this is why i wish the tech/sci fi film canon were wider. im tired of references to Her and Bladerunner and The Social Network etc. in 2026 you should be watching: -Demonlover -Until the End of the World -Videodrome -World on a Wire -Solaris -The 10th Victim -Megalopolis -Stalker -Run, Lola, Run -Seconds -and so many more…

this is a big deal

pat gelsinger (@PGelsinger) designed chips at intel in a period when doing so could involve writing your own hardware description language, which he did for the 486. his team also built a compiler and tools for automating placement and routing. “I wrote my own language,” he says in this pod with @RaghuRaghuram and @appenz. it's a good reminder of how many things had to be invented alongside the processors themselves, and why the prospect of AI taking over more of that work is so exciting. pat then asks us to imagine designing an excellent AI accelerator in three months. getting it fabricated, packaged, and into a working rack takes roughly nine more in his example (the relevant unit is now the rack: “nothing's a chip anymore, it's a rack”). so there's a period in which your very clever design has to sit and wait for its physical existence, while the models whose workloads you've designed it for can keep evolving. his description of graphcore is that it “wasn't a bad design, but the world moved on.” i think about how often people announce a new model or a new way of using one, and nine months seems like a very long commitment to your current understanding of what the hardware ought to do. guido's argument is that agents should make it easier to support a greater variety of architectures, because writing all the software to use an unfamiliar chip has historically been one of the barriers. pat's answer is that somebody still has to pay for manufacturing and for a place to put all these chips. he expects consolidation, even with better software tools. if you're a customer committing to a data center and its electricity supply, you're making a rather different bet than the engineer who's just got a new design to work. which leaves a lot for hardware people to do. pat is especially dissatisfied with hbm (high-bandwidth memory), pointing to problems with density, bandwidth, and heat; he calls it “a hideous memory,” and says he's just funded a new memory company that's still in stealth. he wants better memory closer to compute, optical connections between systems, and less electricity lost in conversion on its way to the chip. he also seems very happy to talk about cooling. “engineers are becoming plumbers,” is how he puts it. if we're excited about being able to design more things with AI, we should be excited about the work that allows us to manufacture and operate them, too. pat has already lived through a period of having to invent quite a lot of the surrounding machinery. it makes sense that he'd see another one as an opportunity.

an important part of the lana del rey fan journey is realizing this isn’t a popstar we’re talking about, but a timeless musical genius on par with frank sinatra or beethoven

of all the takes that age terribly, “dont put ads in a consumer product” is probably the one that’s most guaranteed to make you look sanctimonious and self-righteous in retrospect. every single great consumer internet product and platform with >1bn users delivers some form of ad unit, because that’s how you scale a product to the largest group of people possible. ads are, quite literally, why we can have nice things. and so it shouldn’t have been a surprise that back in jan / feb the following things happened 1) openai announced they were rolling out ads to subsidize their enormous base of free users 2) a bunch of people tried to paint this as a bad thing. now, with the benefit of ~240 days of hindsight, we can audit just how ridiculous this kneejerk reaction was. as of august, oai has ~$1bn in arr for their ad product. in this podcast recorded for the 7th edition of the top 100 consumer ai apps leaderboard, i sat down with @omooretweets, author of the bi-yearly report, and @joshelman, to discuss why ads remain such an important part of the consumer experience, and why subscriptions can only take us so far. some takeaways: - the subscription market is a peculiar slice of the consumer market. olivia's yipit panel suggests around 4.5% of US consumers pay for an ai subscription, and spending is concentrated in people using it to build, create, and get work done. -a founder who needs every user to pay has a strong reason to keep building for that audience (a rather circumscribed group of people who value saving time over spending it). advertising creates room to serve people with different interests and willingness to pay. developers / creators who view ai as a way to get leverage over their lives is an important group of paying subscribers, but a smaller cohort compared with people who view ai solely as a source of information or mild entertainment for now. - josh pointed out that ai companies can become afraid of growing too fast because every new user brings meaningful expense (this explains why, for example, meta seems to be pushing muse a little less aggressively than a product like threads, which cost far less marginally to serve to users). but as inference gets cheaper, ads and transaction fees can support more of the experiences people might want to try. - josh found its ads useful while planning a trip. olivia thinks the context people supply could make the advertising more effective. that creates a product-design opportunity: help someone find what they want, with the sponsorship made overt and a clear value add. - a valuable audience doesn't have to be everyone. olivia points to openevidence's early success with advertising among doctors. specialized ai products can gather audiences whose shared needs are useful to particular businesses. there is room for companies with much more focused purposes than a general assistant. i think this is why the ads question matters beyond oai’s revenue. the business model helps determine who gets products built for them. ads are what enable ai to get built for everyone.

