> Those who realize how much you can accelerate your research with the help of AI will succeed.

This is only true up until a point. If I treat a mid-sized model (say, Qwen3.8 Flash Next) like a pair programmer, then yes, it accelerates my work.

But I can already see the next stage with Fable: If I give it a couple of paragraphs of spec and $50, then I can just leave the room and go wash the dishes. I learn nothing, I participate in nothing, and I bring nothing to the process. I am no longer succeeding at all. Fable's succeeding without me.

Now, in this model generation, Fable starts getting sloppy after a few thousand lines. I can still build better at scale.

But I don't expect AI to accelerate humans or improve our productivity for long. I can already see the first signs of a future where the AI doesn't need us for anything at all.

The alternative is to insist that Nothing Ever Happens, and the future won't get too weird. Which is no longer a bet I'm entirely comfortable with. Weirdness is at least a possibility.

> More precisely, an eigenvector v of a linear transformation T is scaled by a constant factor lambda when the linear transformation is applied to it: Tv = lambda v .

In other words, repeated multiplication of an eigenvector by a matrix can still create exponential growth.

> All problems arise from this statement.

Let me go on the record, then: Given a choice between every living human being becoming absolutely, for-real dead and the human species surviving, then I come down on the side of human survival. There are too many humans that I love personally, and too many innocent people just going about their lives that I would like to protect. Given a choice between the mass death of billions, and their survival, I choose survival.

Human rights are essential. But human rights are difficult to achieve if everyone is dead.

...

> So I feel a strange kind of relief about this possibility of doom.

So let me make sure I understand what you're saying. The pandemic disrupted your life plans for a few years and threw you off the track you planned? (Which does suck.) And now the possibility of actual human extinction makes you feel a strange relief? Am I understanding this correctly?

If so, this is a remarkable level of something. Depression? Nihilism? Jealousy? Something else?

Imagining total human extinction and feeling relief is not a healthy state of mind. And, yeah, you seem to know this. But I kind of wish we could reach some sort of broad public agreement that human extinction would be bad. And if it ever looks like it might happen, we should fight for human survival.

So "fingerprinting" operates on a totally different and basically invisible level, as opposed to the obvious stylistic patterns that the average programmer can identify in about 2 sentences.

To put it politely, this seems like a self-inflicted problem. If you really want your agents to interpret advanced Excel features exactly the way Excel would, have you considered maybe giving your agents Excel?

Someone from Iceland tried to explain this all to me once, and if I understood correctly, it had less to do with the GDP (which also includes money circulating within Iceland) and more to do with the balance of trade, where fishing played a bigger role? But my information may have been misunderstood, or it may be out of date.

In any case, you have a stunningly gorgeous country and very nice people, and I wish you the best of luck in figuring all this out in a way that benefits Iceland.

As noted in the article, Iceland's fishing industry makes up something like 40% of their exports. Which is the only way a tiny island nation can afford essential imports. On top of that, Icelandic fishing is apparently carefully regulated to prevent overfishing, with the result that it is supposedly one of the few truly profitable fisheries in the world.

So maintaining control over their own fisheries may be a survival-level issue for Iceland.

On the other hand, they have a tiny population, no ability to defend themselves, and membership in the fraying NATO military alliance. So I can see why they're debating this issue. Aligning themselves more tightly with Europe doesn't fix their problems, but I can also see not wanting to go it alone.

Of course, if you have a spouse and kids, this all gets easier once you have permanent resident status. Which used to be comparatively easy to get once you were married and you had offspring running around. I don't know what that process looks like now.

In practice, this kind of arbitrary screwing with people makes the US far too risky for educated, professional immigrants. So the scientists and the engineers and the company founders will just go elsewhere and build that country's economy. Which is fine, in the long run: If the US doesn't want to build the future or create new industries, then the voters have every right to vote for that. We've already decided we have no hope of competing with China in cars. And we'll need to cede more industries over time. As far as I can tell, the US has benefited heavily from poaching other people's scientists and engineers and professionals for decades now. But maybe the voters don't buy my argument, and they believe that chasing away skilled professionals and imposing 100% tariffs will lead to the future they want. It's a difference in beliefs about about what makes a country prosperous, and people have a right to vote based on their beliefs.

Yes, a lot of Qwen3.8 27B setups are actually quite snappy, as long as they fit 100% in VRAM. In my testing, I wouldn't go below 32GB of VRAM, though—you really want a 6-bit quant and 8-bit K/V quants minimum. I've seen too much weirdness out of 4-bit quants since Qwen3.8 shipped. I think it may be damaged more than 3.6 at similar levels of quantization?

If hyperscalers hadn't bought up almost all the fast RAM production for the next several years, 32GB of VRAM would be tolerably cheap—a lot by "home PC" standards, but not terrible by "professional tools" standards. Sadly, the RAM market is amazingly ugly right now.

> I doubt the timing differential here, but even still I run my 3090 pretty heavily with inference workloads and it stays cooler than when I use it for gaming.

Yeah, running inference on a laptop is likely to run quite hot. But in an ATX case with decent cooling, it's generally a lower load than gaming. One handy tip: Many Nvidia GPUs (and some from other manufacturers) support power limits. For example, limit a 5090 to 400W instead of 600W, and it will run much cooler. You might lose 11% off your tokens/sec (depending on the exact card).

Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.

So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.

But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.

And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.

We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.

- Use Anki as an "exposure amplifier", not a database of raw facts to memorize. Think "tampering with the relative frequency of input to your brain", not "I will memorize every fact."

