Sourcery

BREAKING: Skydio Hits $4.4B on $110M Series F

Thu, 23 Apr 2026 15:04:26 GMT

Why Skydio Raised Less Than Investors Offered

Today, Skydio announces a $110M Series F at a $4.4 billion post-money valuation. CEO Adam Bry joins Sourcery to dive into the oversubscribed funding process, scaling to over 9 figures in revenue (!!), commercial progress, military + public safety use cases, & their manufacturing edge at Skydio's Hayward factory.

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ICYMI: Skydio is the largest drone factory in the US at scale production.

“The most significant fact in our Series F is how little we are raising. Despite investor demand to put substantially more into the company… our capital needs are rapidly decreasing.” – CEO Adam Bry

Skydio by the numbers:

$100Ms in annual revenue, strong unit economics
~900 employees
60,000 autonomous flying robots shipped
3.4M+ customer flights
3,800+ enterprise customers
1,200+ public safety agencies (doubled YoY)
45 of 51 state transportation agencies
Every branch of the US military plus 29 allies
11M+ Americans live within 2 miles of a Skydio DFR Dock

In this conversation, we cover:

$100M Series F & the unit economics behind a $4.4B valuation
$50M, 3,000 drone US Army contract and tripling production in 2026
R10 indoor drone & F10 fixed wing
Turning down DJI’s acquisition offer in 2014
Getting sanctioned by the Chinese government in 2024
Drone as First Responder: Public Safety customers in 42/50 states, 42% auto theft reduction in SF
Built a China independent supply chain & US manufacturing at scale
Flying agentic AI & the future of autonomous robotics
Adam’s hottest take on the robotics hype cycle

If you’re interested in reindustrializing America, drones, AI, robotics, & fundraising in hard tech, this one’s for you.

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(00:00) Adam Bry, CEO of Skydio
(00:54) Inside Skydio Factory
(01:31) Series F & Scaling
(03:17) Customers And Growth
(04:10) Origins & Reliability
(08:04) Civilian Missions Focus
(09:19) Drones For Public Safety
(12:29) Edge Compute & Partners
(14:20) Expanding Drone Lineup
(17:10) Beating China & DJI
(19:34) US Supply Chain Resilience
(21:33) Reindustrialization Timeline
(22:35) Hiring & Leadership
(27:12) AI Agentic Drones
(30:08) Metrics & Operating Model
(31:49) Hottest Take And Advice

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The Flying Robot Company That Raised Less Than Investors Wanted

Conversation with Skydio CEO Adam Bry on Series F, Autonomy, & the New Physics of Robotics Capital

Most venture headlines in AI and robotics run the same way: more capital raised, more dilution accepted, more runway needed to chase receding horizons. Skydio’s announcement this week runs the other direction. $110 million at a $4.4 billion post-money valuation, a modest check by current standards, deliberately so.

Turns out, when you have strong unit economics + 9 figures in revenue you can think more critically about preserving equity & not taking money just for money’s sake.

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Twelve years in, Skydio has moved from the first drone hype cycle, where “a bunch of capital was invested,” to being, in Bry’s telling, “really the only surviving company that’s building drone hardware from that era.” What makes the round interesting is how Bry himself frames it: “I really think the most significant fact in this whole thing is how small it is. We’re in the very rare and hard earned position amongst robotics and AI companies of actually having rapidly declining capital needs.”

It’s a philosophy he traces back to something older than the current cycle. “One of the frameworks I think about is get rich slowly,” Bry said. “Enduring value and enduring impact is built on a lot of patience and a lot of hard work... the signal trumps the noise. It always does.” What follows is our read on the business, the product thesis, and why the scarcity of this raise may be the loudest signal in the release.

A Humble Series F

The core business is funding the core business.. Skydio reports nine figures of annual revenue, 60,000 flying robots shipped, and roughly 4,000 enterprise customers, including every branch of the U.S. military, 1,200+ public safety agencies, and 45 of 51 state transportation agencies. In a sector pricing in optionality rather than operating income, that reads differently.

The capital is going to unglamorous work. “The number one constraint we’re facing right now is building more drones faster,” Bry said. “We will be tripling production over the course of this year.” A $52 million, 3,000-drone Army order, “actually not that much of an outlier for us these days,” tells you throughput is the bottleneck. Raising only what you need when the market would give you more is a rare signal in robotics.

Autonomy Bet, Compounding 12 Years Later

Skydio’s contrarian call was that drones needed to fly themselves. That bet nearly ended the company: DJI expressed acquisition interest in 2014. Bry declined because “we felt like they didn’t really understand the importance of autonomy and how much that was gonna change, who could use drones and how they could use them.”

The harder insight was about reliability, not capability. “It’s very easy to make a demo, like getting a drone to take off and fly around manually is like 0.1% of the way there,” Bry said. “What’s really hard is... making it work 90% of the time, 99% of the time, 99.9% of the time, like chasing those nines of reliability.”

The analogy he reaches for is self-driving: “Google could demo a self-driving Waymo like 15 years ago, but it’s taken them 15 years to make that reliable.” Skydio’s nines, already “built on hundreds of thousands, millions of flights,” now power what Bry calls a “family of flying robots,” including an indoor drone and a fixed-wing aircraft. Twelve years of narrow focus produced a platform.

Public Safety: Business That Also Saves Lives

Skydio drones now sit in 1,200+ public safety agencies in 42 states, a customer count that doubled year over year. Internal analysis of 6,000 flights across 61 agencies found the drone was first on scene 70% of the time, 23% of calls were cleared without patrol, and the drone provided helpful intelligence 92% of the time.

Bry’s prediction: “I think a default expectation in five years is if there’s an emergency, you call 9-1-1, a drone shows up in a few seconds and that’s gonna be everywhere in the US, hopefully everywhere in the world. And that’s gonna change the way policing works.”

He frames these as transparency tools, not surveillance: “these things are essentially flying body cameras.” The evidence spans the stakes spectrum.

San Francisco has seen a 42% reduction in auto theft since launching its drone program. A rooftop call in SF reported a rifle; the drone arrived first and saw a broomstick. Less than 1% penetration against total 9-1-1 volume gives you a sense of the runway.

The China Problem, & Why the Supply Chain Became a Feature

“We have the great joy and we’ve had it for the last 12 years of being the US underdog against the best of Chinese industry, really state sponsored Chinese industry,” Bry said. Rather than trying to out-spec DJI on hardware, Skydio chose autonomy and end-to-end solutions as the competitive axis.

Then the geopolitics turned operational. “A year and a half ago, we had the great honor of being sanctioned by the Chinese government,” Bry said. “They announced the sanctions and then they showed up at the suppliers that we still had in China, shut them down, stopped them from doing business with us, really tried to, to kill us.” The result: “We now have by far, the most secure drone supply chain in the world, independent from China, that’s operating at increasing levels of scale.” In a market where critical-industry customers are writing Chinese drones out of procurement, being the non-Chinese manufacturer at scale isn’t a marketing line. It’s a category position.

Building “Shenzhen” in the East Bay

Reindustrialization is slower than the rhetoric suggests. “It’s not as fast as people might like,” Bry said. “But it’s not an impossibility. It really just is a matter of like getting the flywheel spinning.” His mental model is ecosystem density. “There’s some alternate universe where this looks like what Shenzhen looks like, and I don’t think there’s anything fundamentally that should stop us from getting there.”

Skydio is positioning as an anchor tenant: an 80,000 square foot facility in Hayward, roughly 900 employees, and a US manufacturing bet placed in 2014, long before “reshoring” became a policy talking point.

The talent math is where it gets interesting. “The talent level’s great at every skill set you need,” Bry said. “There’s not as much of it as we might like, but the talent level is incredibly high.” A meaningful share comes from one familiar source:

“Tesla has generated some great alumni & we have a lot of fantastic folks that have come from forged in the fire of production hell on Model 3.” Seen throughout our interviews Tesla has served, time and again, as the involuntary training program for a generation of American hardware operators.

The hiring filter is unconventional by design. “We just focus on people that we think have enormous potential, whether or not they have sort of like the proven, exact right experience.”

What Bry screens for: “that sort of spark of capability... the combination of the fire and passion to do hard things and then the horsepower to get it done.” The closing pitch is structural: “There’s no other company in the world really, that has the combination of like true cutting edge sci-fi technology... in a real business that’s really working with customers that depend on this in like lifesaving critical missions.”

Flying Agentic AI, & Why Hardware Makes It Harder

The product roadmap, in Bry’s framing, points toward “our drones being like flying agentic AI, just like you have an agent that you interact with on your computer or in the cloud. This thing is an agent that can move & do work for you in the physical world.”

What makes this more than narrative is the underlying infrastructure, “a very rich simulation environment” that lets the team “test and iterate with the real physics that you care about in the loop. So you can do a lot of like the speed of software with hardware involved.” That last phrase is the thesis in one line.

Bry is unsparing about where the sector is heading. Asked for his hottest take:

“I think that we are gonna see a lot of pain and carnage on the investments and expectations that are being set in this sort of like most recent round of robotics companies. ‘Cause it’s just, the physical world does not work the way the software world works... The learning cycles are slower, they’re more expensive.”

A CEO who has already survived one drone trough of disillusionment naming the next one out loud is worth noting.

How Skydio Operates

Bry runs against cascading OKR culture. “I am not big on these like sort of like super granular cascading OKRs... organizing really small teams around them. You want the team to be as small as it possibly can be. And in the AI you can do pretty amazing things with pretty small teams.” The weekly executive meeting opens with customer success, “good, bad, and ugly.”

His advice to founders building into the same bubbly moment is pointed: “get really anchored in the customer problem that you’re trying to solve and building world class technology to solve it.” Easy to say. Much harder when the gravitational pull of fundraising milestones and vanity metrics is core to silicon valley culture.

What to Watch

Skydio’s Series F is a rare artifact in the current cycle: a priced round sized to operating reality, not market appetite. The thesis Bry laid out, autonomy as the compounding moat, public safety as the growth vector, US manufacturing as a structural advantage, and flying agentic AI as the frontier, is as coherent as any in robotics this year.

The risks are the ones Bry named himself: the physical world moves slower than software, reliability is earned one nine at a time, and a lot of peers in this cycle will learn that the hard way.

Near-term milestones are concrete.

Triple production. Scale DFR 3x. Ship the indoor drone and fixed-wing. Extend the end-to-end playbook from law enforcement into site security, utility inspection, and battlefield ISR. If Bry is right that “the signal trumps the noise,” this raise and how it was constructed is one of the clearer signals the sector has produced in a while.

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The material presented on Molly O’Shea’s website are my opinions only and are provided for informational purposes and should not be construed as investment advice. It is not a recommendation of, or an offer to sell or solicitation of an offer to buy, any particular security, strategy, or investment product. Any analysis or discussion of investments, sectors or the market generally are based on current information, including from public sources, that I consider reliable, but I do not represent that any research or the information provided is accurate or complete, and it should not be relied on as such. My views and opinions expressed in any website content are current at the time of publication and are subject to change. Past performance is not indicative of future results.

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BREAKING: General Catalyst's First-Ever Quarterly Review

Mon, 20 Apr 2026 15:19:34 GMT

Is GC Going Public??

Hemant Taneja, CEO of General Catalyst, joins Sourcery to unpack GC’s first-ever quarterly review.

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We cover why he wrote it, the state of GC’s $43B ecosystem, why the software buyout model is broken, Anthropic’s Series G at $380B, the $5B India commitment, buying Summa Health in Akron, the Anthropic–DoW controversy, Mythos & Project Glasswing, Nikesh Arora joining the GC board, and his take on “Tech Lash 2.0.”

Plus: Is GC going public??

HUGE thank you to Deel Co-Founder & CRO Shuo Wang for the introduction to Reggie & the GC team, I am SO happy we were able to make this happen so quickly! What a treat.

Read the Q1 2026 Review (overview below)

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(00:00) Hemant Taneja, CEO at General Catalyst
(01:21) Present state of General Catalyst
(05:08) Seed, Creation & the Customer Value Fund
(06:54) The purpose behind GC’s quarterly review
(12:26) The biggest AI themes right now
(15:06) How AI is changing investing decisions
(19:25) Why seed and Series G require the same conviction
(22:55) Why does he holds two titles at once?
(25:17) Why Silicon Valley glorifies the wrong founders
(27:52) Building an AI-native hospital
(30:20) Managing funds through bubbles and hype cycles
(33:13) How funds sell ownership stakes
(34:59) Why the software buyout model is broken
(38:44) Vista's new software buyout debt fund
(39:20) Global Resilience vs. American Dynamism
(41:49) GC’s $5B bet on India
(45:48) What global investing teaches you
(47:09) The four forces of tech lash
(50:12) Anthropic and the Pentagon
(54:06) Is GC going public?
(55:26) The Puka shell necklace era

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GC’s First-Ever Quarterly Review

CEO Hemant Taneja on AI’s Dominance, Broken Buyouts, & the Gray Areas of Silicon Valley

I just sat down with Hemant Taneja, CEO of General Catalyst, at the top of GC’s San Francisco tower to walk through the firm’s first-ever quarterly review.

It’s a deliberate break from the X-thread, fireside-soundbite, click-bait rhythm that dominates venture commentary, a slower, more honest attempt to make sense of a quarter where, as Hemant puts it, “a lot of large things happened simultaneously, none of them finished.”

Below, I’ve pulled out the most data-oriented sections of the review and our conversation, the software buyout math, the Anthropic trajectory, the India commitment, the CVF credit rating milestone, and tied them back to the broader argument Hemant is making about where durable value lives in the AI era.

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Why the First Quarterly Review, & Why Now?

Hemant’s explanation for the review’s existence is itself a thesis about the industry. The formats venture has defaulted to, X, LinkedIn, conference stages, reward certainty & controversy. None of them, in his view, help anyone actually make sense of the world.

When I asked him why he started writing it now, he told me, “A lot of that generally skews us towards taking very polarized views, because the stuff that amplifies in the world tends to be very black or white, & the progress gets made in the gray.”

The review itself opens on the same note, the volume of happenings, the heat of the discourse, and the open question mark of what the very near future will look like can leave one feeling confused. Hemant’s answer is to slow down, once a quarter, take stock, and be willing to sit with complexity rather than reach for a clean narrative.

The format also forces GC to be honest about the arc it's actually on. In our conversation, Hemant described three core investment strategies:

Seed (the firm's venture capital fund, which he calls the largest seed fund of its kind)
Creation (the build-and-transform strategy run by Marc Bhargava that includes AI-native rollups like Long Lake, Crescendo, Udia, and company hatches like Hippocratic AI)
Customer Value Fund (the investment-grade-rated product for scaling proven businesses)

But those three sit inside a larger ecosystem. Percepta is GC's in-house AI engineering and research company, bringing applied AI into GC's portfolio and partner institutions. HATCo is the healthcare transformation arm behind the acquisition of Summa Health in Akron, Ohio, where the firm is trying to build the first AI-native community hospital. GC Institute is the policy arm operating out of DC, Brussels, and Delhi. And GC Wealth, launched publicly in early 2025 under former First Republic executive Dave Breslin, is the firm's full-service wealth management business for founders and entrepreneurs in GC's Famiglia.

The Anthropic Trajectory & What “Beneficent Singularity” Actually Means

The review leads with AI because, as Hemant writes, “I can’t start anywhere else.” The central data point: Anthropic went from $1B to over $20B (now allegedly at $30B ARR) in annualized revenue run rate in fifteen months. GC first invested in March 2025 at a $61.5B valuation. The March 2026 Series G valued the company at $380B, one of the largest venture rounds in history.

Hemant frames this through the Collisons’ 2026 Stripe letter, which reported over 700 AI agent companies launching on the platform in a single year. Their conclusion: the world may be at the advent of a different and “hopefully much more beneficent singularity.” Hemant lingers on those word choices, hopefully, beneficent, because they frame the singularity as an aspiration, not a foregone conclusion.

His point is that capability is no longer the binding constraint. Trust is. The companies that will matter in thirty years, he argues in the review, are not necessarily the most technically capable ones. The actual transformation happens inside regulated, high-stakes environments, healthcare, education, defense, financial systems.. he makes it clear, those environments don’t reward or value raw capability. They reward embedded trust.

That reframing is what drives GC’s full-arc positioning: 20 seed investments this quarter alongside the Anthropic Series G, Percepta building AI engineering capacity in-house, and Creation doing AI-native rollups of incumbent businesses.

Why the Software Buyout Model Is Broken

The most data-dense section of the review is Hemant’s breakdown of software buyout economics, and it’s worth walking through carefully. The setup: a high-quality B2B software business bought a few years ago with sticky enterprise contracts, 90% gross margins, and 25% annual growth. Entry price: 25× EBITDA (roughly 40–50× FCF), partially financed through private credit, underwritten to a five-year hold. Over that hold, EBITDA grows 50%. The debt comes down. By every operational measure, the deal worked.

And yet the equity doesn’t. EBITDA growth added $625M in enterprise value. Multiple compression from 25× to 12× destroyed $975M. The deal returned 0.68× MOIC, roughly a third of LP capital lost on a deal where the underlying business succeeded. Annualized over 5 years, that’s a deeply negative IRR of approximately -7%.

When I asked Hemant to walk me through this live, he put it simply: the assumption of terminal value is gone.

“In the world of AI today where code is self-writing, to say that a piece of software in a company that has been existing, let’s say five years, the free cash flow, that is worth 30 times, means you’re gonna have 30 years of generating that free cash flow is what you’re paying for. How can you ever make that assumption when technology is changing so fast to say these things are that durable?”

His path forward: structure software buyouts the way mature-category buyouts have always been structured, on cash flows, not exit multiples.

In the review, he runs the counterfactual. Same $50M EBITDA business, 25% growth, but entered at 10× instead of 25×. $500M enterprise value, 3× EBITDA leverage, $350M equity check. Over five years, EBITDA grows to $75M through genuine AI-driven operational improvement. Exit clears at 14×. Enterprise value: $1.05B. Equity recovered: $950M. That’s a 2.7× MOIC and an IRR of approximately 22%.

The CVF Credit Rating & What It Unlocks

The other major financial milestone Hemant discloses in the review is that GC’s Customer Value Fund reached an investment-grade credit rating this quarter, BBB or higher on roughly two-thirds of the capital. To the best of GC’s knowledge, no venture product has ever reached that threshold before.

The unlock is scale. Apollo Global Management runs close to a trillion dollars in AUM, roughly 80% of it in investment-grade credit strategies. That’s nearly $800 billion in capital at a single asset manager that venture has historically been unable to access, because venture investments carry no defined duration or structure. CVF, which launched in 2021, can now bring that kind of capital to bear for GC’s companies, and it did so in under five years.

Fast-growing companies like Fivetran, Kandji, Ro, Travelperk, Superplay, & Upside are already using CVF to fund customer acquisition costs, with CFOs describing it as a dedicated growth balance sheet that scales with unit economics rather than market conditions.

In our conversation, Hemant tied this back to the software buyout argument. CVF gives GC the ability to drive returns in software on a cash flow basis, without the terminal-value dependency that’s impaired so much of the current vintage. Paired with Creation (for transforming established assets) and Percepta (for the AI orchestration layer), it’s the financial infrastructure behind GC’s thesis that the next era of private markets returns comes from AI-driven top-line growth, not cost-cut exits.

GC’s direct exposure to pure SaaS, for context, is approximately $4 billion out of $43 billion in AUM, much of which sits across strong pre-2022 companies in fintech, healthcare, and defense that have embraced AI and are scaling faster than ever.

The $5B India Commitment & Global Resilience

In February, Hemant flew to New Delhi for the India AI Impact Summit and announced a $5B GC commitment to India over the next five years, one of the largest single-country investment commitments in venture history. When I asked him why India, he broke it into three forces playing out at once.

The first is systems. India has a pattern of solving for constraint: generic pharmaceuticals, UPI (which now processes more transactions than Visa and Mastercard combined), and Aadhaar (the largest biometric database on earth).
The second is scale, India will be the third-largest economy within the decade, with one million young people entering the workforce every month.
The third is resilience: India is actively diversifying away from Russian defense procurement, with 75% of modernization spend now going domestic (up from roughly 60% five years ago) and defense exports up 34x over 11 years.

The India commitment sits inside a broader global resilience thesis. Hemant told me in the interview that the geopolitics will force supply chains to shift, Europe won’t buy American defense products at scale, India won’t rely on Russia and America, so defense primes will emerge everywhere.

GC is already invested in Anduril & Saronic in the U.S., Helsing in Europe (Jeannette zu Fürstenberg, Daniel Ek, & Paul Kwan), and Raphe in India.

In Hemant’s framing, this isn’t just TAM expansion. It’s a values statement about inclusive prosperity: if AI concentrates opportunity in a handful of companies in a handful of countries, society will reject it, and the whole transformation stalls.

Anthropic–DoW Controversy & Mythos

The part of the review I most wanted to push him on was the Anthropic–Department of War situation from February. The public discourse simplified fast into “morally superior AI vs. unbridled acceleration.” Hemant’s position is that neither side captures what actually happened.

“I cannot say the perspectives on either side were wrong,” he told me. “It’s just like from where you sit and how you think about the world and what’s important is really what led to those issues. But what it shows you is just the complexity of what we have to go through.”

His concern in the review is less about the specifics of that deal and more about what it revealed.. how quickly the public reached for the exit in the face of genuine complexity. The same questions, where should AI have no role, who decides, will show up in healthcare, education, workforce, and online safety, and the industry needs governance architecture, not leadership preference.

Mythos is the other side of that coin. Anthropic could have released it broadly; instead they rolled it out through Project Glasswing to a tight group focused on eliminating vulnerability debt in critical infrastructure. Hemant calls the restraint admirable, “the opposite of move fast and break things,” and adds news that Nikesh Arora, who’s joining GC’s board, is part of the “Glasswing 40.” His broader point is that the individual moral frameworks from leaders like Dario aren’t a governance system. The industry needs the architecture to make responsible deployment practice rather than preference.

The thread running through all of this, Anthropic, Mythos, Iran, the DoW, is Hemant’s argument that the world is irreducibly gray, and the leaders worth following are the ones who can stay in the gray long enough to design something durable.

Nikesh Arora Joins the GC Board

One of the more consequential governance moves Hemant discloses in the review is that Nikesh Arora, CEO of Palo Alto Networks and one of the “Glasswing 40” working with Anthropic on Mythos, is joining GC’s Board. In the review, Hemant writes:

“I’m very glad that Nikesh Arora, who is joining GC’s Board, is part of the ‘Glasswing 40’. Nikesh has led some of the most consequential technology organizations in the world, and I’ve long admired the clarity and ambition he brings to everything he does. His presence reflects the caliber of judgment being brought to bear on a model that could either do serious damage to or rigorously armor the world’s critical systems.”

Ken Chenault remains Chairman, and Nikesh is joining as lead independent director. When I asked Hemant about the appointment in our interview, he laid out the reasoning:

“He’s been, first of all, incredibly brilliant. He’s an entrepreneurial soul running a Fortune 500 company, & he’s done an incredible job building that. He’s also a great investor mind. So as I think about our GC ecosystem, which is a collection of investors & builders, I just think he’d be a great mentor for all of us.”

The framing mattered. Hemant isn’t treating this as a prestige appointment. He’s treating it as another node in the leadership structure he’s built around himself, Ken Chenault as Chairman, former industry CEOs like Ken Frazier, Kate Walsh, Steve Klasko, and Daryl Tol helping run the company, and now Nikesh as a sounding board on where AI and enterprise security are actually headed.

“I’ve had many conversations with him about where this is going, and every one of those conversations is extremely provocative,” Hemant told me, “and so it made sense to lean on him. Just like we lean on Ken.”

Tech Lash 2.0 & The 4 Forces Driving It

The review’s most pointed section is Hemant’s argument that “Tech Lash 2.0” is already building around AI, and that it’s largely self-inflicted. He cites a Wall Street Journal piece calling this “The Most Joyless Tech Revolution Ever” and agrees with the framing. The anxiety spreading across society about displacement, erosion of human connection, and what intelligent machines mean for ordinary lives is, in his view, fully rational, and largely a response to who Silicon Valley has become in public. He identifies four forces that have brought us here.

The first is the social-media optimization of our minds. Hemant argues the industry has given itself false permission to decouple its social presence from its real-life presence. “Moderation does not get engagement,” he writes. “Nuance does not go viral. And so we have collectively abandoned both.” When I asked him about it directly, he was blunt: saying ridiculous things for attention is “a entirely unproductive exercise if you’re focused on the long term. It is a very short term mindset.”
The second is politics contamination. When Silicon Valley entered the political arena in recent years, Hemant argues it had the opportunity to export its best qualities, bias for action, intellectual honesty, genuine meritocracy. Instead, it imported the worst qualities of politics: tribal sorting, ideological litmus tests, zero-sum conflict performed for audiences rather than resolved for outcomes. GC’s response was to decline political engagement and instead launch the GC Institute, investing in policy education across DC, Brussels, and Delhi. “We had the institutional credibility to elevate public discourse,” Hemant writes. “We degraded it, and our own industry, instead.”
The third is the genius asshole myth. Hemant’s argument here is empirical: organizational behavior research consistently shows that one genuinely toxic person costs an organization hundreds of thousands annually in lost productivity, turnover, and diminished performance. When someone succeeds and happens to be cruel, we assume causation when the evidence shows coincidence. “They succeed despite it,” he writes. “They would succeed more without it.” The theory that brilliance licenses cruelty, in his view, is both statistically wrong and a license most people invoking it don’t qualify for.
The fourth, and the one Hemant has made personal mission to counteract, is contempt for legacy. In Silicon Valley’s culture, “legacy” has become a synonym for irrelevance. He considers this precisely backward. “Legacy is a beautiful word and a beautiful thing to have built. The word itself tells you what matters: you endured. You were trusted long enough, by enough people, to leave a mark.” The industries tech most wants to transform are governed and trusted by people with decades of institutional knowledge the industry doesn’t have. Treating them as obstacles rather than partners is, in his view, how the transformation stalls.

The Case for Kindness (Not Niceness)

When I asked Hemant about the kindness argument in our interview, he was careful to draw a distinction that matters. Kind is not the same as nice. “Kindness and ambition are not at odds with each other,” he told me. “I think in Silicon Valley, we try to glorify the asshole symptom of founders thinking that’s almost a necessary ingredient to succeed. And I don’t think it has to be that way.”

Niceness, in his framing, is often its own form of contempt, conflict-avoidant, performative, unwilling to engage seriously with ideas that challenge. Kindness is different. Patrick Collison delivers difficult feedback and disagrees directly. Satya Nadella inherited a Microsoft defined by stack-ranking and internal warfare and disbanded the toxic culture while raising expectations. Paul Graham described his purpose at YC as “trying hard to prevent assholes from getting funded.” Roger Federer was the most ferociously competitive player of his generation and never once diminished an opponent.

