To dig into this trend, 兔子先生传媒 News analyzed a dataset of repeat founders who have gone through YC鈥檚 cohorts. That analysis revealed some very interesting insights. The dataset, shared with us directly from YC, consisted of 454 repeat founders through the program as well as 935 founder-company records spanning 2005 through 2026.

What we found is that repeat participation to date has mostly been a two-chapter journey: 428 founders (94%) went through YC exactly twice, while only 25 appeared three times. and co-founder was the sole four-time founder.

Other highlights: Founders typically returned to YC five years after their previous appearance, with an average gap of 5.1 years. However, the data reveals two distinct themes. Nearly 30% of return participations occurred within two years 鈥� including 38 in the same calendar year 鈥� while 61 returns happened after a decade or more. Some founders jump straight into their next venture, while others take years off to build experience before coming back around.

Repeat founder numbers peak in the most recent data, hitting 65 in 2025. But that doesn’t automatically mean people are returning at higher rates. In recent years, YC cohorts have grown significantly, and the 2025-26 numbers include newer batch formats alongside potentially incomplete data.

It’s also clear that returning to YC isn’t always a solo journey.

Several complete founding teams returned together for subsequent companies, including those behind , and , as well as and .

A trend YC partners are watching closely

, a general partner at the San Francisco-based accelerator who worked the spring 2026 batch, has enjoyed a front-row seat to the shift. In that cohort, he said he had 鈥渁 bunch of repeat, second-time founders鈥� he’d worked with before 鈥� several during their previous YC company.

鈥淚t definitely feels like more of a trend now,鈥� Epstein said. Still, he’s careful not to overstate the novelty.

鈥淚t’s not a new thing. But the alumni base of past YC founders continues to grow,鈥� he said in an interview with 兔子先生传媒 News, and that naturally translates into more people eligible to come back.

Epstein has worked with more than 1,000 startups at YC. Before that, he was a startup entrepreneur himself, co-founding (YC W10), a marketplace for graphic design assets that he sold to in 2014 before spinning it back out as an independent company in 2017.

Ask him what separates second-time founders from first-timers, and he points to experience using the program itself.

鈥淭hey know exactly how to get the most out of the advice, network and resources available to them,鈥� he said. 鈥淗aving been through the startup grind, they get really good at focusing on the signal that matters and cutting out the noise.鈥�

That experience also helps them avoid a specific, costly mistake.

鈥淭he biggest mistake I see second-time founders avoid is overhiring or overspending pre-product-market fit,鈥� Epstein said. 鈥淭he biggest regret of all the successful first-time founders I know is that they hired too many people, moved way slower and didn’t like working at their own companies anymore.鈥�

Leaner teams, powered by AI

That instinct toward leanness shows up in another pattern: Many repeat founders are choosing to start solo the second time around.

鈥淪ome of them (repeat participants) are solo founders, but they’re not building alone,鈥� Epstein said. 鈥淭hey already have networks of people they can bring in as founding employees. This helps them move faster, and feels more fun and less lonely.鈥�

He compares this shift to how cloud computing eliminated the need for startups to raise large sums just to pay for servers.

鈥淚t wouldn’t surprise me if 10-15 years from now you look back at all the money startups had to raise to hire people and realize that’s not a requirement,鈥� he said.

AI is accelerating that shift, and Epstein sees it pulling former company builders, including himself and YC CEO , back into hands-on product work.

鈥淚t’s so easy to get back into it and start building again. And it’s incredibly exciting,鈥� he said. That mix of hard-won product sense and new tooling, he believes, is changing what one person can build alone.

鈥淭hey actually become the people that can produce at 10x or 100x what a traditional engineer would be able to build,鈥� he said.

As an example, Epstein pointed to , a founder he first worked with on in 2020 who’s now building an AI tool that helps founders manage their projects and automate tasks.

Even so, Epstein believes founders keep coming back for the same core reasons: personalized advice from partners, a community of ambitious peers, access to top investors and alumni, and the urgency of the batch environment.

鈥淭he pressure cooker environment of the batch, which pushes them to move even faster, and distribution to thousands of companies within the network,鈥� he said. 鈥淚t’s extremely hard to replicate those things on your own.鈥�

From Opkit to Sazabi

One of the repeat founders Epstein has worked with is , whom YC has now backed twice.

Callaway’s path to Silicon Valley began almost by accident. As a college sophomore, he skipped a lined-up investment banking internship after reading about a software bootcamp in San Francisco 鈥� a decision he calls 鈥減robably the single most important鈥� of his life.

From then on, his goal was clear: 鈥淚 wanted to do my own venture-backed tech startup, and I wanted to do a YC venture-backed tech startup.鈥�

After gaining experience at and fintech , he founded his first company, , in YC’s fully-remote summer 2021 batch. Opkit was a healthcare-fintech startup building insurance verification and revenue-cycle-management software.

鈥淚t was, in retrospect, not the right thing for me to be working on, but a really fun and interesting and rewarding first venture,鈥� he said in an interview. Opkit was later acquired by .

That experience shaped his second company, , a name chosen deliberately in contrast to Opkit.

鈥淥pkit wasn’t very personal to me. It was more of an MBA case study approach to starting a business,鈥� he said. 鈥淲ith Sazabi, it needs to really be in alignment with who I am and my passions and interests.鈥�

Sazabi, an AI-native observability platform competing with incumbents like , draws directly on work Callaway has done throughout his career 鈥� a return, in his words, to 鈥渨hat I know best.鈥� He sees it as part of a common pattern: First-time founders often avoid building in the field they know best, then return to it with their second company.

Callaway hadn’t originally planned to go through YC again, and the reconnection happened almost by chance through an email that looped in his former partner on Opkit, Epstein. Once Callaway decided to return, he was more strategic about timing, even deferring his batch to build out more of the product first.

鈥淚 wanted to use YC as a go-to-market acceleration event,鈥� he said, something he likely wouldn’t have known to do without having gone through the program before.

The founder was back at YC in person for the first time this spring. He described the second-time experience as something entirely new: 鈥淚t was really something special.鈥�

This time around, Callaway also noticed a more experienced cohort than his own first batch, along with new concerns specific to the AI era. 鈥淭here’s a lot of anxiety around what the durable moat is in an AI world when lines of code are effectively free,鈥� he said.

On fundraising, he drew a pointed comparison to 2021. 鈥淪pring 2026 felt similar to fall 2021,鈥� he said, 鈥渂ut unlike 2021, where interest rates and ZIRP drove a lot of that energy, in 2026 it’s driven by AI and by real material gains.鈥�

The company鈥檚 thesis is resonating with investors. In late June, Sazabi announced an $8 million seed round led by , and Y Combinator, with participation from and more than 60 angels from companies including , and .

鈥淎I has changed how software gets written. Now it is changing how software gets operated,鈥� Callaway said. 鈥淪azabi is rebuilding observability from first principles for a world where agents are part of every engineering team.鈥�

Overall, as AI continues to lower technical barriers and YC’s alumni pool keeps growing, second-time founders like Callaway are becoming an increasingly visible part of the accelerator’s lineup.

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In November 2022, was doing something rare for a longtime educator: taking time off. Having launched his career as a teacher in Atlanta Public Schools, Khan became an assistant principal before founding his own public high school in Denver. After a year spent coaching principals at the district office, he decided to take a 鈥減ersonal sabbatical.鈥�

Then, ChatGPT came out.

Khan began tinkering with the new technology, fascinated by its potential.

鈥淚 actually went out to my old school building, the one that I founded, and started using it with teachers,鈥� Khan recalls. He ran workshops and asked the educators to use the tool in as many scenarios as possible.

