It seems surreal, but there it is right in front of me: a palm-sized array that looks like a cross between a big silicon chip and a petri dish, with what looks like a thin pink film of gunk at the top.

It’s April, and I’m scrubbed up in the San Francisco lab of The Biological Computing Co., a startup founded by two neurosurgeons in 2022.

Co-founder and CEO Alex Ksendzovksy is demonstrating how, by firing about 4,000 micro electrodes in the middle of the dish, he can make the pink stuff — a mass of living neurons, like the ones in your brain — fire responses on a grid on a big screen above.

The brain cells obviously don’t have eyes. They can’t see. (And, importantly, they’re not sentient, without any blood vessels or ability to feel pain.)

But just like your computer translating zeros and ones into visual images you can see, TBC can feed the neurons sequences that represent the image, then create algorithms from the responses.

Sci-fi in real life. Still, you’re probably wondering: why?

On this week’s episode of The Upstarts Podcast, Ksendzovsky explains. The brain, he argues, is a more efficient, cheaper image processor than the best computer. Technologists know this — they’ve talked about chips designed to more closely represent the brain for decades.

But even better than brain-inspired AI, Ksendzovsky, is biologically-derived AI.

“I have about 500 million years of evidence behind me,” Ksendzovsky says. “So our moat is evolution.”

TBC has started by training AI image and video models improved by the algorithms derived from its neurons that it can sell and use to demonstrate that it’s more than a research project. With one, it’s already demonstrated a 27-fold efficiency gain.

While investors have poured more than $25 million into TBC, at least one told Ksendzovsky to leave the neurons out of their pitch, at least for now. He won’t listen.

On this week’s show, we discuss why brain cells are such an overpowered training tool; the ethics of using living neurons; and the implications of this technology going mainstream.

Plus, Ksendzovksy shares his Upstart Moment: giving up, alongside his neurosurgeon co-founder, promising careers in academia to pursue a startup.

If you’re enjoying the show, please help us grow by tossing us a like and review on YouTube, Apple, Spotify, and forward this to a friend.

Our three big takeaways from the conversation are below.

1. Turning obsession into product

2. Winning with today’s tech first

3. Drawing the ethical redline

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Startup lesson 1: Turning obsession into product

Giving up a promising career in neuroscience academia to build a startup wasn’t an easy call for Ksendzovsky and his co-founder, Jon Pomeraniec.

But for Ksendzovsky, TBC represents the culmination of a 20-year-old “obsession.”

“ I worked in a lab, one of the first labs growing brain cells on electrodes, and immediately thought that we should be using it to predict the stock market. That led me onto this journey, and at the time, I kind of pitched it to my professor, and he said it was a bad idea, and he was right.”

The scientist didn’t forget the idea, however. “This has been in the back of my mind ever since,” he says.

Ksendzovsky went on to research growing neurons to help study epilepsy and disease, and found his way back.

“These concepts come together and converge in a dish of neurons where we can abstract from the neural signals, just like we do from epilepsy patients; [we can] keep the cells alive for a long time, just like I had to do in my lab.”

They’d worked on a startup before, and met VCs, so that part wasn’t as scary. The key was to feel passionate enough about the idea to drop their careers and move from Baltimore to San Francisco.

“So [TBC] was really born out of this idea that we were obsessed with this concept, and it had to exist. We were finding the best way to get it out to the world, and try to understand it, and try to create it. And it just so happens that Silicon Valley, San Francisco, is where crazy ideas are born.”

2. Winning with today’s tech first

If TBC’s technology takes off, it’s easy to imagine biological computing at a bigger scale: in data centers. As noted by Upstarts reader and my father, a former IP lawyer with expertise in chips, the concept of “biochips” dates back at least to the 1980s.

If you’re following the possibilities that far, it’s only natural to imagine such chips directly in data centers, operating at huge scale.

Ksendzovsky doesn’t disagree. He says TBC is building the “foundation” first.

“ But to get there, it's going to require probably five, 10 years. And a lot needs to be figured out. What we realized is, in the interim, as we're building this foundation, we can really incorporate our findings into current day AI systems, right? And so we can make significant strides on transformer models, for example.”

In that context, TBC’s approach is a smart one for a startup to take: prove your value in the current generation of tech, and build real revenue, while working toward the deep tech, bigger goal.

“I think the pie in the sky, the North Star is real-time biological computing, where we're actually plugging biological networks or neurons into data centers for real-time inference, for things like intuition, for a personalized AI that's constantly learning, just like the brain does. So that's the vision.

 It's certainly not something that's possible today, but with the tools that we're building on the way to improving software and, and AI systems,, we're learning more and more towards realizing this world of real-time biological computing.”

3. Drawing the ethical redline

One question I knew I needed to ask Ksendzovsky as soon as I’d seen that demo: does TBC employ staff bioethicists? How does it think about the weighty implications of its technology?

 “We talk about this a lot. We think about this a lot, and we interface with bioethicists a lot on this,” he replied.

TBC made some important distinctions with its technology that keep its neurons from “feeling” or functioning like a brain in a sentient way.

“Just to be very clear, what we have in the dish is not sentient, never going to be sentient, does not feel pain, does not have any of the things that, humans, animals, et cetera, have.

It's a tiny, tiny fraction of the brain, what we have in there. They're neurons. Not only is it a tiny fraction, we also remove all the connections from them and reconnect them to serve our purpose, right? There's no blood vessels. There's no three-dimensional architecture to them, right? If you think of what has to happen for you to have the emerging properties in your brain, like consciousness, awareness, sentience, pain, et cetera, it's so different than what we have in the dish that it's never gonna get to that point.”

Limits, Ksendzovsky says, are critical for this area of technology.

“As we continue to drive this technology, we will-- we, we have very clear guardrails of not getting to that point and making sure it doesn't.”

Last month, Squint CEO Devin Bhushan walked the manufacturing floor at Nailor Industries, a maker of commercial HVAC systems, in Houston.

While he was there, he heard about a key employee who, after many years of experience, could tell when the pH balance of the powder coating to be sprayed on its products was off. Without his learned skill, staff would have to regularly titrate and test, slowing the whole process down. Any vacation is stressful for everyone.

Such expertise, which doesn’t necessarily appear in a manual or official list of procedures, is what Bhushan calls “tribal knowledge.” And without such knowledge – across hundreds of skills, discontinued products and company-specific quirks – powerful AI software can’t help an industrial line much, he argues.

“You can’t plug robots in a Cheetos line and expect the robot to know how to debug quality issues when the Cheetos smell weird,” he says. “You have to create a context layer that brings everything together.”

That includes the tribal knowledge, but much more, too: all of a manufacturer’s work orders and asset history, the standard operating procedures (SOPs) it does have written down, as well as the regulations it’s mandated to follow. Only by combining it all can an AI model really understand what’s going on, and suggest how to fix or improve a situation on the ground.

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Enter Squint, which today is raising its hand by unveiling an AI system that it says can beat comparable setups from Anthropic and OpenAI at solving complex industrial problems.

Built on a 2 billion parameter, open-weight small model fine-tuned for manufacturing, Squint’s system then went through multiple rounds of post-training on complex problems in the field. The company then benchmarked it against OpenAI’s File Search tool via their Assistants API; Vector RAG; and Claude Code. Each was tested for its ability to answer thousands of complex questions that would require the system to find and incorporate info from manuals, industry documents and other domain-specific sources.

The result: Squint’s latest system scored 78% multi-document accuracy, compared to Claude Code at 53%, Vector RAG at 47% and OpenAI File Search / Assistants at 46%.

If 78% sounds like a lackluster C+ grade to you (or in the U.K. where we’re writing from today, a score of a “4,” where “1” is best), Bhushan explains that a single person, even an expert one, would likely fail the same test, scoring maybe 10% at best.

“These are questions that would be complex for a human to even attempt,” he tells Upstarts.

Of course, Squint has self-interest in sharing these results – and Bhushan freely admits that the company spent months post-training and refining Squint’s AI to work better.

But the startup, which raised $40 million in Series B funding last year in large part to hire toward, then build, these tools (TCV, The Westly Group, Sequoia and Menlo Ventures are all investors) says it’s not looking to beat Anthropic or OpenAI so much as it’s working to unlock real AI value for complex industrial customers.

“If OpenAI built a model for these knowledge gaps, we’d just use it,” Bhushan says. “It’s more a matter of making possible what hasn’t been possible.”

More on how Squint set up its AI; why Bhushan doesn’t think the model is the moat; and why both he and one of his big customers don’t see AI automating away industrial jobs below.

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When we first encountered Squint several years ago through Menlo’s Tim Tully, the startup’s product was focused on generating manuals and SOPs from filming a person performing a physical task, like running an espresso machine. Bhushan founded the company in 2021 after nearly four years at Splunk, where Tully was his boss; that business reached a customer count in the dozens.

But the bigger problem Bhushan was hearing involved the deeper institutional knowledge passed down by employees, that AI couldn’t learn from scanning a company’s orders, inventory and official processes.

When that knowledge was lost or left the building, it could take hours to manually check a process or find the right artifact with the answer. Sometimes, it would be cheaper for a production line to continue instead of stopping and having to restart; all of that product, like, say, rubber for tires, would be thrown away as a sunk cost.

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Of course, many of these businesses pay for software, but Bhushan argues that current AI integrations were limited by their lack of wider context. A company might hook up ChatGPT to Microsoft SharePoint, for example, or use the tech giant’s Copilot AI tool; but limited by a large language model’s more generalized knowledge and limited access to the customer’s key info, they’d do little more than automate specific processes like tracking inventory or shipments, he says.

Squint’s solution is part AI, part software and part services. Using the system it’s just trained (and which it continues to improve now), it can learn and store the answer to such questions, and make recommendations over time.

Forward-deployed engineers can help the customer build workflows and apps on top that leverage the wider access to data. And over time, Squint will make it easier for customers with more technical chops to vibe-code their own apps and customizations, using Squint’s knowledge graph as their building blocks.

At Nailor Industries, chief information officer Joseph P. ‘JJ’ Jensen is working to update the tech stack of a fast-growing, family-owned HVAC business dating to the 1970s.

Like its peers, Nailor has benefitted greatly from the AI data center manufacturing rush, and it’s a land grab: any point of leverage can mean the difference in Nailor winning more contracts and scaling its facilities faster to meet the demand.

Nailor is trying to complete customer orders as much as 30% faster than bigger competitors, while working with more product SKUs than Costco, Jensen says. Just one newer unit might have as many as 61 serial numbers simply for its copper fittings for its coils. “That makes the complexity as you manufacture a bill of materials become very, very hard,” he says.

Squint has proven valuable, Jensen says, because it “shares the dirty laundry” of how the full factory floor operates, not just what’s in an SOP or documentation (for both of which it uses Squint, as well as for AR training and AI uses that he simply says will “surprise the industry”).

“We could strap GoPro cameras to employees right under their hat, on their safety glasses, and create valuable insights in real time.”

The vertical cliff

Whether Squint can truly capitalize on the gains its system is claiming will depend on customers like Nailor, and two questions immediately come to mind:

1. Why won’t the AI labs absorb this work?

2. Will workforces revolt against AI use on the floor?

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On the first question, Bhushan compares Squint’s work to law, where both Harvey and Legora have quickly built large businesses working with law firms and corporations, leveraging models from Anthropic and OpenAI even as the labs invest in their own legal-focused products.

“The moat in the long run is not going to be that we have the best model system,” he argues. “It will be the app layer, the agent layer, and the workflows that run on top.”

Training its own system allows Squint a head start on the services and app-building side of things, Bhushan argues, meaning that the company can try to stay ahead of more services-focused businesses like Palantir that might need to produce a fully custom build.

