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

[a16z @a16z]

AI is making games faster and cheaper to build, giving developers more room to take creative risks, revive overlooked genres, and let players reshape the worlds they play in.

In this conversation, a16z's Jon Lai and Anish Acharya join Elena Burger to discuss why human taste still determines what makes a game fun, how modding created some of gaming's biggest franchises, the opportunity for new distribution platforms, and games that adapt to whatever players choose to do.

0:00 Intro
0:57 How AI can now build a game
6:21 What shorter dev cycles unlock
10:08 Can AI make a game fun?
12:27 When creating is the game
14:01 Building a Habbo Hotel record shop
16:16 His son rebuilt Ghost of Tsushima in Minecraft
17:54 Mods: where billion-dollar franchises come from
20:36 Decompiling and reviving dead games
22:45 The renaissance of niche genres
25:51 Managing AI agents like an RTS game
28:02 The business of AI games
33:53 What excites and scares studios
35:16 The never-ending game
37:56 The games they'd finally buQild

YouTube: https://t.co/ymq1yYAvwK

@VirtualElena @illscience @Tocelot

[Elena @VirtualElena]

wrote up some thoughts on ai x gaming with @illscience and @Tocelot.

read more below! https://t.co/zIs5TI8IRF

[Elena @VirtualElena]

@sophiadew @MTSlive pumped for this! congrats sophia!

[Elena @VirtualElena]

@zebird0 @speedrun congratulations robin!!! exciting stuff.

[Elena @VirtualElena]

@deepfates dulce et decorum est pro partiful mori

[Elena @VirtualElena]

@being_on_line amazing!!!

[Paid In Full Foundation @paidinfullorg]

During a powerful fireside chat, Nas, Kurtis Blow, Roxanne Shanté, and Rev Run reflected on Kurtis’s legacy—not just as a Hip-Hop pioneer, but as someone who opened doors for the next generation.

#PaidInFullFoundation #HipHopNobelPeacePrize #a16z

[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 @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.