It's arguable that there is a threshold of quality that matter and US and China are both there already. I guess we'll see. Many of us on here are software engineers and I think we've seen that while Opus 4.7 (I think) was some sort of outlier/miracle, today there are several options to choose from. Depending on the task, the harness, etc Astra/Fable are only better when the work demands their level of capability - which much of software engineering does not.

> I cannot see a way out of a major economic crisis, even if we do start to see dramatic productivity wins.

The only thing worse for the economy than failure would be success.

i dont see the connection from 2 model providers relative position to the entire economy? especially in the face of dramatic productivity wins.

Look back to the "too big to fail" banks in '08.

From biggest to smallest, the top three:

AIG controlled $1.05T and market cap crashed from $180B to $3.8B

Wachovia controlled $782B and market cap crashed from $110B to $1.8B

Lehman Brothers controlled $639B and market cap crashed from $60B to $200M.

Massive companies accounting for lots of money in people's retirment accounts. The companies made mistakes leading to what would have resulted or did result in defaulting on their debts. Suddenly billions of dollars vanished from retirement accounts.

We could very easily see a similar cascade of corrections if we get a few more "Alphabet missed earnings" news calls.

> the entire US economy hangs on whether or not Ant/OAI...

... What on earth. Please go outside for awhile.

> whether or not Ant/OAI can stay ahead of the Chinese models.

Ant/OAI/Google are way ahead when it comes to the smartest, largest-scale and most compute-intensive models. By Jevons' paradox, if you make inference for leaner models cheaper (as the Chinese models do, being broadly comparable to GPT Terra or Claude Sonnet; barely approaching Opus/Sol at their very best) that only increases demand for the complementary service of very smart inference.

Sure but routing simpler things to the open models and harder things to opus/astra gave us already a massive boost; rarely we run out of tokens on the subscription models and we recently cancelled a few seats on claude code / codex as we simply were underutilising. And also, we have users of our products ONLY using the cheap models as they benefit enough vs paying more. That was very different only months ago. I can see this gap just growing where the % of higher intelligent model use will get smaller and smaller.

> Sure but routing simpler things to the open models and harder things to opus/astra gave us already a massive boost

You have to look at the effect in the aggregate, not just what any single actor is doing. This is the core of Jevons' paradox: it's based on surfacing additional demand for the same product (here, expensive model inference) that wasn't previously viable.

Hard disagree. I own my gb200 cluster and have unlimited supply of open weight tokens. Whatever is missing from the open weights, they can make it up from speed and multi agent swarm.

Yeah: quantity is a quality all of its own. DS v4.1 Flash running at ~300tk/s makes its verbosity less of a downside, and the efficiencies the Chinese open model space has aimed for makes high concurrency easy for swarms/sub agent sets.

On the other side, I am still shocked at how good Qwen 3.8 Flash Next is on my GB10 Spark-like, even if it is only 45 tk/s decode!

> By Jevons' paradox

Jevons Paradox is not a rule its just a sometimes observed phenomenon

It says nothing one way or the other whether that would happen or not

Shut down stock markets, liquidate the failing companies, use that money to buy out stakes of pension and index funds, leave the debt with the company and let banks write it off. They're supposed to have insurance and risk controls anyway.

If any important bank fails because of this, nationalize it and let their customers exit using money from the federal reserve. Then try and recover as much of that as possible using the banks investment portfolio. Costs a lot less than bailouts.

If you shut down the stock market the financial system craters. As in stone ages no more than economy.

Modern financial system is less „finite amount of money you can move around in a logical way“ and more confidence based definitely totally not a ponzi scheme

> liquidate the failing companies, use that money

What money are you expecting to get int hat scenario? If I had to guess it wouldn't be a fraction of the cost.

> buy out stakes of pension and index funds

Index funds are part of the reason the top companies stock gets so bloated compared to those just outsid of their frame.

>If any important bank fails because of this, nationalize it and let their customers exit using money from the federal reserve. Then try and recover as much of that as possible using the banks investment portfolio. Costs a lot less than bailouts.

I agree. It worked here in Belgium tho they did spin off toxic elements of the bank in a not so great way prior to doing this.(some regular people still got shafted)

> increasingly feeling like the entire US economy hangs on whether or not Ant/OAI can stay ahead of the Chinese models.

I’ve said it for a while now both here and to friends - by Christmas next year, US AI labs will be trying to catch up with Chinese open models.

… and I think once Qwen’s latest open model beats every US labs, it’s over.

There was always going to be an economic "crisis", just a matter of time. I see it more as a forced positive economic adjustment (bleak for those enamored with the current system).

I think whether or not there is an economic impact from AI is really independent of Anthropic and OpenAI.

If there is an economic boost, it will come from broad productivity improvements and deflation of costs for services. If OpenAI or Anthropic can capitalize on that or not is a separate question.

It's not the Chinese models' fault those two companies' business models are doomed. Quantization and model architecture improvements have gotten a few orders of magnitude win, and hardware has gotten another (and I'll bet another 10-100x is coming soon).

OpenAI/Anthropic remind me of Silcon Graphics. Sure, lots of people bought those workstations before MP3 / MPEG video were a thing, and before Voodoo came out with the first cheap 3D accelerators. All the tech trends (software and hardware) back in the 1990's were against them, but their valuation stayed high for a bit longer than rational.

Unless there's some second step function change in AI, LLMs comparable to today's state of the art are going to be running on $1-2K machines in a few years, and they'll be doing it fast and with low wattage. There are already a dozen companies that know how to build that, including in the US, Europe and China.

Unlike SGI: Hopefully, given all the political corruption and fraud in this space, we'll see jail time for the guilty parties.

> LLMs comparable to today's state of the art are going to be running on $1-2K machines in a few years

This is a huge claim. What makes you think this will be the case?

Qwen 3.8 27B, not even the superior Flash Next 125B, running on a single 3090 in my workstation handily whoops ChatGPT 3.5 from just a couple years ago.

Seems like he's just drawing that line into the future. Which seems sensible enough.

I'm drawing the line into the past. Back when Biden was president, a computer that currently runs Flash Next 125B well cost $1400.

But yeah, I assume that when the economy gets back to normal-ish, in 2024-equivalent dollars, we'll be at 500B-1T for < $2000. That'll be 3-4 Moore's Law doublings since computer prices were sane.

SGI did try to pivot their business towards less expensive workstations, but they didn't manage to create a compelling product. The pivot might have been feasible because it would have stayed a hardware business.

But AI labs are datacenter businesses now with the AI part being the driver. Pivoting to selling AI weights for on-premises hardware is a much bigger change. Given the money tied up in data centers, this will be very tough times.