a16z just raised $1.7B for AI infrastructure, and here's where it's going | Equity Podcast

Hello and welcome back to Equity Techrunch's flagship podcast about the business of startups. I'm Techrunch

venture editor Julie Bort in for Rebecca Balon while she's at web summit this week. Techrunch readers have probably

week. Techrunch readers have probably heard some form of agents are the future or agents are coming for our jobs. But

today we're going to find out where one major investor in agents and infrastructure stands on the issue.

Today I'm joined by Jennifer Lee, general partner leading infrastructure investments at Andrea Horowitz.

Jennifer, welcome to the show.

>> Hi Julie. Thank you for having me on.

>> Yeah. Well, you've got some good news that just happened to your team. You

guys recently raised 1.7 billion in new funds. So I guess I mean the the biggest

funds. So I guess I mean the the biggest thing I think people want to know is like what are you going to spend it on?

So, and I'm curious, what are you going to spend it on in 2026, this year, that you probably wouldn't even have considered spending it on last

year or the year before? It's truly a lucky time to be alive. And we're

literally in this super cycle that not just I have never seen that many of the industry vendors on our team have never seen. And infrastructure is getting

seen. And infrastructure is getting rebuilt in real time every single layer, every step of the way. So as an infra investor, it's certainly a very very exciting time which is the reason why we

raised 1.7 billion fund to really back infrastructure founders to go from all the way on the bottom of you know chips chip design building the real hard infra

infrastructure that supports sort of where models are going to run and the software layers, the communication layers, the developer tooling layer to

all the way the model layer. Um and all of these are our mandate to really looking at looking into the future looking at the workloads looking at use cases to see where we need to retool.

And the answer honestly is we need to retool pretty much everything because all the infrastructure >> that's AI running on today are not built for AI workflows.

>> So uh give me a few samples of some of your portfolio companies and why they're infrastructure.

>> I work with a few companies such as 11 Labs ideoggram. These are on uh on the

Labs ideoggram. These are on uh on the foundation model side. They're

developing their own model from pre-training to post- training to the product itself. And 11 labs is uh one of

product itself. And 11 labs is uh one of the voice players that are that is uh building both the creative platform for

for people to uh use synthetic voice or clone voice to uh using podcast, YouTube uh creative expressions uh and also agent platform that are powering a lot

of the voice agents today. be it for customer support use cases for HR for uh sales and marketing purposes and ideoggram is a image generation company

that's you know generating uh graphic design realistic photos typography also from pre-training to post- trainining to the consumerf facing and professional facing products these are on the

foundation model layer and on the infrastructure layer fall fal is a great example of being the inference cloud for all the diffusion models or I'd say

multi media, creative models from video, image uh to audio that is powering a ton of the current scale creative uh expressions, creative tools and

marketing and advertising use cases. And

there's a slew of you know dev tool companies like you know uh Snless, Milifi, MX, Astro. Uh I can talk about them uh probably for the next hour.

>> All your babies. All your babies. You

don't want to leave anybody out.

>> Exactly. Yeah, I get it. But this is sort of below the application layer layer, right? So this is the stuff

layer, right? So this is the stuff that's going to power the application level. Sounds like mostly some of them

level. Sounds like mostly some of them do have their own applications because of this vertical integrated notion of AI company, but yeah, largely these are infrastructure and APIs and and

capabilities that are powering application layer. So you're back in

application layer. So you're back in these companies that do voice that do video that devel. So what's your personal opinion on AI slop? I mean,

mine is it's actually some of the most entertaining stuff on the internet, I feel like, but um also it's problematic. So, I'm curious like

it's problematic. So, I'm curious like you you've got um companies in the trenches of that. So, what what's your thoughts on it and how do you think we're going to get the best out of it

without having it become a real liability? Yeah, I'd say every

liability? Yeah, I'd say every technology go through its infancy phase, go through its adolescence phase and to

maturity. And I I'm in awe of the

maturity. And I I'm in awe of the technology evolution in all of these models. Like three years ago, we're

models. Like three years ago, we're looking at a gender image. Like we can distinctly tell this is a gender image.

