In 2024, Henry AI founders Sammy Greenwall and Adam Pratt were midway through Y Combinator’s summer batch when they delivered their pitch to a skeptical venture capitalist.

Henry was looking to solve back office blockers in commercial real estate using AI-powered software, Greenwall, the startup's CEO, explained.

“You seem like a smart guy,” the investor replied. “I would not build this business, because this is a terrible customer base to sell through.”

Brokers are a tough crowd. The best have honed playbooks over years that they’re not going to easily entrust to an outsider. And it’s a relationship-driven, network-dependent field where multi-million-dollar deals get done over group text.

“100%, it’s historically a terrible market,” Greenwall says now.

That’s historically. Today looks different. Henry’s core Deck product, which generates marketing materials around a property with the requisite branding, maps and data, condenses about 15 hours of work by a designer to a median turnaround time of less than four, with only 30 minutes of that actual human review.

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In less than two years in the market, the tool is now used by more than 150 firms including Newmark and Berkadia, and spanning four of the five largest brokerages in the U.S. Henry doesn’t disclose exact revenue, but with pricing starting at $2,000 per month and scaling to seven figures based usage, it runs in the millions.

And with the launch today of a new product, Henry Deal, the startup is significantly widening the aperture of its work with this seemingly niche, but large industry (combined annual revenue: $112 billion).

Leveraging in-house tech and outside AI models, Henry Deal automates more of the deal process, including buyer lists, memos and underwriting.

To build it, New York-based Henry is also announcing a funding round it quietly raised last fall: $16.5 million in Series A funding led by FirstMark Capital, along with Thomson Reuters Ventures. Other return backers include Y Combinator, Susa Ventures, 1Sharpe, StoryHouse Ventures, Pioneer Fund, RXR Arden Digital Ventures, Karman Ventures, and Coalition Operators.

And that same investor who warned Henry’s founders against commercial real estate – FirstMark’s Adam Nelson – led the round and has joined its board.

The result of a madcap product sprint – more on that below – Henry Deal is still unproven with most of the startup’s customers; the name of the game is now execution, and it’s early, Henry’s founders admit.

“We’re at the forefront of what I would call the hard part of AI,” says Pratt, Henry’s CTO. “Great, it exists. Clearly, it’s disruptive and people are making huge bets on it. But it’s up to us to figure out how different industries, and different people figure it out.”

More on that, including perspective from several Henry AI customers, below.

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The deck guys

One of my favorite questions for founders: did they imagine running a company in their category when they were growing up?

Greenwall might be one of the first that Upstarts has covered who can plausibly respond with a “yes.”

Growing up in the East Bay, his family ran a commercial real estate business, so the shoptalk of brokers entered his consciousness by age 7 or so. In high school and college, he worked ‘coffee run’ internships to learn the industry, and started his professional career as an analyst at Toll Brothers, where he got used to getting “yelled at a lot.”

Greenwall’s move into startups happened when the founder of Lev, a startup looking to build software to optimize commercial real estate (CRE) financing, brought him on as a domain expert co-founder in 2019. They raised multiple rounds of funding on what Greenwall describes as an up-and-down journey before his departure to start Henry AI in 2024.

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(Lev now pitches itself as more of an end-to-end tech stack for CRE, advertising against Henry AI’s search results on Google; Greenwall says he learned about that only recently from an employee, and wishes Lev well as a mostly non-competitive business.)

To build Henry, Greenwall tapped Pratt, a high school friend who had built and sold an app for managing fire department equipment while in college, and later became an engineering manager at Zocdoc. The pitch: to be the Harvey (legal AI) or Rogo (finance AI) for CRE.

That pitch, and their focus on the back office as an area to automate, got them into Y Combinator, where they competed in a friendly rivalry with Greenwall’s wife, Eden Ovadia, the co-founder and CEO of FINNY, a startup building tools for financial advisors.

Henry’s founders were motivated by parting words from their group partner, Michael Seibel, who warned them that if they didn’t stay in the Bay Area, they would fail. “We took that to heart,” Greenwall says. “We were like, ‘We’re going to outwork the SF tech people.’”

Back in New York, Henry and FINNY both worked out of the married couple’s apartment for months; visitors found employees asleep on couches, in a scene befitting San Francisco hacker house culture.

Renaming himself “The deck guy” on LinkedIn, Greenwall reached prospects through his content and personal outreach; both would hop on planes for pitches, with Pratt live-coding changes from inside the meeting room.

For Shaun Riney, a broker at Marcus & Millichap NYC, a backlog of decks was a years-long source of anxiety that would ironically get worse as business was brisk. A hapless in-house designer might get phone calls late at night from brokers looking to expedite a sale, but often the process could run weeks.

With Henry, Riney was able to leave only the highest-value, most important decks to those specialists.

“The freedom of being able to do something on your own at 9pm for the following morning, without involving someone else, it’s huge for the business,” he says. “Part of me now is like, ‘I don’t want too many people to know about this.‘“

Hearing footsteps

But even as Henry grew quickly, its founders had reason to worry early this year, when Anthropic released its Claude Opus 4.6 and 4.7 models.

Against a backdrop of a ‘SaaSpocalypse’ challenging the viability of software businesses, Henry noticed weakening demand among prospects and upselling opportunity in its customer base. It was still growing, but Greenwall and Pratt saw cracks forming if they stuck to decks.

“There was 100% a level of fear here that we have to be moving faster, and providing more value every day,” Greenwall says.

So they put the startup through a grueling, 35-day product sprint to launch Henry Deal for test users by June 15. Unlike the decks product, which still involves some human intervention on the back-end for now (to be phased out over the next year or so), Henry Deal was designed to work fully autonomously from the start.

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And whereas the decks tool might have faced existential risk as just a ‘wrapper,’ Henry designed its new software to swap, and benefit from, whatever state-of-the-art models it needs for a task, more like an interface, says Pratt.

At Grandstone Investment Sales in Ohio, founder and CEO Meir Perlmuter specializes in selling self-storage facilities. “The biggest time suck,” he says, is putting together what’s called a “BOV,” or broker opinion of value, that estimates the property’s market price to help expedite a bidding process.

With Henry, Perlmuter’s firm was able to have one analyst produce three or four BOVs per day; now Henry does it all across the firm’s seven brokers, no analyst necessary. “We’re all in on it,” he says. (Henry says that more than 20% of its customers have similarly cut overhead by using it – welcome for the bosses, not so much those displaced staff.)

‘Front lines’ of AI value

The disruption that Henry’s founders feared from Anthropic hasn’t materialized, they say, but served as a good forcing function to shift into higher gear.

Instead, they say they find at large corporate prospects that buyers are facing pressure to deliver on AI transformations, sometimes running up multi-million-dollar bills on Claude. “It’s brutal,” Pratt says.

“I probably could do this on Claude myself,” counters Perlmuter, who says it’s a topic of conversation among his peers. “But then it would be my problem. I would rather it be their problem, because they do a much better job.”

The Claude (or ChatGPT) versus everyone else debate is reminiscent of what Harvey CEO Winston Weinberg discussed on The Upstarts Podcast back in March. In that conversation, Weinberg conceded that “a bunch of functionality” within Harvey’s basic productivity suite for legal work would get commoditized over time.

What wouldn’t get gobbled up, however, were its integrations deep in a workflow, its vertical-specific knowledge, and its proprietary data not available to the models to train off of, he argued then.

What will prove Henry’s most defensible moat? One guide could be Crosby, an AI-enabled law firm we wrote about last October, whose CEO Ryan Daniels posted on LinkedIn earlier this week about moving to insured, AI-only outcomes for clients. That’s directionally the way that Henry seems to be moving for CRE brokers.

Nelson, their lead investor at FirstMark, says that for now it’s a matter of execution, and out-hustling the field.

“With a lot of these companies right now, we’re seeing this marriage of product and go-to-market,” he says. “Because so much of the product is ultimately going to be augmented and reinforced by the way that the software is used, and the context derived from it.”

Greenwall notes that the night before Henry’s founders spoke to Upstarts a final time last week, they and half a dozen others were in the same conference room at 2am, working on a customer fire drill.

“If there is a reason that Henry isn’t successful, it’s because we’ve failed ourselves,” Greenwall says.

Pratt, meanwhile, says that with all the billions of dollars being poured into AI today, it’s startups like Henry that can “prove the value in the weeds.” “If this doesn’t work at scale, all that infrastructure doesn’t really matter,” he argues.

Still, they know that what they’re building isn’t as romantic as, say, their YC batch mate, Starcloud, looking to build data centers in space. “We’re the deck guys,” Pratt adds. “So of course there’s a chip on our shoulders.”

Every day, across more than 10,000 businesses, millions of people dictate their words instead of typing them out using a tool from a startup called Wispr.

Tapping a button to record, they unload their thoughts and instructions on the product, Wispr Flow, and Wispr’s AI produces the text onscreen – cleaned up and grammatically correct, or more aggressively polished if the user asks.

For slower typers, people on the go or just those who need to verbalize to think through a task, it’s a nifty trick. Since Wispr Flow’s launch in 2024, it’s spread across staff at more than 450 of the Fortune 500, the startup says, with revenue growing at least 150% each quarter for the last year.

But for CEO Tanay Kothari, dictation is just the start. Since he was a 10-year-old kid in India watching Iron Man, Wispr’s co-founder has set his sights on a much more ambitious – but relatable – goal: he wants to build Jarvis (technically, “J.A.R.V.I.S.”), the AI voiced by a soothingly British-accented Paul Bettany in the film, who manages protagonist Tony Stark’s life.

Kothari’s talked about this vision before. Today, Wispr is taking a huge step in realizing it.

Wispr is throwing its hat in the ring as a full-fledged AI lab, Upstarts exclusively reports, with the creation of the Wispr Advanced Interfaces Lab Team under recently appointed chief science officer Ariya Rastrow.

Rastrow, a highly-cited researcher and veteran of the founding team at Amazon that worked on Alexa and later as a distinguished AI lead at Meta working on voice and multi-modal models for its AI devices, has already assembled a team of about a dozen researchers from Amazon, Meta, and academia.

Wispr plans to hire up to 50 researchers for the Lab, representing a commitment of hundreds of millions of dollars. At a high level, their mission will be to develop a new family of voice AI models, and a harness to use them, that can start to work more proactively and intuitively with a user.

Rather than simply dictate instructions, or follow a turn-based conversation with a voice agent, Wispr envisions a more collaborative workflow, in which the interface is continuously learning from your recent actions, following what you’re doing on the screen using computer vision, and learning about you enough to understand nuanced instructions, while suggesting and running others behind the scenes.

“We want to meet people where they are,” says Rastrow. Think of Wispr’s coming system like a chief of staff learning by sitting next to you, and then hovering over your shoulder with a mandate to remove friction from your day, Kothari says.

It’s a big swing for Wispr, which Bloomberg reported was raising more than $250 million at a valuation of $2 billion back in May. Previous investors include Menlo Ventures, Notable Capital, NEA and 8VC.

(“This was part of what motivated us to start raising some more capital to be able to fund this and get the best talent,” Kothari says.)

And it’s one that could put Wispr more on a collision course with OpenAI, which announced its own voice models for natural language interaction, GPT-Live, earlier in July.

“We’re going to realize that this phase of clicking things, and typing things, was never what we wanted to be,” Greg Brockman, OpenAI’s president, said when Upstarts asked if he, too, wants to build Jarvis at a media roundtable this week. “A couple years from now, no one’s going to want to do that stuff anymore.”

Why should Wispr’s new Lab be the ones to drive that new paradigm?

More on that below, but in a nutshell: Wispr thinks its models will operate better for these purposes; that its interface will benefit from more than just the models themselves; and that users will want a neutral partner to guide them across all the apps they use, including but not limited to ChatGPT.

When might we see all this? Kothari says Wispr’s mission to bring movie magic to the real world isn’t years off, but coming soon.

“We are actually now building the model that powers and gets to Jarvis,” says Kothari.

The rest of this post – focusing on what Wispr’s Lab is building, how it’s building it, and why it can compete with the AI giants like OpenAI – is for paid Upstarts subscribers. Take 20% off an annual subscription as part of our Summer Sale, and get more startup news and founder interviews for less than the cost of a sandwich each month.

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As a practicing cardiologist – he still sees patients one weekend per month – Abridge co-founder and CEO Shiv Rao can speak from experience about doctor burnout.

“Nothing crushes my soul more than clerical work,” he says.

But it’s his wife’s experience as a patient that sticks with me more when he brings it up toward the end of our chat on the newest episode of The Upstarts Podcast.

The couple already had a healthy daughter when they found out about a rare disease in the family, and after genetic testing and some tough luck (we’ll leave that to his own words in the episode), decided to proceed with in-vitro fertilization to ensure it wouldn’t pass on.

For three years, Rao says they spent his “overpaid corporate VC salary” traveling around the U.S. from their Pittsburgh home for doctor visits. (“We’re so lucky and we’re so privileged that we could even afford that, and we don’t take it for granted,” Rao notes.)

“I’d be there, and the doctor, or the counselor, would leave, and we’d look at each other and be like, ‘What did they just say?’ Like, ‘What are we supposed to do?’ And I’m a doctor. I can’t even remember.”

