AI companies are starting to build more and more personalization into their products, but there's a huge personalization-sized hole in conversations about AI safety/trust/impacts.

Delighted to feature @mbogen.bsky.social on Rising Tide today, on what's being built and why we should care:

Been thinking recently about how central "AI is just a tool" is to disagreements about the future of AI. Is it? Will it continue to be?

Just posted a transcript from a talk where I go into this + a couple other key open qs/disagreements (not p(doom)!).

🔗 below, preview here:

💡Funding opportunity—share with your AI research networks💡

Internal deployments of frontier AI models are an underexplored source of risk. My program at @csetgeorgetown.bsky.social just opened a call for research ideas—EOIs due Jun 30.

Full details ➡️ cset.georgetown.edu/wp-content/u...

Summary ⬇️

Criticizing the AI safety community as anti-tech or anti-risktaking has always seemed off to me. But there *is* plenty to critique. My latest on Rising Tide (xposted with @aifrontiers.bsky.social!) is on the 1998 book that helped me put it into words.

In short: it's about dynamism vs stasis.

New on Rising Tide, I break down 2 factors that will play a huge role in how much AI progress we see over the next couple years: verification & generalization.

How well these go will determine if AI just gets super good at math & coding vs. mastering many domains. Post excerpts:

Cognitive Revolution (🇺🇸): More insidery chat with @nathanlabenz.bsky.social getting into why nonproliferation is the wrong way to manage AI misuse; AI in military decision support systems, and a bunch of other stuff.

Clip on my beef with talk about the "offense-defense" balance in AI:

Stop the World (🇦🇺): Fun, wide-ranging conversation with David Wroe of @aspi-org.bsky.social on where we're at with AI, reasoning models, DeepSeek, scaling laws, etc etc.

Excerpt on whether we can "just" keep scaling language models:

2 new podcast interviews out in the last couple weeks—one for more of a general audience, one more inside baseball.

You can also pick your accent (I'm from Australia and sound that way when I talk to other Aussies, but mostly in professional settings I sound ~American)

What to do instead? IMO the best option is to think in terms of "adaptation buffers," the gap between when we know a new misusable capability is coming and when it's actually widespread.

During that time, we need massive efforts to build as much societal resilience as we can.

The basic problem is that the kind of AI that's relevant here (for "misuse" risks) is going to get way cheaper & more accessible over time. This means that to indefinitely prevent/control its spread, your nonprolif regime will get more & more invasive and less & less effective.

Seems likely that at some point AI will make it much easier to hack critical infrastructure, create bioweapons, etc etc. Many argue that if so, a hardcore nonproliferation strategy is our only option.

Rising Tide launch week post 3/3 is on why I disagree 🧵

helentoner.substack.com/p/nonprolife...

Lately it sometimes feels like there are only 2 AI futures on the table—insanely fast progress or total stagnation.

Talked with @axios.com's Alison Snyder at SXSW about the many in-between worlds, and all the things we can be doing now to help things go better in those worlds.

Due in 2 days (!): comments on v2 of the EU's draft Code of Practice for general-purpose AI.

For anyone else who'd find it helpful, here's a side-by-side comparison with v1:
draftable.com/compare/NNBy...

One snap take: the rewritten taxonomy of systemic risks (p29) is much stronger.

Positively heartwarming NYT story on the Little Taipei springing up near TSMC's new plant in Arizona. Beef noodle soup, English lessons at church, high-skill immigrant visas... everything a progress studies nerd's heart could desire.

www.nytimes.com/2024/12/29/b...

Fun @jascha.sohldickstein.com post that straightforwardly connects 2 ways of thinking about the perils of optimization (Goodhart's Law and overfitting), then suggests some ways to apply anti-overfitting measures to societal problems. Thought-provoking!

sohl-dickstein.github.io/2022/11/06/s...

By Anton Leicht: how well did AI safety advocates make use of the post-ChatGPT policy window? Some good nuance re: how hard it is to regulate tech that doesn't exist yet, and where the AI safety crowd made mistakes vs. incurred hard-to-avoid costs.

www.techpolicy.press/the-end-of-t...