Whether it's the the hands or the fingers or the eyes, the inconsistency, the lighting, the shadow, like it's very clear that that's a fake or AI generated image. Only 6 months to a year later,

image. Only 6 months to a year later, you really cannot tell anymore. Like the

lighting is perfect. It's hard.

>> Yeah. The facial expression is perfect and we really across Canyon Valley really quickly with image. And same for audio. Like honestly, I was astonished

audio. Like honestly, I was astonished when I first heard 11 Labs clone of my own voice. And it's it's a little

own voice. And it's it's a little uncomfortable, I have to say, that to hear your voice.

>> What did they have you say?

>> I I was just coining it reading a blog a blog post. It it was not the most like

blog post. It it was not the most like glamorous thing, but at the same time, I was like, "Oh my gosh, I can turn this into another language. I can really like hear my voice." And I recently did a

clone of myself speaking in Japanese. I

showed it to my husband who speaks Japanese and he's like, "Wow, it's it's it's quite uh surprising to hear my wife who doesn't speak Japanese speak Japanese to him and it's just like, you

know, it it definitely feels like living in the future." But again, this this technology just evolves into like crossing the uncanny valley face really quickly. Uh I don't think we're there

quickly. Uh I don't think we're there yet for video. Uh that's why I probably see a lot of slobs on on the social media today. But like you said, it's

media today. But like you said, it's entertaining and we already are seeing the progress really with the Gro's recent uh video model that these sort of imperfection is getting close to high

quality, high speed, really hard to tell that it's AI generated pretty quickly and and there's of course good and bad with with that too. It's just, you know, the quality side and uh sort of the

slobs that we're as users were complaining uh are quickly getting removed.

>> I mean, we like knowing that it's AI generated, you know, like a cat jumping on a trampoline. We like knowing that that's AI generated, right? I still love to be able to tell some of the

breadcrumbs, you know, when I'm seeing a video that like, you know, there are these little imperfections to know that it's generated. Yeah, I agree. But I'm

it's generated. Yeah, I agree. But I'm

sure for professionals that's like a a complete no no. So what what are you seeing in 2026 that's that's sort of new

already? I mean 2025 was a crazy year.

already? I mean 2025 was a crazy year.

We went from oh the models are good. We

can maybe use them to companies raising buku amounts of money and attacking every layer of the infrastructure with AI. So, I'm curious

like as we start 2026, what do you think is new right now that we haven't ever seen before?

>> I I'm sure uh you have seen uh mobbot now, not cloudbot uh proliferating through Twitter. Um, I

think that's a great sign of just how people start to realizing agent can be real and they can actually help you with personal productivity for like you know

longer running processes and this is probably outside of the mobile sort of um use cases but that I I'd say it's it's not new as like a concept. have

been talking about agents for a couple years, but it's finally at the stage where we are able to yield real ROI and productivity out of some of the

long-running agents and agentic workflows that I think is a huge unlock.

>> So, do you think this year agents are going to come into the hands of the average knowledge worker?

>> I I definitely think so. with cloud

skills with uh you know open source like like mobile will certainly see a ton of personal adoption. Do you use any

personal adoption. Do you use any agents? What do you use them for?

agents? What do you use them for?

>> Um I definitely use research agents to help me prepare my day.

Uh get to know the topics and also uh pull up notes and information from from uh my own drive and so on. Um, I use a ton of voice agents just just to try out

and test out capabilities and sort of advancements in the fluidity of conversations. So certainly I I'm uh

conversations. So certainly I I'm uh setting up a few days aside to already build my own productivity agent uh to help me arrange my calendar and also

blocking out time for thinking. So these

are really amazing tools and also very easy to to get hands- on with coding agents to build sort of your own productivity assistant. So your first

productivity assistant. So your first knowledge worker agent is going to be managing your calendar, right?

>> Yes. That's what you're thinking. In

addition to trying all the fun voice stuff and speaking Japanese.