Hemant ties this directly back to GC’s investment thesis. Transforming healthcare, defense, energy, and education at the scale the firm envisions requires trust from governments, regulators, legacy institutions, and communities who have no obligation to extend it. Burning that trust for engagement metrics and personal brand isn’t just a cultural issue, it’s a business risk that compounds across every deal GC wants to do. His closing line captures the argument cleanly: “AI can’t cure cancer if we’ve convinced everyone it is a cancer.”

The quarterly review, in that sense, is itself part of the answer: slower, more honest, more willing to sit in the gray. It’s a bet that the industry’s most durable competitive advantage over the next decade won’t be capability. It will be whether we were worth rooting for.

Read the Q1 2026 Review

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ICYMI: Sourcery 🤝 NYSE

Sat, 18 Apr 2026 14:29:28 GMT

Brought to you by Brex:

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Sourcery 🤝 NYSE

Some personal news: Sourcery is officially partnering with the NYSE. 🔔

Sourcery & NYSE are partnering to keep the “brrr” in breaking IPOs, tech financings, & markets coverage — interviews with founders, CEOs, & investors.

My goal with Sourcery has always been to have investing & tech conversations in an authentic, informative, & fun way (jury’s still out on if the Jack Altman/Sam Altman clip is real). Getting to do that from the floor of the New York Stock Exchange with the support of their team is next level. I couldn't have asked for a better start to 2026.

This partnership means Sourcery will be filming & covering select NYSE listings, market events, & milestones throughout the year, plus collaborating on more interviews ( Lynn, if you’re reading this - hot take? ). Expect more IPO days, CEO sit-downs, investor conversations, & the occasional hot take. Bi-coastal Sourcery is about to be in full swing.

Huge thank you to Lynn Martin, Joe Benarroch, Kevin Hawkins, Lance Glinn, & Kristen Scholer for believing in what we’re building & making room for an independent & fast growing voice on the floor. I couldn’t be more excited.

Very special shoutout to Christian for the intro wayyyy way back last fall.

PS — If you’re a very large space or AI company planning a HUGE IPO.. or a CEO or investor with an amazing story, the NYSE floor is waiting. Reply to this email or DM me on X.

PPS — And yes, rumors are circulating this may have been bigger than the Polymarket deal 😂

PPPS — If you have any leads on any of the big names below, would love any help in making these interview happen! Thank you :)

ICYMI: Public @ NYSE

Leif Abraham, Co-CEO of Public, joins Sourcery at the New York Stock Exchange for one of the more fun discussions on finance & how Public is building the next-gen investing platform “for those who take it seriously.” Public represents the first modern alternative to Schwab, Fidelity, and Vanguard — platforms built on decades-old infrastructure that haven’t kept pace with today’s investors or technology.

→ Listen on X, Spotify, YouTube, Apple

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NYSE 🤝 Sourcery Interviews

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🔔 NYSE-Listed Companies

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BREAKING: The AI Brokerage Quietly Taking on Schwab, Fidelity & Vanguard

Molly O’Shea — Fri, 17 Apr 2026 13:58:22 GMT

The Great Wealth Transfer

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The results speak for themselves: millions of users, billions in assets under management, billions in trading volume, and revenue doubling annually as they continue to capture market share.

Public’s differentiators include:

AI agents that automate complex investment strategies
AI-powered custom indices with “Generated Assets”
Comprehensive multi-asset trading across stocks, ETFs, crypto, options, treasuries, bonds
Sophisticated investors with real assets to compound

Leif and I explore how their AI-generated defense index hit 350% returns in one month (big surprise.. lol), the future of financial advisors in an AI world, and why their co-CEO structure works better than solo leadership. Leif explains why they focus on the top 25% instead of chasing mass market speculation, plus his thoughts on AI, wealth inequality, and what it means for society.

For investors seeking a serious platform that combines traditional financial services with cutting-edge technology, this conversation offers valuable insights into where the industry is already aimaxxxxing & what comes next.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Leif Abraham, Co-Founder & Co-CEO at Public
(01:10) Competing with traditional brokers
(03:45) AI agents for investing
(05:22) Are Public’s videos AI or real?
(06:31) Why the co-CEO model works
(09:34) The evolution of Public
(15:06) Growth, scale, and market cycles
(17:59) Breaking down the product ecosystem
(20:45) Public's design philosophy
(22:59) Building portfolios with AI
(26:33) Ideal customers
(30:20) The decline of financial advisors
(33:00) What AI replaces in wealth management
(35:45) Risks and limits of AI agents
(37:32) The “financial super app” debate
(42:39) Prediction markets vs real investing
(45:04) Will everything become tradable?
(46:22) Tokenization: the hype vs. the reality
(50:37) The future of financial platforms
(55:56) VCX, closed-end funds & speculation risk
(59:09) Would Public go public?
(01:02:22) What’s next for Public
(01:04:08) The dark AI prediction
(01:06:52) Wealth taxes vs. UBI

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Public’s AI-First Challenge to Wall Street’s Old Guard

How Leif Abraham Is Building the Next-Generation Brokerage That’s Stealing Accounts from Schwab and Fidelity

In a candid interview at the New York Stock Exchange, Public Co-CEO Leif Abraham revealed how his company is quietly disrupting the investment management industry.. not by chasing retail day traders, but by building a premium platform designed for serious investors in the AI era.

With $400 million raised across five funding rounds and a product suite spanning stocks, bonds, crypto, options, & innovative AI-powered tools, Public represents the first credible challenge to legacy giants like Charles Schwab, Fidelity, and Vanguard in decades. With millions of users, billions in assets under management, and revenue doubling annually.. they’re well on their way.

→ Listen on X, Spotify, YouTube, Apple

The Strategic Pivot: Targeting the Top 25%

Unlike most fintech startups that chase mass market adoption, Public made a deliberate strategic decision in 2022 to focus exclusively on affluent investors — what Abraham calls “the top quartile.”

“In the US, sadly, if you are in the sub 75%, you live paycheck to paycheck, credit card to credit card, you do not have money to compound in the markets. You just don’t,” Abraham explained. “And that’s also why you see the people who acquire those & build products for those [customers], and consumer FinTech, you have to monetize those through loan speculation or interchange or something.”

This “Level Up” strategy fundamentally changed Public’s approach. Rather than building a simplified app for mass adoption, they created a sophisticated platform for investors with real assets to compound. The competitive results are proving it out: Public’s ACAT (Automated Customer Account Transfer) data shows they’re winning established investors directly from legacy incumbents.

“Actually, if you look at where people ACAT accounts from, where they like consolidate brokerage accounts from, for us it’s Schwab, fidelity, E-Trade, Vanguard, and then Merrill, and Robinhood.”

This metric is particularly significant because ACAT transfers represent existing investors with established portfolios choosing to move their entire accounts — stocks, bonds, ETFs, and cash — from their current brokerage to Public. Unlike new account openings, these transfers require convincing someone that Public is superior enough to their current platform to justify the effort of moving. The fact that traditional giants like Schwab, Fidelity, and Vanguard top the list, with newer platforms like Robinhood trailing, demonstrates Public is genuinely disrupting incumbents rather than just competing for first-time investors.

“We’re not gonna build the money super app. We are rather gonna be the investing super app for lack of a better wording,” Abraham said, explaining why they reject the “super app” trend sweeping fintech.

Funding Journey: Social Investing to AI Unicorn

Public’s $400 million funding trajectory mirrors both the retail trading boom and the company’s strategic evolution toward AI-powered investing. The journey began modestly with seed funding of $2 million in 2017-2018, followed by a $9 million Series A in 2019 to establish their initial social investing product.

Momentum accelerated with a $15 million Series B in March 2020, led by Accel and Greycroft, featuring high-profile strategic investors including Will Smith (via Dreamers VC), Sophia Amoruso, and NFL star J.J. Watt. The timing proved fortuitous as retail trading exploded during the pandemic.

Public’s breakout moment came with a $65 million Series C in December 2020, again led by Accel alongside Lakestar and Greycroft, with additional participation from cultural investors like Tony Hawk and the Chainsmokers’ Mantis VC. This was quickly followed by the company’s unicorn round: a massive $220 million Series D in February 2021 at a ~$1.2 billion valuation, with backing from Accel, Greycroft, Tiger Global Management, and Inspired Capital.

After a quiet period during the 2021-2022 market reset, Public resumed fundraising with a $135 million Series D-2 round in December 2024, including approximately $105 million in equity and $30 million in debt. Accel’s continued leadership across multiple rounds signals sustained conviction from their earliest institutional backer.

The funding composition reflects Public’s positioning at the intersection of consumer fintech, media, and next-generation market infrastructure, consistently attracting both top-tier venture firms and culturally relevant strategic investors.

AI Agents: Future of Investment Management

Public’s most ambitious product launch centers on AI agents that automate complex investment strategies. Unlike simple robo-advisors, these agents can execute sophisticated workflows based on natural language instructions.

“What that helps you do is to essentially automate any type of workflow in your brokerage account. That could be a trading strategy. There can be money movements, that can be alerts you wanna set up,”
“It really moves the way you even just like interact with the platform away from just like clicking buttons to just expressing intent.”

The safety mechanism separates reasoning from execution — users collaborate with AI to design strategies, but the final implementation runs deterministically without hallucination risk. “We essentially separate the reasoning from the execution... once you approve it, it is a deterministic model where that can’t deviate,” Abraham noted.

This approach has enabled sophisticated strategies like covered call income generation, where users can simply ask: “Look at my portfolio. Show me if I could do, you know, five grand a month an income potential on it.”

Generated Assets: Custom Indices Through AI

Perhaps Public’s most innovative product, Generated Assets allows users to create custom investment indices through natural language prompts. The AI scans the entire US stock universe, evaluates companies against user criteria, and builds a tradeable index complete with backtesting data.

One particularly successful example caught attention: “I think the best performing generated asset on the platform right now is defense and weapons manufacturers. That is up ~350% in the last month.”

Beyond thematic investing, users leverage the tool for sophisticated portfolio construction. “Give me, you know, the NASDAQ 100, but truly take out anything that’s not tech,” Abraham cited as a common use case, allowing investors to remix existing indices with precision unavailable through traditional ETFs.

The Co-CEO Advantage

Public’s dual-leadership structure with Co-CEO Jannick Malling reflects European business practices but remains unusual in US venture capital. Abraham argues this creates better decision-making through balanced perspectives.

“I think the biggest thing is, I think first it helps really for more balanced decision making,” Abraham explained. “We’re all human to some regard. We’ll all have a time where we go to the office and we’ll feel anxiety or we’ll be in a bad mood or whatever... And it helps to have someone else who experiences everything the same way you do.”

The structure divides responsibilities clearly: Malling oversees engineering and product design, while Abraham leads growth and business development. This allows each co-CEO to develop deep expertise while maintaining shared accountability for major decisions.

The End of Financial Advisors

Abraham predicts AI will systematically disrupt the traditional financial advisory model by replacing three core functions: grunt work, advice, and emotional support.

“If we look at what a financial advisor does for you, we break down these in these three things where it’s like,
#1 grunt work. So like doing the actual tax loss harvesting, setting up some strategy for you, etc.
#2 is advice, which is basically opinions on the back of news, data, reports
#3 is emotional support.”

AI agents already handle the first category. Abraham believes emotional support may actually be easier to replace than most assume: “Considering you have people having full relationships with AI already now... I think that part is actually easy to replicate.”

The advisory industry faces demographic headwinds that compound the AI threat.

“There’s less people becoming financial advisors now. New generations. The average age of financial advisor that goes up by year every year. I think the average age is actually roughly 60 or so already.”

Contrarian Views on Prediction Markets and Tokenization

While billions flow into prediction markets and tokenization projects, Abraham takes a measured skeptical stance on both trends.

On prediction markets: “I think prediction markets or event contracts... are very powerful tool. We’re gonna go into that somewhat ourselves, but I think right now, the use of events, contracts or the use cases are being stretched.”

He sees value in securities-focused contracts for sophisticated portfolio management but criticizes the expansion into entertainment betting: “Using it to essentially do sports betting... I would argue it’s kind of, you know, stretching the use case.”

Regarding tokenization, Abraham cuts through the hype: “Just because you wrap it in a token doesn’t mean it’s trading 24/7... it’s a fucking wrapper. The issue is liquidity. It’s not if there’s a wrapper of a token.”

The Dark Side: AI & Wealth Inequality

Despite his enthusiasm for AI’s potential, Abraham harbors serious concerns about societal implications. He predicts a difficult transition period before any utopian AI future.. hot take incoming..

“Before we live in this oasis where no one has to, where, where no one has to work anymore and whatnot... I think it’s gonna get really dark and it’s gonna get really dark because... The people that have access to the tools that can use them, that own the tools... the power disparity that’s gonna happen there is gonna be so massive.”

Abraham envisions stark societal changes: “New York is gonna feel more like São Paulo than it does like New York right now, I think... in your New York fancy apartment where you have your doorman, it’s not gonna be a doorman, it’s gonna be an armed security guard.”

The solution, he believes, could require both universal basic income and purpose creation: “It’s not just about creating UBI, it’s also about what can we, can we create that, uh, contingency gives people purpose.”

→ Listen on X, Spotify, YouTube, Apple

About Public: Founded in 2017 by Leif Abraham and Jannick Malling, Public has raised approximately $400 million across five funding rounds, with backing from Accel, Tiger Global, Greycroft, and Lakestar. The platform offers comprehensive multi-asset trading, AI-powered investment tools, and serves millions of users with billions in assets under management. Public’s tagline “Investing for those who take it seriously” reflects their premium positioning against legacy incumbents like Schwab, Fidelity, and Vanguard.

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NEW: Giant Magnifying Glass + Hot Rocks Powering AI Data Centers

Thu, 16 Apr 2026 13:50:40 GMT

Giant Magnifying Glass + Heated Rocks

Most announced data centers will never get built. The ones that do are running on gas generators. And the US grid has no answer for what's coming.

→ Listen on X, Spotify, YouTube, Apple

Hannan Happi, CEO & Co-Founder of Exowatt, joins Sourcery for a rare facility tour of their 40,000 sq ft Miami HQ — walking us through the tech, the team, and why the AI power crisis is far worse than the headlines suggest.

Exowatt has raised $140M in total funding from a16z, Atomic, Felicis, Sam Altman, Leonardo DiCaprio, Starwood Capital, Thrive Capital, & more, all betting on a deceptively simple idea: concentrate sunlight with Fresnel lenses, store the heat in rocks at 1,000°C, and dispatch electricity on demand. 24 hours a day, no grid required.

Their P3 system is built from sand, dirt, and steel. Like a large magnified glass and a rock. No lithium. No cobalt. No China. Target cost: 1 cent per kilowatt hour.

In this episode:

→ Why most announced data centers are phantom projects
→ The real bottleneck that replaced GPUs: power & skilled labor
→ How a 200-year-old engine is at the heart of their stack
→ What hyperscalers actually say about sustainability (it's not pretty)
→ Lessons from Tesla on iteration, modularity & vertical integration
→ Why Miami — and why now

With a 90+ GWh demand backlog and commercial deployments live in 2025, this might be the most important energy company you haven't heard of.

Co-Founded in 2023 by Hannan Happi & Atomic CEO Jack Abraham, Exowatt’s mission is to make sustainable renewable energy always available & almost free.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) What is Exowatt? The solar backbone for AI data centers
(01:30) Why the US grid simply cannot handle what's coming
(02:00) Tour begins: Welcome to Lighthouse Miami — 40,000 sq ft facility
(02:45) The P3 explained: three elements, one shipping container
(04:00) The founding story: Atomic venture studio and the modular hypothesis
(05:30) Why solar thermal hasn't followed solar PV's 99.6% cost decline — until now
(07:00) North star: 1 cent per kilowatt hour and where they are today
(11:00) Inside the world's largest solar simulator — the sun, indoors
(13:00) The Fresnel lens up close: a giant magnifying glass for the sun
(15:00) "It's rock science" — the 5-year-old explanation of how it works
(17:30) Domestic raw materials (sand, dirt, steel) as the real competitive moat
(24:30) The phantom data center problem: most announcements won't get built
(26:00) From 100 MW to 10 GW: how data center scale exploded in 3 years
(30:00) Funding breakdown: $140M, a16z, Felicis, Sam Altman, Leonardo DiCaprio
(37:00) Lessons from Tesla: remove parts, iterate fast, build better

Brought to you by:

Turing—Turing delivers top-tier talent, data, and tools to help AI labs improve model performance, and enable enterprises to turn those models into powerful, production-ready systems. Visit: turing.com/sourcery

VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at GetVCX.com

Merge—The leading provider of customer-facing integrations and agentic tools for frontier LLMs, Fortune 500 organizations, and B2B SaaS companies. Visit: https://merge.dev

The AI Power Crisis Is Bigger Than Anyone Is Admitting

Inside Exowatt’s bet that superheated rocks and a 200-year-old engine can power the next wave of AI infrastructure.

→ Listen on X, Spotify, YouTube, Apple

The Phantom Data Center Bubble

There is a version of the AI infrastructure story that gets told in press releases, keynote slides, and analyst reports. In that version, hyperscalers are constructing a grid of colossal data centers across America, a trillion-dollar buildout that will define the next era of computing. It is a compelling story. The reality on the ground looks considerably different.

Hannan Happi, CEO and Co-Founder of Exowatt, works directly with the hyperscalers building these facilities. His read is straightforward: a lot of the data centers being talked about will never get built.

“There’s a lot of headlines about data center projects being planned or initiated.. I don’t think all of them are actually happening. So there’s a lot of like phantom data centers that are being announced.”

The evidence for this is not hard to find. Stargate, the $500 billion AI infrastructure initiative announced at the White House in January 2025 by OpenAI, Oracle, and SoftBank, was positioned as the defining infrastructure bet of the AI era. Within six months, Bloomberg reported that the joint venture had not hired any staff and was not actively developing any data centers. The three partners had been arguing over structure, financing, and site selection since the day it was announced. OpenAI, unable to secure financing for its own data centers, eventually returned to Oracle and SoftBank for a restructured two-way deal. The Abilene, Texas expansion, which was meant to grow from 1.2 gigawatts to 2 gigawatts, was subsequently scrapped. OpenAI also paused its Stargate UK project entirely, citing energy costs and regulatory uncertainty.

Microsoft told the market in January 2025 that it would spend $80 billion on AI data centers that year. By March, TD Cowen analysts reported that Microsoft had walked away from more than 2 gigawatts of data center leases across the US and Europe, canceled a $1 billion campus development in Ohio across three sites, and frozen new leasing activity almost entirely. The company maintained it was reallocating rather than retreating, but the gap between the January announcement and the March reality was significant.

This pattern, large announcements followed by quiet pullbacks, is exactly what Happi is describing. Land acquisition, permitting, grid interconnection, financing complexity, and the availability of skilled labor all create friction between a press release and a functioning data center. The gap between announced capacity and operational capacity is measured in years and gigawatts, and it has become one of the more important blind spots in the AI conversation.

The Underlying Demand Is Real. That Is Where Exowatt Comes In.

Calling out the phantom project problem is not the same as saying the demand for AI power is overstated. It is not. The International Energy Agency projects that US data center power consumption is on course to account for almost half of the growth in electricity demand between now and 2030. That is not a bubble. That is a structural shift in how the country uses energy, and it is happening regardless of which specific projects get built on which specific timelines.

The distinction Happi is drawing is between announced capacity and real demand, and Exowatt’s own commercial traction illustrates it clearly. When the P3 launched at RE+ 2024, North America’s largest clean energy conference, the company reported a pipeline of more than 1.2 gigawatts of orders. That grew to 85 gigawatt hours of signed customer demand by the end of 2024, and exceeded 90 gigawatt hours by April 2025. Those are not letters of intent or press release projections. They are contracts from data centers, energy developers, & hyperscalers who need power.. & need it now.

The speed at which that backlog has grown reflects something specific: customers are not evaluating Exowatt as a 10-year bet. They are evaluating it as a solution to an immediate problem. The P3 can be deployed with or without grid interconnection, ships in standard 40-foot containers, requires minimal on-site construction, and can be co-located directly next to a data center facility. For a hyperscaler staring down a five-to-seven year grid interconnection queue, a modular system that generates power on-site from day one is not a nice-to-have. It is the only durable option that fits the timeline.

Exowatt has since expanded beyond selling hardware. In January 2026, the company launched ExoRise, a new business arm that delivers fully powered land and energy infrastructure for hyperscale data centers across the US Southwest, covering New Mexico, West Texas, Arizona, and Nevada. The pitch is a turnkey alternative to years of utility bureaucracy: show up, plug in compute, & Exowatt handles everything else.

“Hyperscalers should not have to piece together land, power, & infrastructure on their own,” Happi said at the launch. “ExoRise delivers everything together so customers can focus on compute while we handle the power.”

The demand is real. The grid cannot meet it. And Exowatt is already shipping.

Power Is the Known Bottleneck. Here Is What That Actually Means.

The power constraint in AI infrastructure is no longer a hidden insight. It has been covered extensively, referenced in every major hyperscaler earnings call, and cited as the primary reason behind dozens of infrastructure decisions in the past year. What is less well understood is the specific mechanism by which it constrains the buildout, and why throwing money at it does not solve it quickly.

A single gigawatt data center, the kind that was considered large just three years ago, consumes the equivalent energy of nearly one million US households. Hyperscalers now planning five and ten gigawatt campuses are effectively trying to plug small cities into infrastructure designed for suburbs.

“The grid is just really not built for that,” Happi said. “And what that means is the data centers now have to figure out how to bring their own power online.”

The specific mechanism is the interconnection queue, the process by which new power generation projects formally connect to the US grid. That queue is backed up for years in most markets. A developer who applies today for grid interconnection in a high-demand region can expect to wait five to seven years for approval and connection. No amount of capital accelerates that timeline in any meaningful way because the constraint is regulatory and physical, not financial. Transmission infrastructure capable of serving a ten-gigawatt facility in a rural location does not exist and cannot be built on the timelines hyperscalers need.

The outcome is a shift that would have been considered unusual just two years ago. Data centers are going off-grid. Behind-the-meter power generation, building your own power source on-site rather than drawing from the utility, has moved from an edge case to a near-standard consideration for any large-scale build. The question hyperscalers are now asking is not whether to generate their own power. It is what to generate it from, and how fast they can get it online. The answer most are currently landing on is natural gas, with all of the cost, environmental, and geopolitical consequences that come with it.

Big Tech Quietly Moved On From Clean Energy

For most of the past decade, the major technology companies competed on sustainability as seriously as they competed on product. Net-zero commitments, renewable energy certificates, carbon neutrality pledges. Environmental goals were a fixture of earnings calls and annual reports. That has shifted, and the shift has happened quickly.

Happi describes the current priority order inside hyperscaler conversations plainly. Power availability comes first, at almost any cost and from almost any source. Cost comes second. Environmental considerations, which once sat near the top of the agenda, have fallen considerably down the list, behind reliability, speed of deployment, geopolitical risk, and community impact.

“It used to be one of the objectives,” he said of sustainability. “But it’s now time to power, right? I need power. I don’t care how you make it. Squeeze penguins if you have to, I need the power.”

The result is that natural gas has become the default power source for off-grid data center development. Gas generators are available, scalable, and well-understood. They are also expensive over time, carbon-intensive, and in an environment where energy security has become a national priority, strategically exposed.

The longer-term risk is lock-in. Gas infrastructure built today to power a data center will run for twenty years. The clean energy commitments set aside to enable that build do not go away. They accumulate. The costs associated with emissions, regulatory pressure, and rising energy prices will arrive eventually. The current thinking inside the industry is that speed to deployment outweighs those concerns for now.

Why the Answer Might Be Sand, Dirt, & Rocks

Exowatt’s technology is a direct response to this situation. The Exowatt P3 is a modular, shipping container-sized power generation unit built on straightforward physics and a domestic material supply chain.

The system works in three stages. A Fresnel lens, the same optical technology used in lighthouses, concentrates incoming solar energy onto a thermal battery made of specialized rocks. Those rocks are heated to between 800 and 1,000 degrees Celsius, storing energy for up to five days without additional solar input. That heat is then moved via a circulating air loop to a Stirling engine, a technology that has been in use for 200 years, which converts the temperature differential into electricity on demand.

None of the individual components are new. Fresnel lenses, thermal storage, and Stirling engines are all well-established technologies. The value is in combining them in a way that is modular, factory-producible, and built entirely from domestic materials: sand, dirt, and steel, with no lithium, cobalt, or rare earth minerals involved.

“The most important thing is at the end of the day the raw materials that we use: sand, dirt & steel,” Happi said. “And so you don’t have to go import something from China.”

Exowatt’s current cost is just under four cents per kilowatt hour (!!). Their long-term target is one cent, which would make it among the cheapest electricity ever produced at commercial scale. Getting there depends on manufacturing volume, which is the core thesis behind how the company was designed.

Economics of Power & Why Exowatt is Ready Now, Not in 10 Years

Hyperscalers signing power contracts today are paying in the range of 20 to 30 cents per kilowatt hour. Commercial grid electricity typically runs between 8 and 15 cents. Residential customers pay 13 to 20 cents. The premium data centers are absorbing reflects a straightforward imbalance: there is not enough power available where they need it, and they will pay whatever it takes to secure what exists.

Natural gas, the current default for off-grid generation, is not cheap either. Once you factor in fuel costs, transportation, generator maintenance, and long-run exposure to gas price volatility and carbon penalties, the economics of running a gigawatt data center on gas for twenty years are significantly worse than the upfront simplicity of the decision implies.

Exowatt’s current cost of just under four cents ($0.04) per kilowatt hour already undercuts the grid price for commercial customers in most US markets. At scale, with the manufacturing learning curve applied, the one cent ($0.01) target would make it cheaper than any grid-connected power source available today. That is the economic destination. The more immediate question is whether the product works on the timeline the market actually needs.

This is where the modular, container-based approach changes the conversation. A traditional utility-scale power project, whether solar farm, gas plant, or nuclear, requires years of permitting, grid interconnection queues of five to seven years in most high-demand markets, and construction timelines that assume a patient developer with a long capital runway. None of those conditions describe the AI infrastructure buildout happening right now.

The P3 sidesteps all of it. Because each unit is factory-built and ships in a standard 40-foot container, it can be deployed on-site without grid interconnection. A data center that needs power in 18 months does not need to wait for a utility. It needs land, adequate solar irradiance, and P3 units. The US Southwest, where Exowatt is targeting its ExoRise deployments, has all three in abundance.

The comparison to competing approaches is stark.

