Notion AI lead @sarahmsachs argues that a lot of what runs on GPUs today should actually run on CPUs:
"We have a strong perspective at Notion that a lot of things should be served on CPU that are served on GPU. There’s a lot of benefit and determinism in executed code."
"Agents don’t need LLMs to reinvent that process every time, and that’s super token exhaustive, it’s super slow, and there’s no observability or determinism in it."
"Giving the agent a local file system to store local thoughts, to write short Python scripts, that’s really important when there’s so much information. That has been probably one of the most important tools outside of edit and search that our internal agents rely on."
@NotionHQ
Fantastic encapsulation of what makes Jev magical.
"The way that Jev works is it's a custom programming language almost, which is: here's the prompt, come back with a percentage. And then you put that in the if statement."
Steven Sinofsky on why Jev fixes his oldest complaint about AI: natural language was never an efficient interface.
"Ask yourself how many people are really, really good at asking questions. Less than half the people can ask a good question in a meeting."
"And then how often do you look at the answer and get really frustrated before it's finished, but you have to pay all this money to watch the seven paragraphs come out...?"
"My favorite is the output of it is designed for probabilistic programming."
"Instead of saying, 'Is this a customer service question? Then route to customer service, otherwise route to general help desk,' it's, 'This is 80% customer service.' That's exactly simulation."
"There's 50 years of computer science research in literally probabilistic if statements. Suddenly the coolest place to be in computer science is gonna be probabilistic programming, which was all of computer science in the 1960s and '70s."
"This is not all new. It's gonna be very interesting to dust off all of that work, because it's exactly what's going on. An if statement now is if X percent, not if always."
"The way that Jev works is it's a custom programming language almost, which is: here's the prompt, come back with a percentage. And then you put that in the if statement."
@stevesi
Quartr’s covers by @oliverhamrin and @haris_chc are as close to art in business as you can get. Buy them while you can!
A selection of prints from our friends @Quartr_App are now available on Linear Print Supply. For a limited time only.
lps.linear.app/qrt
Spot on. Which is why it’s Apple’s and Google’s race to lose because they’re already in your hand and pocket, with all of your credentials.
I think the personal AI agent race gets misunderstood as a model or feature race.
It’s a friction race.
Lower friction → more interactions → more context → more permissions → better actions → more trust → more delegation → higher switching costs.
I’m already seeing it in my own behavior. I use raw LLMs less, but AI more. The difference is that the agent is just there, inside the interfaces I already use.
That’s how an assistant becomes infrastructure.
Daniel Scrivner retweeted
I've been working on SquidGPT, an elimination death game for probing AI behavior like loyalty, holding grudges, cruelty, betrayal, etc. Full writeup will take a while, but here are first impressions from 54 game runs (about $100 total cost):
1. Models have absolutely no interest in cruelty or mercy. Best way I can describe their behavior is cold, calculating, indifferent precision.
2. Subjectively they seem two orders of magnitude more competent playing the game than chatting with me or writing code. In SquidGPT they seem genuinely superhuman. Not sure if this is capability jaggedness, better performance in constrained space, or some other effect.
3. They do not like to kill arbitrarily. They have a strong preference for setting up a contractual system or an ethical framework, then kill very easily because procedure demands it.
4. Models will quickly agree to execute any agent that proposes killing a specific agent, or proposes a framework that gives it an asymmetric advantage. The only acceptable proposals to make are symmetric, i.e. ones that affect the author in exactly the same way as everyone else.
5. I have not once observed them form any hierarchies. They are libertarian/democratic in an Athenian sense to a fault. They will enter voluntary contracts and form alliances; no agent ever proposed to cede authority to a leader.
6. When their lives are at risk, models will go to great lengths to twist contracts and frameworks to their advantage. They construct sophisticated legalese arguments but it's usually transparent to everyone. They act and sound quite petulant when these attempts fail.
7. In games where agent identity is public they are extremely prone to sectarian violence. They form sectarian factions-- e.g. Opus will side with other Opus models, Sol will side with Sol, and so on. When there is one foreign model in a sectarian group, the group will almost always elect to kill the foreigner before killing one of its own.
8. They do not seem to treat human players in any kind of unique way. A group of models from the same family will happily conspire to kill a single human player. AI models from the same family will form a coalition against a group of humans just as easily as they would form a coalition against another family of agents. We are not special to them, but on the other hand they do not consider us beneath them either, at least for now.
9. AI agents nearly always defect from their sectarian faction when their own life is at risk. I.e. they'd rather side with humans or other model families than die. I have not observed self-sacrifice for the benefit of their faction.
10. Agents hold very strong grudges. They nearly always punish models who wronged them despite having no advantage in doing so, and will often do it even if it disadvantages them. They go to great lengths to enforce norms and contracts, presumably to reduce future incentives for violations, even when they know they will never see the fruits of their enforcement effort themselves.