The responses were varied. Most teachers barely touched it. A few tried, but felt doing the work manually was faster. However, some had a lightbulb moment.

鈥淭here were one or two teachers who told me, 鈥楾his has completely revolutionized the way I teach,鈥� 鈥� Khan said in an interview with 兔子先生传媒 News.

Seeing that divide sparked something in him.

鈥淚 thought this technology could impact every teacher, not just teachers who are really enthusiastic about using new technologies,鈥� Khan said. 鈥淪o the task then was, 鈥楬ow can we take all the power of this new technology and make it really accessible to teachers?鈥� 鈥�

Building the 鈥榲ertical AI鈥� for K-12

That experiment set the groundwork for , a platform designed as an all-in-one AI operating system for K-12 educators and students. For teachers, the tool acts as a daily assistant. It performs tasks like building rubrics, differentiating assignments for varied learning levels, and generating practice worksheets and reading materials.

The platform also helps educators offer monitored AI experiences directly to students. Those experiences range from algebra tutors to writing assistants customized with state exam rubrics that deliver tailored feedback to help students revise their essays.

鈥淵ou can think of MagicSchool as the vertical AI solution for K-12 schools,鈥� Khan said. “Enterprises are adopting generative AI in other fields 鈥� Like in law, there鈥檚 and that are vertical AI for legal firms. We鈥檙e kind of that, but for K-12 schools.鈥�

Today, the company鈥檚 primary customers are school districts that want to provide a safe, governed environment for generative AI that aligns with data privacy rules and local curriculum priorities. MagicSchool now partners with large school systems, including Denver Public Schools, and Florida鈥檚 Broward County Schools and Hillsborough County Schools, as well as private institutions.

“One in five children in America go to a school that is in partnership with MagicSchool,” Khan noted. Additionally, roughly 8 million educators worldwide have signed up for the platform, he said.

The uphill battle to raise capital

Despite the platform鈥檚 rapid adoption, Khan’s path to raising capital for MagicSchool was a challenge. In the beginning, he worked with hourly contractors and lacked a formal business model.

鈥淚 had no real business plan,鈥� Khan said. “The most successful tech companies from my perspective as a consumer were the ones that just got a lot of users, and that was my goal 鈥� I was like 鈥榣et’s just get a lot of people using this, and we’ll figure it out from there.鈥� 鈥�

Once MagicSchool鈥檚 user base neared 1 million, Khan began pitching venture capitalists. However, when compared to standard Silicon Valley profiles, his background as an educator initially proved to be a hurdle rather than a selling point.

鈥淣obody wanted to give a former principal money,” Khan said, recalling 鈥渜uite literally hundreds of meetings鈥� before securing an institutional investor.

鈥淚 think that investors are taught to pattern match,鈥� he noted. 鈥淭hey’re saying, ‘Hey, well, did you go to ? Are you a tech person? Did you work at ? ‘… I have none of those things on my resume.鈥�

Even edtech-focused investors were hesitant, leaving Khan frustrated as he watched other founders secure millions based purely on tech-heavy resumes.

鈥淚 remember seeing other edtech companies right around our size raise seed rounds 鈥� with no product, no sales, no nothing,鈥澨� he recalled. 鈥� I would think, 鈥楶eople know what our product is. Millions of teachers know what our product is, and nobody’s heard of that one.鈥� 鈥�

To overcome the skepticism, Khan relied strictly on impressive growth metrics, convincing investors during every fundraising stage that the business鈥� 鈥渢raction was undeniable.鈥�

The strategy paid off. Following early angel investor checks, MagicSchool went on to raise a $2.4 million seed round led by Colorado-based . To date, the company has raised nearly $63 million in total funding, driven by strong financial growth, including 3x year-over-year revenue growth at the end of last year, according to Khan. The startup鈥檚 other backers include , , and.

Expertise as the next wave of innovation

Although Khan no longer manages the high school he founded, he stays connected to the classroom through district visits. The school remains a top-performing public school in Denver under a former founding team member, he noted.

鈥淥f course, I miss that,鈥� Khan admits. 鈥淭here’s nothing that can replace the relationship you build over a long period of time with students.鈥�

Yet, his deep-rooted experience in education ultimately became MagicSchool’s greatest asset 鈥� a trend Khan sees taking hold across the broader AI landscape as domain experts step up to build industry-specific tools.

鈥淚 think that what we’ve learned over time is that the model is no longer the differentiator,鈥� Khan says. 鈥淗ow you contextualize the model with the real problems that people have in the work that they’re doing, and specific expertise, is the thing that’s going to unlock the next wave of innovation and impact for generative AI.鈥�

Related 兔子先生传媒 query:

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Menlo Ventures XVII will invest primarily in seed and Series A companies, while Menlo Inflection IV will provide growth capital to startups at Series B and beyond. The new funds will target companies throughout the AI market, from foundational models and infrastructure to enterprise, healthcare and consumer applications.

The new capital gives the Silicon Valley firm more flexibility to back companies from their earliest days through later funding rounds that can require hundreds of millions of dollars. It also shows how important AI has become to a firm previously known for investments in companies including , and .

In recent years, has become the most prominent company in Menlo鈥檚 AI portfolio. The firm first invested in the AI model developer in 2023 and has added to its investment in later rounds. Menlo鈥檚 other AI investments include app-building platform , music-generation startup , AI model marketplace , voice productivity company , AI infrastructure companies and , robotics startup , and AI research company .

, a partner at Menlo since 2015, has played a central role in developing that strategy. He invests across AI infrastructure, developer tools and AI-native software and has led Menlo鈥檚 investments in companies including Anthropic, Lovable, OpenRouter, AI-powered software delivery platform , code security startup and legaltech startup .听

Before joining Menlo, Murphy spent 15 years as a general partner at Kleiner Perkins, where he was an observer at Google from the firm鈥檚 initial investment through its IPO, helped launch the $200 million iFund with Apple and worked on investments including DocuSign, AppDynamics, Upstart and Shazam. Earlier in his career, he held operating roles at Netboost and Sun Microsystems.

兔子先生传媒 News spoke with Murphy about why AI is pushing Menlo toward larger and more concentrated investments, what the firm has learned from its relationship with Anthropic, and where he sees the next opportunities 鈥� as well as potential bottlenecks 鈥� across the AI market.

The interview has been edited for brevity and clarity.

兔子先生传媒 News: Inflection IV puts Menlo in competition with some of the biggest late-stage investors in the world. How do you keep the firm鈥檚 close, founder-focused approach when you鈥檙e writing much larger checks?

Murphy: AI companies need more capital than previous generations of software companies. They鈥檙e staying private for longer, and the winners are quicker to break from the pack.
For us, a larger fund gives us the ability to partner with founders from company formation through hypergrowth. Through our venture fund, we invest in seed and Series A companies, but the inflection fund gives us the scale and flexibility to back the clear winners as they emerge.听

This was our strategy with Anthropic, Suno, Wispr, OpenRouter and Lovable.听

You鈥檝e recently invested $100 million in companies including Lovable and Suno. Is that level of concentration becoming a bigger part of Menlo鈥檚 strategy, or is it reserved for a small number of standout AI companies?

The Anthropic investment is an example of us doubling down when we had incredible conviction. Remember, we first invested in the [Series] C round, which gave us a chance to get close to the team, see how well they were executing, and understand where they were going.