Utility grid connection in a constrained market: ~5-7 years.
New gas plant permitted & built from scratch: ~3-5 years
Nuclear small modular reactor, which several hyperscalers have explored as a long-term solution: ~10 years or more
- Despite a lot of support, they’re currently in an early, pre-commercial stage
Exowatt’s P3, deployed in modular increments on pre-permitted land: online within the current build cycle

Customers are not just buying the one cent per kilowatt hour target. They are buying the ability to get power online on the timeline that AI actually demands, from a domestic supply chain that does not depend on a utility, a foreign mineral, or a gas contract that locks in costs for two decades.. or pose a major public health hazard.

The Bottleneck After Power: Skilled Labor

The AI infrastructure conversation has worked through a clear sequence of constraints. First it was Nvidia GPUs. Then, as chip availability improved, attention moved to power. Both received significant coverage and drove substantial capital toward solutions.

There is a 3rd bottleneck developing that has received far less attention: skilled labor.

“The biggest kind of challenge for data centers today is there aren’t enough construction workers to build a data center,” Happi said. “You have to import people from other states to a site to build a data center. And being an electrician these days is actually a very high-paying job because there aren’t enough electricians to even twist wires and hook things up.”

Unlike the chip shortage, this one does not respond quickly to capital investment. Training skilled tradespeople takes years, and the industry did not begin investing in that pipeline early enough to meet current demand. The result is a constraint that will slow the buildout well after the power availability problem is addressed.

Exowatt’s modular approach has a practical advantage here. Units assembled in a controlled manufacturing environment and shipped to site require less on-site skilled labor than bespoke, utility-scale power infrastructure. When qualified electricians are the limiting resource, reducing how many hours of their time each megawatt requires is a meaningful efficiency gain.

Exowatt Careers

The Manufacturing Bet Behind the Technology

The core idea behind Exowatt is not any single piece of technology. It is a manufacturing thesis drawn from two reference points: the cost trajectory of solar PV panels over the past fifty years, and the production philosophy Happi observed while working at Tesla.

Solar PV reduced its cost per watt by 99.6% over five decades. That did not happen because of one breakthrough. It happened because manufacturing scale drives learning, learning drives efficiency, efficiency drives lower cost per unit, and lower cost per unit enables more volume. The curve compounds.

Solar thermal, the technology category Exowatt operates in, has not followed that trajectory. Every solar thermal project built to date has been a large, custom installation. There is no learning curve when every project is built differently. Cost has stayed high because the manufacturing discipline that transformed solar PV was never applied.

Exowatt’s approach is to apply it. Shipping container-sized modules, built in a factory to a consistent specification, create the conditions for that learning curve to develop. Each generation of the P3 informs the next. The one cent per kilowatt hour target is the projected outcome of running that process long enough and at sufficient volume.

“That’s why we chose this modular approach,” Happi said. “Instead of going & building a giant infrastructure project and saying we need ten years and a billion dollars to get to our first demo site, we said: let’s build the smallest thing, learn from that, iterate quickly, & build the next generation, & the next generation.”

Backed by $140 million from a16z, Felicis, Sam Altman, Leonardo DiCaprio, Starwood Capital, Thrive Capital, and others, with a demand backlog exceeding 90 gigawatt hours from data centers and hyperscalers across the US, Exowatt is moving from thesis to deployment. Several commercial projects are going live in 2025. The manufacturing scale that makes the cost curve work is what comes next.

Hannan Happi & What Tesla Taught Him

Before Exowatt, before Atomic, before Miami, & before founding Volansi, Hannan Happi was on the factory floor at Tesla during the launch of the Model S, when the Fremont factory was being stood up from scratch.

“Tesla wasn’t a popular company. I had to fight a lot, uphill, to get there. But it was an amazing, eye-opening experience.”

That experience left a set of convictions that run through everything Exowatt does, and Happi is direct about what they are.

1.) Team >

The team is the primary ingredient. Not the tech, market timing, or capital.

“The lessons learned were to first of all have an amazing, dedicated, mission-focused team. I think that’s the primary ingredient that actually got Tesla through.”

The reason Tesla survived, in Happi’s view, was a group of people willing to absorb a level of pressure that would dissolve a more conventional organization. Exowatt was built with that standard in mind, which is part of why the company chose Miami deliberately rather than defaulting to the Bay Area talent pool, and why the 40,000 square foot facility in Magic City looks more like an aerospace manufacturing floor than a startup office.

2.) Build What People Want

The second lesson Happi traces directly to his time at Tesla and his time at YC: build something people actually want.

“Build a great product that customers really want and enjoy. That’s something I also learned at YC, right from Sam. Build something people want.”

3.) Remove Parts.

The instinct in engineering is to add capability. The discipline that separates companies that scale from companies that stall is the willingness to take things away.

“That constant iteration on: let’s remove parts, let’s make it simpler, let’s scale the manufacturing — that’s the same approach you see at SpaceX or at Tesla. That’s what has led to this.”

Every configuration of the P3 was evaluated against the bill of materials. Every component that could be eliminated was eliminated. The result is a system whose simplicity is what makes it manufacturable at scale.

4.) Short Feedback Loops

Hardware is hard, as Happi puts it, not because the physics are complicated but because iteration cycles are long and expensive if you let them be.

“Hardware is hard. So you want to try to figure out how to create the shortest feedback loops possible & learn from that as fast as possible, & scale that as fast as possible. That’s why we chose this modular approach.”

Exowatt went through more than 50 configurations of the P3 before landing on the version that went to market. The modular approach was not just a product decision. It was a way of compressing the time between building something, learning from it, and building the next version.

5.) Vertical Integration

Or at least the ambition toward it. Happi is candid that Exowatt has not gone as far down that path as Tesla yet.

“Everyone coming out of Tesla is obsessed with vertical integration. You want to build everything in-house. We’ve started working with contract manufacturers because we know we need to scale fast. But Elon really taught us how to vertically integrate everything & build it yourself, build it better, faster, cheaper, & don’t take no for an answer.”

The companies that win in hardware over the long run are the ones that control their own supply chain, their own manufacturing process, and their own cost structure. Every move Exowatt makes toward domestic sourcing and factory production is a step in that direction. The Tesla years gave Happi a clear picture of what that looks like at full maturity. Exowatt is working backward from it.

The Investors & Origin Story Behind It All

Exowatt did not come out of a garage. It came out of Atomic, the San Francisco-based venture studio co-founded by Jack Abraham, who also serves as Exowatt’s Chairman. Atomic’s model is to build companies from the inside, pairing founders with capital, infrastructure, and operational support before a company has a product or a customer. It has produced a number of significant companies including Hims & Hers Health and OpenStore. Happi eventually “moved in as a founder in residence back in 2022 & started working with Jack” ultimately, incubating Exowatt & formally Co-Founding the company with Abraham in 2023.

The founding thesis was not “build a solar company.” It was more specific than that: apply manufacturing discipline to solar thermal in the same way the industry applied it to solar PV, do it at the modular scale, & source domestically. That specificity is part of what attracted the caliber of investors who came in at the seed round.

In April 2024, Exowatt came out of stealth with a $20 million seed round led by Andreessen Horowitz and Atomic, with Sam Altman, CEO of OpenAI, and Leonardo DiCaprio investing as angels. The combination of Altman & DiCaprio in the same cap table is unusual enough to earn attention on its own, but the more meaningful signal was a16z leading the round. Andreessen Horowitz has been one of the more disciplined investors in energy infrastructure, and their conviction at seed reflected a view that the power constraint in AI was not a temporary problem.

The Series A followed in April 2025, a $70 million round led by Felicis and structured as a mix of $35 million in equity and $35 million in debt provided by HSBC Innovation Banking. Additional investors included Starwood Capital, Thrive Capital, 8090 Industries, MCJ Collective, MVP Ventures, GOAT VC, and StepStone Group, with Atomic and a16z both returning. Six months later, Exowatt raised a further $50 million extension led by MVP Ventures and 8090 Industries, bringing total capital raised to $140 million in under two years.

The investor base is worth reading carefully. Starwood Capital is one of the largest real assets managers in the world. Thrive Capital, founded by Josh Kushner, has a track record of backing category-defining companies early. HSBC providing debt at the Series A stage signals that institutional lenders, not just venture capitalists, are taking Exowatt’s revenue prospects seriously. And the fact that both Atomic & a16z came back in subsequent rounds is a solid nod for insider confidence.

What binds all of it together is the founding relationship between Happi & Abraham at Atomic. The venture studio model gave Exowatt something most hardware startups do not have in the early days: time and resources to iterate without the immediate pressure of external market expectations. Happi has described going through more than 50 different design configurations before landing on the P3. That iteration cycle, funded and sheltered inside a studio environment, is what produced a product that was ready for commercial deployment at the moment the market needed it most. The timing was not luck. It was the result of starting early, staying disciplined, and building in a city, Miami, that most of the industry was not paying attention to.

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NEW: Max Levchin, Affirm

Tue, 14 Apr 2026 01:16:10 GMT

Strikes & Gutters, Ups & Downs

Max Levchin, Fmr Co-Founder & CTO of PayPal, now Co-Founder & CEO of Affirm, joins Sourcery for a wide-ranging conversation on building companies, surviving failure, & navigating one of the most important economic shifts in decades, with a steady stream of The Big Lebowski references throughout.

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Levchin is a legendary serial entrepreneur, technologist, & investor, best known for helping build PayPal (as CTO), Affirm, & backing (~100+?) startups including Brex.

We start inside Affirm HQ with a Max Masterclass in the Art of Espressos before going deep into lore of Affirm, Levchin’s career, & an AI macroeconomic outlook.

We cover:

Lessons from PayPal that still apply today
Why the team is the single biggest determinant of success
How Affirm onboarded 800K Shopify merchants in a week
Why you can’t perfectly time an IPO
Why now is the best time ever to be a CS CEO
How AI is collapsing the cost & speed of building software

One of Levchin’s key points: As intelligence becomes more accessible, weaker or “fine print” companies will get exposed faster. The baseline for what it means to build, operate, & compete is rising.. along with the effective IQ of the system.

This conversation was recorded March 30, 2026.

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(00:00) Max Levchin, Co-Founder & CEO at Affirm
(01:35) Inside Affirm's office espresso bar
(14:56) Meeting Alfred Lin & Tony Hsieh over poker
(21:14) Onboarding 800,000 Shopify merchants in one week
(22:59) Big Lebowski quotes hidden in every shareholder letter
(32:11) The PayPal lesson that built Affirm
(35:25) Being a technical CEO & why now is the best time to be a CS CEO
(42:10) Should engineers still learn to code?
(44:48) Side quests with AI
(46:59) Companies AI will destroy
(49:46) How AI has changed engineering at Affirm
(50:54) Agentic commerce & DoorDash
(52:28) Devolution of Credit
(55:06) The biggest misconceptions about buy now, pay later
(57:42) Being a public company CEO
(01:07:01) Advice for private companies
(01:11:09) Creating his own economy
(01:14:29) Can AI help solve the $39T debt problem?
(01:17:46) Will average IQ rise or fall?

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“Fine Print” as a Business Model Is About to Be Destroyed..

Affirm CEO Max Levchin on the devolution of consumer credit, why every digital economy rediscovers capitalism, and what happens when AI gives ordinary borrowers the same leverage as Wall Street lawyers.

Max Levchin takes pride in crafting his own espressos (about five or six shots a day), timed & weighed to the gram. He quotes The Big Lebowski in shareholder letters & on-demand. He recently built an iOS app on weekends using Claude Code because he got annoyed at his home audio remote. Sets PRs on Strava on the daily. Co-founded PayPal. And he’s been CEO of Affirm for over a decade..

In a wide-ranging conversation at Affirm HQ, he covered the history of consumer credit, what AI does to opaque financial products, why every virtual economy converges on capitalism, and what it actually means to be a technical CEO right now.

How Lending Became Extraction

Credit, Levchin argues, started as a borrower-aligned product. Farmers needed capital between planting and harvest. Lenders provided it. The Code of Hammurabi included explicit prohibitions against overcharging borrowers. Early religious law in multiple traditions restricted or banned interest entirely. The original instinct of lending was protective.

The modern version reversed that.

~2000 BC: First documented borrower protections in Code of Hammurabi
100%: Effective gross margin on late fee revenue for major banks
50%: Share of Americans who carry revolving credit card balances
$48B+: Affirm loan volume projected this fiscal year

“At some point someone said, wait a second — that’s a hundred percent gross margin product. When I charge you a late fee, you just pay me more money. There’s no cost other than like sending you a nasty gram. Of course I’ll charge you a late fee. So it’s better if you’re late a lot, because then I’ll make more money and it costs me nothing to create that revenue line.”

Late fees became a near-costless revenue line. The industry optimized around them. The mechanism got buried in fine print. Affirm was built to counter that directly: no compounding interest, no late fees, no deferred rates. The total cost of a loan is shown upfront before the borrower accepts.

“We don’t compound interest specifically to make sure that we can pre-price every loan and say: you’re borrowing $500 and your total interest charges will be $25 or $0 if a merchant is paying your interest for you.”

Early on, consumers didn’t believe it. They’d been conditioned to look for the asterisk. That’s shifted. Affirm employees now regularly get stopped by customers who recognize the logo and want to talk about the product.

The PayPal Lesson That Stuck

Levchin met Alfred Lin & Tony Hsieh before any of them were known for anything. He was a college student, parts of a failed startup had been acquired by Link Exchange, and he flew to San Francisco to help with the integration. He showed up expecting a meeting. It was a poker game.

“I just fed myself and watched Alfred and Tony and a bunch of other people play poker. That was my introduction to the Bay Area.”

His early startups all failed. Rather than walk away, he kept going, less because of any grand vision, & more because starting companies was simply what he was going to do.

“I took exactly zero hours or minutes contemplating is this the right thing for me to do. I was like, oh, like I’m definitely gonna do this again.”

That persistence led to PayPal. As CTO, Levchin was at the center of what became the single densest concentration of founder and investor talent ever to come out of one company —Peter Thiel, Elon Musk, Keith Rabois, David Sacks, Roelof Botha, Reid Hoffman, and others, went on to found and/or lead the most consequential companies of the last two decades.

“The most important lesson is always the team.
It is the alpha & omega, success or failure of a company is the team.”

With the caveat:

“Just having a bunch of brilliant people is not actually enough. You need to organize them, give them a mission. If you don’t have the people, you’re definitely gonna fail. But even if you do have the people and you don’t harness their unique skills and correctly combine them, you might fail anyway.”

He puts the US startup failure rate at 85%. Most fail for people and alignment reasons, not technical ones.

To hammer down the point: The PayPal mafia isn't famous for the exit. It's famous because the talent never stopped compounding.. from founding, building, and backing the next generation of great companies, literally creating trillions in value across decades.

That's the real measure of a successful team. Whether the culture you build keeps generating talent, ideas, and companies long after people have moved on.

Why Every Digital Economy Rediscovers Capitalism

Levchin grew up in Soviet Ukraine. He watched a command economy fail firsthand. His view on capitalism isn’t ideological, it’s observational. Markets work. He’s spent his career inside ones that do.

When asked how he'd construct a new economy from scratch, like the closed digital systems a la Roblox, Second Life, and others.. a common theme emerged. Every one of them eventually arrived at the same structural solutions. They didn't copy the US economy. They independently arrived at it.

"It may be lack of imagination, but if you look at the success arcs of these systems over time, they resemble the US economy more and more. Sort of like basically start out with a fixed money supply and like, well, we'll figure out how to inject more money later. And then suddenly more people come in and you need more liquidity. And so they sort of start pegging their currency to the dollar or floating it — it becomes more and more like the real world."

His answer on constructing a new economy: he wouldn’t. Capitalism works. We’ve had thousands of years to evolve it and the US got it right.

On the $39 trillion national debt — so long as the US produces enough value to compensate those that hold it, the position holds. The follow-up question was whether AI-driven productivity could help cover the difference:

"It's hard to tell. It's a really good question actually, & the question is, where does value accrete in this new age? On the one hand, it's very tempting to say, well, for the country of our resources & our access to physical resources & capability that we have to build and innovate and sort of create things out of atoms, having this incredible boost of productivity expressed in bits is just gonna be incredibly powerful & leapfrog anything and everyone that is remotely buying our supremacy. You could also make the argument that all these countries where there's precious little by way of resources are now boosted tremendously by advances in AI.
I tend to believe in the former, just because I'm being an American by choice. Better be a patriot, otherwise what exactly were you thinking? I tend to think that we'll probably end up with just an amplification of our advantage. That said, we're not without rivals on the world stage and we'll have to compete. But I'm a big fan of competition."

The End of the Fine Print Business Model

Most fintech commentary on AI focuses on underwriting, fraud detection, and operational efficiency. Levchin’s core argument is different: AI is about to kill an entire category of business model.. specifically, any model that depends on consumers not fully understanding what they signed up for.

“No AI is ever going to be like, ‘Oh, you’ve given me a task to find you a good loan. I found one that’s going to take advantage of you. Hooray.’ Absolutely not. I’m not going to let that happen to you. My job as a robot is to take good care of you.”

AI agents acting on behalf of consumers will read terms & conditions that consumers currently don’t. They’ll compare rates across products without fatigue. They’ll catch deferred interest clauses, variable rate triggers, & penalty structures buried in the fine print.

The information gap that consumer finance has operated on for a century — institutions with lawyers on one side, borrowers without time or expertise on the other — closes when every borrower has an AI reading on their behalf.

Levchin firmly believes this will lift the average IQ to a higher standard.

“The average IQ is still a hundred. I think the average IQ with AI in your ear at all times is about to go up to 150 — which is north of the genius definition.”

For Affirm, this is a structural advantage. A company built entirely on transparency has nothing to hide from an AI agent doing comparison shopping. Companies whose revenue depends on consumers not understanding their products are in a different position.

Why CS Still Matters in an LLM-First World

Levchin has been writing code since he was eleven or twelve. He planned to get a doctorate in computer science before startups interrupted that. He still reads AI research papers nightly, not as homework, but because the gap between a paper publishing and someone shipping an implementation has essentially closed. Missing a week of papers means missing something that will matter.

“It is the moment in time to be a CEO with a computer science degree. You can now go from I have an inkling in my head to I’m going to build a prototype to I’m going to ship something that’s production ready — in record short time.
If you do know what you’re doing as somebody with a sense for how to build software, you don’t need to vibe code yourself to a prototype and hand it off. You can actually build something amazing and just ship it.”

On whether deep CS knowledge (like idk the hot topic of coding?) still matters when LLMs write code on demand.. his answer is yes, but for a specific reason.

Software engineering is a science, an art form, and a craft simultaneously. The science is teachable. The art and craft require taste. Taste can’t be fully delegated to a model. You still need to know what elegant code looks like, recognize when you’re not getting it, and be able to steer the model toward something better.

Without that foundation, you accept whatever output comes back. Which might work. But probably isn’t great.

Long-Term Thinking in a Quarterly World

Affirm was ready to go public in under ninety days from the decision to file. The SEC asked them to wait too many companies were rushing to market at the same time. They went public in early 2021.

"In December of 2020, the SEC told us, please, don't come out this year. We're so overwhelmed by all these companies trying to go public. Could you please hang on and do it next year."

While many are overwhelmed by the process of going public, he says it’s actually more manageable than people make it out to be (& maybe they should take the advice Reddit CEO Steve Huffman got: ‘don’t be a little bitch about it’). What’s harder is the discipline not to let quarterly reporting become the company’s primary focus. He’s seen CEOs lock themselves in conference rooms for a week before earnings, obsessing over a period that ended six weeks prior. The quarter is already over. The only useful question is what comes next.

He referenced the Graham/Buffett line: “in the short term the market is a voting machine, in the long term a weighing machine.” Levchin said it’s what he reminds himself of whenever price action moves in either direction. Up or down, the answer is the same: keep building.

Strikes & gutters, ups & downs.

He has no interest in shareholders focused on short-term price action. Affirm is estimating north of $48 billion in loans annually and still represents a fraction of a percent of total US commerce. The company, by his math, has decades before the final grade is in. The IPO is just another data point in the journey.

The Dude Abides.

A running Easter egg in every letter

For twenty-plus quarters as a public company, Affirm’s shareholder letters have contained a Big Lebowski reference. Not once. Every single one. Levchin keeps the corrected Lebowski script open on his desktop while drafting them so he can search for quotes he hasn’t used yet.

Until recently, he wasn’t getting much feedback on them.. a fun hunt carefully curated for each letter. Then, about three quarters ago, an analyst published a note with the line: “Q2 fiscal 25 — a quarter that really tied the room together.”

“Chef’s Kiss”

He’s seen the film enough times that quotes surface unprompted mid-conversation. When asked about his media diet he said, unprompted: “The occasional acid flashback.” “that’s a quote from Lebowski. I have not in fact had any acid flashbacks that I can think of.”

He recently watched it with his fourteen-year-old daughter for the first time. His sixteen-year-old son was inducted a couple years prior. The Dude is in the DNA.

On the cast:

“Philip Seymour Hoffman, very young Philip Hoffman, so he’s kind of funny and spastic. Jeff Bridges is not so bad. Very young John Turturro playing a deranged bowler named Jesus.”

On why it holds up:

“It’s one of these cult movies where you watch it and you’re like, this is amazing, and you watch it again, like no, it’s better than amazing.
You watch it again, it keeps on getting better. Keep on finding these gems.”

His favorite kind of Lebowski moment is the cultural deep cut that has almost nothing to do with the plot. He described one specific scene — the Dude explaining something to Walter, Walter riffing on Lenin, and Donny popping in:

“Donny pops in and says ‘I am the Walrus.’
He keeps on popping in ‘cause he thinks Lenin being referred to is actually John Lennon.”

Then, mid-tour, standing near the office game station, entirely unprompted:

“Also, dude, having an amphibious rodent as a domestic — that ain’t legal either. What are you a fucking punk ranger now?”

And later:

“You said it, man. Nobody fucks with the Jesus.”

On whether any film made today will earn the same status:

"The real litmus test of a great beautifully written movie or well written anything is it survived a test of time.
So I know, but I'll take another ten years. I'm sure there's some amazing scripts that are going to be discovered as the great cult classic of 2026 that are in theaters right now, but we won't know until 2036."

The Dude abides.

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BREAKING: 1st Look Inside Applied Intuition's $15B Physical AI Garage

Thu, 09 Apr 2026 13:23:00 GMT

Automating Everything That Moves.

We got rare access inside Applied Intuition’s autonomous commercial garage with Co-Founders CEO Qasar Younis & CTO Peter Ludwig, where they’re building AI systems that power cars, trucks, mining equipment, defense systems, and more.

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In a never-before-seen tour to the public, Applied Intuition showcases where/how real machines across industries are being rebuilt with AI.

Valued at $15B, Applied Intuition is one of the most important companies in physical AI, powering 18 of the top 20 global automakers and deploying autonomy across automotive, trucking, construction, agriculture, and defense.

Inside this garage, you’ll see:

• Self-driving cars, trucks, & heavy machinery
• Autonomous mining & construction operations
• Defense systems & remote-controlled fleets
• A unified operating system running across every machine

Qasar and Peter break down how one platform can run everything from a car to a boat to a mining site, and why the future of AI is no longer just software, but systems that move the real world.

ICYW: This is what “atoms” looks like.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Inside Applied Intuition physical AI garage
(03:00) Cars are a wiring mess
(04:31) Why modern architecture matters
(06:30) From simulators to autonomy
(07:50) Real-time autonomous operations
(08:34) AI solving labor shortages
(10:09) The mobile AI command center
(12:50) How defense became a core focus
(16:20) Becoming more public
(17:10) Connecting mines, ports, and roads
(19:07) Driverless trucks in Japan
(20:09) Why they don’t operate in China
(21:40) Which country is adopting fastest?
(23:00) Scaling a 1400-person AI company
(25:43) New vs old AI players
(27:49) "Bits are out, Atoms are in"
(29:40) Physical AI is just getting started
(32:06) The "Japan corner"

Brought to you by:

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Merge—The leading provider of customer-facing integrations and agentic tools for frontier LLMs, Fortune 500 organizations, and B2B SaaS companies. Visit: https://merge.dev

Inside Applied Intuition’s Garage: Physical AI at Scale

Applied Intuition just held its first-ever Physical AI Day in Sunnyvale, opening the garage doors for the first time. For nearly a decade they’ve been quietly building across automotive, defense, trucking, mining, construction, and agriculture.

Founded in 2017, valued at $15 billion, nearly $1 billion raised, Applied Intuition now has round 1,400 employees across 18 global offices, and roughly 1,000 of them engineers. They’re helping develop driverless trucks running in Japan, a data engine deployed on U.S. Navy warships, and autonomous mining equipment running live in real quarries.

Most of the industries Applied Intuition operates in share a common problem: they were built layer by layer over decades, each new capability bolted onto whatever came before it. Nobody designed a modern car’s software architecture from scratch. Nobody designed a mining operation’s technology stack from scratch. They accumulated. Every new supplier added their own wiring, their own compute, their own communication protocols. Every new defense program added another siloed system on top of the last. The result, across automotive, trucking, defense, mining, and agriculture, is a sprawling patchwork of legacy architecture that was never meant to be intelligent, autonomous, or connected.

It’s a similar problem to what aerospace faced before SpaceX rethought rocket manufacturing from first principles. The rocket industry had decades of accumulated complexity, with systems designed around constraints that no longer existed, costs that were accepted as fixed, and processes nobody had bothered to question. SpaceX didn’t improve the old architecture. They replaced it. Applied Intuition is making a similar bet across nearly every physical industry.

Since the core thesis is physics is consistent across machines, Applied Intuition is on a mission to create one platform that can power autonomy across all of them. CTO Peter Ludwig put it simply: “The same operating system and platform really runs across all of these vehicle types.”

The Platform

Products fall into three categories: tools & infrastructure, operating systems, & autonomy. The Vehicle OS abstracts hardware and software from one another, letting developers build vehicle applications without needing to know the underlying hardware. The self-driving system covers L2+ highway assistance through full autonomy. The data engine & simulation tooling were originally built to sell to the entire autonomous vehicle industry & now serve commercial + defense.

Customers include:

18 of the top 20 global automakers
The U.S. military & its allies

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Automotive

The garage opened with a fully disassembled production vehicle, electronics laid bare across a table. Every supplier sells not just their component but all the wiring that goes with it, all the way to compute. Multiply that across every system in a car and you get what was on that table.

“If you’re a company that sells a backup camera, you’re not only going to sell the backup camera, you’re going to sell all the wiring that goes to some compute. And then you have to do that for your windows, the sunroof.. that’s why all this is complex.” - Younis

Vehicle OS consolidates that into a unified high-performance compute architecture.

“Making a vehicle with an old architecture fully autonomous is a lot harder than doing that on a modern architecture. And so when we’re working with our customers, one of the goals is -- you want to modernize and simplify the architecture, and then you can add autonomy and all of the AI capabilities on top of it.” - Ludwig

It also changes what manufacturers can do with fleet data.

