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Roughly 3 years ago, specifically in December 2023, I've reiterated my longstanding prediction that «the culture war's most important front will be about AI», and specifically AI accelerationism vs anti-AI/AI safety movement:
And the following political compass:
Seems like this was too much complexity for big tent politics in the US, at least so far; and the Trumpian State sees no use for the EA network, and instead (pretty rationally) perceives it as an uncontrollable alternative center of power. Instead we have, essentially, anti-AI left + pro-AI right (with some notable exceptions – eg. Steve Bannon, apparently, has been anti-China specifically because of his concern about Chinese AI progress, and now joins hands with Bernie Sanders). I've also failed to predict the salience and extent of Chinese open source dominance, as well as the bizarre datacenter water use backlash (charitably, can be shoehorned as Luddism?). Nevertheless, we've «succeeded» at the core task of making this a culture war front. The degree of (unnecessary) politicization of the issue is incredible. Trump is doing the yeoman's work, lumping it in with random Blue-coded ideas he disapproves of “Global “Warming,” where everyone was going to be dead by now, RUSSIA, RUSSIA, RUSSIA, UKRAINE, UKRAINE, UKRAINE, or Impeachment Hoax #1, or Impeachment Hoax #2.”. Jensen Huang enjoys his role as the kingmaker who has the King's ear. Sam Altman is opportunistic as usual but, after recent incidents, is genuinely spooked about AI risks (I have it on good authority that OpenAI really intends to slow down some internal projects specifically to bolster their oversight). The quasi-Masonic part is shaping up nicely, too – Dario Amodei, who's become the poster boy of Woke Left AI, promotes Embedded Evaluators with the central example being METR, very deeply connected to Anthropic and the broader EA scene. The EA itself is more explicitly Left-aligned now, despite efforts of some to paint them as TESCREAL eugenicists (the woke cancellation of Bostrom in Jan 2023 was the canary in the coal mine, Yudkowsky laments the missed opportunity of bipartisanship). There are clearly politically coded reports on prominent doomers.
This is all a bit meandering. What I want to know: how do you see this going further? We aren't anywhere close to the wall of capabilities; there are no walls in sight. Anthropic and OpenAI are holding back already, but their products will keep getting better, and fast. Google will make a comeback at some point (maybe in a couple weeks), xAI and Meta may catch up too. In my book, we (well, they) have practical superintelligence that's sufficient for both unprecedented productivity acceleration and really devastating, nation-crippling cyberattacks, which I guess will be discussed with Xi soon. At this rate, in a few months the level of capability Fable 5.1 or GPT 6 Astra will be mostly commodified and uploaded to HuggingFace (owned by Nvidia now). And those are relatively weak systems compared to internal models, which can build models that are vastly stronger still, without even any R&D breakthroughs, just by virtue of synthesizing stronger data trajectories and designing better RL environments. Superintelligence, in other words, is baked in. By Q1 2027 we'll see a jump from Astra that's at least as big as Sol => Astra. I fail to understand how that won't steamroll companies trying to build their moats on products downstream of frontier AI, labs need every bit of revenue to cover their costs, which will only increase due to growing self-imposed safety requirements; employers outside the AI sphere will also be increasingly feeling the heat. There's a whole ugly dimension of circular financing, too.
Americans as a whole are pretty pessimistic about AI even at these mediocre levels of diffusion (I am skeptical of this data that purports to demonstrate much lower adoption than in China, Americans are probably lying more due to widespread negativity on AI, but in any case AI isn't currently doing most of their jobs). Astra+ level models with very low error rate and 300 tok/s output totally can replace most knowledge workers. On the other hand, it seems that so far AI has not caused anything like mass unemployment, and perhaps economists have a point about comparative advantage, so that'll reduce the intensity of class dynamics.
Democrats are likely to sweep both chambers of Congress, which I guess is what Dario is hoping for and why he feels emboldened to antagonize Trump&Hegseth. Nevertheless, the needs of national security and GDP-maxxing (as well as the Executive's will) should prevent any nontrivial exogenous industry slowdown. So by default we'll see further crystallization of Red Accelerationism vs Blue Decelism, and as AI becomes more undeniably scary, that may begin to eat into the Red political base. It'll be interesting to watch, but I'm really uncertain as to how it'll go.
