But this was also the joke about TED Talks: everyone came away smugly confident they were part of an amazing tide of progress, ignoring any engineering or scaling challenges.

"For the correct analogy for the mind is not a vessel that needs filling, but wood that needs igniting - no more - and then it motivates one towards originality and instils the desire for truth. Suppose someone were to go and ask his neighbours for fire and find a substantial blaze there, and just stay there continually warming himself: that is no different from someone who goes to someone else to get some of his rationality, and fails to realize that he ought to ignite his innate flame, his own intellect, but is happy to sit entranced by the lecture, and the words trigger only associative thinking and bring, as it were, only a flush to his cheeks and a glow to his limbs; but he has not dispelled or dispersed, in the warm light of philosophy, the internal dank gloom of his mind."

-Plutarch, on listening https://books.google.com/books?id=0U-hsAonP1AC&lpg=PA50&dq=p...

I didn't read the whole passage, but it seems to be talking about the same thing.

I'd argue that the US and Europe are bad benchmarks, given the entrenched quasi-monopolies the governments refuse to break up.

But we'll see how that goes for China.

It's still early days for their capitalistic ambitions, and tensions between party and corporations are starting to show (Jack Ma et al.).

Personally, I think the more destructive moment is going to be after they have large national champions competing globally. Is the CCP going to have the discipline to let an Alibaba, BYD, or SAIC be supplanted, if an external or internal more efficient competitor starts growing? Or will they become too big to fail and intertwine with government?

In order to push beyond that (e.g. architecture, size, performance), the human has to bring the constraints. There's only so much AI can/should default assume from "write code for me" (which I expect a lot of bad / non-HN developers are doing).

It is exciting that there's an opportunity for global improvement though! After the general model improvement pace slows, expect there will be a lot of room for code gen AI optimization, and if "write code for me" can be made to generate more efficient code... suddenly all AI-generated code (read: most code) will improve.

Shit code is out in the wild because the company decided to optimize for programmer labor cost instead of code efficiency. If instead apples:apples because AI code gen is being used both ways, everyone wins.

Feels like what I imagine were the early days of optimizing compilers, when I'm sure there were a large number of really bad assembly programmers out there.

Different read: command economies collapse because they invariably have higher levels of corruption and inefficiency.

Both of those are tolerable during boom times of economic growth, and China has kicked the can down the road by pursuing intermittent corruption purges.

Ultimately though, when things start to slow down, the system attempts to conceal the slowdown, which causes even more economic dysfunction, which eventually paralyzes the whole system and leads to collapse.

Making a command economy work sustainably means solving the "People lie to avoid consequences of bad news" problem.

Obviously the blue pill.

I could see a historical moment where Shopify, sans that invariant, massively fucked up inventory counts from uncompleted transactions... then had to unwind all that when a bunch failed to clear payment.

Ergo, now there's an invariant.

Memory absolutely fits both these criteria.

As for the net public good weighing, at the end of the day the question would be if it would be more economically efficient to mass produce memory with a managed price structure (i.e. US agrees to buy X amount at $Y regularly).

Which turns into whether having an anchor buyer allows the producer to economically create (and continually run) more production capacity than they would otherwise.

It feels like for semiconductors, the planet as a whole would benefit from maximizing production to floor cost.

Granted, AI infra mania current times is an anomaly, as was internet build-out.

Personal perspective, but I'd argue there are an entire class of mental health problems that are perspective-based.

I.e. I am acting in a way that is harmful to myself and/or others because I see the situation in a maladaptive way.

Talk therapy (with a sufficiently intelligent and skilled therapist, of which there is a great lack vs demand) can be effective at changing perspectives, which can ultimately allow changing the behaviors they create.

Some mental health problems are biochemical, but there are a lot that are just "The way you see the world is making you an asshole. Don't want to be an asshole? Try seeing it a different way."

   - Built as part of an IRGCN base (that base was closed in 2013)
   - Within 100 yards of the perimeter of an active IRGCN base at the time of the strike
   - Targeted as a result of extensive use of Palantir's Maven Smart System and Claude by the US military to pre-identify targets

Given all of that, I think the most accurate takeaway would be that the US military is deficient in their target approval process. (Arguably grossly so, though that's an international law legal question)

There's been less investigation into the background behind the Lamerd sports hall attack, but it's fairly certainly an intended US strike, likely for the same reason that the Azadi stadium was hit: assumption that it would be used as a muster point for an Iranian military response.

My takeaway from the atrocities is that there need to be substantial updates to international laws of war around (a) automated target approval (even worse excesses by the Israelis in the initial Gaza strikes) and ultimate human responsibility and (b) more strongly defining what constitutes due diligence in intelligence and classification of military targets.

Currently, both are far too nebulous and will likely lead to both incidents being classified as war as usual. Whereas there was a plausible route to both being removed as targets with even non-manual open source intelligence scraping (substantial patterns of school activity over years and the volleyball game schedule, respectively).

If a nation is conducting drone strikes on another nation's territory, the alternative is usually conventional forces at scale on the ground*.

As the Iraq occupation showed, conventional ground forces at scale cause an even larger number of civilian deaths.

So drone strikes, if kinetic intervention is pre-decided, are a less-bad option from a civilian casualty perspective.

* Granted, there might be a decision not to deploy conventional forces because of the risk / cost, but if there are regular drone strikes, it feels like the aggressor country is past that Rubicon.

Lot easier to steal land if there aren't any people living there (because the infrastructure to support modern life was destroyed).

On reddit et al., people talk about LLM brands like their sports teams.

I think the first-party ecosystem moats they're all trying to build are exacerbating this tendency, as now people have a lot of learning time sunk in a company-specific option.

If memory is now upstream of so many companies, then maybe a higher regulatory involvement would be appropriate (like power companies).

Specifically, and it could fix all of this, capping the max % of their supply they're allowed to sell into the AI space.

What was the first thing we did when we digitized media and therefore made it copyable at negligible per unit cost?

Spent a huge amount of time and money to artificially reimplement scarcity via DRM.

Scarcity-based capital interests (read: most) are not going to willingly give up their profit engines.

Unless there are countervailing forces, it will be deployed, capital will hoard the benefits, and everyone else will be told to fuck off.

I don't have faith in any of the AI labs to make hard financial decisions to deploy hypothetical future AGI in a way that's good for humanity as a whole.

There are too many incentives against, including extreme personal financial incentives for key AI lab stakeholders against.

And if we should take anything from tech history, it's that an exceedingly small number of people look fuck-you money in the face and say "Naw, I'd rather do what I believe in."

Maybe it's a consequence of their rigorousness around SRE, but it doesn't seem like there's a plausible and efficient path for that to happen.

Ergo, the only competitive options from Google are ever-Preview products.

The former caused by the latter.

And the latter a problem once you begin to get executives and leaders who have mostly worked inside the company, because then they subconsciously prefer the Company Way(tm) to alternatives.

And Google has some previously-optimal, now-detrimental company ways.