Inside a16z’s $1.7B Infrastructure Bet | Jennifer Li, General Partner

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Jennifer Li, General Partner at Andreessen Horowitz (a16z), breaks down why the firm allocated $1.7 billion of its latest $15 billion fund specifically toward AI infrastructure, and what she’s betting on next.

Jennifer has backed ElevenLabs from Series A all the way through Series D, watching it grow to an $11 billion valuation. In this episode, she explains what she saw in voice AI before anyone else did, what makes a founder worth backing regardless of the tech, and why the next wave of AI infrastructure is being rebuilt from the ground up.

Discussed in this episode

Why a16z bet $1.7B on AI infrastructure (and why now)
The shift from cloud to AI-native infrastructure: storage, compute, orchestration, memory
How ElevenLabs crossed the uncanny valley in synthetic voice
Voice agents as the first AI category to truly scale in the enterprise
What “king-making” in AI go-to-market actually looks like
The traits that made Jennifer write a check for ElevenLabs on founder conviction alone
Open source vs. frontier models: what 2027 looks like
Why world models and vision language models are the next unlock
AI and human creativity: why directors and authors won’t be replaced
How a 1-2 person studio can now make a full movie

Episode highlights

1:06 – Max & Paul intro: are we in a bubble?

1:46 – AI vs. dot-com era: the key differences

4:55 – B2B SaaS disruption and value destruction (Thoma Bravo / Medallia)

6:39 – Intercom / Finn: crossing the chasm from legacy to AI-native

8:08 – Introducing Jennifer Lee, a16z General Partner

8:31 – Paul’s key takeaway: the distribution era

9:41 – Why speed to default brand has never mattered more

11:33 – The ElevenLabs story: a16z led Series A, B, and C

12:02 – Why the seed strategy still works

13:37 – What the best founders do differently with model capabilities

15:58 – Jennifer Lee joins: why a16z raised $1.7B for infrastructure

16:22 – What existing infrastructure is being rebuilt for AI

19:19 – Specific areas a16z is focused on: models, storage, dev tools, security

20:35 – 90%+ of code now written by agents

21:52 – What Jennifer saw early in the 11 Labs / voice AI space

25:19 – Go-to-market in AI infrastructure: what’s working

27:29 – Becoming the default brand: the “Kleenex effect” in AI

28:37 – What makes a founder worth backing on conviction alone

30:48 – Predictions for 2026/2027: open source catching up fast

31:39 – Most exciting new modalities: world models and vision language models

32:07 – AI and human creativity: can they coexist?

34:42 – What’s blocking the creative AI future

36:05 – “The best ideas live in the graveyard”

36:57 – Closing advice: make AI tools your friends

Key takeaways

1. AI infrastructure is being rebuilt from scratch, not just upgraded:
Everything running AI workloads today, storage, compute, orchestration, memory, was built for a different era. The entire stack is being rewired in real time, and that’s where the biggest investment opportunity sits.

2. Voice AI was the first agent category to truly scale:
Jennifer backed ElevenLabs long before voice was mainstream because it was the one modality where accuracy didn’t matter as much as fidelity. Once it crossed the uncanny valley, voice agents took off faster than any other AI category.

3. Speed to market is the new moat in AI:
In enterprise software, the gap between number one and number two used to be small. In AI, the first company to become the default name in a category, like ElevenLabs in voice, builds a lead that’s genuinely hard to close. Getting there fast is the strategy.

4. Great founders balance research, product, and go-to-market equally:
Jennifer’s conviction on ElevenLabs came from three things: personal passion for the problem, technical depth, and the product instinct to patch model shortcomings until the research catches up. Tech alone is only 60% of the equation.

5. AI doesn’t replace creativity, it removes the ceiling on it:
Jennifer’s view is that human creativity is inherently human and always will be. What AI does is give people with big ideas but limited resources the tools to actually execute them, turning a 2-person team into a full studio.

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For an LP-base like ours, with over 350 C-suite and VP-level operators, this kind of white glove service and seamless workflows is so important. It’s also instrumental that we support our institutional LPs that we’re fortunate to work with, and AngelList is able to do so every step of the way.

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Follow Jennifer Lee

LinkedIn: https://www.linkedin.com/in/jenniferhli
Andreessen Horowitz’s (a16z) LinkedIn: https://www.linkedin.com/company/a16z
Andreessen Horowitz’s (a16z) website: https://a16z.com

The GTMnow Podcast shares how the best in tech build, scale and invest.

GTMnow is run by GTMfund, an early-stage venture firm made up of 350+ go-to-market executives from the fastest-growing companies.

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VC 9 Episode Transcript

00:00 – 00:06

Sophie: Andreessen Horowitz raised $15 billion and 1.7 billion of which was allocated towards infrastructure.

00:06 – 00:19

Jennifer: When it comes to storage, compute and all the tooling like, we consider that a big part of infrastructure. Again, like how do we store like memory and so on. Like those are all the opportunities that’s emerging to build infrastructure.

