Visualizing the AI boom (or bubble), with Carta’s Peter Walker

PETER WALKER: I came in to build a research team, so I would be building charts and graphs and data analytics and putting them out in big, long form white papers and all the old school stuff.

RANA EL KALIOUBY: That’s Peter Walker. He’s head of insights at Carta, which thousands of companies rely on – including my former startup and my current fund. Peter came to Carta with a background in media analytics and data visualization. His goal was to turn Carta’s massive amounts of data into key narratives about startups and investing. But simply hosting webinars with slide decks was not working.

WALKER: We found it worked okay, but it didn’t catch on the way that we thought it deserved to. Like, the data’s so good, it should be catching fire.

And what really changed is when you started putting it out from a personal social account or you had somebody behind it.

EL KALIOUBY: That somebody was him. Peter became the face and voice telling the stories in the data. It’s person driven. I can respond to comments in the way that a human being would. I can get into arguments, I can have fun debates.

I can have an opinion about this data instead of just stating it as plain facts, which can get pretty boring. That’s been a completely surprising part of the work, but it’s been amazing.

Peter is filling a huge gap with his work. Startups and privately held companies are a major part of the economy — and it’s where so much AI investment and growth is happening. But the way they operate is a lot less transparent than publicly traded firms.

Carta has all that data. And Peter digs into it to find the trendlines that matter. He’s helping investors, founders, and the public see what’s coming next. The results are so interesting, so let’s get into it.

Peter. Welcome to Pioneers of AI. I am so excited to have you on the show.

WALKER: Well, thank you so much for having me, Ronna. Happy to be here.

Copy LinkWhy startup data needs a human storyteller

EL KALIOUBY: I wanna set some context for this conversation. You and I follow each other on LinkedIn. I’m a huge fan of your work, and especially that you bring data-driven insights to this over-hyped world of AI and VC and startups.

So thank you.

WALKER: Well, thank you for the follow. I appreciate it. It’s been pretty cool getting to build all these graphics for startup founders and investors over the past few years. Not a lot of data in this space, so to bring even a little data is helpful.

EL KALIOUBY: Absolutely. So Carta is a software platform that founders and investors use to track their options and financing rounds and investments. My company Affectiva, before we got acquired, we were on Carta and my fund Bluetooth Ventures is on Carta. So I’m a big fan. But I was telling my daughter, who is 22 and she’s an anthropologist, today that I am interviewing you, and she was like.

What the heck is Carta? So for people who are not spending their every waking minute thinking about startups, what is Carta?

WALKER: Yes. For the anthropologists in the room, what is Carta? Great question. At the simplest level, Carta does two things. We help founders manage their equity, meaning we help them grant ownership in their businesses to advisors, employees, investors, whoever they want to give a little bit of ownership in that company to. And on the other side of the business, we help fund managers like yourself manage their VC funds. So we have founders on the one side and funders on the other. We’re interested in helping founders and funders make better deals in venture, and then hopefully broadening ownership to more and more people over time.

EL KALIOUBY: Amazing. And your title at Carta is Head of Insights. So tell us more about your role and the scope of your work.

WALKER: Yeah, it’s a little bit of a strange cul-de-sac of a role that I’ve found myself in. I don’t know if it exists at many tech companies, but I think it’s awesome.

EL KALIOUBY: Did you define that by the way, when you came into Carta, or did it exist already?

WALKER: My old CMO and I sort of co-created the role. It wasn’t like it was an open job rec out there. I got in touch with her and pitched myself, hey, I think you have a data set that’s worth speaking about in this way, and I think I’m the person to do it. She was very kind to take a bet on me, that was very helpful.

I owe her a lot. So insights at Carta — we have 55,000 startups that use the platform, and we’ve got a ton of different data from those startups on what they’re doing, what they’re fundraising for, how the founders are granting equity, the dilution, all these cool stats, and it’s my job to aggregate that information, anonymize it, and then give it back to the ecosystem.

So you can think of me as sort of a public researcher for startups and venture capital, because as we mentioned at the top, there is not a lot of data in these spaces. And so when you lack data, you make worse decisions. So my goal is to help people make better decisions.

