With AGI we expect a huge return on investment and a GDP growth that could be accelerating at a rate we couldn't even comprehend. Imagine an algorithm that improves itself each iteration and finds ways to increase its capacity every day. Robots suddenly capable of doing dishes, grocery shopping, picking produce from the field. Imagine all your ailments handled... age becomes just a number.
Also with AGI we expect a winner take all situation. The first AGI system would protect itself against any other AGI system. Hence why it's go time for all these AI companies and why they stopped sharing their research.
I recommend people look at the actual study and think about how representative are the subjects, the tasks involved (SAT essay writing), and the way LLMs are being used.
To be concrete, this is taking a task in isolation that LLMs can do much better than humans (writing garbage essays) and using LLMs to do that task. In the real world, tasks have parts and they exist in a larger context. When we use LLMs for one part of a task, there are other things we're doing that the LLM is not helping with. If you compared people doing arithmetic by hand and with a calculator, you would also see very big differences in how active their brains are. But it's not anyone's job to add up numbers. Adding up numbers is a subtask of a subtask in someone's job.
LLMs can tell you exactly how to acquire the materials and manufacture the materials. They might even come up with novel formulations that rely on substances that are easier to get. There might be information about this stuff online but LLMs are much better than random idiots at adapting that information to their actual situation.
On top of LLMs reducing the cost/difficulty, the other reason biological and chemical weapons are such a worry is their asymmetric character — they are much much easier and cheaper to produce and deploy than they are to defend against.
I've never seen an argument like this that, if true, wouldn't also apply to the cognitive offloading we do by relying on culture, by working with others, or working with the artifacts built by others.
Wonderful post and I will be taking inspiration from it. Surprised not to see TypeSpec https://typespec.io/ mentioned, which is a TypeScript-like schema language that I like to describe as "what if OpenAPI was good". I'm guessing they considered it and decided building their own would be both simpler and more flexible. The cost of BYO has come down a lot thanks to agents.
Love TypeSpec, agree it makes writing OpenAPI really easy.
But I’ve moved to using https://aep.dev style APIs as much as possible (sometimes written with TypeSpec), because the consistency allows you to use prebaked aepcli or very easily write your own since everything behaves like know “resources” with a consistent pattern.
Also Terraform works out of the box, with no needing to write a provider.
Looking at the first example - it's far less verbose. Although, seems to be suspiciously minimal, so I can't even tell from a single .tsp route definition what response content type to expect (application/json is the default most likely).
It’s actually human-readable, it has generics, it supports sum and product types in a much more natural way. There’s a lot more, that’s just off the top of my head.
Saw all the replies crying over how verbose these are, clicked through to TFA expecting to see simpler commands. Nope, they're basically the same thing, just slightly shorter. I would never memorize either the jj or git versions if I planned to use them regularly; I'd make aliases.
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