Difference between scheming and alignment here?
GRI
Karma: 194
+1. I was just revisiting this after eating a cheese stick and thinking about how this post is a great concept for my next grocery store trip.
What happens if all of the local datacenter fights across America become way more successful? This functionally seems similar to a data center moratorium, and might actually be easier.
After meeting with a few of these groups, my impression is that the vast majority of American AI datacenter fights are operating with basically zero financial help, and remarkably little legal support. I’ve seen multiple campaigns run by people who basically struggled to raise enough money to even print signs and somehow ended up winning or significantly delaying the project. On aggregate, these fights manage to be very successful with hardly any resources.
In the extreme case, what if you just give a $100,000 grant to every single ongoing AI data center fight in America (source: https://datacentertracker.org/) to get them all equipped with great legal and advocacy help? This would cost around $23 million. (One could imagine weighing each grant by the datacenters projected energy usage.)
To put more emphasis on this point: I think a single medium-sized donor could significantly change the rate of AI data center development in America.
It seems the safety community generally support Bernie’s proposed AI data center moratorium. I think supporting grassroots data center fights is a less robust version, but it seems to captures a substantial fraction of the value, while being surprisingly cost effective. But maybe people just don’t think it’s net positive to slow down development by supporting these communities? If so, I’m super curious to hear why.
Very cool!
A new tracker for American AI Datacenter fights:
I’ve been maxing out my Claude code usage over the last week to compile a pretty comprehensive database of community fights against American AI datacenters. I gathered information on 368 grassroots AI datacenter fights across the country (118 of which are ongoing). As far as I know, a database like this hasn’t been publicly aggregated anywhere before. The closest is datacenter watch, which publishes reports on the topic but doesn’t offer an open, comprehensive database.
I built an interactive map with filterable and exportable data. See here: https://datacentertracker.org/
Here’s what the database looks like when circle size is determined by the number of petitions gathered:
Seems like you can get pretty far by just having current opus 4.6 Claude code run for a week. Only problem is that this is prohibitively expensive.
My impression is that running something like Deepseek for a week straight doesn’t really get you much?
If inference costs per model are declining somewhere between 3x-10x+ per year this alone will get economical quite soon. What projects do you have up your sleeve for when this is viable?
Yeah, agree these transcripts really smell of evaluation awareness
Relatedly, Staknova’s Berkeley Math Circle program was recently shut down due to new stringent campus background check requirements. Very sad.
Also, she was my undergrad math professor last year and was great.
Domain: Music, songwriting
Link: The Beatles: Get Back
Person: The Beatles
Background: the making of the Beatles’ 1970 album Let It Be
Why: Nearly 8 hours of remarkably raw footage, documenting the Beatles creating and recording Let It Be.
One of the best short stories I’ve read in a while
Seems like a huge point here is ability to speak unfiltered about AI companies? The Radicals working outside of AI labs would be free to speak candidly while the Moderates would have some kind of relationship to maintain.
Even if the internals-based method is extremely well supported theoretically and empirically (which seems quite unlikely), I don’t think this would suffice for this to trigger a strong response by convincing relevant people
Its hard for me to imagine a world where we really have internals-based methods that are “extremely well supported theoretically and empirically,” so I notice that I should take a second to try and imagine such a world before accepting the claim that internals-based evidence wouldn’t convince the relevant people...
Today, the relevant people probably wouldn’t do much in response to the interp team saying something like: “our deception SAE is firing when we ask the model bio risk questions, so we suspect sandbagging.”
But I wonder how much of this response is a product of a background assumption that modern-day interp tools are finicky and you can’t always trust them. So in a world where we really have internals-based methods that are “extremely well supported theoretically and empirically,” I wonder if it’d be treated differently?
(I.e. a culture that could respond more like: “this interp tool is a good indicator of whether or not that the model is deceptive, and just because you can get the model to say something bad doesn’t mean its actually bad” or something? Kinda like the reactions to the o1 apollo result)
Edit: Though maybe this culture change would take too long to be relevant.
“Lots of very small experiments playing around with various parameters” … “then a slow scale up to bigger and bigger models”
This Dwarkesh timestamp with Jeff Dean & Noam Shazeer seems to confirm this.
“I’d also guess that the bottleneck isn’t so much on the number of people playing around with the parameters, but much more on good heuristics regarding which parameters to play around with.”
That would mostly explain this question as well: “If parallelized experimentation drives so much algorithmic progress, why doesn’t gdm just hire hundreds of researchers, each with small compute budgets, to run these experiments?”
It would also imply that it would be a big deal if they had an AI with good heuristics for this kind of thing.
I would love to see an analysis and overview of predictions from the Dwarkesh podcast with Leopold. One for Situational awareness would be great too.
Seems like a pretty similar thesis to this: https://www.lesswrong.com/posts/fPvssZk3AoDzXwfwJ/universal-basic-income-and-poverty
I expect that within a year or two, there will be an enormous surge of people who start paying a lot of attention to AI.
This could mean that the distribution of who has influence will change a lot. (And this might be right when influence matters the most?)
I claim: your effect on AI discourse post-surge will be primarily shaped by how well you or your organization absorbs this boom.
The areas I’ve thought the most about this phenomena are:
AI safety university groups
Non agi lab research organizations
AI bloggers / X influencers
(But this applies to anyone who’s impact primarily comes from spreading their ideas, which is a lot of people.)
I think that you or your organization should have an explicit plan to absorb this surge.
Unresolved questions:
How much will explicitly planning for this actually help absorb the surge? (Regardless, it seems worth a google doc and a pomodoro session to at least see if there’s anything you can do to prepare)
How important is it to make every-day people informed about AI risks? Or is influence so long-tailed that it only really makes sense to build reputation with highly influential people? (Though- note that this surge isn’t just for every day people — I expect that the entire memetic landscape will be totally reformed after AI becomes clearly a big deal, and that applies to big shot government officials along with your average joe)
I’d be curious to see how this looked with Covid: Did all the covid pandemic experts get an even 10x multiplier in following? Or were a handful of Covid experts highly elevated, while the rest didn’t really see much of an increase in followers? If the latter, what did those experts do to get everyone to pay attention to them?
Securing AI labs against powerful adversaries seems like something that almost everyone can get on board with. Also, posing it as a national security threat seems to be a good framing.
Some more links from the philosophical side that I’ve found myself returning to a lot:
(Lately, it’s seemed to me that focusing my time on nearer-term / early but post-AGI futures seems better than spending my time discussing ideas like these on the margin, but this may be more of a fact about myself than it is about other people, I’m not sure.)
+1 especially keen to hear from people who didn’t already start with a desktop setup, since I only have a laptop rn.