At the same time, Squint’s business would be more efficient if it were leveraging affordable frontier models instead of having its internal AI team continually post-training, he adds. Building on top, Squint would hope its solution is better tailored to the industrial use case than ones pushed out by the labs, too.

With both Anthropic and OpenAI investing heavily in services operations, that’s a risky bet that a number of startups are currently making. On the jobs side, however, Jensen at Nailor agrees with Bhushan that employees aren’t currently threatened by using Squint.

The people whose expertise is being learned are typically “relieved,” he says. “They’re like, ‘You keep calling me on vacation anyway, in the middle of the night. So yeah, write it down. You’re not going to be as good as me.’”

If you believe in Squint’s business, its AI would necessarily be better eventually, though. At Nailor, Jensen says his company has more of a hiring crunch than a desire to reduce headcount. “We are clear with the staff that AI is not replacing their jobs, it’s here to make it easier,” he says. “So far, we haven’t received complaints.”

Squint’s CEO admits that if his solution continuously improves, it would necessarily surpass humans. But he argues that in practice, industry will continue to need human experts and craftspeople to work alongside the machines.

“It’s going to be a while before AI is fully automating lights-out facilities,” he says.

If you could pick any dream team board of directors, what team who would you choose?

For two years, Notion’s co-founders have quietly asked themselves that question. An ‘Avengers’ style lineup of top startup unicorn CEOs? They considered it. A more Hall Of Fame mindset of former bosses and mentors like LinkedIn’s ex-CEO Jeff Weiner? They explored that, too.

Few – if any – startups in Silicon Valley history have grown as big as Notion, or for as long, without some semblance of a board. But by virtue of a quirk of its own fundraising history and an intellectual internal leadership style, it’s the position Notion found itself in even after more than a decade in operation, and as its valuation reached $11 billion.

But to go public, you need a board. And after a long, gradual process, Notion is unveiling its board for the first time – and talking about the process, and lessons learned, with Upstarts exclusively.

The full Notion board director lineup:

• Ivan Zhao, Notion’s co-founder and CEO

• Jonathan Chadwick, former CFO and COO of VMware

• Christopher Payne, former president of DoorDash

• Gretchen Howard, former COO of Robinhood

• Patrick Hsu, co-founder of the Arc Institute

• Pat Grady, partner and co-steward at Sequoia

“We’ve been slowly adding people the way you’d go and recruit an executive team,” says Notion co-founder and COO Akshay Kothari. “If the executive team covers different parts of running a business, you can map to that, and also have a coach or mentor for each of those executives themselves.”

You can see that map – and the three board committees mandatory for a company to be approved by the SEC for a public offering – in the mix. Chadwick’s served as audit chair on the boards of Samsara and Zoom, and served on the audit committee for Confluent and Stripe; he’s intended to work closely with Notion’s CFO.

Payne, known as ‘CP,’ helped scale DoorDash and serves on the boards of Faire and Robinhood; he can work closely with Kothari in product, as well as with Notion’s CRO. Howard helped scale Robinhood on its own IPO journey; she lines up with Notion’s chief people officer.

Hsu and Grady are the wildcards – not because they’re short on credentials, but because Hsu’s research at Stanford and with the Arc Institute is in another area of AI – generative biology – than Notion’s agents and collaboration tools. He’s intended to help Notion adopt AI – and adapt to its cutting edge – across teams. Then there’s Grady, a VC at Sequoia. Notion opted to voluntarily bring him on because of his market expertise working with companies like ServiceNow, Snowflake and Zoom.

As a group, they’re a less radical mix than Notion might have immediately sought. But its unusual process to get there – and its lessons in leveraging fellow tech leaders, like why CEOs might not make a good director – is worth any startup’s attention, even if it doesn’t have the luxury to start from scratch.

“How you recruit the right complementary skillset, it’s a fun game,” Kothari says. “Think about the mix, rather than the individuals.”

The rest of this post, including analysis of Notion’s board selection process and startup takeaways, is for paid subscribers. Sign up for 15% off for one year.

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Vanta CEO Christina Cacioppo won’t take the bait.

Within the wonky world of corporate governance, and compliance audits, her startup is a big deal. Founded in 2018, Vanta works with more than 16,000 customers today, including tech darlings Factory, Lovable and Ramp; it reached a $4.2 billion valuation last July.

And so the startup once dismissed by VCs as too niche now has to fend off a pack of startups that nip at its heels. Sometimes, they make bold claims about Vanta being outdated. Sometimes, they erupt in scandal.

Cacioppo’s been doing this now for too long to break discipline and call them out by name. But glimpses of her competitive side flash throughout our conversation on our latest episode of The Upstarts Podcast.

A contrast to “newer, and now defunct, entrants in the space” who, like Vanta, had to “fake it ‘til you make it” early on, but, unlike Vanta, “also just didn’t do the work” to make customers compliant. A comparison of business software in 2026 to crypto in 2021 when it comes to chasing hype. A quip that any rivals “have to actually do compliance this time” to pose a threat.

Vanta’s CEO only jabs. She doesn’t name names. I ask Cacioppo if it’s intentional. It is: a lesson in not fanning the flames that even some of tech’s biggest companies, like Sam Altman’s OpenAI, don’t always seem to remember.

“I think I subscribe to the ‘never let them see you blink’ school of thought,” she tells me. “Maybe I have to blink somewhere else, but… Yeah. That’s not here.”

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Through my work co-creating and managing the Cloud 100 franchise for Forbes, I got to know Cacioppo fairly early in her journey, and covered Sequoia’s investment in Vanta back in 2021.

Drawing upon that history – including reading back a favorite quote to her – allowed for one of my favorite podcast conversations so far. (Disclosure: Vanta has partnered with Upstarts on some of our events.)

We cover Cacioppo’s founder journey – the real story, not the neater, widely-told one – as well as how Vanta turned to “cringe” LinkedIn posting and a higher bar for good business hygiene to take off with customers. Plus, we get into her Upstart Moment: rethinking, and future-proofing, Vanta’s software business for the AI era.

“I still think, in 2026, that software can do great things for the world,” she says. You’d think that wouldn’t be a hot take. But with recent AI mania, it’s pretty warm.

Below, we’ll dive into my three favorite lessons from the episode, in Cacioppo’s own words.

1. The changing valuation game

2. Leveraging LinkedIn cringe

3. Managing ‘infinity work’

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Startup lesson 1: The changing valuation game

It has to be a bit strange to be a founder like Cacioppo who, after years of scaling and building a real business, has earned a valuation that a decade ago would’ve been one of tech’s largest, and now is smaller than some AI labs achieve without a product.

When I read back to her that quote from 2021 – “If you confuse financing success with company-building success, you’ll only get one of those things” – she stands by it.

 ”Raising money and having some smart private market investor put a private valuation is really cool and feels good, and kind of doesn’t mean more than that. One person decided to wake up that morning and pay that price for your company.”

Cacioppo “lives in a glass house” saying this, she notes. But she says she still believes in what CEOs like her former boss Drew Houston at Dropbox, and Figma CEO Dylan Field, have said about the public market “enforcing a different rigor” on a business in a healthy way.

In this moment, though, she admits that the buzz of chasing big numbers can matter more than she would’ve liked to previously admit.

“There’s a  hype cycle game that sometimes works. And one model someone gave me was B2B software, AI whatever, in 2026 is much more like crypto in 2021, than [it is like] B2B in 2021. The hype matters. The hype goes into the next round. When you have that round and you have the valuation, you get more investors, you get more customers, you get more employees.

Hopefully, then, you’re using that to build real things, and rinse and repeat. But there is a hype ‘fake it ‘til you make it’ game that I think can work. And I think we probably both know of strong companies that have played that well. We also probably know of some less strong companies that are playing, or going to play that, into a wall. But, yeah, I don’t think it’s as clear which works better in the long term as it used to be.”

Leveraging LinkedIn cringe

Over the course of building Vanta, Cacioppo says that she’s seen the distribution advantage that was once the realm of consumer companies (like Snapchat vs. Facebook) move over into business software.

“If somebody is out-distributing you with a reasonably good product, that is tough.”

Vanta’s approach to distribution: ship as fast as possible, and post about it. “There was a period where I felt like I was, I joke, but I was the launch PMM [product marketing manager] of just posting on LinkedIn: “Here’s one new ship, one new thing we launched.”

“I do think LinkedIn is easier than Twitter, because LinkedIn, it’s just cringe. I’m just hashtag crushing it, and the ‘I’m hiring’ banner. You know? It’s so, so silly. But I think for us it was those two things, and so we did have to increase velocity of some of the things we were delivering to our customers, and then we just had to talk about them constantly.”

An easy proxy for how automated Vanta was becoming was how many integrations it pushed. Today it has more than 400. But when it had 80, Cacioppo pushed the team to add another 100 in a year as part of project ‘Infinite Integrations.’ They reached just under 300.

“The team initially was like, ‘This is crazy, Christina is, you know, politely psychotic. There’s no way we can do this.’ And that was good: be the right amount of unreasonable. Being like, ‘nope, I think you can,’ and we could sit down and talk about it and be supportive. And then we just blitzed through, and emoji-posted on LinkedIn constantly about new integrations and what the number was, and how fast we were moving, and how the rate month-over-month was increasing.

And it built this narrative of, ‘Wow, Vanta moves so fast, and is so much more automated. Their competitors, why would you use one of those?’”

Managing ‘infinity work’

Cacioppo and her co-founder both worked at Dropbox before Vanta, and she name-checked co-founder Houston in our conversation about public valuations. I knew I wanted to bring up Houston again, but for a different reason.

I’ve long been fascinated by startup founders who stubbornly commit to work on the same business well after they could’ve taken a curtain call. Aaron Levie co-founded Box in 2005, took it public a decade later and still runs it today. Brian Chesky, who co-founded Airbnb in 2007, landed a 2020 IPO and is still in charge. Houston co-founded Dropbox the same year, took it public in 2018, and has run the business throughout.

But last month, Houston finally announced a plan to transition out of the CEO role, reportedly to build something new in AI. Chesky seems to be doing similar, per a Bloomberg report, although he may stay in charge at Airbnb.

I was curious: does Cacioppo aspire to a 20-year run at Vanta? Even as an 8-year-old company, it’s already straddling multiple tech eras. What keeps her motivated?

“There’s always problems. Half the things are always broken, all the time. But one of the things I feel very grateful for, amongst many, is that the stuff that’s broken generally changes, and the problems generally change. And I find it interesting and rewarding when you solve them.”

Cacioppo says her hardest times working at Vanta have been when she faced the same problems over and over, without that change, but without a solution.

“You’re like, ‘I’ve tried seven things, maybe I could try an eighth? But this is still the problem.’ To me, that’s when fatigue, burn-out sets in.”

To handle “infinity problems,” Cacioppo leans on frameworks. One is to prioritize that “done” is more important than “perfect.”

“Just pick, figure out how you’re going to filter them. Maybe it’s right, maybe it’s wrong. Pick one, and move, and you will learn as you’re going.

I think that about decision-making, I think that about product development. Movement is so much better than trying to figure out what to do. Because that’s when fatigue, and craziness, sets in.”

For Steijn Pelle, an ideal customer meeting looks very different than your typical startup founder.

His favorite focus group: a bunch of dentists in an Invisalign study club.

Pelle’s startup, Lassie, sells software to help small businesses run their operations. And not just any small business, at least for now. Specifically, Pelle solves toothaches for dentists across the U.S.

And it all started with a cry for help from his own dentist in the Bay Area.

The founder and owner of Grace Dental, Kwon is one of the region’s more successful dental practitioners, with an in-office team of about 20 staff who see nearly 100 patients per day at a new 4,000 square foot office in Menlo Park. But six years ago, Kwon ran a smaller practice out of a 900 square foot space in Palo Alto that was bursting at the seams.