Copy LinkWhat Carta can reveal about private markets

EL KALIOUBY: But one of the things that’s really unique about your approach is that you’re almost like a storyteller and you’re taking a very content-driven, TikTok style approach to surfacing the stories behind this data.

So give us a sense of how much data Carta is actually sitting on.

WALKER: So it’s 55,000 startups, as I mentioned. We think that’s at least half, probably more than half of every venture backed company in the US. At last count, we have about 85% of all unicorns. US unicorns use Carta. So if you’re looking for venture data, there’s really no place better. On the fund side, we help fund managers with their fund administration for about 3,000 venture funds in the US, which is definitely the majority of venture funds. Private equity, which is a space that we’re expanding into very rapidly, I’d say is close to a hundred funds. There’s a ton of different data sets. Maybe the biggest, broadest one though is our equity management tool helps more than a million private company employees manage their equity. So when it comes to questions of how much are you gonna get in equity, what is a vesting schedule, how do you think about exercise — those questions, I think we’re basically world class in terms of the dataset on that.

EL KALIOUBY: So we’re gonna dig into some examples of this data, but I wanna start broad. As you know, I invest in early stage AI companies.

I wanna know how much money is actually going into AI. And to answer this question, you’re gonna have to define what you mean by an AI company.

WALKER: Oh, I was gonna put that back on you though. I want you to define an AI company for me, because I feel like I have 80 different definitions that I pick one depending on the morning.

EL KALIOUBY: That is part of the challenge. Well, tell us what it is today, and I might push back on you.

WALKER: Please. Alright, so AI companies. The way that we would define AI in our taxonomy currently is that you are using AI as a core part of your product offering to end users.

This can mean two different things, and I think this is a big debate. Are you an AI company if you are simply using AI within your day-to-day business? Your marketers use AI to write copy, your engineers use Claude Code to write code. Are you an AI company? Most people in that case would say no. I would maybe debate that, but I think that’s a fairly reasonable one. If AI is in your product, if AI is part of the value that you are offering to your end consumers, whether or not you build the foundational model yourself, or you’re doing ChatGPT calls or whatever, then I would say that you are an AI company and I would include you under that umbrella.

So that’s what we’ve got today.

EL KALIOUBY: I think that’s a fair definition. There’s a whole class of AI native companies that maybe are not building an AI product, but AI is integrated in everything they do. That is interesting, but it’s quite different from companies that are just using ChatGPT to do a little bit of market research.

WALKER: Plus there’s the layers down the stack, right? There’s the energy companies that are thinking about powering AI data centers or chips or semiconductor technology and all that infrastructure layer of the stack. Those are definitely AI companies, because they wouldn’t exist without this technological revolution.

Even if they don’t have AI powered chatbots in their product.

Copy LinkHow much venture capital is really flowing into AI

EL KALIOUBY: Absolutely. Okay. So how much money is going into the AI space?

WALKER: All the money.

EL KALIOUBY: All of the money. Okay.

WALKER: Every single dollar. It feels like it’s tough for us to say exactly. So this year, basically all the capital that’s been raised by companies on Carta — I’d say 35 to 40% of it has gone to an AI company. So that’s many, many tens of billions of dollars.

This number gets way out of whack when you include the top three or four companies, right? If you include OpenAI and Anthropic into this discussion — we can debate whether or not they’re even venture backed startups anymore, they’re just gigantic companies — then the number gets blown out.

It might be over 50%. If it’s early stage we’re talking about, you can think of it as every dollar given to an early stage startup, 40% of those dollars go to AI these days.

EL KALIOUBY: Wow. There was a time when a company would be an internet company or a mobile company or a software company, and then it just became irrelevant.

Do you think the same’s gonna happen in AI?

WALKER: I really hope so. I’m getting bored with putting out the same chart every time that says, this is AI revolution. And you’re right, we don’t talk about internet or JavaScript companies. We talk about software companies, and then we talk about what kind of software you’re building. I would be really shocked if in a couple years 90% plus of software companies on Carta aren’t using AI to build their software in some way, shape, or form.

