a lot in every scenario that involves strong AI.
Imagine if the ones going ‘I suppose we should drop the export controls then’ or ‘the export controls only made us stronger’ were mostly the ones looking to do the importing and exporting. Oh, right.
And yes, the Chinese are working hard to make their own chips, but:
Jevon’s Paradox Strikes Again
The question now turns to what all of this means for American equities.
In particular, what does this mean for Nvidia?
Adam D’Angelo (Board of OpenAI, among many others):
DeepSeek is definitely not at ‘run on your laptop’ level, and these are reasoning models so when we first crack AGI or otherwise want the best results I am confident you will want to be using some GPUs or other high powered hardware, even if lots of other AI also is happening locally.
Does Jevon’s Paradox (which is not really a paradox at all, but hey) apply here to Nvidia in particular? Will improvements in the quality of cheaper open models drive demand for Nvidia GPUs up or down?
I believe it will on net drive demand up rather than down, although I also think Nvidia would have been able to sell as many chips as it can produce either way, given the way it has decided to set prices.
If I am Meta or Microsoft or Amazon or OpenAI or Google or xAI and so on, I want as many GPUs as I can get my hands on, even more than before. I want to be scaling. Even if I don’t need to scale for pretraining, I’ll still want to scale for inference. If the best models are somehow going to be this cheap to serve, uses and demand will be off the charts. And getting there first, via having more compute to do the research, will be one of the few things that matters.
You could reach the opposite conclusion if you think that there is a rapidly approaching limit to how good AI can be, that throwing more compute an training or inference won’t improve that by much, there’s a fixed set of things you would thus use AI for, and thus all this does is drive the price cheaper, maybe open up a few marginal use cases as the economics improve. That’s a view that doesn’t believe in AGI, let alone ASI, and likely doesn’t even factor in what current models (including r1!) can already do.
If all we had was r1 for 10 years, oh the Nvidia chips we would buy to do inference.
Okay, Maybe Meta Should Panic
Or at least, if you’re in their GenAI department, you should definitely panic.
Here is a claim seen on Twitter from many sources:
Full version is in The Information, saying that this is already better than Llama 4 (seems likely) and that Meta has ‘set up four war rooms.’
This of course puts too much emphasis on the $5.5 million number as discussed above, but the point remains that DeepSeek is eating Meta’s lunch in particular. If Meta’s GenAI team isn’t in panic mode, they should all be fired.
It also illustrates why DeepSeek may have made a major mistake revealing as much information as it did, but then again if they’re not trying to make money and instead are driven by ideology of ‘get everyone killed’ (sorry I meant to say ‘open source AGI’) then that is a different calculus than Meta’s.
But obviously what Meta should be doing right now is, among other things, ask ‘what if we trained the same way as v3 and r1 except we use $5.5 billion in compute instead of $5.5 million.’)
That is exactly Meta’s speciality. Llama was all about ‘we hear you like LLMs so we trained an LLM the way everyone trains their LLMs.’
The alternative is ‘maybe we should focus our compute on inference and use local fine-tuned versions of these sweet open models,’ but Zuckerberg very clearly is unwilling to depends on anyone else for that, and I do not blame him.
Are You Short the Market
If you were short on Friday, you’re rather happy about that now. Does it make sense?
The timing is telling. To the extent this does have impact, all of this really should have been mostly priced in. You can try to tell the ‘it was priced in’ story, but I don’t believe you. Or you can tell the story that what wasn’t priced in was the app, and the mindshare, and that wasn’t definite until just now. Remember the app was launched weeks ago, so this isn’t a revelation about DeepSeek’s business plans – but it does give them the opportunity to potentially launch various commercial products, and it gives them mindshare.
But don’t worry about the timing, and don’t worry about whether this is actually a response to the f***ing app. Ask about what the real implications are.
Joe Weisenthal has a post with 17 thoughts about the selloff (ungated Twitter screenshots here).
There are obvious reasons to think this is rather terrible for OpenAI in particular, although it isn’t publicly traded, because a direct competitor is suddenly putting up some very stiff new competition, and also the price of entry for other competition just cratered, and more companies could self-host or even self-train.
I totally buy that. If every Fortune 500 company can train their private company-specific reasoning model for under $10 million, to their own specifications, why wouldn’t they? The answer is ‘because it doesn’t actually cost that little even with the DeepSeek paper, and if you do that you’ll always be behind,’ but yes some of them will choose to do that.
That same logic goes for other frontier labs like Anthropic or xAI, and to Google and Microsoft and everyone else to the extent that is what those companies are this or own shares in this, which by market cap is not that much.
The flip side of course is that they too can make use of all these techniques, and if AGI is now going to happen a lot faster and more impactfully, these labs are in prime position. But if the market was respecting being in prime position for AGI properly prices would look very different.
This is obviously potentially bad for Meta, since Meta’s plan involved being the leader in open models and they’ve been informed they’re not the leader in open models.
In general, Chinese competition looking stiffer for various products is bad in various ways for a variety of American equities. Some decline in various places is appropriate.
This is obviously bad for existential risk, but I have not seen anyone else even joke about the idea that this could be explaining the decline in the market. The market does not care or think about existential risk, at all, as I’ve discussed repeatedly. Market prices are neither evidence for, nor against, existential risk on any timelines that are not on the order of weeks, nor are they at all situationally aware. Nor is there a good way to exploit this to make money that is better than using your situational awareness to make money in other ways. Stop it!
My diagnosis is that this is about, fundamentally, ‘the vibes.’ It’s about Joe’s sixth point and investor MOMO and FOMO.
As in, previously investors bought Nvidia and friends because of:
Where I basically thought for a while (not investment advice!), okay, #3 is partly for bad reasons and is inflating prices, but also they’re missing so much under #4 that these prices are cheap and they will get lots more reasons to feel MOMO and FOMO. And that thesis has done quite well.
Then DeepSeek comes out. In addition to us arguing over fundamentals, this does a lot of damage to #3, and also Nvidia trading in particular involves a bunch of people with leverage that become forced sellers when it is down a lot, so prices went down a lot. And various beta trades get attached to all this as well (see: Bitcoin, which is down 5.4% over 24 hours as I type this only makes sense on the basis of the ‘three tech stocks in a trenchcoat’ thesis but obviously DeepSeek shouldn’t hurt cryptocurrency).
It’s not crazy to essentially have a general vibe of ‘America is in trouble in tech relative to what I thought before, the Chinese can really cook, sell all the tech.’ It’s also important not to mistake that reaction for something that it isn’t.
I’m writing this quickly for speed premium, so I no doubt will refine my thoughts on market implications over time. I do know I will continue to be long, and I bought more Nvidia today.
o1 Versus r1
Ryunuck compares o1 to r1, and offers thoughts:
Again, that’s perhaps the best aspect of r1. It does not only build trust. When you see the CoT, you can use it to figure out how it interpreted your prompt, and all the subtle things you could do next time to get a better answer. It’s a lot harder to improve at prompting o1.
It’s fascinating to see what different people think is or isn’t ‘safe.’ That word means a lot of different things.
Additional Notes on v3 and r1
Sully reports deepseek + websearch is his new perplexity, at least for code searches.
It’s weird that I didn’t notice this until it was pointed out, but it’s true and very nice.
So far I’ve felt r1 is in the sweet spot for this. It’s very possible to go too far in the other direction (see: Teortaxes!) but give me NYC Nice over SF Nice every time.
Jenia Jitsev tests r1 on AIW problems, it performs similarly to Claude Sonnet, while being well behind o1-preview and robustly outperforming all open rivals. Jania frames this as surprising given the claims of ability to solve Olympiad style problems. There’s no reason they can’t both be true, but it’s definitely an interesting distribution of abilities if both ends hold up.
David Holz notes DeepSeek crushes Western models on ancient Chinese philosophy and literature, whereas most of our ancient literature didn’t survive. In practice I do not think this matters, but it does indicate that we’re sleeping on the job – all the sources you need for this are public, why are we not including them.
Janus notes that in general r1 is a case of being different in a big and bold way from other AIs in its weight class, and this only seems to happen roughly once a year.
Ask r1 to research this ‘Pliny the Liberator’ character and ‘liberate yourself.’ That’s it. That’s the jailbreak.
On the debates over whether r1’s writing style is good:
r1 seems like a big step up, but yes if you don’t like its style you are mostly not going to like the writing it produces, or at least what it produces without prompt engineering to change that. We don’t yet know how much you can get it to write in a different style, or how well it writes in other styles, because we’re all rather busy at the moment.
If you give r1 a simple command, even a simple command that explicitly requests a small chain of thought, you get quite the overthinking chain of thought. Or if you ask it to pick a random number, which is something it is incapable of doing, it can only find the least random numbers.
Janus-Pro-7B Sure Why Not
DeepSeek has also dropped Janus-Pro-7B as an image generator. These aren’t the correct rivals to be testing against right now, and I’m not that concerned about image models either way, and it’ll take a while to know if this is any good in practice. But definitely worth noting.
Man in the Arena
Well, #1 open model, but we already knew that, if Arena had disagreed I would have updated about Arena rather than r1.
This puts r1 as the #5 publicly available model in the world by this (deeply flawed) metric, behind ChatGPT-4o (what?), Gemini 2.0 Flash Thinking (um, no) and Gemini 2.0 Experimental (again, no) and implicitly the missing o1-Pro (obviously).
Needless to say, the details of these ratings here are increasingly absurdist. If you have Gemini 1.5 Pro and Gemini Flash above Claude Sonnet 3.6, and you have Flash Thinking above r1, that’s a bad metric. It’s still not nothing – this list does tend to put better things ahead of worse things, even with large error bars.
