back to indexAI CEO: ‘Stock Crash Could Stop AI Progress’, Llama 4 Anti-climax + ‘Superintelligence in 2027’ ...

Chapters
0:0 Introduction
0:47 Stock Crash
2:28 Llama 4
10:55 o3 News
11:59 OpenAI non-profit?
13:13 AI 2027
00:00:00.000 |
Every day it seems to me at the moment there are crazy claims and headlines not just in AI 00:00:06.340 |
but in the wider world. So this video is going to attempt to debunk a few of those headlines 00:00:12.240 |
and just give you what we know. I'm going to look at Llama 4, the model that has been a year in the 00:00:18.140 |
waiting and has many claims and counterclaims about it. Then the blog post slash paper from 00:00:24.680 |
a former OpenAI researcher that has millions and millions of views online and was featured in the 00:00:31.080 |
New York Times essentially predicting superintelligence by 2027. Then some very recent 00:00:36.660 |
news about the release date of what could be the smartest model of them all along with a ton of 00:00:42.960 |
contradictions about whether and when it might come out. I just simply can't resist starting out 00:00:48.680 |
with a quote which could dampen literally all the hype that you see in AI. When Dario Amadei, 00:00:54.780 |
the CEO of Anthropic, makers of the Claude series of models, was asked what could stop AI? What could 00:01:01.080 |
stop the progress? He mentioned a war in Taiwan which we've known is a risk for a long time. I 00:01:06.520 |
highly recommend the book Chip Wars by Chris Miller. He then briefly touched on there being a potential 00:01:11.720 |
data war where they run out of high quality data to train their models on but then he touched on a 00:01:16.280 |
new risk that he hadn't mentioned before. And before you hear this quote, note that it came 00:01:20.880 |
three weeks ago before all of this tariff craziness. What are the top three things that could stop the 00:01:25.960 |
show? If there's a large enough disruption to the stock market that messes with the capitalization of 00:01:30.680 |
these companies, basically a kind of belief that the technology will not, you know, move forward and 00:01:38.520 |
that kind of creates a self-fulfilling prophecy where there's there's not enough capitalization. 00:01:42.360 |
I just want to spend 30 seconds on explaining how that might play out. Companies like OpenAI and 00:01:48.200 |
Anthropic need to raise money to fund those vast training runs that go behind their latest models. 00:01:53.960 |
They don't just have 40 billion or 100 billion sitting around in their bank account to fund those vast 00:01:59.720 |
data centers and everything else that goes into training a language model. The trouble is, of course, 00:02:04.440 |
if investors don't think they'll get their money back, perhaps due to a recession, then they either 00:02:09.240 |
won't invest in these companies or invest less at lower valuations. Less money means less compute, 00:02:15.000 |
which means slower AI progress. That's not a prediction, of course. No one, including myself, 00:02:19.800 |
knows what's going to happen. It's just easy to forget that AI operates in the real world and real 00:02:25.320 |
world things can have consequences for AI progress. And speaking of AI progress, how much progress is 00:02:31.240 |
represented by the release of Llama 4 and two of the three models in the Llama 4 family? Well, 00:02:37.240 |
it's hard to be exact because, as ever, there's a lot more spin than honest analysis in the release 00:02:43.560 |
of this family. But it seems like not too much. There's no paper, of course, that's starting to become 00:02:49.240 |
the norm. But here are the highlights of what we do know. First, the smallest of the Llama 4 models 00:02:55.000 |
has what they call an industry-leading context window of 10 million tokens. Think of that being 00:03:01.160 |
around seven and a half million words. That sounds insane, of course, and innovative, but two quick 00:03:06.520 |
caveats. All the way back to February 2024, we had a model, Gemini 1.5 Pro, that had a 10 million token 00:03:14.120 |
context window. And with that extreme window, it could perform amazing needle in a haystack recovery 00:03:20.440 |
on videos and audio and text. In public, at least, we, quote, "only" got models of up to two million 00:03:26.680 |
tokens of context window, though, perhaps because Google realized something. They realized, perhaps, 00:03:31.640 |
that it's all well and good finding individual needles in a haystack, as demonstrated in this 00:03:36.840 |
Llama 4 blog post. If you dump in all the Harry Potter books and drop in a password, say, halfway through, 00:03:43.480 |
the model will be able to find it and retrieve it. But most people, let's be honest, aren't sneaking in 00:03:48.280 |