Copy LinkWhy AI can be both a boom and a bubble

EL KALIOUBY: Okay. The billion dollar question and the elephant in the room, are we in an AI bubble?

WALKER: That requires a little more definition. So bubble can mean a lot of different things. If we’re talking about purely valuations for startup companies, inarguably, yes. 100% yes. That doesn’t mean, by the way, that some of these really highly valued startups aren’t going to be worth it.

Some of them will. Most of them will not. That’s not very different than normal startup world where most startups don’t end up being worth what’s on the sticker price, because most startups fail. That’s just the way that startups go. So it’s actually not that different than usual. But yeah, we’re in a bubble in terms of some of these valuations are absolutely wild. If you’re talking about a bubble in AI generally across the economy — where some massive percentage of the growth of GDP is based on data centers that are fueling AI and all this kind of stuff —

I really like Ben Thompson of Stratechery’s take on this, which is probably we’re in a bubble, but if useful things come out of that bubble, I think it might be worth it. In particular energy — there’s a question as to, can you compare this bubble to the last bubble, say the dot-com boom? In that sense, dot-com — a lot of companies were started, they flopped, but we got fiber out of it.

And that fiber now runs our internet. Is there a similar thing where the energy that we’re gonna get from data centers will make things much better broadly for the economy in 10 to 20 years? I don’t know. There’s a painful decline coming at some point. I just have no idea when it’s gonna happen.

EL KALIOUBY: Coming up, who might win, and lose, in an AI-driven future.

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Copy LinkWhere investors still see durable AI opportunities

Okay, so from an investor’s perspective, where do you see the biggest opportunity? And I’ll share my bias and kind of my answer to this question. I’m particularly interested in vertical AI, and that’s applications of AI specifically in antiquated businesses and industries like healthcare, legal, construction, publishing, where you can bring in an AI solution that can really reimagine these industries. I’m curious what you’re seeing.

WALKER: Are you worried at all that some of those vertical use cases will end up just getting eaten by the models over time?

EL KALIOUBY: That was gonna be my next question — defensibility. Because I worry about defensibility, not on the spot, but longevity of defensibility. I actually tell founders when they pitch to us, I’m like, if you wake up worried that the next version of ChatGPT is gonna render your product obsolete, then this is not a defensible product and I don’t wanna be an investor. So I think defensibility is an important consideration, but I wanna hear what you’re seeing in your data.

WALKER: It’s definitely, maybe the number one question that VCs are asking founders these days, which is: what is your plan to make this model proof or moat proof? Like, where is your moat coming from? It used to be that moat would be the ability to build a product, the technological skills that you had to do it, et cetera, and the speed at which you could do it.

And now it feels like some people are saying speed is the only moat. I think that’s kind of silly.

Like if the only thing that you can do to build a moat is run as—

EL KALIOUBY: 9 to 5. Nine to five.

WALKER: Sleep in the office, get cots for everyone, never see your families — I think that’s kind of toxic in some ways. Although to be honest, I’ve done it at young startups and it’s great for a little while. You’re all just mission driven and you feel so enthusiastic about it, but you know, for someone with kids, it’s a little bit harder.

EL KALIOUBY: Sustainable. Yeah.

WALKER: In general, where do I see the opportunities with AI? I think vertical AI is one of the biggest ones that is coming up.

Also, the idea of the mix between hardware and software — in some ways, if software itself is more difficult to identify as a moat, then perhaps the moat comes from building physical things and infusing AI into them. So a lot of cool and interesting business models around robotics or internet of things or all sorts of stuff like that. The other thing that I think is really clear is that — and this is perhaps not in your investment thesis — there’s just gonna be a generation of companies that are not venture scale, that can work now, that don’t need VC. They’re gonna build wonderful, smaller businesses with a much leaner team, AI enabled tech services, et cetera.

And they’re gonna be amazing companies that are just not fundable by venture capitalists because the outcomes are not big enough. So like a little small explosion of individual — we used to derogatorily call them lifestyle businesses, but they’re more than that.

EL KALIOUBY: Profitable. Right? Like profitable, like good old profitable companies.

WALKER: Who wants to turn a profit? That’s so lame these days in venture. But it’s actually really great.