“In an old car, you might have an airbag sensor and maybe you get a little icon on the dashboard that you don’t really know what it means. On a modern car, you can have a much more sophisticated diagnostic system and then also with AI, we can surface that to the manufacturer and they can understand across their entire fleet what are the aggregation of the problems that they’re having and root cause those things.” - Ludwig

In March 2026, Applied Intuition announced a collaboration with NVIDIA as a recommended software provider for OEMs building L2+ highway driving systems on NVIDIA hardware. The stack covers ICE, hybrid, and fully electric powertrains, is optimized for the NVIDIA DRIVE AGX Orin and upcoming Thor, and incorporates NVIDIA Cosmos World Foundation Models for data augmentation.

“Advanced driver assistance is becoming a baseline expectation for mass-market vehicles. Our work with NVIDIA raises the bar across the full range of powertrains -- from ICE and hybrid to fully electric -- giving automakers a clear, confident path to deploy L2+ autonomy by combining NVIDIA’s hardware leadership with software designed to meet regulatory, safety and manufacturing realities.” - Varun Mittal, President of Applied Intuition

Trucking

The global freight trucking market was valued at around $2.7 trillion in 2025, with trucks carrying over 60% of worldwide logistics (Global Market Insights). In the U.S. alone, 3.54 million drivers haul 11.46 billion tons of freight annually, moving over 70% of all freight by weight. This critical sector moves over $700 billion in goods yearly, covering nearly every economic sector, including manufacturing, agriculture, and retail. (Geotab, PTPT). It is one of the largest and most structurally important industries on the planet, and one of the least modernized. Most fleets still run on disconnected systems, manual dispatch, and driver-dependent operations with no real path to software-defined infrastructure.

Applied Intuition is running driverless trucks in Japan with Isuzu. The Generation 2 truck was on display at Physical AI Day, one step before full commercial production with rollout targeted within the year.

“This is basically one step before this is fully productionable, and so they will produce this, this is being commercialized in the next year. Japan’s an awesome market for self-driving trucks because there’s a very aging population and there’s a big need for this.” - Ludwig

On March 31, 2026, Applied Intuition and the TRATON GROUP announced TRATON ONE OS, a software-defined vehicle platform powering all new vehicles across Scania, MAN, International, and Volkswagen Truck and Bus. More than a year in co-development, it delivers predictive maintenance, over-the-air updates, and adaptive middleware as a foundation for autonomous driving capabilities. ECU hardware testing starts April 2026, full rollout targeted for 2028.

“With TRATON ONE OS, we combine strong building blocks from Applied Intuition, TRATON and the open source community to create a worldwide cutting-edge EE platform, delivering fast new functions and services to the customer, while still preserving what makes each of our brands unique.” - Stefan Teuchert, SVP EE Platform, TRATON GROUP

“We have driverless trucks running in Japan right now. In the near future, it’s only a matter of time that we have a mine, a road, and a port all connected and we have automation in all three of those.” - Younis

Defense

Applied Intuition entered defense early & expanded significantly with its February 2025 acquisition of EpiSci, an AI and autonomy software company focused on national security. EpiSci brought expertise in autonomous fighter jets, drones, networked communications, and multi-domain operations. They are known for the X-62 VISTA program, which demonstrated AI-controlled aerial combat including a flight with the Secretary of the Air Force.

“The EpiSci team is tackling true systems problems. They are tackling the technical aspects of perception, planning, control, and RF communication in a holistic way. This is what dynamic operating environments require.” - Ludwig
“This work that we do may be, out of all the verticals, the most important. It is our responsibility as a company to field the best technology for the U.S. and its allies, and I think it’s an incredibly important mission.” - Younis

The company now works across the Army, Air Force, Navy, & with prime contractors. Autonomous ground vehicles include a Ford F-150 Raptor platform outfitted in collaboration with Sierra Nevada Corporation with an anti-drone missile launcher.

“We are taking commercially available products which we sell in all these other verticals, and we’re also selling them in defense.. we can frankly deploy more robust systems because they’re already in field and in production for a fraction of the cost.”
“I think one way to think about this in defense is imagine if the department of war said, let’s build a chat app. How long and how many dollars would it take to compete with WhatsApp or Signal? This is extremely difficult. So there are real advantages in the commercial ecosystem.” - Younis

On March 19, 2026, Applied Intuition delivered DECK, the Data Edge Collection Kit, the U.S. Navy’s first large-scale data engine, part of the Navy’s PAE RAS program. DECK performs real-time automated detection and classification using shipboard sensors, manages satellite bandwidth, and allows AI models to be updated over the air with minimal sailor intervention.

“If you do not build a data engine, you do not build an AI-enabled force. That is why the Navy is deploying DECK... it turns ships into learning systems, not static platforms. This is how we move from demonstrations to dominance.” - Secretary of the Navy John Phelan, AFCEA West, February 12
“DECK turns ships into data-generating, continuously improving platforms and gives the DOW a real data engine at sea, not another one-off system. That’s how you deliver AI to the fleet at operational speed.” - Younis

On March 31, 2026, Applied Intuition launched Applied Edge, a field-deployable mobile operations center packaging rugged compute, 5G/Starlink communications, and the Axion autonomy toolchain into a shipping container transportable by C-130. It supports SCIF/SAPF configurations, data, & workflows persist between test events.

“All of the development tools & infrastructure that we are typically able to use to build interesting defense technologies at an office environment, all of that is packaged up & can run completely offline, off the grid, in a shipping container.” - Ludwig

Mining & Construction

The global mining market was valued at around $2 trillion in 2025 (Research And Markets). It is one of the most dangerous and labor-intensive industries on earth, and among the least automated. The global mining automation market sits at under $4 billion today, growing at 8.4% annually (MarketsandMarkets), a small fraction of the overall industry, which means the runway for automation is substantial.

Only 1% of mines currently operate autonomously.

“Mining accounts for 8% of work-related deaths on the planet.” - Younis
“The future of mining & eventually construction is going to be these fully autonomous operations where labor shortage is a real problem in the industry. And so this allows a very small number of people to control a huge number of vehicles.” - Ludwig

Outside the garage sat a Komatsu truck. Ludwig: “You might think this looks like a big truck. This is actually a small truck.” Younis: “This is the smallest truck we could get in this parking lot.” The actual mining trucks Applied Intuition operates are considerably larger.

At Physical AI Day, the company streamed live footage of autonomous Komatsu equipment completing a full load-haul-dump cycle at a remote test site, supervised by a small team in Sunnyvale.

Also on display was an autonomous skid steer.

“Skid steers, there’s like a hundred different tools that can be placed on the front of these. Once it’s autonomous, you can do anything with it.” - Ludwig
“The interoperability of our platform is really important because we can go from a mine, then to self-driving trucks, which ultimately go to a port. And we think automating all of that is really where there’s going to be a huge unlock in productivity.” - Younis

Agriculture & the Jobs Question

The global agriculture market was valued at $12 trillion in 2024 (Research And Markets), making it one of the largest industries in the world. It is also one of the most structurally exposed to the labor crisis. The average American farmer is 58 years old. Farms around the world face a critical labor gap as younger generations migrate to urban areas for higher-paying jobs & leave the farming population aging. (Markets).

Now, the global smart agriculture market was valued at $22.9 billion in 2024, growing at 10.8% annually, driven primarily by labor shortages and the urgent need to maintain food production with a shrinking workforce (Global Market Insights).

Applied Intuition’s autonomy platform extends to agricultural equipment on the same underlying architecture as everything else it builds.

“The AI kind of problem, consternation, heartache & kind of hand wringing that exists right now with what happens with white collar workers.. it’s quite different in the physical AI community because farmers are aging.
People don’t want to work in mines in far away places. These are dangerous jobs. And so AI really in these industries is being kind of pulled out of our hands, because they’re real problems that they’re dealing with. There’s not a new group of people who really want to become farmers. It’s just not there.” - Younis

Global Footprint

Applied Intuition operates across approximately 18 locations. Largest international offices are in Tokyo and Munich. Additional offices span London, Stuttgart, Stockholm, Bangalore, Seoul, and several U.S. cities including Detroit, Washington D.C., and Ann Arbor.

Japan has been the fastest-growing market. Younis attributes it to building a genuinely local team: “Our presence in Japan is a very local presence. It’s not like we just took a bunch of Silicon Valley engineers and put them there.”

Expansion is sequential: Australia is a recent addition targeting mine-to-port autonomy, the Middle East is active, and Latin America is next via mining. China is explicitly excluded, citing defense work and IP considerations.

“The expansion in every one of these global offices is always kind of rate limited by.. we take the folks that are in the Bay Area for granted in the sense of they understand equity, they want to work in high growth startups.
Whereas if you go abroad, they really look at the FANG as a dream job.” - Younis

The Model

Applied Intuition started selling simulators to the autonomous vehicle industry in 2017, then expanded into the full software stack, Vehicle OS, and defense over time. It positions itself as a B2B infrastructure provider to manufacturers, not a competitor to them. Both founders grew up in Detroit.

“General Motors headquarters & General Dynamics land systems technical headquarters are like two or three miles apart. And in between is my high school. The reason is the technology they use to build a tank and the technical experience and the engineers that you need are actually quite similar to the ones you need in the car business.”
“I think we’re as much Midwest as we are Silicon Valley. Even though when people meet us, we’ve spent most of our lives in Silicon Valley.. I worked at Y Combinator, Peter worked at Google. But I think we’re heavily inspired by our roots.”
“We were so hesitant to get into this space. We really thought we don’t want any perception of competition with our own customers. But competition is only when you’re taking out of the same bucket. Our customers are selling to consumers or businesses. We’re selling to other manufacturers.” - Younis

“We’re able to use what’s called end-to-end technology. So we can take raw sensor data in and actually put out vehicle control signals. And we have large scale data collection across a bunch of industries -- not only automotive, but also trucking and mining and construction. And we can combine all of this data together to create these self-driving models.”
“If you look at Waymo, Waymo used to be in self-driving trucks & then they closed that team down because the old way of building self-driving, those are two almost discrete systems. Today, we have the same team working on self-driving on all these different form factors.” - Ludwig

Applied Intuition is privately held, valued at $15 billion, and has raised nearly $1 billion. It is headquartered in Sunnyvale, California.

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BREAKING: Applied Intuition - $15B Physical AI Co. Out Of Stealth

Mon, 06 Apr 2026 17:33:01 GMT

"Cult classic, not a bestseller."

IYKYK: After nearly a decade building in stealth, Qasar Younis (CEO) & Peter Ludwig (CTO) of Applied Intuition join Sourcery to break down how they’ve quietly built one of the most important AI companies in the world reaching a $15 billion valuation.

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Founded in 2017, Applied Intuition is building the infrastructure layer for vehicle intelligence and physical AI, powering systems across automotive, defense, trucking, construction, mining, and agriculture. Today, 18 of the top 20 global automakers and major U.S. Department of Defense programs rely on their software.

The company has raised nearly $1B in total funding, including a $600M Series F at a $15B valuation, co-led by BlackRock and Kleiner Perkins, with participation from global investors such as Franklin Templeton, Qatar Investment Authority, Abu Dhabi Investment Council, Premji Invest, Stripes, Greycroft, BAM Elevate, & 137 Ventures.

Existing investors include Fidelity Management & Research Company, General Catalyst, Lux Capital, BOND, Elad Gil, Addition, and Tribe Capital, alongside early backing from Marc Andreessen.

What makes the company especially unique: despite raising over $1B, Applied Intuition has largely not relied on that capital to fund operations, instead building a profitable, capital-efficient business with long-term customer contracts and deep integration across industries

In this conversation, we cover:

• Why physical AI is fundamentally different from software AI
• How breakthroughs like transformers unlocked real-world autonomy
• The company’s horizontal-first strategy across industries
• Why they stayed quiet for years—and why that changed
• How they deploy autonomy in defense and industrial environments
• What hiring looks like in the AI era

Applied Intuition is building the infrastructure for AI in the real world.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Qasar & Peter, Co-Founders Applied Intuition
(01:15) Most startups are just "hobby projects"
(03:55) Becoming more public
(07:33) The breakthroughs that reshaped their strategy
(11:01) A moon rover simulator?
(11:37) Expansion playbook
(13:55) Raised $1B… Didn’t Use It
(15:04) Hiring in the AI era
(18:17) A team full of ex-founders and CTOs
(19:28) From manual to autonomous in 10 days
(23:18) Why great products drive results
(25:44) Books that shape thinking & why old books matter
(29:38) Turning books into company playbooks

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Building a $15B Physical AI Company for the Real World

Qasar Younis (CEO) and Peter Ludwig (CTO) have quietly built one of the most consequential AI companies of this cycle.

Applied Intuition, now valued at $15 billion, sits at the center of a shift away from software-only AI toward what they describe as “physical AI”: intelligence that operates machines across the real world. Their systems power everything from passenger vehicles to defense systems, construction equipment, and industrial robotics. Today, 18 of the top 20 global automakers rely on their software, alongside major programs within the U.S. Department of Defense.

Despite raising nearly $1 billion in capital, the company has taken a fundamentally different approach than most AI startups. As Younis puts it, “We’ve raised close to a billion dollars… and we’re not using it to pay payroll.”

This is not a growth-at-all-costs company. It is a systems company, built deliberately over time.

From Simulation Tools to Physical AI Infrastructure

Applied Intuition did not start as a self-driving company. In fact, the founders made a deliberate decision to avoid going directly into autonomy.

“We started out building tools. We built simulators and data management offerings and large scale distributed compute offerings,” Ludwig explains.

This horizontal foundation allowed the company to embed itself across industries early. By working with automotive companies, defense contractors, and industrial players, Applied Intuition accumulated a unique vantage point on how AI would actually be deployed in the real world.

That vantage point became critical when the technology caught up.

Two breakthroughs shifted their strategy. The first was the emergence of transformers, initially applied to large language models. The second was the rise of end-to-end deep learning systems capable of directly controlling machines.

“That was the moment when we actually decided to really enter the self-driving space ourselves… not just for cars, but for any kind of machine,” Ludwig says.

This marked the transition from tooling provider to full-stack physical AI company.

Why Physical AI Is Fundamentally Different

A core insight from the conversation is that physical AI is not just a harder version of software AI. It is a different category entirely.

“Physical AI is very different… the machines are different, the geographies and regulatory environments are different, and the diffusion is way, way slower,” Younis explains.

Unlike LLMs, which can scale through browsers and APIs, physical AI must integrate into fragmented real-world environments. Each deployment involves different hardware, different safety requirements, and different operational constraints.

This makes distribution slower, but also more defensible.

Time becomes a competitive advantage. Companies that have been building for years, integrating across industries, and learning from real-world deployments develop a compounding edge that is difficult to replicate.

Applied Intuition has leaned into this dynamic. Nearly a decade into building, they position longevity as a core part of their moat.

A Different Kind of Company

Younis is blunt about what he sees across the broader startup ecosystem.

“I think most companies in the Bay Area are more hobby projects than they are serious businesses.”

That critique reflects a deeper philosophical difference. Applied Intuition is not optimized for narrative or visibility. It has historically avoided attention altogether, only recently becoming more public as recruiting needs scaled.

The company’s operating model reflects this mindset. Financial performance is treated as an output, not an input.

“We don’t have good financials because we focus on financials. We have good financials because we focus on the products.”

This product-first orientation has enabled them to build long-term customer relationships across industries where reliability matters more than speed.

Deployment Speed in the Real World

Despite the inherent complexity of physical AI, the company has demonstrated the ability to move quickly when needed. In one example, Applied Intuition deployed autonomous capabilities onto military vehicles in less than 10 days after receiving them.

“We had a very small team retrofit our system onto the vehicles… and in 10 days we showed them autonomous operation,” Ludwig recounts.

The broader lesson, according to Younis, is that timelines in complex industries are often artificially inflated.

“This view that everything requires many years of planning is just false… we don’t have the luxury of waiting three or five or ten years.”

Speed, when paired with deep technical infrastructure, becomes a strategic weapon.

Hiring in the AI Era

According to Ludwig, there are now two distinct categories of high-value contributors.

“There’s two types of people…

Experts in using AI tools
Individuals who can actually push forward the state of the art,”

This reflects a broader transition in software development. The ability to leverage AI systems is becoming as important as traditional specialization.

Applied Intuition’s hiring strategy mirrors its broader philosophy. It prioritizes people who are motivated by real-world systems, not just abstract models. Engineers interested in cars, defense systems, or industrial machinery are often a better fit than those drawn purely to consumer AI.

Investor Base & Capital Strategy

Applied Intuition’s cap table reinforces its positioning as a long-term infrastructure company. The company has attracted backing from leading venture firms, crossover investors, and sovereign capital.

Early support from Andreessen Horowitz, General Catalyst, and Lux Capital was followed by growth investors such as Elad Gil, Addition, and BOND. More recent rounds brought in global institutions including BlackRock, Kleiner Perkins, Franklin Templeton, and major sovereign wealth funds.

Unlike many AI companies, Applied Intuition has actually not relied on fundraising to sustain operations. Instead, it built a revenue-generating business early, allowing it to preserve capital while scaling.

This combination of deep capital backing and operational discipline is rare, particularly in a category as capital-intensive as autonomy.

The Shift to AI for Atoms

The broader implication of Applied Intuition’s trajectory is that the next phase of AI will extend beyond software.

The current wave has been defined by text, images, and code. The next wave will be defined by machines.

Applied Intuition is positioning itself at the center of that transition, building the systems that allow AI to operate in the physical world.

This is really an infrastructure story, and like many infrastructure companies, it has been built largely out of sight.. flying under the radar.

For nearly a decade, Applied Intuition focused on embedding itself into industries where AI adoption is slow, complex, and high-stakes. That foundation is now compounding.

The result is a company that may not be widely known, but is deeply integrated into the systems that will define the next era of AI.

Or, as the conversation makes clear, this is not a fluffy AI company riding the supercycle wave, or creating another hype narrative.

This is serious AI for atoms.

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BREAKING: The Multi-Billion Dollar Digital Economy Powering Roblox

Wed, 01 Apr 2026 14:05:02 GMT

Big Business Behind Roblox

David Baszucki, Co-Founder & CEO of Roblox (NYSE: RBLX), has quietly built one of the largest & most complex systems on the internet: a real-time, global platform with ~150 million daily active users, 35 billion hours of engagement per quarter, and $6.8 billion in 2025 bookings driving the digital economy running on top of massive complex web of AI & infrastructure.

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Roblox isn’t just a gaming platform, it’s a fully functioning economy. The platform has paid out over $1.5 billion to creators, with top 1,000 developers earning over $1.3 million on average, turning games into businesses and players into entrepreneurs.

Underneath it all is a deeply engineered system.. 40+ global data centers, hundreds of thousands of servers, and hundreds of AI models powering everything from creation, discovery, safety, & real-time interaction.

We go deep on how Roblox designed this economy from first principles, why AI will accelerate (not replace) creators, and what it takes to build a platform that blends gaming, social interaction, and entrepreneurship into one system. This is a curious blueprint for the future of the internet & real life where anyone can create, earn, & participate in a global digital world.

Subscribe to Sourcery for more conversations with the founders and leaders building the next generation of technology.

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(00:00) David Baszucki, Co-Founder & CEO at Roblox
(01:21) A spicy start
(03:53) The 10% global gaming ambition
(05:40) The early vision that built Roblox
(07:45) Vision vs metrics
(09:08) The most underrated part of Roblox
(10:55) Why Roblox data matters for AI
(12:55) The future of AI-powered games
(16:27) AI won’t replace creators
(20:46) Virtual concerts and the future of AR/VR
(22:28) What “4D gaming” actually means
(24:29) Staying relevant across generations
(27:00) The viral engine
(27:58) Lessons on human behavior
(30:51) AI, jobs, and the future of work
(34:00) The culture playbook
(37:03) Mindset and performance
(38:06) Health, diet, sleep, ketosis and biohacking
(46:05) Craziest Roblox moments
(47:45) What’s next for Roblox

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Roblox: A Scaled Digital Economy

David Baszucki, Co-Founder & CEO of Roblox, has built a real-time global platform operating at meaningful economic scale: ~150 million daily active users, 35 billion hours of engagement per quarter, and $6.8 billion in annual bookings.

2025 marked an inflection. Roblox generated $4.9 billion in revenue (+36% YoY) and $6.8 billion in bookings (+55% YoY). In Q4, revenue reached $1.4 billion (+43% YoY) and bookings $2.2 billion (+63% YoY). Daily active users were 144 million (+69% YoY), and hours engaged grew 88% year-over-year to 35 billion. Growth reflects both expansion in users and increased engagement per user, with average daily usage around 2.6–2.7 hours.

Roblox is still early relative to its stated ambition. Global gaming is a ~$200 billion market, and the company has set a target of reaching 10% share.

“The external goal is 10% of global gaming… global gaming’s about $200 billion market. So you know, we did 6.8 billion in bookings last year. 10% would be like $20B.”

Creator Earnings & Economic Output

In 2025, Roblox paid out more than $1.5 billion to creators through its Developer Exchange program. The top 1,000 creators earned an average of $1.3 million, up more than 50% year-over-year. With top, toppp earners crossing tens of millions of dollars.

Sep 4, 2025 “The top 10 developers earned an average of $33.9 million, and the top 100 developers earned an average of $6 million, up over 450% and 500%, respectively, since 2019.^2”

“Our developer creator earned about a billion and a half on the platform and it goes pretty deep. Like top thousand devs are averaging 1.3 million like real people making a live on living. So it goes way beyond the walls of this building.”

The system supports a distributed base of creators operating as independent studios, with leading experiences generating billions of Robux in annual revenue and sustaining long-term engagement.

Roblox’s economic footprint extends beyond the platform. Between 2017 and 2024, it contributed an estimated 22,000 full-time job equivalents and $1.62 billion in GDP to the U.S. economy, with creators generating approximately $416 million in tax revenue. In 2024 alone, Roblox drove $445 million in GDP impact, up 29% year-over-year.

“Very simple economic principles, very well designed, very fair, can create incredibly complicated outcomes.”

²In Robux spent. Our creator ecosystem includes many hobbyists. Of the millions of creators monetizing on Roblox, more than 24,500 are part of our DevEx Program, with the median creator participating in the DevEx Program receiving $1,575 USD during the 12 months ended December 31, 2024.

Engagement & Distribution

The system is anchored in engagement. Roblox reported 35 billion hours of engagement in Q4, one of the highest levels of time spent across consumer platforms.

“Some of the metrics we look at are literally raw engagement time. So I think in Q4 of last year, it was 35 billion hours, so that’s pushing 12 billion hours a month.”

Distribution is tightly integrated. Users transition between experiences in near real-time, creating continuous exposure for creators.

“We work super hard at going from game experience to game as quickly as possible. No one notices it… everyone on Roblox just assume, oh, I’m playing this now, I’m playing this now… like half a second.”

Virality is a primary growth driver.

Roblox content propagates across short-form video platforms & internal discovery systems.

“Roblox has primarily grown virally. Viral means word of mouth sharing links… a third of the gaming content on their platform is Roblox content.”

This reduces reliance on paid acquisition and reinforces creator-driven growth loops.

Underrated: Heavy Infrastructure & Data

Roblox operates a vertically integrated infrastructure stack supporting real-time, multi-user simulation at scale.

“We run on our own cloud. Super efficient, super cost effective.
We have 40 plus data centers all around the world.
We have hundreds of thousands of servers.”

The platform supports tens of millions of concurrent users interacting in shared environments, requiring synchronization across geographies and low-latency compute.

The dataset generated is distinct from traditional internet platforms:

“It’s not just video data, it’s literally 3D positions. It’s what avatars are doing, it’s what they’re looking at. It’s literally data that ultimately can and does allow us to reconstruct anything that’s ever happened.”

This includes spatial, behavioral, and multi-agent interaction data across persistent environments.

Platform Design: 4D Systems

Roblox’s design philosophy extends beyond static 3D environments into what the company refers to as “4D” systems — objects and environments that are interactive, functional, and simulated.

“Games are a lot more fun if I push on a door and like the door opens… or I have a car and I can jump in the car with a friend and drive around.”
“Since the very first day we started building Roblox… we were really into having all of the objects in the world be functional.”

Objects carry embedded behavior from physics, logic, and interactivity, allowing for emergent outcomes rather than scripted experiences. This increases replayability and supports more complex user-generated systems.

AI & Creation

Roblox has deployed hundreds of AI models across its platform, spanning creation tools, discovery systems, safety, and communication.

“We don’t quite know where gaming can go if users have infinite AI.”
“We’re at the verge of not really creator replacement, but creator acceleration as well.”

Creation workflows are shifting toward natural language interfaces and assisted generation. Users increasingly design worlds, assets, and interactions through prompts rather than manual tooling. The number of creators and rate of iteration increase as friction is reduced.

Roblox integrates creation, distribution, and monetization within a single system. Users can transition from consumption to creation without leaving the platform, and successful creators scale directly into businesses.

“The power of providing a platform where all these other creators can work on it and make a living is very powerful.”

The system supports a wide range of outcomes, from hobbyist projects to large-scale studios generating significant revenue.

Category Expansion: Entertainment & Media

Roblox is expanding into entertainment, sports, and media as large-scale, persistent experiences.

A recent Bruno Mars virtual concert reached 12,862,161 concurrent viewers, setting a Guinness World Record for the largest music concert in a video game by a single artist.

Major sports leagues (NFL, NBA, FIFA) and film studios are building persistent environments and experiences on the platform. Seven of the top ten U.S. domestic films activated on Roblox in the past year.

These experiences operate alongside traditional games, using the same underlying systems for interaction, monetization, and distribution.

Economies of Scale

Baszucki points to what he believes is the most overlooked aspect of Roblox: the depth of systems sitting underneath what appears to be simple gameplay.

The platform combines tooling, cloud infrastructure, economic design, and real-time simulation into a single environment where creators can build, distribute, and monetize. The surface experience obscures the coordination required across discovery, pricing, incentives, and interaction at scale.

“I think the most underrated aspect of Roblox is arguably how much deep tech and theories around economics and theories around systems sit underneath it.”

This layering becomes more consequential as the system expands. Infrastructure supports interaction, interaction generates data, data informs creation, and creation feeds back into the economy. Each layer compounds the others, increasing both complexity and output as participation grows.

Baszucki frames the long-term implication less as a product question & more as a societal one:

“There may be a way… that society figures out how to couple UBI with accomplishment… so that people can feel accomplished if they so choose.”

Closing Questions: As AI lowers the barrier to building inside these systems, do digital economies become a primary surface for work rather than a peripheral one? Does creation inside platforms like Roblox evolve into a durable, scalable profession? And as younger, internet-native generations come online, how much of work, ownership, & value creation shifts into these environments? Is that good?