Um, so what what are you using to to build that and what and where are you with it today that you think is going to be quickly solved? Well, Chris and Cloud

Code for sure. This still is is app and and uh I would love to have a nice UI and very uh good integrations with all the systems and tools I'm using. At the

same time, you know, I I just really think the limitation today is time and attention. The reason why the first app

attention. The reason why the first app is a calendar and scheduling one is to really open up time to keep up with all the trends, keep up with all the um

research that that's happening with AI with use cases that people are taking these products and tools for um I am spending hours in just reading X or

Twitter uh about how people are using mobile and it it's this is the reason why I love investing in developer tools and infrastructure is once you give people who are creative tools and a

toolbox. They can literally open up your

toolbox. They can literally open up your imagination that you haven't even designed the use cases that you're thinking of or you're not designing for them, but people are just going to take it and and run with it and then creating

all these different uh and it's super interesting and unimaginable agentic use cases and workflows. So, so that's what I'm trying to set aside my time for and also what I'm trying to use agent for.

>> I love that idea. Like um as a journalist, I'm bombarded by email. like

you have no idea. Like I could check there are uh like 24,000 unread emails in my inbox and I I literally could not

even just managing that is like you know once every six months I have to go through and clean it up. And I would love an agent that did that for me but I feel like we're not there yet. Like the

amount of effort I would have to do to train it and also trust it. you know,

I'd have to trust it because there are definitely things that come in my inbox that are important that I need to jump on, but they're outliers. They're

they're not in the bucket, right? And I

don't trust that a AI is designed for that to see the one thing the human mind's going to see and be like, "Oh, no. I need to deal with this right now."

no. I need to deal with this right now."

So, that's why I think I wonder whether 2026 is going to come to an average knowledge worker. I use AI all around

knowledge worker. I use AI all around the periphery, but you know to actually hand it a task and and say go do I don't even know how an average knowledge

worker would train their own AI for that. So what do you think is going to

that. So what do you think is going to happen there? The example I give is a

happen there? The example I give is a great one on emails and priorities. Like

humans are just so great at connecting dots and figure out unspoken context versus with LLMs. You really need to feed all the like have a clear goal and purpose feed all the context it needs

and understand the things that are happen maybe not spoken in the email itself or on the calendar that are able to gather this information and bring sort of what's what should be the the

top priority for you. That's why I think a lot of the email inbox agents are not quite there yet. Um, but there are certainly areas it can help with when you're really trying to understand a topic. I'm sure you're getting, you

topic. I'm sure you're getting, you know, a ton of inbounds about carving certain things. Um and and that's where

certain things. Um and and that's where I at least find the most productivity lift is whenever I want to you know really figure out what has been talked about on the topic like doing that kind

of research which used to take you know multiple links and going to Google search and open up like a bunch of tabs and coming up with a formed opinion of if this is top of mind if what people

have already talked about like now you literally have it at your fingertips like I just listen to the deep research output and inputting 11 labs reader and just listen on my on my way to a meeting

or on my way to work like that experience has to take hours for me before where we can just like do that in on the go. I agree and maybe it's gonna seep in like a teaag, right? You start

using like it's always it's a much better Google, right? It's a much better Google experience, but it hasn't actually become something I can hand a task off to do. I write and I I write

all my own stories. I have been accused of, you know, of of sounding like an open AI. I think open AI trained on me

open AI. I think open AI trained on me personally, not the other way around. I

love a good M dash. Yeah. Yeah. which I

think it's a good thing that the actual creation still happens with human but these tools really help us to be a lot more productive and a lot more intentional and also a lot more expensive with our views every single

time I'm again going to a meeting coming on to a podcast is where I just spend you know hours to do deep research and just learning um you know about about things that has been explored and talked

before or things that haven't been so like I think that's where the productivity gain is happening but also the trust is being built like this is not a time I I feel like, you know, we

we'll just be ready to hand over agents a ton of things. I'm fairly certain 2026 or maybe even 2027. It's still very much a co-pilot phase. And some some of the pieces might go in into autopilot if

this really is about data entry. Like I

invested in a company called Reduct that turns uh PDFs and and and documents into structured data. Like you really don't

structured data. Like you really don't want people to keep entering data, >> right?