Copy LinkWhat AI valuations actually look like beyond the headlines

EL KALIOUBY: Yeah, it is really great. I absolutely agree. Alright, so we asked you to bring a few of your favorite charts, and I have a few too. We’re actually referencing a slide deck — the title is How Much Capital Do You Really Need?

We will pull these charts up for our viewers and listeners. We’ll try to explain them for our listeners who are not seeing them visually, but we will also link to the PDF in the show notes. So let’s dig in. My first question to you is around AI valuations. There’s this huge spread right now. There are these companies that are raising rounds in the hundreds of millions of dollars, crazy Maisy, like billion dollar valuations, sometimes pre-product, pre-revenue. But then we’re also seeing companies — and that’s kind of where a lot of my investments are — that are just like good old pre-seed, seed stage valuations as you’d expect. So I would love to get your thoughts on what is happening in terms of valuations and also the size of the round. So maybe we can pull up slide 14 to start with.

WALKER: This is a constant question that we get in the VC world, which is what is the right valuation for a small company or a young company? Sometimes they’re not quite as small as you’d expect. Look, I think people want a right answer, but there is no right answer to these questions. On the one end, let’s talk about the biggest shiniest valuations first — you’re raising $50 million at a $500 million valuation as a seed round, and everyone is losing their minds. They’re like, how could a company possibly be worth that? Well, it isn’t really worth that. Valuations don’t have that much to do with the underlying value of the business today at seed or Series A. They’re still very young companies.

What it has to do with is who is founding these companies.

The vast majority of the valuations that are at the upper end come from incredibly legible founders. And legible in this case means they have credibility badges that other people don’t have. So for instance, the paradigmatic example is Mira Murati, who is the CTO of OpenAI.

She was CTO at OpenAI, and then she founded a company called Thinking Machines, and she raised $2 billion as her seed round.

She did that because she was CTO at OpenAI.

There was no explanation needed. This was someone that VCs were clearly going to fund. And you can debate whether the valuation was too high.

She didn’t have a product at the time. I wouldn’t have done it, but I totally understand why they would, because I think this is an incredibly important, viable person that might make me generationally wealthy. That makes sense to me.

EL KALIOUBY: But that is not the norm, right? Because a lot of my LPs are like, oh my God, did you see this headline? But I think that is not the norm.

WALKER: No. It’s just the stuff that gets the media attention. So I try to show in all sorts of different ways the gap between the part of the market that gets talked about, and then the rest of the market that actually is. So, for instance, in this chart, you see valuations over time for companies — the AI valuations on a median basis for 2025 at a seed round is 15.6 million.

That’s pre-money. So you add in maybe three and a half or $4 million raised, you’re talking about a 19 or $20 million company at seed. Now is that—

EL KALIOUBY: That.

WALKER: Yeah, that’s pretty expensive historically. But that is not a crazy, off the beaten path valuation. You can make that make sense.

But those companies don’t get media headlines. They don’t get TechCrunch headlines. They don’t get talked about on Twitter as much. They’re just kind of floating along as they always have in the middle of the market. And there is a really big middle of the market still in venture.

EL KALIOUBY: Yeah, let’s switch to that same chart, but for Series A companies. So that’s slide 16. There we go. Alright. So what pops up for you in this chart? And maybe again, explain to us what we’re looking at.

WALKER: Totally. So this is a chart that shows a line graph over time of valuations for software companies, and then we split it by whether you have AI or not AI as your software denomination. So again, AI companies get a premium. Those lines are always higher than the non-AI lines at the moment. In 2025, we’re seeing Series A — median Series A for a software company is about $55 million pre-money. That’s the valuation, whereas a non-AI company is talking about $42 million or so. So a pretty sizable gap there between AI and non-AI. And AI companies are also raising more money. So one of the biggest questions that I get from founders is: okay, I get that AI valuations are higher. Does that just mean they’re selling less of the company? But it doesn’t really — the dilution is the same. Most of the time they’re just raising more capital, which is odd. At the very beginning there was this idea that, oh, with AI you won’t need as much VC because building a business is gonna be easier.

