Notes

Doing so may well represent the greatest economic opportunity in history. In the prospectus: The founders’ letter (page image) (original filing on SEC EDGAR)

AI could do the same for expertise, giving anyone access to the knowledge and skills of a lawyer, an accountant, or an agronomist, improving economic efficiency and accelerating growth across the world. In the prospectus: The founders’ letter (page image) (original filing on SEC EDGAR) Related: Intelligence on Demand

The idea is closely related to Richard Sutton’s concept of the bitter lesson, which describes how sheer scale has consistently won out over more specific, complex methods of training AI models. Note that scale is not the only source of improvements in model capabilities—algorithmic efficiencies (“compute multipliers”), which allow us to (for example) achieve higher capabilities with the same amount of compute, have played a major role. But these efficiencies are themselves found using the scaling laws: researchers test new ideas and keep the ones that improve the scaling trend the most. In the prospectus: The founders’ letter (page image) (original filing on SEC EDGAR) This is a big lesson. As a field, we still have not thoroughly learned it, as we are continuing to make the same kind of mistakes. To see this, and to effectively resist it, we have to understand the appeal of these mistakes. We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach. ...

Each version of a Claude model assists in building the next, helping to compress development timelines and produce compounding improvements in the speed at which we innovate. We refer to this recursive dynamic as “closing the loop,” and we believe it is one of the most significant structural advantages in frontier AI. In the prospectus: Prospectus summary: Frontier and safety research (original filing on SEC EDGAR)

For the first time in history, intelligence is something humans can build. The cognitive capability to reason, analyze, synthesize, decide, and create is producible at scale and accessible on demand. In the prospectus: Prospectus summary: Overview (original filing on SEC EDGAR) The words “accessible on demand” caught my eye right away, and they immediately made me think of how ordinary workers are hired by the month, not on demand. In some situations that is a huge advantage: a legal adviser handling a case, an accountant doing quarterly reports, a translator used as needed, and so on. Many tasks don’t need continuous employment; they only need someone to step in for a while, do the whole job, and then be done.

“interleaved thinking” refers to a capability whereby an AI model interposes discrete reasoning steps between successive tool calls within a single response, rather than confining its reasoning to a single planning phase at the outset. In the prospectus: Selected definitions: “interleaved thinking” (original filing on SEC EDGAR) So that’s what it means. The phrase “rather than confining its reasoning to a single planning phase at the outset” reminded me of something I wrote in an earlier post: that OpenCode couldn’t reason while its subagents were running. I think the two are related, even though they’re not talking about the same thing. One is whether the harness can collect the subagents’ problems while they run, hand them to the model to reason over, and produce new steps The other is whether the model is able to take in scattered information and make new judgments

In economics, an externality (also called an external effect or spillover effect) is an indirect cost or benefit to an uninvolved third party that arises as an effect of another party’s (or parties’) activity. Externalities can be thought of as unpriced goods involved in either consumer or producer market transactions. Air pollution from motor vehicles is one example: the cost air pollution imposes on society is not paid by the producers or users of the vehicles to the rest of society. Water pollution from factories is another: all consumers are made worse off by the pollution, but the market provides no compensation for the damage. A positive externality is when an individual’s consumption in a market increases the well-being of others, but the individual does not charge the third party for the benefit. The third party is essentially getting a free product. An example might be an apartment above a bakery that gets the smell of fresh pastries every morning. The people living in the apartment do not compensate the bakery for this benefit. ...

Looking back at my own habits: with a long document, I always preview it online and then download it to read locally with my own tools. If the original is HTML, I tend to read it right in the browser, and that’s the best experience. If the original is a PDF and the site “opens” it with some kind of viewer, the experience is usually poor, both in efficiency and in reading. I always end up downloading the PDF, because it’s smooth locally and I can use tools I know better. So I don’t want to try to keep readers on my site. No matter how well it’s optimized, don’t hold them there. Just give them the tools, and leave them free not to use those tools, no conditions attached.

The blackmail test in Anthropic’s “A global workspace in language models”: “fake” and “fictional” show up early in the model’s J-space.

Anthropic’s research on injecting thoughts into a model’s J-space raised two questions for me: what if the subject were a person, and if the model can feel pain, is this abuse? It also brought to mind the idea planted in Inception.