00:19 – 00:22

Sophie: Jennifer Lee is a general partner at Andreessen Horowitz.

00:22 – 00:25

Jennifer: More than 90% of the code are being written by agents.

00:25 – 00:28

Sophie: What did you see early in the Voici space?

00:28 – 00:47

Jennifer: So we first saw the 11 labs demo. I remember if you were using like a Gandalf voice two or just like an arena bug or like, Holy shit, this is really alike and also has all the right pauses stressing intonation. It’s super engaging. I’ve always been a big fan of the One Piece manga. It’s only every couple of years for eight episodes, right?

00:47 – 00:49

Sophie: Yeah.

00:49 – 00:52

Jennifer: Make these products and tools your friends.

00:52 – 01:08

Sophie: The best ideas live in the graveyard.

01:08 – 01:24

Max: All right, we’re back with another fun and amazing episode of the GTM now podcast. The special edition VC bonus episodes we have with myself and my general partner, Paul Irving. What’s up? Paul, how you doing?

01:24 – 01:31

Paul: Doing well. Max, how you doing? I know you been on the road quite a bit. I’m about to hit a road trip coming up, continuing to be pretty busy and exciting times.

01:31 – 01:51

Max: Road warriors indeed. It feels like there’s a certain seasonality to it, for sure. And, this just happens to be one of those extremely busy times, so wouldn’t have it any other way. It’s fun and exciting. And with that, you know what better way to kick off the pod than how fun and exciting it is? Are we in a bubble or not?

01:51 – 02:18

Max: I know it’s nuanced. Does this feel like 1998 or 2001? You know, we just had the all birds pivoting to new birds. I and by and purchasing what, 50 million worth of GPUs to rent out, like, there’s stuff like that that I read and I’m just like, what? You know, we’re doing that again. But then there’s, you know, the growth of anthropic where, you know, you’re looking at that and you’re like, no, this is real.

02:18 – 02:28

Max: This is a platform shift that we haven’t seen the likes of since, you know, maybe mobile or the internet in general. Right. So what’s your take or what’s your what’s your view on that?

02:28 – 02:48

Paul: I think from a high level because if you only look at the high level aspects of it rhyme so nicely, it’s really easy to make that comparison. You say, okay, if I is going to be a shift as transformational and large as the internet when it first launched, there is going to be a huge build out of infrastructure that exists.

02:48 – 03:10

Paul: Companies are going to grow as fast as you’ve ever seen them grow from an equity value perspective at least, and a lot of money is going to be thrown at it. And a lot of that stuff from a very high level will rhyme. If you look at 1998 and 99, 2000 and what the last sort of three years have been post ChatGPT launch, but you have to dive into the numbers like you just mentioned, to really understand that this is completely different.

03:10 – 03:29

Paul: Are there going to be some parallels? Maybe. But the core drivers of is their value today. What are the economic factors which will influence the success of this over the coming, you know, two, three, 4 or 5, ten years or not. So I’m just going to list off a couple of those and would love to get your reaction to to some of them as well.

03:29 – 03:51

Paul: But four years after the internet’s public release, there were 70 million users globally. ChatGPT and AI apps already have 1 billion monthly active users, a completely different scale and in less time, 90% of this I build out. So you talk about data centers, GPUs, 90% of it is pre committed. When we were running fiber in the early 2000 and late 90s, it was 3% pre committed.

03:52 – 04:14

Paul: That’s totally different I think differentiate between the two and you look at them is just unused capacity versus used capacity. Every time that we bring more compute online is almost a 1 to $1 creation ratio for the frontier model companies or the infrastructure companies to have that surfaced. And then you look at the constraints side of things like fiber was pretty cheap in relative terms.

04:14 – 04:36

Paul: And you know what? Over the ensuing 15, 20 years, we really benefited from an overbuild out of infrastructure. But right now, the infrastructure buildout is not simple. Energy is a big constraint. Land is a big constraint. GPUs, if you know, you dive into Nvidia’s earnings every single quarter or listen to Jensen when he talks like they’re still capacity constraint from a supply perspective and meaningfully.

04:36 – 04:55

Paul: And so I think the core drivers of it, you know, is their demand how pre committed that demand is, the amount of users, the amount of value being created, the revenue growth of anthropic opening AI, and even some of the smaller private market startups that we’re lucky to invest in is is night and day different from what it was like in the late 90s and early 2000?

04:55 – 05:18

Max: Yeah. And I you know, I look at this from a B2B SAS standpoint. You just saw, I think, news the other day that Toma Bravo is completely writing off the dollar acquisition that they had made. And, you know, their model was, was largely to acquire these companies that had pretty good, you know, bones, economics, you name it, juice them, and then flip them for 2 or 3 X in a couple of years.

05:18 – 05:46

Max: And, you know, I think a famous one of these was Vista and Marketo, where they got like A3X in just a handful of years. And, you know, with the kind of demise of the traditional B2B SAS and this usurping of these kind of native AI companies, it’s been pretty wild to see companies that I thought 2 or 3 years ago even or, you know, in 2021 of these still worth close to 10 billion that are now, you know, nothing zeros.

