one of the more reliable rules of the internet is that if a sufficiently committed group of people in silicon valley decides something is good/bad/going to change the world/kill us all, you will inevitably learn all about it (and then form an opinion about it yourself). this has been a law that governs everything from ai discourse to health/biohacking to what you eat to how to track your body’s functions. i think people are only just beginning to appreciate just how culturally influential the bay area is. a funny byproduct of this phenomenon is that sf people will sometimes take credit/co-opt cultural trends they aren’t even directly responsible for. so i talked with @being_on_line about how to disentangle a true bay area export from a hijacked narrative, and more generally how to figure out what is actually happening in the world. an unsurprisingly tall task. sf sometimes encounters a subculture years after it’s been incubated, and immediately claims and mainstreams it. chinese peptides are a particularly good example: earlier this year, a lot of writng/blogging about peptides attempted to paint it as a sf-exported phenomenon, but it was really a trend that started in the south and midwest, among the bodybuilding community. a 2013 DOJ case even documents an illinois distributor importing them in 2010–11 for the bodybuilding market. sf is ofc capable of homegrown culture (and productive neuroses). last year, nat friedman's plasticlist project spent about $500k over six months testing 296 food products for 18 plastic-associated chemicals. (specifically chemicals like phthalates and bisphenols). i personally believe this kind of citizen science / gonzo testing is only going to become more in-demand as people increasingly care about food provenance/toxins. i think this is why these trends are worth paying attention to even when parts of them look ridiculous. a small group can change what gets tested, funded, packaged, and eventually made easy for everyone else to do. i also don't think the endpoint of the current body-optimization boom is that everyone gets conventionally hotter and starts to look like clavicular. i think people are going to do increasingly specific, strange things to themselves, depending on what they personally think is worth optimizing (an example of this is that people are now working on wearables to achieve the equivalent of a full night’s sleep in four hours). full convo below!

greg brockman says “we’re now in the AGI era.” one of the most interesting things about this conversation with @bhorowitz and @eriktorenberg is how long he’s been thinking about when that would happen. at the beginning of the pod, @gdb recalls doing some math on compute with ilya around 2016/2017. following the trajectory of moore’s law, they thought agi might be about 15 years away. if you were willing to build enormous supercomputers and spend hundreds of billions of dollars, maybe you could get there in 10. the accelerated scenario they were envisioning a decade ago is roughly the one we’re living through now. the faster timeline depended on people deciding to build an enormous amount of infrastructure. greg describes the present moment as a lot of long-running forces coming together: “it kind of makes sense it’s happening now.” im the kind of person who resists the idea that it’s possible to predict the future, but if you were reading the right research papers 10 years ago, there were a lot of things you could extrapolate out and be roughly right about a decade later. brockman remembers openai discussing agents that could use a screen, keyboard, and mouse at an offsite in napa in 2015. he also points back to 2017/2018-era ideas for how humans might supervise systems that are smarter than they are. put differently, people were thinking about app-layer innovations like computer-use, and ai safety long before capabilities existed. greg now thinks astra is reasonably described as agi, while acknowledging that its abilities remain uneven. in a recent essay of his titled “the defenders window” he describes safety requirements during training and evaluation, and getting powerful defensive tools into the hands of organizations before comparable capabilities spread to attackers (there is now a $ 1bn fund for this, seeded by oai). a personal illustration of this is his own website. after the hugging face incident, greg asked an agent to check his simple blog and got back 13 security vuln findings. then it went into his cloudflare dashboard, changed settings, migrated the site, checked its work, and scheduled a follow-up to finish the email protections. there was still a bunch of ordinary preparation to do on the personal website of someone who had spent a decade thinking about agi. which is kind of funny. you can have a pretty good sense of when the technology/agi will arrive and still have an enormous amount of work to do to get the world ready for it. greg’s account brings those two timescales together: the decade spent building toward this moment, and the relatively shorter window to act on what is now possible.

some takeaways from running a sota llm on a decade worth of lunches: - noted X anon @litcapital had the 4th most expensive meal on record - noted economist and MMT enthusiast @StephanieKelton had one of the cheapest - the most common restaurant was clarke's in london - and perhaps unsurprisingly, london was the most common city

in my mid-20's, a time when about half of my friends hated their jobs (a very normal phenomenon if you're in your mid-20s), i noticed a group of people my age who looked extremely happy and actualized at work. and all of these people somehow seemed to work at stripe. i'll call out @tamarawinter and @orbuch specifically as some of the first people who made me realize that it was possible to be 25 years old and do *singular* work every day, and not just whatever garden-variety boilerplate the company demands. this podcast with @gaybrick and @DavidGeorge83 helps explain why this was, and still is, the case. will says stripe increasingly is a platform for founders internally, just as it's always been a platform for founders externally. he wants each employee to feel like an “auteur”: an engineer, product thinker, and creative person with enough context and authority to make something real. we're now seeing a new generation of this pattern, with stripe's support of stuff like tempo (a team that might have one of the highest concentrations of founder-mindsetted people im aware of? cc @gakonst @dwr @varunsrin @liamihorne etc) the embrace of teams like privy and bridge, and other incubated initiatives like minions. stripe is trying to make employees more founder-like while preserving a shared standard of quality (one of my favorite details on this point: stripe creates simulated customer accounts so teams can experience realistic UIs for disputes and refunds before exposing products to real users). this is something that's actually very tough to do well, so it's very cool to hear will talk about it on this pod.