His office manager had left, and Kwon was personally staying up until 2am each night filling out, and mailing, hundreds of health insurance statements and adjustments for his patients. Even with three hours of sleep and six days a week of grind, he was nearly $1 million behind in payments from his accounts, and worried he’d run out of cash on hand.

“The image that really sticks with me is how paper was just everywhere,” he says. “Stacked on my desk and at home, spilling out of file cabinets, and my backpack. We were just drowning in it.”

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In desperation, or maybe just to vent, Kwon told his patient Pelle, whom he knew had a tech background as a product leader at Robinhood, and who as a native of The Netherlands originally knew what a more efficient system looked like.

But Pelle did more than just give advice, or point Kwon to a website. That month, he embedded for a few months with Kwon as an employee, perched on a bar stool near the fridge and learning how to manage Kwon’s finances and bookkeeping first-hand. At night, Pelle would call his technical co-founder Frédéric Renken in Canada, and the two would work out how they could automate those processes with software.

“I was looking for a hard problem,” Pelle tells Upstarts. “I knew that I didn’t want to build a chat app.”

Fast forward five years, and Lassie has spread, one dentist Invisalign study group at a time, to work with more than 700 practices. For bigger ones like Kwon’s, Lassie’s autonomous system can save more than 100 hours per month in admin work; on average, Lassie saves them at least 30 hours, nearly the cost of a full-time staffer.

Now, Lassie has raised $35 million in a Series A funding round led by a16z to grow even faster. The funding values Lassie around $250 million, a source with knowledge tells Upstarts.

Night Capital and a host of domain expert angel investors joined the round, including co-founders of Superhuman, Plaid, Wise, and Reforge, product leader Gokul Rajaram, and the dentist father of Facebook’s co-founder, Dr. Edward Zuckerberg.

With the funding, Lassie hopes to expand what its software can handle for practices, while making it easier for new ones to sign up. Eventually, the ambition is to move beyond dental practices to other doctor offices, Pelle says.

The story behind Lassie’s product is the stuff of movies – new board director and a16z general partner Alex Rampell compares Pelle to a great Method actor like Daniel Day-Lewis training to play President Lincoln – but it’s got two embedded lessons that are useful for any startup builder.

• Its founders ‘out-obsessed’ the competition

• It operates in a ‘Goldilocks’ zone for AI

“I think this is an optimistic case for AI, where you have business owners and patients also suffering,” Pelle says. “And these small businesses, they can kick us out if the agent doesn’t actually do work that they like.”

We break down each point below.

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Obsessive opportunity

Kwon’s compelling yarn wasn’t enough to launch a startup on its own, but it got Pelle researching. The Dutch entrepreneur had worked at a virtual health practice just after moving to Silicon Valley in 2016, then spent formative stints at Coinbase and Robinhood working in product.

Teaming up with Renken, an engineer and the first product hire at Superhuman (now Superhuman Mail), the two talked to about 80 doctors and business owners to validate the problem. Pelle actually worked a stint at another practice, too, filling in at a gastroenterologist’s office in Scranton, Pennsylvania for a few weeks in mid-2020 before his stint working for Dr. Kwon.

When Renken was able to move to the U.S. and join Lassie fulltime in 2021, the two didn’t rush to ship a product. Over the next two and a half years, they visited and onboarded 100 doctor’s offices onto test versions of their software, starting with basic use cases like managing claims with one insurance provider.

They named their startup Lassie because, according to Pelle, Americans are obsessed with their dogs, and they wanted a product that customers would similarly love; plus, both could help save people from drowning, whether figuratively or on TV.

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Dental practices needed to digitize their records, so they were a good place to start. “You can’t send an AI agent into a file cabinet to automate it,” Pelle says. And small businesses – while they might be harder to onboard or win over at first – provide a strong feedback loop, he argues.

One early customer was Dr. Suffiyah Webb, a New Jersey-based pediatric dentist who operates a practice in Newark, as well as a service providing high-volume pediatric care in preschools and elementary schools.

Webb met Lassie at her industry’s annual conference, where the startup was exhibiting on the show floor, and raised her hand to become a test customer two years ago. Lassie was able to organize her payments, then go back and clean up months of claims and unpaid accounts receivable.

“It was like getting a big broom, and a big dustpan, and cleaning up what was happening,” Dr. Webb says.

She’s become such a loyal customer that peers have asked her if Lassie is paying her to promote it, she notes. It’s a fair question: Kwon, as Lassie’s first-ever customer, received advisor shares and has vested interest in the startup’s success. Not Dr. Webb.

“I’m not getting a kickback for this, I just think it’s excellent,” she says. “It’s allowed me to have more time with my daughter. I was coming home from work at 6pm to post payments, right?”

The secret to Lassie’s success isn’t kickbacks, its CEO argues, but its obsession.

“You will not find many other people that are willing to obsess for so long, and so deep, over this,” Pelle says. Working on a problem for an ‘unreasonable amount of time’ was pivotal at other tech standouts like his former companies Coinbase and Robinhood, but also others like Shopify and Stripe.

“Founders are too impatient,” he adds.

The ‘Goldilocks’ zone

Another explanation for Lassie’s growth: it’s finding enough value in a category that previously would have been considered too small to sustain a startup, but isn’t so big that Anthropic, OpenAI and the big AI labs are bursting down the door.

“It’s in this nice ‘Goldilocks zone,’” argues Rampell, its investor at a16z. “If it were too big, it would be the number one focus. Too small, nobody would spend time on it.”

Lassie’s insight: doctor’s offices, and specifically dental practices, weren’t buying software to manage their back office functions because existing options didn’t do enough; no one was building better software, meanwhile, because the average customer value (ACV) of such a practice, in the hundreds of dollars, wasn’t worth the trouble.

To unlock an ACV in the eight figures ($10,000-plus), Lassie needed to generate more than that in value. Obvious enough. How it did it, though, was to find a way for AI tools to help solve a problem: high turnover, and a lack of qualified applicants, for admin roles at these practices.

By training its AI agent to automate about 15% of the labor needs of keeping up with a practice, Lassie can save its customers $30,000 or more in cost.

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While it might sound like Lassie’s AI is replacing humans, the customers who spoke to Upstarts say that hasn’t been the case. Dr. Kwon’s practice increased the number of patients they could see, allowing him to grow the number of doctors and hygienists he employs from four to 10.

Better conversion of claims and reliable projections, meanwhile, helped Dr. Webb recently open a second location for her practice in Hoboken.

“It’s not replacing people, but freeing them from juggling countless roles and wearing so many hats,” says Dr. Kwon.

Dr. Webb still employs someone to manage her bookkeeping, she notes, with Lassie as a backstop: “AI allows my practice to be a lot more human-connected.”

Consumers are skeptical about AI decreasing their quality of care. Technologists who dismiss those concerns should read threads like this one, from Reddit yesterday, where patients say they’re avoiding offices that check them in automatically.

The key will be systems that don’t just help the small business owners, whether their dentists or other small businesses that Lassie might support in the future, but that also provide a better customer experience, too – from a human who has time to show them they care.

“A lot of businesses still need humans in the loop,” says Pelle. “We just like to build stuff that people love to use.”

If you could fix the genetic lottery to ensure your baby was born healthy, would you?

When I asked this question of startup founder Noor Siddiqui, I pretty much knew how she would answer. Her startup, Orchid, has gained some attention – and notoriety – for claiming to offer such benefits to parents.

I didn’t anticipate how far she’d lean in, and how fast.

“If you have a baby the old-fashioned way, you’re basically rolling the dice, and you get an average outcome,” Siddiqui told me to kick off our conversation from a studio near her San Francisco office. “And if you come to us, you can stack the odds in your child’s favor and have a superbaby.”

For some, there’s probably a fine line between superbaby and supervillain. Not for Siddiqui. At first, she was taken aback by negative reactions to her product. Now she leans into the narratives to drive more attention to what she believes is massively important work.

And it’s partly why we were excited to have Siddiqui kick things off on Season 2 of The Upstarts Podcast.

If you followed our first season, you got to meet a wide variety of ambitious founders, from Canva’s Melanie Perkins to Runway’s Cris Valenzuela, with dog longevity, math, nuclear energy and space companies in between.

This season, we’re looking for more Upstarts: contrarians, iconoclasts and challengers who are punching above their weight, and doing things differently from company building approaches to weird and exciting technology.

Event alert: We loved seeing some of you at our Boston Tech Week event yesterday. Join us in New York next week to hear from the CEOs of Crosby, Merge and Modal next, and stay tuned for info about a special event at London Tech Week coming up.

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That brings us back to Siddiqui. The first startup to read the entire genome of an embryo, Orchid is a category creator in an area that — due to a combination of stigma, hard science and historical lack of capital — doesn’t get the attention it deserves. (Occasional trend story and ideological sparring match aside.)

This isn’t sci-fi: since its 2019 founding, Orchid’s novel approach to genetic testing of embryos during the IVF process has assisted with a number of successful births; it already works with more than 100 clinics in the U.S. and abroad.

On this episode of The Upstarts Podcast, Siddiqui talks about her founder journey as a Thiel Fellow turned Stanford research dropout; why she compares previous testing to proofreading only the chapter titles of a book; and how she’s gotten used to the backlash that comes with ‘superbabies’ and a high price tag of $2,500 per screened embryo.

Plus, she talks about her Upstart Moment: sparking a new wave of reproductive tech startups that she sees carrying the field far beyond where Orchid can go alone.

This season of the show is presented by Rippling. As part of their support, we’re trying something new: in-depth show notes and highlights published each week, without a paywall.

Below, we’ll share three of my favorite lessons from the episode: two focused on startup-building, one more philosophical.

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Startup lesson #1: Silicon Valley’s secret is risk-taking

On the show, this point starts out as a hot take: Siddiqui “hates the East Coast.”

But her bigger point is more nuanced: becoming a Thiel Fellow at age 17, and moving to the Bay Area to be around other startup founders, broke Siddiqui free from tracks she perceived would take her to law school, or medical school, or interning on Capitol Hill.

“The tolerance for failure is just so much higher. I just met so many people who just burned through tens of millions of dollars on what seemed a trash idea, and it was a trash idea!

And then it was like, ‘Okay, great, well now I’m going to do the other thing.’ And then the next thing is also a trash idea. Great, they get to put $100 million into that other trash thing. And the third thing is amazing, and then it’s a multi-billion-dollar company.”

Siddiqui notes the irony that she went to Stanford next, and had to be encouraged by an investor to drop out to start Orchid. But it’s not always a linear journey.

“I think the real value of the experience [as a Thiel Fellow] wasn’t the mechanical things I learned, it was more just, I don’t know, the license. The license to actually do the thing, and to have seen it go sideways for people, and be successful for people.”

Startup lesson #2: Even semicolons spark holy wars

Every episode, I ask our founder guest about their Upstart Moment. I’ve started to get better at deflecting the most boring answer: every day!

With Siddiqui, I didn’t have to worry. She went straight to an interesting topic: handling haters.

Coming of age in San Francisco, Siddiqui was insulated by a “positive environment” where backlash isn’t really worried about. Outside that echo chamber, she says she was surprised by how much she was affected by the “mob of angry people” reacting negatively to Orchid, or genetic testing for embryos, or even IVF altogether.

“I think I just didn’t expect them to affect me at all. And then because I was sort of like, ‘Well, why would you read mean comments? You could just stop reading them… but I think a lot of it was also just breaking my brain, as to why. I just really didn’t understand, honestly, a lot of the hate.”

We’ll go deeper on Siddiqui’s moral clarity in the next section. To stick to her founder process, her lesson from the backlash: “You can’t make things palatable for everyone.”

There will always be haters. Even technical founders, or builders in seemingly uncontroversial areas, can get caught up over a line of code, or a semicolon, or a logo change.

