transhumanism, transindustrialism
Queenstown-Lakes District
Joined December 2015
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Jason Potts retweeted
I asked Claude: "What types of businesses outside of the labs will get stronger as raw intelligence explodes?"
Honestly, spot on response.
Jason Potts retweeted
Quick thread on AI and productivity:
Lots of people say this chart shows AI is now showing up in the productivity data.
But I made this chart to argue the opposite: a trend that pre-dates AI and only shows up in the US doesn’t look like AI.
Here’s how I’m thinking about the data and underlying dynamics:
Jason Potts retweeted
Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. *Significantly* better viewing experience, ~2 hours with Opus 5.5:
hackertube.org/
- Excellent recs without any logins (state is kept in local browser session)
- Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads
- Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version
- HackerTube Longs ✊
A bit more on the process:
I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing.
Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.
“A large fraction of architectural firms’ billings is for technical drawing production. AI will almost certainly make technical drawing cheaper to produce, and competition between firms will put downward pressure on fees”
Coding, math, law, … now architecture
Excellent post on the changes coming to the architecture profession from Stephen Gallagher (H/t @Mollykinder)
stephenjgallagher.substack.c…
The other reason is the type of questions asked. ABMs, along with evolutionary approach in general, is dynamics first. It’s a theory of change, not of equilibrium states.
This is true and I have always thought it was a big shame. Part of the reason (as Andrey notes in the replies) is that historically ABMs had not been taken to the data. But this is changing. Here is my colleague @RMaria_drc’s recent work bridging this gap:
arxiv.org/abs/2412.16591
ABMs x AI hold a lot of promise for economics, and it’s an active area of research for our team.
All good monetary systems need elite military forces for exactly this reason
Vale William Nordhaus. His history of lighting is still something I assign almost every class I teach. Such a great story of long run innovation and economic dynamics
nber.org/system/files/chapte…
RIP to an absolute lion of the field news.yale.edu/2026/10/31/wil…
Interesting
Nice (although SFI-centric) interactive history of agent-based modelling in social sciences.
santafe.edu/features/agent-b…
Jason Potts retweeted
The op-ed claims that video format (YouTube/tiktok) would favour optimistic takes. To me it seems technological determinism the other way around. But possibly interesting to test in a cultural ecology of social media framework
cambridge.org/core/journals/…
Jason Potts retweeted
Here's the link to the book
econjwatch.org/file_download…
Love this “The machines can do critique now, at the level of depth and rigor previously only possible for the academically deranged”
People who have been in research fields develop a felt sense that most "research" (in almost any field that isn't exactingly technical) is research theater, and many superstar careers enjoy star status not because of a unique connection to the truth but because of a unique connection with an audience. No one really has the time or stature to, from a position of objectivity and trust, bring popular researchers to heel. But it will get increasingly hard to hide from serious critique. The machines can do critique now, at the level of depth and rigor previously only possible for the academically deranged. We're going to have to revise a lot of what we "know" about the world that isn't really knowledge but is actually just received popular consensus - like floating voltages without grounding wires. The grounding wires are coming.
Jason Potts retweeted
It’s almost incomprehensible how wide the distance is between someone who’s working at the frontier with AI and basically everyone else right now.
The crazy thing is these lines are not converging, but only growing farther apart. If the curve of AI progress had leveled off, it would certainly have given most people and companies time to catch up or implement their workflows in a standardized way. The technology would have neutralized competitive advantage because everyone would be using it the same way.
But what’s happened is the opposite. The curve of AI progress is basically going vertical right now, which means if you had codified your processes 3-6 months ago you’re likely already behind again. Now imagine most companies that kicked into gear a year ago and thought that they were done. Even further behind now.
I’ve never seen a technology in my life and career that improves at this rate, which consequently means you need to upgrade your expectations at a similar rate. Our brains weren’t wired for this, and our organizational change management processes certainly weren’t either.
