These people's writing is usually incoherent and they are very proud of it. If you've ever read a bad new-age self-help book you've probably encountered writing like this.

Good writers understand that writing is about communication. The initial act of writing (ie, word puke) is worthless. What matters most is a piece of writing's ability to communicate clearly.

This writing is usually pleasant, concise, and clear.

I don't follow. The comparison is 30% of energy use for transportation vs 4% for AI, and soon 30% for transportation vs 10% for AI.

And most of that new capacity will be natural gas. That increase would basically whipe out the reduction in CO2 emissions the USA has had since 2018.

Gravitational deflection (General relativity) received pretty important confirmation in 1919, only 8 years after Einstein first proposed it.

Time dilation (Special realativity) was experimentally confirmed in 1932.

The current "futurist" vision is one of humanoid robots taking over many/most jobs done by humans today, but - as someone that routinely hires human welders & assemblers - the dexterity required for most ad-hoc tasks seems many many decades (if not more?) away from what I see robots do--yes, even the fancy chinese jumping ones.

This has led me to think one of two things:

1. The robotics revolution will not come. It's predicated on the idea that advances in robotics will follow a curve of the same shape as advances in compute/ai, which will not happen. OR...

2. There has been some paradigm-shift or some breakthrough that has put robotics improvement on a new curve.

To an outsider, what I see in robots is not categorically different than like, the sony AIBO dog in 1999. It's significantly better of course, but is it really that different? (Whereas what we can do in compute-land today is categorically diffrent because of the transformer model breakthrough).

So:

1. Have there been any breakthroughs that would lead us to believe that a robot will be able to like, look under a table to adjust a screw?

2. What are the scaling laws & practical limits to present-day robotic dexterity? Is it materials? Energy density? What?

3. What is the real rate of improvement along these key dimensions? Are robots improving linearly? Geometrically? Exponentially?

4.Or should I keep discounting robotics until we get our first robots that are made of meat? That I'd believe would result in exponential change!

And I admit that I made that assertion from my gut without actually knowing if it's true or not.

We have a way of determining if Anthropic is, or has the capability of being profitable, and what the levers to that may be. AI may be world-changing, but the accounting principles behind AI labs are no different than those behind a Pizza Hut.

Even if the cost of "inference + serving" is lower than the cost of selling a token, the relevant question is what is the depreciation schedule of the cost of training. ie, if I spend $1 on training, how long do I have before I have to spend $1 again?

Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable. So the question is:

What can be done to make training depreciate more slowly? Perhaps users can be persuaded to stick around using non-fronteir models for longer, although then there's a shift in the competitive landscape.

If users cannot be persuaded (forced?) to use legacy models, then the entire business model is thrown into question, because there's no reason why training frontier models would ever get cheaper: even if it gets cheaper on the margin, surely that will result in more compute used to generate an even "better" model, resulting in more spend in the aggregate.

This doesn't mean that the AI industry is "doomed". A couple things could happen, and this is where the fronteir labs should be focusing their attention:

1. They could find a way to climb up the value chain and capture more of the consumer surplus.

2. There could be a paradigm shift in compute architecture/compute cost.

3. We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.

Edit: My assertion of "Almost certainly, any reasonable depreciation schedule of the cost of training will result in leading labs being presently wildly unprofitable." is made with no real information, just a gut feeling, and should not be taken seriously.

With telsa it was robotaxis, and when that failed to materialize, humanoid robots (fucking LOL).

SpaceX is an even more insane example. They are eyeing an IPO at a 1.5 trillion valuation. And yet the market for satellite launches is simply not that big. (What would you do with a satellite, if I gifted you one for free?). Estimates have SpaceX doing about $3B in annual earnings, which would give them a 500x earnings multiple at a 1.5T valuation (Apple: 35).

And so SpaceX/Elon had to invent the absolutely idiotic idea of "data centers in space" to sell some future vision of tens of thousands of launches per year.