“These were really arcane technical decisions that the vast majority of people don’t even know anything about, and then there was just a massive holy war over that decision, and people were getting so much hate for it.

And I was like, okay, so it actually doesn’t matter whether you touch something that affects 100 people, or a million people, or a billion people. There’s always going to be people who feel really activated about it, even if you feel completely certain that what you’re doing is [right].”

Alex’s lesson: Facing a true believer

In our interview, Siddiqui tells the story of her mother’s blindness, and how it inspired her to work to ensure that, even if she couldn’t cure her mom, she could prevent her mom’s condition from being passed to the next generation.

It’s a relatable, even noble reason to launch a startup. It’s also one that might spark metaphysical questions for other people, at least among writers: Would Siddiqui’s grandmother, if an Orchid customer, have selected a different embryo? Would that baby have still grown up to be Siddiqui’s mom, as we think of her?

Siddiqui herself doesn’t seem to worry about hypothetical timelines. She’s out to right wrongs. “I just thought that was insanely unfair.” Trying to prevent such genetic conditions is, to her, “the most obvious thing.”

It’s with that lens that I find her answer about Orchid’s current high cost so fascinating.

In her future reality, Siddiqui envisions everyone with the chance to access IVF and Orchid, should they so choose. She applauds California for following some countries, such as Belgium and France, in covering IVF treatment under health insurance.

But ultimately, she has trust in society – and moral pressure – to push the government to step in.

 ”What I think is insanely cool about Orchid is, I think it is going to light a fire under everyone’s seat. Because obviously everyone is so disgusted by this idea of, ‘Well, why should it be the case that some segment of society gets to have super healthy babies and everyone else gets a random outcome, and we have no cures to actually reverse these diseases later?’

So, I think that that is actually a really great forcing function, that I hope will actually just lead to better outcomes for everyone. Which is that, ‘Hey, because this is going to lead to such massive health disparities and is such a big deal, we do need to cover it for everyone.’”

There’s an economic case to be made, too – healthier babies, like healthier adults, are cheaper to care for.

But Siddiqui almost sounds reminiscent of the leaders of AI labs when they argue that AGI will solve economic disparity, or that at a certain level of risk, AI will need to be safeguarded by the state.

In that future, the West Coast technologists bring fire down to the people; the East Coast lawmakers pass it around.

When venture capitalist Jake Saper posted about a tech CEO with hundreds of millions in revenue who was pivoting his software business — despite hundreds of millions in revenue — to services earlier this month, it immediately caught our attention.

Saper, a general partner at Emergence Capital, has beaten the drum about this shift for more than a year. At Upstarts, we raised our hand: When this CEO will unmask themselves, let’s talk.

Today, we can yank off that mask. The mysterious guinea pig is Gainsight, the retention-focused software business owned by Vista since late 2020.

And the CEO behind the move is Chuck Ganapathi, a startup veteran that co-founder Nick Mehta brought in to lead the retention-focused company two years ago.

At Gainsight’s annual conference in Las Vegas today, Ganapathi is announcing the company’s shift to what it calls an ‘AI-Native Services,’ or AINS, business centered around its AI agents solution called Atlas.

In an exclusive interview, Ganapathi tells Upstarts that the shift is the culmination of a year’s work to reposition Gainsight for an AI-disrupted market.

Gainsight is self-funding the shift from its profits from that aforementioned hundreds of millions in annual recurring revenue; as such, while it’s moved some top engineers to the new product, it’s not shuttering or deprecating its traditional software product used by corporations like IBM and Workday.

Instead, the Atlas unit will operate as its own business within the company, with a general manager, dedicated team, and separate profit-and-loss bookkeeping.

“There would be a revolt if we said we were going to kill that business,” Ganapathi says. “But our fundamental belief is that Atlas, and the AI native services business, has a larger market.”

In a tumultuous time for enterprise software, Gainsight’s example is the latest in a series of signs pointing to the disruption of the subscription model. Last fall, we wrote about a shift to outcome-based pricing, and how high-growth companies like Cursor were handling customer sticker shock.

The big AI labs continue to apply pressure from the product side, as seen in the recent tensions between Figma and Anthropic about the AI company’s design-focused release, which we scooped last month.

Public software companies have been hammered by Wall Street investors who currently prefer chips. Even at billion-dollar startups, $300 million in annual recurring revenue is no longer enough not to disrupt your own product.

Across the ecosystem, it’s out with the SaaS (Software-as-a-Service), and in with the SaS (Service-as-Software).

And at its core, it’s an attempt to leverage AI tools to get more bang for your buck – meaning companies can deliver white-glove-caliber results for customers, and charge higher margins – for automated services that historically weren’t valued much.

Event alert 🚨: We’re in Boston tomorrow for a founder-focused lunch event with three active startup CEOs, where we expect this will come up; we’ll also get into it next week at our New York Tech Week event, featuring a panel discussion with the CEOs of AI unicorns Crosby and Merge. Apply to join that conversation here.

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Coming out on the other side of this major effort at Gainsight, Ganapathi has some advice for other software leaders looking to follow suit — and warnings. He identifies three must-have conditions for success in any repositioning from SaaS to SaS.

Paid subscribers can learn more about Gainsight’s process, and those practical lessons, below.

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How does a software leader from the last generation make the transition into AI’s brave new world?

At Zendesk, the 19-year-old customer service company, CEO Tom Eggemeier hopes one answer is to invest deeper in its startup-serving roots.

Zendesk is announcing a refresh to its startups program today centered around a two-year, $100 million commitment of resources, Upstarts exclusively reports.

Zendesk is widening the aperture of its startup program, too: offering free access to its software, AI agents, and employees for startups through their Series B stage of funding, or until they reach about 250 employees, for up to two years.

That money number is more of an estimate, attempting to price a value of the various offerings, than a direct outlay from Zendesk’s balance sheet. But it still represents a major increase of 5x to 10x what Zendesk was previously allocating, according to its CEO.

Zendesk is also expanding its startups program to offer access to benefits from partners like Lovable and Notion, as well as to provide benefits directly through venture capital programs at a16z, Techstars and others.

“This is a new AI era, where people want different things,” says Eggemeier, who stepped in as CEO after the company was taken private at a valuation of about $10 billion in 2022.

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Beyond the value for startups – and Upstarts spoke to one new participant you can hear from directly below – Zendesk’s commitment is notable as another smoke signal of how software businesses are adapting to the generative AI-infused moment.

We wrote last week about a startup unicorn, Remote, transitioning to focus more on infrastructure and services instead of subscription software despite $300 million in annual recurring revenue.

At Zendesk, we’re seeing a different flavor of the same transition play out: one where the business has already gone public, faced choppy markets, dipped back under the waves with private equity backstopping, and now poked its head back up again with a story around AI.

And Eggemeier sounds familiar, too, when he talks about how Zendesk will partner with a new crop of startups moving forward. “We want to move at an AI pace,” he says. “Our old program was not agile and flexible enough for the current environment.”

More on Zendesk’s revamped startup program and the takeaways from its AI push below.

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This will ruin the tour

Zendesk was a darling of startups when it was one itself. Today, it still claims graduated startups like Box and Uber, as well as scaleups like Canva and Discord, among its customers. Among the more recent crop of AI breakouts, it works with ElevenLabs, and acquired a customer service agent startup we’ve covered in Upstarts, Forethought, in March.

But on a listening tour of several hundred startups across the Americas, Europe and Asia in recent months, the company heard repeatedly that it was now competing not just with rival solutions, but opportunity cost.

Eggemeier met with more than 30 startup founders personally, including for a breakfast last month, and heard variations on this theme:

“‘We really like your platform, we like a lot of things you’re doing with AI. But I’ve got a question right now,’” he retells it. “‘Do I go put some money into my service software, or do I go focus more of that on my product? And we heard that loud and clear.’”

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Among the startups that Zendesk targets, budget is focused more on product and go-to-market right now, with fledgling companies turning to the cheapest or most easily vibe-coded or adapted agents to cover as much of the rest as possible.

They’re also looking to stretch their own revenue with as small a team as possible – a trend we’ve covered at Gamma and others – in what Eggemeier believes will be the new normal moving forward.

Zendesk for Startups’ new program is meant to meet that moment by being more flexible on when a startup outgrows it, not so strictly defined by a time period or size. It’s also added more features requested by AI-minded founders like an ecosystem marketplace, so that startups can pull in credits and access from other partners like Amazon Web Services and Lovable in one place – an attempt to expedite that process for “pressured” small teams.

The company will also now provide benefits for the networks of VC firms, including a16z, Techstarts, Lvlup Ventures and 500 Global.

The new approach is appreciated at one new admitted startup, MERIT, where Jovan Silva and Lauren Burke Silva are building a business focused on helping smaller contractors participate in federal contracts.

“We are a very lean team, and Zendesk helps us extend ourselves,” Burke Silva says.

MERIT is integrating Zendesk’s AI agents to help with customer support tickets and voice-based responses, adds her co-founder and spouse Silva. Based in Washington, D.C., MERIT has a full-time CTO, but its founders are domain experts in government contracting, not AI, they note.

“If you’re coming into building a platform without the deep technical expertise, or without a bunch of relationships in that [Silicon Valley] world, it can be very useful.”

The bigger picture

Zendesk’s efforts to reinvent itself in AI obviously go far beyond its startup program. At its annual conference this week, the company announced a number of agent-focused capabilities as part of a wider ‘Autonomous Service Workforce’ where companies can plug in Zendesk agents, connect Zendesk through APIs or its MCP to their own, or build new agents on top.

More than 1,000 people within Zendesk currently work on AI-focused product design and engineering, Eggemeier says; the company hopes to finish the year having supported more than one billion API calls, and with about $500 million in AI-based revenue, which is increasingly shifting to outcome-based pricing.

As such, Zendesk’s CEO says he’d be happy if the startups program exceeds the $100 million commitment. Success will be measured primarily by Net Promoter Score with participants, he adds.

“If we blow through that, and it’s $150 million or $200 million over two years, we’re going to be really happy,” Eggemeier says. “Ultimately, it’s going to be good for Zendesk’s top and bottom line, and good for our innovation.”

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Our take: the move is relatively low risk for a company like Zendesk, particularly without the immediate market pressures of public earnings; the number is squishy enough that if fewer than anticipated startups participate, it won’t cost Zendesk much except in terms of customer opportunity and possibly embarrassment.

So we would expect more ‘last-gen’ or mature software businesses to follow suit, beefing up their startup programs and credits, while startup unicorns of a certain scale (like Canva or even ElevenLabs) will deepen their own startup programs, too.

For busy startup builders, that means it’s worth taking a break from managing your own agents to shop what your tech vendors can offer, at Zendesk, or others who follow suit.

Less than a month ago, Remote co-founder and CEO Job van der Voort got in front of his entire company to tell them that for many of them, their jobs were about to change.

By traditional startup accounting, things were going well: the San Francisco-based startup with a distributed workforce (van der Voort lives in Amsterdam) had recently reached $300 million in annual recurring revenue for its software, which helped businesses across a range of functions and HR.

Remote – San Francisco based but fully remote like its name, with van der Voort based in the Netherlands – had recently become cash-flow positive. It still had money from a $300 million funding round raised in 2022, valuing the seven-year-old startup at about $3 billion. It had just announced a new chief business officer.

And yet here was van der Voort, appearing by video, telling many of 2,000 employees that the products they were working on were going to be deemphasized – put in ‘maintenance mode,’ in tech parlance – while other unfinished ones, some weeks from launch, were going to be shut down.

Why take such drastic measures? Van der Voort’s own board of directors had posed the same question. The answer, as you may have guessed, has everything to do with building a last software business in the age of AI.

“We were in a really great place as a business,” van der Voort says. “But the cost of spending time on things that aren’t important, or that you believe don’t have a future, is immense.”