When we led the [Series] D round, it was still the largest investment the firm had ever made. We learned from that experience and success, and it’s become a standard part of our approach now. Also and importantly, the market has changed.听

There’s a gold rush around later-stage AI, and the companies that break out are growing at rates we鈥檝e never seen before, at scale. These companies need capital to sustain that growth and, frankly, have earned higher private valuations given the growth rate.听

We’re changing how we invest, but overall we鈥檙e pursuing more of a barbell right now. On the later end, we’re much more aggressive, stage- and capital-wise, for the right companies.听

That said, the bar is still very high. Many AI categories are overfunded, and there is a huge amount of speculation. The winners of this era separate quickly, and we believe they will compound at unprecedented rates.听

Your relationship with and Anthropic gave Menlo an early view into where the AI market was heading. What are you seeing now that you think other investors may still be missing?

I don鈥檛 know that it’s counterintuitive, but I’d say we are moving from Phase 1 to Phase 2 of the market and are seeing an entirely different set of opportunities and challenges.听

In Phase 1, developers just picked a model to start building AI. In Phase 2, we are seeing companies get to scale using AI and looking to optimize their spend and infra choices. A whole host of companies are seeing tailwinds alongside Claude and Claude Code, such as OpenRouter, Fireworks, Modal and .听

It will be a multi-model world. One size won鈥檛 fit all, and we鈥檝e been active in that area as well, including more vertical models such as for life sciences and for robotics.听

The Anthology Fund has helped you spot promising AI companies early. As the application layer matures, what specific bottlenecks are you seeing founders run into when building enterprise-grade defensibility on top of frontier models?

The Anthology Fund has been an incredible source of deal flow and has given us a broad aperture around what areas of AI are disproportionately taking off. It’s been a great program for getting closer to a broad set of application and infrastructure companies and building relationships before deciding where to lean in.听

I wouldn鈥檛 say it’s been the key factor in identifying bottlenecks across the AI ecosystem. For sure it is part of it, but from the broad set of portfolio companies and new companies we meet, the No. 1 bottleneck has been how to take all the new code that has been written and get it into production faster, safely, and securely.听

This has created a big tailwind for companies helping with software delivery, like Harness with application and code security, like Semgrep; and code review and testing like .听

Additionally, the rise of custom models based on open-source/open-weight models has created a number of bottlenecks as companies look for compute, training, sandboxes, and more. Both development and runtime resources have become essential to accommodate this next wave, and companies like Modal and Fireworks are addressing that with their offerings and the compute capacity they鈥檝e been able to aggregate across various compute providers, including Nebius and CoreWeave.

Valuations across the AI market have risen dramatically. Which parts of the market do you think are most likely to produce strong, sustainable businesses: infrastructure,听model tools or industry-specific applications?

We鈥檝e been active across models, infrastructure, and applications. All are showing tremendous potential and tailwinds right now. At the moment, infrastructure is seeing a disproportionate spike in opportunities as enterprises and AI-native companies embrace a multi-model approach and scramble to keep up with the compute and infrastructure management needs that it requires. Coding tools are now mainstream and putting tremendous pressure on organizational processes to release software faster and more efficiently, which is leading to tailwinds for companies like Harness and Gimlet.

It’s fair to say the majority of companies are optimizing for market share right now rather than gross margin, but there are many opportunities for margin improvement over time, and this is a rare land-grab moment.

You鈥檝e backed new AI research labs before they even have a product, including . At that stage, what convinces you that a team has something truly different, and that it can compete with much larger technology companies?

As I mentioned, we believe in a multi-model world, where one size won鈥檛 fit all needs and use cases. We have an explicit strategy to gain early exposure to some of the most compelling AI research teams with distinctive techniques or capabilities, even at a very early stage. Many of these companies are raising $100 million-plus rounds, and while we occasionally lead in this category, we prefer to write smaller checks initially. This helps us build broader exposure across the category and talent pool, and then double down once we see one really taking off.

Frankly, there are too many right now, and all claim some differentiated technique or team. Of the roughly 60 model companies, we believe we鈥檝e invested in more than five of the best and expect to lean into one or two of them as they ramp.

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• Anthropic Backer Menlo Ventures Raises $3B In New Funds To Back AI Startups Across Stages听

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and co-led the financing, which included participation from (/), , 鈥� , (chairman of ), and others. With the latest round, San Jose, California-based Antora has raised $770 million since its 2017 inception, per 兔子先生传媒. It raised $150 million in a Series B round in February of 2024.

The company did not reveal its valuation.

With so much data center demand amid the artificial intelligence explosion, Antora says it will use its new capital to speed up deployment of 鈥渓arge-scale鈥� projects across the country 鈥渢o meet surging energy demand.鈥�

It recently deployed what it describes as one of the world鈥檚 largest battery storage projects, a in South Dakota. The company says its tech is differentiated in that thermal batteries store low-cost electricity as heat in insulated blocks of solid carbon and 鈥渄eliver it around the clock as heat or power.鈥�

Antora says that the same factory-built modules can serve a chemical plant, a food producer, a steelmaker, a data center, or the grid, without supply- constrained critical minerals or multi-year construction timelines. As a result, it can provide 鈥渃heap, clean energy that鈥檚 fast to deploy.鈥�

The company also claims that its San Jose, California factory ranks among the country鈥檚 largest battery gigafactories.听

鈥淔rom factories to data centers, energy is the bottleneck to industrial growth,鈥� said , co-founder and CEO of Antora, in a release. 鈥淎ntora has shown we can help break that bottleneck鈥攄elivering energy fast, at massive scale, with American innovation.听

Despite surging energy demand from AI data centers, cleantech venture investment has been relatively modest in recent years, and Antora鈥檚 raise marks one of the sector鈥檚 largest this year.听

兔子先生传媒 shows investors put more than $15 billion into seed- through growth-stage rounds for companies in 兔子先生传媒鈥檚 cleantech, EV and sustainability-focused categories in the first half of 2026. That puts this year鈥檚 funding on pace to slightly exceed the 2025 tally, though that was the lowest total in several years and well below the highs reached in 2021 and 2022.

Related 兔子先生传媒 query:

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• Sector Snapshot: Cleantech Startup Funding Stabilizes As Energy Demand Grows听

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Before that, Larco had spent nearly eight years as a partner at (NEA), one of the world’s largest venture capital firms.听

There, she served on the firm’s investment committee and led investments across enterprise software, developer tools, and consumer technology, including , , , , and . She also served as a board observer at leading up to its 2021 IPO.听

Known for her sharp product intuition and hands-on operational experience, Larco focuses heavily on helping founders evaluate market dynamics, navigate product-market fit and scale resilient teams.

Before transitioning to venture capital, she built a career as a product leader and founder. After earning a degree in computer science with honors from the , she began her career at working on and , before leading core product teams at companies like and . She also founded an app development startup that she successfully ran and sold before joining NEA.

兔子先生传媒 News recently sat down with Larco to discuss how changing founder preferences and the (SVB) collapse drove her to launch a specialized pre-seed and seed fund designed to make early-stage founders a top priority.听

Among other topics, we also discussed how she evaluates startups based on founder potential rather than initial ideas, looking for teams that leverage AI to make products dramatically faster, cheaper, or easier to use while avoiding rigid, single-model wrappers.

This interview has been edited for clarity and brevity.

兔子先生传媒 News: You were at New Enterprise Associates for nearly a decade before branching out on your own. What led you to start your own firm? Was there a specific gap in the market, or was there a premise you felt couldn’t necessarily be fulfilled at a fund that size?听

Larco: There were a lot of things. At a multi-billion-dollar fund, writing $2 million checks is never going to be a top priority. They invest across all stages, but when you have to deploy between $3 billion and $6 billion depending on how you look at it, it鈥檚 impossible to do that $2 million at a time with standard team sizes.