05:46 – 05:55

Max: Right. So how do you kind of reconcile that in this, you know, trend or bubble as part of that. Yeah.

05:55 – 06:15

Paul: It’s it’s a difficult thing to reconcile in the sense that I do believe both things can be true, where you can say there is more value being created today than maybe we’ve ever seen in the history of technology, investing in innovation that usually on the other edge of the sword is value destruction, like entropy has to exist somewhere.

06:15 – 06:41

Paul: And I think the, the whiplash of it all is the part that’s difficult for a lot of companies and executives and operators and founders and investors to manage, which is investments that you made or product decisions. If you’re a founder that you made 18 months ago that you would normally have a long runway to prove out whether this is going to be viable or not are re underwritten on what seems to be a week by week, month by month basis indefinitely, a model released by model or at least basis.

06:41 – 07:00

Paul: An interesting counterpoint to this though. And I was talking to an executive and we’ve talked about them on the podcast a couple times, the intercom team and the release of Finn. What’s really interesting about that, and I think an under discussed aspect is the core intercom product is now re accelerating. So it took this re pivoting to say, hey, we’re going all in.

07:00 – 07:16

Paul: I fin is the future of this company. It’s own product. It’s AI native. You sell it to all your customers. But there is still a use case for some of the traditional software that they build out over a decade. And now that part of the business is re accelerating. But you know what? That never would have happened if it wasn’t for fin.

07:16 – 07:36

Paul: And so I think you can follow the success cases. Understand that they’re really hard to pull off. And there’s going to be a lot more value disruption than creation for companies that are trying to cross the chasm of traditional B2B software to AI. But it is possible, and it is cool to see the almost traditional offering at a company like intercom start to accelerate again.

07:36 – 07:54

Max: Yeah, there’s definitely, a handful of employees, in two different buckets, ones that, like, were there in the early days, had some shares to exercise irons out there, like, oh, I don’t know where this thing is going. I’m going to save my money. And then the other bucket that’s like, yeah, you know what? I’ll pick up a flier on this.

07:54 – 08:11

Max: And then while the company, you know, I comes back, the company really accelerates in a massive way. And then, you know, you see a competitor that I think was growing incredibly fast, but probably not to the scale that fin was in. Qualified get acquired by Salesforce, which I heard was upwards of a billion. So obviously intercom doing very well.

08:11 – 08:29

Max: Now, on the other side of that, today’s guest on the show is Jennifer Lee from Andreessen Horowitz, partner at Andreessen Horowitz. We’ve done some deals with Jennifer Love, working with her fantastic show. What were your kind of, you know, key takeaways or things you’d like to dig at from that episode.

08:30 – 08:57

Paul: Where GTM funds? So I have to start with their distribution commentary, which I thought was spot on, though, and it’s something we talk about a ton internally, which is the speed to become a default brand has never been more important if you’re an AI native company, and then the gap between sort of one and 2 or 1 and two in the rest of the field just seems to widen on a day by day basis and be as large of a chasm as ever before.

08:57 – 09:18

Paul: And, and I think there’s some really good examples of this. And they obviously and rightfully get, I think, a lot of praise for the work that they’ve done. But like Harvey becoming a default application AI native application for the legal community across top 100 law firms and the rest of the ecosystem beneath that as well. It wasn’t that the product could do everything you wanted it to do in the early days, but they did it.

09:18 – 09:42

Paul: Incredible job establishing the brand and then the product back, filling that from an execution perspective. Laggards are a good example of a company not far behind and growing incredibly fast. But you start to look beyond that and it is hard to see, you know, what the rest of that market could look like. You know, lovable and Replit is a good example of this in sort of vibe coding in non-technical user app creation where distribution matters.

09:42 – 10:08

Paul: And we’ve said that for a long time. But in this AI native era, we’re calling it the distribution era. For a reason. You need to get your go to market right in your distribution right from day zero, because you know the fastest way you become the default brand in the new category. And there’s a lot of new categories and products being created on a day by day basis is you need to move fast, and you need to have the foundations in place to be able to, you know, build a flywheel and build an engine.

10:08 – 10:34

Max: Yeah. And once you do move fast, you get rewarded for it pretty quickly because there are funds like Andreessen with 15 billion under management, I think 1.7 billion just for infrastructure alone. And they’re going to invest pretty ferociously. Right. So there I think she did the A, B and C for 11 labs. Right. And so you’re you know you’re kind of king making on your own by growing quickly and building great distribution.

10:34 – 10:42

Max: But then of course, you know the mega funds can come in and really give you this mountain of money that puts a pretty good big gap between you and the rest of the pack.

10:42 – 11:06

Paul: Yeah. And I think a lot of it also comes from some of these verticals or some of these use cases like business users knowing they need to adopt AI and C-level executives and boards having a mandate to adopt AI, but not always knowing where to start. And, you know, like any network, anyone who’s had a professional job realizes and understands this is it’s just people trading and sharing notes.

