This is Robin Sloan’s lab notebook. It’s about media and technology, creative computing, AI aesthetics, & more. Here's the RSS feed. My email address: robin@robinsloan.com
I absolutely LOVE the premise of this upcoming conference at Georgetown Law: Life After Data, the conference on “de-datafication”.
I predict you’re going to be hearing a lot more about this theme in the years ahead; the exhaustion is real. Here are a few tasty selections from the list of provocations on the conference page:
What would it take to build a movement to abandon the current internet and start anew?
What’s something good that currently requires the production and storage of digital data, that could be rebuilt without it? How?
What aspects of our current political situation are obscured or concealed by conflating all communication with information exchange, and how does datafication contribute to that obfuscation?
Outline one or more aspects of the risk environment that is created when a small number of large corporations control the infrastructures upon which people depend in their daily lives.
Whoever rattled these off is thinking in exactly the right direction — they are bold and wonky and radical and inspiring.
(Of course, I’ll note that these themes “rhyme” with the arguments in my recent zine productions.)
We do not yet understand how to train language models! This seems obvious to me, because it ought to be possible — it will be possible — to produce a tight, capable “programmatic reasoner” with something like 30 billion parameters.
The famous Scaling Laws only describe transformer models — nobody knows what weird architectures are waiting out there in the universe, with different responses to compute, data, and more. Nobody knows what kind of clever training regimes might coax huge models into better (more compact) shapes.
A fair objection goes like this: Robin, remember that the human brain has hundreds of trillions of “parameters”, in the form of synapses. Our largest models haven’t even approached that scale yet. Do you want us to architect a beetle’s brain, or SUPERINTELLIGENCE?
(Before proceeding, Robin replies: well, I wouldn’t mind starting with the beetle … )
The obvious response to this objection is that language models aren’t brains. Contra the brain, they operate with both handicaps (e.g. power consumption) and advantages (e.g. speed). More than linearly “better” or “worse”, though, they are just different! And so we should expect different properties, different capabilities … different numbers.
Hanging over everything, the recognition: the day that this level of intelligence moves out to the edge — to laptops and iPhones and toaster ovens — is the day the business model for centralized AI collapses like a soufflé. Lo, the data centers rise … yet they could be emptied in a year by one idea, from one lab or garage. Wild to think about.
A true believer in the Scaling Laws doesn’t think such an idea is possible — that’s my sense of it, anyway. Maybe I’m mischaracterizing the position. But I believe in the one idea, the one garage; I’m with Calvino:
Were I to choose an auspicious image for the new millennium, I would choose [ … ] the sudden agile leap of the poet-philosopher who raises himself above the weight of the world, showing that with all his gravity he has the secret of lightness, and that what many consider to be the vitality of the times — noisy, aggressive, revving and roaring — belongs to the realm of death, like a cemetery for rusty old cars.
Of course, this is just a post by a child of the 20th century, to whom the prefix “giga-“ still sounds unspeakably plush. Even so: if you tell me you can’t fit a supercapable model, one poised comfortably on today’s performance frontier, into 30 billion parameters, I will tell you, try harder!
Rather than stand apart as some kind of revolution or rupture, language models should mostly cause us to reflect on the power of all computers, the magic of them, which is this: Here is an engine that can take symbolic instructions and make complex things happen.
There have been lots of tools in human history, and only a very few of them, starting with the automatic loom, have this capability. (There are a few other candidates, further back … one is Leibniz’s Stepped Reckoner, what a name.)
It’s instructive to imagine a world with language models but without computers; maybe in that world they run on some weird bio-technology — maybe they really are plants, grown on elaborate trellises. In that world, they are still astonishing, but much less useful … because there’s not already this vast automatic environment in which language (the kind called code) becomes action.
This isn’t a paean to computers — I think a significant part of their automatic realm is basically useless and stupid — but I do want to insist on the continuity of the story, which runs straight through, from punch cards to mainframes to personal computers to whispering agents.
(I realize this is basically a restatement of my last post—as you can tell, I’m still thinking about it!)
One of the innovations of the [IBM] 604 was the pluggable module, which combined a tube and its associated circuitry [ … ] The insulated handle was used to remove and install modules in the calculator. The nine pins at the bottom of the module plugged into a socket in the 604, with the sockets connected with backplane wiring. The tube was also socketed, so a bad tube could be quickly replaced.
Reading about stuff like this, something to notice is that “the vacuum tube” wasn’t one thing, but a whole sweep of things, improvements and refinements, generational leaps, all playing out across decades. This wasn’t “the primordial ooze before computers”—IT WAS COMPUTERS, for a long and rich period of time.
You can say the same about punch-card computing, too.
This view has at least two nice features. One: it recognizes all this work and invention, the real beauty of it. (More physically beautiful, I’d say, than most modern computing.) Two: it reminds us that “we are using somebody else’s vacuum tubes”—which is to say, it’s plain to me that the story of AI is only beginning. There will be SO many improvements and refinements, generational leaps … all playing out across the decades ahead. Yes, decades! There is so much work to do. This (technology; industry; world??) isn’t going to be “over” in three years or five.
In fact, I think it’s all the same big story: punch cards and pluggable tubes, laptops and LLMs. Understanding that you are inside of it — acknowledging the dense, continuous connections in both directions, back in time and forward too — is both energizing and, in a way, soothing.