when we think about ai in the physical world, we usually default to thinking about humanoids or self-driving cars or or models helping scientists discover new drugs and materials. but there’s another version of physical-world ai that i think deserves more attention: using intelligence to orchestrate all the benign-seeming things that have to happen for something to get built. which is especially important because the physical world is this wildly chaotic unpredictable place. what happens when the physical world refuses to cooperate. your shipment falls off a ship. or a strait closes. where, exactly, can an agent intervene? this is the problem we get into in my conversation with @askvladi, cofounder of lio, and @seema_amble. lio builds ai agents for procurement, in other words the work of figuring out what a company needs to buy, finding suppliers, negotiating terms, and making sure the right things arrive. when you’re building an aircraft or a data center, those decisions connect engineering, logistics, and finance in very consequential ways. vlad’s answer to the question of how to wrangle the physical world was about probability. suppliers have different records of reliability and routes have different risks. understanding those things in concert changes what you should buy / what you should pay: a cheap component that holds up an entire project can become a very expensive component. this all requires a much richer understanding of the world than the price and delivery date sitting in an enterprise database. i think this is a very compelling application of ai. a lot of our ability to build more ambitious things depends on making thousands of these decisions well. robots justifiably get a lot of attention; getting everything required to build them in the same place at the right time is just as important!

so excited to see this launch. @eriktorenberg and @david__booth have been laser-focused on networks and founder ecosystems for their entire careers, and the startup world is better because of it. @cosign is the culmination of this work, but also still just the beginning...

all the world’s a sim / and all the men and women merely agents

ncaa but for h16n / uatx / thiel fellows / yc / speedrun / 1517 etc who's building this?

in 1882, thomas edison's pearl street power station (the first commercial power plant in the us) began serving 59 customers in lower manhattan. that same fall, mit introduced the country's first electrical engineering curriculum. this co-evolution of enterprise on one side, and pedagogy on the other is one of the many forces that brought america firmly into the industrial era in the late 1800s/early 1900s. i think this is useful context for the conversation between @eriktorenberg @bhorowitz @gaganbiyani about what education for the ai era should look like. the institutions we now treat as permanent fixtures had to make decisions about emerging technologies and whether it was worth teaching a field whose applications were still being discovered. in this pod announcing horowitz andreessen academy (@theacademysf), ben makes the case that a technological change on the scale of ai should prompt a similarly ambitious rethinking of education. one of my favorite details is ben recounting a conversation with stanford's dan boneh about making problems hard enough that students *need* ai to solve them. that seems like a much more interesting educational ambition than spending the next decade trying to establish whether someone used a chatbot on their homework. there's something very regressive about knowing young people have access to tools that can help them do unprecedented things, and then evaluating them entirely on what they can do without them. we should all be excited to see what happens when a school like haa takes the opposite approach, and prepares students for the machine age.

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!

it’s the machine age now. plan accordingly.

the datacenter-themed chanel spring summer ‘17 RTW collection. you couldn’t do this kind of thing today, they’d cancel you.

pleased to announce jalapeno chip early access at the a16z office

in true borderless fashion, i just ran into @GEVS94 on his way to the airport