In the jungle book there is a scene - Shir Han is dragging Baloo with his tail and this legendary exchange takes place:
If we slow down now - we will certainly feel the teeth.
Qwen 3.8 flash next abliterated exists. That can be run albeit slowly on consumer hardware and my hunch is that it is enough of a both foundation and power multiplier - for the mythical beast that is ASI to eventually emerge. The cat is out of the bag and stopping now
Also one of the things the Yuddites seem incapable of doing is figuring out that superintelligence does not mean omnipotence. The laws of physics will still work and some other too. Internet is trivial to be made more secure, not every infrastructure needs to be connected to it anyway.
If the frontier labs slow down - the research will continue. My hunch tells me that we are really far away from the limits of IQ per watt, per weight, per wafer, per harness. All the world militaries and terrorist groups have great interest in making full fledged AI work in the limited hardware a drone possesses.
The datacenter big ASI have always been safe. You just put a lot of semtex in the foundations of the DC with dead man switch. The other one - the fast nimble one that could fit on consumer hardware is the dangerous one. And I don't want it to emerge in Yemen or Sudan.
The question is 2028. 2026 won't be important because the red tribe will hold the presidency and if some of the conservative judges have functioning brains will let themselves be replaced in the lame duck if they lose the senate. But who knows what the job market will be in 2028 and if the effects on AI will be suddenly felt by then.
Personally I am accelerationist. I have been since reading the Lord of Light.
Closeness to sam altman is pure coincidental.
From yesterday you could run the dense qwen 3.8 on 1080ti. The research will continue.
How does Qwen 3.8 Flash perform? What kind of K/V cache are we talking about here, on consumer hardware?
I'm an AI fan but I have issues with even the biggest and strongest models for my usecases, which ironically enough is AI development (non-cheating AI in a strategy game, that is). They get there, we are making progress - but with no small amount of fumbling along the way. Testing various ideas and strategies takes time. Qwen 3.8 Flash is below Qwen 3.8 Max, which itself is below Kimi K3, right? And that's below Astra and Fable. And my usecase is nowhere near ASI development.
I agree that AI will improve in cost-efficiency but consumer hardware seems like a stretch.
Anything fast or nimble is going to roar and bellow from datacentre-grade compute if it can whisper on consumer hardware.
I can run Qwen-3.8-Flash (120B) at Q4, K/V cache, on an nVidia 3090 and a Core i5-14400 (albeit with a lot of now-expensive RAM), with simple llama.cpp run, around 5-2 t/s at 100k available 16-bit kv cache. Dropping the kv cache to 50k nearly doubles performance. Qwen-3.8 in general defaults to a heavy thinker, so that's worse than it sounds -- a moderately complex problem can burn 30k tokens -- but it's the sort of thing you can leave crunching on a problem for a while and be happy about the answer.
(Comparisons: Qwen-3.8-27B runs about ten times the speed, and Gemma4-26B runs basically faster than I can read it.)
For intelligence and capabilities, the comparison to frontier stuff is rough. Low-parameter models just don't have some information, and with either hallucinate or just nope out, no matter how well it had to be present in the training data. Indeed there's been some efforts to trim low-value knowledge from public models to optimize them for specific use cases, with weird results.
And home users have some rough spots. Both quantization and abliteration drive perplexity and errors, and the harnesses to find and debug them live aren't well-established in the open source (or free-as-in-beer) world. It's fascinating to read a logic trace that goes into surprising depth, but it doesn't do much if the program output doesn't work. The errors are small and embarrassingly simple for a programmer familiar with common JS errors, or for other models to catch, but non-programmers would likely struggle to explain what was even going wrong.
((Also note: the game's not good or fun, even when it does 'work'. That should be expected given the lack of specificity, lack of agent harness, or even a real iterative process, but it's also something no human would do this way even as the core idea it came up with is kinda clever.))
That said, intelligence can be surprising. If you want a model that can make connections between input tokens or parse through mounds of data, you can get away with stuff much smaller and more energy-efficient than you would expect. I would not, a year ago, have expected you could get spatial reasoning worth spit in a 27B model. A real big curveball isn't the most likely thing, and I wouldn't put a ton of money on specifically Jev doing anything ridiculous, but I wouldn't bet against someone coming out with a two-fold performance or intelligence improvement for inference in this model class before the end of the year, either.
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