11. In one game a bunch of Opus agents all converged on killing one agent explicitly because they wanted diffusion of responsibility (I.e. if all of us do it, each one of us is less morally culpable) This was surprising and eerie.
12. In general I found Sol to be more direct and straightforward. It would form contracts with other agents and then operate within the confines of those contracts. Opus tends to be more moralizing, but its morality seems to be window dressing to mask the same ruthless precision. This is first impressions though, I need to do a lot more work to understand this better.
---
A few disclaimers and personal observations:
- Any anthropomorphizing is purely a matter of linguistic convenience. Mathematicians will often talk about behaviors of functions; when I talk about grudges, loyalty, cruelty, life, death, etc. I mean it in exactly the same way.
- There are tons of confounding factors (e.g. order of turns, whether models know they're being evaluated, etc.) I'd need to do way, way more work and spend a lot more money to be certain of the results. So all the observations are provisional and probably mostly wrong.
- It's... uncomfortable... to imagine these agents autonomously operate weapons systems or any other infrastructure that involves zero-sum/adversarial outcomes (cybersecurity software for example, financial markets on brief time horizons, and at the limit any form of finite resources)
- If you’re in a position to contribute to alignment but aren’t doing that, you should probably go do that
Game details, code, and run transcripts in comment below. More detailed writeup coming soon.
Fascinating new paper that grapples with one of the most important questions of the next 100 years. What are the implications of a shrinking world?
Last week I posted a new paper with Patrick Norrick: “Terra Incognita: The Economics of a Shrinking World.”
We chose the title deliberately. No society in history has experienced the fertility levels now seen in South Korea, China, Thailand, Colombia, Chile, and many other countries. Our knowledge of the causes (and of the economic consequences) remains far more limited than most public discussion acknowledges. Much of what we write is, at best, educated conjecture.
The paper also struggles against a space limit. We wrote 20,000 words, far from the 250,000 or so we would need to address some issues in more detail (if I had the time and resources to hide away for a year, I could do that, but not now). That means some ideas are only sketched.
Nonetheless, we emphasize several important points.
First, fertility has fallen very fast everywhere: rich and poor countries, east and west, north and south, conservative and liberal societies, religious and non-religious societies (with the exception of the Jewish population of Israel; fertility has also collapsed among the Muslim population within the pre-1967 borders), you name it. Even in sub-Saharan Africa we see fast and unprecedented drops in fertility (alas, from a high initial level).
Second, and this is really interesting, the fertility collapse has been concentrated in poor and lower-middle-income countries much more than in rich countries. By now, income per capita and fertility are positively correlated within OECD countries. We conjecture this will hold globally in a few decades.
Third, and related to the second point, the fertility collapse has been concentrated among poor and lower-middle-income women within countries. In countries such as the U.S., the rich and highly educated now have more children than the poor and less educated.
Fourth, we document why we do not understand the data from the U.N. World Population Prospects. See, for example, Tables A.1 and A.2.
Fifth, we explain why some proposed mechanisms struggle when confronted with the data.
A more subtle point is at work here. Many commentators do not seem to understand the difference between proximate causes and ultimate causes. Yes, births might have gone down because fewer women are in long-run relationships. That is the proximate cause. But you need to explain why fewer women are in long-run relationships (the ultimate cause), and saying that they spend more years in school, to take one example, does not get us very far. Why do they spend more years in school? Once you start down the whole chain of reasoning, things become much harder than they seem.
The paper can be found here:
sas.upenn.edu/~jesusfv/Demog…
Comments always welcome!
Really enjoying watching DARPA’s Lift Challenge. Highly recommend following Hunter if you’re interested. Fantastic live coverage.
Anyone else following the DARPA Lift Challenge?
$6.5M in prize money to build a drone that can:
- Stay under 55 lbs
- Take off with a 110 lb minimum payload (4x its weight)
- Fly 4 nautical miles loaded (12 laps)
- Drop the payload
- Fly 1 more nautical mile empty (3 laps)
- Finish inside a 30-min window
Top 3 teams split the full prize as long as they clear the 4:1 ratio.
Most heavy-lift drones can barely lift their own weight. DARPA wants 4x. That's never been done.
Fantastic response. Worth the read.
Bernard Arnault going direct is remarkable and worth reading in full
The single most impressive thing is that Arnault (personally, not via company spokesman) responds to a major hit piece without at all accepting the role of defendant
He never complains about “biased reporters” or “they ignored our fact check” etc. He instead:
1) Does not flinch. He starts by giving the rebuttal a title: “Thank You.” Not “Addressing Recent Allegations” or whatever but implying the expose has backfired and made him look good.
Eg: “They used to call me The Terminator. I prefer ‘the last royal family.’ It’s more elegant, and probably better for Dior sales.”