The story has a happy ending – they now have 10-year-old sons, identical twins – but it speaks powerfully to what Abridge is trying to achieve: “Finding a way to help both the patient and family member, but also the clinician, feel like they can do and be their best, is really at the end of the day what this is all about.”

Of course, there are lots of business reasons to pay attention to Abridge. Founded in 2018 by Rao and two others with deep Carnegie Mellon ties, Abridge is a first mover among the newer generation of AI-enabled healthcare startups, serving more than 300 health systems that reach 250 million Americans.

Active in AI long before ChatGPT, Abridge recently partnered with Nvidia in its model development, and with Eli Lilly in pharma. It’s raised nearly $800 million to date, achieving a $5.3 billion valuation last year in a round led by a16z. A host of other firms, including Bessemer, CapitalG, IVP, Lightspeed, Redpoint, Spark Capital, USV as well as strategics from Alphabet to CVS Health Ventures and the Mayo Clinic are also on the cap table.

Initially taking on Microsoft’s $20 billion Nuance acquisition, Abridge competes in a big market identified as ripe for AI impact by seemingly everyone. A challenger, Ambience Healthcare, has raised hundreds of millions of its own from investors including General Catalyst, Kleiner Perkins, Sequoia, and OpenAI – and, perhaps controversially, also a16z.

Microsoft hasn’t gone away, and Epic Systems, the health records giant, isn’t turning a blind eye. Palantir has gotten into the hospital business. And in January, we wrote in Upstarts about how Anthropic and OpenAI have made concerted life sciences pushes.

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Is Abridge still the Upstart, or at risk of being disrupted? I was eager to ask Rao about it, after last sitting down with him in person at my final Cloud 100 launch event for Forbes in the fall of 2024: a stacked panel that included Rao, Anthropic co-founder Daniela Amodei, Perplexity founder Aravind Srinivas, and Scale co-founder and now Meta executive Alexandr Wang.

Taking Microsoft head-on in the early days, Rao tells me, was a “YOLO move.” Against other startups, Rao argues that no other can claim the same breadth and scale. “In some ways, we’re still creating a new category, yet we are still in a category of our own,” he argues.

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On this episode of the podcast, Rao talks about how an early bet on AI got Abridge going, before ChatGPT; why reaching scale in healthcare requires years of “eating glass” and “kissing frogs;” and why he thinks Abridge can help doctors and nurses feel like “superheroes.”

Plus, he shares his Upstart Moment: that “YOLO” decision to take on Microsoft, with his “one shot” to get it right.

Below, you’ll find my two favorite business lessons from Abridge for busy startup builders – plus my own perspective on Abridge’s battle.

• Speedrun at the boss

• Own your destiny (AI stack)

• Alex’s take: It’s not all about the money

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Speedrun at the boss

Around 2022, when Abridge was still just about 50 people, Rao says the startup walked through a “one-way door” to go after enterprise customers. That meant taking on the “huge bosses” of software working with them, and the obvious Big Boss was Microsoft and Nuance.

“It was a YOLO move. I think that’s why most VCs tell their startup to go down-market first, figure out PMF [product-market fit] with a small clinic, build something that the end user loves, and then swim over time upstream, and get that large system.

For a lot of reasons, including the amount of money we had in the bank, there was no other way. ‘Cause ultimately you have to get those big systems. That’s where 75% of the doctors in this country practice. If you’re going to build a big, impactful, legacy-leaving, generational company, you need to get there as fast as you can.”

That kind of move is ultra-high risk for a startup, Rao says now. Why? Buyers in big companies – be they CIOs, CISOs, or other similarly-titled execs – tend to talk to each other. They have WhatsApp groups.

“When they see something they don’t like, they tell each other,” Rao notes. Startups that blow it might get a chance to rebrand, or recap, and come back.

Abridge did make it work, though, and Rao says they did it by showing their full hand of all they were trying to build, and being clear about a much bigger thesis.

“It turned out being ourselves was such a counter-positioning advantage against Microsoft,” he says. “Just saying, ‘Hey, this is what our true north is; this is what we’re trying to build.”

The key slide in their presentation stressed that Abridge wasn’t about AI taking notes, but about a conversation layer that kept the technology out of the way. “There was something paradoxically profound about AI actually removing all of the other tech, or pushing it into the background,” Rao says. “People resonate with that.”

Rao’s startup advice: Take it all on, fast.

“I think some companies might feel that you can play a longer game, and I think on some level, you’ve got to play really long games, but the world is moving so quickly right now, that it’s hard to triage. It’s hard to say, ‘This is tomorrow’s problem’ anymore. Everything is kind-of today’s problem.

And you’ve got to be so forward in terms of making clear why you’re different, and in healthcare, how you’ll be a 10-year, a decades-long partner for them. The urgency, and the emergency, the level of existential crisis you get to at moments in a company, just force you to focus, and do the thing you have to do.”

Own your destiny (AI stack)

Abridge announced its partnership with Nvidia last month, with the two announcing they would train a new AI model to be used exclusively by Abridge for documentation and clinical decision support, per the Wall Street Journal.

When I ask Rao about it, he describes the partnership as answering a more basic question: “We’ve got this incredible proprietary data set. What else can we do with it?”

Abridge’s hope is that it can maintain a number of agents for each doctor, around the clock, without breaking the bank. That means Abridge has to be “very careful” about how it uses frontier models from the big AI labs, Rao says.

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“On any given week, 40% to 60% of our model outputs are probably driven by in-house work,” he says. Abridge has put a lot of work into its model routing, orchestration and evaluations to ensure that it’s only sending the most difficult problems to expensive models.

“This is the operating system for every single AI company out there. If you do a really good job of that, you’re going to deliver a better product, but you’re also going to be able to compete with your P&L [profit and loss] as well.”

Rao expects the question of frontier model costs to only heighten as Anthropic and OpenAI go public and face other priorities. “The companies that can reach down lower into the stack, the ones that can really own and control their own destiny, are going to be able to separate.”

That said, Rao expects Abridge to partner more closely with the labs like Anthropic and OpenAI over time.

“The moment that we feel like we’re going against the grain of what is absolutely incredible about this moment – how fast things are changing, and how profound this platform shift is – if that ever happens, we’re screwed.”

Advances from the AI labs need to “feel like a tailwind,” Rao says.

“When they do new things, cool, awesome. We evaluate. We see where we should point those models, and then we’re delivering a better product experience on some level, but we’re also getting all of the edits and adjustments. We’re getting the feedback loops.”

The need to decide which tasks end up where, and which Abridge should distill or fine-tune itself, won’t go away. Startups building vertical AI tools, or AI-enable software, will always be riding the wave, Rao believes: “I think it’s going to be that sort of orchestration, or portfolio, approach to models that’s going to win the day.”

Alex’s take: It’s not all about the money

One company that never came up in our conversation: OpenEvidence, which provides more of an AI copilot that helps doctors keep up with medical information and answer clinical questions. OpenEvidence is currently higher valued than both Abridge and Ambience combined, reaching a $12 billion valuation in a funding round this year.

A mix of investors from each of those companies are also in OpenEvidence, another sign, like a16z being in both, that this market is one prone to overlap. Above all, it’s just really, really big.

It can be hard to wrap one’s head around just how much money goes to healthcare in the U.S. A category accounting for nearly 20% of GDP that is embracing technology should produce a number of big winners, not a winner-take-all market dynamic. OpenEvidence partners directly with medical societies and works with doctors directly, giving it more of a bottom-up go-to market approach than Abridge.

That means its reach can spread faster, helping to explain its higher price tag; it also puts it likely on more of a collision course with the AI labs. (Short-hand of ‘ChatGPT for doctors’ adds fuel.)

Abridge is playing a different game, and while it may grow total users more slowly alongside a smaller valuation, that also makes it feel a safer bet.

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If I’m an investor in either Ambience or Abridge right now, I’m likely feeling good about my chances of a successful exit, either as another Nuance-type acquisition, or eventually a public company like Veeva Systems, a life sciences software company I profiled in Forbes magazine in 2017, and which currently trades at a $30 billion market cap.

But venture capital is a Power Law business, and if you mention one or the other to an investor, or another founder, you’ll often hear a take on how they seem to be doing in comparison to the other. “Ambience is catching up fast,” a backer will say. “Abridge has widened the gap,” another will respond – “Surely, that’s why a16z lined up the money truck!”

In some areas of AI, the horse-racing style updates seem pertinent: engineers change coding tools fast, and who is winning in a given week can mean the difference between an acqui-hire or a $30 billion deal for SpaceX stock.

In this case, the competitive dynamics maybe sell the impact of this category short. Ultimately, these tools are intended to help fix a partially broken system, and deliver better care. Spend any time talking to, or even just listening to Rao in this episode, and I expect that you’ll believe it, too.

For years, CEO Eric Yuan avoided work travel — partly from personal preference, partly to push the use of his own video conferencing software at Zoom.

But when he did venture to New York, he would visualize his personal dream: ringing the bell at Nasdaq, on the day of his startup’s IPO. “I always told myself, ‘I’ve got to work hard,’” he says.

As we chat last week, Yuan is back in New York to celebrate Zoom’s fifteenth birthday. He’s loving the local energy. “Every time I’m in New York, I feel like we are in the middle of a new chapter. I feel my energy is up, and I want to do more things,” he says.

Our interview is a reunion. In April 2019, when Yuan’s dream finally came true and he rang the opening bell to take Zoom public, I was there, scribbling madly in a reporter’s notebook, for the first in-depth profile of Zoom, which ran in the next issue of Forbes.

When, as Covid-19 shut down in-person activities the next March, Yuan impulsively decided mid-Zoom interview with me that he would give away his software to every K-12 school in the U.S. (and in a bunch of other countries) for free, my resulting scoop went viral. It’s probably still the most-read story of my career. With Zoom on everyone’s mind, I followed that up with another profile, this time a Forbes cover story.

The pandemic is long over, and so are those heady days for Zoom’s stock. Its market cap sits under $30 billion, compared to nearly $140 billion at its peak.

But 15 years in, Yuan’s still running the show, meeting with customers and pinging engineers about bugs (more on that later). What motivates him, I wonder, when he’s achieved his dream — twice?

Now, Yuan focuses on work goals, he says: doing right by Zoom’s customers and employees, while pursuing positive impact in the world.

“My business goal is just to keep going,” he says.

He’s inspired by Jensen Huang, still calling the shots at Nvidia after 33 years, and Larry Ellison, still active in leadership at Oracle after nearly 50.

And he’s still fighting for you to use Zoom more, not less, in the AI era, even if that means personally annoying some of his engineers.

Why pay attention to what Zoom’s founder has to say? Our interview covered a lot of ground, with bits that should be relevant for startup founders, builders, and anyone curious about how one of tech’s most successful leaders sees AI impacting our society.

For startups, we cover Zoom’s current strategy; its approach to acquisitions; and Eric’s take on Anthropic and OpenAI.

For builders, we talk about why Zoom’s CEO still de-bugs code; its reinvestment in product-led growth; and how Eric thinks about AI meetings.

For society, we discuss the risk of deep fakes, where Zoom is working on a new product; how AI could spark more in-person interaction; and Eric’s bet on a four-day work week.

But let’s be clear: Yuan, despite a net worth estimated by Forbes north of $5 billion, doesn’t take himself too seriously.

“No matter what, the uptime, downtime, AI or not AI, just enjoy everything,” he says. “That’s the most important thing. This is in life, or startups.”

In the spirit of Yuan’s call to “enjoy,” we’re running this full interview for all subscribers, for free. If you enjoy it, we hope you’ll consider supporting Upstarts by purchasing an annual subscription: 20% off in our Summer Sale.

Zoom 2.0 (AI Edition)

The lightbulb moment for Zoom’s AI efforts wasn’t the rise of the transformer, or even the launch of ChatGPT. It came later, Yuan says now, when he and leadership realized how much faster some of the company’s engineers were shipping code using Cursor in early 2024.

“Looking back, I was not smart enough” to spot the trend sooner, Yuan adds.

Now, the focus is on what Zoom calls “completion”: a shift from orienting its products around the conversation itself (happening over video), to the business action or outcome discussed or agreed-upon in that virtual room.

It’s a “much bigger market opportunity,” Yuan argues, that means Zoom is investing more in capabilities to bring in more context before meetings, and to send updates to a CRM, or AI agent, after them.

Zoom has offered some of these tools for a while now, including a Granola competitor for note-taking called My Notes. (I agree to run both, in a bake-off of sorts. My verdict: Granola’s AI summary wins as more useful, and less corporate-coded; Zoom’s transcription is more accurate, although both struggle at times with the Chinese-American Yuan’s accent.)

Because Zoom comes from the starting point of a live conversation, however, Yuan believes that the company will only lightly overlap with partners like Salesforce, the owner of Slack, over time.

“Today, you cannot find any two companies where it’s very clean, there’s not any overlap,” he says.

Back to startup speed

Engineers who join Zoom expecting a slower, public company pace are often in for a bad time. When Zoom was a startup, much of its technical team had worked with Yuan for a long time, he says, and knew what to expect. Now, some complain that their CEO is pushing them hard. Directly, too: Zoom’s CEO says he engages directly with technical staff about issues, bypassing managers.

“It’s so slow, guys,” he admits he’ll say. “Competitors are shipping products. What are you doing?”