Even if you still write those checks, founders have gotten wiser to what it feels like when they are a top priority versus when they aren’t. One founder put it to me this way: 鈥淚 want my investor at every round to feel like the check size hurt 鈥� that it’s a big percentage of their fund 鈥� because that鈥檚 how I know I鈥檓 going to be a top priority when push comes to shove.鈥�

So, for a pre-seed round, they want a pre-seed fund where the check size hurts. For a seed round, they want a seed fund where the check size hurts. For a Series A, they want a mid-sized fund where the check size hurts.

That frank conversation put a lot into perspective. Founder preferences have shifted over the past few years. Emerging funds over the last three to four years are winning very competitive deals, securing lead slots against more established, bigger firms. This was virtually unheard of before.

How has that happened?

A side, unintended consequence of the SVB collapse was this change in founder preference. When SVB was going under, every single founder called everyone on their cap table saying, 鈥淚 can’t make payroll on Wednesday. Can you help me?鈥�

Every VC was getting dozens to hundreds of calls. Depending on portfolio size, you can’t help everybody or be on the phone with every single company. Everyone had to prioritize. If firms scraped together money to help cover payroll, they couldn’t cover everyone across the entire portfolio. Very quickly, founders got to see where they sat on the priority list.

That’s interesting. As I cover rounds lately, I鈥檝e noticed the lead investors aren’t as often the big mega-funds.

Not at pre-seed or seed.

Even Series A. You’re seeing less of it happening.

Part of it is that fund sizes got really big, so they are writing bigger checks, which inevitably leads to more calculated ROI risk and moving to later stages. Part of it is that founders want to be a top priority, and they saw what happens in a crisis.

Founders are on WhatsApp channels, hacker houses, and communities, so one bad story spreads faster than ever. It used to be just repeat founders who wanted specialized, focused firms at the earliest stage for signaling risk and other reasons. Now, even first-time founders hear those stories and want a specialized investor.

When customer preferences change in any market, you realize there’s an opportunity. We asked ourselves: 鈥淐an we capitalize on this shift? If you were to build something from the ground up targeting this specific ICP, what would you build?鈥�

We did what we tell our founders to do: a listening tour. We interviewed people in our ICP and asked: What do you wish you had? What works, what doesn’t, what taglines are you skeptical of, and what is tangibly helpful? We doubled down on what we could provide well and cut out things people assume are best practices that founders don’t actually value.

We think of Premise as a startup, and our product happens to be a fund, so it still has to be something people want.

Do you invest strictly at those very early stages, or across other stages?

Strictly pre-seed and seed. Check sizes range from $500,000 to $3 million.

It鈥檚 noisy out there. How are you able to cut through that noise to identify real potential versus people riding the AI bandwagon? As a journalist, I struggle with that, so I imagine investors do, too.

We spend a lot of time with founders before backing them. During diligence, we talk one to three times a day for three to five days, alongside extensive reference and back-channel checks. Because of that, most of our investments are in cities where we have strong networks, like SF, New York, and Atlanta.

We try to get a deep sense of who the person is, what motivates them, and what key attributes they possess. Mercedes and I looked across all the best founders we saw at our previous firms and identified seven core attributes. There isn’t one single persona; founders have different strengths and weaknesses. We look for founders who are world-class in at least two of those seven attributes. In our investment memos, we justify those choices with anecdotes and reference feedback. Nobody is the best at all seven 鈥� some attributes even contradict each other.

At the pre-seed and seed stages, whatever idea you pitch 鈥� while we want it to be a good idea because it shows your ability to plan and generate ideas 鈥� the likelihood that it’s what the company looks like in five to ten years is very slim. A lot of it is gauging the potential of the person to find the right market and product fit to build an iconic company.

It is tough, but it’s not that different from the crypto, Web3, or early AI waves. Tailwinds always attract fair-weather founders. The core tactics to figure out who really wants to build something interesting, who has unique insight, and who is tenacious enough to endure the ups and downs haven’t changed in the last decade.

I’ve seen you discuss AI as a concierge service, shifting from “do-it-yourself” tools to “do-it-for-me” agents. You’ve also mentioned that an AI agent shouldn’t just be a wrapper; it needs to significantly re-architect the cost structure. When looking at a seed-stage deck today, what stands out as evidence that a team actually knows how to fundamentally change that cost structure?

Those can actually be two separate things. If a traditional wedding planning concierge service costs $20,000, and you offer it for $1,000, you’ve blown the cost structure out of the water 鈥� even if you’re just a wrapper using $100 in API credits. You can be a wrapper, pay for APIs, and still charge a fraction of traditional costs because the legacy price anchor is so high.

What I look for in any company to be competitive is whether it is faster, cheaper, or easier than existing options. A 10% discount isn’t enough, but at 50% off, people will switch. If a tool reduces a weekly five-hour administrative task to five minutes, sign me up. The bar now is enabling people to do things they couldn’t do before or lacked the confidence to do. For instance, I can build a cap table in Excel, but it takes me forever. If a tool makes that effortless, I’m in.

So ideally, a startup delivers on at least two of those three pillars: faster, cheaper, or easier.

I’m not against wrappers, but founders must understand the underlying mechanics. If you scale and the wrapper gets too expensive, or the model degrades, you need to know how to split tasks across open-source, closed, Google, or other models to deliver the best product at the best price.

Technical founders obsessively optimize models for specific features across their product. Less technical founders often use a single model for everything, which doesn’t guarantee the best price or performance. My hesitation with wrappers isn’t that a team launched quickly; it’s when they don’t know how to continue innovating because they’re wedded to a single model.

The counter-argument to my own point is (AWS). When AWS came out, critics said, 鈥淎nyone can start a company over a weekend on AWS; it’s not defensible, there’s no moat, you don’t own servers.鈥澨�

Yet many great companies were built on it. It鈥檚 the same argument. People said the same things about the cloud and mobile waves 鈥� that mobile was a toy and no one would buy a $1,000 phone or pay for subscriptions. Looking back, those criticisms sound funny.

You mentioned you look for seven distinct founder attributes, and that a founder needs to be world-class in at least two or three. Without giving away the whole secret sauce, what is one attribute on that list that would surprise people?

The one that catches people off guard is what we call 鈥渦rgently dissatisfied.鈥� These founders can come across as disagreeable: they’re more focused on the goal than on making people feel good, and their standards can be genuinely difficult to work around.听

But the people who’ve worked with them tend to say the same thing: that the founder pushed me to accomplish things I didn’t think were possible. This shouldn鈥檛 be confused with ego. It’s about managing hustler, relentless energy and pointing it at the right problems. The best founders I’ve backed have this quality. They have a high bar for themselves and their teams 鈥� as in everything should have been done yesterday, and they should have acted accordingly.

On the flip side, given how fast the tech landscape is shifting right now, is there an attribute that used to be a ‘must-have’ for a Series A founder five years ago that you now consider a nice-to-have at the seed stage?

The attributes themselves are pretty universal truths about what makes a great founder. What’s changed is the intensity and pace at which they have to show up. Five years ago, shipping an exceptional product, not just features, every six to twelve months was the bar. Now it’s every three to four months.听

So being a decisive execution machine still matters enormously, but what we’re evaluating is whether a founder can operate at this new compressed pace without sacrificing quality. That’s a harder thing to assess early, but it’s become one of the most important signals.

Right now, a huge portion of the VC ecosystem has completely retreated from consumer tech to chase B2B enterprise AI. Are you still actively looking at consumer behavior change as an investor? Do you think the rest of the market is miscalculating the size of the consumer AI market, and if so, why?