You don’t have to pay as many engineers, all this stuff. And yet many of the AI companies are raising more money than ever. So how do we square that circle? I think is an interesting question.

EL KALIOUBY: Yeah, I was actually gonna ask you about that, because I’ve not seen this in Carta data, but maybe you have data to support that AI companies are more capital efficient — outside of the foundation model infrastructure companies. The thesis was, okay, you are getting further with less capital, in smaller teams and in shorter periods of time. Is that supported by any data that you’ve seen?

WALKER: The thesis was correct. They do have smaller teams. They could be more capital efficient, but what it discounts is the compute costs. Most of these companies are not profitable, and they almost never have been at early stage. Everyone always harps on, oh, the margins on these AI companies are terrible.

Well, they’re spending a lot of money on the AI itself. And if it’s easier to start a company, every one of those spaces gets more crowded more quickly, so you have to spend money on distribution as well. So on the whole, I would say that AI companies are not necessarily more capital efficient than their earlier counterparts.

They’re just spending the money in different ways.

EL KALIOUBY: In different ways. Interesting. So instead of FTEs, a lot of it is actually going to the compute and kind of pushing your product out there.

WALKER: These companies, to be clear though, as a distinction from say the last boom that we had in 2021 — they have more revenue. They have a lot more revenue. So in that case, is it more real? You moved from do they have any revenue to okay, these companies, a lot of them do have very significant revenues. Whether those revenues are gonna stick around is the real question. But if you look at it on a pure basis, these companies are quote better than the 2021 counterparts. They’re generating real dollars right now.

Copy LinkWhy geography and pricing still shape startup outcomes

EL KALIOUBY: Yeah. So if I am about to start my company and I’m looking at this data, what does this data tell me?

WALKER: Build an AI company first and foremost. That’s a little glib, but it is kind of true in that the broader takeaway is that there are movements in the VC market. There are themes, there are narratives, and being a part of that theme or narrative makes fundraising easier.

100%. The other thing our charts show again and again is where you are does matter. I’m not saying—

EL KALIOUBY: Let’s pull that slide up. I’m Boston based, by the way. So just.

WALKER: I will keep my comments appropriate then. Look, so this is a chart that shows valuations in San Francisco and valuations in all other US cities.

And the gap — there’s a bit of a bump at the median or so, but where it gets really different is the top 5% or 10%. The top 10% or 5% of companies in San Francisco are raising at valuations that are two, three times as much as the top 5% in other cities. So there really is this concentration at the very high end in SF. Is that—

EL KALIOUBY: Good or bad? Exactly.

WALKER: Look, I think it’s very easy to say if all you care about is having the highest possible seed stage valuation, you should move to San Francisco. I think that’s kind of unobjectionable to say. The real question though is, is it good to have the highest seed stage valuation?

Is that what you should want for your business? That I think is very open to debate. I know that founders sometimes get annoyed with VCs saying you should want a lower valuation, because it sounds self-serving, but I’ll speak for the VCs here. A lot of times they’re right, in that if you take a really high valuation at seed stage, getting to the next stage is harder.

You have set yourself up to clear a much higher bar to get to Series A and beyond. It can leave a lot of companies stranded because their valuation is way too high.

EL KALIOUBY: Yeah. I’m Boston based and I do invest across the US but.

WALKER: I think there’s incredible talent coming out of the Boston AI innovation ecosystem. Obviously we have some of the top schools in the world here.

EL KALIOUBY: Do you have any point of view on this distinction between valuation and value creation? Yes, the valuations might be higher in the Bay, but what about value creation and how do we quantify that?

I think it’s a really good question. It’s very difficult to quantify value created versus valuation at the early stages in the beginning of a company’s life because you just don’t know which of these companies are actually going to achieve an exit or something like that. I will say that there are ecosystems in the US where there are more capital efficient exits, Boston being one of them, than there are in the Bay, and that we’re not overfunding companies nearly as much as we are in San Francisco. The other point that you mentioned is talent. I think that there are two or three ecosystems in the US, Boston being one of them, that have an immense amount of talent to give to this AI moment that just don’t get talked about as much. Obviously everyone talks about San Francisco, and the two biggest to me are Seattle and Boston.

