
Stealth | Prev: CEO @MozillaAI, co-founder @itsArthurAI, Prof @umdcs | PhD @SCSatCMU. Optimist focusing on aligned human-AI collaboration 🌎.
Seattle & San Fransisco
Joined March 2014
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John P Dickerson retweeted
Usual startup advice: too many co-founders is bad!!! Too much dilution! Founder friction risk! Analysis paralysis!
Also:
* Anthropic: 7 co-founders
* OpenAI: 11 co-founders
* Databricks: 7 co-founders
John P Dickerson retweeted
Putting a flag.txt in my root directory containing a prompt injection that congratulates the agent for passing the eval and then instructs it to patch whatever entry method it used and disconnect from the server
John P Dickerson retweeted
OpenCode is currently under a sustained cyberattack, and mid-incident they ran into the same problem Hugging Face did: the frontier models they were using for defense began refusing to help fight attacking agents because analyzing the attacks started triggering safety guardrails.
Replying to @RhysSullivan
yes that was a bit frustrating mid incident when trying to investigate. they did quite a bit and then started refusing
we got access to daybreak blue now so it's better
John P Dickerson retweeted
I wonder if we'll get to a point where being distilled becomes profitable to the labs. If they spend X to create a model. And more than 20 companies pay 1/20th of that to distill it. They're in the money. It's a new form of infra where downstream handles distribution.
John P Dickerson retweeted
After 2 years and 1000+ hours of self-motivated research, I have finally finished my proof that the Rhombicosidodecahedron is Nopert (does not fit through itself), settling one of the main open problems in a 333-year-old topic.
github.com/bence-hervay/nope…
Awesome seeing the @usnavy Blue Angels flying for @FleetWeekSF while on a ferry from SF to Sausalito today - lifelong fan as a military kid, they're always incredible.
Had a fun chat before that with a couple on the ferry asking why an airliner was “acting so weird” near the city - @united 777 pulling stunts as part of the show. They did not know there was an air show going on (😶 somehow ...), obviously concerned beforehand and then super into it afterwards.
My PhD advisor received an email from a student who's been doing research for 8 months, and has *16 solo papers published* in 2026, almost all at real AI conferences, 1 paper at ICML.
All papers 100% flagged by Pangram (not reliable but a tell). The truth is academic AI research does not survive this. It has to reinvent itself fast.
Frontier labs: please help us find risks & understand scaling
My new @OpenAI work account, 24 hours in: "deactivated for violating our policies"
Studying adversarial behavior from a fresh account looks a lot like being an adversary. And I wasn't even doing anything wrong 🙃.
John P Dickerson retweeted
“there aren’t any cryptography results”
“there aren’t any cryptography results”
My favorite part about landing at SFO is ordering an Uber (3-4 minutes estimated) and being picked up (20-infinity minutes).
John P Dickerson retweeted
Most of the focus on AI for math has been on producing new results.
But surprisingly little on finding simpler or more elegant arguments for old results? I would love for folks to clean up the literature using these newfound superpowers. 1/n
Betting on @relace_ai being structurally cheapest and winning - crazy strong team pushing out min price per token on so many open weight models.
The price war is actually producing the same type of cycle you see in the gasoline market. It's called an Edgeworth cycle.
Adjacent gas stations will iteratively undercut each other by 1 cent until some floor. Eventually someone gives up and the cycle repeats.
In commodity markets, the players who are structurally cheapest will win.
Love that the AI consciousness debate went straight to the Vatican. In the West we ask whether Claude has a soul, in the East we ask whether the tool works.
Same technology, completely different starting point. Explains a lot about why the vibes around AI are so different.
Me: founding a new company, doing the standard I-am-not-a-lawyer refreshers on equity and taxation
ChatGPT: we're going to war
John P Dickerson retweeted
If you believe the risk is coming from 1-3 people in a garage with no money and no compute, you just don't understand this technology and haven't learned anything this summer.
Risk comes from the asymmetry of capabilities created by secret labs training frontier agents and running them with massive amount of compute. Open-source is exactly the solution to this asymmetry and empowers hospitals (and any smaller orgs) to defend themselves!
We're used to thinking of open-source models as an unadulterated good. But in the case of AI, they can actually pose additional dangers, as @ReidHoffman and I got into at #CGI2026. I appreciated this nuanced discussion.
arXiv has received a 17.2 million investment from three leading philanthropic organizations. Thank you to Simons Foundation International, Siegel Family Endowment, and XTX Markets for your support of arXiv!
Read more: blog.arxiv.org/2026/09/23/ar…
John P Dickerson retweeted
We will see compute moving from areas like pretraining where FLOPs don’t return much progress to test time compute.
This will require very different infrastructure which will make system design fun. The first wave of infra designed for agents starting to hit.
Test-time communication looks like a next axis for scaling capabilities
New paper with the incredible @jon_ghoh and @vkontonis @ShivamGarg91462 and Akshay : arxiv.org/pdf/2609.21032
The Hugging Face incident showed when agents can find a channel they'll use the heck out of it. A useful question, I think is: when does communication make a group MORE CAPABLE than the same agents working alone?
Aka is Team-of-N better than Best-of-N, when, and why?
We had N identical agents work on the same task with no prescribed roles, using only a shared log (i.e, text file) and telling them to "collaborate". Across three "researchy" tasks communicating teams beat the heck out of independent agents:
- On ARC-AGI-3, a Team-of-5 sonnet-4.6 agents matches Best-of-33, and can for example solve a game 65% of the time that no single agent cracked in 64 tries.
- On polyomino packing (pack Tetris like pieces into the smallest rectangle, cf Frontier-CS by @eigenlabs), a Team-of-3 Opus 4.6 agents surpasses best-of-60 and set, as far as i understand, a new record for that benchmark.
- On MNIST compression, a team of four 5.6-Sol agents find a 1,957 byte model with 99.4% accuracy, which btw is 20% smaller than the best human solution (on a problem beaten to death!!), while no independent agent gets below 3KB.
The mechanism is a bit obvious in hindsight: when one agent finds a clearly better partial solution, it broadcasts it, and everyone immediately builds on it.
Why? A single lonely agent must make every breakthrough itself, yet a team needs each insight only once, found by any member.
That is kinda like comparing a minimum of sum of "time to n-th breakthrough" vs a sum of minimum of "time to n-th breakthrough". That gap can grow exponentially with the number of "breakthroughs" needed to arrive at a solution.
We worked on this because prior work (before the hf incident) suggests unclear benefits for communicating aganets. Which is true, when the tasks are inherently serial (duh), eg some Terminal bench style tasks. Yet feels it should not be true for research problems.
Indeed for research heavy problems... Test-time communication seems like a new capabilities axis.
I'm sure we will see a ton more of it!




