It’s a warning – and potential guide – for any software startup building in the shadow of the frontier AI lab. Remote’s CEO shares why he pushed the big button to rethink his entire product roadmap, and his advice for fellow founders, exclusively with Upstarts subscribers below.

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The agents are coming.

For security leaders inside the world’s big banks, retailers and corporations, that means a wakeup call akin to Paul Revere’s ride. For cyber startups, it means opportunity.

“The ground is shaking,” says security investor Oren Yunger. “The surface is completely revamped for technologists, and security professionals specifically.”

Yunger’s firm, Notable Capital, recently surveyed the cyber chiefs at 100-plus large companies alongside Morgan Stanley for their Rising in Cyber 2026 report.

What they found: while 71% of responding organizations said they already use AI agents in production; more than 50% categorize their security for such AI tools as early-stage or immature.

Startups – both veteran ones and brand-new, AI native ones – are eager to help bridge that gap. Early-stage cyber startups are flush with venture capital and able to open doors earlier than they might have in the past. They’re able to integrate faster, and use AI themselves to cover more ground.

And the redrawn rules of engagement for the AI era are bringing back into play certain security risks, like phishing and identity theft, that previously seemed mostly solved.

“This reinvigoration of areas of security that have been around for a long time, they’re getting revitalized with new and better ways of doing things,” says Sublime Security founder and CEO Josh Kamdjou.

Sublime – which counts customers ranging from tech leaders Ramp and Snowflake to pharma maker Roche and candy giant Mars – is one of several AI-enabled email security to appear on Notable Capital’s Rising in Cyber 2026, its third annual list of 30 most promising cybersecurity startups.

Upstarts teamed up with Notable as a media partner on the report and related unranked list. We played no part in the selection process, which weighed input of nearly 150 practicing security professionals, but received early access to review the findings and discuss them with several prominent chief information security officers (CISOs).

We also reached out to several list-making startups of our own choosing for their perspective, including Sublime and 1Password, making a push in agentic identity management. (Nine of this year’s 30 listees are Notable portfolio companies; neither interviewed startup is backed by the firm.)

Below, we break down:

• Why in cyber, it pays to be early-stage right now

• Why securing agent comes in so many different faces

• And how startups can better impress potential buyers like Glean

Plus, we hear from 1Password CTO Nancy Wang about why there’s more to the the equation than who has the best model.

This post is sponsored by Notable Capital as part of an exclusive media distribution partnership for the Rising in Cyber list. Upstarts retains full editorial control of this post, and Notable had no influence on its reporting, nor any early look at its content.

We’ll be moderating a panel discussion tonight with OpenAI and the CISOs of Cerebras and TransUnion at a private event for list makers and security leaders hosted by Notable and the New York Stock Exchange. If any clips become fair game, we’ll be sure to share them in days to come.

Read the full Rising in Cyber list and report here.

When it pays to be early

According to the report’s data, only one segment of cyber startups grew their total funding raised from VC in 2025: Series A and B startups, which combined for $5.5 billion in total funding, on a higher count of deals.

From our conversations with practitioners, that’s no surprise for a few factors:

• Security teams at bigger companies are meeting with early-stage startups frequently right now

• Such startups are more likely to have launched post ChatGPT, on the ‘AI native’ side of the divide

• They have an easier time selling, as big companies are more willing to solve last-mile integrations or customize their software themselves versus wait

At Glean, CISO Sunil Agrawal says he only hires engineers capable of building their own tooling to correct gaps, even on the security team. Every startup should offer an MCP or other hooks like a skill, or APIs, for them to do so on top. And Agrawal says he often tells startups to focus more holistically on the bigger picture problems they’re solving, not just how they’re winning in one capability.

“I don’t need you to create that fancy dashboard, or alerting, and so on,” he says. “I will do that myself.”

Secret agents

With most CISOs reporting they’re early in their agentic security journey, they also voted for access and control for agents as their most concerning AI-based threat (21%) for the next 12 months.

It’s no surprise that the Rising in Cyber list features a number of startups tackling different agent angles: access, identity, observability, runtime. Agentic security will only gain more prominence as users begin to authorize agents to automatically make financial decisions like purchases and refunds, notes Yunger.

Even as a builder for the category at 1Password, Wang says she “hounded” her security team for months to be able to take advantage of Anthropic’s Claude and OpenAI’s Codex agents in production, which required a two-month review.

How many tools one buyer will really want is unclear. “The market is still very much in exploration mode,” she says.

A smaller number of companies on the list offer “AI for security,” where the AI tooling itself is the differentiated product, like Terra Security, which promises continuous AI driven penetration testing to find vulnerabilities.

Model, genius

Against the backdrop of Anthropic’s Project Glasswing, a response to supposed game-changing capabilities in its new Mythos model; and OpenAI announcing its Daybreak security program yesterday, it seems like everyone in security is debating how frontier models factor into capabilities both for attackers and defenders.

Glean’s approach, as a potential buyer of new tools: give the best model access to the security team to try to wreak havoc from the inside, says Agrawal. His team is the biggest user of tokens among the engineering organization. “We’re not waiting for these models to be widely available,” he says.

At 1Password, Wang has a different spin. If, as we’ve covered previously in Upstarts, Claude Code using existing models is already powerful enough to expose vulnerabilities, the game changes.

“It’s less about who has the best model, and more like who has the model that I can best control,” she says.

Startups can impress, according to our anonymous leading CISO, who wasn’t authorized to speak on the record, by showing more context around their findings. It’s one thing to identify 100 vulnerabilities; it’s more valuable to identify three urgent ones, and flag 97 for a future patch.

“Context is going to be king,” they say.

Heat check

To hear Yunger tell it, we are just weeks away from the kinds of large scale attacks that could have major economic impact, enough to be “impossible to ignore.”

What is the sense of urgency right now among buyers of security startup tools? Per the survey, budgets for CISOs are growing — but not as fast as they could to meet demand for AI tooling, or for AI enablement to become a bigger part of the budget.

“It’s coming from a place of concern, but not necessarily fear,” says Wang at 1Password.

At Sublime, Kamdjou warns that while AI-enabled attacks will be more unpredictable and fast-evolving, many of the solutions out there are ‘snake oil.’ “There’s a lot of noise and flashy tools out there,” he says.

From a CISO perspective, Agrawal at Glean says there’s no point in being “scared" about AI.” “Stop worrying, start going back to your first principles,” he advises. “Start identifying the entire risk surface.”

And our unnamed corporate CISO says that for some security teams, this is a moment they’ve expected to come.

Does that make the job more fun, more stressful, or both?

“I think the industry is ready for the challenge,” they reply. “There’ll be moments where it’s not going to be fun.”

When SF Compute co-founder Evan Conrad set out to raise a Series A funding round last fall, he hit a wall. Business at his AI computing marketplace was booming, but investors weren’t biting.

“Super candidly, I was really terrible at fundraising,” Conrad says. “We had all the numbers to be quite successful, but I don’t think I gave off the impression that people expected me to have.”

To get the process back on track, Conrad flew out to New York for emergency sessions with one of his earliest investors, Sumeet Singh. The former a16z investor had invested personally in SF Compute; when he launched his own fund last year, Worldbuild, he doubled down on his bet.

In those meetings and others back in San Francisco, Singh and Conrad “hammered home” the vision and upside for SF Compute, which had started out as an audio model company and backed into a more lucrative role connecting other companies to flexible computing capacity. And Singh introduced Conrad to Wing Venture Capital, which ended up co-leading a $40 million Series A in SF Compute last November.

“There’s lots of people who will swarm around you when you’re doing well,” Conrad tells Upstarts. “Sumeet was one of the people who swarmed around us when things were not.”

In venture capital circles, Singh is still more of a behind-the-scenes fixer, a character also known for his Renaissance side pursuits in music (he’s a DJ and creator) and fashion (he previously ran a custom clothing line)

But as Worldbuild announces it’s raised a $30 million debut fund after one year of operation, Singh’s already building a track record worth watching.

Through Worldbuild, he’s backed SF Compute, space energy startup Aetherfux, and a number of promising newer startups, like AI hardware maker Truffle, in his first year. Through his stints at Nyca Partners and a16z, Singh helped the firms get into anti-fraud startup Sardine and fintech Revolut, among others.

But it’s Singh’s approach – taking the time and reputation risk to publish theses (like this one in Every) around where the intersections of technologies will go – that has made him valuable to founders, who have used them to raise from firms like Founders Fund and Sequoia.

“There is so much noise in venture today, that actually leading with original thoughts can have a competitive advantage,” Singh says. “We want other, larger platforms to view Worldbuild as a tastemaker for where they should be spending time six months, or 12 months from today.”

For startup builders, Singh represents another signal: in venture capital’s current climate of boutiques and mega funds, more ambitious emerging investors are going the founder investor route.

That’s especially true among the ranks of bigger firms like a16z, where investors including Bryan Kim, Anj Midha, Arianna Simpson, Satish Talluri have joined an alumni network of founder investors that includes Singh, Kristina Shen, Michelle Volz, Vijay Pande and others.

They’re motivated by a few factors: the ability to move fast and invest in non-consensus (even non-AI areas); economic upside with fewer mouths to feed; and the ability to extend themselves more with new tech.

A16Z declined to comment. But we spoke to a few sources to get more info on what several of those recent departures are up to below.

“VCs look at companies and talk about how AI is going to disrupt them,” says one. “We never look in the mirror and say why AI is going to disrupt me.”

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The family business

Even before Worldbuild, Singh has always been surrounded by entrepreneurs.

His parents emigrated from India in the 1980s; they, and many other relatives, spent the ‘90s running small electronics shops all over Manhattan, selling cameras and cell phones.

Singh spent early years running around those stores before his father moved on to owning Dunkin’ Donuts franchises in Long Island; the two still talk about potential new ventures regularly, Singh says.

After starting his career in finance, Singh talked his way into Nyca, a firm without $1 billion in assets, where he learned the venture trade. As a junior investor, he eventually “pounded the table” for one potential investment, consumer finance startup Brigit, threatening to quit if Nyca didn’t do the deal;, conscious of his own lack of startup building experience, Singh decided to join Brigit to help lead strategy.

He started two weeks before Covid-19 shut down business across the U.S.; Brigit was exposed as its customers were hit hard, and had to scramble. “I thought we were going to die every day for six months,” says Singh. “I now recognize that as not near-death moments, but moments where you need to punch through a glass ceiling.”

In 2021, with Brigit stabilized (it was later acquired for $460 million), Singh joined a16z as one of its first New York hires. Singh’s job was to go down ‘deep rabbit holes’ to identify potential opportunities, then bring them to more established partners like David Haber and Angela Strange.

A thesis around the intersection of crypto and fintech led to a bet on Sardine, now valued at $660 million; one around super apps in the ‘global South’ helped a16z to make an investment in African app maker Carry1st, which announced a $27 million round in 2023.

But in a larger organization like a16z, with hundreds of employees, Singh grew restless. While established investors at a16z can write checks unilaterally, Singh often found himself pursuing buy-in from two or three teams and funds within the firm.

And at a moment when multi-stage firms are writing checks in the hundreds of millions to billions of dollars, getting a firm excited about company formation-stage checks could prove tougher.

“Early stage, where there’s the most change and the most chaos, that’s really what excites me the most,” says Singh. “Ultimately, I needed to build a firm to reflect that.”

In launching Worldbuild, Singh tapped his network from angel investing while at Brigit and his work at a16z. Four of Worldbuild’s portfolio are previously backed founders, like Browserbase CEO Paul Klein IV, whom Singh personally invested in while he was working on another venture, Stream Club.

