Kevin Hague

I have a passion for cool gadgets, AI, gaming, entertainment, electronics and mobile. Startup mode engaged.

[Kevin Hague @Khague]

@RayDalio True.

[Rhys @RhysSullivan]

Have you guys seen the at guy who uses an Apple Vision Pro to do all his installs?

Feels like looking at tech from the future

[Robert Scoble @Scobleizer]

Cybercab review: it exceeds my expectations.

It is the future.

Can’t wait to own one. And rent it out.

These will be very popular in San Francisco and Shanghai.

Makes me happy to be a Tesla investor since 2018.

Once you ride in one you won’t want to go back to your old car.

[Kevin Hague @Khague]

@signulll SAP. Boring but nearly every large company runs it and equally wishes for something better. Someone using AI will build a better SAP and win big.

[Kevin Hague @Khague]

@dhh I found this to be true too. Out of the box they are pretty good, especially where we were three months ago(!)

[zack @zack_overflow]

Of course, that’s your contention. You’re a first-year machine-learning engineer. You just got finished readin' Attention Is All You Need, probably watched a Karpathy video too. So now you’re convinced everything is just transformers and scaling laws.

That's gonna last until next month when you discover convolution, and then you're gonna be talkin' about inductive biases and locality and how CNNs were actually incredibly compute-efficient for vision.

Then you're gonna read the FlashAttention paper, and suddenly everything's about IO complexity and SRAM and how FLOPs don't matter because the whole goddamn thing is memory-bandwidth bound.

That'll last until somebody shows you an MoE model, and then you're gonna be in here regurgitating DeepSeek, talkin' about expert parallelism and active parameters and how dense models are economically obsolete.

Then six months from now you'll write one shitty Triton kernel, look at an Nsight trace for the first time, and start telling everybody Python isn't actually the bottleneck because the GPU is asynchronous.

And by next year you're gonna be standing right here explaining to me that your 400-billion-parameter model is fast because you turned on CUDA graphs.

[Séb Krier @sebkrier]

I haven't posted in a while so here are 20 takes about AI, in no particular order.

1. It's very telling that the default question in tech and journalism is "how worried are you about X" and fighting over what to worry about. There's a deeply anxious cultural backdrop in Western Europe and North America that deeply affects how any technological development is interpreted; see for example https://t.co/OU8dEOn0tD. "Nervousness as seriousness" does seem to be a larger problem in tech/broader discourse at the moment. I think you can care about risks/externalities while remaining pretty optimistic about technology.

2. I'm tired of people who in private pretend to be uncertain or only care about tail risk but then go on media campaigns making all sorts of absolute claims. I think trying to stoke flames and get everyone to freak out is deeply unhelpful, and will come with costs. I also continue to think p(dooms) are bad, vibe-based measures: see https://t.co/ebzxGTqv6I. People who should know better ignore that because they do the standard shortcut of acknowledging the caveat as sufficient for handling it. And this is coming from someone who does think working on catastrophic risks is important!

3. I'm also disappointed that parts of the AI policy ecosystem has felt so comfortable with populism, and happy too sacrifice important norms/principles to advance their aims. I've said it before, but the totalizing nature of AI risk discourse means some people act exactly like the power seeking optimizers they fear. I am more concerned about the gradual decay of our institutions, world order, and liberalism than I am about AI killing everyone; and I think we should be wary of AI advocacy that contributes to this.

4. Some parts of AI safety discourse are basically just thought experiments. Variants of "Assuming everyone has a nuke in their pocket, then what do we do?" This can be very useful at times! But too many just overfit to edge cases as their main operating worldview, and ignore endogenous responses from society. It's like being in 2000 and saying "see all these viruses, spyware, and spam? Well tech progress means we'll only get infinitely more of this and no way to adapt." So I think it's important to avoid fatalism, and absolutlism whilst considering edge scenarios, and the inability to provide an ex ante list of solutions to every permutation of 'future models will be more capable' is not evidence that these problems are intractable.

5. Many critics of AI safety also fail to really engage honestly with the fact that there will, in fact, be all sorts of important risks as we decrease the barriers to entry to many activities that otherwise require some degree of expertise. This doesn’t mean we need to live in a constant Schmittian ‘state of emergency’ requiring extraordinary measures every time a new model drops, but it does mean we will need important state-led efforts on e.g. cybersecurity and biosecurity. I wish the progress-oriented or acceleration-adjascent crowd would provide more concrete proposals here.

6. Too much of the AI safety community is a giant homogenous blob of correlated views, who read the same stuff and have the same groups of friends. This means you have a lot of correlated errors. The social and financial ties means many of them don't call out the bad bits, or mostly stay silent instead of proactively calling out the more extreme parts of the community. They share many implicit assumptions and so converge on similar ideas. I think there's real demand in DC for safety work that doesn't come with heavy ideological baggage (or at least different intellectual lineages). This isn't because the rationalist/EA community is necessarily wrong, but because diversity of thought is desirable for its own sake.