All these years later, Yuan still finds and reports bugs in Zoom’s products himself. “I’ll write a summary: ‘Guys, I only have 24 hours to myself. You have so many engineers, add up how much time you guys have. Why don’t you guys find those bugs? Why have only me find those issues?” It’s enough, he says, to drive him “crazy sometimes.”

Zoom’s recently-announced acquisition of Common Room isn’t about simply re-injecting some startup hustle into the organization, Yuan says. He pushes back on the notion that Zoom is looking to be more acquisitive now. “It’s hard to balance organic growth versus M&A,” he says. “We are still very disciplined.”

A near-miss acquisition validates that approach for Yuan. In 2021, Zoom announced its planned acquisition of Five9 for just under $15 billion. Five9 shareholders didn’t support the deal, and the two companies walked away that fall.

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Instead, Zoom launched its own contact center solution recently; Five9 trades at a market cap of just about $2 billion. “Our progress is much better, and we didn’t spend so much money on that,” he says.

When I ask him why companies like Anthropic and OpenAI are seemingly buying up more AI startups, besides the disparity in equity value (with their private shares carried closer to valuations approaching $1 trillion), Yuan argues that the AI labs have more product needs across a wide surface area of infrastructure, hardware, models and agents, with more appetite for paying for talent.

Zoom, meanwhile, looks to university programs, up-skilling of veteran engineers, and to others who resonate with its mission in order to compete.

“No matter how much better you think a [pay] package is, somebody else will for sure offer a better one,” Yuan warns.

The gospel of PLG

The back-and-forth around me choosing to use Granola leads to a “pain point” for Yuan and Zoom: customers still think of it as “just a meeting company,” when it offers a much wider range of tools.

To change that, Yuan believes Zoom needs to get back to more of its product-led growth roots. My Notes grew to 1.5 million monthly active users in four months; that’s the type of adoption he wants to see more widely with ZoomMate, the AI companion the company offers in its productivity suite.

The challenge for Zoom: enterprise products and launches will still be driven by the sales team, and close engagement with big customers. Other launches need to inspire bottom-up new users. And, Yuan says, until recently, Zoom wasn’t systematically tagging each as one or the other.

“Now we try to have a formula for each product before we launch. We should say, ‘This one should start from product-led growth, or this one should start from a sales team approach,’” he says. “We should have a clear, defined go to market strategy.”

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It’s the opposite challenge of many startups that lean heavily on PLG, then find out that it can only take you so far. They then scramble to build out enterprise tooling, customer success and forward-deployed capabilities to smooth over at-scale adoption.

Zoom, meanwhile, almost sounds like it’s fighting back against the Innovator’s Dilemma: “We thought inertia could help us,” Yuan says. “And that’s not right.”

For PLG products, Yuan believes cost and ease of use are most important. For sales-driven ones, trust and reliability.

“This is probably one of the things that keeps me up at night,” he adds. “We still need to address it.”

Digital twins, deep fakes, and the four-day work week

What Zoom is building towards above all – a selling point with those potential hires, Yuan says – is the concept of a ‘digital twin’ that Zoom can help power to work like us, and alongside us.

For the past three quarters, Yuan has used an AI avatar to read through the script of his earnings call, allowing him to join just for the live Q&A, the part he likes.

As another example, Yuan imagines an hour-long contract negotiation happening over Zoom. Most of that time – maybe 45 or 50 minutes – is spent running through the document and details, he posits; only the last 10 minutes or so is the actual live negotiating. Handing over the first part to our digital twins, we could spend more time conversing and building rapport or catching up, he argues, or moving on to other tasks.

Talking to AI every day is lonely, he argues, and most people will want an emotional connection with other humans. Delegating work to a digital twin, he hopes, will free up more time for that.

Yuan is skeptical about AI simulation models replacing those negotiations – he thinks the tech isn’t ready yet to replace us altogether – and he doesn’t think human workers will embrace mixed meeting environments where half a Zoom is people, the other half avatars.

Zoom plans to announce a product soon around deep fake detection, he adds, that will be able to verify human identities in a meeting, or flag when videos are AI-generated and not labeled as such. “We need to make sure to tell participants,” he says. “Otherwise, if you don’t manage this well, it’ll be very dangerous.”

The tech leader’s boldest prediction, though, comes around the four-day work week. Yuan insists it’s coming. How soon? A few years to five, he replies, comparing the looming moment to 1926, when Henry Ford adopted an official five-day work week for his factories.

A big benefit of new technology is that it can handle more work for us, Yuan believes – freeing up more time for us to travel, work on philanthropic projects, or in our communities.

What’s the point in working hard if you can’t enjoy yourself, Yuan asks rhetorically. “‘Enjoy’ doesn’t mean this is a waste of time, a waste of resources,” he elaborates. “No, this is to do more meaningful things.”

I’m skeptical that Yuan’s peers in public companies will follow suit. Or if not them, their competitors in China. Getting Wall Street investors onboard seems like a challenge, too.

Yuan goes back to Ford, noting that in other countries, the five-day work week eventually caught on, too.

“It’s going to happen,” he insists. Will he lead by example, then? “I should lead by example, right?” he replies.

We’ll have to circle back in another five years, to check in on how Yuan is spending his Fridays.

It’s tough to keep a big startup valuation under wraps for too long. Filings or investors might disclose it; reporters will sniff it out.

But founder Eléonore Crespo has a rare distinction: her startup, Pigment, had its funding milestone scooped by an unlikely leaker: the President of France.

“One more French unicorn!” Emmanuel Macron posted on LinkedIn two years ago. (Naturally, he said this in French, so the exact word was “licorne”.)

And given that Pigment is one of France’s most prominent AI startups alongside Mistral and Dataiku, I was excited to ask Crespo about that experience. What was her reaction?

“I was like, ‘So be it,’ you know?” She tells me in our latest episode of The Upstarts Podcast, recorded from London. “We didn’t want to announce anything, because we found that it was not necessarily the milestone we wanted to celebrate.”

Crespo laughs now. “Thanks to the government, I’ve met so many of the top CEOs in France that are now buying Pigment.”

As a community, AI startups in France have been “very lucky” to have that support, Pigment’s co-CEO says. “But at the same time, I’m here to create a global success from France,” she notes.

That ambition is a big part of what makes Crespo and her startup more interesting than you might think from just scanning its description: “business planning, powered by agentic AI.” Or translated again, a new crack at an old category of software, financial planning and analysis, or FP&A, taking on the likes of Oracle, SAP and Workday.

Founded in 2019 by Crespo and Romain Nicoli, the former co-founder of Criteo, Pigment counts tech darlings including Anthropic and Notion as customers, alongside corporations like Coca-Cola and Unilever.

The startup’s AI agents help finance teams at those businesses to make important financial decisions faster by reducing the amount of time they spend collecting, managing and pulling data from spreadsheets. (A problem that Crespo encountered first-hand while working at Google.)

More interestingly for someone not working in financial planning, Pigment is also starting to help its customers rein in runaway AI budgets, better managing their token and model spend. On the show, Crespo and I talk about Uber, which made headlines when one of its executives revealed in May that the company had blown through its annual AI budget in just four months. Uber is a Pigment customer, but not for AI spend tracking yet.

“I would love to build something with them now,” Crespo tells Upstarts. “It’s going to be a big topic for everyone, and it’s a topic that goes beyond just understanding your costs.”

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In our conversation, Pigment stands out for other reasons: it maintains an unusual co-CEO structure, with Crespo’s co-founder Romain Niccoli building an engineering team in Paris that Crespo calls “probably one of the most loyal in tech history,” with zero regrettable attrition to date.

Its Paris roots also help it compete for talent with what Crespo compares to a “Chinese buffet,” where AI heavyweights like Anthropic and OpenAI pass through and grab all the steak and lobster first.

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But most interesting to me is Crespo’s policy of “unreasonable hospitality” – a customer-obsessed mindset that is reflected, she says, in everything from late-night WhatsApp messages to personal touches like baked goods: she recently visited Meta with cookies featuring dual Meta and Pigment logos.

More on those big takeaways – including a guest-starring role by Tom Cruise – below. Plus, per reader feedback, we’ll try something new: signing off with my own personal thoughts.

• How to make a co-CEO set-up work

• Competing at the AI talent “buffet”

• Using “unreasonable hospitality” to win

• Alex’s (armchair expert) take

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How to make a co-CEO set-up work

Before Pigment got started, Crespo ran a painstaking process to identify her ideal co-founder. She knew she wanted an experienced enterprise leader who could build an engineering team, and Niccoli stood out as a former co-founder of Criteo, which went public in 2013.

Crespo spoke to a number of mutual contacts about Niccoli before first approaching him: “I did my own CIA due diligence, and he had no idea.”

To win him over, she brought a 100-page memo book of potential projects to show him. After three afternoons together around Christmas, he told her, “Let’s go.”

How do they make co-CEO status work in practice? The key, Crespo tells me, is organization. “He leads tech, product, and HR. I lead everything else, and we are extremely clear about that.”

The duo have a policy of making tactical decisions on their own, and syncing up on big decisions. For key strategic product updates or company milestones, they’ll spend two hours together in a workshop to reach agreement.

“We have this concept internally of direct ownership, or DRI [directly responsible individuals, an Apple-coined term], to make sure that there is always a single individual that is an owner of a specific topic.”

Most recently, Crespo and Niccoli shared several calls to discuss the right criteria, and compensation package, for a potential C-suite hire. But when it comes to fundraising, Niccoli defers to Crespo, who previously worked as an investor at Index Ventures.

“That’s the type of thing he doesn’t care about,” she laughs.

Competing at the AI talent “buffet”

Building an engineering team in Paris has proven an “incredible edge” for Pigment, Crespo argues, because France currently produces high-quality, AI-trained engineers, who are loyal and mission-driven.

“It’s something that would have been impossible for me to replicate in the Bay Area,” she says, noting that she was recently talking to one of Intercom’s co-founders, and they compared competing with Anthropic and OpenAI for talent to a “Chinese buffet.”

“You have lobster, and you have, whatever, let’s say a good steak, and then you have the noodles, and then you have the rice. And so you come, and Anthropic and OpenAI, they take the lobster and the good steak. And there are only some noodles left, and you’re like, ‘Oh my God.’

That’s what happens today, and it’s very, very, very hard not only to compete, but to retain the talent. So for us, building in Europe has been an incredible strength.”

Maintaining offices in France and the U.S. has also helped Pigment “think globally” from day one, Crespo says, with employees from a number of countries – far more than just those two – and a mindset she argues is worldlier than some Silicon Valley peers.

That said, Crespo spends a lot of time in San Francisco personally, and plays nice with both Anthropic and OpenAI as partners.

“We spend a ton of time with their teams, and we will keep doing so for the foreseeable future, because it’s true that’s where a lot of things are happening today.”

Using “unreasonable hospitality” to win

To Crespo, “unreasonable hospitality” means being memorable.

“When we come to meetings, the way we show up, we deliver the experience of when they buy Pigment. We make it extremely memorable, and we spend a lot of time trying to really deliver delight there.”

For customers, that means thoughtful, tailored swag, or the cookies like Crespo recently brought to Meta: “We try to create some bonds that go way beyond the prospect, that can create a friendship over time, I would say.”

A “fantastic” customer experience also means Crespo personally responding to WhatsApp messages on the weekend, or late at night.

“I will always answer within the next five minutes, and it’s the same for the rest of my team. We are extremely reactive. We try to fix issues, and we never push back. We really try to be there all the time, and we always do the extra mile.”

That means hopping on planes whenever a customer has a more complex problem, too.

Working with Pigment should feel like buying a Ferrari, Crespo argues, not a mass-produced car. Like Ferrari, that means it remains up-market; such an approach wouldn’t work with SMBs or many thousands of customers, Crespo admits.

“It’s not a mass play here. It’s a play where we serve the largest companies in the world with a solution that is tailored for them.”

That white-glove experience also extends to closing key hiring candidates. One of Crespo’s biggest challenges has been hiring a chief revenue officer, she says; she met with dozens of candidates. When she found the right one, she was eager to find an “unreasonable” touch to close them.

As it turns out, one of her investors, ICONIQ, works with a number of celebrities through its family office practice, and Crespo was invited by founding partner Divesh Makan to a private 90 minute meeting with Tom Cruise, who was going to talk to portfolio founders about filmmaking and what it took to be a standout actor.

“I begged Divesh, ‘Can I bring my candidate? I think it would make a big difference,’” she remembers. Makan agreed.

“It was quite a lifetime moment for [the candidate] and for myself,” Crespo says. “That’s how we managed to get him.”

Alex’s (armchair expert) take

Pigment’s technology isn’t as high-risk as a dog longevity drug or a new system of nuclear reactors. Given its customers, the fact that it partners with the big AI labs, and the straightforward nature of its software, it seems a safe bet to continue growing and build a lasting business.

What is less clear to me is that Pigment will be a $100 billion company versus an attractive acquisition for one of the last-gen giants it’s taking on. I wish I had asked Crespo straight-up: how big do you think this can get?