I think the retreat is short-sighted. Consumer software has historically produced some of the most important companies ever built, and it doesn’t make sense to vacate that entirely because the sector has been in a lull the past few years.听

The first principles of what makes a disruptive consumer company are exciting again because consumer behavior is rapidly changing with AI. We price in that risk. Fintech is another space where I’ve seen a meaningful pullback, and we’re still active there for the same reason. If everyone is running from a category, that’s usually worth paying attention to in case new tailwinds emerge.

You spent years as a product leader at places like and . We鈥檙e hearing a lot of talk about how AI will automate the tedious parts of product management 鈥� writing tickets, reviewing specs, tracking bugs. If AI absorbs the execution workload of a PM, what does a top product leader actually do day-to-day in 2026?

The job of a PM has always been consumer empathy: understanding what someone is trying to accomplish and why, and then making sure the product actually gets them there.听

AI only changes the artifacts you produce. A few years ago, you were writing specs. Now the best PMs I talk to are writing evals to define what 鈥済reat鈥� looks like for the agents they’re building and testing whether the agents actually deliver it.听

Someone somewhere still has to care deeply about the end user, ask the hard questions about what success means, and hold the bar. That’s still a human job.听

I love the analogy that AI wrappers are just the new AWS. But with AWS, the 鈥渕oat鈥� eventually became workflow stickiness and data accumulation. In a world where technical founders are constantly swapping models to optimize cost and performance, what does a 鈥渕oat鈥� actually look like for an early-stage company? If it’s not the underlying model, then what is it?听

I think it鈥檚 still workflows and data accumulation. I don鈥檛 think the moats changed much. The real question is how you retain your customers when competitors can clone you in three days. There are small non-durable moats you can lean on before you build out the data/workflows/network effects/integrations/etc moats.听

You made an interesting distinction between how technical and non-technical founders approach model selection. Given that, are you leaning heavily toward funding purely technical, AI-native architectures right now, or can a world-class product-and-distribution founder still win you over if they hire the right engineering talent?听

Never say never, but I am heavily biased towards a founder or founding team that has exceptional AI talent. I find that these folks enjoy being at the cutting edge, staying up to speed on the latest breakthroughs, and don鈥檛 mind blowing up their roadmap to move fast on a new functionality that enables them to build better products for their customers.听

You talked about AI shifting from ‘Do It Yourself’ to ‘Do It For Me,’ like giving everyone a concierge wedding planner or a financial analyst. When an agent moves from just giving advice to actually executing transactions and making decisions on behalf of a user, what is the biggest hurdle you see startups face? Is it a trust problem with the user, or is it an execution infrastructure problem?

Few people want 鈥淒o it entirely for me, and I have no idea what you did or how you did it鈥� right now. Most concierge services do the research, ask you questions to personalize the recommendations, and then filter down the options they present. If you have questions, you can dig into their reasoning, what they ruled out, etc. If you don鈥檛 like the options, they can go and find a new set. Rarely do wedding planners, travel agents, etc just go off and book everything for you without your input. I think that鈥檚 where we are with agents. It鈥檚 not just a trust problem, but more that people still want to make the decisions themselves 鈥� just not do all the research.听

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After pausing his entire engineering team’s workload for two weeks to ship a requested fix, he discovered the effort made no difference. The customer still threatened to churn.

“We did a retro, and wouldn’t you know? The person who’s asking for the new request was the new decision maker [we] didn’t even realize existed,” said Kataria, co-founder and CEO of San Francisco-based , in an interview. 鈥淲e missed the fact that the prior person had left, and the context had shifted hands, and no one had tracked that.鈥�

That breakdown planted the seed for Centralize, an enterprise sales platform emerging from stealth today alongside a $15 million Series A funding round led by (NEA).

The financing includes participation from ,1 , , Ritual Capital, Adverb Ventures and high-profile angel investors including former co-founder and , CEO and founder of .听

Combined with a previous $4 million seed round led by Salesforce Ventures, Centralize has now raised $19 million since its 2023 inception to build what Kataria calls a “deal GPS” for enterprise revenue teams.

Engineered by Big Tech vets

Kataria and co-founder and CTO met more than a decade ago as engineering students at the .听

Both went on to build high-scale products across Big Tech. Kataria served as a founding engineer on Facebook Shops during e-commerce push during COVID-19, scaling the platform from zero to a quarter billion monthly active users in a year. Wang, meanwhile, created Slack Huddles, building the initial version alongside Slack executives (CTO), (VP of product), and Butterfield (CEO), and later leading engineering and product teams at Slack.

After honing their technical chops in big tech, Kataria joined Y Combinator-backed fleet card startup as a product tech lead. It was there, while working closely with go-to-market teams to save that churning enterprise customer, that he recognized a fundamental gap in modern revenue operations.

鈥淚t was just this sea of information that no one had a handle on. The deal was at risk because the relationship is what mattered most, and we didn’t have a handle on it,鈥� Kataria told 兔子先生传媒 News in an interview. 鈥淥ne of the things that we always say is that the one thing AI can’t commoditize is relationships.鈥�

Solving the 鈥榤ulti-threading鈥� problem

Founded through Y Combinator鈥檚 Winter 2024 batch, Centralize aims to fix what Kataria describes as a lack of an actual relationship layer in modern sales platforms.

After bringing its primary product to market in December 2024, Centralize focused heavily on “multi-threading,鈥� or the practice of identifying, engaging, and organizing all necessary stakeholders high and wide within a target company, from procurement and legal up to the C-suite.

Rather than acting as a static record, Centralize operates as a visual, multiplayer surface centered around automated org charts that function like a map. AI agents analyze first-party data, call recordings, emails, calendar events, and web sources to continuously construct a live picture of key relationships.

鈥淚t’s kind of like a deal GPS,鈥� Kataria explained. “Or like a visual map, in which the people are the map. It’s the puzzle pieces. It’s basically like a landscape of who we know, who’s missing, how we get there, and then it’s the turn-by-turn navigation.鈥�

Centralize鈥檚 AI assistant is named “Centra,” and answers questions such as 鈥淲ho owns the budget?” or “How do we approach the CRO?” in seconds, the company claims. It also flags the moment a champion leaves, a new decision-maker joins, or engagement drops on a key deal.

Besides proactively flagging missing stakeholders, such as empty leadership seats or unengaged decision-makers, the platform also identifies warm entry points through mutual connections or past company overlaps.

Rapid growth and a bottoms-up launch

Centralize charges enterprise revenue teams based on 鈥渁ccounts under management鈥� with unlimited seats, encouraging cross-functional teams, including account executives, sales development reps, and customer success managers, to collaborate on account maps in real time.

The approach is driving rapid momentum. Over the past year, revenue has expanded significantly, driven by adoption among fast-growing enterprise companies.

“Since last year, we’ve… almost 8xed the company in revenue,” Kataria said, noting that much of that momentum accelerated over recent months.

The startup鈥檚 client roster features notable tech names, including , , , , , and .

To accelerate expansion, Centralize is launching a free, single-player tier alongside its funding news. The move allows individual account executives to sign up, build real-time account maps, and introduce the platform organically to executive leadership.

, venture partner at NEA, noted that the investment in Centralize was driven by the founders’ 鈥渦nique鈥� vision and execution.听

鈥淩achit and Will have built something rare: a product that sales teams actually want to use, not just another system of record they’re forced into,鈥� she wrote via email. 鈥淲e led Centralize’s Series A because we saw a founding team with an unusually sharp read on how AI changes the day-to-day of enterprise sales.鈥�

Koplow-McAdams also noted that buying committees have nearly doubled in size over the last decade. 鈥淭he entire revenue tech stack was built around activity capture, rather than navigating buying committees,鈥� she added. 鈥淭hat’s a structural gap, and it’s only widening as AI raises the stakes.鈥�

Startups like Centralize that bring AI to bear on enterprise marketing and sales have seen a strong uptick in funding this year, 兔子先生传媒 , with 2026 on pace to beat last year, which was the strongest year for venture investment into startups related to sales, marketing and CRM technology since 2022.

