And I don't think anyone in their right mind would argue the AI industry isn't being fairly compensated.
To go further: most people in the world wouldn't feel bad at all if we found a way to slow what's likely the biggest socio-polical-economic change in human history down a bit.
> These kinds of countries are the only ones that matter, because they're the ones that have to answer about what people were thinking when they chose to make use of their power in a way that is relevant on a larger scale.
This is an extreme claim, and incorrect. If you'd like to see counterexamples, you'll see many corrupt regimes in Africa, which did extreme harm to their own people. You'll see many regional powers.
One does not need to be a global superpower to be good or evil.
There's also nothing magical about Germany or Japan. Many countries had similar resources. Both chose to invest those resources into industrial militarism.
One can make the argument for a handful of countries which our outliers for land area or population, but in general, if any country chooses to invest in military and attack its neighbors, it has good odds of success.
> You have the UK, France, Germany, Japan, Russia, China and the US
Germany was an outlier, on the evil end, but otherwise, it's a selection of which facts one picks.
A comparison would require deciding which facts to compare on. For the US, the "evil" argument comes back to things like slavery and the genocide of the native peoples.
One can pick hundred of examples like Guantanamo Bay, fake vaccines in Pakistan, the Tuskegee Syphilis Study, the Tusla massacre, police violence, corrupt court, ...
The US does pretty nasty things, even if they don't always make US news or grade school textbooks.
> It's very cheap to label anything as propaganda without taking the time to appreciate whether it has any merit in terms of the overall behavior of a country or its people
This is an ad hominem, and a poorly placed one. You're discounting what people are telling you. A lot of the people here went through the US school system, learned "US rah rah rah" propaganda, and only deconditioned themselves as adults.
Many of us were where you are when we were younger.
I don't believe in US good, non-US bad. I also don't believe the same about my religion or my political party, for that matter.
How you measure depends on weights you assign (cultural system of values) and what information you use (media bias).
You can rank in the extremes (e.g. North Korea as worse than Belgium), since they come out that way by almost any set of information and values. Comparing the US to most other countries, there isn't a clear ordering. If you believe the things you wrote, I think the other comment summed it up well: "You sound like you've swallowed pro-US-propaganda hook, line and sinker."
Most countries have similar propaganda, by the way.
They're not "attacks." Anthropic calling them "attacks" doesn't make them attacks. Companies are collecting transcripts of conversations with Anthropic, and giving users a discount to share them.
As many have pointed out, they're collecting data in ways much less aggressive than Anthropic itself about what Anthropic does.
Anthropic doesn't like it, but I don't see this as "Chinese companies stealing IP," any more than if Google tried to ban competitors from seeing how Google Docs or an Android phone behaves, or Ford trying to ban anyone from Toyota from seeing what their car looks like.
Please stop calling them "attacks." It's distillation training. It's looking at what Anthropic does -- as a block box -- and trying to duplicate or beat it. It's how progress happens.
"The bet of using AI to speed up AI research is starting to pay off.
OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.
The AI R&D progress multiplier: what do we mean by 50% faster algorithmic progress?
Several competing publicly released AIs now match or exceed Agent-0, including an open-weights model. OpenBrain responds by releasing Agent-1, which is more capable and reliable.28
People naturally try to compare Agent-1 to humans, but it has a very different skill profile. It knows more facts than any human, knows practically every programming language, and can solve well-specified coding problems extremely quickly. On the other hand, Agent-1 is bad at even simple long-horizon tasks, like beating video games it hasn’t played before. Still, the common workday is eight hours, and a day’s work can usually be separated into smaller chunks; you could think of Agent-1 as a scatterbrained employee who thrives under careful management.29 Savvy people find ways to automate routine parts of their jobs.30
OpenBrain’s executives turn consideration to an implication of automating AI R&D: security has become more important. In early 2025, the worst-case scenario was leaked algorithmic secrets; now, if China steals Agent-1’s weights, they could increase their research speed by nearly 50%.31 OpenBrain’s security level is typical of a fast-growing ~3,000 person tech company, secure only against low-priority attacks from capable cyber groups (RAND’s SL2).32 They are working hard to protect their weights and secrets from insider threats and top cybercrime syndicates (SL3),33 but defense against nation states (SL4&5) is barely on the horizon."
I think good governance would listen to polls over metrics.
A good example of how this works is cocaine.
Capitalism and competition isn't always good governance. It works brilliantly in many places, such as restaurants or commodity goods. It fails completely for medicine or banking. It's in between for tech or education, but it's clearly failing for AI.