But at the same time, founders are wired to respond to that the same way: boundlessly big. It’s hard to reach a $5 billion or $10 billion acquisition outcome if that’s what you set out to build at the start. And if that’s Pigment’s eventual fate, it would be an excellent one worthy of celebration. (One dark horse outcome would be that it joins Anthropic or OpenAI directly, gaining a permanent seat at the “buffet”.)

I’m also satisfied with how Crespo presents her co-CEO setup, although I usually am skeptical of such an arrangement (‘too many cooks’). Crespo can bring the energy, public face, customer-facing sales and service, and fundraising; meanwhile, the more veteran, proven co-founder can focus on going deep on product and tech.

It seems like a good match, in large part because of how carefully Crespo approaches it. Generally speaking, I’d be cautious about startups following suit – it seems to carry a lot of risk if you don’t match the right levels of ego.

One place where I’m not sure I fully buy Crespo’s optimism is when I ask her in the episode about Pigment’s AI’s impact on jobs. Crespo concedes: “Probably there are companies that are going to reduce headcount,” as well as rethink processes.

But overall, she takes the techno-optimist angle that most customers have more financial planning to manage than they can handle, and that analysts at customers welcome an AI agent freeing up some of the 80% of time they spend on busywork.

I’m optimistic in many ways about the technology itself, but less about corporate interests and public company executives. If the incentives set by shareholders encourage companies to instead cut headcount and depend more on AI agents for this kind of work, it’s easy for me to picture a CFO who manages an increasingly agentic team of analysts instead.

The Deep Dive

Adapter CEO Adam Ghetti didn’t expect to be building another startup after selling his previous venture – the product of a decade’s work – to Twilio five years ago.

Then the transformer emerged, and many of the smartest people he knew in tech started racing towards its obvious applications: text generation and large language models.

“It was obvious to us that 10,000 other people were going to go do the content thing,” he says.

For Ghetti, those “flashiest” uses missed the most important questions: how could we use technology to truly understand our own digital footprint, our own precious data? More importantly: if we got there, how would we control those treasure troves, so that we weren’t putting the big AI labs like OpenAI in charge of our own “personal worlds”?

“Ten years from now, could I sleep well knowing I could have helped and didn’t? I couldn’t,” Ghetti says.

Then, as he puts it, a prominent computer scientist focused on data science, professor David Bader (formerly of Georgia Tech, now at the New Jersey Institute of Technology) and a VC firm that had previously backed him, GV, called his bluff. In November 2022, just days before the public launch of OpenAI’s ChatGPT, they got started on a new startup: Adapter.

Now, after three and a half years of operating in stealth, Ghetti is unveiling Adapter to the world. Based in Austin, Texas, the startup employs 17 people and has raised $17.8 million in capital to date from GV, Bond Partners (an early-stage fund within BOND), and a number of other investors including Eric Schmidt’s family office Hillspire, Byers Capital, the funds of former Lookout founder Kevin Patrick Mahaffey and Shana Fischer, and personal investors including Paul Judge, Zach Sims and Ted Schlein.

What they’re betting on isn’t a new AI model or app, but what Ghetti calls ‘Cognition as a Service’ – a new-look infrastructure layer that is intended to better leverage and control your personal and work data for use by AI agents and applications.

Adapter is making available a developer tool and API, called Adapter Mind, intended for developers and tinkerers to start using immediately to connect to existing applications, and to spin up new ones. Users can drop Adapter’s model context protocol, or MCP, into whatever tools they prefer, like OpenAI’s Codex, Anthropic’s Claude Cowork, or Cursor.

Adapter says it will continue to set up Slack channels and ship best-practices and starter kits in a public library to help early adopters test the limits of what it can do in upcoming weeks.

It’s also shared with test users a consumer-facing harness, called Adapter Life, as an example of a tool that can build upon Adapter to operate like a super-charged chatbot or agentic assistant, communicated with via iMessage or WhatsApp.

“Adapter is designed to adapt to your life, your workflow, your needs,” Ghetti says.

But Adapter is explicitly not a new model company or agent builder itself.

Instead, it’s looking to establish new ‘primitives’ that Ghetti compares in potential impact to Amazon Web Services’ release of S3 online data storage, which helped to kick off a new era of businesses from Dropbox to Zoom.

“We don’t want to produce many things, but the things we produce, we hope will enable a whole new world of new things to do,” he says.

If that sounds wide-reaching, even amorphous, it is: Adapter is an ambitious, big-swing venture that looks headed for binary outcomes: kicking off a whole new wave of AI capabilities and startups if it succeeds; failing to land a killer use case and collapsing in on itself if it does not.

But Adapter is already provocative across three hot-button topics in AI today:

• the token economy and efficiency of AI spend

• capabilities and true ROI from AI tools

• transparency and data sovereignty

On the cost front, Adapter claims its knowledge graph, in large part because it lives online using CPUs, not costlier inference-friendly GPUs, is much cheaper to set up and maintain than spending on model tokens.

From a capabilities perspective, Adapter and its advocates say its graph unlocks context currently lacking even from the leading AI interfaces, because it’s not context limited.

And on the privacy and control standpoint, Adapter says it’s a better “custodian” of your data because it doesn’t access or train models off of it, and because Adapter can then parcel out that info to the model labs and wider web on a need-to-know basis, keeping their hands off, too.

Of note: Adapter’s CEO says it currently partners with a number of the big labs behind the scenes, but explicitly not OpenAI. (“Our entire mission is built around trust, transparency and sovereignty. OpenAI does not appear well aligned to these,” Ghetti says.)

Here at Upstarts, we spoke with several backers and users of Adapter’s tools – and went through the onboarding process ourselves to see it first-hand. Our impressions, and Adapter’s efforts in each of those three areas, below.

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The prime directive

To sign up for Adapter, you give it access to a personal email initially – the idea being that this hub of your data is long-lasting, perhaps more so than any particular job – and then connect a bunch of tools to it, like Google Drive, Notion and Slack. The savvier can set up their own custom connectors to access photos or old file repositories; the more you feed in, the smarter it gets.

Ingesting it all takes a while – for me, 15 minutes or so – and then you can start asking questions. In a demo, Ghetti demonstrates Adapter correctly identifying the earliest touchpoint between us, more than ten years ago when I was at Forbes, despite there being no obvious article link or paper trail of that moment. He’s also used Adapter to write investor updates that know the depth of his relationship with each investor; on the personal side, Adapter Life (his is called Ghetti Life) knows everything about his kids’ pediatrician visits.

Mahaffey, the former Lookout founder and an early backer and user, asks Adapter to serve as a research assistant to update him on any developments in geothermal energy, a current area of focus, as well as for reviews of mountain e-bikes using a new motor he finds promising.

Erik Nordlander, a general partner at GV who previously backed Ghetti’s last startup, Ionic Security, has used it to triage his inbox. His Adapter flags qualified experts and executives in his network to help answer inbound requests from founders, beyond what he says he can do in LinkedIn.

Adapter’s able to do all of it in large part because its system takes the time to understand the “prime intention” behind any task or query, Ghetti explains. (The company has a patent on its prime directive work.)

When using Adapter directly, or when another tool calls the Adapter API, it can then run as few or as many loops of searches as needed to get the answer it believes you need, at the depth it predicts you want. The more you use it, the better your Adapter Mind gets at balancing that.

Unlike an LLM, Adapter doesn’t forget context over time, or inject unwanted context from a past conversation. And unlike many agents that load up a prompt behind the scenes to re-search for all the relevant information, once Adapter knows something, it stays that way.

“You can do this with all the questions you wouldn’t typically think to ask an AI, because it has nuance,” Ghetti says.

Pricing power of CPUs

How is Adapter doing it? The big breakthrough is the knowledge graph, explains Bader.

Bader’s government-level work and Ghetti’s experience in data security allowed them to build with less than $20 million a system they argue might take a corporation hundreds of millions to set up.

Adapter’s graph only has to read your data one time; then it maintains it on a secure, always-updated private website hosted through Cloudflare. Adapter continuously updates that information as new data points (an email, a Slack message, a sale) come in, but once Adapter knows something, it doesn’t necessarily need to call an LLM or search the wider web, only doing that for “truly novel” questions.

That allows Adapter to ingest many gigabytes of data and sift through much of it for just one query, far exceeding the context windows of most AI models, Ghetti argues. And because the data is in memory, performant on CPUs, they can do much of that searching locally, on device, and using smaller parameter models.

The hope for Adapter is that it can then pass on much lower costs to customers, making it a cheaper and more efficient building block for other applications, and making it more practical for prosumer users. (In my personal use case of managing email inboxes, I’ve hit token limits asking AI agent applications to ingest and label the high volume of PR pitches I get.)

It all adds up to “bell curve” cost to unlock “log curve” value, Ghetti argues, which means Adapter is cheaper to operate the longer it works with a customer. “The delta from ‘never’ to ‘now’ is a lot, but the delta incrementally between ‘now’ and ‘what’s next’ is very small.”

Guardians of your galaxy brain

In an era in which AI labs and advertisers are both hungry for your data, Adapter’s supporters describe it as a third option in a brewing battle between secure walled gardens of data that must be entrusted with one company and not shared widely, and open ecosystems where your privacy and data aren’t secure.

Adapter users who access models from Anthropic, OpenAI or elsewhere don’t share their specific data with the LLM, Ghetti notes, because Adapter sends the models an aggregate stream of traffic.

Mahaffey compares it to a data ‘Switzerland,’ sharing your data on a need-to-know basis that could serve as a compromise moving forward for advertising and agent-to-agent communication.

Imagine a model or site communicating with your Adapter graph to share a few versions of an ad, he posits. Instead of the company scraping all of your data and web behavior to target the ad to you directly, Adapter could respond with what ad might resonate best, without sharing any additional info.

“We need a world with less noise, not one with more,” he argues.

At GV, Nordlander argues that even the tech giants can find value in such infrastructure: “I don’t think in OpenAI’s wildest dreams they think you’re going to be doing all of your productivity stuff, and your social stuff, in the model.”

Ghetti, for his part, sees himself as a ‘custodian,’ or protector, of your data. When I note the irony that a startup founder is effectively asking people to trust it – and him – with that data in order to secure it, Ghetti responds that users can verify their own data security cryptographically with Adapter’s documentation.

He’s also exploring legal ways that Adapter can establish itself as a long-term good actor more structurally, and says he has no plans to ever cede board or equity control. “You should have sovereignty over your coal mine,” he argues.

Adapter’s CEO says he’s confident his startup has found a way to build a successful business model ensuring all three.

For it to work, it needs one thing above all: people to try it out.

“When people feel like the information and understanding asymmetry in their life begins to balance in their favor — not just because they come to understand more, but because they come to achieve more simply — when that is true, we are winning,” Ghetti says.

Materials science hasn’t changed much in 150 years. Enter Radical AI CEO Joseph Krause.

In a big new lab in Brooklyn’s Navy Yard, the startup founder is building a “next-gen” approach that he says makes other autonomous labs look like hand-free driving mode on a car, compared to a fully self-driving Waymo.

It’s an approach that plenty of corporate scientists tell Krause must be too good to be true. “How could you really discover more alloys in a week than we have discovered in our company history?” he says they ask.

But since its founding in 2024, Radical AI has already produced valuable, and secret, alloys for customers, Krause insists. And the startup has done it after raising just $55 million in its last funding round, not the $1 billion of certain well-heeled competitors.

We’ve written about other material science startups at Upstarts already, including our scoop of Apple’s former AI chief joining CuspAI. Another, Periodic Labs, was reported by Forbes to be raising $500 million at a valuation of $7.5 billion in May. Radical AI stands out not just because it’s based in New York, but because it’s arguably the only startup that isn’t outsourcing the actual lab work to partners.

Krause is also a straight shooting, no-nonsense founder who makes for a lively interview. Will he ‘win’ this category when DeepMind alumni, Jeff Bezos and others can also raise huge sums? It’s hard to definitively say. But it’s easy to see Radical AI competing, and thriving, in this fast-evolving category.

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On The Upstarts Podcast, Krause explains how a SpaceX breakthrough helped kick off a category; why cloud labs and deep tech companies often fail; and why concurrent engineering is as important as next-gen lab equipment itself.

Plus, he shares his Upstart Moment: a 100-year-vision to become one of the world’s most important companies.

Our three biggest takeaways for busy builders can be found below.

• Look for ‘something more’

• Embrace resource pressure

• Stick to your guns

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Look for ‘something more’

Krause was in a PhD program and working as a fellow at the U.S. Army Research Lab when he cold-emailed 100 VCs about in New York about working with them to invest in materials science.

Kevin Ryan, the serial entrepreneur and AlleyCorp founder, offered him an internship.

“This AI wave was taking off, everyone was investing, and there was pressure to deploy dollars in really exciting companies that are going to be the big companies of the future. “

Reviewing AI pitches with one of his Radical AI co-founders, Jorge Colindres, that Krause got the idea for Radical — out of frustration.

“We were just like, ‘How many more recruiting pitches can we get to ‘redefine the recruiting industry,’ right? I'm sure it's a good problem. Nothing against those businesses, but we were looking for something more.”

They thought about ideas in materials, robotics, manufacturing, and physical industry, but materials science seemed the best chance to have a “fundamental impact” on humanity.

“Regardless of what industry you care about, automotive and aerospace, manufacturing and defense, climate, energy, semiconductors, electronics, the most important industries in the world are all a direct result from materials R&D.

