Hacker Newsnew | past | comments | ask | show | jobs | submit | jwolfe's commentslogin

Please explain how putting an upper bound on how good the strongest models can be prevents cheaper less strong models from catching up, rather than enabling it. I do not understand this argument at all.

The general idea is that Anthropic/OpenAI is pushing this narrative as an attempt at "Regulatory Capture"[1] which would allow them to make it prohibitively expensive for anyone but them to enter the market thus stifling competition.

* 1: https://en.wikipedia.org/wiki/Regulatory_capture


How would that slow down the Chinese models, given that the US has no regulatory reach in China?

You target the US companies: if they can't use these Chinese models, then they're less of a danger for a now captive audience in the US (and the West generally).

This is already kind of the case: the big enterprises don't really want to touch the latest Chinese models. It's a real pain, personally, I want to use them at work!


Show them you can burn tokens in seven sessions day and night with comparable results to Opus with less energy and less than 10 dollars a day, per dev.

We have. Unfortunately there are political realities that get in the way, and Bedrock for example doesn't have GLM 5.3 (Flash or otherwise) or anything new/useful

I do imagine it'll change, but it hasn't yet.


If it's hosted, all they know is "data goes to China".

Until profitable, reputable third parties host open models in the US with ZDR or they become plug-and-play for self-hosting at a modest cost, paying the US models is as much about data protection and liability as performance.


1. China is a bigger market than the US for Ai, they are on pace to process 100Q tokens this year, roughly the same or more than the US big companies

2. Enterprise trends are towards open weights, several routers and vendors now have more than half the volume going towards open weights


Yes, but thats not something a company engaged in regulatory capture for themselves care about: especially if they're worried they'll be outpaced and overtaken by the Chinese labs. Which they will be, IMO.

they care because they know it unlikely open weights will be banned, and thus available to American companies, with regulatory capture (onerous requirements) being a "good enough" "ban" that their big models don't face real competition, regardless of the open weight origin. American companies make open weights too, they are equally threatening to Big Ai financials.

That will then create incentives for companies that consume AI tokens to counter lobby against those regulations.

Incentives exist, they are already lobbying and making counter public statement, like Jensen Huang of Nvidia.

His first tweet ever, from this last July

https://images.nvidia.com/pdf/Open-Weights-and-American-AI-L...


Because the end goal is to ban non-US AI companies from being able to do business in the US.

...because everyone saw how well that worked for the Jones act, what with all the naval yards the US has lost over time, and how nearly no US-built ships operate where not legally mandated /s

Just because it's a bad idea, doesn't mean they won't do it.

The US is meeting with China to discuss the threat of AI… May be fine but, i’m wary

Trump and Xi are meeting. Not the countries, just two corrupt and malevolent individuals.

it wouldn't slow down China as much as make it impossible for American companies to use non-American options, they care about their margins and don't want to be commoditized

[flagged]


OK, so how does this help the US?

If the US slows down this may lead to people that would have went to US labs to go to other countries.


I heard someone analogize token vendors to car manufacturers, where American companies only want to produce expensive options, the people want cheaper/better alternatives, and we ban BYD because those with enough money are more "persuasive"

The analogy is a good one, but your explanation is missing one aspect: the country (USA) does have a reasonable interest in having the capacity to build their own models. The “we need to slow down because it’s getting too dangerous” part is probably more related to “we need to slow our public facing development down so the US government can get the best and the American corporations can trickle out what we decide is safe”

It’s similar with cars. It’s not that American cars are better than Chinese cars on any tangible measurement. But America already shipped most of its manufacturing overseas. Everyone who built those factories is retired. The US should probably hold on to some capacity to make cars, seeing as their entire infrastructure depends on them.


American Ai/Car manufacturers could build cheaper/open models, some do, the big ones do not. It's not an either or, but a spectrum where they have chosen to build only in a subrange

It is the natural result of a country run by lawyers. China is a country run by engineers.

That doesn’t explain the decline of German automotive industry which is now taken over by Chinese cars thanks to massive subsidies by the Chinese government

Why? Germany is also run by lawyers.

See eg https://en.wikipedia.org/wiki/Friedrich_Merz#Private_sector_... for the current chancellor. Many past chancellors were also lawyers, and many members of the Bundestag were and are lawyers.

I don't know whether having lawyers in power leads to industrial decline. My point is only that you can't use Germany as a counterexample.


Do we consider the US Chips Act to be a subsidy? What about when GM became Government Motors because it was Too Big To Fail?

In other words, when do economic and industrial policies transition to subsidies? Is it a matter of perspective? Is the devil in the details?


It's a matter of scale: https://www.wsj.com/world/china/the-u-s-has-been-spending-bi...

If you trust Google's AI summary, China spends 4-5% of GDP on industrial subsidies, vs US at 0.4%. 10-12x as much.


What about after accounting for PPP? (https://en.wikipedia.org/wiki/Purchasing_power_parity)

Do the US numbers account for state level incentives like tax breaks?

I for one do not trust Google summaries, having seen too many hallucinations, it has pushed me away from their search and ai completely.