passwords into seven volumes of Harry Potter. So these results from the release 48 hours ago 00:03:55.400 |
seem less relevant to me than this updated benchmark from 24 hours ago. It's called Fiction Live Bench for 00:04:02.600 |
Long Context Deep Comprehension. This is the benchmark where language models have to piece together plot 00:04:08.520 |
progressions across tens or hundreds of thousands of tokens or words. In my last video on Gemini 2.5 Pro, 00:04:15.080 |
I noted its extremely good performance on this benchmark. In contrast, for Llama 4 for the medium 00:04:20.280 |
sized model and the smallest model, performance is pretty bad and gets worse. The numbers at the top 00:04:26.120 |
refer to the clues being strewn across, say, 6,000 words or 12,000 words or even 100,000 words. Things 00:04:33.880 |
then get stranger when you think about dates. Why was Llama 4 released on a Saturday? That is 00:04:39.560 |
unprecedented in the entirety of the time I've covered AI. If you were going to be vaguely conspiratorial, 00:04:45.800 |
you would think that they released it on a weekend to sort of dampen down attention. Also note that its 00:04:51.320 |
knowledge cutoff is August 2024. That's the most recent of the training data that Llama 4 was trained on. 00:04:58.920 |
Compare that to Gemini 2.5, which has a knowledge cutoff of January of 2025. Kind of hints to me that 00:05:05.640 |
Meta were trying desperately to bring this model up to scratch in the intervening nine months or so. 00:05:11.720 |
In fact, they probably intended to release it earlier, but then in September we had the start of the O series 00:05:18.360 |
of models from OpenAI and then in January we got DeepSeek R1. By the way, if you want early access to 00:05:25.160 |
my full-length documentary on DeepSeek and R1, it's on my Patreon. Link in the description. But I will say, 00:05:31.640 |
before we completely write off Llama 4 as in this meme, there is some solid progress that it represents. 00:05:38.280 |
Especially the medium-sized model, Llama 4 Maverick, as it compares to the updated DeepSeek 00:05:45.640 |
V3. Both of these models are of course not thinking models, like Gemini 2.5 or DeepSeek R1. Meta 00:05:52.440 |
haven't released their state-of-the-art thinking model yet. But just bear in mind for a moment that 00:05:57.960 |
for all the hullabaloo around DeepSeek V3, Llama 4 Maverick has around half the number of active 00:06:05.000 |
parameters and yet is comparable in performance. Now yes, I know people accuse it of benchmark maxing or 00:06:10.920 |
hacking on LM Arena, but check out these real numbers. Assuming none of the answers made it into 00:06:16.280 |
the training data for Llama 4, the performance of its models on GPQA Diamond, the google-proof stem 00:06:22.680 |
benchmark that's extremely tough, is actually better than the new DeepSeek V3. Or of course, 00:06:27.560 |
GPT-4.0. So if you were making the optimistic case for Meta or for Llama 4, you would say that they 00:06:33.800 |
have a pretty insane base model that they could create perhaps a state-of-the-art thinking model on 00:06:39.480 |
top of. Only problem is, Gemini 2.5 Pro is already there and DeepSeek R2 is coming out any moment. Also, 00:06:47.080 |
when you take Llama 4 out of its comfort zone, its performance starts to crater. Take this coding 00:06:52.280 |
benchmark, Ada's Polyglot benchmark, testing model performance on a range of programming languages. 00:06:57.720 |
Unlike many benchmarks, it doesn't just focus on the Python programming language, but a range of 00:07:03.560 |
programming languages. And as you can see, Gemini 2.5 Pro tops the charts. Now yes, you might say that's 00:07:09.160 |
a thinking model, but then look at Claude 3.7 Sonnet, that's without thinking, it gets 60%. DeepSeek V3, 00:07:16.920 |
the latest version, gets 55%. And you unfortunately have to scroll down quite far to get to Llama 4 00:07:25.080 |
Maverick, which gets 15.6%. Now is it me, or is performance like this quite hard to square with 00:07:32.520 |
headlines like this one from Mark Zuckerberg, which is that his AI models will replace mid-level engineers 00:07:39.480 |
soon? As in, Zuckerberg says, this year, 2025. Was he massively hyping things out of all sense of 00:07:46.200 |
proportion? How dare you have that thought? Four more quick things before we leave Llama 4, 00:07:51.320 |
and yes, I did pick that number deliberately. And the first is on the tentative signs from their 00:07:56.920 |
biggest model, the unreleased one, Behemoth. Now Meta have deliberately made the comparisons with models 00:08:02.600 |
like Gemini 2 Pro and GPT 4.5, and the comparison is somewhat favourable. Though if you look closely 00:08:09.960 |
