matthewgee retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
matthewgee retweeted
➕➕ New research ➕➕
On the design of agents for markets and markets for agents.
AI agents are increasingly entering markets, acting on behalf of people—
How do we make sure they represent their people well, and that these markets are set up safely and fairly for everyone?
matthewgee retweeted
The analysis here is super cool but also HUGE shoutout to @ernietedeschi and the Stripe team for building out a new, reputable private-sector data index which is a really nasty, complicated thing to do in a robust way - huge amount of under appreciated work
New from @Stripe Economics: the SaaSpocalypse was a massive market sell-off earlier this year, but how did actual SaaS firms fare?
We built the Stripe SaaS Index to find out: a metric of payments to non-AI SaaS firms.
Turns out revenues *accelerated*. /1 stripeeconomics.com/p/the-sa…
matthewgee retweeted
AI is the most important technology of our era.
I’m delighted to see so many insightful contributions to this volume that grapple with the big economic questions raised by transformative AI.
New NBER Book released: The Economics of Transformative AI
nber.org/books-and-chapters/…
matthewgee retweeted
We need more complex systems, SFI-pilled scientists involved directly in AI research.
I spent 7 years at CMU in the same department as some of the best complex systems researchers around (aside: CMU's Social and Decision Sciences department was heaven on earth). It is a huge shame that scientists who have spent decades studying multi-agent systems and the emergent properties from those interactions are not more central to the conversation.
matthewgee retweeted
My best interview in some time.
Rohin Shah leads AGI alignment/safety at DeepMind.
And he has a lot of spicy personal takes:
We probably won’t get catastrophic misalignment (00:49)
Safety 'commitments' have severe limitations (10:38)
The intelligence explosion probably isn't imminent (1:52:44)
Why he's not working to pause AI advances (51:44)
Pre-deployment evals aren't the right focus (for catastrophic risks) (37:41)
Signalling concern for safety sometimes diverts resources from actually making AI safe (01:09:51)
Reading AI thoughts is v useful for safety – and we'll probably be able to for years to come (54:17)
Governance is somewhat more likely to be the bottleneck than alignment (43:55)
Rohin's team doesn't have a veto, and that's OK (27:36)
Central banks are a promising model for regulating AI (33:34)
Also:
Google DeepMind's actual plan for building AGI safely (1:40:29)
How external researchers can positively influence big AI companies (2:21:55)
The roles GDM most needs to hire for (2:37:03)
On the 80,000 Hours Podcast. Links below - enjoy! (@rohinmshah)
matthewgee retweeted
In the BBC with a hot take*: maybe individual company announcements are the worst way to evaluate the impact of AI on the labor market and we should take a more data-centric approach
*ok it’s not that hot let’s be real
matthewgee retweeted
Event announcement 🗣️
Registration for the #WomenInData Virtual Conference is now open! We are partnering with WID Steering Committee members to help @D4D_Nepal host the conference.
Link: asiafoundation.zoom.us/meeti…
More info coming soon :)
#WomenInData #WIDNepal #GenderData
matthewgee retweeted
We’re hiring DevOps Engineers that have deep understanding of Chef. 👨🍳
Join the @AppDirect team in SF!
jobs.lever.co/appdirect/1db6… – at San Francisco, CA
matthewgee retweeted
"This is one of the most reckless & reprehensible decisions ever made by a U.S. president." bit.ly/2rvGJf7
Looking forward to hosting a webinar with @karlfogel next Tuesday on open source collaboration in energy analytics! bit.ly/2njEy9f
matthewgee retweeted
Wonderful maturity in the community to have learned the limitations of technology xitter.cf/chihacknight/status/83… #opendata #civictech
Technology alone can’t solve Chicago’s murder problem - our piece in @CrainsChicago chicagobusiness.com/article/…
matthewgee retweeted
Why is US prime-age labor participation rate lower than in most other advanced economies? Goldman Sachs offers an explanation:
Required reading: xitter.cf/kirkwjohnson/status/82…
I served in Iraq, as USAID’s man in Fallujah. Lived alongside Marines and interpreters as they fought terrorists. @realdonaldtrump (1/many)
matthewgee retweeted
To all Researchers: If US Muslim ban remains, lets move our conferences elsewhere. Science must be open to all. – at California, USA
SLC 3:45am. Getting ready for my first point-in-time count! So inspired by these amazing volunteers collecting good data to #endhomelessness
matthewgee retweeted
Great news: Thanks to the #ACA, the share of Americans without health insurance has fallen to an all-time low.
matthewgee retweeted
How Data Science for Social Good has “started a movement” among governments and non-profits, from @govtechnews: govtech.com/data/Data-Scienc…
matthewgee retweeted
Thrilled to annouce the @WhiteHouse Data Innovation Summit w/ Solutions Showcase on 9/28 (submission due 9/1!) #USA
whitehouse.gov/opendatasummi…
































