Jeff Forbes retweeted
As William Gibson said, "The future is already here – it's just not evenly distributed". I like the funnel metaphor since it also hints at the friction involved with these capababilities diffusing more widely/quickly (despite SV rhetoric).
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.
Jeff Forbes retweeted
How does ChatGPT change the work students produce? And where does critical thinking fit in?
As a professor and father of three, these questions are especially important to me. 🧵
openai.com/index/what-studen…
Jeff Forbes retweeted
The FY 2027 NSF Graduate Research Fellowship Program competition is now open. Learn more and apply: brnw.ch/21x5cGo
📷: Charlotte Geary/NSF
Jeff Forbes retweeted
Friends don't let friends continue to give exams and papers that are not adapted to the current AI landscape (this is AFTER 27 bailed so the reality is even worse)
PS Props to students 1, 22, and 31
Jeff Forbes retweeted
I asked a (brilliant female) colleague about the apparent gender gap in AI uptake in our field and she suggested that it was driven by women being more likely to be judged as incompetent if they use AI. This resume study suggests, sadly, that she may be correct.
Jeff Forbes retweeted
We're launching the OpenAI Economic Research Exchange: a new program for external researchers conducting independent research on the economic impacts of AI.
We are looking for rigorous empirical projects on questions that matter for workers, firms, institutions, and the broader economy.
openai.com/index/economic-re…
Jeff Forbes retweeted
For 45 years, Berkeley built virtually no new housing. By the mid-2010s, it was the most expensive college town in America. Shortly thereafter, YIMBYs took over and kicked off a building boom. Today, nominal rents are below 2018 rates—remarkable progress on affordability.
Jeff Forbes retweeted
Pretty wild I got my PhD 4 years ago to the day. I feel very lucky that I got to do it and make my switch into AI.
Lot's of people today in AI are underselling the value of going through the process of a PhD.
Jeff Forbes retweeted
Many folks seem to be confused, and think the collapse of the CS major graduation numbers at Berkeley could be linked to the "AI is taking SWE jobs" hysteria narrative. Here's the easiest way to see that this is false: the timeline doesn't fit.
The graduating class in 2027 (first small CS cohort graduating) has students who arrived on campus as freshmen in Fall 2023, with freshman admission targets set (i.e. shrunk) by the university in Fall 2022. So, the hysteria narrative obviously doesn't match the timeline; ChatGPT didn't even come out until November 2022.
Now consider the plot below; orange curve is what % of bachelor's degrees are CS degrees each year at Berkeley, and blue curve is what % of applicants applied to be a part of that graduating year, intending to be a CS major in their application (combining both junior transfer and freshman applicants). In other words:
* orange measures CS graduate production
* blue measures CS demand (via % of all applications to the university)
What do you notice? The collapse in orange (CS grads) isn't because of a collapse in blue (demand). In fact it's the opposite: orange collapsed at a time when blue was going up. 1/
Jeff Forbes retweeted
One of the cruelest changes here is changing benefit time limits for families with kids. Only families with kids 14 and under are exempt—used to be 18
Underlying expectation there is for the 15+ kids to work to eat
Disgusting
New work requirements for SNAP are set to go into effect on Feb. 1 and it could mean that millions of Americans lose their benefits.
Read more: abcnews.link/B4R1RoI
Jeff Forbes retweeted
Women are chronically underrepresented in tech/programming sectors.
This new preprint examines whether hiring bias--especially in interviews--is to blame and, if so, why.
Using 60,000+ coding interviews from a peer-to-peer interview platform, it finds that even when women perform similarly to men, they are penalized in interviews.
But which theory of bias actually explains this gap?
The authors test four hypotheses about the source of gender bias in technical hiring.
Hypothesis 1: Ability differences.
Explanation: If women receive lower interview scores because they perform worse, evaluation gaps should mirror performance gaps.
Finding: Rejected. Performance differences on objective coding tasks are small and cannot explain the much larger evaluation penalties women receive.