So to us, it felt like the perfect blend where you have this technology that is completely changing the paradigm in discovery, and you have this field that's 100-something years old and hasn't had it happen yet. That's a place that we should attack.”

Embrace resource pressure

Radical AI’s most recent funding round, a $55 million seed announced a year ago from RTX Ventures, Nvidia, AlleyCorp and others, seems quaint compared to some of the billion-dollar raises for pre-launch companies in AI.

When I mention this to Krause, he first stresses that Radical AI’s competitive edge doesn’t come from automating tools, or navigating a robotic arm, but from the interdisciplinary team of scientists he’s put together to run such experiments, capture the results, and build AI systems for analyzing them on top.

 “The connection and the collaboration between all of those units is imperative. It is not impossible — we did it — but it takes serious time to really build it. You cannot get interdisciplinary work unless you build interdisciplinary products. So the timeline to even just catch up to us is two, three years, and we are compounding that more and more, the more technology we build on top of it.”

Still, I ask Krause straight-up: if he could have raised $1 billion to start, would he really have turned it down?

“ I think it's dishonest to say that more money is not helpful. It is,” he replied.

Money means hiring faster, bigger facilities, and more data collection.

“In the material science space, we do not have experimental data sets, and so the more experiments you can run, the larger the data set gets, so the better the AI models perform. The number of experiments you could run is correlated to the amount of capital you have, because of the tools that you can buy.

So more money's helpful, but you do not need a billion dollars to start this. As a matter of fact, we didn't have a billion dollars, as you know, and yet we've still proven the self-driving lab works.”

Krause says he doesn’t want a billion dollars today, because he doesn’t need it, and it keeps his team focused.

“I'm a big, big believer as an entrepreneur in ‘back against the wall.’ When you get comfortable is where great ideas go to die, because you have no incentive and no chip on your shoulder to execute.”

In the future, Radical AI will likely need $1 billion, and it will raise it. But for now, it would be a distraction, says the CEO.

”Having pressure to deliver is a very healthy thing.”

Stick to your guns

At this point in Radical AI’s journey, Krause says he and his team are confident that they’ve found the “correct way” to build in materials science. It will take time for the market to agree, however, in what he calls Radical’s “capital-efficient, but very directed, approach.”

 “We don't want to boil the ocean in materials science, because you'll never drive any value, and then you'll never prove the system drives value.

So for us, now it's about sticking to your guns, not being persuaded by VCs, by the market, or even by what talent is telling you. Staying true to the mission, true to the execution plan, and driving against that.”

According to Krause, this is a particular challenge for deep tech companies, specifically.

“They go to market, they hit a wall, and then they quickly try to start moving around that wall with a lot of input from the outside world.

Not at all do I think you shouldn't pay attention to what customers or what people are saying. I think you can't be influenced by what people are saying, either, particularly if you are confident you're right. It is making the bet that you are right that typically pays off in the long run.”

GetYourGuide CEO Johannes Reck has a love/hate relationship with Google.

The search platform is still a key source of new customers for GetYourGuide, the still-private online marketplace that Reck co-founded with three others in 2009; even in the age of AI chat tools, Reck doesn’t see that changing any time soon.

But then there’s all that Google doesn’t do: such as seem to care much about whether a travel ticket site is official, or a tourist trap paying the tech giant to appear as such.

Take the Sagrada Familia church, which I visited earlier today with my spouse’s family here in Barcelona. Tickets to see the recently-completed final tower, which opened in February, sell out weeks in advance.

When I meet with Reck in New York earlier this spring, I’m already on the hunt, running into posts warning that many of the top ticket pages on Google aren’t official, but scams.

“We have a lot of beef with Google with it,” Reck scowls. “We believe that they should be filtering this, and that it’s not in the consumer interest.”

Small comfort: Checking back today after snapping photos atop the tower, I notice that the seemingly most prominent offender has been taken down and de-listed from Google; perhaps Reck’s visit last month during Pope Leo XIV’s official visit, during which the church recognized GetYourGuide as one of 15 official collaborators, has paid off.

But it’s a problem that persists across the world’s most popular tourist sites. A few days ago, in Rome, I have to wade through sites offering up Galleria Borghese tickets; ironically, the official website is broken that day, leaving only scalpers.

I buy three entry tickets from one; one ticket from another. The last two of our group — including my brother-in-law Zach D. Roberts, whose excellent photography you’ll find below and on his website — I book onto a tour they immediately ditch so we can sync up.

And it’s one that seems like it could easily get more challenging in the AI era. When it’s easier than ever to marshal armies of bots to make listings, simulate visits and skew site traffic, what will appear bona fide, and what AI slop?

At GetYourGuide, Reck says he’s optimistic about the future of AI-assisted travel.

“There are still a lot of question marks in the market,” he concedes. “But right now, I think it’s a huge accelerator.”

A few factors underpin that confidence.

First, there’s what AI can do to help make an overwhelming number of listings – GetYourGuide currently shows about 200,000 experiences across 50,000 supply partners to an audience of about 33 million travelers each year – feel more tailored and personalized.

Then, there’s the competitive dynamic: GetYourGuide and similar sites like Viator and Headout have databases of proprietary info that an LLM can’t easily scrape from the web.

Zooming out, there’s how the travel booking experience evolves alongside AI tools. Reck envisions a world where travelers talk through experiences with AI in a more haptic, visual experience.

Below, we break down how GetYourGuide is leveraging AI, threatened by it, and the “geeky” future its CEO envisions.

• AI horses, human guides

• Resisting our AI overlords

• Travel’s TikTok-ification

We get into the risks, too. But Reck is optimistic: “The good news is that agents can’t really go on the trip for us.”

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As I sit down to meet Brian Elliott, co-founder and CEO of Boston’s new startup unicorn Blitzy, I’m excited to ask him about his experience as a U.S. Army Ranger.

Not what it was like to go through Ranger School, the famously grueling program that has been the subject of TV shows. I want to know about something more hardcore: Elliott’s LinkedIn profile.

There, more prominent than any word about his combat deployment in Afghanistan, or his Bronze Star, Elliott shares that he “managed $30M in assets” as chief of staff of a 140-person team with, he adds, 10 direct reports.

There’s something so perfectly LinkedIn about it: the framing of elite, brave service to America as valuable within the corporate ladder.

When I get to ask Elliott about it, he tells me he’s been fascinated by systems – how things work – since he was a kid. Even a ‘direct action raid’ involving ground and air movements is, to hear him tell it, a “very elegant system.”

“Synchronizing all of that is IRL orchestration, and I loved it,” Elliott says. “Because precision, and operating under stress with precision, mattered more than anything”

It’s an answer that any ‘LinkedIn Lunatic,’ would approve of, but it’s helpful in understanding how Elliott, who co-founded Blitzy with friend Sid Pardeshi in 2023, has been able to build it into a promising business valued at $1.4 billion so fast.

Blitzy’s agents can understand 100 million-plus lines of code, helping corporations to automate and update systems that would otherwise take millions of dollars, paid out to consulting firms over months, to barely crack.

“For eons, we have been limited by how much a human context can hold in their brain,” Elliott argues. Now Blitzy can offload that context to its AI systems: “We can do changes at a size and scale that were previously impossible.”

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On this July 4 edition of The Upstarts Podcast, Elliott shares how he built Boston’s newest tech unicorn by becoming a CFO’s friend; why Cursor and Claude Code only see enterprise code through a straw; and what West Point and the Army Rangers taught him about high-stakes performance.

Plus, he shares his Upstart Moment: catching the 6am train to New York to try to close a customer, with his co-founder’s visa at stake.

Our three biggest takeaways for busy builders can be found below.

• Follow the ‘dissonance’

• A CFO’s ‘best friend’

• A Ranger’s lesson

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Follow the ‘dissonance’

The best time to build a startup, Elliott argues, is when “you have a view on the world that everybody tells you is incorrect, and despite that, you know it’s true.”

For Blitzy, that was the conviction that large language models, for all their capabilities, would not grow into efficient tools for updating large-scale code bases like the one he’d seen at what’s now called the Department of War.

“We would go everywhere and be like, ‘The model's not gonna do it all. Inference-time compute is the most important scaling law for code quality.’

This is before reasoning models, way before reasoning models were a thing, right? And if that was true, the limit case was gonna be understanding the underlying code base.”

This was not, Elliott says now, a popular position to have in 2023. “Everybody told us it was the stupidest thing they’d ever heard.”

 “They just thought that the model would solve the problem, right? It was like, ‘Oh, if GPT-3.5 can't do it, then GPT-6 will do it.’

It's not true. It's a physics problem, right? But these models are so amazing, it creates this illusion that it will be able to do everything, because you can experience GPT-3, you can experience GPT-3.5, right? And then you extrapolate what that's going to mean for size and scale in a way that's very logical, but it's not rooted in the reality of what a transformer is, right?”

This view informed how Blitzy set up its software: to run continuously, without a human in the loop, for weeks. The big unlock was what Elliott calls a knowledge graph, or orchestration system, that could handle millions of lines of code — not the thousands of an LLM’s context window.

 “What we're doing is we are removing the pressure or the load of having something... As a human, it would be like, ‘What do you kind of remember top of mind?’

So we're removing that load off of the agent, and we're only putting in what they need, just in time, every time, dynamically, done hundreds of thousands of times. And so at any single moment in time, an agent can only do one thing. We parallelize work, and then we sequence work based on how it can be sequenced.”

A CFO’s ‘best friend’

If you listened to our episode with May Habib last week, this one might sound familiar.

But Blitzy also considers itself a CFO’s ‘best friend,’ and Elliott is also bearish about ‘token maxing,’ which Blitzy’s CEO compares to evaluating a sales rep ‘SDR’ by the number of phone calls they make.

“It could be related. Sometimes it's related. But really you should be counting an SDR by bookings, right? That is their incremental value to the company. So, if an SDR created a script that dialed a billion people, that's not necessarily good.

That's my general view on token maxing: It's not sufficient, although it is not an unreasonable way to think about who is using AI. It's really not a sufficient way to think about adoption.”

Blitzy keeps its work usage-based, but aligned with a company’s existing roadmap, Elliott says.

“You're like, ‘Literally, I am buying these projects.’ What are the jobs to be done? These projects, way faster, from Q4 brought into Q2. That's what I'm buying, and there's incremental value both in labor savings and top-line revenue if those things are generating product value.

That is against the fixed amount. It's gonna be roughly X lines of code, right? So there's no variance swing from the CFO, like they're experiencing when they just buy random licenses for other AI products. What you're doing with the other AI products is you're throwing Opus 4.7 at everything, you're on 'max think' mode. And you're just putting that on loops, right?”

Blitzy’s value-add rests in part with the fact that it selects the best model for each task in real-time, Elliott says.

“Might be Gemini, it might be Flash, it probably won’t be Flash, but it might be Opus, it might be Sonnet, right? It might be an earlier version of Sonnet, right?

It’s baked into how we build our product in a way that really resonates with the CTO, the CIO, and the CFO.”

A Ranger’s lesson

When I ask Elliott what his top lesson across West Point, his active service and Harvard would be, he invokes a slogan from the Rangers: sua sponte.

"It means ‘of your own accord,’ and for the tech world, this is high agency,” Elliott says.

 “You will not succeed in the regiment if you're not an extreme sua sponte leader, because you're often on the ground, and you don't have comms, you don't have information, et cetera. You just have to make decisions live, based on understanding the larger intent. And you're not asking for permission. You are operating against a designed intent and mission.

That's exactly what it's like to be an entrepreneur, right? You are operating, and you are owning the outcomes of those decisions, and you're doing so at an incredibly high pace.

It goes all the way back to the conversation of: ‘Action begets information.’ Information doesn’t beget action, right? And so the ability to move with incredibly high agency translates from a prior life to this current life.”

When Elliott says this, it reminds me of startup founders who’ve said they hire especially from the ranks of elite collegiate athletes, who can combine initiative with discipline. The CEO agrees.

 “We hire a disproportionate amount of former athletes. Beth behind the camera [Beth York, Blitzy’s head of media and creative, and our volunteer camera person for the taping in Boston] is an Iron Woman. A really high signal way to vet through people is looking for athletic backgrounds, both on the technical side and go-to-market.”

In September 2024, James Stirrat-Ellis was hanging around a drop zone outside Seville in Spain when he struck up a conversation with another skydiver in between jumps.

His new friend was a senior executive at a data center provider, and when the two met up back in London, the exec urged Stirrat-Ellis – an architect by training, and recently a manager at 11x, the controversial AI agents startup – to apply his skill set to challenges in the AI-fueled data center boom.

“He said, ‘I know that you can automate this stuff, there’s a ton here,’” remembers Stirrat-Ellis. But it wasn’t love at first sight for Stirrat-Ellis and his co-founder, Ben McClusky. “To be frank, we weren’t that excited.”

Building software for the pre-construction phase of such projects – seemingly a small piece of the puzzle – didn’t seem like a high-octane startup niche. But as the founders spoke to more potential customers, they realized that the services involved ran into the millions of dollars – and involved much more of a challenge than initially met the eye.

If they could automate some chunk of that using AI agents, they could build a highly profitable agency. More, and they might have a venture capital-scale winner. And if they could make large-scale building projects mostly autonomous – taking tech-style margins, not traditional services ones – they could build a generational company.