>> That's like looking at human written form, right? That's like probably the

form, right? That's like probably the job and the role that should go away first that turn these humans into something way more intelligent uh type of work than just like sitting in front

of computer doing data entry from these overseas services providers. So those

are the things I think will go out to the more autonomous versions first in 2026. Yes. So the average knowledge

2026. Yes. So the average knowledge worker is going to be able to hand off boring tasks like that. There's I still have a lot of those tasks that I can't

get AI to do for me. Collecting

information and putting it in a different format. It's still pretty

different format. It's still pretty wonky. You know what I mean? Stuff like

wonky. You know what I mean? Stuff like

that. And so maybe 2026 it'll come along. I I I do have a beef though with

along. I I I do have a beef though with um Silicon Valley and even VCs to some extent that keep talking about agents as

human replacements like full human job replacements. I think in my opinion from

replacements. I think in my opinion from having experienced and played with these tools just as the year you know the couple years as it's come out that that

we're going to hand off undesirable tasks to agents but I think that this whole oh you're never going to have a salesperson again or things like that. I

feel like that's just marketing so that these software companies can price their agents comparing them to labor you know.

Okay, you can pay us $15,000 a year, but you'd have to pay someone $100,000 a year. It's not really replacing a human.

year. It's not really replacing a human.

And so, okay, am I right or am I wrong?

What do you think? Are these agents really going to replace a human? And are

they going to do that in the next year or two? I think it really depends on the

or two? I think it really depends on the job and the role. It goes back to what I just said. It's like, you know, if this

just said. It's like, you know, if this is a job really not bu, you know, sitting in front of the computer doing data entry or like being on the phone just talking about the same thing again and again like explaining

the same concepts or like answering the questions about where is my order like how do I return this like these are just inherently very cumbersome, mundane and soul crushing tasks that I I wish you

know agents can replace and elevate these humans into more intelligent type of work. um where there is still a large

of work. um where there is still a large amount of knowledge work that really requires human interaction and human creativity to involve and that's

probably I'd say the last 20% of let's say customer service or like sales SDRBDR like you still want to interact with a warm human voice that understands sort of the human intent and also solve

sort of a complex issue build that relationship build sort of the connection uh so it's not going to be end all be all or like a binary answer.

I think it again it really depends on the type of work. I feel like we're in agreement. It's they're going to replace

agreement. It's they're going to replace tasks but not human jobs in my opinion.

Sure, there's still a few hang out human jobs like data entry from that hailed back from the 1960s or whatever. But I

mean technology has always automated those kinds of things. Correct. Like

back in the day, remember the receptionist, you know, like companies don't have that anymore. And that was long before AI agents came in, right?

>> So I think tasks, not jobs and that all of this jobs are going away talk is something Silicon Valley needs to cut out, needs to stop doing. What do you think?

>> Um, you like them pitching that.

>> I'd say certain jobs probably should go away and the job titles will change.

>> Okay, fair enough. Fair enough. Fair

enough. I think we're mostly in agreement on this, but I do want to know, so what is your most unhinged opinion? What is the thing that when you

opinion? What is the thing that when you go to a cocktail party, you're like, "This is my opinion, and I'm standing to it, and uh people are want to tell you you're wrong." I know these models are

you're wrong." I know these models are really creative. They're able to create

really creative. They're able to create in lightning speed in seconds, but I inherently still think creativity belongs to humans, and the best ideas

will eventually still come coming out of human. So your unhinged opinion is that

human. So your unhinged opinion is that you're I'm putting words in your mouth.

You're a little skeptical about AGI.

Sounds like I think our AGI definition.

>> I think it's reasonable to be skeptical about this thing that everyone's imagining.

>> Yes. Yes. I I think in the in the in the best form of AGI is where every single human are able to express our creativity in its maximum and that's what I hope is

what AGI brings us is you know there's a lot of intelligence that really helps us take care of the things um either we don't like to do like it is not

inherently pushing limits on the individual boundaries and really help each single human being expand our aperture domains. means knowledge and

aperture domains. means knowledge and really reach the maximum creativity because I do think a lot of our daytoday like we really just are in this crunch of getting things done like tell me how

many times you feel you're creative and are able to create something in the last year probably you know it's countable >> it's not fair to ask me because I write for a living so >> that's true that's true those are all

all creative moments um >> but but I'd even say like if if you have have help to go through those 2400 100 emails every day.

>> Yes, >> you'll be able to create way more and probably >> I would be able to create more >> also push further of your best ideas.