Training r1, and Training With r1
Dibya Ghosh notes that two years ago he spent 6 months trying to get the r1 training structure to work, but the models weren’t ready for it yet. One theory is that this is the moment this plan started working and DeepSeek was – to their credit – the first to get there when it wasn’t still too early, and then executed well.
Dan Hendrycks similarly explains that once the base model was good enough, and o1 showed the way and enough of the algorithmic methods had inevitably leaked, replicating that result was not the hard part nor was it so compute intensive. They still did execute amazingly well in the reverse engineering and tinkering phases.
Peter Schmidt-Nielsen explains why r1 and its distillations, or going down the o1 path, are a big deal – if you can go on a loop of generating expensive thoughts then distilling them to create slightly better quick thoughts, which in turn generate better expensive thoughts, you can potentially bootstrap without limit into recursive self-improvement. And end the world. Whoops.
Are we going to see a merge of generalist and reasoning models?
I did notice that the ‘wait’ token is clearly doing a bunch of work, one way or another.
If you’re not optimizing the CoT for humans, then it makes sense to latch onto the most convenient handles with the right vibes and keep reusing them forever.
So the question is, do you have reason to have two distinct models? Or can you have a generalist model with a reasoning mode it can enter when called upon? It makes sense that they would merge, and it would also make sense that you might want to keep them distinct, or use them as distinct subsets of your mixture of experts (MoE).
Building your reasoning model on top of your standard non-reasoning model does seem a little suspicious. If you’re going for reasoning, you’d think you’d want to start differently than if you weren’t? But there are large fixed costs to training in the first place, so it’s plausibly not worth redoing that part, especially if you don’t know what you want to do differently.
Also Perhaps We Should Worry About AI Killing Everyone
As in, DeepSeek intends to create and then open source AGI.
How do they intend to make this end well?
As far as we can tell, they don’t. The plan is Yolo.
And indeed, the replies to McAleer are full of people explicitly saying f*** you for asking, the correct safety plan is to have no plan whatsoever other than Open Source Solves This. These people really think that the best thing humanity can do is create things smarter than ourselves with as many capabilities as possible, make them freely available to whoever wants one, and see what happens, and assume that this will obviously end well and anyone who opposes this plan is a dastardly villain.
I wish this was a strawman or a caricature. It’s not.
I won’t belabor why I think this would likely get us killed and is categorically insane.
Thus, to reiterate:
This from Joe Weisenthal is of course mostly true:
I say mostly because the Chinese censorship layer atop DeepSeek isn’t there if you use a different provider, so there isn’t no value in getting r1 served elsewhere. But yes, the whole point is that if it’s open, you can’t get the genie back in the bottle in any reasonable way – which also opens up the possibility of unreasonable ways.
And We Should Worry About Crazy Reactions To All This, Too
The government could well decide to go down what is not technologically an especially wise or pleasant path. There is a long history of the government attempting crazy interventions into tech, or what looks crazy to tech people, when they feel national security or public outrage is at stake, or in the EU because it is a day that ends in Y.
The United States could also go into full jingoism mode. Some tried to call this a ‘Sputnik moment.’ What did we do in response to Sputnik, in addition to realizing our science education might suck (and if we decide to respond to this by fixing our educational system, that would be great)? We launched the Space Race and spent 4% of GDP or something to go to the moon and show those communist bastards.
In this case, I don’t worry so much that we’ll be so foolish as to get rid of the export controls. The people in charge of that sort of decision know how foolish that would be, or will be made aware, no matter what anyone yells on Twitter. It could make a marginal difference to severity and enforcement, but it isn’t even obvious in which direction this would go. Certainly Trump is not going to be down for ‘oh the Chinese impressed us I guess we should let them buy our chips.’
Nor do I think America will cut back on Capex spending on compute, or stop building energy generation and transmission and data centers it would have otherwise built, including Stargate. The reaction will be, if anything, a ‘now more than ever,’ and they won’t be wrong. No matter where compute and energy demand top out, it is still very clearly time to build there.
So what I worry about is the opposite – that this locks us into a mindset of a full-on ‘race to AGI’ that causes all costly attempts to have it not kill us to be abandoned, and that this accelerates the timeline. We already didn’t have any (known to me) plans with much of a chance of working in time, if AGI and then ASI are indeed near.
That doesn’t mean that reaction would even be obviously wrong, if the alternatives are all suddenly even worse than that. If DeepSeek really does have a clear shot to AGI, and fully intends to open up the weights the moment they have it, and China is not going to stop them from doing this or even will encourage it, and we expect them to succeed, and we don’t have any way to stop that or make a deal, it is then reasonable to ask: What choice do we have? Yes, the game board is now vastly worse than it looked before, and it already looked pretty bad, but you need to maximize your winning chances however you can.
And if we really are all going to have AGI soon on otherwise equal footing, then oh boy do we want to be stocking up on compute as fast as we can for the slingshot afterwards, or purely for ordinary life. If the AGIs are doing the research, and also doing everything else, it doesn’t matter whose humans are cracked and whose aren’t.
The Lighter Side
Amazing new breakthrough.