Centaur Era Starts Now
Step one: find problem
Human creativity and imagination, augmented by the raw search power of AI. The computer - the workhorse - automates away the predictable, zero-entropy decisions, freeing the human - the helmsman - to steer the ship through idea space.
The Centaur Era is well underway. We do not know how long it will last or whether it has an expiration date. But standing here today, you are a thousand times more capable than any human at any previous point in history, with a trillion more opportunities available. How will you decide what to do?
What’s possible?
Here’s a nice little appetiser for you to start sparking some ideas:
How about building a nuclear fusor in your bedroom using Claude 3.5 Sonnet and Claude projects?
I primarily relied on a giant Claude project filled with documentation from forums, call transcripts, mail threads and more…
I also used o1 pro for helping me with very complicated assembly or electrical wiring stuff. Found it much more useful for these cases. - Twitter thread, video explanation, podcast, blog post
Or perhaps you’d enjoy building a GPU from scratch:
I've spent the past ~2 weeks building a GPU from scratch with no prior experience.
My most important resources were Claude Opus, GPT-4, and these 2 repos…
More: decoding images from a portable brain scanner, home chip fab
Those are all tech and hardware focused, but consider this list of academic disciplines. For every academic discipline X there will be a computational variant of that field.1 Here’s an imaginary project I made up for “Computational History”:
I extended the work of historian Penelope J. Corfield, who studied optimistic and pessimistic attitudes in Georgian Britain. Using AI search tools, I located thousands of 18th-century novels, plays, poems, letters, diaries, guidebooks, journalism, sermons, songs, and sayings. I then designed an AI-assisted method to analyse national mood shifts over time.
The possibilities are endless.
Problem Search
Everyone’s capabilities have been amplified a thousandfold. We walk around with PhDs in our pockets. Soon, we may each have small armies of intelligent agents at our disposal.
As the cost to build drops, the value of people, ideas, interesting problems and intelligent starting assumptions increases. Problem valuation > implementation. The most urgent questions are philosophical not technical.
Finding a problem is our first problem. Here are some things to try:
Debate valuation frameworks (one, two, three, four) and collect lists of ideas
Survey many big open problems or deep-dive into a general topic like energy
Get inspired by art eg. mind uploading from Pantheon (TV Series)
The best measure of success - falling in love with a problem that brings life meaning.
(The search continues…)
Judges and Juries
But given a particular problem, how can you decide whether it’s worth pursuing? Sometimes it’s good to go in guns blazing because when under extreme uncertainty over-rationalisation is often a poor substitute for action.
But over the past 6 months I think I personally would have benefitted from discussing my ideas more with others. There’s actually an interesting evolutionary argument in favour of this - the argumentative theory of reason - which claims that reasoning evolved primarily for evaluating others’ arguments, not constructing our own.2
For example, research shows that groups outperform individuals on tasks like the Wason selection test.3 Of course, group decision-making is vulnerable to biases like groupthink, so the challenge is to create an environment that enhances rather than diminishes reasoning, which is something I’m actively thinking about:
Just like with AI, human decision-making benefits from reasoning, criticism, best-of-n and other test-time search methods. Here are some things to try:
Get feedback on your ideas from ensembles of human and AI “judges”
Write and discuss recursive “why?” documents4
Write socratic dialogues arguing against yourself (I’ll share mine soon)
Learning
My sense is that in the Centaur Era, pre-requisites are largely a myth, and just-in-time learning is the modern way. The perfect AI-powered learning environment doesn’t exist yet, but it’s becoming easier and easier to learn while working in an authentic context, like an IDE, on a project you care about.
For example, I used the socratic tutor prompt (see the video below) to learn about hashing algorithms SHA3 and BLAKE3 while working on these bounties. I also used it inside Cursor to understand the DreamCoder paper and codebase.
Something more speculative that’s on my mind is what meta skills will be valuable to learn. I think one of the main ones will be task delegation. There was promising thread titled If I wanted to spend WAY more on AI, what would I spend it on?, but the lack of good answers from commenters suggests that people find it difficult to even think of what things they could be delegating to AI.
Maybe the skill of task delegation, or capital/compute allocation will become as important as the domain-specific knowledge of whatever problem you are working on?
Thanks for reading and get in touch on Twitter or Discord if these ideas are interesting to you!
The Computational X idea is from Stephen Wolfram
The Enigma of Reason - Rough summary: “Reasoning is a flawed superpower that arose as an adaptation to the human hyper-cultural, hyper cooperative social niche. Its function is not really to enhance individual reason (even though it may do so as a side effect), instead it evolved to evaluate others' arguments so we can secure the benefits of communication while mitigating the risks - (falling prey to manipulation, lies etc)”
Basically the idea is that you open up a blank document, start writing about why you are taking a certain action, eg. why you want to go to grad school, or why you are learning math, or why you want to make money. Then for each bullet point you come up with, you recursively ask "why?" until you reach a level where you find your true motivations.
Oh, this is good to read, only recently I suddenly realised I could no longer use the excuse 'this is too complicated for me to understand!"
Note: typo = 'nut the lack of good answers '