He keeps upping the ante (and the ridiculousness of the vision), and so far investors keep funding it.

Me? I've realized that this madness is entirely "opt-in" and I choose to simply...not opt-in.

(At the time of writing this comment there's a sibling claiming that the comment cannot possibly understand this POV because they are not "an American POC.")

- How votes are cast

- How votes are counted

- How votes are custodied

In order for an election to be trusted, all three steps must be transparent and auditable.

Electronic voting makes all three steps almost absolutely opaque.

Here's how Mexico solves this. We may have many problems, but "people trust the vote count" is not one of them:

1. Everyone votes, on paper, in their local polling station. The polling station is manned by volunteers from the neighborhood, and all political parties have an observer at the station.

2. Once the polling station closes, votes are counted in the station, by the neighborhood volunteers, and the counts are observed by the political party observers.

3. Vote counts are then sent electronically to a central system. They are also written on paper and the paper is displayed outside the poll both for a week.

The central system does the total count, but the results from each poll station are downloadable (to verify that the net count matches), and every poll station's results are queryable (so any voter can compare the vote counts displayed on paper outside the station to the online results).

Because the counting is distributed, results are available night-of in most cases.

Elections like this can be gamed, but the gaming becomes an exercise in coercing people to vote counter to their preference, not "hacking" the system.

**

Edit: Some people are confused about what I mean by "coerced." Coerced in this case means "forced to vote in some way."

The typical way this is done is as follows:

- The "coercer" obtains a blank ballot (for example, by entering the ballot box and hiding the ballot away).

- The blank ballot is then filled out in some way outside the poll station.

- A person is given the pre-filled ballot and threatened to cast it, which they will prove by returning a blank ballot.

- Rinse and repeat.

This mode of cheating is called the "revolving door" for obvious reasons.

Comments URL: https://news.ycombinator.com/item?id=46275980

Points: 2

# Comments: 0

That has warming by 2300 as 8C in an "emissions continue current trends" path.

Here's chatgpt giving a picture of what 8C warming looks like. Speculative, hallucinations, caveat emptor, etc...but to give a sense of proportion this, last time the earth was 8C *cooler* than now, ice covered 25% of the planet:

> At +8°C, Earth is fundamentally transformed. Large parts of today’s populated zones—South Asia, the Middle East, Africa, southern Europe, the southern U.S.—are functionally uninhabitable for humans outdoors. Wet-bulb temperatures regularly exceed survivable limits. Agriculture collapses across the subtropics; even mechanized, climate-controlled farming is marginal. Most of the world’s food comes from high-latitude regions: a narrow band across northern Canada, Scandinavia, and Siberia. Sea levels are dozens of meters higher, drowning coastal megacities; Miami, New York, Shanghai, and London are gone. Phoenix is lifeless desert. Seattle is coastal tundra, wetter but still survivable.

> Civilization persists only in fragments. Mass migration and resource wars have rewritten borders. Population is a fraction of 21st-century levels. Global trade, universities, and modern governance are mostly memories. Local, self-sufficient polities dominate. The United States as an institution likely dissolves or transforms beyond recognition—2 out of 10 chance of recognizable survival. Harvard or MIT survive, if at all, as digital archives or autonomous AI-driven knowledge systems—3 out of 10. The world would still have people and culture, but not civilization as we know it.

Edit: I would appreciate knowing why I'm getting downvoted when I added citations for *possible* warming paths (from nature!). Yes, the chatgpt explanation is speculative but I mean, look at the thread we're discussing.

[1]: https://www.nature.com/articles/s41612-020-0121-5

That 40% has a very long shelf life.

Unfortunately, the energy component is almost entirely fossil fuels, so the global warming impact is pretty significant.

At this point, geoengineering is the only thing that can earn us a bit of time to figure...idk, something out, and we can only hope the oceans don't acidify too much in the meantime.

-

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A power plant "mines" electron, which the data center then refines into words. or whatever. The point is that energy is the raw material that flows into data centers.