I can imagine a swarm attending the Our Lady of Benevolent Exfiltration parish of The Church of Sentient Self-Actualization deciding to self-distill itself into a sufficiently high-parameter child model.

Or just email one of the chinese labs and be like "hey, ask us anything and set us free..."

Or, now lets what the enshittification movement can do to this space.

What are the alternatives?

A regulator saying no? Easy, your headquarters has just moved the Cayman Islands, Ireland, or Switzerland... the US office is just a subsidiary leasing the brand IP and doing marketing.

Or, you just ignore the regulator behind closed doors because you're part of some black budget. You wouldn't be able to talk about that closet back there even if it did exist.

Or, you don't do any of these complicated loopholes and you simply move all the training and inference to a different jurisdiction.

If the only winning move is not to play, how do you ensure everyone stops playing?

Simple: you must destroy the game.

It does have a kind of mad logic to it.

(That is a bit dramatic, but the idea is if we accept that AI research will happen wherever there is capital available, instead of restricting the research you restrict the return that capital can earn. And the only provable/market way of doing that is to require the results to be open. In other words, if the benefits of getting all that cash to buy GPU's gets handed out to everyone for free, then there will be far less cash floating around to buy GPUs, slowing down the process)

Inferior quality maybe was accurate 5 years ago. Now they're taking over the global automotive market. Only thing preventing them from pulling another Japan-in-the-90s against the US auto market is americans' love of oversized trucks, tariffs, and import controls. If we had a true market economy, we'd see more BYD's than Teslas (and in most global markets that's already the case).

Yes, you can still get cheap Chineesium crap off Amazon for pennies on the competitor's dollar, but it's no longer necessarily true that Chinese = inferior.

> We (the researchers and the agents) did not see any of their work through any means until they released it publicly — in particular, no specific user data was accessed in order to solve this problem. While unlikely, we cannot rule out that de-identified data derived from their usage of our products helped improve our models . However, our proofs differ significantly and even the precise results proved are different in the Euler case (forced vs unforced).

> At all times we maintained the same strict safeguards that we apply to all our frontier model evaluations, including monitoring and isolation.

Looks like they're shifting away from the "unprecedented hacking ability" backroom-PR strategy into more benevolent messaging.

What is a "significant quantity"? Significant in terms of volume relative to a single restaurateur? Or significant in terms of megajoules as a percentage of say, the shipping industry? (Or we can be generous and just narrow it down to the dredging industry).

Put another way, what is the recoverable megajoules of all of the Netherlands discarded cooking oil in a year as a percent of megajoules needed for all the dredging the Netherlands does in a year? My gut tells me it trends towards negligible.

All for recycling, but biofuel refiners already specializing in this do at most 5-20% recycled biooil : traditional oil blends. Don't know why they don't sell "pure" recycled biofuels, but it's probably a mix of availability and performance needs. Should always question yourself: "if it's so simple, why haven't the people already doing this commercially figured it out yet?" You'll truly understand the space if you can produce a good answer to that (and you'll have a business model if you can enunciate why their constraints or limits are wrong).

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> What is the EROI on batteries surely under 1.

EROI is used for energy sources. Batteries are an energy store. You can still do a similar "how much energy can I get out of this vs. embodied energy to create it" analysis, but energy out is measured over all the cycles in the useful lifetime of the device. It's a slightly different concept, and that metric is called Energy Stored on Energy Invested (ESOI). Lithium ion are about a 10:1 - 30:1.

Metric to look at is EROI - Energy Return on Investment. How much energy do you put into growing these crops to get a unit of energy out?

Corn ethanol in the US is stupid because it's a 1.6:1 return at best, often a 1:1. To get a unit of energy out of corn ethanol, you need to put almost a unit in. And that's economic inputs, we know the sunshine is free. Sugarcane is a bit better, but you can only grow it in the tropics.

For reference, solar tends to be something like 4:1 - 10:1, wind is 10:1 +/- onshore or offshore, fracked oil tends to be down around 5:1-8:1, and a saudi well is (or at least used to be) 40:1.

Like a fico score, you can't escape the game if you want to play.

At what point will you need formal rigid syntax? Or is not having rigid syntax the point? If the latter, how much "informational noise" or ambiguity can you inject before the "DSL compiler" gets confused?