>> I think so. My my unhinged opinion is I do think that LLMs have a limit. You

know, I think they're interesting now, but I I think that uh world models, which is where the in my unscientific uh

opinion is is where the AI starts interacting with the real world. I feel

like that's gonna get us to this imagined future where you have robots, you know, picking apples and not humans anymore, that kind of thing. I think

that that LLMs are are fascinating, but I don't think they're the be all end all. So, would you agree with with that?

all. So, would you agree with with that?

Do you think that LLMs are going to hit a limit? that that I also think it's

a limit? that that I also think it's it's a pretty uh agreed upon sentiment at this point that LLMs are good for certain things but not going to be the answer for everything.

>> I don't think they're going to turn into AGI. I do not think LLMs are going to

AGI. I do not think LLMs are going to turn into AGI.

>> Yes, we'll need multimodality. We'll

need ways to interact with the real world. Uh we can't just live in sort of

world. Uh we can't just live in sort of token generation and next token prediction. So yeah, very much in

prediction. So yeah, very much in agreement with that.

>> Oh, see that's good. So the last uh VC I talked to did not agree. still just

thought we were in the early stages of LLMs and we are. We we are but I think we're starting to see their limits. I

do. The other really interesting thing that's happening in 2026 is chip design.

So this is like right up your alley. So

we're now seeing AI well we're seeing the early stages of a few startups that are using AI for chip design. I mean the goal is like the AI is going to design

its own substrate level you know and um iterate faster. What are you seeing that

iterate faster. What are you seeing that happening at all? Is it all just an idea from a few labs? I personally haven't spent a ton of time on this topic, but I've been just keeping up with the literatures and the research our team

has done. So, so I think this goes back

has done. So, so I think this goes back to what I talked first that a lot of the AI workloads today are not running on the infrastructure that's built for it and we're really pushing towards a

future where there will be more purposeful boot workload specific uh hardware and software that's either for just you know large scale inferencing

fast speed latency. So like a a lot of again on the chips level um that that we're seeing people are spending time researching uh what's needed for again pushing limits on on getting performance

out of these chips. So if you think about where you know LLMs and and AI can really help with is this this is like a waterfall takes years to design a chip

getting sort of into prototyping into production like now we can really shorten that that first phase of designing and prototyping and this not just applies to chips I'd say like this

really applies to all software creation today is that shortened period of ideation and creation for the initial phase. So um it's definitely coming to

phase. So um it's definitely coming to to chip design. So, one of the other things that's unusual about this particular stage in investing in AI is

how quickly these companies are going from, you know, we just were founded to whatever 100 million ARR, you know, in a

tweet. Um, so I I'm just curious uh if

tweet. Um, so I I'm just curious uh if you have any stories or examples of some of your fastest growing startups and what what is that, you know, for

founders, what does that mean when you're going to get to these big numbers so much more quickly than has historically ever happened? I'll first

caution the listeners and the viewers that not not all AR are created equal and not all growth are equal either.

Like now we're literally talking about ARR, revenue, run rate, GMV in one concept,

especially on Twitter. And there's a lot of uh sort of missing nuances of the business quality, sort of retention and durability that's missing in that

conversation. And so this is actually

conversation. And so this is actually one part that's introducing a lot of anxiety to some like newer younger founders. It's like how do I do how do I

founders. It's like how do I do how do I go from zero to 100? I'm like you don't sure it's a great aspiration but you don't have to build a business that way to only optimize for the topline growth.

Like you can still build a sustainable business that's growing five to 10x year over year which is again unheard of.

>> And also don't believe everything you read on the internet. Exactly. Exactly.

they're they're using this number but how they're how they're constructing that number uh there's a lot of variables in there right >> totally totally so so coming back to your question of like the the businesses

that we have seen that have have reached those milestones cursor 11 labs fall have all gone from zero to 100 200 million in a couple years or as short as

you know a year and these are there there's real reasons behind each of them is coding agents is the actually the first agent that works in in real life

and producing really incredible results.

And that's cursor for 11 labs. Voice

agents plus synthetic voice has a ton of use cases that are lower hanging fruits, less prone to errors and also again talking about the the crossing and Kenny

Valley being one of the first modalities doing that like really drove 11 Labs growth from zero to I think last reported was 300 million by end of last year and fall where they're really

powering all the creative workloads from image to video um and and we're really seeing the proliferation of open source where LLMs are really controlled by a

few big labs and the soda models on the image video side there is a ton of demand >> smaller models. Yes. Yeah.