back in 2017 i was interning as an equities analyst at a fund in nyc, which had a 9am morning call every weekday. the purpose of this call was to discuss quarterly earnings or current events or whether we should be buying nvidia (at the time, on a tear because of the '17 crypto boom), but one morning in june the call opened with a bunch of people freaking out because some guy named "travis" had resigned. after a couple of rounds of "travis is gone" "did they fire him" "bloomberg says he resigned," i was especially confused because the firm had a portfolio manager named travis, and i looked over at his desk and he was still sitting there, not being escorted out by security or anything. who was travis? it was only after things had calmed down that i realized the people on this call were talking about *travis kalanick* of uber, a figure who loomed so large in the minds of wall street investors that even *two years before* uber ipo-ed, the news of his departure was treated like a six sigma event. everyone wanted to know: if travis was gone, what would happen to his company? even in his uber days, travis was the bits and atoms guy (watch the video clipped in the first ~2 minutes of the below podcast for proof of that). and in order to be a bits and atoms guy - as in, the kind of guy who's so discontent with the slow, staid, schlerotic state of things that he has to painstakingly reorchestrate the physical world with computers, perhaps pissing off some powerful interests along the way - some people might call you difficult. some people will not understand your vision. some people will try to get you fired. but if you're a bits and atoms guy, you never stop being one. you might retreat into near-obscurity, or spend years wandering in the wilderness, biding your time in exile. but you will not stop building. and one day you will return triumphant like a modern-day charles de gaulle, even better than ever, and ready to prove wrong everyone who didn't understand the first time around. being in a room with travis leaves you pretty pumped up. you can see why he's such a rallying leader, and why (by my count) at least six top uber execs have chosen to follow him to his new company, atoms. it isn't simply that the job isn't finished (although that's a big part of it, as travis attests). it's also because it's clear that there are very few leaders like travis out there: people who have such a holistic worldview that they can get people who would otherwise be founders themselves to join the ride and help build an extremely specific vision for the future. here are some things that stood out to me from this event: -travis is a branding/comms mastermind. he deploys memorable phrases like “food computer,” “wheelbase for robots,” “atoms as bits” and describes his vision for cheap meal delivery as "autonomous burritos." you can see why people get excited for his vision for the future. there's extraordinary complexity in what atoms is trying to do, but travis makes each step along the way feel manageable because he can distill these ideas into extremely concrete images. -atoms is building full-stack autonomation for three different divisions: food, mining, and transportation. food supplies the real estate, software, robotics, and logistics laboratory; mining supplies a commercially deployed autonomy business; and transport is intended to become the reusable movement platform connecting multiple physical industries. travis argues humanoids make sense for diverse, low-volume work in spaces designed for people. but at industrial scale (which atoms is doing), purpose-built machines should win on throughput, space, cost, and reliability. -the best way to get over your last company may be the same as the best way to get over your ex: fall in love again. travis says anger and fear of failure can carry a founder through years of pain, but they also produce long nights where very little gets done and sharp elbows felt by everyone around you. atoms is what happened when he learned to build toward a future rather than against an enemy. -doing hard things is a reminder you can do more hard things. in the q&a ben remembers travis describing attacks by chinese ride-sharing companies as proof that, if he could survive china, the rest of the world would be manageable. travis still deliberately creates problems when things become easy. but the rate of problem creation cannot exceed the organization’s rate of problem-solving. such a fun talk and q+a with @travisk @bhorowitz @eriktorenberg ! check it out.

a big reason we haven’t seen massive ai-related job losses is because large legacy companies are full of people who derive power from the number of subordinates below them on the org chart and you simply can’t replicate those dynamics with agents (unless you have eg a tokenmaxxing leaderboard…)

learned a lot from this conversation with @simon_mo_ and @BornsteinMatt. biggest takeaways for me: -there are a lot of reasons why we should like open-weight models. a lot of these arguments stop at handwavy things like "what if the labs stop releasing frontier models to the public" or "it's lower cost." but simon's position as lead maintainer of @vllm_project and CEO of @inferact give him authority to talk about some of the other, more interesting and concrete reasons to pay attention to open-weight models, namely that they allow end-users to calibrate latency / other performance metrics with way more customizability than what any of the frontier closed-source labs offer (and without the fear that your job might be met with a refusal at some random point where you're deep in a 2 hour job) -re: the above point...for this reason, a lot of US companies (inferact included!) choose to use open-weight models over their closed-source alternatives. this also isn't limited to internal workloads / research - on a recent a16z podcast the team at @DecagonAI spoke about how something like 90% of their customer service ai agents run on open-weight models that they've fine-tuned. -we should really appreciate how many companies/teams came out researchers fascinated by the wave of very small open-weight models that were being distilled from e.g. gpt-3.5 and earlier models in 2022/2023 (prior to the release of chatGPT!). these small models motivated the development of pagedattention, which then led to vlmm/inferact (at other layers of the stack with similar origin stories, you can look at teams like openrouter or ollama). in other words, we have open-weight models to thank for a bunch of the orchestration infra we now rely on. i think yet another, indirect, way we can point to open-source/weight infra pushing the frontier forward. anyway, a lot more in this convo, it was a lot of fun!