11:06 – 11:24

Paul: What do you use? What did you try out? What worked. And there’s this virality. I also think in a lot of AI product demos, but but also when people sharing use cases in success cases where once you have the momentum behind you, it’s just a snowball rolling downhill and hats off. I wanted to make sure we tipped our cap to, Jen for.

11:24 – 11:34

Paul: Yeah, leading the A, the B in the series C for 11 labs is just what an incredible team to work with. And not just one two, but three unbelievable investments for their team, into their company.

11:34 – 11:55

Max: And it shows that their strategy can work. Right. If you raise 15 billion and you if you find the winner at a which you kind of are starting to know who the winner is, you can put a ton of money to work in that one company, but it also shows that our strategy and kind of the seed strategy works, because there were rounds done in 11 labs before they got there.

11:55 – 12:06

Max: Right? So, you know, some smaller firm found that company. It was able to get in before, you know, and then, Andreessen Horowitz, ABC. What do you think about that?

12:06 – 12:26

Paul: Yeah. It’s the preceding seed rounds. It’s just a question. We get a lot the preceding seed rounds, they capture the TechCrunch headlines and, you know, go viral on LinkedIn or both are the mega seed rounds. Those are a really small, you know, drop in the bucket in the larger, I would say, investment landscape for pre-seed and seed.

12:26 – 12:45

Paul: And you look at a company like 11 labs, and it’s a great example of that. There was a $2 million round that got done at company inception, and the angels and the preceding seed specialist funds that invest in to that particular round. I mean, not only a fund making investment, but potentially a firm making investment. And we see that all the time.

12:45 – 13:12

Paul: You know, pre-seed in seed is not an efficient marketplace. By any stretch of the imagination. There is incredibly smart founders and builders all over the world with exceptional teams and exceptional ideas, and it’s hard to capture all of those at the inception stage of the company. But, you know, firms like A16z do a exceptional job of making sure that if those companies break out, they’re on their radar and you know they’re ready to to concentrate capital into them as they grow.

13:12 – 13:37

Max: Yeah, I mean, it’s it’s certainly validating, obviously, to the strategy of the kind of emerging manager and, and seed only funds. But wow. Kind of what a, an amazing way to deploy capital is to be able to kind of wait to see who the winner is going to be and then go all in behind them. It really kind of is attestation to what Andreessen Horowitz has built from a brand standpoint over the years.

13:37 – 13:56

Max: And I think there’s probably a few firms that could pull that off. Sequoia, Lightspeed, Excel index, thrive. You know, there’s but it’s a short list, probably some ten, ten firm list lasting on this one, but one to point out founder qualities. What are some of the things that she was looking at? You know, when sizing up founders to invest?

13:57 – 14:29

Paul: There’s a couple that stood out to me, but one in particular, which, was the best founders understand where the puck is going from a model capability perspective and builds their product roadmap in concert with that. But the very best founders go one step further, which is they know where the models are going in, the model capabilities are going, and they’ll build a patchwork version of that functionality into the application ahead of time and then get it into customers hands.

14:29 – 14:46

Paul: And then when the model capabilities are there to backfill that one quarter later, two quarter later, you are way ahead of the rest of the market. And she she cites 11 labs is a company that’s done a great job of that. And it’s easy to see why and how. And if people are using the product, you’ve experienced it firsthand.

14:46 – 15:00

Paul: But that level of understanding of where the world’s going, where these AI models are going and building your product roadmap around that is, I think, what the best founders, you know, building AI, native software and AI native applications are going.

15:00 – 15:23

Max: Yeah, and she’s got a great AI phenomenal track record. All right. So with that, let’s get right into it. Today we have our SVP of marketing Sophie Bueno CC, doing the episode in person with Jennifer Lee, general partner at Andreessen Horowitz. We’ll let them take it from here. Our LP base spans from individual operators to institutional allocators and angels, has been instrumental and supporting all of them.

15:23 – 15:51

Max: They handle everything from investor onboarding and accreditation to distribution and tax documentation, creating a seamless experience across geographies and fund types. Plus, all of this is available on a single modern platform for an LP base like ours. With over 300 C-suite and VP level operators, this kind of white glove service and seamless workflows is so important, also instrumental, that we support our institutional LPs and we’re fortunate to work with an angels is able to do so every step of the way.

15:51 – 15:59

Max: If you’re looking for a platform that can support any type of LP investing in your fund. Learn more atangels.com/gtm fund.

15:59 – 16:01

Sophie: Jennifer, welcome to GTM now.

16:01 – 16:03

Jennifer: Thank you for having me here.

16:03 – 16:23

Sophie: Absolutely. It’s great to have you here and have a lot we want to cover is Andreessen Horowitz raised $15 billion and 1.7 billion of which was allocated towards infrastructure. And I believe that means that infrastructure is actually tied with apps for the largest vertical bat in the race, which is huge. Why now? Why infrastructure?