>> Also demand for variability like you don't want your design to all look the same like you kind of want different styles characteristics but still have the consistent characteristics of low latency high throughput. So that's a

reason driving false growth in the past two years. Um, so I'd say these are

two years. Um, so I'd say these are quite durable business models and reasons why these companies grow really fast. Okay. Fair. Fair. So not everyone

fast. Okay. Fair. Fair. So not everyone grows that. Be careful with the the

grows that. Be careful with the the number believing the number. But what

does it mean for a founder? I mean,

normally it would have taken years and then you would have built up the structure to support that kind of revenue or those that many customers or what whatever it's looking like for the business. They they don't have that

business. They they don't have that time. they're all of a sudden they're

time. they're all of a sudden they're they're just managing this may possibly sometimes without CFOs without the financial boundaries that a CFO brings.

So what can you give me some examples of of the kinds of things that startups that are growing this fast face that you you've never seen before? Yeah, I can

tell you um the hardest thing today which again shouldn't be a surprise but it's just consistently being the topic is like how do we hired not fast but the

right people that can really jump into this type of speed and culture like these companies again when they reach those milestones we're all under 100 people

>> and like you said there's no you know CFO or like CRO Like a lot of the growth happened with the initial teams and people stepping up but then when we're thinking of you know how to grow the

team further like hiring best people that can move at AI speed they're just very limited talent out there right now which is crazy to to say given like you

know there's this other side of fear of people losing jobs like I've never seen hiring market like this in all of these AI companies that there's such shortage of talent and there just you know a lot

of desire to find people who able to move fast and also AI native at the same time there's also a ton of like unknown and open topics and and problems that we

have never faced before like a lot of the deep fake how to counter that and uh legal and compliance requirement like these are all the things that are in the new territories that we're consistently

figuring out as a companies. So like

those really requires a ton of creative thinking like you know being also composed measure also think about how you know the public audience and the global audience will react to certain actions. So like those are always very

actions. So like those are always very nuanced topics that I've been having with these companies. They're not used to that. Suddenly one wrong word and you

to that. Suddenly one wrong word and you know people are up in arms or a mistake uh cursor had a sort of a a mistake in how they rolled out new pricing right

that caused people to go up in arms you know the young company stuff that happens only >> they're not a tiny company anymore I I think it's hard right yeah think about

like a developer tool IDE change pricing becoming a huge deal three years ago right now. It's a big deal.

right now. It's a big deal.

>> All right. So, finally, what's one kind of start startup that you are on the search for that you'd you'd really love to fund with this 1.7 billion your team is sitting on right now? What's one

startup you hope to find this year? It

goes back to the the word you just said, search. I think there's still a ton of

search. I think there's still a ton of potential in search. Be it web search, um >> interesting.

>> Yeah. Uh be a personalization type of search. I think LLM needs tools. It

search. I think LLM needs tools. It

really needs um most up-to-date accurate information and that's a search problem.

Uh and the search infrastructure has evolved quite a bit but still needs further involvement to make the search more personal, make the search um also

more expensive, faster to really um keep up with the high throughput, high frequency agentic search uh and also high accuracy too. Now we have further

demand of what we're getting from the language models. We can't allow a single

language models. We can't allow a single entry in the search results being wrong, right? Like the hallucination problem or

right? Like the hallucination problem or like you know the out of context that that we're defining for for this search query like we just have higher requirement for what we're hoping for

from LLMs. And that's again coming back to a search problem and I'm still looking for great teams building in that.

>> Okay. So, someone handling LLM search with accuracy, if if that's your startup, come talk to me.

>> Where would someone How would listeners reach you?

>> They can reach me uh with DM on Twitter, jenniferhi. Uh they can also email me.

jenniferhi. Uh they can also email me.

It's jayac.com.

>> Well, great. Thank you for talking to me today and um I think you're going to have a fun year. You've got quite the little pot to find startups and offer great terms for people. Thank you so

much, Julie. This has been a lot of fun.

much, Julie. This has been a lot of fun.