at the footnotes, it says, Llama model results represent our current best internal runs. Did they 00:08:15.720 |
run the model five times and pick the best one? Three times? Ten times? We don't know. Also note they 00:08:21.320 |
chose not to compare Llama 4 Behemoth with DeepSeek V3, which is three times smaller in terms of overall 00:08:29.160 |
parameters and around eight times smaller in terms of active parameters. In dark blue, you can see the 00:08:35.000 |
performance of DeepSeek V3, the latest version, and you'd have to agree it's pretty much comparable to 00:08:41.480 |
Llama 4 Behemoth. In other words, if you wanted to put a negative spin on the release, you could say 00:08:46.280 |
Llama's biggest model, many times the size of the new DeepSeek V3 base model, performs at the same level, 00:08:53.400 |
basically. Now, yes, I know Llama 4 Behemoth is still, quote, in training, but pretty much all models are, 00:08:58.680 |
quote, in training all the time at the moment with post-training. Second, just a quick one I saw 00:09:03.320 |
halfway through the terms of use, which is you're kind of screwed if you are in the EU. You can still 00:09:09.320 |
be the end user of it, you just don't have the same rights to build upon it. Next comes a little nugget 00:09:14.920 |
towards the bottom of the page in which they've tried to make Llama 4 lean a bit more right. They 00:09:19.640 |
say it's well known that LLMs have bias, that they historically lean left when it comes to politics, 00:09:26.280 |
so they're going to try to rectify that. I'm sure, of course, that had nothing to do with 00:09:30.680 |
Zuckerberg's relationship to the new administration. Finally, Simplebench, in which Llama 4 Maverick, 00:09:36.440 |
the medium-sized model, gets 27.7%, which is around the same level as DeepSeek V3. Now, 00:09:43.800 |
that is a lower than, quote, non-thinking models like 3.5 Sonnet that don't take that time to lay out their 00:09:49.720 |
chain of thought before answering, but it's a solid performance. Meta are definitely still in 00:09:54.440 |
the race when it comes to having great base models upon which you can build incredible reasoning models. 00:09:59.800 |
Now, as it happens, I did get some juicy hints recently about what the performance of O3 would be 00:10:05.880 |
on Simplebench, and that's the model coming in two weeks. I'll touch on that in just a second. 00:10:11.160 |
And let's just say that it's going to be competitive. I know that's kind of like an egregious hint that I'm 00:10:16.360 |
not backing up, but that's all I can say at the moment. Now, what you may have noticed in the 00:10:20.440 |
middle of the screen is that Simplebench, which is a benchmark you can check out in the description, 00:10:25.560 |
I created it around nine months ago, is powered by Weave from Weights and Biases. They are sponsoring 00:10:31.480 |
this video and indeed the entire benchmark, as you can clearly tell with the link at the center of 00:10:36.760 |
the screen. That will open up this quick start, which should be useful for any developer who is 00:10:41.400 |
interested in benchmarking language models, as we do. To be honest, even just those who are 00:10:46.040 |
interested in learning more about LLMs, you can check out the Weights and Biases AI Academy down 00:10:51.320 |
here. As you can see, they are coming up with new free courses pretty much all the time. Now, 00:10:55.800 |
I did say I'd mentioned the O3 news, which came just a couple of days ago from Sam Altman, 00:11:00.200 |
in which he told us that O3 would be coming in about two weeks from now. This is from my newsletter, 00:11:05.560 |
but do you remember when OpenAI and Sam Altman specifically said, "We want to do a better job of 00:11:10.440 |
sharing our intended roadmap? As we get closer to AGI, you guys deserve clarity." Well, clarity would 00:11:17.000 |
be great, because initially O3 was supposed to come out shortly after O3 Mini High, which came out 00:11:23.800 |
towards the end of January. So, naturally, we expected it in February. Then, OpenAI did A180, 00:11:29.080 |
as you can see in this tweet, and Sam Altman said, "We will no longer ship O3 as a standalone model." Now, 00:11:34.920 |
perhaps prompted by the Gemini 2.5 Pro release, or their GPUs melting because of everyone using Image 00:11:40.840 |
Gen, they've pushed back GPT-5 and are now going to release O3 indeed as a standalone model in two weeks. 00:11:47.080 |
So much for clarity then. We're also apparently going to get books about Sam Altman's misdeeds 00:11:52.280 |
and dodgy documented behaviour, but that's a topic for another video. One thing I bet OpenAI don't want 00:11:57.800 |
us to focus on is their new plans for their non-profit. Remember that $300 billion valuation you saw 00:12:03.640 |