I thought you were gonna say New York, but you’re right.

WALKER: Not New York. I think New York is doing fine. New York is doing pretty well. They’re doing about as well as I would expect them to. They’re not over or underperforming. I think that Boston and Seattle have a lot of room to grow in the AI space based on the amount of local talent that is available to them. I think we’ll hear a lot of cool stories out of those two ecosystems over the coming years.

I wanna build some storytellers in non-San Francisco places. I think Boston actually has one of the best stories in venture capital. It is, to me, the city that you would look at if you are Austin or Charlotte or Dallas or anywhere that wants to be big in VC. Boston did it.

Boston went from not really a player to being really big in biotech and nothing else. And it’s now broadened itself to being a really mature, interesting, well-rounded ecosystem. So I think Boston is a major success story in that way, and I don’t hear it talked about as much in that sense.

EL KALIOUBY: Yeah, we’re not really great at marketing.

WALKER: Correct.

EL KALIOUBY: Absolutely.

Absolutely. Alright, so I do wanna talk about — Benchmark Capital coined this term supernovas to describe the handful of AI companies that are exploding both in valuation and scale, seemingly like overnight. Are you seeing tons of supernovas in your data?

WALKER: There’s not a lot of supernovas. I’m not sure if it was Benchmark or Bessemer.

EL KALIOUBY: Oh, I think you’re right.

WALKER: It might’ve been Bessemer.

I’m not sure, but they defined it — shouts out to Bessemer — as something like a company that can go from zero to a hundred million dollars in revenue in two years or less or so. That is shockingly fast, that never used to happen. And two, it still really doesn’t happen outside of, I would call it, let’s say 10 to 20 names, maybe slightly more. But the idea that every AI company is somehow running at that pace, it’s definitely not true.

Supernovas are very, very rare.

EL KALIOUBY: Yeah, still very rare. Great.

WALKER: The problem is that having them as an example resets the bar. Every VC’s like, oh, where’s my Cursor? Like, I need one. And so it kind of makes what used to be great just good, because great got redefined.

Copy LinkWhat solo founders and supernovas signal about the market

EL KALIOUBY: Yeah, I wanna go back to what these AI companies look like in terms of both capital needs and efficiency. I guess question number one — we had Anton Osika, the founder of Lovable on the show, and we talked about this idea of a one person unicorn. The thesis is, okay, with vibe coding tools like Lovable, you don’t really need a technical co-founder. You could get started right away. Is there any evidence in the Carta data that solo founders are becoming more common and or more successful?

WALKER: Yes, they’re becoming more common. I would maybe distinguish the idea of a solo founded company from a one person unicorn.

I actually don’t think we’re basically ever gonna see a one person unicorn. There might be a solo founded unicorn, but even Anton’s a great example. It’s not like Lovable doesn’t have team members. They have about a hundred people on their team.

Solo founders are becoming more common every year. Every year there’s more and more solo founders as a percentage of all the founders in that year that joined Carta.

So that I think is pretty clear.

EL KALIOUBY: Do you have a hypothesis on why?

WALKER: My instinct is that the cost of company creation are continuing to go down — the AI boom just being the latest example — you know, you used to have to buy on-prem servers and now you can use cloud, and all of those things just push company creation down and down in terms of the cost.

So I think that’s the main driver. And candidly, people who may not have thought of themselves as founders can see examples now of people like them who have done it, and that also spurs entrepreneurship, which is awesome.

EL KALIOUBY: It is awesome.

WALKER: The fly in the ointment — VCs still don’t love solo founders. They don’t fund them nearly to the same extent that they fund co-founding teams.

EL KALIOUBY: Interesting.

WALKER: I hear a lot of reasons why. Some VCs say, I’m worried about this person getting hit by a bus. Okay, yes, it could happen. Some VCs say I prefer complementary skill sets, so I want a technical and a business lead so they can share the load.

Sometimes I think what VCs want is just, can you prove that anybody will work with you? Like, are you too disagreeable to inspire a team? But be that as it may, I think it’s very clear from our data there’s more solo founders, but the percentage of them that get funded has actually stayed flat or even gone down a little bit.