Hypothesis 2: Statistical discrimination / lack of information.
Explanation: If interviewers underestimate women’s ability, giving them objective performance signals should reduce the gap.
Finding: Rejected. Providing interviewers with automated coding test scores does not reduce the gender gap.
Hypothesis 3: Taste-based discrimination.
Explanation: Interviewers may simply prefer men.
Finding: Not supported. The gap disappears when evaluations are blinded and non-interactive.
Hypothesis 4: Interaction-based bias.
Explanation: Bias arises during live interpersonal interaction (voice, confidence norms, expectations).
Finding: Supported. Gender gaps appear only in interactive interviews, not when the same code is evaluated blindly.
Bottom line: Interviews don’t just measure ability. They produce bias through interaction itself. Reducing gender gaps may require redesigning interviews, not just debiasing interviewers.
Jeff Forbes retweeted
In the 1970s-80s measles was the single leading killer of children, 2-3 million died annually. Through EPI @WHO we brought it down to 450,000 kids who died annually by 2000. Through @gavi it came down to <100,000. Now the ignorant careless chuckleheads want to erase all our gains
Joe Rogan: "Measles was what everyone got when I was a kid, and what happened was you'd get sick for a few days, and then you'd be immune for life. They're making it look like everyone's dying from measles."
Readers added context they thought people might want to know
Joe Rogan was born August 1967.
Due to Vaccines Measles rates dropped significantly - so when he was a kid "everyone didn't get Measles".
He might be confusing Measles with Chicken Pox, which was prevalent at that time
britannica.com/biography/Joe-…
statista.com/statistics/186…
Jeff Forbes retweeted
Huge kudos to @NSF on this effort.
Today, the NSF announced Tech Labs, an ambitious program to build new independent research organizations to accelerate American science. This comes off of momentum in metascience that has been building for years around concepts like FROs, fast grants, and new research and funding organizations like @arcinstitute, @ArcadiaScience, @ARIA_research, and more. I sat down with @AdamMarblestone and @CorreaDan from FAS @scientistsorg to discuss what we have learned from 7 years of FROs; where things are going with the gradual reorganization of American science; and how government can play a role.
Read about it here:
fas.org/publication/nsf-supe…
And check out NSF’s announcement: nsf.gov/news/nsf-announces-n…
Jeff Forbes retweeted
NSF is launching one of the most ambitious experiments in federal science funding in 75 years.
The program is called Tech Labs, and the goal is to invest ~$1 billion to seed new institutions of science and technology for the 21st century.
Instead of funding projects, the NSF will fund teams. I’m in the @WSJ today with a piece on why this matters (gift link): wsj.com/opinion/science-fund…
Here’s the basic case:
1) Most federal science funding takes the form of small, incremental, project-based grants to individual scientists at universities.
2) The typical NSF grant is ~$250k/year to a professor with a couple of grad students and modest equipment over a few years. This is a perfectly reasonable way to fund some science, but it's not the only way.
3) A healthy portfolio needs more than one instrument. Project-based grants are like bonds: low-risk, steady, safe. But no one trying to maximize long-run returns would put 70% of their portfolio in bonds.
4) Yet that's basically what our civilian science funding portfolio looks like. Around 3/4ths of NSF and NIH grant funding is project-based.
5) Tech Labs is NSF's attempt to diversify that portfolio. The Tech Labs program is aiming for:
- $10-50 million/year awards per team
- 5+ year commitments
- Measuring impact through advancement up the Tech Readiness Level scale rather than papers published
- Up to ~$1 billion for the program
- Supporting research orgs outside traditional university structures
6) Scientific production looks very different than it did when the NSF launched 75 years ago. The lone genius at the chalkboard can only do so much. Frontier science + tech today is increasingly team-based, interdisciplinary, and infrastructure-intensive.
7) The team behind AlphaFold just won the Nobel Prize in Chemistry. It came from DeepMind, an AI lab with sustained institutional funding and full-time research teams. It would be near-impossible to fund this kind of work on a 3-year academic grant.