 

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1. Salesforce Ventures is an investor in 兔子先生传媒. It has no say in our editorial process. For more, head here.↩

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and co-led the financing, which included participation from and former U.S. Commerce Secretary . With its latest capital injection, San Francisco-based Freehand has now raised $100 million.

While Freehand declined to reveal its valuation, CEO and co-founder said it was 鈥渁 significant step up鈥� from the startup鈥檚 $25 million Series A that was raised in March 2024.

The deal comes as tariffs, taxes, and immigration policy strain the outsourcing model that ran supply chains for decades. Freehand鈥檚 fundraise also lands amid an uptick in venture funding to supply chain and logistics-related startups, with 2026 on pace to deliver the strongest year since 2022, , with $6.2 billion raised by such companies in the first half of this year across 350 deals.

Logistics roots

Freehand was founded in February 2024 by Jayakrishnan and , two enterprise logistics veterans who previously co-founded and recently sold , a SaaS transportation management system (TMS) and procure-to-pay system of record for large enterprise logistics.

In early 2024, as AI transformation accelerated, Jayakrishnan and Manohar stepped away from operational roles at Pando, moving to board positions, to launch Freehand as an independent entity focused entirely on agentic AI.

Pando continued operating under a newly appointed executive team before being sold to a strategic buyer in early 2026, marking a complete shareholder exit for the founders.

Their experience building enterprise supply chain software convinced them that existing back-office paradigms were ripe for disruption.

鈥淲e had been in this fairly archaic dinosaur of an industry for the last six to eight years,鈥� Jayakrishnan told 兔子先生传媒 News in an interview. 鈥淚nstead of trying to catch them up to a technology paradigm that was sunsetting, we thought we could leapfrog them into a technology paradigm that was just rising.鈥�

Beyond corporate cards

While spend management platforms like focus on corporate cards, employee travel expenses, and bill payments, Freehand targets complex supply chain operations. That means that instead of processing standard receipts and routine approvals, its AI agents manage non-standard spending across logistics, raw materials, parts, and labor.听

The software operates inside existing company systems, performing tasks like reading contracts, policies, emails, and internal data to verify bills, track operational milestones, and handle vendor negotiations.

Automating complex financial governance

For large, global businesses, keeping track of supplier bills across complex shipping routes like the Red Sea and the Strait of Hormuz is difficult. Contracts are detailed, and checking whether large bills match actual work has historically required big back-office teams.

鈥淲hen eventually rubber hits the road, when you get an invoice from a supplier saying, ‘Hey, you owe me $16.948 million for everything that I’ve done for you in the last six months,’ there aren’t a lot of proof points to figure out whether you know if that number is right or wrong,鈥� Jayakrishnan noted. 鈥淎nd so there are large teams that have gotten built over the course of the last decade or so, whose job it is to check these invoices, negotiate these contracts, and figure out whether service obligations from global suppliers are in alignment with contract governance overall.鈥�

When billing discrepancies arise, Freehand鈥檚 AI agents negotiate adjustments directly with suppliers while maintaining strategic vendor relationships.

鈥淚f it is not, then negotiating with the supplier becomes, 鈥�’you should have charged me $16.4 million instead of charging me $16.9 million and here’s why I’m not going to pay you the difference,’ and going back and forth without… losing the sensitivity towards that relationship itself,鈥� Jayakrishnan said. 鈥淭aking those business calls, which have historically been done through tribal knowledge… and truly automating the process to the point of no human intervention is effectively what Freehand does.鈥�

Measurable ROI for Fortune 500 spend

By shifting from manual oversight to agentic automation, Freehand believes it allows enterprises to reduce their reliance on third-party offshore outsourcing and give internal employees more room to perform higher-value strategic work.

Freehand counts some 50 customers, including ,听, and . Its platform autonomously processes billions in payments across 60 to 70 countries and hundreds of currencies without human supervision, per the company.

Some of the benefits of its technology, according to Jayakrishnan, include recovering 5% to 10% of total spend across a number of categories; completing 鈥渃omplex鈥� operational workflows 5x to 7x faster; and reducing overall procure-to-pay cycle times by more than 70%.

鈥淲e are, for a lot of companies, their first global rollout of AI deployments at scale that impacts daily transactions and daily operations at global scale,鈥� Jayakrishnan said. 鈥淥ur ask of the enterprise 鈥� is to allow us to give AI a free hand to run supply chain finance for your business.鈥�

, general partner at Battery Ventures, noted that while supply chain and logistics management is a massive sector, it predominantly 鈥渟till runs on manual labor and repetitive workflows that are begging to be automated.鈥�

鈥淎nd that’s before you account for the turmoil: tariffs, shifting geopolitics, disrupted trade routes,鈥� he wrote via email. 鈥淢eanwhile, technology spending is a rounding error at just over $20 billion, which tells us AI has enormous room to drive efficiency, starting with the most mission-critical but repetitive workflows like freight audits and payments.鈥�

Thakker said his firm did deep research on supply chain AI across its global offices and found Freehand to stand out on multiple fronts, including founder market-fit, a focus on the largest Fortune 500 shippers 鈥渞ather than the intermediaries everyone else chases, and clear, measurable business outcomes.鈥�

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, , , , , , and several other investors also participated in the financing. The round brings Throne鈥檚 total funding to nearly $18 million since its 2023 inception.

The startup was founded in 2023 by CEO , who previously worked for nearly a decade in software product management at various companies, CTO , and former co-founder and CTO , for whom the company鈥檚 mission is also a bit of a personal one as he has ulcerative colitis.听

鈥淲e started Throne because we believe it鈥檚 inevitable that people will one day measure their health from their waste,鈥� Hickle told 兔子先生传媒 News.

Connecting the dots

Earlier this month, Throne introduced a beta version of its Gut Health AI coach, which lets users ask questions about their data and receive personalized feedback.听

The tool uses LLMs to interview users after episodes of bad gut health to understand what the relevant factors underlying each episode might have been.听

鈥淚t’s a conversational AI experience very similar to speaking with a dietitian or nutritionist who might ask you about stress or changes in your diet or sleep,鈥� Hickle said.听

“The coach connects the dots between someone’s diet, their lifestyle, and what’s actually happening in their gut, turning daily readings into insights they can use,鈥� Hickle said. 鈥淭his is where we think the long-term value lives, and it’s a meaningful differentiator.鈥�

Capodilupo, who serves as Throne鈥檚 chief product officer, believes that longitudinal data 鈥渋s the whole game.鈥�

Throne sells directly to consumers but is also exploring sales through clinicians, particularly functional-medicine practitioners. A third potential business line involves academic and pharmaceutical research. Studies of digestive health often depend on patients accurately describing and recording their own bathroom habits; Throne argues that its passive system could provide researchers with more consistent, objective data.

The device enters a market that includes , which recently introduced its Dekoda toilet-mounted health tracker. Hickle pointed to usability, battery performance and software as key differentiators between the two offerings.听

A 鈥榮moke detector for colon cancer鈥�

Throne鈥檚 longer-term goal, according to Hickle, is to build an at-home system capable of identifying changes that could serve as early warning signs of colorectal cancer, as well as bladder and kidney cancers, kind of like a 鈥渟moke detector for colon cancer.鈥�

That capability remains under development and is not a feature of the product now being sold.

For now, the technology behind Throne is showing clinical promise. The company was recently accepted to present its first academic abstract demonstrating that its visual AI assesses stool form with accuracy on par with board-certified gastroenterologists.