So the duo founded Build.inc, a startup that runs dozens of AI agents to automate everything from the PDFs and Excel documentation of a project to site recommendations and permitting process.

Build already works with the U.K. government advising its AI growth zones initiative, among more than 100 major infrastructure projects, with other customers in real estate and data center development, the startup says.

Build claims it can compress months-long pre-build processes into several weeks, at half the cost. Contracts start in the six figures; and Upstarts estimates Build to have bookings into the millions in future sales; the startup declined to confirm that, but said that it had “tripled its booked revenue since April.”

The bigger ambition is to handle more of a large-scale building process across other categories, too, from energy and industrials to residential.

“In five or 10 years, we’re going to make building autonomous,” claims Stirrat-Ellis. “You can email Build, and your building goes from A to B, from concept to completion, as autonomously as possible.”

Just nine people today in New York, San Francisco and London, Build is now publicly launching with $8.5 million in seed funding led by Index Ventures, with Pebblebed, Puzzle Ventures, Tiny.vc and a number of personal investors including OpenAI CFO Sarah Friar and Blackstone CTO John Stecher.

With the funding, Build – which has relied almost entirely on word-of-mouth so far – will need to grow its go-to market and sales, says new lead investor Martin Mignot at Index.

Build is very early in its journey. But the startup’s launch is already interesting to the wider ecosystem for two reasons that we’ll explore below:

• The rise of the ‘neo firm’

• The data center wedge

“We’re going through the world’s greatest infrastructure build-out ever, full stop,” says Stirrat-Ellis. “These data centers will happen regardless. Build exists to free up the headroom to actually say, ‘Well, how do we build this the right way?’”

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‘Neo’ pilled

Build is the latest in a wave of startups to pursue some combination of AI-native services and agents. When Stirrat-Ellis and McClusky started building this craze hadn’t started yet, and Build’s CEO says it was “very, very unpopular.”

But the duo figured that if they could generate a massive technical due diligence document for a data center site that would typically take six weeks in just six hours, they could charge $25,000 for it and enjoy margins more like a tech company than a consulting shop.

They spun up dozens of agents to handle different tasks; in the initial workflow, one of the founders would email those agents to get PDFs back, and they’d grind long hours to turnaround a finished document for a client. “It looked kind of pathetic, but behind the scenes, a lot of stuff was going on,” Stirrat-Ellis says.

Since then, they’ve hired experts from giants like Starwood, Tishman Speyer and data center businesses to embed their domain expertise alongside the agents. It’s an approach similar to how Crosby, another New York startup we’ve covered in Upstarts, looks to deliver faster and cheaper legal work around contracts.

Such businesses don’t sell software subscriptions, crucially but focus on customer outcomes, which they believe can better align with customer needs while still creating a healthy margin.

Outcomes over software was an idea Stirrat-Ellis says he first developed while still at 11x, although he ultimately cut his time short at that company amidst its turmoil and accusations around how it represented revenue and customers. (Of that experience, Stirrat-Ellis says: “One of the biggest lessons is to do things the right way.” Adds Mignot, his backer: “You learn a lot about someone from what they take away from these types of experiences, and he was very transparent and open.”)

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So if Build isn’t a software company, and it’s not just a services firm, what is it, exactly? Does it call itself AI-native services, or services-as-software, like in our recent coverage of Gainsight? Build’s CEO tosses out other terms: neo firm, or AI operating partner, but there isn’t an ideal phrase yet.

Its revenue model is different, too: retainer pricing that starts in the six figures and can run into the millions as customers hit usage caps. Build’s agents are expensive to run – as much as 100x the cost of other agents in simpler use cases, per its CEO – but that’s baked into its healthy margins for now.

“We’re figuring out pricing, like every AI native company today,” Stirrat-Ellis says.

Data center crunch

Build is currently playing into the urgency to build large-scale data center projects globally – and, somewhat counterintuitively, the blockers and backlash.

For customers like the U.K., Build helps to map out the local power grid and site the centers in optimal locations, factoring in other variables including political community sentiment.

Stirrat-Ellis says he and his team understand why locals near some of these proposed developments object – “living beside a data center with massive amounts of noise is not a good place to be,” he agrees – but he argues that Build can help bring those concerns and discussions earlier into a process, helping all parties to reach a happier outcome.

“Without Build, these data centers are going to be really cumbersome, and annoying, and difficult to live with,” he claims.

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Speeding up the time process to get permits and reach such compromises will meaningfully lower the cost to build such projects, argues Index’s Mignot, increasing the pace of AI development, which can then further drive down costs.

There’s a sovereign AI angle, too: “Every country is trying to compete with their neighbors for compute capacity throughout the world,” Mignot says.

But for Build to grow into the ‘generational’ company that its CEO aspires to lead, data centers will eventually need to be a smaller part of its business. Similar tech could help build nuclear giga-factories like the one that Valar Atomics aspires to introduce, or for large-scale new housing projects.

For Stirrat-Ellis, who didn’t grow up dreaming of launching such a startup, and who says he was taken more by the market than the idea initially, there’s a lesson:

“I think it’s small markets, that appear really dull, that actually end up building really, really great companies,” he says. “Because you kind of have to break some rules to make something happen in an excellent way.”

It’s hopefully a turn-of-phrase – or at least all clearly ethical rule-breaking – this time around.

In a New York office earlier this week, General Intuition CEO Pim de Witte receives me wearing a Netherlands jersey — it’s the World Cup, after all — to demonstrate his company’s pet robot.

Known to its data analyst handler as Clippord, the robot’s about the size of a big golden retriever or husky, and utilitarian looking. De Witte asks me not to take photos on aesthetic grounds.

Trust our word for it that this robodog eventually clomps around the office successfully, with just the occasional bump into the side of a column, although the team doesn’t trust it to make it past a glass meeting room wall just yet.

If you’ve seen robotics demonstrations before — quadruped clankers are a fixture at tech conferences these days — it’s nothing state-of-the-art.

But it’s how General Intuition, which announced a $320 million funding round led by Khosla Ventures yesterday at a $2.3 billion valuation, is “zero-shotting” Clippord’s basic navigation that has de Witte fired up.

Clippord has no maps or specific data on the office layout as a reference; it’s approximating its own size, and how to handle obstacles, entirely from a corpus of video game usage data, topped off with 8 minutes of real-world data collected on the sidewalk below.

Only one sensor, a camera in the front where its eyes would be, is processing what it sees as pixels, sending that data back to a data center, predicting a next action, and returning that command 10 times per second, faster than a human’s reaction time.

Why does that matter?

According to de Witte: robotics companies are spending way too much time and money on their own training.

“Everybody is collecting way too much,” de Witte tells Upstarts. Better to turn to Fortnite.

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Writer CEO May Habib has a message for the parents out there in tech, and especially the women founders: if you think what you’re doing isn’t harder, you’re kidding yourself.

What started as an amusing anecdote about the recent discourse of founders sharing VC horror stories — “Vinod [Khosla] also fell asleep in my partner meeting at Khosla [Ventures],” Habib quips — has quickly gone somewhere more serious.

Reflecting on Writer’s Series A — ultimately a $21 million round led by Insight Partners in 2021 — Habib reflects that her startup would’ve had an easier time if she’d switched roles with co-founder and CTO Waseem Alshikh.

“If you’re a woman pitching VCs, we’re all fooling ourselves to think that in the back of their minds, they don’t think you’ll work as hard,” she says. “I think me and Waseem should have switched roles. Is that crazy? Have the technical man pitch.”

Sequoia, the famed VC firm we’ve mentioned a lot in Upstarts, rejected Writer during that Series A process. Sequoia’s rejection letter included a line about how the firm found it ‘inspiring’ that Habib was building her startup with two little kids at home, she recalls — a detail she thinks was a giveaway that the firm was concerned in a way it wouldn’t be with a CEO dad.

“Everyone wants to hear you say there is nothing more important to you than being a billionaire,” she says. “And the reality is: My kids are kinda more important than that. I think anyone would say their kids are more important than that.”

But don’t mistake that for a lack of ambition. Habib says she works 15 or 16 hours a day anyway. She’s got plenty.

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And she believes that Writer, valued at $1.9 billion, represents the best chance for Fortune 500 boardrooms in weekend crisis meetings about AI adoption to see real results. Accenture, Cigna and Vanguard already use Writer’s AI agents to do everything from create morning sales briefings to personalized product test invites.

Writer isn’t as buzzy a partner as working with the big AI labs, but it’s building what Habib calls AI for “normal” people.

"Anthropic and OpenAI salespeople walk in like heroes, get a contract, and leave to literally never be seen again,” she argues.

And as those companies, well down the IPO path, gives away millions in tokens to secure business from each other — what Habib calls “absolute insanity” — she is calling for more accountability in what return on investment companies actually get from all their AI use.

There’s an adage in tech: nobody ever got fired for buying IBM.

Habib proposes an update: "People should be getting f***ing fired,” she says. “That’s the best way to infuse some sanity into this market.”

On this episode of The Upstarts Podcast, Habib goes into her journey starting and pivoting machine translation startup Qordoba into Writer; the VC who fell asleep in a pitch meeting; and how the real moat in enterprise is not leaving customers behind.

Plus, she shares her Upstart Moment: hiring top talent in the middle of a “gold rush.”

This whole episode is a must-listen, but our three biggest takeaways for busy builders can be found below.

• “Survive, don’t sell, don’t quit.”

• Bringing sanity to tokenomics

• You don’t need to be a lab to own the stack

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“Survive, don’t sell, don’t quit.”

The key to success in more competitive, harder startup categories isn’t any one decision, Habib says: it’s just staying in the game.

“This space rewards those teams that are physiologically set up to do hard things for a long period of time,” she says on the show.

“ The hype will pass, and that competitor that gets all the headlines, they haven't built the right thing for the right people, and [don’t] truly understand the customer. They may have their moment in the sun, but will not survive.

And for us, [it’s] the maniacal focus on customer outcomes, customer ROI. I know it's lame. No one wants to talk about ROI, right? All the cool boys aren't talking about ROI. ‘Why is May talking about ROI?’ Customer cares about ROI, right? Are they paying for software that does something?”

Writer is waiting for a “runaway market” that hasn’t fully materialized yet in the roles it sells to.

 “We have a process that is really well structured to be able to turn human and AI collaboration into working code, right? Does that happen in sales? Does that happen in marketing? Does that happen in HR? We don't have the scaffolding to be able to take agents and get them into production, with human oversight that is well-known, well-established, and easy to audit.

That’s the scaffolding we are building for these teams. And that's the difference between production and scale.”

What’s missing today, Habib argues, is trust. When teams trust agents as much as they do people, agents can take off in those roles the way they have in code.

“That explosion is incredibly latent, right? We haven't announced these things, but we've met a lot of the milestones that people brag about. I think for us, the thing that we will be bragging about is when we have the same kind of agentic autonomy in the enterprise that we all have in engineering and coding domains, and we're not there yet, because the customer is not there yet. The trust is not there yet.”

Bringing sanity to tokenomics

As noted above, Habib advocates for more buyers to face consequences for spending resources badly on AI — the new IBM.

 “Almost every one of our customers has everything under the sun, and they don't get results. And our ability to come in and help them get results is just about thoughtful product meets thoughtful implementation, meets thoughtful rollout.

And the token maxing across all aspects of the business… I think on the coding side, you know, Cursor and others have built in the actual budget constraints, right, and that functionality. Anthropic hasn't.

And so I'll meet people where it's a $3 million overage on two weeks of the plugin in Excel. Like, what's the ROI on that? Zero, right? Excitement, maybe. But if you're not learning the right lessons, then that is not money that is well spent. And CEOs are just having to get involved. I think CFOs will have to get involved.”

If she’s right, startups should take heed: and find ways to be on the right side of ROI. Writer found a sweet spot as a “weapon” for its own buyers, CIOs and CMOs, to reduce their spend on more expensive software from Adobe, Salesforce, and Workday.

At the same time, those companies are all customers of Writer, too.

“Internally, everybody is trying to understand how to get this kind of leverage and get the form factors right.”

Writer benefits now from the tailwind of companies adding, or shifting, people to have “AI” in their title and remit.

“I think I agree with Aaron Levie [CEO of Box] who wrote about this recently. I don’t know if he gave a percentage, but I personally think 10% to 15% of most sales and marketing teams are going to be AI-related titles.

And us being able to bring that kind of aggregated data to an executive team who has two people in the business dedicated to AI, is what allows them to say, ‘Oh, we need to 10X the human investment here, not just the software investment.’

And so, yes, you’re giving people leverage over this cost structure, but you’re also helping them take that into retraining and in a lot of cases, rehiring, for the kind of capabilities and skill sets they want in the company.”

You don’t need to be a lab to own the stack

We heard a version of this insight from HeyGen CEO Joshua Xu in our story about how his video AI startup reached $200 million in ARR with minimal burn: controlling the whole infrastructure stack, you can avoid getting caught passing along revenue to others via API.

At Writer, the company’s internally-trained Palmyra models power 90% of their LLM calls, Habib says.

Because Writer started training those models before ChatGPT’s release, the big decision wasn’t so much to train models, but to continue training them in recent years, she adds.