When Klein was thinking of launching Browserbase, which we covered in detail last June, Singh helped Klein to frame the company as more than AI infrastructure, Klein says. “He’s just been a great guy to bounce ideas off of,” says the CEO. “He’s the first call I’ve made on every single funding round, as one of their architects.”

Singh gets in front of new founders, meanwhile, through his theses and blog posts, which he started posting on Notion after leaving a16z, and which he now shares via Discord channels, Reddit and other social media.

“When you meet a founder and you can send them, here are four PDFs and blog posts I wrote on the very space you’re working on, they’re super impressed,” says Village Global co-founder Ben Casnocha, a limited partner in Worldbuild. Other LPs include a top 20 university endowment, according to Singh, as well as several prominent multi-stage VC firms.

“It’s very rare to have someone who is both plugged into what everyone is talking about, but also bold enough to conceive their own ideas.”

Offshoots from the money tree

Worldbuild slots into a fast-changing venture landscape that is absorbing outsized AI investments that one fund manager compares more to “banker type deals” than traditional venture ones.

And the splintering is seen more than anywhere at Silicon Valley’s biggest firms, where emerging partners with established brands are increasingly tempted to set up their own shops, a trend we covered in our final story at Forbes last year before striking out on our own with Upstarts.

Since then, Pande left a16z’s biology practice to start VZVC last year; Simpson left the crypto team three months ago; and Kim announced his departure in April.

They join a long line of investors who have founded firms immediately after departing a16z. We asked around to compile a list; if we expanded the criteria to former a16z employees who run firms but worked elsewhere in between, it would grow much longer, including investors like Kristina Simmons and Elizabeth Weil.

Why have so many folks left a16z? It’s partly a numbers game, given the size of the firm and the number of partners its funds support.

“A measure of the strength of an organization is how many people leave to start their own thing versus leave to join a competitor,” Volz writes Upstarts, comparing the alumni network to Palantir’s network, which we discussed with Chapter’s CEO two weeks ago. “It means you’ve hired smart entrepreneurial people and the only reason they’d leave is to build their own thing.”

But several investors who spoke anonymously with Upstarts say that driving factors include the ability to write checks faster, with fewer internal politics, while owning more of the upside. “You will never be poor at a16z,” says one. “But it’s harder to be generationally wealthy.”

As for the new wave, Upstarts poked around about what Midha, Simpson, Talluri and Kim are up to.

In Midha’s case, AMP isn’t a VC firm per se, calling itself an independent system operator for global compute; governed by a public benefit charter, AMP includes a venture fund that has received “billions of dollars of interest,” per a source, but functions more as a holding company to maintain that grid.

Simpson, meanwhile, plans to invest in seed-stage startups in crypto as she did at a16z, but also widen her focus to additional verticals in tech, per a source.

Talluri, who worked with AI companies including Pinecone and Grepr, is working on an unannounced fund in AI infrastructure, another insider says.

Kim is expected to continue to focus on consumer and prosumer AI, where he was an early investor in ElevenLabs and video maker Mirage among others.

Upstarts reached out to all three for interviews, and will update this story with comment if we hear back.

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For his part, Singh says entrepreneurs can use more “first believer” fund managers, whether they’re at a new firm or bigger one. For founders of investors curious who might be the next VCs to leave a multi-stage firm, he advises to look for more untraditional backgrounds like his own.

“You could probably scan through the LinkedIns of any big firm and say, ‘Who has the most non-linear experience? That’s probably the person that’s going to start their own firm,” Singh says.

Ironically, that’s more of a headache with a16z: in reporting this story, we identified a number of fake LinkedIn profiles that purport to be former a16z partners, copying backgrounds from several real employees.

So, maybe don’t bet on new funds coming from folks with sketchy AI looking photos, and just a few hundred followers. But if they have their own podcast, like Singh does, that’s probably another sign.

Back then, investors weren’t sold — not just on their new video AI startup, Runway, but on generative AI altogether.

“There’s always doubt when you get 50 rejections,” Valenzuela says. “I’ve realized that building something special, you have to be able and willing to be non-consensus driven.”

Fast forward, and Runway works not just with many film studios and Hollywood, but creative teams and engineers at corporations like Allstate, Siemens and Robinhood. It’s had no recent trouble fundraising, reaching a $5.3 billion valuation. And it’s outlasted gen AI’s heavyweight, OpenAI, which recently shuttered its Sora app.

On our final episode of Season 1 of The Upstarts Podcast, we circle back with Cris — whom some Upstarts readers may recall spoke at our launch party in New York a year ago — for a special interview at the New York Stock Exchange. We’re both proud to building in the city (and Cris is the only guest we’ve run into on the street, totally by chance, in the days before filming).

On the show, he talks about his origins working with artists at NYU’s program; what the big AI labs missed in video; how Runway works with expert and amateur filmmakers alike; and why he believes creators should look past the field’s controversy to embrace technology in their work.

Plus, he shares his Upstart Moment: collecting those rejection emails from top VCs and sharing them with the whole company to motivate his team..

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This season of The Upstarts Podcast is presented by Mercury: banking, redesigned from the ground up.

Chapters
00:00 Introduction
02:32 Why world models matter
03:57 What OpenAI got wrong
06:32 Runway’s NYU origins
13:10 Like going to the gym
16:06 A fan at ‘The Late Show’
20:36 Cris’s Upstart Moment: VC doubts
25:30 Building at the speed of AI
30:28 Darren Aronofsky and amateur users
33:12 A new normal in filmmaking
39:05 Up next: Characters, robots and games

Just looking for the highlights? As always, we break down three startup lessons from the episode for paid subscribers below.

1. What OpenAI got wrong in video

2. How to keep up with the pace of AI

3. A defense of AI tools for skeptics

That’s a wrap on Season 1! If you want to unlock all of our past episode show notes and lessons, you can take 15% off an annual subscription here.

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We have big plans for Season 2, and we want to hear from you! Please send us show ideas, including killer founder guest subjects, by responding to this email or pinging Alex directly. Thanks for watching and listening!

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The Upshot

Serval co-founders Jake Stauch and Alex McLeod credit much of their recent success back to their time at another startup, Verkada.

As the heads of product and engineering at the security unicorn, they worked closely together on a feedback loop: talk to customers, build for their pain points, figure out how to sell it more widely, then talk to more.

“It’s the kind of founder magic that leads to such a great end experience for the customer,” says Stauch, Serval’s CEO. “Why would you ever give that up?”

But Serval, which launched two years ago with software automating systems for IT teams, and which has now expanded into areas like HR and finance, is growing too fast for its founders to still have their hands in every loop.

The company raised $75 million in December at a $1 billion valuation after revenue grew 6x in three months. A team of just about 60 staff now work with customers including tech high-flyers like Notion, Perplexity, Together AI and Vercel, but also almonds cooperative Blue Diamond Growers and media conglomerate Fox.

So Serval has come up with a novel experiment for scaling founder-level care: a program that explicitly hires aspiring founders to work as forward deployed engineers.

Called Serval Start, the program is launching with a cohort of one dozen members, with four already committed, and eight reserved for net-new hires.

Cohort members will be thrown at the company’s most strategic and biggest accounts, with the expectation that they’ll be able to troubleshoot and hustle their way to maintaining Serval’s “vertical line” growth.

What they get in exchange:

• Accelerated equity vesting schedule with a six-month cliff (compared to typical one year)

• Early exercise rights at Serval’s valuation from when they start

• Direct access to Serval’s execs including its founders

• Time with its VCs including First Round, Redpoint and Sequoia

• Weekly and monthly meetups and other programming

The program will be led by Christine Kim, who joined Serval two months ago after spending five years investing at Greylock. Its success will be measured, she says, not just by how participants boost Serval’s business, but also by the startups they eventually leave to build.

“There’s a lot of programs targeted toward founders that already have an idea, or a company in mind,” Kim tells Upstarts. “We wanted to find something that would be even earlier than that.”

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In a frenetic startup environment where investors and longtime operators are launching new-look residencies and founder matching programs to try to launch – and invest even earlier – in new businesses boosted by AI’s new capabilities, Serval’s program stands out as arguably the first formal, in-house program of its kind.

It’s not without risk, from hiring pressure to team dynamics, and what could work for Serval may not apply to every startup. Still, the program is worth the wider ecosystem watching closely – if anything, to see what new startups emerge.

How Serval set up the program, thoughts from two of its early members, and the risks and implications for other startups are below.

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‘Extended bootcamp’

Serval’s CEO Stauch hopes that Serval Start can spare founders from living his own experience before his current company.

Coaching companies isn’t new for him: at Duke, he helped build out the university’s entrepreneurship club, later serving as a mentor alongside his spouse Tatiana Birgisson, also Serval’s COO. But Stauch spent seven years building his first startup, which focused on brain-controlled marketing research and then video games, before moving on to lead product at Verkada.

“I could have cut off a lot of time there,” Stauch says. “This is the program I wish existed then.”

The idea of such a program first came up in a board meeting. It got legs – and Kim as its champion – because Serval leadership and investors were grappling with a problem of how to serve overwhelming customer demand.

The answer, Stauch and co. believed, was an employee empowered to build new features at customer requests, take initiative and problem solve like Serval’s founders did when they led all sales.

That doesn’t mean any old forward deployed engineer, argues Stauch: “There’s been a proliferation of what’s called an FDE in tech right now, it’s everywhere,” he says. “But if you look under the hood, most of these folks are just implementing software. It’s a rebranded solutions engineer or implementation consultant.”

Some elements of the program already existed informally. Employees known to be interested in starting companies might already get introduced to Serval’s investors; the startup also already intentionally hired former founders who might want to try again.

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That was the case with Spencer Lang, an internal selection for the upcoming cohort. After working at Microsoft for several years, Lang had joined a startup as a founding engineer and then co-founded a startup intended to help non-profits use video in direct mail. When his co-founder departed the project, Lang decided to join Serval last October as an FDE to retrench and build up his network.

“I was pretty explicit upfront with Jake that I would start another company, and he was super supportive of that,” Lang says.

Working closely alongside fellow hires over a sustained period of time can give you a better understanding of who might make for ideal co-founders than searching through Y Combinator’s Co-Founder Matching Platform, or even joining a residency like HF0 or South Park Commons, he argues.

“I can think of five people here that I would start a company with, and have extremely high confidence,” Lang adds.

High talent density is what attracted an early outside applicant selected to participate. Felicitas Binz spent nearly three years working in product at healthcare automation startup Luminai before leaving in November to pursue her own startup idea.

Like with Lang, Binz’s project with her planned co-founder friends didn’t work out, and no natural co-founders materialized in her network. Joining Serval, she decided, will allow her to meet other high-achieving peers, while “challenging” her own product knowledge at the cutting edge of how AI software is being deployed.

“A lot of stars need to be aligned before you can go full-time on a startup,” she says. “Serval is a place where I can be more strategic about my next step.”

‘Great takes time’

It seems likely that Serval will have no difficulty filling the eight remaining spots in its first batch.

That’s when the hard part starts, Kim knows. The bar for success includes Serval Start hiring even more exceptional talent than its typical standards, she says; those hires will then need to meaningfully grow the startup’s business, while also taking advantage of the program’s opportunities.

On the business side, Serval already has a customer pipeline bigger than it can handle for 2026, Kim says. Whether it can accelerate deployments and revenue will depend in large part on its FDEs, and specifically this Start squad.

At the same time, Serval will expect Start members to work nights and weekends on their entrepreneurial journeys – not regular company hours.

If the cohort is too mercenary, lagging on their day work to prioritize the networking side, and leaving as soon as their equity vests in six months, the program will fail.

Serval’s leaders say that’s not as big a concern as it might seem from the outside, because personal performance is tied closely to the company’s. “We’re still small enough where you can’t really hide, and you can’t really look good without doing something that’s helping our customers,” Stauch says.