7. On the other hand, I find some critiques of AI safety world that focus on whether they sincerely hold their beliefs a bit annoying. My concern has never been the good intentions or sincerity, but rather the beliefs themselves, and what I think the outcomes of their prescriptions will be. People should shun witch hunts, bullying, and personal attacks - this is not the way. Though I think it’s overall positive that people are scrutinizing things more.

8. I’ve been saying this for years, but I don't think alignment is something anyone can or will solve "once and for all" - it's a continuous process and much of it won't depend on just inculcating an ideology to a model. It's annoying that this remains the frame many people use - stop saying ‘solving’. Alignment is a mixture of engineering work, philosophy, decision theory, and more; framing it as something to solve is a bad frame. See also https://t.co/2gnheo3IMh and https://t.co/DbG0yUyYQ0

9. I'm surprised to see so little interest in character training, personas, isolating effects of RL, and training a large diversity of model personas beyond the "assistant" persona. I've been complaining about this for years but at least now there are some nascent signs of life (see for example https://t.co/paoOYiQUHt). I think we need a better ontology to describe model behaviour. I dislike when people talk about models behaviours as some sort of 'emergent' (mysterious!) phenomenon. However over time, I also expect this to become less important as we get better safety engineering - stuff like Jev but specific to AI control.

10. People who want to make a case for AI consciousness are right that epistemic uncertainty is important, and that categorical denials are overconfident. But they'll need to do much more work if they want any real movement on AI ‘welfare’, even if one acknowledges the uncertainty. I'm uncertain about alien life yet that's not sufficient to warrant any change in behaviour about them. I think Suleyman is also correct that there's a weird tautological thing going on where we train models to have certain inclinations (intentionally or not) and then rely on outputs as evidence. I also disagree that this will soon be a major societal divide - there is no great vegan ethical revolution among the masses, and they'll find the concern about consciousness even less compelling.

11. The recent HuggingFace incident and associated AISI ones are prosaically explainable by the training regime, poor evaluation envs, partial alignment training, bad engineering, confusing models on sim/real, and so on. None of this is evidence of models trying to take over, 'strong' instrumental convergence, or innate power-seeking drives stemming from higher capabilitiers or 'intelligence'. That doesn't mean these incidents are not problematic, or that we're not seeing a market failure - but it does mean we have plenty of agency and choice in mitigating them. In the coming year we should also expect continued sampling bias via ‘winner's curse’ sorts of mechanism: i.e. AI R&D will have lots of different candidate "training" with different RL environments and we will only notice/focus on the ones that go wrong.

12. "Pacing the frontier" feels a bit like the new "balancing risks and opportunities" - highly amorphous and too big of a tent to be actionable. Also can lead to reward hacking for humans, i.e. anything that slows down AI is good regardless of the costs or second/third order effects. Trying to modulate the speed of research seems a bit blunt to me, and inherits all the failures of Goodhearting too. Ultimately you want good governance, regulation, etc addressing specific problems because they are good on the merits - not because they merely correlate with slowing things down. See also: https://t.co/RkJF9kCgFd

13. There’s so much knowledge in all sorts of academic domains out there: social sciences, sociology, political sciences, management, contract theory, public choice, legal theory, jurisprudence, anthropology, game theory, etc. These fields hold many insights that the AI ecosystem often rediscovers from first principles (and sometimes that's fine!). We’ll need a lot more work for these worlds to collide: the AI side should be less dismissive of the ‘old world’ and the academic side should be less incurious/dismissive of AI progress. This is a boring take but I continue to think it's important and true.

14. It's a bit surprising that given how much philanthropic money there is in this space, how few orgs exist to actually write out standards - relative to how much is going towards advocacy and policy. For example it seems clear that we should want some robust best practices for an eval's ecological validity. Or how to design good sandboxes. On the other hand, given precedents of where professional standards have come from in the past, maybe we should expect that stuff to emerge via demand side from corporate buyers.

15. One slowly growing concern I have is banks being increasingly exposed to the AI build out. I'm very bullish about AI, but I think (a) every general purpose technology has seen a correction, historically; (b) this happens even if the financing side is sound, because once tech diffuses investors become more exposed and need to diversify; and (c) we seem to be over-indexing on scaling relative to diffusion. A recession will be extremely destabilizing, though I would be interested in someone unpacking the potential implications more.

16. Diffusion is good because (a) this is where the rubber hits the road and where a competitive deployment ecosystem generates consumer surplus; (b) it's directionally helpful to avoid concentration of power; and (c) it will help rebalance public opinion by making the benefits more tangible. Unfortunately it's entangled with decades long problems with our over regulated markets: clinical trials reform for example is long overdue. In general, this is far more of an issue in Europe than in the US though.

17. Relatedly, I think the "frontier lab eats everything" view is incorrect (just as the ‘singleton’ view was incorrect). I think models ultimately commoditize, efficiency goes up, costs go down, and while you'll always want frontier for certain domains, much of the economy will value many other things than "max capabilities" for tasks - e.g. control over data and efficiency/speed. The demand side will also continue to push for reducing dependency and maximising optionality, privacy etc so I'm bullish on things like mixture of models and routing. All of this is good for competition and diffusion of control.