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BREAKING: Inside VCX — The Public Venture Capital Fund

Mon, 30 Mar 2026 16:50:31 GMT

The Rise of Public Venture Capital

Fundrise CEO Ben Miller breaks down the launch of Fundrise Growth Tech Fund (NYSE: $VCX) — one of the first publicly traded venture capital funds.

→ Listen on X, Spotify, YouTube, Apple

VCX debuted at roughly $700M valuation, then surged to ~$6.5B within 3 days, then hit +18x, with shares spiking to $575, way above the estimated net asset value (NAV) per share of $18.97, giving over 100,000 investors access to a portfolio of top private companies including Anthropic (~20%), Databricks (~17%), OpenAI (~10%), Anduril, Ramp, and SpaceX.

The timing reflects a broader shift: private markets are now where most value is created. VCX portfolio companies grew ~193% vs ~25% for public tech benchmarks, highlighting the gap between private and public market growth. Meanwhile, IPO timelines have stretched from ~3–5 years to 10–15+ years, meaning public investors are increasingly missing the highest-growth phase.

We discuss how VCX works as a closed-end fund, why it has traded at a premium (despite most closed-end funds trading at discounts), & how Fundrise accessed top-tier companies during the 2022–2023 venture downturn — including buying from distressed sellers and stepping into competitive rounds.

Finally, we explore what comes next: whether public venture capital becomes a standard allocation, how cycles and volatility impact the model, and what happens if public markets begin directly funding private tech at scale.

Topics Covered:

• VCX launch & NYSE debut dynamics
• Portfolio composition (Anthropic, OpenAI, Databricks, SpaceX)
• Private vs public market growth gap (193% vs 25%)
• Macro shift: value creation moving to private markets
• IPO window + why companies stay private longer
• How Fundrise sources and wins allocation
• Closed-end fund structure, NAV, premiums/discounts
• Risks: volatility, cycles, and downside scenarios
• Future of venture: rise of public VC funds

Subscribe to Sourcery for more conversations with the founders, investors, and operators shaping the future of tech, markets, and capital.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Benjamin Miller, Co-Founder & CEO at Fundrise
(01:12) The idea that almost got rejected
(04:27) How the 2023 crash created big opportunities
(05:54) From $700M to $6.5B in days
(07:38) How a closed-end fund works
(11:09) Inside the VCX portfolio (OpenAI, SpaceX, Databricks)
(14:50) What Robinhood and Destiny are doing
(17:02) Why private markets are pulling ahead
(21:38) IPO environment right now
(22:17) The SaaS apocalypse & market volatility
(25:55) What happens if VCX trades down
(27:39) How VCX moves through cycles
(31:25) How they decide where to invest
(35:45) Investment size and scale
(36:59) Most underrated portfolio company
(40:37) Biggest lesson: pain = success
(43:30) Origin story: why Fundrise exists
(47:12) Will big VC firms go public?
(50:52) Future of venture capital
(54:05) Biggest risks ahead
(57:18) Democratizing venture capital
(01:00:56) What’s next for VCX
(01:03:05) Dealing with skeptics

Brought to you by:

VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at GetVCX.com

Inside $VCX: A New Structure for Accessing Private Technology Markets

NYSE Public Debut & Early Trading Dynamics

On March 19, 2026, the Fundrise Growth Tech Fund (VCX) began trading publicly, marking one of the first instances of a venture-style portfolio being made accessible through a listed vehicle.

The fund debuted at an approximate $700 million valuation and subsequently experienced significant upward trading activity, with shares in the last week reaching as high as $575, representing a substantial premium to its estimated net asset value (NAV) of $18.97 per share. This divergence reflects strong early demand for exposure to private technology assets, as well as the structural characteristics of closed-end funds, where market price is determined independently from underlying asset value.

At the time of listing, the fund had over 100,000 investors, indicating broad participation relative to traditional venture vehicles.

Portfolio Composition & Exposure

VCX provides investors with exposure to a concentrated portfolio of private technology companies, with a notable emphasis on artificial intelligence and data infrastructure.

As disclosed, key holdings include:

Anthropic (~20%)
Databricks (~18%)
OpenAI (~10%)
Anduril (~7%)
SpaceX (~5%)

This composition reflects a strategy focused on high-growth, category-defining companies, many of which remain private for extended periods. The ability to access these companies through a public market instrument is central to the fund’s value proposition.

Structure Details: How VCX Trades & Prices

A key aspect of VCX is how its closed-end fund structure differs from more familiar public market vehicles. While it provides exposure to private companies, investors are not directly buying shares in those companies. Instead, they are purchasing shares in a listed fund that holds those positions.

“The fund is listed, not the companies.”

VCX operates as a closed-end fund, meaning trading occurs at the fund level rather than through continuous creation and redemption of underlying assets. This distinction has important implications for pricing, liquidity, & how closely the fund reflects its underlying portfolio.

“The people are buying & selling the actual fund itself in the public markets.”

Because of this structure, the market price of VCX is determined by supply & demand for the fund’s shares. This can result in meaningful divergence from the fund’s net asset value (NAV), especially given that the underlying holdings are private and marked based on periodic funding rounds rather than continuous market pricing.

“You invest in private companies… typically mark at their last round valuation.”

This creates a dual-layer dynamic: a publicly traded price driven by investor demand, and a privately marked portfolio value that updates more gradually.

Summary of Structure

Investors buy shares of the fund, not the underlying private companies
The fund is a closed-end structure, not an ETF
Shares trade between investors on the public market
The fund does not continuously issue or redeem shares based on demand
Market price is driven by public supply and demand, not directly by NAV
Shares can trade at a premium or discount to NAV
Underlying holdings are private & typically marked to last funding rounds
The structure separates public market liquidity from private company operations

Massive Growth in Private Value Creation

The emergence of VCX is closely tied to broader structural changes in capital markets. Over the past decade, a growing share of value creation has shifted to private markets.

As discussed in the conversation, portfolio companies within VCX have demonstrated significantly higher growth rates relative to public benchmarks:

“The weighted average growth rate for our portfolio for VCX was 193%… the weighted average growth rate of public tech companies last year was 25%.”

At the same time, the timeline to public listing has extended materially. Companies that historically may have gone public within 3–5 years are now remaining private for 10–15 years or longer, concentrating the highest-growth phase outside public markets.

Structure: Closed-End Fund Mechanics

VCX is structured as a closed-end fund, which differs meaningfully from more familiar public market vehicles such as ETFs.

In this structure:

Shares of the fund trade on an exchange
The fund does not continuously issue or redeem shares based on demand
Market pricing is determined by investor sentiment rather than strictly by NAV

“A closed-end fund… the shares in the fund are trading rather than shares in the underlying asset portfolio.”

This structure allows the fund to hold illiquid private assets while still offering liquidity to investors. However, it also introduces the possibility of sustained premiums or discounts relative to NAV, as seen in early trading.

Sourcing & Access to Private Companies

A key question for any vehicle of this type is how it gains access to highly competitive private investments.

Fundrise’s entry into several portfolio companies occurred during the 2022–2023 market downturn, when venture capital sentiment was negative and liquidity pressures forced some investors to sell positions. This created opportunities to acquire stakes through secondary transactions and participate in rounds where capital availability was constrained.

“In 2023… people were selling their best assets… we bought from distressed funds who were having to sell.”

This combination of timing, opportunistic capital deployment, and positioning as a long-term partner enabled Fundrise to build exposure to companies that are typically difficult to access.

Implications for Venture Capital & Public Markets

VCX reflects a broader evolution in how capital may flow between private & public markets. As private markets expand, and companies delay or avoid IPOs, new structures may emerge to provide access to earlier stages of company growth.

Miller frames this as a longer-term shift:

“I think that like 10 years from now, every single person… will have 5% of their portfolio in public venture capital.”

At the same time, the model introduces new considerations, including pricing volatility, NAV transparency, and the cyclical behavior of premiums and discounts. The early trading of VCX (at a significant premium) highlights both the demand for private market exposure and the complexity of integrating illiquid assets into a public framework.

What Happens Next?

The Fundrise Growth Tech Fund (VCX) represents a notable experiment/innovation/development in market structure: a publicly traded vehicle providing access to private, high-growth technology companies.

Its early performance reflects strong demand for this exposure, while its structure raises important questions about pricing, liquidity, and long-term sustainability. More broadly, VCX sits at the intersection of two ongoing shifts, the expansion of private markets and the search for new ways to access them.

Whether this model becomes widely adopted will depend on how it performs across market cycles, but it clearly signals a growing interest in bridging the gap between private value creation and public market participation.

Questions to consider:

If private markets continue to capture the majority of value creation, can a public structure like VCX sustainably bridge that gap?
Can venture capital truly be made public without changing the characteristics that drive its returns?
Does this mark the beginning of venture capital becoming a public asset class, or the limits of trying to make it one?
Can liquidity & venture returns coexist, or are they fundamentally at odds?
If this model works, does it redefine how investors access innovation over the next decade?

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BREAKING: Inside Marc Andreessen & Ben Horowitz’s Multi-Family Office

Sat, 28 Mar 2026 13:40:47 GMT

Michel Del Buono, CIO of Andreessen Horowitz’s multi-family office Perennial, breaks down how Silicon Valley’s top founders and investors manage wealth after massive liquidity events.

→ Listen on X, Spotify, YouTube, Apple

Perennial was built after Marc Andreessen and Ben Horowitz identified a structural gap: traditional wealth managers optimize for service and asset gathering, not investment performance, while institutional asset managers are built for non-taxable capital and don’t optimize for after-tax outcomes.

That leaves a growing class of $50M–$1B+ personal portfolios in a “no man’s land” — too complex for retail wealth models, but not served by institutional frameworks.

In this episode, we go deep on how these portfolios are actually constructed: how to manage concentrated stock, structure around taxes, and build multi-asset portfolios that behave more like endowments than individuals.

We also cover how founders should prepare for large-scale liquidity events, including potential mega-IPOs like SpaceX, & why most people make the wrong wealth decisions at the exact moment their hard-earned capital becomes liquid (& how to avoid that).

We cover:

Why most wealth management portfolios are misaligned
The structural flaws of RIAs vs institutional asset managers
How to manage concentrated stock positions pre- and post-IPO
Why venture capital returns are driven by extreme dispersion
How taxes, fees, and structure can drive hundreds of basis points of alpha
Why volatility is one of the biggest opportunities for long-term investors
The rise of multi-family offices in Silicon Valley

If you’re building, investing, or approaching a major liquidity event, this is a masterclass in how wealth is actually managed at scale.

Special thank you to Dave Maloney for the intro!

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Michel Del Buono, CIO a16z Perennial
(01:25) The idea behind a16z Perennial
(03:38) What’s broken in wealth management
(09:05) How wealth has changed over time
(11:57) How fee structures shape portfolios
(15:26) Why single family offices are hard to run
(19:47) Who wealth management is really for
(23:26) What makes Perennial different
(22:21) Preparing for massive liquidity events: SpaceX, OpenAI...
(24:01) How to choose the right wealth manager
(26:39) Why switching firms is so hard
(28:01) How portfolios are actually built
(31:29) Why volatility is an opportunity
(32:47) Why real estate is so powerful
(34:55) Taxes and the Billionaire Tax debate
(38:46) Should you move to save taxes?
(40:59) Chamath: SPAC losses and how they affect taxes
(42:21) Secondary deals, SPVs and the risks
(46:16) What drives returns in Venture Capital
(49:42) Biggest Lesson from Marc Andreessen & Ben Horowitz
(51:27) The biggest mistake founders make with money
(52:53) How to invest after a big exit
(54:16) Concerns around private credit
(56:38) What big IPOs mean for markets
(58:08) Keeping up with markets
(59:35) What’s the focus this year at a16z

Brought to you by:

VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at GetVCX.com

a16z Perennial: Why Wealth Management Is Structurally Broken

Michel Del Buono, CIO of Andreessen Horowitz’s multi-family office Perennial, frames the current wealth management landscape as fundamentally misaligned with the needs of modern wealth.. particularly in Silicon Valley.

“The two standard approaches… aren’t really great for someone with institutional levels of wealth.”

At a high level, the problem is not a lack of capital, access, or even sophistication in isolation, it’s that the system itself was never designed for individuals managing institutional-scale balance sheets. Today’s founders and operators routinely exit with $50M, $100M, or $1B+, yet the infrastructure serving them still reflects models built for retail investors or tax-exempt institutions.

This creates a structural mismatch at the exact moment capital matters most.

The Two Broken Models

The first model: traditional wealth management, prioritizes service over investment performance. These firms, often spun out of large banks, are designed to be highly responsive, relationship-oriented, and operationally helpful. They excel at logistics, coordination, and lifestyle services, but their investment arms are not structured like performance-driven organizations.

Critically, they are compensated through AUM-based fees that do not change based on portfolio complexity or outcome. This leads to a predictable behavioral pattern.

“If you’re paid the same to do something easy or something difficult… you’ll do the easy thing.”

In practice, this means portfolios skew toward simplicity like public equities, bonds, and basic diversification, because building sophisticated, multi-asset portfolios requires hiring expensive talent, underwriting complex investments, and operating with long feedback cycles. Most firms are neither incentivized nor structurally equipped to do this.

The second model: traditional asset management, has the opposite problem. Hedge funds, private equity firms, and endowment-style allocators are highly sophisticated, but they are optimized for non-taxable capital. Their largest clients are pensions, sovereign wealth funds, and foundations, all of which focus on pre-tax returns.

For individuals, especially those in high-tax jurisdictions, this creates a critical gap. The most accessible and reliable form of “alpha” is often tax optimization, yet these firms are not designed to deliver it.

The result is a bifurcated system where one side lacks investment rigor and the other lacks tax awareness.

The “No Man’s Land” of Modern Wealth

Between these two systems sits a rapidly growing class of individuals with institutional-scale wealth & personal tax exposure. This segment is expanding quickly, driven by accelerated wealth creation in technology, AI, & venture-backed ecosystems.

Despite this, the quality of investment advice has not kept pace.

“Most folks would agree they felt sort of underwhelmed with the quality of the investment advice.”

This is not simply a service gap, it is a structural gap. These individuals require portfolios that behave like endowments, but operate within the constraints of taxable entities. They need access to private markets, real assets, and complex strategies, while simultaneously optimizing for after-tax compounding.

Perennial’s approach is to unify these requirements into a single system: institutional-quality investing, implemented through a tax-aware lens, and customized around the realities of concentrated wealth.

Liquidity Events: The Most Critical Transition

(Hint.. ~$2T SpaceX IPO)

The most consequential moment in a founder’s financial life is the liquidity event. Whether through an IPO, acquisition, or secondary sale, this is the point where illiquid equity transforms into deployable capital.

“Your wealth is the product of your life's work… invest the time to understand what you're buying before buying.”

Increasingly, these events are occurring at unprecedented scale. A potential SpaceX IPO, for example, would represent one of the largest wealth creation moments in history. Yet the infrastructure to support thousands of newly liquid individuals remains underdeveloped.

The most common mistake is reflexive behavior, reinvesting proceeds back into early-stage ventures, often through informal networks and referrals. This reflects familiarity rather than strategy.

A more effective approach requires balancing multiple competing objectives: preserving exposure to high-conviction assets, gradually diversifying risk, structuring for tax efficiency, and maintaining liquidity for future opportunities.

Del Buono emphasizes that the goal is not immediate liquidation, but controlled evolution of the portfolio. This includes strategies such as monetizing volatility through options, staging exits over time, and aligning investment decisions with long-term objectives rather than short-term sentiment.

“Structuring your estate structuring your trust is very important pre IPO… post IPO it’s all about how do I diversify gradually.”
“Part of their strategy should be to diversify but part of it should be to hold on to that stock long term.”
“I would not go and suggest they dump SpaceX 100% immediately and go buy stocks and bonds.”
“These stocks are volatile by nature and so you can monetize that volatility without necessarily exiting the stock.”

Portfolio Construction for Taxable Capital

Portfolio construction at this level diverges meaningfully from institutional models. For taxable individuals, asset selection is not just about expected return, it is about after-tax return, liquidity, and correlation.

Real assets, particularly real estate, play a central role because of their tax advantages and structural position within the financial system. Much of the tax code and lending infrastructure was built around physical assets, creating embedded benefits that persist today. When managed correctly, these assets can generate attractive after-tax returns while diversifying exposure away from technology and public equities.

At the same time, liquidity becomes a strategic asset in itself. Holding treasuries or cash equivalents is often misunderstood as a drag on performance, but in reality, it provides optionality: the ability to deploy capital quickly during market dislocations.

As Del Buono puts it:

“Volatility is not the enemy… it’s a huge opportunity.”

Periods of drawdown, when assets trade at significant discounts, are not risks to be avoided, but opportunities to be captured. The ability to act during these moments depends entirely on prior portfolio construction.

Venture Capital: Extreme Outcomes, Not Average Returns

Venture capital occupies a unique position within this framework. It offers the potential for outsized returns, but those returns are highly concentrated among a small subset of managers.

The dispersion between top and bottom performers is wider than in any other asset class. This creates a dynamic where average exposure is insufficient, outcomes are determined by access and selection.

For many investors, this reality is misunderstood. Without exposure to top-tier managers, venture can underperform public markets on both a risk-adjusted and absolute basis, particularly after accounting for fees and illiquidity.

This reinforces a broader principle that asset class allocation is less important than implementation quality.

Chamath, SPACs, & the Role of Capital Losses

While the recent viral hit on X around Chamath Palihapitiya & SPAC-related losses (now resolved) didn’t go so great.. it also highlights a misunderstood part of portfolio construction: capital losses are not just downside, they’re tools.

In isolation, a loss is simply a negative outcome. But within a properly structured portfolio, losses can be used to offset gains and materially reduce tax liability. The key distinction is where those losses sit.

“You can’t use a loss that comes to you personally… the way to use a loss is inside a fund vehicle that generates its own gains that can be offset by its own losses.”

This is where structure matters. Individual investors often assume that losses from failed investments can be directly applied against their broader income or gains. In practice, tax rules limit that flexibility. Without the right structure, those losses can be underutilized or effectively stranded.

Inside fund structures, typically partnerships, losses and gains are netted internally before distributions. This allows portfolios to generate returns that are already tax-adjusted, rather than forcing investors to manage offsets manually at the individual level.

The implication is that capital losses become part of the strategy, not just the outcome.

This dynamic is particularly relevant in venture & speculative markets, where dispersion is high and failure rates are expected. Losses are inevitable, but when structured correctly, they can be used to improve net returns across the portfolio.

The takeaway is less about any single SPAC outcome and more about how portfolios are engineered. Without intentional structuring, losses remain losses. With the right framework, they become a mechanism for tax efficiency and long-term compounding.

The Compounding Effect of Structure

Across all of these dimensions, the most powerful driver of long-term outcomes is structure. Fees, tax efficiency, and investment execution may appear incremental in isolation, but their effects compound over decades.

“Just a couple hundred basis points… could mean hundreds of millions of dollars over time.”

This is especially true for large portfolios, where even small improvements translate into significant absolute gains. Reducing fee layers, building in-house capabilities where appropriate, and optimizing for tax efficiency are not tactical decisions, they are foundational to long-term wealth creation.

A Structural Shift in Wealth Management

What emerges from this analysis is not simply a critique of existing models, but the outline of a new one. Wealth management is transitioning from a service-oriented industry to an investment-driven one, from standardized portfolios to customized allocation frameworks, and from static strategies to dynamic, tax-aware systems.

At the same time, control of capital is shifting. Increasingly, founders and operators are choosing platforms that reflect their own expectations around performance, transparency, and long-term alignment.

This is why firms like Perennial exist, not as incremental improvements on existing models, but as a response to a structural gap in how modern wealth is created and managed.

The implication is clear: as wealth creation accelerates, the systems that manage it must evolve just as quickly.

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BREAKING: Reddit CEO Steve Huffman | Why Reddit Is Winning the AI Era

Fri, 20 Mar 2026 14:56:15 GMT

Steve Huffman, Founder & CEO of Reddit, joins Sourcery at Reddit’s San Francisco HQ for a deep dive into how Reddit became one of the strongest businesses in social media — and why it may be even more important in the AI era.

→ Listen on X, Spotify, YouTube, Apple

On the eve of its 2-year anniversary as a public company (NYSE: $RDDT), we cover Reddit’s March 21st, 2024 debut on the New York Stock Exchange (NYSE), including how the company brought its community along for the ride through one of the largest directed share programs in tech IPO history — allowing Reddit users to buy shares at the IPO price.

From $12M in revenue in 2015 to $2.2B in 2025, Reddit has quietly built one of the most durable social platforms on the internet. A core idea from this conversation: Reddit is built on real humans, & as Steve puts it, there needs to be an “ass in seat.”

As AI agents, bots, and synthetic content flood the internet, Reddit is doubling down on authenticity, human conversation, and community-driven moderation — while also becoming one of the most valuable data sources powering top AI models.

We cover:

Reddit’s NYSE IPO and why Steve recommends going public
Why Reddit’s ad business is working while others struggle
How Reddit became critical training data for companies like OpenAI and Google
The “ass in seat” philosophy and why human presence matters
How Reddit handles bots, agents, and AI-generated content
Why Reddit may have cracked the social media business model
Steve’s contrarian take on IPOs and building long-term companies

Reddit by the numbers:

121M Daily Active Uniques
471M+ Weekly Active Uniques
100K+ Active Communities
24B+ Posts & Comments
$2.2B Annual Revenue (2025) +69% YoY

If you want to understand where the internet is heading & why human communities may matter more than ever — subscribe to Sourcery.

Subscribe now

𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Steve Huffman, Co-Founder & CEO at Reddit
(01:14) Why going public made Reddit stronger
(05:40) What drove Reddit’s IPO success
(08:30) Letting users buy shares at IPO price
(10:51) What people get wrong about Reddit communities
(14:10) Why Reddit ads work so well
(18:40) Where does AI get its training data?
(21:00) Bots vs AI pretending to be human
(24:40) Reddit as the internet’s truth detector
(25:57) Why Reddit never became social media
(28:10) How brands should actually use Reddit
(30:20) Fake stories and why they still work
(36:30) Human verification vs privacy online
(40:30) What Reddit protects vs what it changes
(42:10) Incentives and real human behavior
(43:10) What metrics Reddit actually cares about?
(44:50) From $12M to $2.2B how it happened
(48:30) Where Steve gets his advice
(50:40) Advice from Rich Barton on going public
(51:00) Inside Reddit’s SF office
(53:20) How AI is changing engineering productivity
(54:40) Why AI will not reduce engineers

Brought to you by:

VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at GetVCX.com

Inside Reddit: IPO Lessons, AI Data Power, & the Fight for a Human Internet

A conversation with Steve Huffman, Founder & CEO of Reddit

Nearly two decades after its founding, Reddit has emerged as one of the most important platforms on the internet—and increasingly, one of the most strategically valuable.

Under CEO Steve Huffman’s leadership, Reddit has transformed from a scrappy forum into a $2.2 billion revenue business with over 121 million daily active users. More importantly, it has done so while preserving something most platforms lost along the way: authenticity.

In a wide-ranging conversation on Sourcery, Huffman breaks down Reddit’s evolution across three defining fronts: becoming a public company, building a scalable business model, and positioning itself at the center of the AI ecosystem—without sacrificing its human core.

The IPO 2 Years Later: NYSE Debut, Community Ownership, & a Contrarian Playbook

Reddit went public on the New York Stock Exchange (NYSE) in March 2024, marking one of the most closely watched tech IPOs following a prolonged slowdown in public listings.

The path there was anything but straightforward. Reddit spent nearly two years in registration, initially planning to go public in 2022 before market conditions forced a delay. Huffman is unequivocal in hindsight: going public was the right decision.

“It’s made us a better business. The rigor, the routine, the pressure—I think all of that has really helped us.”

He frames the IPO not as a liquidity event, but as an operational upgrade, one that introduced discipline, accountability, and long-term alignment across the company.

A defining difference: Reddit brought its users with it

One of the most unique aspects of Reddit’s NYSE debut was its directed share program, one of the largest ever executed in a tech IPO.

Rather than limiting access to institutional investors, Reddit enabled thousands of its users to participate in the IPO at the same $34 share price as institutional buyers.

“I wanted our users… who have a deep sense of ownership over Reddit… to actually be owners of Reddit.”

This decision reflects a deeper philosophy: Reddit’s value is created by its communities, and therefore its communities should share in its upside.

Huffman’s only regret was that more users didn’t participate—largely due to negative sentiment from prior platform changes and the restrictions of the IPO quiet period.

Still, the move set a precedent for what a community-aligned public offering can look like.

The contrarian IPO lesson: price for momentum, not ego

Reddit’s IPO also carried a key tactical insight.

The company priced at $34 per share—well below its prior private valuation peak of $61. Rather than defending the highest possible valuation, Reddit chose to create room for upside.

“Go out at a lower price so you can have that momentum… it’s the best marketing dollars you may ever spend.”

The result: strong post-IPO performance, broad investor goodwill, and a stock that traded significantly above its listing price.

In Huffman’s view, this approach builds trust across all stakeholders—employees, investors, and the market.

From $12M to $2.2B: Cracking the Social Media Business Model

When Huffman returned as CEO in 2015, Reddit generated approximately $12 million in revenue. By 2025, that number had reached $2.2 billion.

The most important takeaway is not just the scale—but how Reddit got there.

Unlike many social platforms that engineered monetization layers on top of engagement, Reddit discovered that its core behavior was already commercial.

“40% of conversations on Reddit are commercial… people are asking: what should I buy, what should I watch, what should I wear?”

This insight reframes Reddit entirely. It is not just a social platform—it is a decision engine.

That dynamic has made Reddit uniquely effective for advertisers:

Intent-rich conversations rather than passive scrolling
Community-driven recommendations rather than influencer-driven promotion
High trust environments where authenticity drives conversion

At the same time, Reddit made a critical strategic decision early: to build its own advertising infrastructure.

“We wanted to own the relationship with our customer… and we couldn’t do that without our own ad tech.”

Moving off third-party ad networks allowed Reddit to control targeting, measurement, & sales, transforming advertising into a scalable, high-margin business.

The result is a platform that now operates effectively as both a brand and performance channel, a combination many competitors have struggled to achieve.

Reddit in the AI Stack: “The Fuel for AI”

If Reddit’s second chapter was business model clarity, its third is even more consequential: becoming foundational infrastructure for AI.

Huffman describes Reddit’s role in stark terms:

“Reddit is the fuel for AI.”

With over 24 billion posts and comments, Reddit represents one of the largest repositories of human conversation on the internet—rich in nuance, opinion, and real-world context.