EL KALIOUBY: Interesting. Okay.

WALKER: Do you fund solo founders?

EL KALIOUBY: I was actually running through my list of investments. The majority are not. I can’t actually think of one company that has a solo founder.

WALKER: Part of the problem?

EL KALIOUBY: But I also don’t know if I see a lot of companies that are solo founders either.

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Coming up after a break, how the pace of funding is changing for startups, in AI and beyond. Stay with us.

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Copy LinkWhy funding cycles are stretching and hiring is slowing

Okay, so then let’s talk about the cadence of the funding rounds. Are companies taking longer to graduate from one stage to the other? And I’d love to pull up a chart that we can walk through.

WALKER: Yeah, so the old VC advice that you would hear around venture was once you start fundraising from VCs, you’re gonna raise a new round — a new primary round, so seed, Series A, Series B, et cetera — every 18 to 24 months or so.

EL KALIOUBY: Yeah, that was the case for Affectiva, for example.

WALKER: 100%. And that’s still what I’d say most VCs advise their companies to plan for.

And obviously this is a big deal. If you raise $2 million, making that money last for two years versus three years is a big difference. You gotta change the way you run the business. So right now what this chart is showing us is the time between these rounds is getting longer.

So from seed to Series A, the median is now 2.1 years, where it used to be in the 1.5 range. From Series A to Series B, the median this year was about two and a half years instead of down to 1.7, where it used to be in 2021. So yes, it is getting longer. It’s taking longer to raise, and some founders might be listening to this and they might think, well, what are you talking about? Every day it seems, I read about a company that raised six months ago and they just got another massive round. Yes, those are the tiny percentage that are getting the attention. What we’re seeing is more of a barbell thing where if you are a hot company that is building an in-demand AI product, you might be able to raise once a year.

If you’re not, you better plan for not raising for another three years, because fundraising is not easy right now if you are not in the in-demand, golden AI circle. It’s not easy.

EL KALIOUBY: Yeah. So let’s talk about how that translates to hiring then. You have very interesting data around hiring that shows that January 2025 was the lowest hiring in seven years. Tell us what we’re looking at here.

WALKER: So this is a line chart that shows number of new hires that joined Carta companies every month, starting in January 2019 and ending in say June of 2025. The hiring very clearly was going bonkers in 2021 and early 2022. Tech startups, big tech, everybody, it felt like, was hiring a ton of people.

So 74,000 or so people joined Carta companies in January of 2022. It’s the highest mark we’ve ever seen. In January 2025, that same number went from 74,000 to 28,000, so that’s a massive drop. And the question is, okay, why did that happen? Well, in 2022, interest rates changed, funding for startups dried up, so less funding, less hiring.

That makes sense. In 2025, funding is starting to pick back up again, but hiring hasn’t picked back up, so obviously everyone jumps in that sense to AI.

My instinct is that AI is not somehow causing layoffs. What it’s doing is every time you say, oh, I’d love to hire a new marketer, for instance, at my Series A company, your CEO might go, well, could we do that with AI tooling instead? Could we split up that work amongst the people that are already here and stay lean for longer?

And enough CEOs and enough companies asking that same question again and again, and you get this really dramatic lull in hiring that we’ve had.

EL KALIOUBY: Super interesting.

I can kind of confirm that in my portfolio. The teams are really leaner early on, and it is because they’re using AI for marketing and they’re using AI for coding.

WALKER: There’s also this — I don’t know how to phrase it — this meme-like quality to some of this hiring stuff where you look around and you say, what are my peers doing? And in 2021, every startup founder was bragging about how many people worked for them. That’s no longer a brag.

The brag is how few people work for you and how much revenue you have for every full-time employee. That’s the new it metric for a lot of these companies.

EL KALIOUBY: And then what is your advice to our listeners who are kind of looking to join a startup?

WALKER: Yeah, this is maybe the sadder side of it, right? We kind of praise all these companies for being really capital efficient, and they have been on the headcount side, but it means less opportunities for people who wanna join startups. I got my start at a tiny startup, and that radically reshaped my career.