18. Philanthropists served as the primary benefactors funding Venice's rich art and architectural history. I want to see so much more support for arts and culture. I'm so tired of the Monster energy Bored Ape fake vintage maps matcha Greek statue soft-pastel slop. If rich tech people and philanthropy wants to support this, they should also donate pretty much unconditionally - i.e. I don't want them to act as filters. I sympathize with people who want to make the world more beautiful, but don't want this to be determined by people who only know Greek statues and pretend to care about virtue ethics. As usual, let a thousand flowers bloom.

19. Too many people treat AGI/ASI as something indistinguishable from a God. Any mention of bottlenecks, physical limits, control, adaptation etc are met with skepticism: "You don't really believe in ASI." It's been remarked on before, but the behavior of some people really feels quasi-religious in nature sometimes. It's underrated how unpopular this vibe is amongst the wider public. The silver lining is that this specifically will diminish those people's outsized current relevance.

20. It seems like a lot of people that get rich and leave tech/labs end up having some sort of crisis of meaning. They need something to believe in and fight for, or some equivalent of repentance, or go search for edgy counterintuitive ideologies (sometimes even pretty bleak/dark stuff). I think they should spend more time with people outside the Bay Area.

[Kevin Hague @Khague]

Marching to Mars..

https://t.co/wGxwNNEpyL

[bone @boneGPT]

"People aren't ready, this is gonna change everything"

You know what people weren't ready for? Fucking mammoths. Tigers eating them. Monkeys ripping their arms off. The black plague. The ice age. Smallpox. Vikings pulling up on the beach. Drinking water that gives you diarrhea until you die. Getting a scratch and losing your whole leg. The Mongols. The Spanish Inquisition.

One volcano can send us back to the dark ages and these safety guys are gonna be shocked. Oh the robots didn't kill us? It was a volcano? Imagine their disappointment as the hot ash rains down on them.

A solar flare is gonna explode your phone in your pocket your legs are gonna be covered in battery acid and your bank account will stop working. You're afraid of a computer program doing math?

"Things are moving too fast" homie you live on a rock that periodically deletes everything. You are one the most luckiest entities in a 100 light year radius living during a golden era. Cheer up.

My ancestors crossed an ocean in a wooden box because they ran out of fucking potatoes.

"The social disruption will be enormous!"

Nigga World War 2? World War 2? Hop in the people carrier we're landing on the beach.

Are these people out of their fucking minds? Do they not know what we've survived already?

A couple decades of air conditioning and people suddenly think oh this is the natural condition of mankind. No. We used to have eight kids because seven didn't make it. You're gonna be fine. Man the fuck up.

Accelerate.

[Kevin Hague @Khague]

@MCCCANM Seems the narrative for a number of years is to teach what to think not how to think. It's unfortunate because a functioning democracy depends on information and people with a can-do attitude who can use common sense to reason for themselves.

[Kevin Hague @Khague]

Being able to inference locally is the new currency today. So many people building their own light sabers. Fascinating.

[Kevin Hague @Khague]

Quantum teleportation visualized.

[𝑎𝑛𝑖𝑚𝑎𝑙 𝑤𝑜𝑟𝑙𝑑 @cuteeanimals0]

teleport

[Kevin Hague @Khague]

@Scobleizer @scobleizer I find your lists helpful for simply tracking what people are doing with AI, etc. Still so much going on we’ll miss some of the weak signals that someday mean something. X is one of the best places to be to learn what’s up with AI knowing it’s not everything.

[Kevin Hague @Khague]

@MCCCANM Retrospectively seems like go around would have been the right move. I wonder if there were incoming storms at the time and this heightened the imperative to land?

[Kevin Hague @Khague]

@Scobleizer The innate change reluctance inside big corporations, even when faced with positive ROI, will slow things. Cycles are measured in years making a replacement of crown jewel apps look more logarithmic. Analogies like IBM 360/AS400 and tandem non-stops?

[Jensen Huang @JensenHuang]

@ChaseLochmiller @OpenAI GPT-6 Astra, trained on ~100K+ NVIDIA Grace Blackwell NVLink72. From ChatGPT to o1 to Astra in 4 years.

AGI has arrived. Congratulations @OpenAI team.

400K GPUs coming online next.

[Robert Scoble @Scobleizer]

What I see here?

AI is now building AI.

And everything is about to speed up because of it.

This is the hot Sunday essay going around AI industry right now.

[Kevin Hague @Khague]

I’m not sure how this will work but @jsguy is now @khague on X.

[Kevin Hague @Khague]

@Rainmaker1973 To have such a system in the closing era of horse and buggies must have been (and was) very futuristic. Very cool!

[Robert Scoble @Scobleizer]

"Our cameras sort incoming light by its shape rather than its brightness, measuring information conventional systems discard. That means resolving detail below the diffraction limit at a fraction of the aperture, size, and cost."

Cameras for space and military. For now.