The key question is on direction of LLMs. Right now, LLMs are taking over human jobs. If the cost of silicon+power < cost of human being doing the same work, what rational reason is there to employ a human being?
If this applies to SWEs, lawyers, business analysts, many research scientists, .... this situation could persist for a long, long time. While capital costs less than the inputs of labor (nominal food, housing, etc.), there is no need for labor.
The key question is about continued progress in models, and of the tooling around them:
- Plateau: Old silicon obsoletes in due course
- Rise quickly: Old silicon maintains value for a long time
This is not a tool which can be used to assume information is anonymized.
The way OpenAI describes it is ...
... concerning.
"Our goal is for models to learn about the world, not about private individuals. Privacy Filter helps make that possible." This means they're using sensitive PII to train models.
A smart AI will re-identify all the information -- including that in the 96% -- in a snap. That's already a solved problem.
I had a physics professor I worked with who had a Nobel Prize.
He didn't win it. It was won by a team of students / collaborators / mentees, who felt he deserved it. I can't disagree with them. Among the nicest people in the world.
I don't think anyone meant it in the sense of "You're a Nobel Prize Winner," so much as "We couldn't have done this without your mentorship, and you deserve to hold onto this." He certainly doesn't consider himself to be a Nobel Prize winner.
This was painful to read. It become better and simpler with a basic signals & systems background:
- His breaking up images into grids was a poor-man's convolution. Render each letter. Render the image. Dot product.
- His "contrast" setting didn't really work. It was meant to emulate a sharpen filter. Convolve with a kernel appropriate for letter size. He operated over the wrong dimensions (intensity, rather than X-Y)
- Dithering should be done with something like Floyd-Steinberg: You spill over errors to adjacent pixels.
Most of these problems have solutions, and in some cases, optimal ones. They were reinvented, perhaps cleverly, but not as well as those standard solutions.
Bonus:
- Handle above as a global optimization problem. Possible with 2026-era CPUs (and even more-so, GPUs).
Years ago, I picked cell carrier because of this. When I ran out, it switched to O(200kbps), which is fine for email, basic web search, etc.
It was actually a bit ironic that, at the time, you could burn through the whole high-speed quota in seconds or minutes, if you went to the wrong web page. Most carriers would stop or bill you an arm-and-a-leg after.
I think you're wrong, and you're underestimating the transformational impact of Ad-Words.
Free internet existed before paid internet, true, but mostly because people did things for other motives (like fun). Altavista was a tech demo for DEC. Good information was found on personal web pages, most often on .edu sites.
Banner ads existed, but they were confined to the sketchy corners of the Internet. Thing today's spam selling viagra. Anyone credible didn't want to be associated with them.
What Google figured out was:
1) Design. Discrete ad-words didn't make them look sketchy. This discovery came up by accident, but that's a longer story.
2) Targeting. Search terms let them know what to ads to show.
I can't overstate the impact of #2. Profits went up many-fold over prior ad models. This was Google's great -- and ultra-secret -- discovery. For many years, they were making $$$, while cultivating a public image of (probably) bleeding $$$ or (at best) making $. People were doing math on how much revenue Google was getting based on traditional web advertising models, while Google knew precisely what you were shopping for.
By the time people found out how much money Google's ad model was making, they had market lock-in.
Pick a university, and given them $1B to never use Windows, MacOS, Android, Linux, or anything other than homebrew?
To kick-start, given them machines with Plan9, ITS, or an OS based on LISP / Smalltalk / similar? Or just microcontrollers? Or replicate 1970-era university computing infrastructure (where everything was homebrew?)
Build out coursework to bootstrap from there? Perhaps scholarships for kids from the developing world?
Software is bloated in part because it's built in layers. People wrap things over, and over, and over. Stripping down layers is neigh-impossible later. Starting from scratch is easy.
Starting from scratch fails in practice because you don't get feature parity in time short enough for VC (or grant) funding cycles.
If we build a tech tree around 200MHz 32MB machines, except for things like ML and video, we'd have a tech tree which did everything existing machines do, only 10x more quickly in 0.1% of the memory. Machines back then were fine for word processing, spreadsheets, all the web apps I use on a daily basis (not as web apps), etc.
Need would drive people to rebuild those, but with a few less layers.
IP is the code and possibly the weights.
No one is stealing IP.
And I don't think anyone in their right mind would argue the AI industry isn't being fairly compensated.
To go further: most people in the world wouldn't feel bad at all if we found a way to slow what's likely the biggest socio-polical-economic change in human history down a bit.