16:23 – 16:58

Jennifer: Yeah, I’ve been, longtime enterprise investor, but I spend most of my time on infrastructure. And as I’ve been looking back in the last eight years in venture like the, the role of infrastructure does become more and more prominent for multiple reasons. One is we’re still, you know, on the end of like shifting everything to cloud. But along that race like we have this thing called machine learning came about around, you know, 2017, 2018, and a lot of toolchain that’s built upon that, both on data, both, around, you know, ML tooling or coming through and 20, 22, of course, everybody knows the target time.

16:58 – 17:23

Jennifer: But even before then, we’re just seeing a lot of great like AI and research, came to the market. And all of that requires new infrastructure built out. And now of course, we’re seeing that in full, fruition. But I think this is not like overnight. This has been like a gradual, change that everything that we are running all these AI work clothes on are actually not built for the specific workload.

17:23 – 17:46

Jennifer: Like, sure, we have GPUs that are, specifically tailored for large scale inference and training, but everything else when it comes to storage, compute and all the tooling, like we consider that a big part of infrastructure are actually being revamped in real time. Like what are the agents using as tools? What are the orchestration layers again? Like how do we store like memory and so on.

17:46 – 18:06

Jennifer: Like those are all the opportunities that’s emerging, to build infrastructure. So that’s why we raised, the 1.7 billion round, and on top of all of that, we’re still in the very early, innings of developing new research, new algorithms to develop even more powerful models. So all of that, again, will require a large amount of capital.

18:06 – 18:11

Jennifer: So want to be able to support all the founders building in the research and engineering space.

18:11 – 18:25

Sophie: Rapid change. Absolutely. And what percentage would you say is existing infrastructure that needs upgrades or net new that, you know, like you said, there’s so many new developments. Aren’t things that necessarily existed before with AI?

18:25 – 18:53

Jennifer: Yeah, a lot of these two goes hand-in-hand. I think, again, this is my personal view, like a like consumer space, like we, invent completely new paradigm. New interactions, like infrastructure tend to be layered there, build upon each other, and it’s layered upon like, you know, old existing infrastructure was new layers. Like, we always had transactional databases for the vector storage was new to store embeddings.

18:53 – 19:19

Jennifer: We have always again, had, you know, memory and file systems know now agents are using them. So how to access these tools like MCP is can be new layer. But API has been around forever. So these are just like newer layers stack on top of each other to serve like either a different persona or different paradigm of like how tools are being plugged in together and utilized by either human developers or agents.

19:19 – 19:29

Sophie: And you’ve raised the 1.7 billion and you’re deploying on the infrastructure. And are there specific companies or areas that you’re particularly interested in?

19:29 – 20:03

Jennifer: Yeah, we define, infrastructure in a pretty broad way, but there are certainly very focus areas we tend to spend time, time on definitely foundation research. Of AI models. We have been long time backers of open AI and thinking machines. I see a lot of creative models from World Labs. 211 labs to BFL and, ideal gram like, these are all the like first party model developers starting from pre-training, building sort of either full stack application or like launching the the model itself as a API and so on.

20:03 – 20:30

Jennifer: We spent a lot of time in the core AI infrastructure, and that includes, again, all the things I talk about, like systems, storage, data pipeline. I imagine the company corporate doctor, like what they do is really using visual language models to turn PDF documents into a ready data structure. Like that itself is a core piece of system that required for building agents and automating knowledge work.

20:30 – 21:00

Jennifer: We still spend a ton of time in developer tools. Again, because the developer persona is changing. It’s not just humans writing code anymore. Now, probably like more than 90% of the code are being written by agents. And we need a new set of tooling record review for like CI, CD for pretty much the whole toolchain of, software development curse are, of course, is one of the very prominent investment of ours on the on the ID side and will continue to double down in this, killer use case.

21:00 – 21:22

Jennifer: I think there’s a lot of interesting opportunities. And after that, there’s security like it is. How do you how do we secure both the software perimeter, but also the team and the people? Organizational perimeter. We’re seeing a lot of, both sort of fear mongering scare of, like, what? Yeah, hackers will do. By the same time, we can also secure that realm much, much better.

21:22 – 21:43

Jennifer: And now we have these really capable coding agents. The software we’re writing and launching hopefully will be much more secure, too. Yeah. And then we’ll look at you know, application stack as well as, sort of traditional like data pipeline and data systems like these, again, are just like fundamental building blocks. That’s required for building great application.

21:43 – 21:44

Jennifer: The software.

21:44 – 21:52

Sophie: Incredible. And one of the companies that you’ve invested in, you mentioned is 11 labs. You, I believe, led their series B in 2024.

21:52 – 21:57

Jennifer: We actually had serious AB and C and participate in the D.

21:57 – 22:12

Sophie: Okay. Okay. So you’ve been with 11 labs since they’re series A? Yeah. Through to their series D. Yeah, and they’re now valued at over 11 billion. Yeah. What did you see early in the Voici space. Because that was a little bit prior to voice was as widely recognized.