“We started Writer to commercialize transformers. As we continued to train models, we saw that while the labs might be a few weeks ahead, everybody’s following the same research breadcrumbs.”

Writer started to use synthetic data as early as 2023 to help account for having fewer resources in its own training.

“We just never really felt the capital gap meant a capabilities gap.  Sure, they may be ahead, but there are plenty of eras of this chessboard, where we were ahead.

The thing is, with the enterprise, it doesn't really matter. Because they're still trying to catch up to the innovation that you launched 24 months ago. And so we never felt like we needed to go use third-party models to be able to deliver for customers, and to be able to deliver frontier performance for customers.”

Customers will use Palmyra models when they’re faster and cheaper, but Writer’s position is that customers at big companies like Mars and Unilever shouldn’t be bound to any one model provider. Others will want to fine-tune their own.

“It gives folks the optionality, but we're never not going to be a company that builds and trains its own models,” says Habib.

One other distinction: even as Writer now tests its latest model, Palmyra 6, it doesn’t see itself as a lab.

That’s because Writer doesn’t operate research, product and go-to-market separately. “It’s everything from a single Slack to a single force in front of the customer,” adds Habib.

In one recent example, Cigna gave feedback to Writer’s head of research, Dan Bikel, that in a certain use case, Palmyra 5 performed better. “This is why I will be perpetually working 90 hours a week,” Habib says.

“It resulted in a deep amount of data I was able to provide to the research team, so it’s that continuous loop of product and research. And then our go-to-market teams who are literally on site with the customer saying, ‘This works great. This works shit. We need to improve this,’ right? ‘We need the model to be able to do X, Y, Z.’ And that is what makes us a product company, not a lab.”

You’re probably sleeping on HeyGen.

The AI video startup doesn’t attempt to compete with models from ByteDance, Google or Runway in offering up film-worthy, “cinematic” experiences.

Based in Los Angeles, founders Joshua Xu and Wayne Liang have raised a paltry amount of venture funding compared to their peers; they lack an eye-popping valuation.

And while HeyGen users span 85% of the Fortune 100, the company says, its lifeblood customers aren’t heavyweights — they’re small business owners and real estate agents, personal trainers and online course instructors.

“We want to make visual storytelling accessible to all,” Xu says. “What’s more important is the expertise they are getting shared.”

But in its own not-so-little world, HeyGen is growing fast. More than 30 million users have produced about 120 million videos across 250-plus languages to date. And they’re quietly paying for HeyGen, too: annual recurring revenue has recently passed $200 million, Xu tells Upstarts exclusively, doubling from $100 million eight months ago.

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In a moment in which AI businesses have claimed to shatter revenue records for startups, what’s more interesting is how HeyGen reached that ARR milestone: doing it while remaining cash flow break-even for 2026 so far, and only burning $25 million, about one-third, of the $74 million in funding it’s raised to date. (Investors include Benchmark, BOND, Conviction, SV Angel and Thrive Capital.)

According to Xu, HeyGen hasn’t really burned cash since it reached $10 million in ARR three years ago. Instead, the company proudly tracks a couple of other metrics: revenue for every dollar raised (now up to $2.70 for each $1 invested) and revenue per employee (now up to $1.5 million per full-time staffer).

HeyGen attributes its ability to grow so sustainably thus far to 3 things:

• It keeps its team as lean as possible

• It owns its own full stack, from model to app

• It focuses on a less competitive, more efficient use case

“We are doing this very differently,” Xu claims. “We focus on the identity and the message first, not the cinematic.”

And that means that HeyGen also believes that its platform is “extending humans” in useful ways, instead of proliferating more AI ‘slop.’ “We’re all about extending humans,” says chief marketing officer Andrew Mok.

Whether you fully buy that is another story. Upstarts spoke to three of HeyGen’s small business customers for their perspective. More on how HeyGen has structured its startup not to bleed cash, and their insights, below.

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Self-constrained growth

HeyGen’s architecture plays a significant factor in its growth profile to date. One layer deeper, the startup made two key decisions: own its own full stack; and avoid video use cases that would require more expensive development.

Xu divides the video AI market into three buckets:

• Message-first video

• Cinematic video

• Advertising video

Because HeyGen focused on generating a ‘digital twin’ for a single person to be delivering a message, the first bucket, it was able to train models using only 1% to 2% of the total video data available, he says. HeyGen’s models — the most recent is called Avatar V — needed less data and far fewer GPUs than OpenAI’s Sora, or Google’s Veo 3 series.

While the latest from Google, Veo 3.1, can produce videos in the same ballpark as HeyGen’s, the startup’s model generates them 10x more efficiently, Xu claims.

Owning its own full stack allows HeyGen to optimize its inference more efficiently than by using third parties, according to its CEO, while avoiding more costly API calls for features like voice. HeyGen does offer its own API, though, allowing users to generate videos without filming their own baseline video or opening its app.

HeyGen’s pricing is usage-based, and scales based on how many credits users need, and which of its models. The startup’s goal is not to lose money on each — what sounds like basic business sense, but which Xu says is rare in the field today.

The startup has avoided the temptation to expand its capabilities more into the ‘cinematic video’ or ‘advertising video’ markets, where Runway, Higgsfield and others compete. “We’ve been very constrained,” says Xu.

The company has also pushed its own employees to do more with less. Earlier in June, Xu posted a chart on X showing that HeyGen staff were contributing 16.7x more code per person than in 2022.

More important than simply using a lot of AI, Xu says, is that employees are ‘AI native’ finding ways to improve their own workflows. Every job applicant is interviewed for AI ‘fluency’ as part of the hiring process.

“We really believe in a lean team of top performers who are AI native,” Xu adds.

‘A marketing avenue, instead of a clone’

Who are the users actually driving HeyGen’s revenue growth?

Fifteen months ago, YouTube creator Julia McCoy suffered a health crisis including a case of long Covid, among other diagnoses. After more than a decade regularly posting videos about content marketing and SEO, she found herself unable to record, she says.

When she switched over to an AI avatar from HeyGen, her loyal subscriber base of about 50,000 followers rebelled. "People hated it,” she says. “We got such negative comments.”

But McCoy says she had no choice but to keep putting her AI-generated videos out, 2x to 3x per week, “like the most stubborn person on Earth.” After about six months, she saw a significant change. The followers who hated a digital twin had churned out. A new audience, that didn’t care or even embraced it, had found her channel. They gave her avatar, which she dressed in a Star Trek style uniform, a name: Bones McCoy.

“The new audience enjoyed the content for the content’s sake,” she says.

Today, a team of five helps McCoy put out videos to 300,000 YouTube subscribers on her channel; she generated about 700 personal videos last year, to go with monthly courses and promo videos. One video last fall eclipsed 800,000 views; last summer, she was invited on — alongside her avatar — to talk to Dr. Phil.

In each video, McCoy discloses she’s a digital avatar, but she says she reads comments, and followers respond accordingly.

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At CrystalNinja, a 20-plus-year-old rhinestone-focused shop in San Diego, founder Kellie DeFries has expanded into an online shop and training platform, centered around a patent-protected rhinestone pick-up tool. She’s got more than 100,000 followers on Instagram for work like a diamond-encrusted Patrón bottle featuring Knicks champion Josh Hart.

Making videos for her VIP members was starting to get overwhelming, DeFries says by email, so she turned to HeyGen for avatars to cut down on time spent on prep, shoots and reshoots. “The time it saves me alone is worth” the price, she notes, signing her email: “Sparklingly yours.”

But while HeyGen says its users follow best practices about disclosing they’re using AI generated video, the distinction doesn’t always seem obvious to these users’ audiences.

Vancouver-area based real estate advisor Craig Veroni spent a decade-plus making videos to promote his clients and listings before learning about HeyGen from a colleague about a year ago and undertaking a 3-day training.

Now his AI-generated hyper-local news reels regularly outperform his human content, he says, with some surpassing 250,000 views. His Instagram following has quadrupled to nearly 6,000, and he’s started to close business through the platform for the first time.

If you know what to look for, it’s fairly obvious when Veroni is using HeyGen: there’s something uncannily perfect about his appearance in them, the lighting too perfect, the backgrounds like the virtual ones we’ve all come to recognize on Zoom.

But if there are disclosures, they weren’t immediately obvious to Upstarts. Veroni himself recently posted a video on Instagram, detailing how his AI had “destroyed the real me.” “Most of you never noticed,” he added.

Veroni is a former working actor, with his own Wikipedia page for his work on Stargate Atlantis. I ask him how he feels about the ethics of using HeyGen to present a digital twin as him.

“I’m firmly in the camp that no production company should use an actor’s likeness without their consent,” he says. “But if an actor says, ‘Yeah, I want a digital version of myself to do stuff,’ that’s cool. This was my choice to make a digital clone, so that it could actually help me focus on my clients, and spend more time in person with them.”

The YouTuber McCoy offers her own advice for people still squeamish about it: think of it as another business channel.

“Look at it as just a marketing avenue instead of as a clone,” she says. “Your whole perspective changes.”

Salespeople have a problem: they only spend 21% of their time actually selling to customers.

Founder Jaleh Rezaei set out to fix that – and business was booming.

Her startup, Mutiny, reached $10 million-plus in revenue for software that helped take some of the busywork out of the sales process. Companies like Snowflake and Uber were customers. Sequoia, Insight Partners and Tiger Global were all backers.

Which makes what happened next all the more shocking.

Mutiny’s software helped expedite the sales process for sellers by helping them manage other assets they’d need to close a deal — case studies, graphics like battle cards, custom pricing — and Rezaei saw the writing on the wall with AI tools.

Halfway measures didn’t cut it. So Mutiny’s founders made the tough decision to fire their customers and light their revenue ablaze. They reduced headcount down to 15 people. And they started scrambling to ship like a new startup, not a Series B one.

“When you start a company, the expectation is failure,” she says. “It’s really hard to accept that you’re going to burn it to the ground for the possibility of a better future.”

Just a half year later, Mutiny has won back old customers like Snowflake and Uber. Sales of its AI agent, which now handles a similar process with just a prompt, are growing 150% month over month.

“In my eyes, it’s a six-month-old company, seven-month-old company now,” Rezaei says. “I don’t think you need a lot of people to get to product market fit. Having lots of people just slows things down.”

On this episode of The Upstarts Podcast, Rezaei shares why busywork follow-ups are the sales killer; how AI is reinventing SaaS, like it or not; and how to get over ‘Claude spookies’ about the threat of big AI labs.

Plus, she shares her Upstart Moment: convincing her co-founder to start over again, on a fateful New York walk.

Our three big takeaways from the conversation are below.

• ‘Benevolent dictator’ founders stand on business

• You don’t need long runway, but you do need to pick a lane

• Speed is (mostly) all you need

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‘Benevolent dictator’ founders stand on business

Rezaei credits her co-founder and CTO, Nikhil Mathew, with turn of phrase for what it’s like to be a zero-to-one founder: “a benevolent dictatorship.”

“ Founders hold all the context in their head, and they make really fast non-linear calls. And while it may feel chaotic, if you zoom out, you just see the product, ‘Wow, it's getting better so quickly every month.’

 You need to sit in the sales calls. You need to sit in the customer support calls. You need to try to grow the account and renew the accounts. You need to be in the room building the product. You need to understand every part of the company, to be able to very quickly identify where the problems are.

Otherwise, it would just take 20 years of doing customer interviews, if you weren't operating off of those gut instincts. So that's the zero-to-one journey.”

That changes as a company scales, and a CEO has to learn how to delegate, manage, set up processes, and be able to make decisions that carry more friction. One thing that doesn’t change, Rezaei says: it’s key for a CEO to focus on company-building, not optics.

“If the business isn't good, you lose all of those optics eventually anyway. And this is hard for founders, right? Because, you are on a roller coaster, and sometimes founders look for external validation, right? ‘Okay, well, if I have a lot of people, or if I have investors or whatever, it means that I'm doing well.’

But really, the only thing that matters is, are you serving a really large market of customers, and do they love your product? And those are all of the signs of whether you're building a successful business.”

Rezaei Is a loyal Y Combinator alum, and she invokes Garry Tan, the accelerator’s CEO.

 “Garry had this really good example of: The chart that you don't want to show anybody, and you want to hide, is the chart you should bring as the first slide of your board meeting, and then talk about it, admit it, and, and fix it.”

You don’t need long runway, but you do need to pick a lane

The founders of Intercom, rebranded as Fin and acquired by Salesforce for $3.6 billion earlier this week, were instrumental in helping Mutiny navigate its re-founding moment.

Like other startups we’ve covered in Upstarts recently, such as Remote, Rezaei naturally considered maintaining Mutiny’s software business at first, to help fuel its AI agent development. Fin had gone through a similar process.

 “They made it very clear that it was going to be really, really hard, and that these two businesses are very, very different. Because they were further ahead at implementing, right? And so, you know, they were going through it.

The way, for example, you optimize your SaaS product is A/B testing the UI [user interface] and things like that. The way you optimize an agent product is based on outcomes and evals — it's just a very different process. And so seeing how they were doing that, a couple of years ahead of us, it helped further confirm that how I see the two businesses diverging, it was only going to get more and more that way.”