earlier on in this video, that depends on OpenAI becoming a for-profit company. So, 00:12:08.440 |
what's going to happen to that non-profit which was supposed to control the proceeds of OpenAI 00:12:13.880 |
creating AGI? Remember, in the slim chance that Sam Altman is right and OpenAI are the company that 00:12:20.120 |
creates trillions of dollars of value, as he predicted, this non-profit might have ended up 00:12:25.720 |
controlling trillions of dollars worth of value. More importantly, it would have controlled what would 00:12:30.520 |
have happened to AGI should OpenAI be the company that created it. Now, put aside whether you think 00:12:35.640 |
it will be OpenAI that creates AGI, or whether AGI is even well-defined or feasible in the next three 00:12:41.160 |
to five years. Just focus on the promise that Sam Altman and OpenAI made. We've gone from that 00:12:46.120 |
non-profit controlling what could have been, in theory, a significant fraction of the world economy, 00:12:51.400 |
to supporting local charities in California, and perhaps generously across America and beyond. 00:12:58.200 |
Now, hardly anyone, if anyone, is focusing on this story as OpenAI are no longer the dominant players 00:13:04.920 |
in the race to AGI, but nevertheless, I think it's significant. Now, if you are feeling somewhat 00:13:09.800 |
dehyped about AGI after hearing about Llama 4 and these OpenAI stories, well, you could spend a few 00:13:16.120 |
hours like I did on the weekend reading AI-2027. This was written by a former OpenAI researcher 00:13:24.280 |
and other super forecasters with a pretty impressive track record. Also, as you may remember, 00:13:29.400 |
Daniel Cocotagelo put up an impressive stand against OpenAI on their non-disparagement clause. He was 00:13:35.880 |
essentially willing to forfeit millions of dollars, and yes, you can make that much as a safety 00:13:40.120 |
researcher at OpenAI. He was willing to forfeit that so he wouldn't have to sign the non-disparagement 00:13:45.240 |
clause. Because he made that stand, OpenAI were practically forced into dropping that clause for 00:13:51.080 |
everyone. So, well done him on that. Nevertheless, I was not particularly convinced by this report, 00:13:56.280 |
even though I admire the fact that they put dates on the record for their predictions. To honor that, 00:14:01.880 |
I will try to match some of their predictions with predictions of my own. Their central premise 00:14:06.920 |
in a nutshell is that AI is first going to become a superhuman coder and then ML researcher and thereby 00:14:13.960 |
massively speed up AI progress, giving us superintelligence in 2027. They draw fairly heavily 00:14:20.120 |
on this paper from Meta, and I'm going to cover that in a separate video because I am corresponding 00:14:25.080 |
fairly closely with one of the key authors of that paper. Anyway, we start off fairly lightly, 00:14:30.440 |
basically with a description of what current AI can do in terms of being an agent like ChatGPT's 00:14:36.280 |
operator and deep research, essentially describing what we already have. We then get plenty of detours 00:14:42.200 |
into alignment and safety because you sense the authors are trying to get across that message at the 00:14:47.560 |
same time as making all of these predictions. I start to meaningfully diverge from their predictions 00:14:52.920 |
when it comes to early 2026 when they say this: If China steals the state-of-the-art AI, Agent 1 they 00:14:59.720 |
call it, weights, they could increase their research speed by nearly 50%. Based on all of the evidence 00:15:04.840 |
you've seen today about DeepSeq and Llama 4, you would say it's almost equally likely that the West will 00:15:11.080 |
be stealing China's weights. Or wait, they won't need to because DeepSeq continued to make their models 00:15:16.600 |
open weight. Just like Leopold Aschenbrenner and Dario Amadei, everything is a race to the jugular, 00:15:21.800 |
which is a narrative that's somewhat hard to square with DeepSeq pioneering certain research and giving 00:15:27.400 |
it to everyone. Then, apparently in late 2026, the US Department of Defense will quietly begin contracting 00:15:33.720 |
OpenAI or Google directly for cyber, data analysis and R&D. But I'm kind of confused because already, 00:15:40.200 |
for at least a year, OpenAI have been working directly with the Pentagon. Yes, before you guys 00:15:44.920 |
tell me, I'm aware that Daniel Cocotagelo, who is the main author of this paper, did make some amazing 00:15:50.120 |
predictions back in 2021 about the progress of AI. I can link to that in the description, but that 00:15:55.720 |