22:12 – 22:33

Jennifer: Yeah. So there are two things really are diverse behind on the thesis side of investment, of course, you know, it’s just such a compelling team and founders that, you know, on that alone, I think would have, written that like, by myself. But but I’ve been tracking the first synthetic voice space for many years, and it’s just never crossed, uncanny valley.

22:33 – 22:55

Jennifer: Like, you can literally just hear this is robotic sound. You, know, it can automate some stuff to, like you know, narrate, paper. But nobody will be, like, paying attention to to that narration for a long period of time just because, again, it’s not engaging. It’s not like we’re having this conversation more human like and having all the intonation, emotions embedded in that.

22:55 – 23:15

Jennifer: So when we first saw the 11 labs demo, I remember you’re using like, Gandalf voice to or just like narrate a bug or like, holy shit, this is really a like and also has all the right like pauses stressing intonation that it just like it’s super engaging and you can imagine how that it’s being used for all the creative use cases.

23:15 – 23:36

Jennifer: Yeah. We had a thesis around like, yes, language model is really important. And it’s like dragging all the intelligence, but all the media and creative models is actually where I was having the the best run and the best use cases just because there’s no accuracy related to it. Right? Right. Like you’re not really judging these models by of course we’ll see like six fingers five fingers.

23:36 – 24:08

Jennifer: But sometimes even the imperfection is like creation is creativity. And we want really to double down in the investing and flourishing creative ecosystem. And voice model just is such an important pillar because we need that in the video. We need that tool like, you know, for podcasting, for again, creative, expressions and so on. So betting on, sort of the best in class standalone voice model was also one of the thesis driving behind and what didn’t really came into a picture or like we didn’t really have the foresight here and there.

24:08 – 24:38

Jennifer: Right. Is again, all these voice agents. Yeah. Like in 2022, it was still very early when we’re still just like prompting the box, like creating images and videos and generating like 30s or like a minute episodes of like, voice interactions. But I’d say voice agent was one of the first few agents really took off because all the desks, workers, customer service, front desk, like a lot of these use cases or just like really easily automatable because it’s first, repetitive.

24:38 – 25:07

Jennifer: Second, it is natural human language. It doesn’t really involve a lot of like jargons and so on. So you can easily, bring sort of that synthetic voice into the picture and having like a fluid conversation and language model. So it’s kind of good enough to, complete many of those tasks, being the driving force behind. So where we were saying, like, this voice agent took off and like 2014 and we backed also decade on in the customer support space, many, many others, that are all using Levin’s voice.

25:07 – 25:19

Jennifer: So that was, again, just another unlock of how big the the space and the market could be. While you really nailed the accuracy and also the fidelity of of the model itself.

25:19 – 25:34

Sophie: Absolutely. And you’ve been with 11 labs and launched many other companies through multiple races and stages of their journey. So you observe a lot on the go to market side of how they’re building. Curious what you’re seeing in the infrastructure space, what’s working and go to market?

25:34 – 26:02

Jennifer: Yeah, it’s such a great question because I’ve always thought I’m like an enterprise person through and through. Like, in 2022 was the first time I turned into more of like, a consumer die hard. Because, again, all of these tools are first being picked up by the consumer and Prosumers. And that was a great learning is like there’s not really a clean line between like, what are your side hustle hobby use cases and which ones are really like, you know, workforce enterprise ready.

26:02 – 26:32

Jennifer: Like then it was like a lot of the activities were happening just for fun. And people are tinkering with the tools, but really quickly. And this was the biggest difference from all the prior, like technical evolutions or like paradigm changes is like these products gets into the team enterprise work scenarios really fast because you just see the ROI really quickly to like gain either productivity, improve efficiency or just like, you know, creating things that’s never been done before.

26:32 – 27:03

Jennifer: So we think there’s a lot of potential to build like vertical integrated products. And that’s what 11 has done. Like they have a killer developer API, but they also have a really comprehensive product suite since day one from the creative studio to now, the agent platform. So they can tailor for both of the, the audiences. But we also have companies like fall where they have always been developer driven, but really quickly, they’re able to go after the enterprise customers with sort of the workflow product that’s built on top of it.

27:03 – 27:29

Jennifer: So there’s just a lot of opportunity to, like, own a market and on the interface and persona really quickly and that sort of either you can call it King making or like a brand. The effect was already prominent. I to like people tend to go to a default product when they think about certain functionalities, like it’s pretty much synonymous of 11 labs versus voice models and fall versus general media, or like video image models.

27:29 – 28:03

Jennifer: Like this is a phenomenon that hasn’t really happened before because it’s always a bit of like oligopoly for enterprise space. There’s number one, number two, but they’re not far distances. But in this AI landscape, again, because everything happens so quickly, like how to get your brand and developer or like consumer recognition really fast to become the default choice is what I think are the go to market challenges and opportunities are, if you found that open space, just go as quickly as you can to become again the name that everybody knows and default to and that’s, you know, does a great advantage.