Still, everyone leaving within six months, and Serval having to start over with those FDE roles and the batch, would be a mixed outcome. “It’s a risk that we have to be excited about,” says Kim.

Bill Trenchard, the First Round partner who invested early in both Verkada and Serval and is volunteering to work with the batch, says he’s “not too worried,” and expects many of the cohort to last up to the intended two years.

“Actually learning what great looks like takes time,” he argues. “And the best people will want to make a huge difference, wherever they are.”

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What if those startups bring engineers or other staff along with them, plunging Serval into a brain drain? The startup’s founders left Verkada amicably, and the company invested in the new idea. Serval’s best case scenario is probably that the Start members pair off with each other, but that’s not a given.

“There’s a balance to be struck there, and I’m sure there’s a threshold where it’s less cool,” says Lang. “That’s a bridge to cross when we get to it.”

A new playbook?

If you’re a founder or startup staffer reading this far, we’re guessing it’s because you’re at least curious to explore whether a version of Serval Start could work elsewhere, too.

It’s obviously too early to know whether Serval’s experiment will work out. But its organizers note a few criteria that made it seem worthwhile for Serval, that can serve as a checklist for exploring at other startups.

Traits for a company to benefit from founder FDEs:

• Serval is high-growth: Enterprise and Fortune 500 sized accounts come in every week, Kim says, meaning there is constant demand for staff to jump on ever-bigger, more complex deals.

• But it’s not too big: With under 100 employees, Serval still operates at a scale in which FDEs can actively build and ship features for customers – something harder to do at bigger organizations like OpenAI.

• It’s enterprise-focused: Such a program only makes sense where FDEs are at a premium, and when aspiring founders want to build in B2B or enterprise themselves. It won’t make sense for a consumer app.

• Its product has wide reach: Serval’s automations touch upon a number of teams within a customer, from procurement and finance to legal and security, as well as product and engineering. Such exposure might be less valuable for FDEs at companies that only sell to one team, such as engineering or sales.

Companies that could naturally set up such a program, per Stauch: Ramp in fintech, or AI agent builders Decagon and Sierra.

There’s one more variable to consider: ability to attract premier talent. If the people coming in aren’t more skilled in a certain area than your status quo – meaning you’re not unlocking access to stars you couldn’t hire otherwise – it’s unlikely to be worth the trouble for the company and investors alike.

“Maintaining selectivity is going to be the main challenge,” says Kim. “We already push ourselves to look for the top tenth of a percent of engineering talent, and now we have to look for the top one-hundredth of a percent.”

The Upshot

Apple’s longtime former AI chief John Giannandrea is joining a science AI startup, CuspAI, to help build out its U.S. operations, Upstarts has learned.

The former boss for efforts including Apple Intelligence, robotics and Siri plans to work part-time with CuspAI, three sources say, leading the Cambridge, U.K.-based materials lab’s efforts to open a Bay Area office with heavyweight AI talent.

The advisor role doesn’t yet have a formal title attached to it, but represents Giannandrea’s first move back into the ecosystem since his news-making retirement from Apple two weeks ago.

Giannandrea and CuspAI both declined to comment.

The move comes as CuspAI, which uses AI to help customers discover new materials, is reported to be raising $200 million or more in funding at a valuation of more than $1 billion. CuspAI previously announced a $100 million Series A in September, on top of a $30 million Seed round in 2024, the year of its public launch.

The reported upcoming round aside, Upstarts understands that CuspAI had raised more than that — more than $200 million total already — through a previously unreported Series A extension. Investors include NEA, Singapore’s Temasek, Hoxton Ventures, Basis Set Ventures, Lightspeed and the venture arms of Nvidia and Samsung.

What, exactly is CuspAI? And why is Giannandrea joining it, of all startups?

We have some early answers — both from the perspective of the exec known to colleagues as “JG,” and from that of the startup looking to compete in a fast-developing area of AI research — below.

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In 2020, Blumenfeld-Gantz’s exposure to his own parents’s struggle navigating Medicare — faxes, phone calls, even a trip to the Social Security office — led him down a rabbit hole of whether someone could build a better way.

“I was just appalled at how absurd the entire ecosystem is,” he says. “And not just the technology, which is a big part of it. There are also just terrible incentives that make every broker push plans that pay them more.”

So he launched Chapter: a startup focused on connecting Americans to better Medicare plans, saving seniors thousands of dollars per year. Recently, Chapter reached $100 million in annual recurring revenue and a $3 billion valuation after receiving backing from Al Gore’s fund Generation Investment Management.

For Blumenfeld-Gantz, that’s proof that his mission is distinctly bipartisan. Founded and led in New York City, Chapter has deep GOP ties, from Blumenfeld-Gantz’s legacy at Palantir to Chapter’s co-founder Vivek Ramaswamy, who was just a billionaire businessman then, but has since run for president and is currently pursuing the GOP nomination for governor in Ohio. One of Chapter’s earliest investors was current Vice President J.D. Vance, when he was just a VC.

Chapter is the only startup we know of backed by vice presidents from both sides of the aisle.

“When people spend time in the government, they see how big of a problem and how complex these systems are,” its CEO says.

On The Upstarts Podcast, Blumenfeld-Gantz shares his journey from foreign policy studies to forward deployed engineer at Palantir; what he learned at the tech company and why it’s proven a startup breeding ground; how Chapter’s nationwide approach helped it reach scale faster; and why he proposes a twist on a typical startup metric: product-market-channel fit.

Plus, he shares his Upstart Moment: when last year’s government shutdown cut off Chapter’s data right at the opening of Medicare’s busiest enrollment window, and his team had one week to reverse-engineer their way out.

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This season of The Upstarts Podcast is presented by Mercury: banking, redesigned from the ground up.

Chapters:
00:00 Introduction
01:04 Solving his parents’ problem
08:08 From foreign policy to Palantir
12:01 Why Palantir breeds so many startup founders
14:28 Getting started and managing VC no’s
20:24 Why product market channel fit matters
26:51 Cobi’s Upstart Moment: the shutdown
32:33 “One of the most hated people in the industry’
35:41 Tuning out noise and talent density
42:37 Up next: a wider financial platform for senior spend

Just looking for the highlights? As always, we break down three startup lessons from the episode for paid subscribers below.

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1. Palantir tips: ‘unstructure,’ ‘meaningful problems,’ and ‘eating the pain’

2. Going national from day one

3. The value of ‘product-market-channel-fit’

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At an exclusive AI-focused event on Monday afternoon, Figma board director Andrew Reed asked CEO Dylan Field to talk about his relationship with AI lab Anthropic.

It was a loaded question. Just three days prior, Anthropic had announced Claude Design, a product that seemed to compete with Figma’s software. That move came only three days after Mike Krieger, co-lead of the same Labs team that developed it, had formally resigned from Figma’s board.

Towards Krieger, known for his role as co-founder of Instagram and until recently Anthropic’s chief product officer, Field was polite. “Mike is a great guy, and he was awesome on the board,” Field said, as confirmed by two sources who were in the room.

For Anthropic itself, Field chose more loaded words:

“They were not consistently candid in their communications,” he said.

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The Upshot

Some people are ‘inbox zero’ types, making a point to read – maybe even label or archive – every email that comes in each day.

Naveen Gavini is not one of those people. At least not when it comes to his personal life, where he calls his habits more like ‘inbox infinity.’

Until his new startup, Gavini worked as chief product officer at Pinterest, where email “ran his day” on the job. Meanwhile, in his personal inbox, other, arguably as important, notes would slip through: friends’ birthdays and major family updates, notes about his kids, even his pet’s insurance coverage lapsing with an expired credit card.

“Every time I’d open up my inbox, I felt this little anxiety of the never-ending to-do list,” Gavini says.

So after teaming up with two friends from Pinterest – Albert Pereta, its former executive creative director, and Steven Ramkumar, a former distinguished engineer – Gavini decided to take on a problem left largely unsolved since the launch of Gmail more than 20 years ago.

Their new app, Extra, leverages AI capabilities to make inboxes more personalized and proactive.

And after five months in beta testing with about 1,000 users, Extra publicly launches its availability today. Initially open to Gmail users via iOS and web, Extra will onboard people who sign up off its waitlist daily, as fast as they can manage, Gavini says.

(The first 100 or so Upstarts readers to sign up can skip the line with the code UPSTARTS – we’re choosing to share this, and are not being compensated in any way to do so.)

In a few days testing Extra at Upstarts, we saw first-hand what the team is trying to build: a new-look, more responsive, more fun inbox experience; albeit one that is still a work in progress.

If Gmail looks, as Gavini argues, like a spreadsheet of columns and rows of people and brands asking you for something, Extra presents a more colorful executive summary of what it thinks actually matters to you.

And while “email is the hook,” as Ramkumar puts it, there’s a bigger vision in play here: to build your “life inbox,” part personal assistant, part smart directory to the thousand little things going on in your life.

That’s why the startup behind Extra has a wider-reaching name, BuildForever; it also helps explain how the startup has already raised $9.5 million from investors including Felicis Ventures, Abstract Ventures, A* and Elad Gil.

BuildForever’s founders know the history here, too. Many startups have tried to tackle email since Gmail, from Mailbox to Superhuman; many have failed; the best have been acquired.

They’re making two bets provocative enough to deserve discussion by the wider ecosystem, even if you’re skeptical.

• Tools like Gmail weren’t designed for the LLM era

• AI builders are under-serving the consumer market

So much money and attention is focused on re-thinking our work tools and processes today, Gavini notes, with many of tech’s smartest people bringing AI to verticals like legal or healthcare.

“My workflow changes every three months now,” he says. “So why are the apps that I use on my phone every day still the same?”

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Extra difficult

Email startup veterans have simple advice for the BuildForever team: don’t do it.

“We’ve learned a lot in the past year,” Gavini responds. “But as a consumer, I would love better personal email, so we’re going to take a shot.”

For such a potentially Quixotic venture, their credentials are strong. At Pinterest, they not only worked in consumer at scale, but launched Shuffles, a standalone collage app aimed at Gen Z that temporarily crested the U.S. App Store in 2022.

And the trio spent months chatting and researching the problem before diving into email eyes wide open. Their thesis: many of the more recent tools to emerge, like Superhuman, have focused on work, because that’s where the obvious money is. But how we use email for work is fundamentally different than when we’re off the clock – much more about continuous, repetitive back-and-forth or outreach; much less about passive consumption.

Busy professionals also tend to try to optimize their workdays first, then scrap together the rest. Not everyone was ‘inbox infinity’ like Gavini, but even ‘inbox zero’ purists like Pereta would spend hours a week tending to their manicured inbox gardens.

Then there are younger people, like the college students who tried Shuffles, who see Gmail as “for old people,” Gavini says. Many of them are now younger than Gmail itself; their names are already taken on the service.

Extra tries to solve the needs of all those groups, although it tends to resonate first with people like Gavini who want to ‘Marie Kondo’ their email backlogs.

At launch, Extra summarizes your daily meetings and what it thinks are key messages, like the delivery status of an Amazon package, or in my case, a looming deadline to purchase college reunion tickets.

For newsletters like Upstarts, Extra could be a double-edged sword. A handful show up in the front-page daily briefing, summarized to potentially drive more follow-through; the rest get grouped into one of Extra’s smart categories, which users can create and edit. Others might include a Kids one for parents, or Reservations for foodies.

Extra has leaned into smart actions and search. According to Pereta, too many apps still take the approach of a “blank box” – putting the pressure on a user to know what to ask, or retrieve. Emails in Extra suggest a few actions, like adding to calendar or going to a ticket page; you can also query a chatbot similar to productivity tools to ask for suggestions and find old context.

In chats with three test users identified by Extra, we heard three different reasons for adopting Extra. Emma Bates, a startup founder and fractional exec, finds it easier to toggle between six different email accounts without feeling overwhelmed. She’s set up a category for planning her wedding, too.