doesn't mean he's going to be always right going forward. Also, he himself has admitted that those 00:16:00.040 |
predictions weren't that wide-ranging. Anyway, things get wild in January of 2027 because, as you can see 00:16:06.120 |
from this chart up here, we get an AI that is better than the best human. The first superhuman coder, 00:16:13.240 |
in other words. This is the key crux of the paper because once you get that, you speed up AI research 00:16:19.160 |
and all the other consequences follow. But as I have been discussing with the authors of the meter paper, 00:16:24.040 |
there are so many other variables to contend with. What about proprietary code in Google or Meta or Amazon 00:16:30.440 |
that OpenAI can't train their models on? What about benchmarks themselves being less and less 00:16:35.240 |
reliable indicators of real-world performance because the real world is much messier than benchmarks? 00:16:40.600 |
This superhuman coder may need to liaise with entire teams, get certain permissions and pass all sorts of 00:16:46.280 |
hurdles of common sense. And even if you wanted to focus brutally just on verifiable benchmarks, not every benchmark 00:16:52.840 |
is showing an exponential. Take MLE Bench or Machine Learning Engineer Bench from the Deep Research or O3 00:16:59.240 |
system card from OpenAI. That dataset consists of 75 hand-curated Kaggle competitions worth $2 million in 00:17:05.720 |
price value. Measuring progress towards model self-improvement is key to evaluating autonomous 00:17:10.120 |
agents' full potential. Basically, if models get good at machine learning engineering, they can obviously 00:17:15.400 |
much more easily improve themselves. And let's skip down to progress and zoom in a bit and you can see 00:17:21.000 |
the performance of O1, O3 mini, Deep Research without browsing, Deep Research with browsing, GPT-40 even, 00:17:28.840 |
and I'm not noticing an absolute surge in performance. Obviously, I am perfectly aware of benchmarks like 00:17:34.440 |
humanity's last exam and others which are showing exponential improvement. I'm just saying not every benchmark is 00:17:40.040 |
showing that. Also, January or February of 2027 is less than two years away and this model would 00:17:45.880 |
have to be superhuman in performance. So much so that it could autonomously develop and execute plans to 00:17:52.360 |
hack into AI servers, install copies of itself, evade detection, and use that secure base to pursue whatever 00:17:57.880 |
other goals it might have. Notice the hasty caveat, though, how effectively it would do so as weeks roll by 00:18:03.480 |
is unknown and in doubt. That happens a lot, by the way, in the paper. I even noticed a co-author say, 00:18:09.240 |
well, this wasn't my prediction, it was Daniel's. There's a lot of kind of heavy caveating of 00:18:13.400 |
everything. Notice, though, that not only would an AI model have to be superhuman at coding to do all of 00:18:18.280 |
this, it would have to have very few, if any, major flaws. If one aspect of its proposed plan wasn't in 00:18:25.240 |
its training data or it couldn't do it reliably, the whole thing would fail. And that leads me to my 00:18:30.760 |
prediction. I mean, they've made a prediction so I can make one that models, even by 2030, will not be able to 00:18:38.120 |
do this. I'm talking reliably, with 95 or 99% reliability, autonomously, fully autonomously, 00:18:45.320 |
develop and execute plans to hack into AI servers, copy itself, evade detection, etc. If, on the other 00:18:50.440 |
hand, Daniel is right and models are capable of this by February 2027, then I will admit I am wrong. 00:18:56.840 |
That, by the way, brings me back to this chart, which, if you notice, says that only 4% of people 00:19:04.200 |
at that point would say, what do you think is the most important problem facing the country today? 00:19:09.080 |
And they'd answer AI. Well, I don't know about you, but if I or my friends or family heard that there's 00:19:13.800 |
AI out there that just can hack things and replicate itself and survive in the wild, I think more than 4% 00:19:19.240 |
of people would say it's the most important issue. I mean, man, actually, the more I think of it, 00:19:23.080 |
like, look at the clickbait headlines you get on YouTube and elsewhere about AI today. Can you 00:19:27.960 |
imagine the clickbait if AI was actually capable of copying itself onto different servers and 00:19:33.720 |
hacking autonomously? Actually, it wouldn't even be clickbait at that point. I would be doing headlines 00:19:37.880 |
like, oh my god, it can hack everything. Anyway, you get the idea, and that's not even mentioning the 00:19:43.320 |
fact that these agents can also, almost as well as a human pro, create bioweapons and the rest of it. 00:19:48.920 |