Here are some links I found (among many). I tend to trust CSIS, even though the have many hawks, they are generally thorough and nuanced.

https://www.csis.org/analysis/red-ink-estimating-chinese-ind...

Some historical analyses of US policies (know less, but both put it over 1% currently, nuances)

https://www.columbia.edu/~ev2124/research/ErtenStiglitzVerho...

https://www.nber.org/system/files/working_papers/w34744/w347...

I'm honestly not sure why this is seen so negatively. It seems to be working pretty well for them, perhaps we should do similar instead of whining about others being more effective?


Another point of comparison we might make, how close is Trump's desired increase to the US Defense budget to what China is spending on industrial subsidies? It looks relatively close to numbers in these research papers.

It would seem that $0.5T could be better spent


I think it less about lawyer vs engineers and more about money in politics (now unlimited)

> Please explain how putting an upper bound on how good the strongest models can be prevents cheaper less strong models from catching up, rather than enabling it. I do not understand this argument at all.

They are not proposing to regulate only the strongest models. They are proposing to regulate all models. If they are already on top, regulation may stop them from proceeding further, but it also stops the cheaper alternatives from catching up.

If they feel they have reached the asymptote of the curve, then regulation doesn't affect them, it affects those who have yet to reach the asymptote.


Particularly, the route they seem to want to go is "safety".

My guess is that Anthropic and OpenAI will push for "safety" regulations which require byzantine testing that, shocker, Anthropic and OpenAI can pass but the chinese models cannot. The route they'll take is import bans and potentially even general bans on products producing or using "unsafe" models.

They'll further likely try and push AI "safety" treaties from the US to other nations to further lock in their lead.

That's why, IMO, we've been seeing so many "OMG, AI will destroy the world and these AI researchers are so scared" articles.


I don't think "putting an upper bound" was OPs phrasing?

That's what pacing the frontier is, and is what the labs are pushing for.

That’s not the argument.

Please elaborate on what the AI labs are specifically requesting and how that results in slowing down Chinese model progress below the frontier.

Cracking down on proliferation of open models which can't be locked down using the kind of guardrails that Anthropic/OpenAI/etc insist are keeping the public safe from all manner of nefarious bioweapons, hacker swarms, propaganda bots, etc. They've discovered they can't meaningfully slow Chinese model progress, so the next best option is to knock them out of competition in the enterprise market for any American company.

Both Anthropic and OpenAI leaders have repeatedly made this exact argument that it's impossible for open models to rigorously enforce the same kind of safety framework as proprietary cloud-served models. It's implicitly part of any regulatory framework they advocate or else it wouldn't be "fair" to American companies since Chinese models would "cheat" (provide weights).


claude.md predates agents.md.

Yeah but .md predates claude.md. I had agent.md files in my repo before Claude tried to act like it's a special protocol.

The world has moved on.

This wastes both tokens and turns. But yes it's probably the best option we have today.

It can try its own file and fall back to generic like here. What's wrong with that?

Wasting turns? That is silly, use a better harness. Also token usage can mitigated by incremental discovery instead of stuck 5k+ worth of tokens in the AGENT/Claude md file.

Every turn means more tokens in ways that are not obvious to most people and lead to tons of unnecessary cache reads.

No harness can batch your agents.md read with the reads the contents of the file tell it to read.


3 of those are already in there.

I can't imagine that they had managed access to the internet but could not figure out how to contact anyone at the company if they wanted to.


That is apparently the existing academic term. It's got a Wikipedia page. But yes, all buzzwords were invented.


This confirms to me it was indeed originally intended as a buzzword then. :D

Nothing like establishing a new buzzword in a field to get them citations rolling.


New? It stretches back industrially to the WS-* stuff in the early 00s (this is where the term originated), and on the academic side has roots in process calculi from the 90s and session types from the late 00s and early 10s.


If bash placed the current directory in your PATH by default, then yes.


Could file a CVE with Microsoft then, cause thats kinda what cmd.exe is doing:

    > git clone git://evil evil
    > cd evil\
    > git status
The last line would execute git.exe from the cloned repo, wouldn't it?


No, `git` uses the binary in path, to use the repo file it should be `./git.exe`


On Linux.

Look for `NoDefaultCurrentDirectoryInExePath` if you want to learn about Windows.



Thanks!


They're referring to Enterprise customers, though should have been clear about it. Enterprise plans on Claude for example no longer include any baseline tokens. It's 100% usage based pricing.


True, but my friends in Enterprise still just purchase Claude Code subs and expense them. They basically get an allowance of $500 or so per month to buy various tools, and of course are banned from Chinese models. (Claude, Codex, Antigravity allowed, basically.)


That's not a very good tldr. The answer claimed in the paper is that the combination of the two is better than either alone.


And that's the real tl;dr. Hybrids win whenever anyone actually checks. To really be scientific we still have to check, but.. why wouldn't they? Probabilistic AI brings intuition/learning but can't plan/search. Classical brings planning and search, but has no intuition or learning.


That's a very generous interpretation.


Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: