# AI Infrastructure Investment, Distribution Moats, and Founder Strategies

**Podcast:** AI + a16z
**Published:** 2026-05-12

## Transcript

Andreessen Horowitz raised $15 billion and $1.7 billion of which was allocated towards infrastructure.
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.
Jennifer Lee is a general partner at Andreessen Horowitz.
More than 90% of the code are being written by agents.
What did you see early in the voice AI space?
So when we first saw the 11 Labs demo, I remember you're using like a Gandalf voice to just like narrate a book 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, yeah.
Make these products and tools your friends.
The best ideas live in the graveyard.
What does it take to build the infrastructure behind AI?
This episode, originally aired on the GTM Now podcast, features Jennifer Lee, general partner at A16Z, in conversation with Sophie Buonassisi.
They discuss the rapid evolution of AI infrastructure and the systems powering the next generation of software.
From model development to developer tooling, Jennifer explains why infrastructure is becoming the most important layer of the stack.
and how distribution is emerging as the key differentiator in a crowded market.
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 are you doing?
Doing well, Max.
How are you doing?
I know you've 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.
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?
I know it's nuanced.
Does this feel like 1998 or 2001?
You know, we just had the all birds pivoting to new birds AI 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.
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 view on that?
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 AI 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.
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, 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.
Are there going to be some parallels?
Maybe.
But the core drivers of is there value today?
What are the...
economic factors which will influence the success of this over the coming, you know, two, three, four, five, ten years or not.
So I'm just going to list off a couple of those and would love to get your reaction to some of them as well.
But four years after the Internet's public release, there were 70 million users globally.
ChatGPT and AI apps already have one billion monthly active users.
A completely different scale and in less time.
90% of this AI build out, so you talk about data centers, GPUs, 90% of it is pre-committed.
When we were running Fiber in the early 2000s and late 90s, it was 3% pre-committed.
That's totally different.
I think a differentiator between the two when you look at them is just unused capacity versus used capacity.
Every time that we bring more compute online is almost a one-to-one dollar creation ratio for the frontier model companies or the infrastructure companies to have that surfaced.
And then you look at the constraint side of things, like Fiber was pretty cheap.
in relative terms.
And you know what, over the ensuing 15, 20 years, we really benefited from an overbuildout 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 there's still capacity constraint from a...
supply perspective and meaningfully.
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 meet and invest in is night and day different from what it was like in the late 90s and early 2000s.
Yeah.
You know, I look at this from a B2B SaaS standpoint.
You just saw, I think, news the other day that Toma Bravo is completely writing off the Medallia acquisition that they had made.
And, you know, their model was largely to acquire these companies that had pretty good, you know, bones, economics, you name it, juice them, and then flip them for two or three X in a couple of years.
And, you know, I think a famous one of these was Vista and Marketo.
where they got like a 3x in just a handful of years.
And, you know, with the kind of demise of the traditional B2B SaaS and this usurping of these kind of native AI companies, it's been pretty wild to see companies that I thought two or three years ago, even, or, you know, in 2021, at least, they're worth close to 10 billion that are now, you know, nothing, zeros, right?
So how do you kind of reconcile that in this?
you know, trend or bubble as part of that.
Yeah, 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 and innovation.
That usually on the other edge of the sword is value destruction.
Like entropy has to exist somewhere.
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 longer 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 and definitely a model release-by-model release basis.
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 AI.
Fin is the future of this company, its 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 Finn.
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 destruction and 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.
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, Ion's out.
They're 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 flyer on this.
And then, wow, the company, you know, Ion comes back, the company reaccelerates in a massive way.
And then, you know, you see a competitor that I think was growing incredibly fast, but probably not at the scale that.
Finn was in qualified to get acquired by Salesforce, which I heard was upwards of a billion.
So obviously Intercom doing very well 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?
We're GTM Fund, so I have to start with her 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 two or one 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.
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 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 an incredible job establishing the brand and then the product backfilling that from an execution perspective.
Lagora is 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 and non-technical user app creation where distribution matters 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 and your distribution right from day zero because...
you know, the fastest way you become the default brand in a 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.
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 they're...
I think she did the A, B, and C for 11 Labs, right?
And so you're kind of kingmaking on your own.
by growing quickly and building great distribution.
But then, of course, you know, the megafunds can come in and really give you this mountain of money that puts a pretty big gap between you and the rest of the pack.
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.
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 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 also in people sharing use cases and 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 leading the A, the B, and 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 that company.
It shows that their strategy can work, right?
If you raise 15 billion and 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, right?
So, you know, some smaller firm found that company and was able to get in before, you know, Andreessen Horowitz ABC.
What do you think about that?
Yeah, it's the pre-seed and seed rounds.
It's just a question we get a lot.
The pre-seed and seed rounds that capture the TechCrunch headlines and, you know, go viral on X, LinkedIn or both are the mega seed rounds.
Those are really small.
you know, drop in the bucket in the larger, I would say, investment landscape for pre-seed and seed.
And you look at a company like Eleven 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 pre-seed and seed specialist funds that invested into that particular round, I mean, not only a fund making investment, but potentially a firm making investment.
And we see that all the time.
You know, pre-seed and 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 A6 and Z 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 concentrate capital into them as they grow.
Yeah, I mean, it's certainly validating.
obviously to the strategy of the kind of emerging manager and seed-only funds.
But wow, kind of what an amazing way to deploy capital 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.
And I think there's probably...
a few firms that could pull that off, Sequoia, Lightspeed, Excel, Index, Thrive, you know, but it's a short list, probably sub 10 firm list.
Last thing on this one, but I want to point out, founder qualities.
What are some of the things that she was looking at, you know, when sizing up founders to invest in?
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 build 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 and 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.
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 cites 11 Labs as a company that's done a great job of that.
And it's easy to see why and how and if people are users of the product, you've experienced it firsthand.
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.
Yeah.
And she's got a great eye.
Phenomenal track record.
All right.
So with that, let's get right into it.
Today, we have our SVP of marketing, Sophie Buenassisi, doing the episode in person with Jennifer Lee, general partner at Andreessen Horowitz.
We'll let them take it from here.
Jennifer, welcome to GTM Now.
Thank you for having me here.
Absolutely.
It's great to have you here.
And how 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 bet in the race, which is huge.
Why now?
Why infrastructure?
Yeah, I've been...
long time enterprise investor, but spend most of my time in infrastructure.
And as I've been looking back in the last eight years in venture, like the role of infrastructure just 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 tool chain that's built up upon that, both on data, both around, you know, ML tooling or coming through.
And 2022, of course, everybody knows the TATGPT time.
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 build 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 workloads on actually not built for the specific workload.
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, we consider that a big part of infrastructure, are actually being revamped in real time.
What are the agents using as tools?
What are the orchestration layers?
Again, how do we store memory and so on?
Those are all the opportunities that's emerging to build infrastructure.
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.
So we want to be able to support all the founders building in the research and engineering space.
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.
Yeah.
A lot of these two goes hand in hand.
I think, again, this is my personal view, like, unlike consumer space, like, we invent completely new paradigm, new interactions, like, infrastructure tend to be layered.
They're built upon each other.
And it's layered upon, like, you know.
old existing infrastructure with new layers.
Like we always had transactional databases, but the vector storage was new to store embeddings.
We have always, again, had, you know, memory and file systems and now agents are using them.
So how to access these tools, like MCP is completely 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 different persona or different paradigm of like how tools are being plugged in together and utilized by either human developers or agents.
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?
Yeah, we define infrastructure in a pretty broad way, but there are certainly very focused areas we tend to spend time on, definitely foundation research.
of AI models.
We have been longtime backers of OpenAI and Thinking Machines, SSI, a lot of creative models from World Labs to 11 Labs to BFL and Ideogram.
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 model itself as API and so on.
We spent a lot of time in core AI infrastructure and that includes Again, all the things I talk about of like systems, storage, data pipeline.
I invest in a company called Reducto.
Like what they do is really using vision language models to turn PDF documents into LM ready data structure.
Like that itself is a core piece of system that required for building agents and automating knowledge work.
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 for code review, for like CICD, for pretty much the whole tool chain of software development.
Cursor, of course, is one of the very prominent investment of ours on the IDE side.
And we continue to double down in this killer use case.
I think there's a lot of interesting opportunities.
And after that, there's security.
Like it is how do we secure both the software parameter, but also the team and the people organizational parameter.
We're seeing a lot of both sort of fear mongering scare of like what AI hackers will do.
By the same time, we can also secure that realm much, much better.
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 software.
Incredible.
And one of the companies that you've invested in, you mentioned, is Eleven Labs.
You, I believe, led their Series B in 2024.
We actually led Series A, B, and C and participated in the D.
Okay.
Okay.
So you've been with Eleven Labs since their Series A.
Yes.
Through to their series D.
Yeah.
What did you see early in the voice AI space?
Because that was a little bit prior to voice was as widely recognized.
Yeah.
So there are two things really are the drivers 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 check myself.
But we've been tracking the first synthetic voice space for many years.
And it's just never crossed an uncanny valley.
Like you can literally just hear this is robotic sound.
You know it can automate some stuff too.
like, you know, narrate a paper, but nobody will be like paying attention 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 and emotions embedded in it.
So when we first saw the Atlanta Labs demo, I remember they were using like a Gandalf voice to just like narrate a bug or like, holy shit, this is really alike and also has all the right like pauses, stressing, intonation that it just like, It's super engaging.
And you can imagine how that's being used for all the creative use cases.
Yeah.
We had a thesis around like, yes, language model is really important.
It's like driving out intelligence.
But all the media and creative models is actually where AI was having the best run and the best use cases just because there's no accuracy related to it.
Right.
Like you're not really judging these models by its accuracy.
Of course, we'll see like six fingers, five fingers.
But sometimes even the imperfection is like creation, is creativity.
And we want really to double down and invest in flourishing creative ecosystem.
And voice model is just such an important pillar because we need that in a video.
We need that to like, you know, for podcasting, for, again, creative expressions and so on.
So betting on sort of the voice model was also one of the thesis driving behind.
And what didn't really came into picture or like we didn't really have the foresight to render right is, again, all these voice agents.
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 30 seconds 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 desk looks workers, customer service, front desk, like a lot of these use cases are just like really easily automatable because it's first repetitive, 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 the synthetic voice into the picture and having like a fluid conversation.
And the language models was good enough to complete many of those tasks being the driving force behind.
So we were seeing like this voice agent took off in like 2014 and we backed also Decagon in the customer support space and many others that are all using Eleven's voice.
So that was...
Again, just another unlock of how big the space and the market could be when you really nailed the accuracy and also the fidelity of the model itself.
Absolutely.
And you've been with Eleven Labs and many other companies through multiple raises 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 in go-to-market?
Yeah.
It's such a great question because I've always felt I'm like enterprise person through and through.
Like in 2022 was the first time I turned into more of like a consumer diehard 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 back then.
It was like a lot of the activities were happening just for fun and people were 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.
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 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.
So there's just a lot of opportunity to like own a market and own the interface and persona really quickly.
And that sort of either you can call it king making or like a brand effect was really prominent in AI too.
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.
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 that default choice is what I think all the go-to-market challenges and opportunities are.
If you found the 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, just a great advantage.
And talking about Moat, really defensible position to be in.
Yeah, everybody's striving to have that Kleenex phenomenon now on the B2B side and the speed to market on the go-to-market side and speed to own that category now is something that we haven't really seen before.
So totally.
Yeah, it's very interesting.
And one thing that you mentioned earlier about 11 lads was the founder caliber.
You said, I would have written them a check 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?
I think if I simplify, there are a lot of like, you know, really amazing qualities now that I've worked with Maddie and Peter over a few years.
But I think if I dumb it down to like the beginning and what are the most outstanding qualities, it's really, I think, the passion, the conviction to the problem.
It can come from anywhere, but like to them, it's a very personal story that they have just seen these like adult movies that are really boring with like only one monotone voice narrating through the whole.
movie that like really takes away all the emotions and 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 mindset.
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, we'll win.
But that's only like half or maybe 60 percent of the equation.
Like you still need to.
package it into a product that's easy to consume, easy to understand, because these models are really capable, 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 Eleven Labs even talk about this in one of our podcasts in length of like, how do they advance on the research side, but also use product to, and product functionalities to patch some of the imperfections of the research until it's ready.
Let's say like we know in three months or six months, this research will be ready, but now it's not.
But it is something users are really craving for.
How do we turn it into a product functionality that sort of feels a bit of the shortcoming of the model, but can still deliver the premise, not in the best way or in the most perfect way, but again, like bring the future forward a bit.
And then when the model is ready, you have the model replace that.
Like that kind of understanding of like where a product can shine, where the technical and research can shine is a really important trade integration on both sides.
And the last part is, you know, they took go-to-market really seriously, not just winning consumers, but also going after enterprises with, you know, the API product, with the voice agents and agent platforms.
And they're certainly winning in that space now, too.
Definitely.
That's great advice for founders, too.
And now as you look out.
across 2026, which we're almost midway through, which is crazy.
It is crazy.
What are the biggest changes that you anticipate seeing in 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?
I think we'll continue to see acceleration on the frontier SOTA models.
But the best news for the ecosystem is open source is catching up really closely and really fast.
And I'm very excited about that because that's, again, how a lot of startups, companies can sort of.
Combined different levels of intelligence, also like characteristics, economical value of them into, again, their system and composite workflow instead of just one model run through all the way, like, which is both slow and expensive.
Very excited about a couple of 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.
So those are the ones I'm super excited about.
Very exciting.
Well, 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?
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 a meme.
Now it's so easy.
Like it's at 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 and resources they have.
Now they can.
literally have like a one to two people studio, but making a full movie.
Like we're 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, show their ideas.
Actually just came from the pitch this morning.
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.
We're thinking about, again, like really premium storytelling for like luxury brands and so on.
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 work models to create something that, again, used to take teams of like tens of people, month of work to come about.
Right.
And lastly, I'll just tell like sort of a personal sort of 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.
Yeah.
You have all the materials and even like animated version.
Like, how can we make it faster?
Like to.
And that's a super long manga.
I don't know if you know, like the whole book can like 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 action, engaging movie storytelling?
Like now, again, we have these really capable tools that.
We don't have to wait for years and years until like we can get to see the full episode.
So that's what I'm looking forward to.
Good.
That is the goal.
Somebody'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, too.
What's going to stop us from getting there?
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 guard reeling direction to get to the point of like being able to be perfectionized.
I think more so of the like attitude and the adaptation 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.
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.
The models will never be able to replace that, in my opinion.
And I would love to see more creatives just embracing these tools and making it part of their daily workflow and like becoming way more productive in being able to express what is in their mind, what's in their heart and tell that to a much more broader audience.
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 ship and actually express, like you said, bring out what's in the mind to reality.
So very, very exciting times.
No, 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 tier and A tier, you know, creative storytellers can use the tools to become even better than where they are today.
A lot of, you know, us can use these tools to tell stories or like show really creative, pretty things to our family and friends.
Like that alone is just making everything better.
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 something that's been in kind of the back of your mind that maybe you haven't brought to the world yet that you'd want to tell people?
I would say just make 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.
And as much as, you know, as founders, as operators can spend time and just like living and breathing it, I think.
you know, a lot of good ideas will come out of it.
I love it.
Jennifer, this has been fantastic.
Appreciate the time.
Thank you for joining.
Thank you so much.
Thank you for the questions.
Absolutely.
and subscribe to our Substack at a16z.substack.com.
Thanks again for listening, and I'll see you in the next episode.
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