Des Traynor, Fin’s co-founder, walked Rezaei through the potential process of isolating its business or deprecating it, and how Fin would’ve thought about each option.

“At the end of that conversation, I kind of concluded there's really no good reason for us to keep the other business. We have the cash, we can fund the investment, and we can be a lot more efficient with the way that we use the cash that we have, if we are really nimble and small, and don't have any of these frictions to deal with.”

That said, Mutiny’s CEO adds a self-described ‘hot take’: startups don’t need a lot of money in the bank to pull off such a transition, she says.

Mutiny has multiple years of cash to burn in the bank, but she notes that at YC, founders reach product market fit on $100,000 or $200,000 in spend.

 “I don't think you need a lot of people to get to product market fit. And a lot of times, having lots of people just slows things down, and makes it so that context is now split across more people. So, I would say if you're a founder, and if you have six months of runway, I get it [not turning off your revenue source].

But if you have the money that a seed stage company can raise in the bank, you can get there.”

Speed is (mostly) all you need

When Rezaei was an early employee and growth leader at Gusto, the software business with $1 billion in revenue, she says there was a common thread across managing sales, marketing, and customer success teams.

“ When I hired good people, and then I figured out how to help them move as fast as possible, that's when we grew the fastest. So if I were to simplify everything around growth channels and all these different things, it really just comes down to speed.”

Setting aggressive goals for her employees, and then encountering the blockers they faced to keep them from achieving them, helped set her down the path to founding Mutiny.

“It was just so clear to me that speed was the growth advantage,” she says.

Years later, when Mutiny’s founders shifted the business dramatically, it was once again all about speed.

“Day one, post November 2025 pivot, we are in the office working together around the clock. You know, I personally was like, ‘This needs to work. I am pouring everything I have into this, and I know it'll work, but we're going to have to work our asses off.’

Every hour mattered. One of the things that I try to bring to the table is I do have a really good sense of urgency, and timing, and pacing, for the company. And so if things aren't fast, it just, it kills me. I can feel it.”

The obsession with speed reflected itself in Mutiny’s founders reading every error and bug their test users were encountering with the new agent, and pushing code to fix them late into the night.

”I love speed.  I think speed is the only thing that matters, and if we're building something, and somebody else is building that thing ,and they're getting to the correct answer faster than us, we're going to lose.”

The lesson for other startups: if you know more about your customers than competitors, you might win. If you out-hustle your competitors and move faster than they do, you might win. If you can have both — the knowledge and the pace — it’s suddenly much harder to lose.

The Upshot

On an afternoon in late March, weeks before the Knicks will captivate the city with a historic title run, the best place to be in New York tech is a low-key office space in Chinatown.

Past a front door that doesn’t lock, and up a somewhat sketchy elevator, a group of eight companies are presenting to a few dozen people in the loft-style office of Gutter Capital, a local venture capital firm.

It’s the first-ever demo day at Elbow Grease, the early-stage accelerator created by Gutter late last year. In front of a portable screen, the participants present their products to rows of a few dozen fellow founders, program mentors and speakers, closely watched and guided along by Gutter’s founding partners, Dan Teran and James Gettinger.

There are jokes. “Please form a line at the back of the room to sign up for your policy,” declares the co-founder of a startup helping small businesses find insurance, to laughter. There’s plenty of lukewarm pizza.

What there isn’t: the gaggle of tech press and investors you’d expect at a startup demo day. These pitches are for feedback, not buzz. “This is the same deck we used to pitch the U.S. Space Force,” notes the team behind Galileo Space, who are working on cheaper, better low-orbit satellite imagery.

“We’re not a finishing school to teach you how to fundraise,” says Gettinger. “We want to do this with you, and for things to go well, for decades.”

It’s a promise that Gutter is putting its money – and staking its future – behind. The firm is announcing the close of its $75 million Fund III today, up from Gutter’s previous fund sizes of $25 million and $44 million.

The new fund will focus on Elbow Grease, both through new batches and capital for follow-on rounds, Teran says. As part of the announcement, Gutter is also officially opening up applications for the program’s second batch.

Elbow Grease 2 is seeking 12 to 15 early-stage startups for its upcoming batch. Startups can apply here through July 31.

For a program that was framed as an experiment last October, it’s a big step forward, with commensurate risk attached.

“We just feel Elbow is the logical entry point,” says Teran, who previously built and sold local startup Managed By Q. “The earlier we can get involved, the bigger impact we can make for companies.”

Note: Gutter Capital didn’t compensate us in any way for our reporting on this project. We put in the extra work purely out of journalistic curiosity, and our mandate to write trustworthy, fun unique reporting on the startup ecosystem.

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What’s in the juice at Elbow Grease to create such momentum, so fast?

At Teran’s invitation, Upstarts embedded exclusively with Elbow Grease to sample its programming, including a happy hour, founder lunch training session, fireside chat, museum outing and Demo Day, between January and March. (We skipped the Nets game, but can wave from the Knicks parade later this week.)

We also checked in repeatedly with a few of the founders about their experience over the period, and asked visiting speakers like Stripe veteran Claire Hughes Johnson and CEO mentors like Electric founder Ryan Denehy for their feedback.

The portrait that emerges: a low-ego, even wholesome startup program where an eclectic mix of founders become New Yorkers, at least for a few months.

That sleeves-rolled-up ethos, encouraged by Elbow Grease’s name, is set by Teran and Gettinger, co-founder of web app Givlet and later a professional poker and daily fantasy sports player. They lean hard into their networks and experience – and they leverage themselves, and their firm, in a high-conviction bet that means long hours and low pay, at least for now.

“The reality is that James and I barely pay ourselves, and we work 80 hours a week as seasoned hands,” Teran says. “The biggest risk to the whole thing is our capacity… We’re offering a lot of service on a pretty lean budget.”

But for all the feel-good energy out of Elbow Grease, it’s a highly capitalistic operation. This is increasingly what it takes to get ownership in high-potential startups, Gutter’s partners argue; get it right, and big paydays await all involved (including their portfolio founders, cut into the firm’s profits).

“We’re in it to the hilt, very much betting on it,” adds Teran. “We have a love of the game, and I think long-term we will outperform every fund of our size.”

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‘No crazy egos’

When Teran and Gettinger announced Elbow Grease last October via Medium post, they weren’t sure how many applications they’d receive, or of what quality. They got more than 600.

One was Galileo Space, where CEO Kazi Farabi and team brought backgrounds at Lockheed Martin, SpaceX and Google to bear on a solution to take cheaper, better image sensing via a low orbit satellite.

With ties to USC and the famed NASA JPL center, Galileo had received encouragement from Y Combinator to apply to its next cohort, but Elbow Grease committed faster: move to New York for eight weeks, and receive $300,000 for 9% ownership (implying a $3.3 million valuation) rolling into a future funding round.

The Galileo team knew literal rocket science, but not much about investor pitch decks. Gutter paired them with Denehy, a longtime peer of Teran’s and LP in Gutter’s funds. Denehy didn’t know much about space, but he did know about launch timelines and capital raising, with Electric valued at $1 billion across a handful of rounds.

“It’s training us, getting us ready to be customer facing and raising more capital,” Farabi told me when we first met in January. “They were the first believer, the first check that matters,” he tells me at Demo Day in March.

When Denehy met the wider batch, he claims he could spot a ‘through line’ across the companies: “No crazy egos.” “There was a vibe with everybody, where I was like, ‘this all makes sense,’” he says.

Elbow Grease’s full first batch:

• Galileo Space

• GRAIL

• JoyWork

• Keeper Systems

• LegacyAI

• Punch

• QuickSecure

• SwitchBoard

AI played a big role in shaping Elbow Grease’s curriculum, but not for the reasons you might expect. In many accelerators, startups spend the program looking to ship and validate a minimum viable product, or MVP, readying it to a point to pitch to investors by demo day.

In Elbow Grease, founders mostly knew how to use the various vibe-coding and agentic tools already in the market from companies like Anthropic and OpenAI; they could ship a product in a matter of days, notes Gettinger. Programming and workshops instead oriented around recruiting, sales and other aspects of company building.

Guest speakers included Lotus co-founder Mitch Kapor, Notion’s head of design, and Teran’s pal from after he sold his startup to WeWork (an eventually turbulent era we covered while at Forbes), WeWork co-founder Miguel McKelvey.

Upstarts tagged along for one fireside chat as David Politis, who built and sold BetterCloud (and had just hosted Upstarts on his podcast) interviewed ServiceChannel ex-CEO Tom Buiocchi.

In the room, Buiocchi seems tickled by the close attention paid to him by the attending founders, especially when he tells a war story about Frank Slootman, the former Snowflake CEO. Afterward, he holds court, encouraging founders to come pitch their product in the office, and fielding tactical questions.

On another afternoon, in a workshop with Chris Thompson on founder-led sales, the Bikky co-founder asks a conference room of batch staffers if any of them have hired a salesperson. No one has, so he deftly shifts his presentation to focus on avoiding common mistakes in doing so. It’s a full-circle moment for him, he says, as Teran would come to board meetings for Bikky, which manages customer data for restaurant chains, and ask him why he hadn’t hired more.

Those sales and hiring insights were especially appreciated at QuickSecure, where Georgia Tech dropouts Rohan Kumar and William Susskind are building a system that combines software and hardware to allow schools to respond faster, and more safely, to emergencies and drills.

Gutter helped QuickSecure run its first engineering recruiting process as it closed a private school with more than 50 campus buildings.

“I think Gutter hit the right balance of being present and supportive,” says Kumar, the startup’s CEO. Later, on a private tour of the Whitney Museum of American Art’s Biennial exhibition, Susskind reflects that he’s glad to be pushed out of the office occasionally by Elbow Grease to experience more of New York. “We know we need to do stuff like this,” he says. “Maybe you see a painting, and you’re inspired.”

Out of the gutter

A few weeks after demo day, four of the eight startups in the batch work at least partially out of Gutter’s offices, which recently added a second floor for portfolio companies. Several, with deeper ties in Los Angeles and San Francisco, have moved home; the batch’s Atlanta-based company, Punch, is considering sharing a local space with another Gutter company.

With the dust settled, highlights of their progress in Elbow Grease include:

• First enterprise customer wins for JoyWork, Keeper Systems, and big customer wins for LegacyAI, Punch, QuickSecure and SwitchBoard

• Adoption of GRAIL’s agentic science editor by researchers across 75+ academic institutions

• Endorsement of Galileo Space’s first mission capability (targeting next year) by the Air Force Research Laboratory’s Director of Space-Based Sensing

• Fundraising discussions still confidential for now

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One big question for Elbow Grease: how that connection scales. It’s no secret that seed funds are looking to incubate startups and work with founders earlier than ever, partly in response to how quickly a startup can get going today, partly to make the venture math work securing more ownership in a company at higher valuations.

“You have a lot of funds that all look the same competing for the same eligible companies,” admits Teran.

Elbow Grease also isn’t the only game in town. Just in New York, promising startups have come out of recent programs at Betaworks and Newlab, as well as the South Park Commons founder community; stalwarts like Techstars and Entrepreneurs Roundtable Accelerator (ERA) still operate. Plus, in the AI era, the pull of San Francisco remains strong to a host of options, from YC to a16z speedrun, Neo, PearX, and more hacker house-style residencies like HF0.

From their structure to the type of startup they seek, each projects its own signature style. But Teran is aware that high-pedigree founders might apply to several. Gutter is simultaneously embracing the competition, while adding systems to try to vet applicants for the new batch for their genuine interest and fit.

Much of Elbow Grease’s momentum falls on the shoulders of its small team: Teran, Gettinger, and a small team including operating partners Richard Hughes and Vince Li, and operations lead Carli Casteel. Gutter leverages their personal and professional networks hard to attract top mentors and speakers. A fund, called Gutter Infinity Fund, that shares 5% of fund profits with its portfolio founders helps; so does giving mentors upside in Elbow Grease.

But as Gutter hopes to review 1,000-plus applications for its second batch – and settle into a rhythm of two batches every 18 months or so – the pressure is on its selection process, first for the right startups, and then for the right mentors, Teran notes.

“It comes down to who you’re choosing,” he says. “We only have so many hours to focus on interviewing and screening the right subset of companies.”

Participants in Elbow Grease’s first batch are bullish, from the founders to the experts tapped. About half of mentors still communicate weekly with their batch founders, according to Teran, and have made personal investments or taken formal advisor roles.

At Electric, Denehy says he’s still in a Gutter-created Slack channel riffing with the Galileo team several times per week.

“If you’d told me from the outset, ‘Hey, our expectation is that you’re going to be talking to these guys multiple times per week, you’re going to be reading pitch decks,’ I’d have said I don’t have time for that,” he says. “But the reality is, busy people always have time for one more thing, if they want to do it.”

Stripe’s Hughes Johnson has spoken at more accelerators and founder dinners than most. She says she was impressed by the level of preparation of Elbow Grease’s founders to go beyond ‘the ABCs’ in their questions, which she sees as a testament to the Gutter team.

“You’ve got to love that they call themselves Gutter, and this program Elbow Grease,” she says. “They actually put their hands in and get dirty.”

But while there’s something “pretty special and unique” about Elbow Grease for Hughes Johnson, she doesn’t think it needs to be.

“We need more of this, in more places across the country.”

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.”