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I’m a [software, electrical, mechanical] engineer (ordered by preference—CV on website) currently looking for a role doing hard things and solving important problems, especially problems standing in the way of a free, abundant future for all humans. Appreciate any leads!
I definitely think it’s interesting that it’s possible for N-D-substrate-computations to imagine / intuit N+1-D, but yeah, I feel like that’s mostly a given because we have the concept of N+1-D in the first place.
There are different levels of “imagine / intuit” though. Some people have particularly good or bad intuition for the 3D space we live in. I took your claim to be something like “the average brain could intuit 4D just as well as 3D, maybe requiring slight modification”. I think the modifications to reach true parity would be pretty extensive, because of how much 3D-specific architecture (as opposed to weights) human brains have. I do agree the modifications are theoretically possible, but the modifications to give a fruit fly human-level cognition are also theoretically possible with arbitrary modification.
This argument seems to work for N-D space for any N which doesn’t seem right. I think we definitely do know less about 4D space than 3D, partly because we’re much more interested in 3D, partly because there’s just (a lot) more going on in 4D.
Intuitively it feels like current AI should be much better at learning navigation in 4D than human brains. Brains have real architecture-level, baked in task-specific circuits, which AI lack, and reconstructing a 3D world is arguably the most important of those. Sure, you could modify them with neurotech to change that, but you could do that for virtually any task so it doesn’t seem very meaningful.
There’s also the problem that human sensors are inherently 3D. It’s not clear how you would translate eyes into 4D. If you do pick a way to do this, and leave visual processing circuits the same, the circuits aren’t getting their expected data stream anymore. Brains are clearly pretty good at coping with this, like in blind people where visual processing circuits are (at least partially) co-opted for other things, but blind people are clearly worse at navigating the 3D world than sighted people, and it seems like the same would be true for humans vs 4D-native beings (like AI).
It seems to me like both this post and discussion around Buck’s post are less about LLM generated content and more about lying.
Opus giving a verifiable mathematical counterexample is clearly not lying. Saying “I think” is on somewhat shakier but mostly fine ground. LLMs saying things like “When I was a teenager” when not editing a human’s account is clearly lying, and lying is bad no matter who does it, human or not. Extensively editing personal accounts indeed gets into very murky waters.
Skimming Starcloud’s whitepaper: They say the radiators are “held at 20 C” without mentioning at all how they’d actually do that. For effective heat distribution you need fluid and small tubes, which are very prone to micrometeorite impacts, which means you need a lot more shielding mass. If you don’t have effective heat distribution your chips get too hot.
This is what I was talking about, I should have been more clear. Year-average daily ground irradiance in the US seems to be about 4-5 kWh/m^2 (this includes night + weather + atmosphere effects, does not include panel efficiency) ~= 200 W/m^2. In space (assuming sun-synchronous or non-LEO, which are both more expensive than LEO) you get 1361 W/m^2. So about 7x. In the Sahara Desert it’s ~300 W/m^2 so ground-solar worst case is 4-5x.
Yes, I’ve worked on spacecraft and you have to put about as much effort into dissipating your energy as collecting it. The benefits I’ve heard claimed for space based compute are minor at best, and the economic downsides of cooling are show-stopping before even considering the (many) other negatives. Electronics also have to run much hotter than otherwise if your only cooling method is radiation. I’m honestly struggling to understand why some people that I normally consider reasonable and realistic are giving it much consideration.
I wish that article included any justification for that argument and I was unable to find any elsewhere by Chris Stott. Space energy today is certainly far more expensive than earth energy, probably 1000x.
Even as launch costs approach 0, things that can operate in space are more expensive than not. Vastly more difficult maintenance and cooling, radiation hardening, latency… I don’t see how even 7x cheaper energy per area (current solar is ~200 W/m^2 avg, space maximum is 1360 W/m^2) can make up for those things. (edit: this accounts for weather + night + atmosphere effects, see below comment)
There is a recent intense interest in space-based datacenters.
I see almost no economic benefits to this in the next, say, 3 decades and see it as almost a recession indicator in itself.
However, it could allow the datacenter owners significantly less (software) scrutiny from regulators.
Are there any economic arguments I’m missing? Could the regulator angle be the real unstated benefit behind them?
I’m not sure how to even engage with this category of claims because they totally ignore the obvious question that’s automatically implied when you assert them, that being how exactly human intelligence differs from whatever term you use to say that LLMs don’t have real intelligence.
Maybe belief in some of these terms is another good poll question. I suspect all of them are only going to get more common and dogmatic.
I hope that’s the case but feel there are drastically more and better reasons for people to believe in it compared to flat earth.
I find that Claude is very bad at pushing back on the user’s beliefs when there is any nuance involved. I just selected 3 random conversations that had this pattern:
Initial message from user asking neutral question about murky but factual topic—“would you say x thing is more like y or z?” not “how tall is the Eiffel tower?”
Model response giving evidence for a certain belief
Weak user pushback “but what about a and b?” where a and b are common, short talking points about this topic in opposition to the model’s stated belief
Model immediately concedes and significantly retracts: “You’re right to push back. I was conflating / being too hasty / making questionable assumptions …”
In each case the user’s response didn’t address a specific element of the model’s response, and the user’s belief is not the consensus belief so the model responds in opposition to it at first. This means the two user messages can be run across models without changing anything. I did this for [Sonnet 4.5, Grok 4, GPT-5, Gemini 2.5 Pro] using OpenRouter which makes it a very quick and easy process.
In 2⁄3 cases, Claude was the worst at sticking to its beliefs, and it wasn’t particularly close. In 1⁄3 no model conceded much (The initial conversation was with an earlier Claude). GPT-5 and Grok 4 generally stuck to their guns and Gemini was somewhere in between.
There is a selection bias because the conversation pool is all Claude failures because Claude is my main model. But results line up well with my vibes—I pass important questions by all the current top models.
I can see this being made into a benchmark by rounding up common non-consensus beliefs and the best arguments for them and using a neutral judge LLM, or human, to decide whether there was significant concession.
Does anyone else notice this tendency? Are other models actually any better at this? I’m strongly considering ditching Claude, at least for this type of question, even though I mostly prefer it otherwise.
Great points. I think a lot of people yearn for some ultimate form of communication that is unmoored to and unencumbered by social norms and Overton windows and emotions, and thus much more efficient at transmitting information.
But that form of communication is (a) simply inhuman, and (b) not that much more efficient.
Wrapping your ideas in a thin veneer of politeness is similar to encoding them in words. It’s fuzzy and non-optimal, yes, but it’s just how humans transmit information. It’s not possible to fully mentally separate idea criticisms from personal attacks, even for those who try very hard to do that. So if you want to communicate more effectively, you put in a little extra effort to make it clear that you’re talking about ideas and not people.
The cost of the veneer varies depending on the social acceptance of the idea being transmitted but it’s really not very costly most of the time, especially when you weigh it by how much it improves your transmission ability. If you want to get better at convincing people of your out-there ideas, being polite about them is the lowest hanging fruit.