That makes it uniquely valuable for training large language models.

Reddit has entered into formal data partnerships with companies like Google and OpenAI, while also navigating the reality that its data has historically been used without permission.

But the relationship is symbiotic:

AI systems depend on Reddit’s data for human signal
Search and AI products drive discovery back to Reddit
Increased visibility brings in more users and content

At the same time, AI is increasing the value of Reddit’s core differentiator: real human conversation.

As the broader internet becomes more synthetic and summarized, Reddit becomes more distinct.

“When the whole internet becomes sanitized by AI… people appreciate the humanness of Reddit.”

“Ass in Seat”: Philosophy of Human Presence

Perhaps the most defining idea from the conversation is Huffman’s high sophisticated advanced framing of Reddit’s future in the AI era:

“We want to make sure there’s an ‘ass in seat’.”

It’s a simple but powerful principle: Reddit only works if there is a real human behind the screen.

This is where Reddit draws a hard line between:

Helpful AI tools
AI masquerading as humans, which is not allowed

Reddit has long allowed bots—but only when they are clearly labeled and additive. The new challenge is AI-generated content that is indistinguishable from human content. Huffman describes this as an “immune system” emerging within Reddit’s communities:

“Reddit is the world’s greatest bullshit detector.”

To reinforce this, Reddit is developing new verification layers:

Third-party identity systems that verify humanness without revealing identity
Token-based verification so Reddit confirms you passed—but doesn’t know who you are

The goal is a balance: preserve both authenticity and anonymity.

“Anonymity is safety… what drives behavior is context, not identity.”

Leadership Lessons: Authenticity >

Huffman’s leadership philosophy mirrors Reddit’s product philosophy: authenticity above all else.

“People can feel authenticity… you have to say what you believe.”

Key principles:

Be stubborn about the right things
Reddit resisted engagement-driven models that could have scaled faster but damaged the platform.
Evolve everything else
Formats, UI, and distribution changed constantly—but the core stayed intact.

“Be stubborn about the values… but evolve everything else.”

Balance metrics with meaning
Huffman emphasizes both “the number & the story.”

“If you’re only focused on numbers… you risk losing what’s important.”

Learn from principled operators
He points to leaders like Warren Buffett, Charlie Munger, and Rich Barton as models of long-term thinking.

The Next 20 Years: More Change, Same Core

Looking ahead, Huffman expects rapid change in how Reddit is experienced—but not in what it is.

Interfaces will evolve
Discovery will evolve
AI will reshape interaction

But the core remains:

“Everything interesting about Reddit is created by our users.”

Communities, conversation, and human judgment are the constants.

Reddit’s trajectory offers a rare counter-narrative in tech.

While much of social media optimized for scale, virality, and algorithmic engagement, Reddit optimized for authenticity, community, and human signal—often at the expense of short-term growth.

That decision is now compounding.

Reddit is not just a social platform. It is:

A decision layer for consumers
A data layer for AI systems
A community layer for the internet

And in a world increasingly filled with synthetic content, its core bet is simple:

The future of the internet still requires humans.

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BREAKING: Deel Hits $1.4B+ ARR | CEO Alex Bouaziz

Tue, 17 Mar 2026 15:23:29 GMT

Alex Bouaziz is the co-founder & CEO of Deel, the global payroll and workforce infrastructure company that has rapidly become the #1 platform for hiring & paying employees worldwide. Co-Founded in 2019 with CRO Shuo Wang, at only 7 years old, Deel has scaled to $1.4B+ ARR, a $17.3B valuation, & 40K+ customers across 150+ countries.

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In this conversation, Alex breaks down how Deel became one of the fastest-growing software companies in the world, building the infrastructure that allows companies to hire, manage, and pay talent anywhere globally.

We discuss Deel’s unique M&A playbook, including 10+ acquisitions used to rapidly expand payroll, HR, and compliance capabilities across markets, and how the company has built its own global payroll infrastructure across countries.

Despite its rapid growth, Deel has also achieved profitability while scaling past $1B+ in revenue, an uncommon milestone for a company growing at this speed.

Alex also discusses the company’s recent $17.3B valuation & funding round backed by Ribbit Capital, Coatue, and a16z, how Deel scaled globally, and why he believes AI and autonomous agents will fundamentally reshape the future workforce.

Topics include:

• Scaling Deel to $1.4B+ ARR
• Serving 40K+ companies across 150+ countries
• Deel’s 10+ acquisition M&A strategy
• Building a profitable hypergrowth SaaS company
• The future of global hiring and distributed teams
• How AI agents will transform the workforce

Subscribe to Sourcery for conversations with the founders and investors shaping the future of technology, AI, and global business.

This conversation took place in London February 12th, 2026. Special thank you to J & family for letting us record in your gorgeous home. (Shoutout to Charlotte!)

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Alex Bouaziz, Co-Founder & CEO at Deel
(02:05) Working with his co-founder and CRO Shuo Wang
(03:54) What Deel actually does
(05:05) Biggest customers
(05:54) $17B valuation and latest funding round
(06:35) How Deel approaches fundraising
(07:50) Hitting $100M ARR and early growth
(10:35) Inside the latest funding round
(12:03) Why payroll is a huge global opportunity
(14:21) How Deel expanded its product stack
(16:18) Running a 7,000 person fully remote company
(18:29) Tips for building remote teams
(19:50) Why Deel went global from day one
(22:47) How AI is changing hiring
(26:19) A question from Micky Malka
(26:57) From airlines to oil and gas: who uses Deel
(27:58) M&A strategy and integration playbook
(34:33) Biggest mistakes companies make with acquisitions
(35:56) How to retain founders after acquisitions
(39:09) What it takes to be IPO ready
(40:29) How AI could affect Deel’s future
(42:55) The "Dubai founder controversy"
(46:00) Leading in a competitive market
(50:47) The biggest misconception about Deel
(53:27) The right people around you
(58:33) What’s next for Deel
(01:01:05) The future of autonomous agents
(01:03:35) Biggest lessons from Shuo
(01:06:34) What makes a great salesperson
(01:08:32) Partnering with Arsenal

Brought to you by:

VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at GetVCX.com

Deel 7 Years in:

$1.4B+ ARR, $17.3B Valuation, 40K+ Customers

After years of operating at extraordinary speed, Deel is finally revealing the playbook behind the scale of what they have built.

On the eve of its 7-year anniversary, the global payroll and workforce infrastructure company disclosed numbers that confirm what many in the venture ecosystem have quietly suspected: Deel continues to be one of the fastest-growing enterprise software companies in the world.

The company now generates $1.4B+ in ARR, serves 40,000+ customers across 150+ countries, and carries a $17.3B valuation. Those numbers place Deel among a rare group of hypergrowth SaaS companies that have crossed the $1B revenue threshold in under a decade.

Deel was founded in 2019 by Alex Bouaziz and Shuo Wang, two close co-founders who built the company around a simple thesis: work was becoming global, but the infrastructure to support it didn’t exist yet.

As Bouaziz explains:

“We built Deel so companies can hire anyone, anywhere, without needing to open entities in every country.”

Seven years later, that thesis has turned into what many operators now view as the operating system for global work.

Source: OnlyCFO

Hypergrowth in One of the Hardest Markets in Software

Global payroll is not an obvious startup category.

It is one of the most complex and regulated areas of enterprise software. Every country has different labor laws, tax structures, benefits systems, and compliance requirements. Historically, the market has been dominated by legacy providers built decades ago.

Yet Bouaziz and Wang approached the problem differently. Rather than expanding country by country like traditional payroll providers, they designed Deel as a global-first platform from day one.

That decision fundamentally changed the speed at which the company could scale.

Within just a few years, Deel crossed $100M ARR, then continued accelerating as remote work and distributed teams became structural shifts across the global economy. Today the company has surpassed $1.4B in ARR, making it one of the fastest-scaling enterprise platforms of the past decade.

Even more unusual: Deel has achieved profitability while scaling past $1B in revenue, something very few companies growing this quickly have managed.

Deel reached $1bn revenue faster than many high-growth tech industry peers due to its impressive organic growth engine and ability to scale at speed. Source: S-1 Filings, GS Research, FactSet

The Infrastructure Layer for Global Hiring

Deel’s customer base reflects the shift toward globally distributed teams.

More than 40,000 companies now rely on the platform to hire, manage, and pay employees across borders. While this includes startups and some of the largest technology companies in the world, Deel’s customer base extends far beyond Silicon Valley — spanning financial services, healthcare, manufacturing, logistics, & global enterprises, as well as fast-growing sectors like gaming, fintech, IT services, marketing and advertising, crypto, AI and data labeling, business process outsourcing, + even oil & gas.

Notable customers include Shopify, Coinbase, Lockheed Martin, ElevenLabs, Lucid, Zillow, On, Puma, Reddit, Klarna, OpenAI, Jelly Belly, NuBank, Instacart..

For these companies, Deel functions as the infrastructure layer that handles payroll, compliance, tax management, benefits, and HR operations across jurisdictions.

“Companies shouldn’t have to think about the complexity of hiring globally. The infrastructure should just work.”

That infrastructure now spans 150+ countries, enabling companies to build truly global teams without establishing legal entities in each market.

Deel’s M&A Playbook

Another driver of Deel’s rapid expansion has been its acquisition strategy.

Over the past several years, the company has completed 10+ acquisitions, targeting specialized payroll and HR companies across different regions. Rather than building every capability internally, Deel has used M&A to integrate local expertise and accelerate product development.

This approach has allowed the company to quickly expand its platform across multiple categories, including global payroll, employer-of-record services, HR software, and compliance infrastructure.

“M&A is a way for us to move faster — to bring in deep local expertise and integrate it into a single global platform.”

And at a higher level, the philosophy is consistent with how Deel has approached the entire market:

“Speed matters in building global infrastructure. You can’t wait to build everything from scratch.”

TLDR: How It Really Works

What’s typically a multi-year integration cycle, Deel compressed into months.

“We built a pretty unique playbook… the TLDR is when we do acquisitions of smaller companies for product, we basically build a way to fully integrate their product and their customer base in the span of 12 months.”

Instead of treating acquisitions as long, disruptive integrations, Deel flips the model entirely — prioritizing speed to market and distribution from day one.

“For the first three months we’re going to build on your backend… within the span of a month, you already have the first version of the product.”

This allows Deel to immediately layer acquired products into its platform, rather than waiting for a full rebuild. The real unlock, however, comes from aligning product integration with go-to-market readiness.

“Before a salesperson is comfortable selling a product, it takes nine to 12 months… by bringing the product after month one, you can get your salespeople trained and comfortable.”

While most companies rebuild first and sell later — often creating 18–24 month delays — Deel runs both processes in parallel.

“Most companies would rebuild a product for 12 months, then show it to their go-to-market team… then have another lag of nine to 12 months before they can sell.”

By the time Deel fully rebuilds the backend, the sales team is already trained, the product is already in market, and distribution is already activated.

“That little trick here gets us much more accelerated… you can see it in the growth of the business.”

The result is not just faster integration.. it’s a compounding advantage across product velocity, revenue expansion, and distribution.

Why Raise Capital If You’re Already Profitable?

One of the more interesting parts of Deel’s story is that it has continued to raise capital even after reaching profitability. In startup land, fundraising is often framed as survival — runway, burn, and the next milestone. Deel’s posture has been different. The company was already profitable, already scaling past $1B+ in revenue, and already operating from a position of strength. That changed the meaning of capital entirely.

As Bouaziz explains, Deel has long tried to fundraise from a position of leverage rather than need. Earlier in the conversation, he notes that one of the company’s core operating principles was always to build the business so that raising money would be optional, not existential. That discipline shaped Deel’s culture early and gave it unusual flexibility later.

“When you’re fundraising, you’re basically selling a part of your business… in my opinion, you should always be in a position where you don’t actually need it.”

That framing is important. Deel did not raise because the business required outside capital to continue operating. It raised because additional capital could accelerate a playbook that was already working — especially around acquisitions, infrastructure buildout, and global expansion.

The company’s investor base reflects that ambition. Deel is backed by Ribbit Capital, Coatue Management, and Andreessen Horowitz, among others, and Bouaziz makes clear that part of the value was not just the capital itself, but who was writing the check. He specifically points to the importance of bringing in people like Micky Malka of Ribbit and deepening relationships with investors who understood the business at a detailed level.

“Being able to bring him onto the cap table was something we’re very excited about.”

That helps explain why Deel was selective. Bouaziz says the company was not planning to raise, and that the process came together only after investors had spent months getting close to the business. In other words, this was less a traditional roadshow than a case of high-conviction investors pushing their way in.

“We weren’t planning on raising money.”

That dynamic matters because it reinforces Deel’s broader strategic posture: if you are already profitable and growing quickly, capital becomes a tool for compression. It lets you do more, sooner. It lets you acquire faster, enter new markets faster, and strengthen the platform before competitors can catch up.

“We don’t raise because we need to survive. We raise to accelerate.”

That may be the simplest way to understand Deel’s fundraising strategy. Rather than using outside money to cover losses, the company has used it to compress time — expanding into more countries, integrating more products, and pushing harder on M&A than would otherwise be possible. In Deel’s case, capital is less about extending runway and more about increasing strategic velocity.

There is also a second layer to this. Bouaziz acknowledges that bringing in a major external investor like the legendary Micky Malka at scale also serves as validation. For a company that spent years growing outside the traditional Silicon Valley spotlight, an outside-led round functions not just as financing, but as a signal to the market.

“Having a completely external investor come and invest into the business is also a great validation of where you are as a company too.”

That combination — profitability, investor selectivity, and strategic use of capital — is part of what makes Deel’s story unusual. Most startups raise because they need oxygen. Deel raised because it wanted jet fuel.

The Micky Malka Question

One of the more interesting questions raised from Micky Malka is how a founder evolves as the company scales this quickly. Bouaziz is candid about how much the role has already changed:

“This year my skills improved a lot… I was not exactly super well versed in litigation… or crisis PR.”

The past year, in particular, forced rapid growth across areas far outside product:

“I’ve learned more this year than I’ve learned in the last six years of building the company.”

Looking ahead, his focus is on scaling himself alongside the company, especially through hiring and organizational buildout:

“Scaling myself, bringing the right people at the right time… those are the things that are going to be my focus.”

At the same time, he’s focused on maintaining clarity amid scale:

“Making sure the company stays true to itself… and not get carried away with all of the noise.”

Is AI Taking Everyone’s Job?

Another question prompted by Micky is one of the most risky questions around Deel’s core business.. what happens if AI reduces hiring altogether? If companies need fewer people, does that break the model?

Bouaziz doesn’t see that happening.. at least not in the way people expect.

“First of all, we’re not seeing that trend yet… we’re seeing a little bit in operations, but not a big deal.”

Instead, he expects a more nuanced shift. Some roles may shrink, but the overall number of companies and types of jobs will expand.

“I think they’ll hire less, but there’ll be more companies… and every single industry revolution, new jobs will be created.”

It’s a familiar pattern. Technology reshapes labor markets, but rarely eliminates them outright. As productivity increases, new categories of work emerge, often in places that weren’t obvious before.

That perspective also ties directly to Deel’s expanding customer base, which now spans far beyond tech. Bouaziz points out that the platform increasingly supports industries many wouldn’t initially associate with a global payroll startup:

“Every company in the world needs payroll… we can basically cover them.”

That includes everything from airlines to oil and gas to retail and the moment when that clicked for him was surprisingly tangible.

“When we closed our first airline, I was like, I didn’t know we could get to this type of customers.”

Today, that reach shows up in everyday moments.

“I walk into a store in London… and I’m like, we run payroll here… I’m a little proud of that.”

The underlying insight is simple but powerful: regardless of how AI reshapes work, companies will still need to hire, manage, and pay people.

And if anything, a world with more companies, more global talent, and more distributed work only increases the importance of infrastructure like Deel.

The Future of Global Work

Co-Founders CEO Alex Bouaziz & CRO Shuo Wang

Looking forward, Bouaziz believes the shift toward global hiring is still in its early stages. The rise of AI, automation, & autonomous agents could dramatically increase productivity, allowing companies to operate with smaller, more globally distributed teams.

“AI will make teams more productive, but it will also make global hiring even more important.”

In that future, the ability to hire talent anywhere becomes even more valuable.

“The future of work is global.”

Seven years after launching Deel, the numbers suggest that future is already unfolding.

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BREAKING: Inside a16z's $22B AUM Growth Fund

Fri, 13 Mar 2026 17:20:12 GMT

Alex Immerman, General Partner on Growth at Andreessen Horowitz (a16z), joins Sourcery to break down how one of Silicon Valley’s most influential firms is investing across AI, fintech, autonomous vehicles, and growth-stage software.

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With a fresh $6.75B Growth fund, a16z Growth now manages over $22B across five Growth funds. Alex discusses how a16z evaluates category leaders, what separates private markets from public markets today, and why firms are rethinking old metrics in the AI era. He also goes deep on some of a16z’s highest-profile portfolio companies and investments including Waymo, Kalshi, Coinbase, Robinhood, Stripe, Revolut, ElevenLabs, Flock Safety, and more.

We cover:

The Marc Andreessen Value Lesson: Cadillac or Steak Knives?
Why private markets now hold much of the highest-growth opportunity
Underwriting Waymo’s growth & the future of autonomous driving
Why a16z backed Kalshi
ElevenLabs, voice AI, & how breakout AI companies build defensibility
The new AI underwriting framework: growth, engagement, retention, & margins
How a16z scaled from startup VC to capturing 18% of all U.S. venture dollars
Why market leaders capture outsized value and how a16z thinks about winning

If you enjoy interviews at the intersection of venture capital, AI, fintech, public markets, and frontier technology, subscribe to Sourcery for more.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Alex Immerman, GP a16z
(00:53) Flock Safety: The most underrated company in America
(03:43) Private vs public market growth gap
(04:43) Do gross margins matter in AI companies?
(06:33) Waymo’s growth and $16B funding round
(08:13) The future of ride sharing and autonomous vehicles
(10:01) Where the value in self driving tech will accrue
(12:27) Tesla FSD vs Waymo’s full stack approach
(13:50) The growing brand power of Waymo
(15:43) What comes after autonomous cars? Humanoid robots
(17:41) Are people really worried about AI taking over?
(18:55) Inside ElevenLabs’ $500M funding round
(22:12) The real world use cases of voice AI agents
(22:55) How a16z invested in Kalshi
(24:11) Why Kalshi over Polymarket?
(26:35) Prediction markets vs g*mbling
(29:33) Is fintech making a comeback?
(32:44) Why stablecoins are becoming important
(33:30) Lessons from Coinbase founder Brian Armstrong
(34:19) How a16z evaluates growth companies
(36:52) Where startups spend money: R&D vs marketing
(38:44) Why startups need a strong Act 2
(40:08) Why venture outcomes are getting bigger & bigger
(42:24) Why backing the market leader matters
(43:49) The best books for founders
(44:59) How a16z built its media machine
(47:03) Who will be the first Trillionaire?

Brought to you by:

VCX—VCX is the public ticker for private tech, allowing investors of all sizes to invest in venture capital. View The Portfolio at GetVCX.com

Top 5 Key Lessons

1. The fastest-growing companies are still private
Public software companies growing 30%+ are increasingly rare, while many private AI companies are scaling at multiples of that pace. Growth investors now find the most explosive expansion in private markets rather than public equities.

2. AI companies are rewriting traditional SaaS metrics
Some AI companies launch with 0–50% gross margins due to model costs, forcing investors to look deeper at engagement, retention, workflows, and data defensibility rather than just headline margins.

3. Category leaders capture the vast majority of value
In technology markets, the winner often accumulates disproportionate market cap. Venture investors increasingly focus on backing the market leader early, because second place can mean dramatically smaller outcomes.

4. Autonomous vehicles could massively expand the transportation market
Ride-hailing today represents less than 1% of vehicle miles traveled in the U.S., suggesting robotaxis like Waymo could unlock a much larger market rather than simply competing with Uber or Lyft.

5. The scale of venture outcomes is increasing dramatically
Historically the top 1% venture outcome was roughly $1.5B. Today it’s closer to $10B, and could reach $40B within the next decade as technology markets expand globally.

Inside a16z’s $22B Growth Fund:

Andreessen Horowitz has grown from a startup venture firm in 2009 into one of the most influential capital allocators in technology. Today the firm manages more than $22B across five Growth funds, recently raising a new $6.75B vehicle to continue investing in category-defining companies.

Across that platform, the Growth team has invested in companies such as Waymo, Kalshi, Coinbase, Robinhood, Stripe, Revolut, ElevenLabs, Flock Safety, Harvey, EliseAI, Hebbia, Roblox, Anduril, and Sardine.

In a conversation on Sourcery, General Partner Alex Immerman outlined how the firm evaluates breakout technology companies, how AI is changing the metrics investors use, and why the scale of venture outcomes continues to expand.

The discussion reveals a clear theme: the center of gravity for innovation and growth has shifted toward private markets and AI-native companies.

The Fastest Growth in Technology Is Happening in Private Markets

One of the most striking shifts in the technology landscape is the divergence between private and public market growth rates. While public technology companies once represented the fastest-growing segment of the economy, that dynamic has changed.

Today, many of the most rapidly expanding companies remain private for longer periods, particularly in AI and frontier technology sectors.

As Immerman explains:

“If you were to scan public internet software, fintech, there are fewer than five companies growing north of 30% this year. We didn’t invest in a company last year growing under 30%.”

This dynamic is partly driven by the scale of opportunity in emerging markets such as artificial intelligence. AI companies are reaching extraordinary revenue levels at unprecedented speed.

“If you look at OpenAI and Anthropic alone, those two companies added as much revenue last year as half of the public cloud universe excluding the Mag Seven.”

For growth investors, this means the most explosive opportunities increasingly appear before companies reach public markets.

AI Is Forcing Investors to Rethink Traditional Software Metrics

Artificial intelligence is not just producing new products—it is reshaping how investors evaluate businesses.

Historically, venture investors relied heavily on classic SaaS benchmarks such as high gross margins, predictable revenue growth, and capital efficiency. AI companies challenge those assumptions.

Many AI startups begin with far lower gross margins due to the cost of running large language models and inference infrastructure.

“A lot of these AI companies are showing up with zero to 50% gross margins, and we’re used to looking at companies that have 70% gross margins.”

This forces investors to ask a deeper question: are those margins temporary, or do they reflect structural weaknesses?

“Are the lower margins because of LLM costs? If they are, we can make sense of that.”

Instead of focusing solely on traditional metrics, growth investors are increasingly analyzing engagement and usage behavior to determine whether a product is becoming essential to users.

“Engagement is a leading indicator of retention.”

In other words, strong product engagement often signals that a company is building the type of durable platform that can eventually produce high margins at scale.

Physical AI & the Expansion of Massive Markets

Beyond software, AI is beginning to reshape physical industries. One of the clearest examples today is autonomous vehicles.

Andreessen Horowitz has invested in Waymo, the autonomous driving company spun out of Alphabet. The company recently raised capital at a $126B valuation, reflecting growing confidence in the long-term potential of robotaxi networks.

Early data from San Francisco suggests autonomous ride-hailing can gain market share rapidly once deployed.

“In San Francisco it got to 25% market share really quickly with very few cars.”

But the larger opportunity may not simply be replacing existing ride-sharing services. Instead, autonomous vehicles could dramatically expand the transportation market.

“Ride hailing in that aggregate large number I just shared is less than 1% of vehicle miles in the U.S.”

This implies that the current ride-hailing industry represents only a small fraction of the potential demand for autonomous transportation.

As costs decline and fleets scale, autonomous vehicles could unlock entirely new categories of usage.

All Eyes on Flock Safety

Among the companies in Andreessen Horowitz’s growth portfolio, Alex Immerman pointed to Flock Safety as one of the most underappreciated technology platforms in the country.

Flock builds a nationwide network of license plate readers and safety cameras that help law enforcement agencies identify vehicles linked to crimes and share intelligence across jurisdictions. The system allows investigators to move far faster than traditional investigative methods.

According to Immerman, the scale of the impact is already enormous.

“Flock solves over 2,800 cases every day.”

That volume translates into a meaningful portion of crime investigations nationwide.

“That’s about 15% of reported crime in America.”

Immerman noted that while the company occasionally receives attention when a major case is solved using the platform, the technology is quietly being used by law enforcement every day.

“When we look at it, that’s just a normal day in Flock’s life.”

The broader point, he said, is that companies like Flock demonstrate how software can deliver measurable real-world outcomes beyond traditional consumer or enterprise applications.

“There’s a lot of discussion about whether tech and AI are good for society. Flock is an incredible example.”

As AI and software continue expanding into sectors such as public safety, defense, and infrastructure, platforms like Flock Safety illustrate how technology can become a foundational layer for solving problems at national scale.

Venture Outcomes Are Growing at an Unprecedented Scale

Another major shift in the venture ecosystem is the increasing size of successful company outcomes. Historically, billion-dollar exits represented extraordinary results for venture-backed companies. Today, those outcomes are becoming far more common.

“Back then the top 1% outcome was a bit under one and a half billion dollars.”

In the current technology landscape, the scale of those outcomes has increased dramatically.

“Today that’s $10 billion.”

And if technology markets continue expanding globally, the next decade could produce even larger results.

“If you fast forward another eight to ten years, that’s $40 billion.”

This trend explains why venture firms like Andreessen Horowitz have raised significantly larger funds in recent years. As markets grow and companies stay private longer, the capital required to support those businesses also increases.

The venture model itself is evolving alongside the companies it finances.

The Larger Shift in Technology Markets

Taken together, these insights illustrate how quickly the structure of the technology ecosystem is changing.

Private markets now contain many of the fastest-growing companies. AI is reshaping the metrics investors use to evaluate businesses. Entire physical industries, from transportation to robotics, are being rebuilt by software.

And perhaps most importantly, the scale of potential outcomes continues to grow.

For growth investors, the challenge is identifying the companies capable of becoming the next generation of category leaders before those markets fully emerge.

The Marc Andreessen Lesson:

Cadillac or Steak Knives?

One of the most enduring principles inside Andreessen Horowitz comes from co-founder Marc Andreessen: in technology markets, first place captures the majority of value. Alex Immerman explained that Andreessen often illustrates this dynamic using a line from the film Glengarry Glen Ross:

“Alec Baldwin in Glengarry Glen Ross is managing a failing sales team and says: first place gets a Cadillac. Second place gets a set of steak knives. Third place, you’re fired.”

The quote highlights how dramatically outcomes diverge between the market leader and everyone else. In venture capital, the difference between backing the winning platform and backing a strong but secondary player can mean the difference between a generational return and a modest outcome.

As Immerman put it:

“In our business it is critical to back the market leader.”