- Automate card creation as a much as possible. This will almost always require custom tooling. Card creation is part of the learning process, but you still want to automate everything you can possibly automate. Imagine tools to turn ebook highlights into cards, or to turn entire movies worth of subtitles into listening cards.

- Delete cards ruthlessly. If a card makes you say "Ugh", just delete it. If you're automating as much of card creation as possible, and if you're treating Anki as an "exposure amplifier", then deleting cards is cheap. And a significant amount of review pain comes from a small minority of the cards. Cull them, and reviews start to feel like popping bubblewrap, and not like getting a root canal.

- Prioritize large context and small active recall. For language learning, you might capture several sentences of context from a book or web page you're reading. Read or ignore the context depending on your mood. The active recall portion of a card significantly boosts how much you retain. But oddly, it seems OK to make the active recall portion really small. Try hiding half a word, or a single preposition in a phrase. For passive cards, just boldface an interesting expression in context, and mark the card as passed if you mostly understand it in that context.

- Introduce new cards slowly. Total review burden will stabilize around 5-6 times the number of new cards you learn a day. So 10 cards is a good default, and 20 is a significant commitment.

- The critical window seems to happen around 20-30 days after you introduce a card. This is when either your memory magically "consolidates" somehow (and formerly challenging cards suddenly become blindly obvious), or when a card enters repeated failure territory (and should thus be deleted ruthlessly). You could probably do just fine if you auto-suspended any card that you successfully recalled after a 20- or 30-day gap.

Also, this kind of tooling is the sort of thing you could just vibe code these days. If you do, make the "Delete" button really big.

But their current catalog is badly impoverished, and they're just going to recommend the same 30 Netflix originals they always recommend to me, plus a few films or series that are rotating through on a temporary license. If I actually try to search for something specific I'd really like to rewatch, it's almost never there. They haven't quite regressed to the level of a small-town, early 90s Blockbuster, but it sure feels that way sometimes.

So honestly, how much good can the Netflix recommendation algorithm do these days, given the much smaller catalog of movies and films it apparently has to work with?

(The one that I don't get is the Kindle recommendation algorithm. If I read one really good book with a certain theme, the Kindle immediately replaces my recommendations with 40 bad knockoffs, 25% of them clearly AI written. There's apparently no signal for "actually good.")

I've been working with DeepSeek V4 Flash 0731. I'd say that it's maybe not quite as smart as Opus 4.5, but it's willing to think things through carefully and keep going until it gets a good answer. So it's a decent Opus 4.5 replacement. Just let it cook.

It isn't Opus 5 or Fable 5. But it's nearly free on Open Router, and it's self hostable on a Mac Studio with plenty of RAM, or using an RTX Pro 6000 Blackwell or two. Which is chump change for any company that employs programmers.

It would absolutely have been a frontier model last December.

There's no reason why Google's public stuff is this stale, overpriced and underwhelming. But at least until their next round of models drops, even calling them a "frontier lab" is starting to feel like a stretch. Which is weird!

Well, given the literal trillions being spent, the only ways this pays off are:

1. AI replaces a non-trivial fraction of human employees.

2. Someone builds a Culture Mind, and humans become (hopefully) pampered pets of AIs we don't understand. Seems unlikely, but it would arguably count as a payoff even if it made money meaningless.

Or maybe the AIs don't want pets, and you get SkyNet. Which definitely doesn't care about paying off anyone's investments.

When you look at various news articles about investors, yeah, there are definitely a lot of rich people who think that they're going to automate all human labor or just bring about the Singularity. Possibly with them in charge of the rest of us. If you don't make these kinds of wild assumptions, then yeah, this is looking like one of the biggest bubbles ever.

Well, that's why they want the robots! If your robots are capable enough, and if your AIs are smart enough, why, they could just build the yachts directly!

Right now, ordinary humans are needed by the economy because we do all the work, and because robotics hardware is still far behind AI. But there's no inherent logical reason why you need a human to turn raw materials into luxury products. The really important questions are: Who controls the AI and robots? How good will they get? Who or what does all the work? And who controls the natural resources?

> Of course (let me go distopic)

Yup, that is a possible end state.

But the goal is to expand what the AI can do in each generation. At this point, Fable 5 can ace almost any greenfield project a skilled developer might have written in a few days. But it's bad at refactoring, bad at keeping the code clean as it goes, and bad at discovering new insights as it codes. So Anthropic will train Fable 6, using benchmarks like SlopCodeBench that test maintenance over time.

Now what about project management? Train Fable 7. What about product management and talking to stakeholders? Train Fable 8. What about market research and sales? Train Fable 9.

By this point, Anthropic doesn't need to actually release these newest models to the public. Why, that might be dangerous! Instead, they write, "deisgn [sic] a successful software product and sell it plz." And they spin up a million dollars worth of compute and let it crank out SaaSes, iPhone apps, etc., driving entire software companies out of business.

Then they spin up some more instances, and say, "make robot plz" and "try a thousand ways to make yrself smrater." I mean, Qwen and DeepSeek keep finding ways to pack more smarts into a given number of weights. Fable 9 will likely be able to do the same. Hell, Fable 5 can probably run 1,000 machine learning experiments now, just grinding through ideas the way ChatGPT's internal models grind through proofs.

And this is my problem. If it were just programmers losing their jobs, well, sometimes professions die. But what makes you think it will stop with us? How far will this go in the next 4 years? The next 20?