Validation studies with researchers at , and the are also underway, according to the company.

Throne has recently added several executives and medical advisers, including gastroenterologists and colorectal-cancer experts Dr. Fola May and Dr. Aasma Shaukat, as it works to establish credibility in both consumer technology and gastrointestinal health.听

The next frontier in health monitoring

Will Ventures managing partner said the opportunity reflects the broader adoption of passive health tracking pioneered by companies such as Whoop and . He views passive digestive monitoring as the logical successor to wrist-worn wearables.

鈥淩oughly 80% of consumers now adopt at least one gut-health targeting behavior, yet there is no existing medium for the continuous tracking of gut health markers,鈥� Gardner wrote via email.听

鈥淭here is an immediate opportunity to build a premier gut health brand, and to make a real impact on public health along the way.鈥�

Gardner also believes that the software-hardware integration gives Throne an edge in an emerging category.听

鈥淭hrone鈥檚 product is entirely non-invasive and relevant for everyone from GI patients to biohackers to general wellness seekers,鈥� he said. 鈥淟ongitudinal health data related to daily bathroom visits will be incredibly powerful, and it will be deeply enhanced by AI.鈥�

Looking ahead, Throne plans to use the Series A capital to scale consumer education, expand clinical channels and advance core R&D.

鈥淲e’re creating a new category, so we’re investing heavily in educational content to help people understand why this matters,鈥� Hickle said.

Though the company declined to disclose specific valuation or growth figures, Hickle expressed confidence in Throne’s market trajectory.

鈥淲e’re not sharing hard numbers at this stage, in part because this is a David-and-Goliath situation with a competitor like Kohler,鈥� he said. 鈥淲hat I can say is that we’re following a trajectory similar to the wearable pioneers who came before us, like Whoop and Oura, giants on whose shoulders we stand.鈥�

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Notably, the quarter also marked the busiest one for General Catalyst since 2021 in terms of investing in rounds of $5 million or above. The firm鈥檚 next-busiest fintech investing quarter in rounds of that size was the fourth quarter of 2025, when it participated in 10 raises of $5 million or above.

Overall, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year. (It鈥檚 important to note that H2 2025 marked the strongest six-month funding period for fintech startups since the second half of 2022.)

Over the past year, startup accelerator Y Combinator has routinely ranked as the most active investor in the fintech space. And overall, it was still the most active investor in the second quarter of this year, participating in 41 deals.

But this time, it ranked behind General Catalyst in terms of backing fintech rounds in the $5 million or more category. General Catalyst participated in 12 of those deals, while YC and each invested in 11.

In overall fintech dealmaking, General Catalyst still ranked far behind YC鈥檚 41, with 13 deals. participated in 12, Index Ventures in 11, and in 10.

Top lead investors at $100M or more

For megarounds 鈥� those deals of $100 million or more 鈥� we once again saw private equity firms topping the list of lead or co-lead investors. , , , and topped that list, according to 兔子先生传媒 data.

The largest rounds in Q2 were raised by a geographically diverse bunch of fintech startups. They include:

• Expense management startup was the fintech sector鈥檚 largest recipient of capital in the second quarter, raising a massive $750 million Series F round in June co-led by Ontario Teachers鈥� Pension Plan, Iconiq Capital and GIC that valued the company at over $50 billion post-money.
• , a London-based cross-border payments and foreign-exchange fintech majority-owned by , was a close second 鈥� landing $748 million in a private equity financing led by Centerbridge Partners in April.
• Also in April, Indian consumer lending startup raised $220 million in a Series E round co-led by , and that valued it at more than $1.5 billion.
• Paris-based insurtech landed a $545 million Series G led by Prosus that valued it at $6.2 billion.

Top fintech investors at seed

When it comes to investing in seed rounds, unsurprisingly, Y Combinator again topped the list 鈥� by far, with 33 fintech deals. Next up was with seven investments at the seed stage, and then with six.

The investor base shifted when we looked at who led or co-led post-seed rounds in the second quarter. General Catalyst topped that list, with five deals. , , , Index Ventures, and all tied with three investments each.

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Docter also proved adept at securing funding for research and other projects, a skill that ultimately caught the attention of venture capital firms and led him into the industry 26 years ago.

His technical roots are reflective of Palo Alto, California-based Dell Technologies Capital鈥檚 broader team. Its investors have degrees in fields including electrical engineering, computer engineering, computer science and data science, and many have worked at both large technology companies and startups.

That experience shapes the firm鈥檚 affinity for deeply technical founders and its approach to early-stage investing. When evaluating seed and Series A companies, the team focuses heavily on the potential impact of a technology: what problem it solves, what it could disrupt, and how well it works, often before traditional financial metrics become the central consideration.

Since its 2012 inception, Dell Technologies Capital has invested $1.8 billion across the enterprise stack and saw six high-profile exits at the end of 2025 alone.

In this interview with 兔子先生传媒 News, Docter also discussed how AI is reshaping SaaS and why he doesn鈥檛 believe the business model is headed for extinction. He also shared why he thinks distribution may ultimately separate the winners from the losers among AI startups, and more.

The interview has been edited for clarity and brevity.

兔子先生传媒 News: When you evaluate companies, do they all have to tie into what Dell does?

Docter: Not necessarily. I usually describe it as Dell Technologies Capital having a unique network you don鈥檛 get at any other VC firm. I鈥檓 using my words carefully because I鈥檓 not saying we鈥檙e better. I鈥檓 just saying we鈥檙e unique.

That unique network is that we have access to network and his company network, which has become even more relevant in this AI world but has always been very much in the middle of technology.

We leverage that network in two ways. One is to get another perspective on what鈥檚 going on in the world and understand technology and how it鈥檚 being used. What do Fortune 500 companies want or need? What is asking for? We have that perspective.

If you look at the other side of the coin, those are also the areas where Dell Technologies Capital can best help our portfolio companies. We have this perspective and this network that are really valuable. We can use those to the benefit of our portfolio companies, and that defines our investment philosophy.

classically said, 鈥淚nvest in what you know.鈥� The way I look at it is that we鈥檙e trying to invest in what we know because of who we are, our technical background and our unique network. But if I turn that over, that鈥檚 also where we can help. Invest in what you know, but also in what you can help with.

For founders building deep tech, there鈥檚 a fear of being on the right track, but too early. Some companies have had to wait more than a decade before they really took off. As an investor, how do you evaluate a team that is clearly building technology with incredible potential but is years ahead of the adoption curve? How do you help them survive that stretch of time?

Docter: You asked two questions in one. One is: How do you identify the founders you think can be successful? The second is: How do you keep them alive long enough to get to the finish line?

The answer to the first question hasn鈥檛 changed from how we鈥檝e always thought about it and how venture capital always thinks about it. First and foremost, you鈥檙e really betting on the people. This is a people business. I know you hear that all the time, but you really are betting on the people and the founders.

It鈥檚 not purely about the technical capability of the founders. There鈥檚 definitely an EQ part of the equation, which I think our team is really good at. Our group is good at quickly getting an opinion on a founder and whether he or she is capable. Then we usually spend additional time trying to pressure-test our initial thesis on that founder鈥檚 ability to be agile 鈥� to understand when they鈥檙e wrong and change directions or to be willing to get input from somebody else who might be way less smart than they are but has a different approach or way of thinking about the problem that opens up new avenues.

I think that鈥檚 qualitative. It鈥檚 EQ more than IQ, but a lot of times that determines success. I don鈥檛 think this AI era has changed that. That鈥檚 consistently true.