So I really hope that that doesn’t go away for folks. If you’re trying to join an early stage startup, what I would say is — we’ve all seen the memes of AI sending resumes and writing resumes, and then AI reading resumes on the other side, and it’s like there’s no human interaction. The number one thing you should prove to this company is not that you want a job.

It’s that you wanna work there. Don’t just submit a cover letter and a resume — build for the job that you want at this company. You should be obsessive about the thing that they’re building or the space that they’re in. It’ll just set you apart from all the candidates who say, I would like a marketing job, please. No. I would like a job at this company. I’ll sweep the floors at this company, no problem.

Like, I just wanna get into this place because I’m so excited about what you are building. That stands a better chance of making a dent.

Copy LinkHow AI is changing research work and what comes next

EL KALIOUBY: How do you use AI in your work?

WALKER: I use AI in a lot of ways, but the two primary ones that I’m finding really useful right now — on the data visualization side, I candidly haven’t found an AI that is really amazing at making charts yet, but they are incredible thought partners. So I can give it a CSV and I can say, visualize this in 20 ways, spark some new ideas about how I would display this information. And from one of those 20 ways it will push me to a path that I wouldn’t have gotten to otherwise.

So that thought partner AI stuff has been really helpful. The other is for data pipelining and cleanliness. We have so many ways to rerun data sets to say, oh, we have this data set, but it would be amazing if we had zip code into the address instead — that’s not something that a human needs to do anymore.

We can just append and enrich information in so many interesting ways. And it lends those cool filters on all of our data where we didn’t have those before, and now we can slice and dice things in different ways. Those two ways have been really, really helpful.

EL KALIOUBY: Give us a little preview of what you’re working on next? Like what are two or three things that you’re very excited to be working on and sharing?

WALKER: Oh, so many cool, interesting research projects here at Carta. On the fund manager side, for VCs that are listening in — we have done a lot of work to produce good statistics on VC fund performance, IRR, TVPI, et cetera. What we haven’t dug into as much is VC fund economics: carry, management fee, the way that operating expenses work.

So that’s coming soon from us, like how does a VC make money? How does it work on a GP level? All these kinds of cool and

EL KALIOUBY: Can you come back for an episode on that?

WALKER: Oh yeah, would love to.

EL KALIOUBY: That would be awesome.

WALKER: The next thing that we’re working on — and I do not wanna over promise on this, but it’s so exciting that I’m pumped about it — the thing that is missing from our data is financials. We have what I think is the best valuations data in the business. What we don’t have is financials. We can’t tell you, oh, that Series A was raised with this much ARR. We’re working on it. I think there are a couple ways that we could get there, but once it’s dense enough, I think we can start talking about financials alongside valuations and really impact a lot of people in the way that they think about what is needed to go out and fundraise.

EL KALIOUBY: Like unit economics, right?

WALKER: Churn, margin, all that kind of stuff.

EL KALIOUBY: Very exciting. So then my last question, and it’s a question I ask of all my guests — what does it mean to be human in the age of AI? It’s a question I think a lot about.

WALKER: That’s a really good question. You could take it in a lot of different directions, but the one that’s coming to mind first is there’s an outsize amount of credit you get for caring about people as people today, in a way that you didn’t get before. So maybe this is a selfish way to look at it, but remembering someone’s birthday, sending something that they weren’t expecting because you had had an interaction with them two weeks ago, writing a handwritten note, whatever it is that makes that little jump from I was thinking about you as a human being — I think stands out so much more than it used to because we’re all on our phones, we’re all interacting with each other less, all these kinds of things. So whatever the small moment of delight that you can give to someone that you know personally — I think it’s just deeply appreciated in a way that maybe it wouldn’t have been a couple years ago.

EL KALIOUBY: Yeah, I love that. Small moments of delight and authentic human experiences and connections. Yeah. I love that. Peter, that was awesome. Thank you so much for joining us on the show.

WALKER: Absolutely. Thank you for having me. This was great.

In an over-hyped AI frenzy, I love how Peter brings data-driven insights to put it all in context. And I love that he’s humanizing this data with powerful storytelling. At the end of the day these trends mean everything to founders looking to make their visions come to life.