With any form of recursive self improvement, things just get wilder. It’s a time when you will need to be constantly reevaluating the things that didn’t work just a couple months ago to see if they now can be solved. AI native companies certainly have a strong advantage due to this right now, so now it comes down to can the bigger companies keep up. Wild times.
Jason Potts retweeted
New paper: Rather than automating review, can we target AI to known weaknesses of human review? Consider lottery variance, where differnet reviewer draws change outcomes.
We show how LLM-implemented checklists learned from prior review can reduce variance w/limited added bias 🧵
Jason Potts retweeted
💥 Thrilled to share my new essay "A New Dawn For Mathematical Progress" out today in @NoemaMag! Read the essay here: noemamag.com/
As frontier AI systems solve some of the most difficult open questions in mathematics such as the Millennium Prize problems, many fear human mathematicians are becoming obsolete.
I argue that whether AI ends human mathematics or begins its golden age depends on which of two models of "mathematical progress" we embrace: the checklist model or the generative encounter model.
Right now, the impacts of AI systems on mathematics has left the mathematical community in deep disorientation. A PhD student recently wrote of a "profound spiritual crisis"; a Fields medalist called for "new theories of care and love."
These reactions make sense under the checklist model, where progress consists of resolving well-defined, pre-specified problems (Olympiads, famous conjectures, Millennium Prizes) to crown lone human heroes.
However, the checklist model was always a narrow view; one that elevated "pure" math as the only "real" math, while treating "applied" math as secondary.
As the mathematician George Dantzig once said: "I consider myself a mathematician. I don’t differentiate between pure mathematics and applied mathematics."
History shows that so much of our richest math comes from the generative encounter model on which mathematics makes breakthroughs when human mathematicians tackle complex and messy real world problems.
If we embrace the generative encounter model of mathematical progress, a golden age of mathematics, rooted in human purpose & new visions of education & collective reward, may have just begun.
Jason Potts retweeted
Very happy to share a new paper, “Technology Bottlenecks: Measuring Where AI Could Raise the Value of Work It Cannot Yet Do.”
AI exposure measures ask about the work AI is capable of doing. We ask instead where the marginal value of work AI cannot yet do may rise as AI speeds up the rest of the job.
To do so, we employ 288 million U.S. job postings mapped line by line to their closest O*NET tasks and GPT-4 feasibility ratings from Eloundou et al. (2024) to construct a measure, “bottleneck pressure,” for each task in each occupation that posts it.
Using a simple task-based framework, we derive this pressure at the task level, showing that for an AI-infeasible task it is the exposure of the occupation that posts it, so the same task can face very different bottleneck pressure in different jobs.
Reporting bottleneck pressure alongside AI exposure shows both the scope for AI to perform work and where improving the work it cannot do may pay off more as capabilities expand.
Jason Potts retweeted
In 1969, two men bought a lion cub from the Harrods department store in London. They raised him in their apartment until he grew too big, eventually releasing him into the wild in Kenya with the help of conservationist George Adamson.
Later, the men returned to Africa. Despite being the head of a wild pride, Christian recognized them immediately and ran to greet them with hugs.
Jason Potts retweeted
Agent-Based Modeling: Worlds Within Worlds, a new interactive story from the Santa Fe Institute, chronicles the rise of ABMs in the social sciences, with a focus on SFI contributions.
Over the next few weeks we’ll highlight pieces of this story. First, how it begins:
PART 1: In a former convent in Santa Fe in the late 1980s, economists, physicists, and computer scientists scribble equations on a blackboard where an altar used to be. They are discussing a promising new approach to scientific modeling, one that represents individual actors in, say, an economy or a pandemic, and the rules those “agents” follow.
Today, the method — known as agent-based modeling — offers powerful insights for predicting financial crises, managing pandemics, and even uncovering truths about the past.
Read the full story and run the models yourself: santafe.edu/abm