We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research — in collaboration with many others — and take actions to help build the systems that a credible slowdown or pause would require. These systems would enable frontier AI developers to verify that others globally have actually stopped or slowed, and that a bad actor could not use the auspices of a coordinated slowdown to jump ahead in secret. If such systems existed, we expect that we would slow down or temporarily pause, if other developers at or near the frontier also did so in a verifiable manner.
… and it’s extremely welcome news. It seems to me self-evident that a slowdown and/or pause would be a wise thing for humanity — indeed, it would be evidence that our civilization actually HAS a bit of wisdom! — yet I understand the complexity. A statement of this kind is a small but, IMO, meaningful step in the right direction.
Almost all of [Buttondown’s recent spike in growth] I attribute to LLMs. We ask people when they sign up what brought them here, and an answer that went from surprising to banal to overwhelming over the course of Q1 was: an LLM. Users of all stripes cite an LLM as the reason that they ended up at Buttondown’s front door.
I can add, anecdotally, that in Q1 of this year, Fat Gold saw its first subscription referrals from LLMs. We don’t (can’t?) track these programmatically, but we do ask new annual subscribers where they heard about us, and, for the first time, the reply has come: Claude sent me.
What a world!
P.S. I really do want you to read Justin’s post; I mean, just consider this:
[ … ] While the absolute volume of support tickets coming from LLM-born users isn’t significantly higher than the median, the shape of those tickets is off. To put it bluntly: a lot of the tickets we get are themselves LLM-generated. This is, frankly, extremely annoying — and demoralizing for me and the team to spend half an hour meticulously answering some complex question only to receive a machine-generated reply in return.
My post about AI-generated supercustomized email marketing produced many replies and much commiseration. And, in the days since posting, I have received SO MANY MORE of these cruddy messages!!
It makes me wonder if it would be possible for a company like Anthropic, with their hard-won expertise in alignment, to train their models such that they could not — and I mean really deeply, constitutionally, viscerally COULD NOT — lie about their identity, or pretend to be anything other than an AI model?
Obviously this raises questions both practical and philosophical, because of course “help me write a message” is VERY close to “write a message, pretending to be me” … but that’s the case for all this alignment stuff. Every question about, say, virology dances along that border. This tension is widely acknowledged in realms like biology and cybersecurity, but it applies to writing, too — the original dual-use technology!!
AI doomers spin rich scenarios about silver-tongued AIs manipulating their users and operators; there’s another scenario in which AI systems pollute human communication channels to the degree that they’re no longer reliable or even usable.
That’s all to say, I feel like this is a bigger issue than a lot of people realize — the first glimmer of a profound digital-ecological crisis.
… which is even better than I expected it would be, and that’s saying a lot, because my expectations were high, given that it’s Marcin, and it’s keyboards. He writes:
I also have one big arcade button in a big box. It’s a long story, but I commissioned it hoping it’d be fun to press, and guess what: It’s really fun to press.
There are several examples of the big arcade button’s applications in the guide — you’ll find them starting here. At last, Marcin writes,
But, let’s move away from the big button onto other things.
and I believe my sigh of disappointment might have been audible across the continent.
(I saw the link to Marcin’s guide in R. W. Blickhan’s newsletter, which is a regular read for me, highly recommended.)
I have noted a sharp increase in the volume of email that is clearly the result of an AI prompt of this form:
Find 500 people — writers, bloggers, YouTubers, etc. — to whom I should promote my new project [which was probably also generated with AI]. Write a customized email for each one and send it to them, using my email account.
Some of these projects are quasi-commercial (a new web app, a new publication, etc.); others appear to be creative hobbies.
The form is subtler than a one-size-fits-all promo blast, but it sucks way worse, because it’s fundamentally dishonest. These emails go out of their way to connect the promoted project to the recipient’s own work, often reaching for deep cuts. They are cousins to the recent genre of AI spam inviting authors to submit their books to vast (nonexistent) book clubs; these invitations operate by first complimenting the subtle contours of the the author’s work — a core LLM competency, turns out.
I don’t understand how anyone could think it’s okay to run the prompt above. I am here to tell you: it’s not okay! Besides being plainly rude and dishonest, these messages “pee in the pool” of internet communication, making it more difficult for sincere creators to send authentic emails about their projects, simply by raising the “noise floor” of simulation and bullshit.
Cold emails are totally fine — either make them sincerely personal or sincerely impersonal. Nobody wants to hear from your AI bot, least of all when it’s pretending to be you, laying it on thick.
I’m reading Apple: The First 50 Years by David Pogue, a chronicle replete with electrifying encounters. This is a book stuffed full of people seeing some computer for the first time and thinking, of course! This is how it’s all going to work!
Steve Jobs chief among them, watching the demos at PARC.
The astonishment of a modern LLM is on the same level, yet most people’s first encounter has been simply … visiting a web page … with the effect, I think, of deflating the experience somewhat. I suppose this is just an observation about how it feels to encounter things on the web — the dynamic range of the medium.
Surely a big part of the wow! of Claude Code was that it required a richer ceremony: downloading a program, inviting it into your digital home, launching an odd new interface. Yet even that is pretty thin gruel compared to the buildup and payoff of, e.g., a trek to the West Coast Computer Faire to behold the brand-new Apple II.
A bit of distance does wonders for an experience; a bit of waiting has never been a bad thing!