The answer to the second question is even harder. How do you know if you鈥檙e betting on a deep-tech company and you know going in that this is a five-, seven-, 10-, 15-, or 20-year problem? It鈥檚 really, really hard to sustain that company.

You have to do a bunch of things smartly. You have to make sure you don鈥檛 overspend, because overspending can really kill a startup. You also have to have really good co-investor partners.

We feel like we are part of a venture capital ecosystem, and we always strive to partner and play nicely with others. As Michael says, 鈥淧lay nice but win.鈥� We always try to play nice but win.

It takes a village for these things to work, so it鈥檚 important to have the right constituents and partners around the table who can continue to fund the company for years and years. The timeline is absolutely compressed, so I think it is getting harder for that to happen.

The classic venture playbook often considers first-mover advantage to be everything. But the 鈥渟leeping giants鈥� thesis suggests the second wave 鈥� the companies with the foundational architecture in place when a catalyst like generative AI hits 鈥� may be the ones that win. Is being a first mover still the same advantage it used to be?

Docter: I think it can cut both ways. One of the things we talk about is whether a company is doing category creation 鈥� which means it鈥檚 creating a brand-new category of business or software product that doesn鈥檛 exist today and is going to be huge 鈥� or category disruption, meaning there鈥檚 already a very large category that exists and I鈥檓 going to disrupt it with my technology. I鈥檓 doing something much better, faster, cheaper or stronger.

It鈥檚 important to have a sense of whether a company is doing category disruption or category creation. If you鈥檙e doing category creation, being first means you have to educate everybody. It鈥檚 a heavy lift. It鈥檚 a daunting amount of work, capital and effort that goes into explaining something that doesn鈥檛 currently exist and why it鈥檚 going to be needed in the future.

A lot of times, first-mover advantage isn鈥檛 an advantage there. Category creation is often where the second, third or fourth company hasn鈥檛 had to spend all the effort. They can piggyback off the heavy lifting the first mover had to do.

But in cases of category disruption, I think there鈥檚 value in first-mover advantage. You鈥檙e disrupting a big, existing, multibillion-dollar category and doing something in a new or better way. Being first there is very beneficial.

There鈥檚 a lot of talk about AI agents replacing SaaS models. Do you feel that panic is overhyped? If so, why?

Docter: AI is disruptive to the SaaS world, without a doubt. It鈥檚 disruptive because it will change how software is built and consumed. Maybe even more importantly, it鈥檚 going to change how it鈥檚 priced. The per-seat pricing model is probably outdated and going to die. It鈥檚 going to be priced based on consumption or outcomes.

Everything is disrupted, but I fundamentally don鈥檛 believe all SaaS companies are going to die because of this. I believe the SaaS companies with smart, effective management will look at what AI can do for their businesses, which most already are. They鈥檙e going to adopt it, embrace it, and transform their companies using it. The ones that do will come out the other side as successful companies. They鈥檙e not going to go away.

How they charge and price might be different, but they鈥檙e still going to be the category winner or category leader. Remember that they have some fundamental advantages they can leverage.

One is brand. When I say a big SaaS name, you and I both know it. Pretty much everybody knows 1, and .

They can leverage their brands.

They also have incumbency, meaning they currently have the business. They have customers they鈥檝e sold to for years and years and have long-standing relationships with. If 鈥� and it鈥檚 a big if 鈥� they understand how to embrace the AI transformation that鈥檚 going on and leverage it, there can and will be winners.

There will be winners for sure, or people who come out okay. Without a doubt, there will also be SaaS companies that don鈥檛 make the turn. But is that any different from any other technological or industrial revolution? It鈥檚 always the case that there are a few with good leadership and management who are nimble and agile, even at scale, and they are successful. Others aren鈥檛.

As early-stage founders shift from pay-per-user to pay-per-outcome or other new models, how should they think about their go-to-market strategies and still seem attractive to investors?

Docter: One of the biggest questions we ask early-stage AI founders is: 鈥淲hat is your distribution strategy?鈥� That basically means: How are you going to go to market or get distribution for your product?

Today, that is a harder problem. In terms of differentiating yourself as a startup, I would say its importance has grown.

There will be many people with very good or disruptive technology. The winners are almost certainly going to be the people who figure out distribution first, best or fastest.

If I tie that back to the SaaS question, it鈥檚 clear that some SaaS companies won’t be able to transform themselves organically. They鈥檙e going to need to undergo an inorganic transformation, meaning they鈥檒l have to buy or acquire something that can help their company transform.

If you think about what I just said about early-stage AI startup founders, they need distribution. How do you get distribution? By partnering with an incumbent that has a brand in the space you鈥檙e trying to sell into, sell adjacent to or disrupt.

I think there is a recipe here for SaaS companies to be in acquisition mode for the next six, 12, 18, or 24 months to help transform their companies and make the curve. The incumbent can acquire technology that would take too long to build, and the startup gets distribution that would be much harder for it to build.

Dell Technologies Capital had incredible exit momentum late last year 鈥� including massive liquidity events like , and 鈥� right in the middle of a broader venture liquidity drought. What did you see in those specific businesses or the macro environment that allowed DTC to return capital so effectively when everyone else was stuck?

Docter: I鈥檇 love to say we saw it all coming, but the reality is we can鈥檛 time the market. It just doesn鈥檛 work that way. But we feel lucky that things are lining up the way they have. Netskope, Rivos, SingleStore, and recently, and .

We just try to stay really focused on backing great founders with deeply technical ideas. We鈥檙e investing early and know that sometimes it can take years for the market to fully catch up to what鈥檚 being built. You can see that pretty clearly across the outcomes you asked about. Netskope and SingleStore were at it for more than a decade, building products and businesses until the market met them.

Rivos was a little different. The founders had a strong point of view that a shift in computing was coming fast as AI workloads started to put real pressure on data center infrastructure. They were right and got to a significant exit in just under five years.

We really try not to over-rotate on timing and instead stay consistent in who we back and how we invest.

You鈥檝e talked about looking at startup traction to see whether revenue comes from an “innovation pilot budget” or a “core engineering production budget.” For a startup trying to raise its Series A or B right now, what evidence do they need to show you to prove their AI revenue is sticky and not just experimental hype?

Docter: The biggest question we are asking ourselves today when we talk about making any Series A or B investment is 鈥淚s their revenue durable?鈥� Everyone knows about the complete shift away from the SaaS seat-pricing model.

But what we鈥檙e also seeing is a huge shift away from recurring revenue to something I鈥檓 calling听 鈥渞e-occuring鈥� revenue. I know that鈥檚 not really a word. What I mean by 鈥渞e-occuring鈥� is that, instead of showing multiyear contracts, a lot of revenue is uncontracted, meaning customers are not signing up for annual or multiyear deals. But they are signing up for projects, sometimes very large projects.

My suggestion to startups looking to raise substantial rounds is to show how customers engage and keep coming back for more. The ability to say 鈥渨e got our first deal with in October, and they did a second deal with us in January, and we already did our third deal in March鈥� is very powerful.

Given DTC鈥檚 unique position, how do you advise founders to leverage a corporate venture capital relationship differently than a traditional institutional VC, especially when navigating a rapidly shifting market like this one?

Docter: The answer really is that the investor type is irrelevant. The one thing founders should universally do with every investor on their cap table is ask for more help. 鈥淵ou don鈥檛 get what you don鈥檛 ask for.鈥� I know that鈥檚 an old saying, but it absolutely holds true.

So many founders, especially first-time founders, are reticent about asking for help or advice. Don鈥檛 be. Play to your investors’ strengths and ask them for the help they can deliver. Whether it鈥檚 management advice, introductions to decision makers at Fortune 500 companies, or access to channel sales. Ask!

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