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Emily Yi, a game designer, finds the interface more engaging and less “administrative” than using Gmail on her computer or the Mail app on her phone. As she helps manage care for two elderly parents, she values Extra’s voice capabilities: “The personal assistant part is huge.”

And Courtney Knights, who works in fundraising and development at Columbia University, has used Extra to help break a pattern of relying on two personal emails: one for important info, and another for marketing sludge. Extra has helped remind her to fill out a form for her kids’ summer camp; she’d like a way to connect multiple users, so she could drop updates around their school straight onto her husband’s page and calendar.

Our own experience with Extra has been more of a mixed bag so far. Little things like the reminders and package tracking are appreciated and immediately useful; so is having a simple, visually-pleasing central dashboard of personal tasks.

At other times, the app’s growing pains are more visible. One of the most amusing was when Extra made its first recommendation of a potential email source for me to unsubscribe, due to disuse: official emails coming from Extra itself.

Extra also appeared somewhat lost in time, recommending a year-old dinner reservation as needing action, and offering to check me in to a flight happening in two months.

Gavini says that’s partly the app showing a new user what might appear in certain categories; the app also seems to struggle a bit more with people like me, who take the time to label and archive email whenever possible.

As noted by the test users, it’s easier to get into the swing of using Extra on mobile. It’s also more valuable for people who already use multiple phones, or apps, for work and personal accounts, versus connecting everything into one Gmail inbox.

Racing ahead

How will Extra make money? It’s free for now, and to go up against existing tools, some version of it will likely need to stay free forever.

“We need to be able to support a billion people using the product, and I don’t think a billion people will pay $5 a month when they have a free service,” Gavini admits.

As it adds more assistant-like capabilities, Extra could explore a premium offering; it could also lean into marketing. You can already organize all of your emails from brands into one category to keep track of sales; would a brand gladly pay an affiliate fee to stay top of mind there? If Extra knew you were going on a trip, would Uber or Lyft pay to have a one-click option to book a car from the airport?

“Layering on that funnel is very interesting,” says Pereta. It’s also dangerous – similar to the tension of OpenAI exploring putting ads, or commerce buttons, directly into ChatGPT.

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Gavini brainstorms that Extra has much of the info needed to track and identify rewards and special offers coming in from your various credit cards or loyalty programs. Would you, or the cards, pay to keep those top of mind and aggregated in one place? That opportunity reminds us of Paribus, the price-matching tool previously built by Ramp’s founders, and which was acquired by Capital One after being throttled by Amazon. The receipts are still in the email.

A more existential question facing Extra’s builders: if they’re right, and consumers want a new tool here, why won’t Anthropic and OpenAI simply gobble the market up?

It’s not a question that worries Peter Deng, who led the startup’s most recent round at Felicis, and who previously helped oversee ChatGPT at OpenAI. Besides BuildForever’s founders’ own experiences at Pinterest (the now cliche word “taste” comes up), he takes heart in the fact that the labs are focused on the revenue opportunity of work use cases: “They are currently in a world where the user talks first, and the model doesn’t talk at all,” he says.

From tools built DIY using OpenClaw, the recently popular open-source agent builder, to a hypothetical Claude chief of staff, it all might converge on the same super assistant opportunity. For consumers, more competition could mean better options.

“I’m not trying to convince other people to root for this company,” says Deng. “I just want people to try it.”

The Upshot

Trusting AI model output as the gospel truth is already a bad idea. It’s an even worse idea in women’s health, where Midi Health co-founder and CEO Joanna Strober is quick to point out that clinical research routinely excluded women as recently as 1992.

That’s a big problem when AI models are training off of, or drawing inferences from, research data before that cut-off to inform current-day insights.

“A lot of the research you see out there is just incorrect when it comes to women’s health,” Strober says. “A woman is not a small male. Unfortunately in most data models, they are.”

It’s one of a few reasons that Midi Health and Oura Health are both training their own AI models, alongside decreased funding at NIH for research more broadly. “It’s a systemic problem,” says Oura CEO Tom Hale.

Upstarts interviewed both health tech leaders on the main stage at HumanX last week. We also led a discussion on how AI is changing collaboration at software unicorns Gamma and Miro. Gamma co-founder Jon Noronha and Miro founder and CEO Andrey Khusid both had interesting points on how agents are transforming their businesses.

(Our other HumanX interview, on how DoorDash co-founder Stanley Tang set up its robotics division, is available for all subscribers here.)

For our paid subscribers, we’re breaking down the key takeaways from all four startup leaders below, covering AI’s impact in patient care, wearables, management and presentations.

“Everybody is becoming an executive,” says Khusid. “Most of us never wanted that life.”

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• Listen on Apple Podcasts

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This season of The Upstarts Podcast is presented by Mercury: banking, redesigned from the ground up.

Chapters:
00:00 Introduction
01:11 Cohere’s ‘takeoff phase’
05:09 Where vibe coding falls short
08:05 Smalltown Canada to AI epicenter
14:27 Co-authoring the Transformer paper
18:52 Starting up Cohere
25:22 Why AI sovereignty matters
30:25 Aidan’s Upstart Moment
32:56 Playing on ‘hard mode’
36:55 Ethical questions of AI
39:44 Why Cohere should IPO

Just looking for the highlights? As always, we break down three startup lessons from the episode for paid subscribers below.

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1. Be more like Jensen

2. Control > hyperscaler funds

3. IPOs are a good forcing function

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When DoorDash co-founder Stanley Tang pictures you ordering lunch, or a new phone charger, he doesn’t see a person driving it to you. He doesn’t even see a car.

Robotaxis were designed with transporting people in mind, not a sandwich.

“You’re just trying to retrofit that form factor into a delivery use case,” he argues. “If you think about it from first principles, why would you need a 4,000 pound car?”

That line of thinking helped lead Doordash to design the Dot, an electric autonomous delivery robot that it started publicly testing in Phoenix, Arizona last year. About four feet tall, the Dot can travel up to 20 mph, use sidewalks, bike lanes and roads, and carry up to 30 pounds on its own 350 pound frame – what the company bills as a safer and more efficient way to move goods.

When Tang, one of four now-billionaire founders of DoorDash, first set up its Labs unit in 2018, the company didn’t expect to be building its own robots. Instead, its autonomy strategy was focused on partnerships, Tang says.

We talked about that journey and lessons about setting up an R&D unit, as well as DoorDash’s thinking about autonomous vehicles and robotics more specifically, last week at the HumanX conference in San Francisco – on the main stage, as well as backstage for more to bring our Upstarts readers.

(Tang unsurprisingly didn’t give us a heads up that DoorDash would be participating in a controversial ‘delivery’ and press conference with the White House several days later; The Guardian can catch you up there if curious.)

You can find our key takeaways and video clips from the conversation below.

On Friday, we will publish more startup takeaways from our other interviews at HumanX:

• AI and data in health and wearables, and how women have been left behind, featuring Oura CEO Tom Hale and Midi Health CEO Joanna Strober

• How two productivity unicorns are thinking about AI in workplace productivity both internally and for their customers, featuring Gamma co-founder Jon Noronha and Miro CEO Andrey Khusid.

That post will be for paid subscribers only. If you’re curious what they had to say, or simply want to support our work here at Upstarts, it’s a great time to participate in our first birthday sale: 25% off an annual subscription.

That comes out to less than one of DoorDash’s sandwiches each month.

Partner vs. build

DoorDash initially intended for Labs to set up partnerships with other hardware companies for its autonomous strategy, not build its own.

DoorDash would offer APIs to connect to its software, handle operations and distribution, and leave the rest to other startups, Tang says.

In practice, things weren’t so simple. DoorDash’s surface area was broad and heterogeneous – urban areas, suburban, rural; short, medium, long distance – meaning few startup partners had more than a solution for one use case or persona.

Where hardware partnerships have ended up making the most sense so far: drones.

There, DoorDash partners with Wing, the delivery company owned by Google parent Alphabet that was itself a product of its ‘moonshot’ X Lab. (Co-founder Larry Page also launched and eventually wound down a flying car startup, Kittyhawk.)

DoorDash also works with Flytrex, an Israeli drone startup that operates in the U.S., and Manna Air Delivery, an Ireland-based startup.

(In 20223, I watched a Manna flight firsthand outside of Dublin with Stripe co-founder John Collison, one of its earliest backers, as part of a Forbes cover story on Collison and his brother Patrick.)

Worth the weight

Where DoorDash wants to operate now: the platform between merchants and customers that can connect any autonomous or human delivery modality to best fit the environment, distance and order type.

Drones will make sense for more remote or rural deliveries; robots like Dot for more dense suburban ones. DoorDash doesn’t have a good answer for high-density urban environments like the streets of New York City yet; most of its business happens in the suburbs anyway, says Tang.

DoorDash calls the system deciding its best delivery mode the Autonomous Delivery Platform, and it’s a selling point for Tang with potential partners and hires. “There’s a lot more to autonomy than just autonomy,” he says. “You’ve got to build the ecosystem around it.”

Doing so has led DoorDash to ship another product its founders didn’t envision, besides the Dot: a new version of an old-school scale, for weighing items.

The problem: drones have strict weight limitations, and DoorDash didn’t know how heavy the food orders it facilitated were. In order to work with Wing, the company had to design and send customers a scale to check them; then it started collecting that data.

“We started realizing, ‘hold on a second.’ This is actually super valuable data that not only works for drones, but could potentially work for robots, too,” Tang says.

Today, DoorDash has shipped more than 20,000 of its SmartScales to merchants. The scales have had another unforeseen benefit beyond autonomous delivery, too: the data helps restaurants know when orders might be missing items, because they’re coming in too light; participating customers have seen a 40% reduction there, says Tang.

Working backwards

As DoorDash expands its use cases into parcel delivery, Upstarts asked Tang if we should expect more versions of its Dot robot, or other robots that could specialize in packages of varying sizes.

Tang, the first DoorDash leader to confirm publicly that it’s currently testing autonomous package delivery, said it’s a fair guess. Test users have already ordered items like cameras and Mac Minis for setting up OpenClaw agents, he says.

Where lab, R&D or ‘innovation’ teams have failed inside scaling startups or recently public tech companies, Tang argues, is that they don’t work backwards from a business outcome, Tang argues. (The current wave of AI and robotics labs could take note, too.)

“A trap a lot of these kinds of research labs or hard tech labs fall into is they start with the technology first, build that in a vacuum, and then they try to figure out what use cases to try to fit it into later,” says Tang. “Whenever that happens, it always feels like the product isn’t quite the right fit.”

Startups or companies looking to design programs like DoorDash’s autonomous one should also ask themselves where the data sits, Tang adds. DoorDash had 10 billion lifetimes deliveries to use as data, and even then realized it was missing a key component with order weight.

It also needed data on a number of last-mile questions, like how to avoid parked cars, navigate roaming dogs, or reach a doorbell. “The hard part isn’t building a prototype,” Tang says. “How do you deploy the autonomy across multiple categories, at a global scale?”

To reinforce the point, Tang notes that DoorDash manages 9 million deliveries per day. A one-in-a-million chance of something happening will typically feel pretty remote. For DoorDash, that means it could be happening eight or nine times every day.

As DoorDash’s autonomous deliveries roll out, it’s easy to imagine a bunch of edge cases that could range from headache to fiasco. What if people realize its package delivery drones are carrying high-value items, and are easy to break into? What if more videos go viral of delivery robots getting stuck? What if one of its drones misses and puts a package through a sunroof?

Those are problems for Tang and partners to watch, but Tang believes that having the data and distribution is the most important place to start. Then DoorDash will keep building solutions for use cases where it doesn’t find strong options, startup or not.

His last word for now: “This is here already. It’s not a science fiction thing.”