8) Same pattern at the @arcinstitute (8-year appointments, cross-cutting technical support teams) and @HHMIJanelia (massive infrastructure investments to map the complete fly brain). Ambitious science increasingly needs core institutional support, not a series of project grants stapled together.
9) Similarly, Focused Research Organizations (@Convergent_FROs) have showcased a new model supporting teams with concrete missions and predefined milestones to unlock new funding.
10) There’s a whole ecosystem of philanthropically-supported centers doing amazing research, like the Institute for Protein Design, the Allen Institute, the Flatiron Institute, the Whitehead Institute, the Wyss Institute, the Broad — the list goes on.
11) But philanthropy can’t reshape American science alone. The federal government spends close to $200 billion each year on research and development, an order of magnitude more than even the largest foundations.
12) If we want to change how science gets done at scale, federal funding has to evolve. And the NSF and NIH don’t have dedicated funding mechanisms to support or seed these sorts of organizations.
13) Earlier this year, I started working on a related framework called “X-Labs” that built on all this exciting institutional experimentation that’s been happening within the private and philanthropic sectors. It’s time for the federal government to step into the arena: rebuilding.tech/posts/launch…
14) Traditional university grants are still important for training the next generation of scientists and for certain kinds of curiosity-driven work. But after 75 years of putting nearly everything into one model, we should try something different.
15) And key program details are still being developed! You can reply to the Request for Information with suggestions or feedback on how to design this program here: nsf.gov/news/nsf-announces-n…
16) Science is supposed to be about experimentation. Science funding should be too.
Jeff Forbes retweeted
Bill Gates famously said "People often overestimate what will happen in the next two years and underestimate what will happen in ten."
This holds for AI, but with both quantities divided by 4.
For years skeptics kept moving goal posts and making overconfident takes about what language models could or couldn't do, to be disproved immediately after. I'm glad they were proven wrong again and again.
But on the other hand, more subtle or ambitious expectations are clearly important for progress and not always goalpost moving. Models outperforms humans in many areas while falling short in others, so it makes sense to adjust your targets as you learn more about their limitations.
Relatedly, the desire to convey to people the (correct) intuition that AI is going to continue rapidly improving makes some people resistant to acknowledging or exploring existing limitations. They brush them off a bit too quickly - after all, line goes up, so why waste time?
That's a shame because they end up discussing AI only in abstract terms, rather than building concrete models of what these systems actually are and do. Without that grounded understanding, the more abstract predictions feel a lot less reliable.
Jeff Forbes retweeted
Today's the day! Tonight at 6 pm ET, join us for the livestream of the 2025 #AERABrownLecture, featuring James A. Banks with a discussion forum to follow. aera.net/Events-Meetings/202…
Jeff Forbes retweeted
"There has always been—and there remains—a wide gap between America’s democratic ideals and its practices." James A. Banks, 2025 #AERABrownLecture
Jeff Forbes retweeted
Life is just more complex than “there’s a cabal behind everything” and “the establishment decides” and that’s more frightening for people to deal with because it involves looking in the mirror and looking at your friends and neighbors.
Jeff Forbes retweeted
From teaching to Director of CS Equity Initiatives at the #KaporFoundation, Shana V. White has always centered equity in education and building community in classrooms.
Tune into her @RoadtripNation feature to see how she’s shaping a more inclusive CS future for K12 students. Learn more about Shana and her work here: bit.ly/3Gh5vZt
In addition to his other accomplishments that truly impacted me, I am shocked to learn Malcolm-Jamal Warner directed Special Ed’s I’m the Magnificent! That’s likely one of my top 10 songs! Thanks @byjoelanderson
Malcolm-Jamal Warner, most remembered for playing Theo Huxtable on ‘The Cosby Show,’ passed away Sunday. But his legacy spans much more than one character.
@byjoelanderson:
theringer.com/2025/07/21/tv/…




































