Elena

podcasts @a16z new media

[Elena @VirtualElena]

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…

[scott belsky @scottbelsky]

films set the ideas and narratives that kick-off passions and careers

[Elena @VirtualElena]

@alive_ @a16z @cdixon @bhorowitz @pmarca What a run @alive_!! So excited for your next chapter, congratulations.

[Elena @VirtualElena]

@danshipper @OpenAI wow amazing, cant wait to read this!

[Elena @VirtualElena]

this is a big deal

[Nick @nickcammarata]

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had

with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works

https://t.co/zHij5zwKGT

[Elena @VirtualElena]

@pratikg @gajesh @akiaeki @liangsays @katiekirsch @schisofrenia @Max @0xkydo @emmajo @richardzphotoz @AryanNNaik @smc90 @ishverduzco @eriktorenberg @sophiadew @kashvi @soubhikdeb @benroy good company!!

[Elena @VirtualElena]

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.

[a16z @a16z]

Former Intel CEO Pat Gelsinger with a16z's Raghu Raghuram and Guido Appenzeller on the next wave of semiconductor innovation and the physical constraints shaping the AI buildout:

Drawing on his experience designing Intel's 386 and 486 processors, Pat explains how AI could transform chip design, but also why faster design alone won't solve the industry's biggest problems.

They examine the bottlenecks in manufacturing, memory bandwidth, advanced packaging, and power, and why today's explosion of specialized AI chips may eventually consolidate around a smaller number of architectures.

They also discuss the potential for new memory technologies, the shift from copper to optical networking, and why energy capacity could become a major constraint on AI growth.

Finally, they revisit Pat's VMware years to ask what virtualization might look like when infrastructure is built for agents rather than humans.

YouTube: https://t.co/ZJFvOky68H

1:05 Hired by Intel at 18
3:50 Arguing with Stanford professors
7:25 Will AI design the next gen chips?
8:45 Designing faster than fabs can build
11:55 Why GPUs are stuck with bad memory
13:40 Why 100 AI chips will collapse to a few
19:15 Agents make new chips cheap to run
22:10 No new memory chip since the 90s
27:20 Why 16-layer chip stacks are a fantasy
32:15 The physics that keeps memory next to compute
34:55 "I declared the death of copper"
38:05 AI traffic breaks the internet's design
42:40 "Energy capacity equals economic capacity"
46:25 The 130-year-old power war AI reopened
48:50 VMware for agents

@PGelsinger @RaghuRaghuram @appenz

[Elena @VirtualElena]

@samkkaufman @netcapgirl @katiekirsch @richardzphotoz 🥰back atcha

[Elena @VirtualElena]

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

[Antonio García Martínez (agm.eth) @antoniogm]

My most embarrassing musical confession is that I like Lana Del Ray actually.

[Elena @VirtualElena]

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.

[a16z @a16z]

The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.

a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:

The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.

They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.

00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment

YouTube: https://t.co/bxo4NbwAvt

@omooretweets @JoshElman @VirtualElena

[Elena @VirtualElena]

@arakharazian @stlouisfed huge!!! congrats ara!

[Elena @VirtualElena]

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!

[a16z @a16z]

Lio co-founder and CEO Vladimir Keil joins a16z's Seema Amble and Elena Burger to discuss where AI-native startups have an advantage when incumbent software companies already own the customer, the data, and the system of record:

Their answer comes down to the work that happens outside those systems. In procurement, a final price in an ERP can hide hundreds of emails, spreadsheets, supplier conversations, engineering analyses, and decisions across legal, finance, and operations. Vlad explains how Lio uses multi-agent systems to take on more of that end-to-end work, from sourcing and RFQs to negotiation, shipment tracking, and invoices.

They also discuss how enterprises learn to trust agents with increasingly consequential decisions, why the last 20% of an internal AI build can require most of the effort, and what happens when both buyers and suppliers have agents working on their behalf.

00:58 Why AI startups still beat incumbents
04:19 The hidden work behind an $8K line item
06:18 Retrieval, process, policy, principal
09:27 The incumbent's internal conflict
14:48 What procurement actually looks like
21:13 Procurement at Boeing-scale
28:03 A bolt order, end-to-end
37:57 What a durable vertical AI company looks like
44:46 When both sides deploy agents

YouTube: https://t.co/xfipxFaUYM

@askvladi @seema_amble @VirtualElena

[Elena @VirtualElena]

@tamarawinter so cool

[Elena @VirtualElena]

openai actually has the right idea with a september dev day but the other labs dont sufficiently respect the calendar

[Elena @VirtualElena]

my proposal for “pacing the frontier” is that model releases should be limited to 2x/year and coordinated as staggered seasonal collections, like fashion week.