That pattern appears repeatedly across the technology landscape. Google ultimately dominated search. OpenAI has established an early lead in foundation models. Stripe emerged as the core payments infrastructure layer for the internet economy. Coinbase became the most trusted regulated crypto exchange. ElevenLabs is rapidly defining the category in voice AI. Anduril has built one of the most important new defense technology platforms.

Across each of these markets, the leading company has captured a disproportionate share of adoption, capital, and market value.

“The market leaders accumulate most of the market cap in a given market.”

For venture investors, the implication is straightforward: the goal is not simply to invest in promising companies, but to identify the one that has the strongest path to becoming the defining platform in its category.

In venture capital, the difference between the leader and the runner-up is rarely incremental.

It is the difference between the Cadillac and the steak knives.

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BREAKING: Google Completes $32B Wiz Acquisition | Gili Raanan, Cyberstarts

Wed, 11 Mar 2026 18:05:19 GMT

$6M Seed Check → $32 Billion Exit

Gili Raanan is the Founder of Cyberstarts and one of the earliest investors behind Wiz, one of the most important cybersecurity companies of the modern era and now a $32B outcome. Before founding Cyberstarts, Gili spent nearly a decade at Sequoia and earlier built and sold 2 security companies to IBM and EMC. He also helped build the first real CAPTCHA implementation and the first web application firewall.

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In this conversation, Gili breaks down the Wiz story from the beginning: why he backed the team early with a $6M seed investment, how the company changed direction, what made its product market fit so powerful, & why its go to market motion scaled so unusually fast. He explains what made the founding team exceptional, what he learned from watching Wiz scale from a $66M seed valuation, & why he believes the next great cybersecurity company could be even bigger.

Beyond Wiz, Gili shares the Cyberstarts framework for investing before product, team, or code exists, why he underwrites people over ideas, & how AI agents are changing the future of cybersecurity.

Today Cyberstarts has raised $1.5B+ across 7 funds, backed 30 companies, and its portfolio represents over $61B in combined value (50% of the worldwide private cybersecurity companies market !!! ).

We cover:

• The early days of Wiz and how the company evolved from seed to a $32B outcome
• Why Wiz achieved such strong product market fit
• What made Assaf and the founding team special
• How Cyberstarts picks founders before there is product or code
• Why Gili believes AI could create the darkest decade in cybersecurity
• What the next generation of $50B–$100B cyber companies could look like

This conversation took place in London February 12th, 2026. Special thank you to J & family for letting us record in your gorgeous home. (Shoutout to Charlotte!)

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Gili Raanan, Founder Cyberstarts
(01:05) From Sequoia to building a Cyber only venture fund
(04:39) Building CAPTCHA and the first web application firewall at 27
(09:15) Why he decided to focus only on cybersecurity
(12:45) The kind of founders he looks for
(19:00) A portfolio with 50% of private cyber market value
(26:30) What makes a company truly important
(30:48) Why do Israeli founders dominate cybersecurity?
(31:05) Helping founders become the best version of themselves
(37:06) Israeli culture, pressure, and resilience
(41:25) Breaking the ceiling with companies like Wiz
(46:15) Facing fears, Yoga & overcoming internal limits
(51:55) Why building startups is an emotional journey
(56:25) How cybersecurity ideas actually emerge
(01:06:45) Asimov’s laws of robotics and AI guardrails
(01:09:55) The three biggest cybersecurity risks in 2026
(01:12:25) Inside the $32B Wiz deal
(01:22:23) What makes Wiz co-founder Assaf exceptional
(01:28:48) The biggest takeaways from his time at Sequoia: Doug Leone, Mike Moritz
(01:32:05) What’s next for Cyberstarts?

Brought to you by:

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Gili Raanan, Cyberstarts, & the Human Side of Building Cybersecurity Giants

The “Godfather” of Cybersecurity

Cybersecurity has quietly become one of the most important technology markets in the world. As software, cloud infrastructure, and now AI agents reshape how organizations operate, the attack surface has expanded dramatically. Behind many of the most influential companies addressing that problem is Gili Raanan, founder of Cyberstarts.

From backing Wiz at the seed stage to helping build a portfolio worth tens of billions of dollars, Raanan has spent decades at the center of cybersecurity innovation. His story reflects not just the evolution of the cyber market, but a deeply human philosophy about what actually creates great companies.

Gili Stats:

Recipient of the Israel Defense Presidential Prize (1996)
Served in Unit 8200, the elite Israeli Intelligence Corps within the Israel Defense Forces
Recipient of the IDF Innovation Award (1992) for a decade of distinguished service
Inventor of CAPTCHA, one of the world’s most widely used security algorithms - which was acquired by EMC and IBM
Helped build the first Web Application Firewall (WAF)
Holder of 10 additional U.S. patents across security and application management
Later went on to build two enterprise software companies that were acquired by EMC and IBM.

Decade as GP at Sequoia

Joined Sequoia Capital in 2009 as a venture investor, where he led cybersecurity, internet, mobile, and technology investments in Israel
Served nearly nine years as a General Partner before leaving in 2018 to found Cyberstarts

Building Cyberstarts

Cyberstarts was founded with a simple but unusual thesis: cybersecurity deserved a dedicated venture platform built specifically for the category.

While most venture firms operate as broad generalists, Cyberstarts focuses almost entirely on cybersecurity at the earliest stages. The firm often invests before products, teams, or even code exist, partnering with founders at the idea stage.

Today the firm has:

Over $1.4B in capital raised across multiple funds
Around 30 portfolio companies
More than $60B in combined portfolio value

Some of the most notable companies backed by Cyberstarts include:

Wiz
Cyera
Island
Fireblocks
Upwind

The firm has also produced a series of successful acquisitions across the cybersecurity ecosystem, helping establish Cyberstarts as one of the most focused and successful investors in the sector.

The Wiz Story

Among Cyberstarts’ investments, Wiz stands as the most visible example of the firm’s model working at scale.

Founded in 2020, Wiz quickly became one of the fastest-growing enterprise software companies ever built. The company developed a cloud security platform designed to help organizations understand and secure complex cloud environments.

Raanan was the first seed investor in Wiz, backing the company with an early investment of roughly $6M.

Within a few years, Wiz grew into a global cybersecurity leader, culminating in a $32B acquisition by Alphabet, the parent company of Google. The deal is widely considered the largest cybersecurity exit in history.

For Raanan, however, the success of Wiz was not simply about timing or market opportunity. It came down to people.

Investing in People Before Ideas

One of the defining elements of the Cyberstarts model is Raanan’s belief that people matter far more than ideas at the earliest stages of a company.

Markets evolve. Products change. Entire technology categories can appear overnight. But founders, their motivations, and their resilience tend to persist.

For this reason, Cyberstarts frequently invests before there is a defined product. The firm focuses on identifying founders with the conviction and stamina to build something meaningful over many years.

Raanan often looks for individuals who have experienced adversity or who possess a strong sense of personal mission. In his view, building an enduring company inevitably involves periods of difficulty and uncertainty. Founders who have already developed resilience tend to navigate those moments better.

This philosophy was evident in the founding team of Wiz, led by Assaf Rappaport, whose previous company Adallom was acquired by Microsoft.

The Kind of Founder Gili Raanan Looks For

One of the defining principles behind Cyberstarts is Raanan’s belief that founders matter far more than ideas in the earliest stages of a company.

Markets change quickly. Technologies evolve. Entire categories can emerge overnight. But the character of the founder tends to remain constant.

Because Cyberstarts often invests before a product exists, Raanan focuses intensely on the personal traits of the founders themselves.

Several qualities consistently stand out:

Resilience Through Adversity

Raanan frequently speaks about the importance of adversity in shaping founders. In his experience, the best builders are rarely people whose paths were completely smooth.

Instead, they are individuals who have faced meaningful challenges early in life and learned how to recover from setbacks.

Building a company inevitably involves moments when things go wrong — product failures, market shifts, financing challenges. Founders who have already developed resilience are often better prepared to navigate those periods.

Mission Over Status

Another trait Raanan looks for is genuine motivation to solve an important problem, rather than simply building a company for financial outcomes or prestige.

The founders who succeed in cybersecurity often have a deep personal connection to the problem they are trying to solve. Many have spent years working inside security systems, understanding their weaknesses and the real-world consequences of failure.

That sense of mission tends to sustain them through the long process of building a company.

Intellectual Curiosity

Cybersecurity evolves quickly, which makes intellectual curiosity essential. Raanan values founders who constantly ask questions, challenge assumptions, and adapt their thinking as technology changes.

The most successful founders, in his view, are those who treat company building as a continuous learning process.

Humility and Team Orientation

Finally, Raanan pays close attention to how founders work with others. Great cybersecurity companies are rarely built by a single individual. They require strong teams that combine technical depth, product design, and go-to-market execution.

Founders who are collaborative, self-aware, and capable of building trust within a team often scale their organizations more effectively.

These traits were clearly visible in the founding team of Wiz, led by Assaf Rappaport, whose previous company Adallom was acquired by Microsoft.

Israel’s Cybersecurity Advantage

Cyberstarts is deeply connected to Israel’s cybersecurity ecosystem, which has produced an outsized number of influential security companies over the past two decades.

Several structural factors contribute to this phenomenon.

Israel’s technology ecosystem is heavily influenced by elite military intelligence units such as Unit 8200, where many engineers gain early exposure to real-world cyber threats and complex infrastructure systems.

That experience often translates into founders who have already worked on large-scale security problems before entering the startup world.

Additionally, the Israeli startup culture emphasizes speed, technical depth, and direct communication. These characteristics can produce companies capable of moving quickly through early development cycles.

Companies like Wiz demonstrate how this environment can generate globally significant cybersecurity businesses.

Lessons from Sequoia

Before founding Cyberstarts, Raanan spent nearly a decade as a partner at Sequoia Capital, one of Silicon Valley’s most influential venture firms.

During that period, he worked alongside prominent investors including Doug Leone and Michael Moritz.

From Sequoia, Raanan absorbed several core principles that still shape his approach today.

First, the importance of conviction. The most successful investments often look uncertain or unconventional at the moment they are made.

Second, the importance of long-term thinking. Building a great company can take a decade or more, and investors must be willing to support founders through that journey.

Finally, Sequoia reinforced the idea that venture capital is ultimately about identifying and supporting exceptional people.

Purpose Beyond Returns

Despite the scale of Cyberstarts’ financial success, Raanan often frames his work in broader terms.

For him, the goal is not simply to build valuable companies, but to help founders create important companies — organizations whose products become essential infrastructure for their customers.

In cybersecurity, that mission carries additional weight. Security companies play a direct role in protecting businesses, governments, and individuals from increasingly sophisticated threats.

As artificial intelligence, automation, and cloud infrastructure continue to expand, the importance of cybersecurity will only grow.

In that sense, Cyberstarts is not simply backing startups. It is helping shape the next generation of companies responsible for securing the digital world.

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BREAKING: Alfred Lin | The Future According to Sequoia

Mon, 09 Mar 2026 14:41:55 GMT

Navigating The AI Paradigm Shift

Alfred Lin, Partner & new Co-Steward of Sequoia Capital (& #1 investor on the Midas) List, joins Sourcery for a conversation on AI, founder-market fit, enduring companies, & how Sequoia measures this moment of accelerating technological change.

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For more than five decades, Sequoia has backed many of the most consequential companies in technology, from Apple and Nvidia to Airbnb, DoorDash, Stripe, and more. But as Alfred explains in this conversation, Sequoia does not think about the 54-year-old firm the way many others do. Rather than optimizing around AUM, Sequoia focuses on DPI and being a net liquidity provider to LPs. Since 2020, the firm has distributed more than $43 billion back to investors (as of Oct 27, 2025).

In this conversation, Alfred breaks down why AI is the biggest paradigm shift of his career, why the narrative that “AI will kill SaaS” is too simplistic, why startups are reaching meaningful scale faster than ever, and why the most vulnerable companies are the ones that fail to embrace change.

We also discuss what’s happening in boardrooms right now, how moats evolve during platform shifts, why the next generation of great companies may be dramatically larger than the last, and how Sequoia identifies outlier founders across companies like Airbnb, DoorDash, Kalshi, Zipline, Clay, Commure, Nominal, Physical Intelligence, Crosby, OpenAI, and Citadel Securities.

Recorded live February 26th at the Upfront Summit 2026.

Special thank you & many shoutouts to Valerie for handling us (& our tequila shots) 😂

Topics include:

Why AI is accelerating startup growth and product velocity
Why “AI kills SaaS” is the wrong framework
How moats change during paradigm shifts
What Alfred is hearing in boardrooms right now
Which companies are most vulnerable in the AI era
Founder-market fit and the importance of a founder’s “spike”
Why the next generation of companies could be much bigger than today’s giants

Subscribe to Sourcery for more conversations with the people building the future of technology, finance, and markets.

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Alfred Lin, Partner & Co-Steward at Sequoia Capital
(01:00) The metric Sequoia actually cares about
(02:54) $43B distributed since 2020
(05:41) The biggest wave Alfred has seen in his career
(08:19) Amazon did not k*ll Walmart
(10:09) Paradigm shifts and changing moats
(14:38) Boardroom conversations about the future
(17:11) Companies that fail to adapt fall behind
(19:31) From waterfall development to agile teams
(23:06) Sequoia’s AI investment strategy
(24:48) Managing context switching between portfolio companies
(26:00) Becoming Co-Steward of Sequoia Capital
(29:31) How to navigate the AI wave

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The Future According to Sequoia

Why the Next Companies Could Be $10 Trillion

Every major technology cycle reshapes both how companies are built and how investors evaluate them. The internet transformed distribution, mobile reshaped product design, and cloud computing rewrote software economics. Artificial intelligence now appears to be accelerating each of those dynamics simultaneously — compressing development cycles, increasing productivity, and expanding the potential scale of technology companies.

Few venture investors have operated through as many of these transitions as Alfred Lin, Partner & new Co-Steward of Sequoia Capital and the #1 ranked investor on the Forbes Midas List. His portfolio spans early partnerships with companies like Airbnb, DoorDash, Zipline, as well as more recent investments in companies such as OpenAI, Kalshi, Nominal, Citadel Securities, & Clay.

We unpack how Sequoia is analyzing the current AI cycle, from why startups are scaling faster than previous generations to how the definition of a “legendary” venture outcome continues to expand. Before getting into the specifics of AI, however, Lin emphasized the key differentiated structural point about how Sequoia evaluates itself as a venture firm.

Alfred’s Partnerships

AI Is the Fastest Technology Cycle Yet

Lin has invested through multiple technology waves, including the rise of the internet, the mobile revolution, the transition to cloud computing, and the emergence of SaaS. What stands out about the current AI moment, he argues, is the speed at which companies are building and scaling.

AI tools have dramatically lowered the barriers to product development, enabling startups to launch products, iterate on them, and reach meaningful revenue far faster than in previous eras. The traditional milestones of company formation — building a product, reaching product-market fit, and scaling revenue — are happening on compressed timelines.

“You see companies today going from zero to 10 million ARR in very, very fast timeframes with which we have not seen in the past.”

The implication is that the startup ecosystem may become more dynamic and experimental than ever before. Companies can test ideas faster and pivot more quickly, but the speed of innovation also increases the difficulty of building durable competitive advantages.

$1T → $10T

Another implication of the AI wave is the potential for dramatically larger technology companies. When Lin joined Sequoia in 2010, the largest companies in the world were valued in the hundreds of billions. Today, several technology firms have already crossed the multi-trillion-dollar threshold.

“When I joined Sequoia the largest market cap company was probably $300B or $400B dollars. Today we have companies that are worth $4-5T.”

If those companies continue to compound at their current pace, Lin believes the next phase of technology giants could reach an entirely new scale.

“If you give it another five to 10 years… those companies continue to compound, it’ll be worth $10 trillion or more.”

That shift is already influencing how Sequoia thinks about legendary outcomes. Inside the firm’s Valentine conference room hangs a wall dedicated to its most successful investments. When Lin first joined the firm, an investment needed to generate $100 million in gains to qualify for the wall. Today, the threshold has increased to well over $1 billion, and Lin expects the definition of a legendary outcome to continue expanding.

Why “AI Will Kill SaaS” Is the Wrong Narrative

One of the most common narratives surrounding AI today is that it will replace traditional SaaS companies entirely. Lin pushes back strongly on that framing, arguing that history suggests technology shifts rarely eliminate previous categories outright.

“The simple narrative is AI is gonna kill SaaS. And I just don’t think that’s the case.”

Lin recalls making a similar prediction himself in the late 1990s, when he left his PhD program in statistics to pursue opportunities created by the internet. At the time, he believed e-commerce would destroy traditional retail.

“When I dropped out of my PhD program in statistics… I made the proclamation that e-commerce was going to destroy brick and mortar.”
“Amazon was going to kill Walmart. And that just didn’t happen.”
“Walmart is 20 times larger today than it was in 1997.”

Instead of eliminating physical retail, the internet forced retailers to evolve. Companies that embraced the new paradigm survived — and in some cases thrived. Lin believes the same pattern will likely unfold with AI.

Most Vulnerable Companies Right Now

While Lin avoids naming specific companies that may struggle in the AI transition, he points to a clear pattern among the organizations most at risk.

“The companies that are most vulnerable are the companies that don’t embrace change.”

Every major technology transition reshapes the nature of competitive advantages. In legacy enterprise software, distribution often revolved around selling to CIOs and embedding products deeply inside organizations. SaaS shifted that model toward bottom-up adoption, where individual users began adopting products before companies formally purchased them.

AI may once again change how products are built, distributed, and adopted. For founders and incumbents alike, the key question is not whether change will occur — it is how quickly companies adapt to it.

Founder Spikes & the Sequoia Playbook

Despite all the technological shifts underway, Sequoia’s core philosophy of venture investing has remained surprisingly consistent. The firm continues to prioritize founder-market fit and unique founder insight above nearly everything else.

Lin describes looking for what he calls a founder’s “spike” — a rare capability, experience, or perspective that differentiates the founder from everyone else.

“What is your spike? Why are you different than everybody else in the world?”
“We try to magnify that spike.”

Rather than attempting to smooth out a founder’s weaknesses, Sequoia focuses on amplifying their strengths while ensuring weaknesses do not become fatal liabilities. In some cases, AI may even help compensate for those weaknesses by automating skills that previously required years of development.

Navigating the Mid-Game of Technology Shifts

For investors, the most challenging aspect of technological transitions is that the end state is often clearer than the path to get there. Lin describes this dynamic as playing the “mid-game.”

“We know what the end state is going to be… all these companies that shift will embrace AI.”
“But who wins & who doesn’t along the way — that’s the mid-game.”

This uncertainty is precisely where venture investors attempt to generate outsized returns. By partnering early with founders navigating these transitions, firms like Sequoia aim to identify the companies that will ultimately define the next era of technology.

“Inevitable ≠ Imminent. So many board conversations are about the end-state without regard to the opening or the midgame, where the crucible decisions are made.

End Game Play, Will Manidis: "The middle is two thousand years long and counting and we are somewhere in it and the eschaton is not ours alone to force.

Know your vocation. Play the game. Burn the clock."

Alfred’s Latest X Articles:

Next Decade of Company Building

Despite the uncertainty surrounding AI’s long-term impact, Lin remains deeply optimistic about the opportunities ahead. While AI is already automating many repetitive tasks, he believes its greatest impact will come from enabling entirely new forms of creativity, productivity, and company building.

“The fact of the matter is a lot of our most mundane tasks will get automated away.”
“That is great. See it as an opportunity to do the things that you are uniquely good at.”

In an environment where technology continues to accelerate and tools become more powerful, the defining advantage may not simply be access to infrastructure or capital. Instead, it may be the insight, creativity, and ambition of founders willing to build something entirely new.

For Sequoia, that pursuit remains constant.

“We’re only as good as our next investment.”

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Cheers!

Read / Watch More Alfred:

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Inside Dept of War: Emil Michael, Under Secretary of War for R&E, & DoW CTO

Molly O’Shea — Sat, 07 Mar 2026 13:54:27 GMT

Resharing this interview / profile from December 13th 2025

With great honor, Mr. Emil Michael, the Under Secretary of War for Research & Engineering (USW(R&E)) and Chief Technology Officer for the Department of War (DoW), joins Sourcery in front of an F-117 at the Reagan National Defense Forum 2025 to dispel common misconceptions about the DoW, share innovations in modern defense technology, & the role of ‘new primes’ like Palantir, SpaceX, & Anduril.

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Drawing on his unique path from hyperscaling Uber into a global technology company to now overseeing the DoW’s research, development, & prototyping enterprise, he breaks down how the system actually works behind the scenes.

He also outlines the DoW’s 6 Critical Technology Areas — Applied AI, Scaled Hypersonics, Scaled Directed Energy, Biomanufacturing, Contested Logistics, & Battlefield Information Dominance — and explains how these priorities reflect the realities of modern warfare rather than the myths often portrayed around the sector.

Bonus: Newer companies Emil Michael highlights as emerging leaders in defense innovation include:

Planet Labs for commercial satellite imagery
Neros as a next-generation drone company
Hadrian for AI-driven advanced manufacturing

And a broader group of frontier AI companies now engaging with the DoW.

Top 10 Quotes Below.

𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Emil Michael
(01:16) Oversight of DARPA, MDA, CDAO & DIU
(02:11) From Uber to the DoW — how he entered government
(03:34) Modernizing the largest bureaucracy in the world
(05:30) China’s advancements & shifting U.S. priorities
(10:14) Why the DoW reduced 14 tech areas to 6
(11:00) Applied AI & adapting commercial AI for defense
(11:47) Hypersonics, directed energy & counter-drone systems
(12:36) Contested logistics, biomanufacturing & new materials
(13:41) America’s “1938 moment” & rebuilding industrial capacity
(15:35) Preparing & protecting future U.S. warfighters
(17:05) Lessons from Ukraine: drone-on-drone warfare
(18:49) Expanding partnerships & accelerating AI across the DoW
(23:13) Clearing misconceptions about Palantir & defense tech companies
(29:35) Emerging innovators: Planet Labs, Neros, Hadrian & frontier AI

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EMIL MICHAEL

Under Secretary of War for Research & Engineering & Department of War Chief Technology Officer

Mr. Emil Michael serves as the Under Secretary of War for Research and Engineering (USW(R&E)) and Chief Technology Officer for the Department of War(DoW), spearheading the Department’s efforts to ensure U.S. military technological superiority and keep the DoW at the forefront of innovation. In this critical role, he provides strategic direction and oversight for the DoW’s entire research, development, and prototyping enterprise, including direct oversight of organizations such as the Defense Advanced Research Projects Agency (DARPA) and the Missile Defense Agency (MDA).

Prior to his appointment as USW(R&E), Mr. Michael distinguished himself as a White House Fellow, serving as Special Assistant to the Secretary of Defense. In that role, he managed key projects in Afghanistan, Iraq, and Pakistan and oversaw the ‘tooth-to-tail’ reform effort aimed at reducing overhead and bureaucracy to provide resources to our warfighters. His contributions earned him the Secretary of Defense Award for Outstanding Public Service.

After his initial tenure in the Department, Mr. Michael served on the Defense Business Board (DBB), where he drew on his vast private-sector experience to introduce commercial best practices into the department’s operational framework. In this role, he helped craft policy recommendations that modernized procurement processes, optimized resource management, fostered a culture of agile decision-making, and facilitated closer collaboration between industry and the Department.

Beyond his government service, Mr. Michael has developed significant business experience in the private sector. He has helped build several successful companies, including D-Wave Systems, Tellme Networks, and Uber Technologies. Mr. Michael has a proven track record of anticipating technology shifts and identifying companies ripe for hyperscale, with investments spanning a broad range of companies across AI, financial technology, quantum computing, and consumer logistics.

During his tenure as Chief Business Officer at Uber from 2013 to 2017, he created global partnerships, drove over $20 billion in capital-raising initiatives, and helped orchestrate strategic mergers and acquisitions. He launched the UberMilitary initiative to support military families during relocations, demonstrating an ability to translate commercial success into meaningful support for the defense community. He also helped drive the creation of Uber’s Advanced Technology Group, which was Uber’s primary initiative to develop self-driving car technology. By blending his private sector insights with the unique imperatives of national defense, Mr. Michael aims to accelerate the transition of breakthrough technologies from prototype to operational reality, ensuring that the U.S. military maintains its technological edge.

Top 10 Quotes

Defense Innovation & Industrial Strategy

1. “My job is to create five more companies [on top of the ‘new primes’] that work in the department and bring new capabilities.”

2. “It’s literally the biggest bureaucracy in the world — three million people — and I get to say, clean sheet of paper, what should we be doing differently?”

3. “If something’s critical for ten years, that means you’re not making much progress on it.”

4. “You don’t want to fight a war. You want to prevent a war… and you can only do that by being strong.”

AI, Technology & Modernization

5. Of the 6 Critical Technology Areas, “Applied AI is number one… the big companies are already spending hundreds of billions of dollars in AI and AI infrastructure… all I need to do is take that and adapt it for the Department.”

Geopolitics, Warfighting & Drones

6. “China started investing in really sophisticated capabilities while we were fighting terrorists with improvised explosive devices.”

7. “The Ukraine war became a robot war — drone on drone… the front lines are robots against robots.”

Defense Tech, Palantir & Misconceptions

8. “Palantir is really a nuts-and-bolts software company… we didn’t know where our stuff was… we were spending trillions of dollars over the years… where are our tanks, where are our munitions… how do we understand where they are in their life cycle, how do we repair them?”

9. “If helping the American government do lawful things makes you evil, I don’t know who’s left.”

Private Sector Perspective & Leadership

10. “The biggest surprise is the quality and camaraderie of the people at the top of the government… there’s not a lot of political infighting — this is about a team effort.”

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BREAKING: Thomas Laffont, Coatue

Molly O’Shea — Fri, 06 Mar 2026 16:05:16 GMT

Anthropic, AI Volatility, Next Mag 7

Thomas Laffont, Co-Founder of $70B AUM Coatue, joins Sourcery to break down how AI is reshaping both private & public markets—from Coatue’s investment in Anthropic’s $30B Series G at a $380B Valuation to the growing volatility AI is introducing across SaaS and the broader tech complex.

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Recorded live at the Upfront Summit 2026 in Los Angeles on February 25th, 2026, Laffont shares his take on the Citrini “Global Intelligence Crisis” paper, why boardrooms are rapidly expanding AI spend, and which private companies could emerge as the next “Magnificent 7.”