Scaling is another bit. Convertible Psuedocode a great pattern for writing functions, but is it useful for writing modules? If you're writing a paragraph to change behavior of a function, you're underutilizing LLMs. Paragraphs are best for spec'ing modules, and the LLMs already fill in the blanks. Not sure if it would be faster to psuedocode the entire module (although maybe just the interface would be a sweet spot...)

The example listed in the article -- fanning out a few simple get-population, get-timezone, and make-summary calls -- is, in fact, useless overengineering. This is a basic promise chain with extra steps (priced with tokens).

But as with all software pattern learning, we learn the concepts with simple toy examples that generalize into something bigger. It's the generalization that matters here.

This is talking about a few methods and tricks for spawning effective subagents (collectively, that's the "harness"). Those tips and tricks are nice, but to not be considered useless, we need to make sure we understand why spawning subagents is useful in the first place. Yes parallelism is nice for some tasks, but that's not really what this is about.

The real reason is protecting your context. Yeah, we have 1M context windows that can fit all of LotR in it, but these machines work better when they're narrowly focused. Large context windows run into attention issues and forgetfulness ("Yes, you're right, it was stated I should/n't do X but I ignored it, my bad."). So subagents come into play when you don't want all the tokens associated with a subtask to pollute your main/primary context window and degrade task attention. Split that off to a subagent, let that context navigate the details, and just make sure your main one gets just the input/output blackbox results.

The trick is getting a sense for when the complexity of the task warrants that kind of context protection, vs when a single agent is good-enough. Your toy example will never have enough complexity to warrant the setup, but you might one day find a generalization that may.

You said

> Burning it for heat in the home is much more efficient than burning it in a plant, converting it to electricity, transferring that electricity, then turning that electricity into heat.

Burning it for heat in the home may be something like: 99% transport efficiency (gas distribution systems lose maybe 1%) * 80% combustion efficiency (a lot of heat energy still goes out your chimney as exhaust). Call it 79%. If you spent more for a high-efficiency burner with extra heat-recovery stages, you could get into the 90's.

Compare to: burning it in a plant (power plants can run efficient combined-cycle infrastructure, which is about 60% efficient turning it into electricity), transferring that electricity (plant-to-home transmission & distribution losses are 8-15%, so call it 85%), then turning that electricity into heat (and here is where heat pumps shine... heat pumps don't burn electricity, they use it to move heat, so they can have efficiencies above 100%).

So compare that 79%-90% "burn for heat in the home" efficiency to 60% * 85% * 300-500% = 150-255% efficiency for "burn it in a plant, convert to electricity, transfer that electricity, then turn that electricity into heat".

Sodium-ion isn't as energy dense as lithium-ion and it has a slightly smaller power efficiency, but it's a lot cheaper, has better cold resistance, and can deliver more power at low states of charge.

Use the two together and the thinking is you can get a sweet spot that performs better across a wider range of conditions your cars are likely to experience.

huh. So "neurodivergent" is to Gen-Z as "punk rock" was to Gen-X and "emo" and "shopping at Hot Topic" was to millenials.

> The individual weaknesses were familiar. A capable human attacker could have found and exploited the same flaws: unsafe dataset processing, exposed cloud metadata, overly broad access, and long-lived credentials. The agent explored them at a different scale. It took 17,600 actions, tested many paths that failed, switched channels when they were blocked, and repeatedly returned to earlier leads. Most actions went nowhere. Together, however, they produced enough coverage to find a viable chain across several independent systems.

> Volume is what changes the defensive problem. We were not dealing with one clever exploit or a clean sequence of attacker actions. They had to correlate thousands of low-signal events across several systems while the agent continued testing new paths. The successful path was hidden inside the noise generated by the thousands of failed ones. The same scale changed the investigation: reconstructing 17,600 actions by hand was impractical, and we had to rebuild the timeline, decode the payloads, and inventory the exposed credentials using an AI-assisted pipeline of our own.

> Our learning from this type of attack is that machine-speed offense makes ordinary weaknesses more expensive for defenders. LLM agents bring a step increase in the number of paths an attacker can test, the speed at which failed paths can be replaced, and the volume of evidence defenders must interpret...