[Elena @VirtualElena]

so excited to see this launch. @eriktorenberg and @davidbooth 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...

[Erik Torenberg @eriktorenberg]

Excited to introduce @Cosign: the curated professional network for the startup community: https://t.co/zhjIYSCigC

Our goal is to create the following:

- A comprehensive startup directory of investors, companies, and operators, including what they worked on and who they worked on it with.

- Cosign graph: Who shaped your career? Who were you actually in the trenches with? Who would work with you again? Who thinks you’re someone to watch?

- Durable reputation: Great endorsements happen every day on X and disappear into the feed. Cosign attaches those signals permanently to people and companies.

- Discovery: Who are the best fintech angels? Which AI companies should I watch? Who are the best designers?

- Intent network: Privately signal “I’d invest in them,” “I’d hire them,” “I’d work with them,” or eventually even “I’d acquire this company.” Match people when interest exists on both sides.

Imagine if Wikipedia, LinkedIn, and OG AngelList had a baby. Though Cosign is a community, not a business.

In order to join, you need to be cosigned. Or you can apply directly.

David Booth and I started this idea 7 years ago but didn’t have the firepower to make it work. Now we do.

No one has really touched LinkedIn in 20 years. Excited to take a swing.

https://t.co/zhjIYSCigC

[Elena @VirtualElena]

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

[Elena @VirtualElena]

@justinglibert @v_maini congrats justin! we gotta do a lab visit soon!!

[Elena @VirtualElena]

@goodside @repligate lmao

[Elena @VirtualElena]

ncaa but for h16n / uatx / thiel fellows / yc / speedrun / 1517 etc

who's building this?

[Luke Metro @luke_metro]

do you think that the a16z college and the joe lonsdale college are football rivals

[Elena @VirtualElena]

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.

[a16z @a16z]

Gagan Biyani with Erik Torenberg and Ben Horowitz on HAA: The Horowitz Andreessen Academy

HAA is a private, full-time, in-person school in San Francisco for young people coming out of high school. Its mission is to nurture the best young people in the world, and equip them with the tools to shape it.

The Industrial Revolution rewrote how we train young people. It's time for a new model for the AI era.

HAA is project-based – less lecture time, more time with tools and AI, structured social life, co-ops/internships, with students embedded alongside working adults.

Founding Partners include Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe.

It's the best time in history to be 18. The vision is grads with careers they love or their own companies founded, and hopefully other schools copying the model down the road.

0:55 Why a16z is backing a school
4:40 Why other college alternatives failed
6:55 AI is this era's Industrial Revolution
9:35 The model: live in SF, intern at top startups
12:10 Ben: the best time in history to be 18
15:25 No big idea at 18? Join someone who has one
17:40 People skills come from reps, not lectures
23:15 Why it has to be San Francisco
26:45 Why it's a separate, for-profit company
31:00 Every curiosity becomes a project
33:30 Main character or NPC?
35:15 You can't learn to be a CEO from a book
37:30 Picking a problem, and failing the right way
40:30 What success looks like in five years

YouTube: https://t.co/5TvdKHL6cH

@gaganbiyani @bhorowitz @eriktorenberg

[Elena @VirtualElena]

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!

[a16z @a16z]

How do you differentiate a real cultural shift from a manufactured one?

Cyber ethnographer Ruby Justice Thelot studies culture through TikToks. He scores thousands of them by category and sentiment to measure what people do versus what they post.

In this conversation with a16z's Elena Burger, they point that method at the wellness boom: why Silicon Valley gets credit for a peptide trend it did not start, what 400 years of thinness reveals about what we value, why the people who quantified everything are turning to energy healing and acupuncture, and why the body is the last thing left to optimize once AI handles the rest.

00:00 Intro
01:45 How to spot a fake trend
04:55 Silicon Valley did not invent peptides
10:15 The ten-to-one rule of trends
12:12 Why you know Ozempic and not the company
14:00 How to determine a trend's life stage
16:31 Why watching something feels like doing something
19:22 How thin became a sign of intelligence
23:08 How a beauty standard goes mass market
25:10 The body ideal follows the economy
26:46 Why biohacking went mainstream
29:28 When the Bay Area decides something is bad
33:14 Two signs a trend has peaked
36:38 Your tracker can make you feel worse
39:15 Sleeping four hours and feeling rested

YouTube: https://t.co/ev2bwjRecP

@being_on_line @VirtualElena