We discuss:

• Coatue leading Anthropic’s latest funding round (recently hit $19B ARR)
• Why AI coding tools are spreading rapidly inside organizations
• The Citrini paper and how investors should interpret it
• Why SaaS valuations are being repriced
• The “Next Mag 7” candidates in private markets
• Coatue’s philosophy of Big Idea Investing or (“BFI) and risk management

𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Thomas Laffont, Co-Founder Coatue Management
(01:18) The rapid rise of Claude Code
(04:15) Anthropic’s revenue growth and trajectory
(05:25) Where capital is flowing: private vs public markets
(08:10) The Citrini paper and AI market volatility
(10:15) Are new Claude releases hurting SaaS companies?
(17:22) Will AI reduce the number of engineers?
(19:38) The ATM analogy for AI and jobs
(21:02) Could autonomous agents automate investing?
(23:18) How Coatue got conviction on Nvidia
(24:18) Why TAM does not matter
(26:43) Running Coatue with his brother Philippe

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Brought to you by:

TL: 5 Key Takeaways

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1. Anthropic’s growth surprised even its investors
Coatue led Anthropic’s latest funding round, but even insiders underestimated how quickly AI coding products like Claude Code would scale. Boardrooms across industries are now rapidly expanding their spending on AI tools.

2. The “Next Magnificent 7” will likely come from private markets
Laffont believes companies like SpaceX, OpenAI, Anthropic, Databricks, and Revolut could become the future equivalents of today’s tech giants, highlighting the growing importance of private market exposure.

3. SaaS valuations are being challenged by AI and slower growth
Traditional SaaS companies once commanded premium multiples due to high growth. As growth slows and AI threatens to rewrite software layers, investors are reassessing the long-term terminal value of many software businesses.

4. AI will likely increase productivity more than eliminate jobs
Instead of shrinking engineering teams, most companies are planning to use AI tools to dramatically expand what their teams can build. Increased productivity could lead to more innovation and larger markets, not fewer engineers.

5. Big idea investing matters more than TAM
Laffont emphasizes that the size of today’s market matters less than how much that market can grow over time. Companies like Uber and Apple massively expanded their markets after launching — which is where the real upside came from.

Investing at Scale: Coatue Across Public + Private Markets

AI Adoption Is Now Showing Up in Boardrooms

One observation Thomas Laffont shared is how quickly AI adoption has moved from experimentation to board-level discussion. Coatue sits at the intersection of public and private technology markets, managing roughly $70B across funds, including about $30B invested on the private side, which gives the firm visibility into operating data across dozens of technology companies.

In recent board meetings across its portfolio, Laffont noted a consistent pattern: management teams are beginning to report AI tool usage and spending as part of regular board updates. That includes companies integrating tools like Claude Code from Anthropic, developer tools like Cursor, and other AI coding or productivity systems.

“I was in a board meeting yesterday… most companies are now reporting back to their boards the adoption of these tools inside of their organizations…
There was a slide in one of the board meetings that said, look, we’re spending $X on this tool, and we think it’s way too low.
We expect the spend to at least 3x next year… when you see the same pattern repeating itself across companies, you know that you’re onto something big.”

This pattern is consistent with what Coatue is seeing across its broader AI exposure. The firm has invested heavily across the stack — including frontier model companies like Anthropic, infrastructure providers such as CoreWeave, and developer tooling platforms like Cursor (Anysphere). Anthropic alone recently raised $30B in funding at a $380B valuation, with Coatue among the lead investors.

Across many enterprises, AI spend appears to be shifting from early pilots toward broader operational deployment.

The New Mag7: Next Generation of Market Leaders May Still Be Private

Coatue is unusual among large investment firms because it invests across both public and private technology markets. The firm started as a public equity hedge fund but expanded aggressively into venture and growth investing, building a portfolio that spans companies such as Amazon, Microsoft, Meta, Nvidia, and TSMC on the public side, alongside private companies like OpenAI, SpaceX, Anthropic, Runway, Figma, Harvey, Glean and Databricks.

That positioning shapes how Laffont thinks about where the next generation of market leaders might emerge.

“My default view has always been that the public market is the best kind of valuation mechanism, and it offers transparency, it offers liquidity, it offers opportunity of access… So then that leads you to think, well, what would the next Max seven look like… names like SpaceX… OpenAI and Anthropic and Revolut and Databricks.”

The dynamic he describes reflects a broader shift in the technology ecosystem. Some of the most valuable companies in AI and infrastructure — including OpenAI, Anthropic, and SpaceX — have been able to raise billions & billions of dollars in private markets without going public (See OpenAI’s recent $110B round).

For institutional investors, this raises an important portfolio question: if the next generation of large technology companies remains private longer, access to those markets becomes increasingly important for capturing future index-level returns.

AI Risk, the Citrini Paper, + “Doomerism”

The conversation also touched on the widely circulated Citrini research paper, which sparked debate across the technology and investment community by warning that artificial intelligence could trigger severe economic disruption. Laffont acknowledged reading the report but pushed back on the idea that discussing potential risks should be framed as alarmism.

He argued that raising concerns early is not inherently dangerous and may actually strengthen the ecosystem. In his view, the worst outcome would be a collective blind spot that allows risks to build unnoticed.

“I don’t think that screaming fire in a crowded room is productive or safe. However, I don’t view bringing up these conversations early the same way.”

Instead of dismissing the warnings, Laffont sees value in forcing companies, regulators, and investors to continuously examine how AI could reshape industries. In practice, he believes this scrutiny creates a healthier environment for decision-making.

“If everyone thinks we’re in a bubble, then by definition we’re not in a bubble.”

From an investor’s perspective, volatility driven by AI narratives, whether optimistic or pessimistic, is simply part of the market adapting to a transformative technology. Laffont emphasized that daily questioning and debate are preferable to a scenario where concerns go unexamined until a sudden collapse forces a correction.

Market Volatility Around AI May Be a Useful Signal

AI developments have produced sharp market reactions over the past year. Major model releases, research papers, and product launches frequently lead to rapid re-pricing of entire technology sectors — particularly application software.

Laffont’s perspective is that this kind of volatility may actually be beneficial for the market.

“I would much rather have daily volatility, daily questioning, then no volatility or no questioning, and then like a massive crash like three years later.”

In that sense, he sees the current wave of debate around AI risk not as doomerism, but as a necessary pressure test for the technology sector. The fact that governments, corporations, and investors are already actively thinking about AI’s long-term implications signals a level of awareness that could ultimately reduce systemic risk rather than amplify it.

From Coatue’s vantage point — investing simultaneously in infrastructure providers like CoreWeave, model developers such as Anthropic, and application software companies — the rapid feedback loop may accelerate strategic adaptation across the ecosystem rather than allowing disruption risks to build unnoticed.

Why SaaS Multiples Are Being Re-Evaluated

The conversation also touched on the shifting valuation environment for SaaS companies in public markets. For much of the past decade, SaaS businesses commanded premium valuations because of sustained high growth.

“A lot of SaaS companies were compounding mid 20s to low 30s for a long period of time… what’s happened now is by and large SaaS companies have significantly decelerating… investors are now saying, well hold on, I can own a semi company… growing almost 40%… and it’s trading at a cheaper multiple of GAAP earnings.”

The comparison he references reflects a broader market rotation toward AI infrastructure. Semiconductor and compute-related companies tied to the AI build-out have recently delivered faster growth rates, which affects how public investors compare opportunities across sectors.

For many SaaS companies, the central strategic question now is whether AI can help re-accelerate product adoption and revenue growth, or whether valuations will gradually normalize relative to other technology sectors.

AI May Shift Enterprise Software Toward Selling Outcomes

Another idea Laffont raised is the possibility that AI changes the fundamental business model of enterprise software.

Historically, software platforms were designed to help employees perform their jobs more efficiently. AI agents introduce the possibility that software systems might directly execute some of that work.

“What if they move actually from selling you software… to selling you work… I’m actually selling you the work of an HR professional.”

Under that model, the value proposition shifts from software features to completed outcomes — such as handling payroll inquiries, reimbursements, or internal HR requests.

This concept aligns with the rapid development of agent-based systems emerging across the AI ecosystem, from coding assistants to enterprise automation platforms.

Coatue’s “Big Idea” Framework for Investing

Laffont also described how Coatue evaluates large technology opportunities internally. The firm refers to these internally as “BFIs” — Big F**** Ideas — which reflect long-term structural trends rather than short-term market movements.

“We have a moniker internally… BFI, which… stands for big fucking idea.”
“I personally have a view… the size of the TAM is irrelevant… what I do think is, is that TAM gonna grow… and then I care a lot about additional TAMs.”

Rather than focusing solely on the current size of a market, the framework emphasizes whether a company can expand the market itself and create new adjacent opportunities over time.

Many of Coatue’s largest positions historically reflect this logic. Investments in companies such as Amazon, Nvidia, & Apple benefited from expanding markets like cloud computing, accelerated computing, and mobile ecosystems, that grew far beyond their original addressable markets.

For firms investing across both public and private markets, identifying those structural shifts early remains central to long-term portfolio construction.

ICYMI: Coatue’s CTEK Fund

A new addition to Coatue’s strategy is its CTEK fund, a vehicle designed to give investors exposure to the firm’s highest-conviction technology investments across both public and private markets.

CTEK—short for Coatue Technology—reflects the firm’s core philosophy: that the most important technology companies increasingly span the boundary between private and public markets. Rather than treating venture and public equities as separate asset classes, Coatue structures the fund to capture opportunities across the entire lifecycle of a technology company.

The fund holds positions in many of Coatue’s flagship technology investments, including leading companies in AI, software, semiconductors, & internet platforms. By concentrating capital into a portfolio of major technology winners, the strategy is designed to compound alongside the most dominant companies driving the modern technology economy.

This approach aligns with Thomas Laffont’s broader view that innovation cycles are accelerating and that many of the companies shaping the next generation of markets are still private.

Vehicles like CTEK allow Coatue to maintain & offer long-term exposure to these companies as they mature, whether they remain private longer or eventually enter public markets.

In practice, the fund serves as a central access point to Coatue’s technology investing engine, combining the firm’s venture, growth, and public market insights into a single strategy focused on long-term compounding in technology.

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BREAKING: Nominal Hits $1B - Founders Fund Preempts $80M B-2 Acceleration Round

Molly O’Shea — Thu, 05 Mar 2026 15:53:55 GMT

Nominal hits $1B valuation after closing preempted $80M B-2 Acceleration Round led by Founders Fund, with participation from Sequoia, Lux Capital, & General Catalyst — just 10 months after Nominal’s $75M Series B led by Sequoia.

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Cameron McCord (Co-Founder & CEO, Nominal) & Trae Stephens (Partner, Founders Fund; Co-Founder & Chairman, Anduril) join Sourcery to break down why the round was preemptive, what Founders Fund was tracking from inside its portfolio (including Anduril), & how Nominal is becoming core infrastructure for teams building mission-critical hardware — from aerospace & defense to autonomy, energy, & advanced manufacturing.

We get into the “GitHub for software-defined hardware” analogy, what’s broken in the current federal testing stack (yes: Excel + MATLAB + PDFs), how Nominal can cut major test campaigns by 50–60%, and why the real competition is bureaucracy + legacy incumbents.

Cameron also shares how Nominal thinks about TAM expansion, dual-use strategy (and why Trae hates the term), strategic M&A, hiring 120–140+ people in 2026, and how AI changes the hardware engineering workflow (from post-test analysis to agentic parallelization).

All Systems Nominal.

𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Trae Stephens & Cameron McCord
(01:11) Nominal raises $80M from Founders Fund
(03:32) Why Founders Fund made the investment
(05:22) From Palantir to Anduril: Trae’s investor-operator journey
(07:14) Nominal: the GitHub for hardware testing
(12:33) Why Sequoia believed Nominal’s TAM was much bigger
(15:36) Inside Nominal’s growing defense customer base
(17:32) Why government hardware testing still relies on Excel and MATLAB
(22:22) Cutting hardware testing time by up to 60%
(26:55) How AI changes hardware development
(37:22) Why the government is backing new defense tech companies
(33:35) Nominal's sales strategy
(45:44) Competing with legacy software giants
(46:24) Recruiting top engineers
(50:56) Early Anduril stories from the desert

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All Systems Nominal

Nominal now serves more than 60 customers, including 4 of the world’s 5 largest defense contractors, while reporting 7× year-over-year revenue growth.

As McCord described the ambition:

“We’re building the platform modern hardware companies will run on for the next fifty years.”

Product: The Software Stack for Hardware Engineering

Nominal’s core thesis is that hardware engineering workflows still rely on outdated software infrastructure.

Modern software development evolved around collaborative platforms like GitHub and cloud-native tooling. By contrast, many hardware programs still manage testing and validation through a fragmented set of tools including Excel, MATLAB, PDFs, and PowerPoint.

Nominal replaces these manual workflows with a unified platform for ingesting, analyzing, and operating on hardware test data.

When complex systems are tested—whether drones, aircraft, or energy infrastructure—they generate large volumes of telemetry, logs, sensor signals, and video. Engineers must analyze this data to determine whether systems performed correctly and met required conditions.

McCord described the experience that inspired the product:

“We’d fly systems all day and then spend hours late at night trying to figure out what happened. There was an explosion of telemetry, sensor data, logs, video, audio—and the process just sucked.”

Nominal centralizes these workflows into a single platform where engineers can query and visualize test data in real time. The company is now integrating AI interfaces that allow engineers to interact with hardware data using natural language queries.

“You can say ‘plot the kinematics of the drone,’ and the system will generate the analysis automatically.”

Over time, Nominal aims to enable one engineer to manage and validate many hardware systems simultaneously, increasing productivity in complex engineering environments.

Market: An Industrial Renaissance

Nominal’s growth coincides with a broader resurgence in industrial technology.

Autonomous systems, robotics, aerospace, energy infrastructure, and defense technologies are advancing rapidly. These systems increasingly combine sophisticated hardware with large software stacks, making testing and validation more complex.

Nominal entered the market through hardware testing and post-test analysis, a step in the development process where engineers face immediate operational pain.

“We started with a simple problem where the pain was extremely acute and we could make it 10× better.”

From that initial wedge, the platform expands into adjacent areas including manufacturing testing and operational monitoring as customers scale their hardware programs.

Given the scale of global manufacturing and defense technology spending, Nominal sees a large and growing opportunity.

“The global manufacturing TAM is massive. I’m not worried about the addressable market.”

Funding & Use of Capital

Nominal: Nearly $200M in Total Funding

$7.5 million Seed led by Lux Capital with participation from Founders Fund
$20 million Series A led by Paul Kwan, Managing Director at General Catalyst.
$75 million Series B in just 10 days, led by Sequoia’s Alfred Lin (joined the board)
$80 million Series B-2 preempted & led by Founders Fund 10 months from Series B

Pictured: Delian Asparouhov, Cameron McCord, Molly, Trae Stephens

Nominal was not actively fundraising when Founders Fund approached to lead the new round. McCord explained the timing:

“You always want the external reality to match the internal momentum you’re feeling inside the company.”

The additional capital allows Nominal to accelerate expansion across several areas.

First, the company plans to continue investing heavily in product development as the platform evolves into a broader industrial software ecosystem.

Second, Nominal intends to pursue selective acquisitions, particularly small teams or technologies that complement its platform.

“The winner in this market will ultimately build a platform.”

The company is also expanding geographically, recently opening a London office to support growing European demand.

Finally, the funding provides the resources needed to scale the organization as demand increases.

Anduril, Founders Fund, & Early Validation

Nominal’s connection to Anduril played a key role in its early development and adoption.

McCord previously worked at Anduril, where he experienced firsthand the operational challenges of testing hardware systems. Those experiences directly shaped Nominal’s initial product direction.

Trae Stephens, partner at Founders Fund and co-founder of Anduril, explained that Nominal’s traction within the firm’s portfolio helped drive the decision to lead the new round.

“Founders rarely agree on tooling. When multiple companies and our entire engineering team at Anduril independently told us Nominal had become essential infrastructure, the decision was obvious.”

Stephens emphasized that Nominal stands out from typical enterprise software tools because of its operational impact.

“This isn’t a nice-to-have tool. It’s actually saving us money.”

The platform is now deeply integrated into engineering workflows at companies building mission-critical hardware.

Customers: The Companies Building the Physical World

Nominal’s platform is increasingly used by companies operating at the frontier of hardware development—organizations building systems where performance, reliability, and safety are mission-critical.

Today the company supports more than 60 customers worldwide, including startups, defense contractors, government programs, and advanced industrial companies developing aerospace systems, autonomous vehicles, robotics, and energy infrastructure.

One of Nominal’s most prominent customers is Anduril, the defense technology company co-founded by Founders Fund partner Trae Stephens. Engineers at Anduril use Nominal to unify test data across autonomous systems programs and accelerate review cycles across large engineering teams.

According to Stephens, Nominal has become deeply embedded in engineering workflows:

“Nominal is one of those examples where you look at the ROI and realize this isn’t a nice-to-have. It’s actually saving us money.”

Nominal is also used by Shield AI, whose autonomous aircraft programs generate enormous volumes of flight telemetry during testing. Engineers use Nominal to review test data immediately after flights—compressing analysis cycles that previously took hours or days.

Beyond defense startups, the platform has expanded across the aerospace sector. Hypersonic aircraft developer Hermeus uses Nominal to analyze flight & taxi tests of its Quarterhorse prototypes as the company pushes toward operational hypersonic flight.

Government testing facilities are also adopting the platform. Nominal has partnered with the Arnold Engineering Development Complex (AEDC) at the U.S. Air Force’s Arnold Air Force Base to improve data analysis for aerospace system testing.

The platform is also being used by emerging autonomy companies such as Scout AI, which relies on Nominal as the central system for reviewing robot test data and managing annotated training datasets used to improve autonomous systems.

Across these organizations, Nominal is used to capture telemetry, synchronize multimodal data streams, and analyze system performance during testing and operations. The company’s products—Nominal Core and Nominal Connect—enable engineering teams to bring together telemetry, logs, video, simulation outputs, and instrumentation data into a single collaborative workspace.

The result is a growing customer base across sectors including aerospace, defense, robotics, manufacturing, and energy, industries where testing cycles are expensive and where better data infrastructure can significantly accelerate development timelines.

As McCord summarized, the goal is simple: build the data infrastructure that allows engineers building complex hardware systems to iterate faster and ship reliable products sooner.

Government Relationships, Policy Tailwinds, & Hiring

Nominal operates in industries closely tied to government customers, particularly defense and aerospace.

Adoption in these sectors requires credibility, reliability, and compliance with complex regulatory environments. McCord said that once trust is established, word-of-mouth adoption can accelerate across programs and organizations.

“Eventually people start asking, ‘Are you using Nominal?’”

Stephens also pointed to broader shifts within the Department of Defense that could accelerate adoption of modern technologies.

“Individual people can make a difference. The people in place now understand technology and the importance of acquisition reform.”

At the same time, Nominal is preparing for significant internal expansion. The company currently employs roughly 125 people and plans to grow to more than 220 employees over the coming year.

McCord believes the mission itself is a powerful recruiting advantage.

“Engineers realize they can build software that helps autonomous aircraft, fusion reactors, or robotics systems. That’s a powerful draw.”

As Nominal scales its platform and team, the company aims to become foundational infrastructure for the next generation of industrial technology.

“We’re building the software layer that helps engineers build the future.”

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BREAKING: Inside M13 — The LA VC Firm Quietly Outperforming

Molly O’Shea — Mon, 02 Mar 2026 16:14:06 GMT

Courtney and Carter Reum, Co-Founders of M13, join Sourcery for a deep dive into building one of LA’s most focused early-stage venture firms, with an emphasis on venture math, operational expertise, and winning.

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With $1.9B AUM across three funds, 200+ direct investments, 54 exits, and 15 unicorns seeded or backed at Series A, M13 has built a reputation for pairing venture math with hands-on execution.

Portfolio investments include companies like Lyft, Pinterest, Ring, Bonobos, Matterport, Shake Shack, Solana, and emerging leaders like Rho, Prepared, OpenFX, Arena Club & Niural AI. The firm is ranked Top 20 globally in the HEC-Dow Jones Venture Capital Performance Ranking and was named one of TIME’s Top 100 US VCs for 2025.

M13 targets 30–35 core investments per fund, aims for ~20% ownership, & structures its portfolio around power-law outcomes, not incremental 3x bets. Over the last three years, companies behind M13-led rounds have raised ~$800M in follow-on capital, reached Series B ~30% faster, and at ~25% higher valuations than the median.

We cover:

• Why M13 targets 20% ownership
• How they generate alpha in a hype-driven AI market
• The second and third wave of AI opportunities
• Portfolio construction & power law math
• Stablecoins and the future of money
• LA’s evolving tech ecosystem
• Talent wars in the AI era
• Founder execution vs idea quality
• Biohacking, longevity, and performance
• Lessons from pro sports, Goldman Sachs & culture

“M13 is one of the brightest star clusters in the northern sky. It’s full of remarkable individual stars. But when those stars come together, they create something that’s truly greater than the sum of its parts. That’s the M13 advantage.”

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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒

(00:00) Welcome to the Pod House: M13’s LA Origins
(01:10) $1.9B AUM & 54 Exits — What M13 Focuses on Now
(02:20) Early Wins: Ring, Lyft, Pinterest & 15 Unicorns
(04:10) Alpha vs Multi-Stage Capital — Two Sports of VC
(06:00) Avoiding AI Hype & Sticking to Venture Math
(08:40) Why M13 Increased Ownership to 20%
(10:00) iPhone → DTC → AI: Investing Through Technology Cycles
(11:50) Stablecoins & Blockchain as Infrastructure
(12:40) Why AI’s Biggest Winners Aren’t the LLMs
(16:20) Healthcare, GLP-1s & AI-Driven Longevity
(19:10) Inside M13’s Hands-On “Propulsion” Model
(24:00) The War for AI Talent
(27:50) Is LA Taking a Siesta? The Future of LA Tech
(35:40) From VC to Pro Soccer at 45 — Culture Lessons
(46:44) The ritual behind a strong marriage
(47:10) Relationships, Mentorship & the Power of Compounding

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M13: Power Laws & Portfolio Geometry

Carter characterizes the current VC market as divided into “two sports”

Large multi-stage funds deploying vast pools of capital into a small number of potential generational winners
Early-stage firms seeking alpha through concentrated ownership & asymmetry

M13 typically writes 30 to 35 core checks per fund. Its ownership target has evolved from approximately 10% in earlier funds to roughly 20% today, reflecting a deliberate shift toward higher conviction positions.

The economics are calibrated accordingly.

At M13’s fund size, the firm does not require outlier trillion-dollar outcomes to generate fund-level returns. As Carter explains:

“When someone says to me, this could be a $20 billion company, I say statistically speaking, it’s very unlikely. It could be, but there is math to it. There is pattern recognition.”
“If you have a 10% chance at 100x, you gotta take that shot on goal.”

The implication is that venture capital remains governed by power-law distributions. Incremental outcomes are structurally insufficient.

Courtney reinforces the asymmetry:

“You can’t aim for 3x if you want a 3x portfolio. You’ll end up under that.”

In other words, targeting moderate outcomes in a high-variance asset class often produces suboptimal aggregate performance.

Operational Leverage and Measured Outcomes

Unlike many traditional venture firms, M13’s team composition is heavily operator-weighted. Of approximately 40 professionals, only one previously worked at a venture capital firm. The remainder come from operating backgrounds across brand, growth, data, and product.

This structure underpins the firm’s Propulsion model — an operating platform designed to work directly with portfolio companies on specific performance drivers.

The firm tracks burn multiple, compounding growth rates, and business-specific indicators. In applied AI companies, for example, daily query growth may serve as a core metric.

An internal review cited by Carter offers measurable outcomes:

“We are doing an internal review the other day, and our portfolio companies have raised about $800 million behind rounds we’ve led the last three years. They’re raising those Series Bs about 30% faster at about 25% higher valuations than the median Series B. That means our hands-on approach is working.”

Whether this outperformance persists across cycles remains to be seen. But the data suggest a measurable relationship between operational engagement and follow-on financing velocity.

The competitive landscape, however, is increasingly dense.

“There are five or 10 companies going after every idea right now. They’re all well pedigreed, they’re all well funded. We wake up every day and say, look, we just gotta out execute everyone.”

He frames incremental improvement as compounding leverage:

“If we can get 1% better every day, we can be 37 times better by the end of the year.”

AI: Competing with Incumbents vs. Investing in Ripples

M13 views artificial intelligence as a horizontal layer reshaping multiple sectors. Yet they express caution toward foundation-model investing at peak valuations.

Carter describes the competitive reality bluntly:

“Innovators are competing with innovators who are competing with the best companies on the planet.”

The presence of hyperscalers with deep pockets of capital, data, and distribution, severely challenge any potential for return distributions for early-stage entrants at the base model layer.

Instead, M13 emphasizes second- & third-order effects.

Carter draws a historical analogy to the 1920s automobile industry: while car manufacturers represented the core technological breakthrough, significant wealth accrued to infrastructure and adjacent sectors enabled by the car.

“The job of a venture capitalist is to understand the latest technological innovation & then have a prepared mind to find where the best opportunities are.”

In practice, this translates to investment in applied AI businesses, infrastructure layers, and blockchain-adjacent use cases such as stablecoins. The thesis is less about headline breakthroughs and more about derivative value layers.

Los Angeles Value Creation

Los Angeles has historically been viewed as secondary to Silicon Valley in technology density. Yet over the past cycle, LA-based companies have generated hundreds of billions of dollars in enterprise value.

The potential SpaceX IPO at over $1T illustrates the catalytic effect of large liquidity events.. and Carter was clear to make sure we don’t undervalue it.

“We’re early investors, so we hope $1.5T — come on, Molly. Don’t jinx us.” lol.
“Anytime you have a big IPO like that, it puts a lot of wealth in people’s pockets. It allows people to go figure out what they want to do next. And so I think it will be fantastic for the ecosystem.”

Historically, large public offerings recycle both capital and talent. Early employees become founders. Founders become investors. Wealth compounds locally.

M13 does not typically invest directly in capital-intensive aerospace sectors, but Carter notes:

“We tend to invest in the software layers of some of these industries, but no doubt about it, it will be a big event & I think we’ll have very positive ripples.”

If AI’s first wave clusters in San Francisco, LA’s intersection of aerospace, media, creators, and consumer platforms may position it uniquely for second-order value creation.

Disciplined Approach

Across themes from portfolio construction, AI strategy, culture, & geography, M13’s framing returns to one central principle: disciplined probability management in a high-variance system.

“A data point is just a data point until they coalesce” Then, that’s the signal.

In venture capital, where outcomes are lumpy and narratives are abundant, the differentiator may not be pure-optimism, but calibration.

“I think we're probably the rare VC that doesn't think — although we have 15 unicorns under our belt — that we just have some special superpowers and we invest in special people and then special things happen.”

Whether M13’s model continues to perform will depend on execution across cycles. But its thesis with concentrated ownership, asymmetry, operational engagement, and second-order AI exposure, reflects a deliberate response to an increasingly capital-saturated environment.

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