28:03 – 28:06

Jennifer: And talking about moat really defensible position to be in.

28:07 – 28:17

Sophie: Yeah. Everybody striving to have that Kleenex phenomenon now on the B2B side and the speed to market on the go to market side and speed on that category now is something that we haven’t really seen before.

28:17 – 28:18

Jennifer: So totally.

28:18 – 28:37

Sophie: Yeah, it’s very interesting. And one thing that you mentioned earlier about 11 labs was the founder caliber. You said I would have written them a chat based on that alone. And I’m paraphrasing a little bit here. Yes, but for any founders listening, what is that trait or traits that makes an investor feel like that?

28:37 – 28:54

Jennifer: I think if I simplify, there are a lot of like, you know, really amazing qualities, not workers. Marion Theater over, few years. But I think if I had done it down to like, the beginning and, and what are the most, outstanding qualities is really, I think the passion and conviction to the problem it can come from anywhere.

28:54 – 29:16

Jennifer: But like to them is a very personal story that they have just seen these like, adult movies. Yeah, that are really boring. Was like only one monotone voice narrating through the whole movie that like really takes away all the emotions and the excitement from it. Like they want to solve that problem. But second, they’re like, not only technically very talented, but also have a great product line sight.

29:16 – 29:36

Jennifer: I think that’s again, something always being overlooked by, especially in these sort of technical driven evolutions, is like, if you have the best, tech will win, but that’s only like half or maybe 60% of the equation that like, you still need to package it into a product that’s easy to consume, easy to understand, because these models are really capable.

29:36 – 29:59

Jennifer: But sometimes, you know, they still need some guardrails and guidance to really bring the best quality out to the consumer and users, too. And 11 labs even talk about this in one of our podcasts in terms of like, how do they advance on the research side, but also use product to end product functionalities to patch some of the imperfections of the research until it’s ready.

29:59 – 30:19

Jennifer: Let’s say, like we know in three months or six month this research will be ready, but now it’s not. But it is something users are really craving for. How do we turn that into a product functionality? That sort of fills a bit of the shortcoming of the model, but can still deliver the promise, not in the best way or in the most perfect way, but again, like bring the future forward a bit.

30:19 – 30:44

Jennifer: And then when the model is ready, you have the model replace that. Like that kind of understanding of like where product can shine, where the technical and research can shine is a really important trade integration on the both sides. And the last part is, you know, they go to market really seriously, not just winning consumers, but also going after enterprises with, you know, the API product, with the, the voice agents, the agent platforms.

30:44 – 30:48

Jennifer: And they’re certainly winning in that space now, too.

30:48 – 31:09

Sophie: That’s great advice for founders, too. And now, as you look out across 2026, which we’re almost went way through, it is crazy. It is crazy. What are the biggest changes that you anticipate seeing the landscape? Because like you said, everything is moving so, so quickly. So if you had to put a prediction out almost perhaps 2027 at this point, what does the landscape look like?

31:09 – 31:39

Jennifer: I think we’ll continue to see, acceleration on the frontier. So the models. But the best news for the ecosystem is open source is catching that pretty closely and really fast. And I’m very excited about that because that’s, again, how a lot of startups, companies can sort of combine the different level of intelligence, also like characteristics, economical value of them into again, their their system and compose that work close up just a lot model run through all the way like which is both really slow and expensive.

31:39 – 32:01

Jennifer: Very excited about a couple new modalities that are becoming more prominent. One is work models. The second is vision language models. A lot of use cases are really just emerging from them, whether it’s, you know, robotics or like real time intelligence. These are again, our new unlocks that we have not had in the past because the model either was not good enough or not economical to run at large scale.

32:01 – 32:04

Jennifer: So those are the ones I’m super excited about.

32:04 – 32:22

Sophie: Very exciting. Exciting times ahead. And you’re investing out of, I mean, a $1.7 billion infrastructure and AI fund. But at the same time, you’ve got some strong perspectives on human creativity. So I’m curious to hear what does the future look like? With so many advancements on AI and human creativity, can they coexist?

32:22 – 32:40

Jennifer: I have always been a mature but huge fan of like using all the creative tools from product design to now, all the like video image models. Sometimes maybe just for a simple like birthday cards or like, you know, sharing with a team. Like something you have like visually in your head, but you don’t know how to make it into me now.

32:40 – 33:08

Jennifer: It’s so easy, like it’s your hand. Again, these are like very small use cases. But again, I think about how you know, some are really creative, have a lot of ideas, but they are limited by the tools and the people are resources they have now. They can literally have like a 1 to 2 people studio, but making a full movie like with back a few of those creators and we’re seeing their creative workflows, and it’s just really incredible to observe how the AI tools have given them power and given them the opportunity to tell the story.