China is then going to steal that improved Agent 2 from the Pentagon, and still obliviously 96% of 00:19:56.360 |
people are focused on other things. Being slightly less facetious, I think the paper over-indexes on 00:20:02.360 |
weight thefts and it all being contained in the weights of a model. I think progress between now 00:20:07.160 |
and 2030 is going to much more depend on what data you have available, what benchmarks you have created, 00:20:12.600 |
what proprietary data you can get hold of. Now, don't get me wrong, I do think AI will help with 00:20:18.760 |
the improvement of AI. Even if it's just verifying and evaluating, replicating existing AI research, 00:20:25.720 |
which is a new benchmark released by OpenAI just a week ago, already models like Claude 3.5 Sonnet can 00:20:31.720 |
replicate 21% of the papers in this benchmark. But when you have a limited compute, and potentially very 00:20:38.040 |
limited compute if there's a massive worldwide stock crash or war in Taiwan, but when you have limited compute, 00:20:43.800 |
are you going to delegate the decision of which avenues to pursue to a model which might be only 00:20:50.520 |
80% as good as your best researchers? No, you would just defer to those top researchers. Only when a model 00:20:56.440 |
was making consistently better decisions than your best researchers as to how to deploy compute would you then 00:21:02.040 |
entrust it to them. The authors definitely bring in some real-world events that may or may not have 00:21:05.960 |
occurred at OpenAI when they say, "AI safety sympathizers get sidelined or fired outright" brackets 00:21:11.720 |
the last group for fear that they might whistleblow. Personally, I would predict that if we have an 00:21:16.600 |
autonomous AI that can hack and survive on its own, I don't think safety sympathizers will be sidelined. 00:21:23.480 |
If I am wrong, then we as a human species are a lot dumber than I thought. Anyway, just two years from now, 00:21:29.880 |
June 2027, most of the humans at OpenAI/Google can't usefully contribute anymore. Again, I just don't 00:21:37.160 |
think feedback loops can happen that quickly when you reach this level. I could well imagine benchmarks 00:21:42.760 |
like the MMMU or SimpleBench being maxed out at this point, but imagine you're trying to design a more 00:21:49.400 |
aerodynamic or efficient F-47. That's the new fighter jet announced by the Pentagon. Well, that AI self-improvement 00:21:56.760 |
is going to be bottlenecked by the realism of the simulation that it's benchmarking against. Unless 00:22:02.760 |
that simulated aircraft exactly matches the real one, well then you won't know if that "self-improving AI" 00:22:08.520 |
has indeed improved the design unless you test it out in a real aircraft. Then multiply that example 00:22:13.880 |
by the 10,000 other domains in which there's proprietary data or sim-to-real gaps. I guess you 00:22:20.040 |
could summarise my views as saying the real world is a lot more messy than certain isolated benchmarks 00:22:26.520 |
online. The model, by the way, at this point is plausibly extremely dangerous being able to create 00:22:32.520 |
bioweapons and is scarily effective at doing so, but 92% are saying it's not the most important issue. 00:22:40.360 |
Man, how good would TikTok have to get so that 92% of people wouldn't be focused on AI at that point? 00:22:46.600 |
I'm going to leave you with the positive ending of the two endings given by the paper, which predicts 00:22:51.480 |
this in 2030. We end with "People terraform and settle the solar system and prepare to go beyond. 00:22:57.720 |
AI's running at thousands of times subjective human speed reflect on the meaning of existence, 00:23:02.680 |
exchanging findings with each other and shaping the values it will bring to the stars. A new age dawns, 00:23:09.080 |
one that is unimaginably amazing in almost every way, but more familiar in some. Those in the 00:23:14.520 |
audience with a higher PDOOM can check out the other scenario, which is rather grim. Notice though, 00:23:19.880 |
I'm not disputing whether some of these things will happen, just the timelines that they give. I still 00:23:24.520 |
think we're living in some of the most epochal times of all. Just that it might be a more epochal decade 00:23:30.760 |
rather than couple of years. Thank you as ever for watching. I know I covered a lot in one video. 00:23:35.480 |
I will try to separate out my videos more in future. I'm super proud of the deep seat 00:23:40.760 |
documentary I made on Patreon, so do check it out if you want early access. But regardless, 00:23:46.360 |
thank you so much for watching to the end and have a wonderful day and wonderful decade.