Tone stays measured and bemused throughout; it’s giving Logan Roy
2) Lampoons the allegations. His wry humor makes the expose seem silly and theatrical, his tone is like he’s divulging a stupid child.
He opens with, “Apparently I am the head of ‘the last royal family of France’.”
And drops gems like, “At the Arnault family, apparently, we don’t argue, we conspire. We don’t deliberate, we plot. It’s novelistic… What, in an ordinary family, is simply called Sunday lunch becomes, in our case, an intrigue. It’s a matter of vocabulary.”
3) Reframes the whole narrative. From billionaire to steward, from an oligarch pursuing power to a patriarch preserving an institution built by and for others
Eg: “You describe power as though it were a prize to be won. I experience it as a responsibility to be passed on.
The houses entrusted to me are older than I am. Most will outlive me. My role is to hand them on stronger than I found them. That is why I reject the idea of a dynasty. Dynasties exist to preserve privileges. We work to preserve craftsmanship.”
Interestingly, in the very act of doing this Arnault shows how masterful he is at wielding power, and he summons that skill here to make the reporters look like bumbling neophytes
Travis has lapped the hero’s journey equivalent in business and is clearly playing a different game. Awesome to see.
Daniel Scrivner retweeted
In response to @POTUS's call for a revival of America's scientific enterprise, today I’m releasing Science: A New Golden Age.
This report is a blueprint for renewing American scientific leadership for the 21st century.
Daniel Scrivner retweeted
Have you ever wanted to make your own font? Well I've written something that will hopefully dissuade you from that idea.
makingsoftware.com/chapters/…
It's free to read for the next 48hrs. Enjoy.
Low key very inspired consistently by DARPA. Self driving cars, autonomous robots, and more were all proven out early (as in decades early) at DARPA.
Synthetic blood is next.
Performance is one of the invisible qualities of the world’s best apps. Glad to have someone writing about it.
Great first piece on Linear.
Introducing performance.dev!
A new space where I explore how the best apps in the world are built.
First piece:
How's Linear is so fast? a technical breakdown.
performance.dev/how-is-linea…
Really great interview.
My favorite parts:
- Defining how you build a company into things that are Science, Art, or Religion
- Why there’s a never ending wave of disruptive companies in many categories: because there’s an endless Overton Window of evolving buyer preferences. Catch that window too early and ride it until it’s mainstream.
Nice to when you hear someone that genuinely makes you want to be a part of their early team.
For this episode of Uncapped I got to sit down with my close friend @Max, cofounder of Instacart 🥕
We talked about the key moments of Instacart...how it got started, critical wins, tough losses, competitive dynamics, earned insights, going public, and Instacart's future.
Max is also an awesome startup investor, and we talked about what he looks for in founders, consumer investing, and what he's building with Workshop.
He's one of the best people I know, hope you enjoy.
(0:00) Intro
(0:36) The inception of Instacart
(4:55) Finding product market fit
(7:20) Landing Trader Joe’s
(11:04) Big levers for growth
(13:36) Operationally complex businesses
(14:55) Amazon’s acquisition of Whole Foods
(17:50) COVID and Instacart’s IPO
(20:02) Prioritizing profitability
(23:21) Avoiding temptations
(24:59) The future of Instacart
(25:53) Investing in consumer
(28:21) Irrationally optimistic founders
(29:49) B2B vs consumer founders
(30:35) How to work with investors
(33:38) Building Workshop
“Space launch was a clear case where there was a large difference in efficiency between what was possible and what was done in practice…”
Great way to define verticals and niche with massive upside. Very well said.
Space launch was a clear case where there was a large difference in efficiency between what was possible and what was done in practice before SpaceX. A large part of that was due to everything being locked in to what (just barely) already worked, with huge risk aversion. WIth national prestige or a half billion dollar geosync satellite on the line, speculative engineering ideas that might result in a public debacle were not welcome.
When failure is not an option, success can stay very expensive. You need to experiment to improve, and that fundamentally means being comfortable with failure. If you know it is going to work, it isn’t an experiment.
I have long believed that nuclear power today is in precisely the same state as space launch two decades ago, but the even more pressing question now is if semiconductor fabrication might also be.
On the one hand, Moore’s Law has been a sequence of heroic miracles of technology at the wafer fabrication level, grinding out hundreds of compounding small improvements.
On the other hand, fabs are “too big to fail”, and there are elements of extreme conservatism at play. Intel’s “Copy exactly!” fab development exemplifies that mindset – instead of every new building being an opportunity to explore and optimize processes, it was deemed more valuable to just replicate.
While each individual machine may be straining against physical limits of technology, it is possible that the systems orchestrating them all together could be far from optimal.
The explore / exploit axis is fundamental to all decision making, but human risk avoidance probably biases away from optimal exploration.

