33:08 – 33:34

Jennifer: So their ideas actually just came from from the pitch this morning. Yeah. Another really exciting piece is like the model quality have been improving in the last three years, but it’s finally gotten to a place that is ready for professionals. Like it’s been great for consumer prosumers. It’s been great for entertainment advertising, but now we’re thinking about movie making or thinking about again, like really premium storytelling for like luxury brands and so on.

33:34 – 33:53

Jennifer: And like the models now have gotten to a place that can really maintain brand consistency, having like really complex workflow tools that can compose both image audio video models and word models. Yeah, to create something that again, used to take teams of like tens of people month of work to come about.

33:53 – 33:54

Sophie: Right.

33:54 – 00:00

Jennifer: And lastly, I’ll just tell like sort of a personal, sort of a dream list is I’ve always been a big fan of the One Piece manga, and they have this, like live action movie on Netflix. It’s only every couple of years for eight episodes, right?

34:09 – 34:10

Sophie: Yeah.

34:10 – 34:32

Jennifer: You have all the materials and even like animated version, like, how can we make it faster? Like till and that’s a super long manga. I don’t know if you know, like the whole bug kind of like a wrap the Earth and so on. Like, how can we tell more of the audience, like how amazing the story is through sort of a live, actually engaging, movie storytelling.

34:32 – 34:41

Jennifer: Like, now again, we have these really capable tools that we don’t have to wait for a year, a year until, like, okay, you got to see the full episode. So that’s what I’m looking forward to.

34:41 – 34:54

Sophie: Good. That is the goal. So he’s got to change that. And I mean, it’s a very inspiring, prosperous future from what we’ve talked about. But there’s often always blockers to that to what’s going to stop us from getting there.

34:54 – 35:25

Jennifer: I think you know, the technical advancement will keep happening. It’s going to go faster. Like I’m not worried about that. Of course, like I’m not saying any of these models are perfect. They still require a ton of really direction to get to the point of like being able to be professionalized. I think more so of the like attitude and, and that that passion of, you know, the creative industry, the individuals who are still like having a bit of like insecurity around what do we where does our job go and what happens when, you know, these models are becoming so powerful?

35:25 – 35:52

Jennifer: I still think inherently creativity is human expression, and it is always going to be, you know, our nature and owned, the origins being owned by humans. These are just great, great tools. Like it’s not going to be able to replace, you know, a director or like a book author, someone that just have these great stories in their mind, like the models will never be able to replace that, in my opinion.

35:52 – 36:06

Jennifer: And I would love to see more creatives just embracing these tools and making that part of their their daily workflow and like becoming way more productive in being able to express what is in their mind was in their heart, and tell that to a much more broader audience.

36:06 – 36:24

Sophie: Definitely. I always think about this quote I heard a long time ago, but it’s the best ideas live in the graveyard, and I think this is a time that suddenly we’ve hit an unlock where people can be creative, they can ship. It’s never been easier to build and share and actually express. Like you said, bring out what’s in in the mind to reality.

36:24 – 36:26

Sophie: So yeah, very exciting times.

36:26 – 36:56

Jennifer: You know, 100%. I think like every single human is creative inherently, but not every person have the best tools for them. Now, it’s not only like, you know, all the s, tr and eight year, you know, creative storytellers can use the tools to become even better than where they are today. But a lot of, you know, us can use these, these tools to tell stories or like, show really creative pretty things to our family and friends like that along is just making everything better.

36:56 – 37:14

Sophie: Definitely. Definitely democratizing access to it. And if you could leave founders leaders listening just one message, whether it’s about the future or just an inspirational tidbit, personally, what’s the thing that’s been in kind of the back of your mind that maybe you haven’t brought to the world yet, that you do want to tell people?

37:14 – 37:46

Jennifer: I would say, just make it a habit. Make these products and tools your friends like. It’s taken us some years to like, just get used to and now so dependent on our smartphones, smart devices. Yeah. Now not only we have these really powerful machines, we also have such intelligent models that runs inside them. I think it’s such a blessing that we’re able to live in such a time and again, with any power, you kind of have to learn to harness it.

37:46 – 37:54

Jennifer: And as much as, you know, as founders, as operators can spend time and just like living and breathing there, I think, you know, a lot of good ideas will come out of it.

37:54 – 37:59

Sophie: I love it, Jennifer, this has been fantastic. Appreciate the time. Thank you for joining.

37:59 – 38:00

Jennifer: Thank you so much. Thank you for the question.

38:00 – 38:11

Max: It’s actually that was another fantastic episode of the VC series on the GTM now podcast. Head over to Apple, Spotify or YouTube and give us a like and subscribe and we’ll see you on the next one.

Sophie Buonassisi

Sophie Buonassisi is the SVP of Marketing at media company GTMnow and its venture firm, GTMfund. She oversees all aspects of media, marketing, and community engagement. Sophie leads the GTMnow editorial team, producing content exploring the behind the scenes on the go-to-market strategies responsible for companies’ growth. GTMnow highlights the strategies, along with the stories from the top 1% of GTM executives, VCs, and founders behind the strategies and companies.