Venture Capital's Blackstone Problem
with Rick Zullo, Founder & Managing Partner of Equal Ventures
Rick Zullo is the Founder & Managing Partner of Equal Ventures, a seed-stage fund with over $200M in committed capital investing across energy, insurance, retail, and supply chain. Rick has been investing in vertical software, marketplaces, and services for 14 years. One of his earliest exits was Vettery, a staffing platform acquired by Adecco, and his portfolio includes companies like Threeflow, Leap, Smarthop, and David Energy.
Rick brings a value-investing lens to venture that’s equal parts Munger and Thiel, focused more on competitive advantage and long-term free cash flow than most VCs are typically. In this episode, we dig into one of his core convictions: in a market flooded with AI-driven growth, much of the growth managers are are seeing is beta, not alpha. Caveat emptor.
Rick has also build a defined practice around services, well before “AI-native services” was a category. He’ll share his POV on why it’s never been easier to start a services company… and never been harder to build one that lasts.
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Today’s Episode
Venture capital is strange business these days. AI-native startups are growing faster than software ever did. Services — historically anathema — are in vogue. Physical AI and defense tech are absorbing billions, despite hardware being a VC graveyard and defense LPA-restricted for decades. Valuations (and hence revenue multiples) at seed have detached from any rational framework. And the narrative has shifted so dramatically that some LPs are questioning their exposure to software and embracing “deep tech” (although the vast majority of that category is still software, making that dichotomy deeply confusing).
Naturally, the instigator of these shake-ups is AI. Rick Zullo believes many investor assumptions in this moment are misplaced. His argument: the current boom is producing the illusion of product-market fit at scale. When a technology wave lifts every company in a category simultaneously, you can’t mistake it for idiosyncratic company success. Conflating beta with alpha1 can turn out very costly.
Beta-Driven Product Market Fit
Rick has a term for what happens when a technology catalyst makes everything in a sector work at once: beta-driven product market fit. “Because of a technology or market catalyst, everything in a single category is working,” he explains. “That long-term means a lot of these companies are going to struggle as they become more well-capitalized, compete against each other, and invite more competition.”
He feels the AI-Native Services wave is a perfect example. When an LLM makes it trivially easy to automate insurance claims processing, and five companies all go from zero to $5M in six months, the growth looks like signal. But it might not be the signal founders are looking for. When the technology wave lifts your boat, it lifts competitors and incumbents too.
The fundamental distinction between software and services still matters. Historically, SaaS entered greenfield markets: the product was new, adoption required friction, and success in overcoming that friction yielded defensibility. Services companies operate in brownfield territory. “You’re largely trying to steal share from other competitors in the industry,” Rick argues. “That’s a very different game.” The incumbents have distribution, cost of capital advantages, and they’re implementing AI as aggressively as anyone.
The insurance TPA market is his clearest case study. Equal has studied it extensively and found what you’d expect in a mature services industry: tremendous multihoming behavior, low switching costs, and a cost curve that every player is riding down together. “Are you going to assume that because you have early growth, competitors aren’t going to respond?” In most services categories, they already are.
In a world where buyers are trying to figure out how to use AI, it makes sense to be more consultative. Forward-deployed everything is all the rage, for good reason. When used for the right reasons — to find a unique, defensible, compounding application of AI — it’s powerful. When the footing is win business and hope the product will come, it’s dangerous.
Summarized: SaaS revenue was hard to get, so strong growth meant product-market fit. If AINS revenue is easier to get, it’s likely easier to lose as well. So VCs touting their zero to $5M ARR as proof of market leadership and justification of valuation might be in for a rude awakening.
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AINS & The Revenue Multiple Trap
If beta-driven PMF is the disease, revenue multiple investing is the symptom. Rick is blunt: “Revenue multiple investing in services has never worked.” The reason is mechanical. Services companies carry cost structures that software companies don’t, even fully automated ones. The question isn’t what your gross margin looks like in year one.
The long-term question is: what is your net margin? …At early stage, it’s really hard to tell what net margin is unless you understand the moat structure of that industry.
He sees investors underwriting AI services companies at 500x to 1,000x revenue, in some cases calling GMV “ARR.” Hidden costs compound the distortion: companies loading up on risk assets and keeping the cost of capital below the line, out of the gross margin calculation. In insurance, opex optimization barely moves the needle. What matters is loss ratio, and most AI-enabled carriers have no idea what their loss ratio will be until their book matures. “A lot of them are trying to outrun their book with growth,” Rick says. “We saw that play through in 2021.”
WeWork, for example, had strong revenue growth, positive early margins, and a compelling narrative. Benchmarked against any legacy REIT on unit economic ratios, it was terrible. Rick sees the same trap in AI services: companies that look exceptional in isolation and mediocre when compared to the incumbents they’re supposedly disrupting.
As we’ve argued many times before, the “death of software” narrative misses so much nuance that it’s more or less worthless. Rick believes the same effect is at play with the blind trust many VCs are placing behind AINS. Like in all categories, there are of course excellent businesses. But if the market broadly prices services revenue as if it carries the same margin structure and defensibility as SaaS ARR — when much of the time, it doesn’t — that is a potentially fatal mistake.
Venture’s Blackstone Problem
So why is capital flooding into categories with these competitive dynamics? Rick’s answer sits upstream. It’s an incentive problem that starts with VC fund economics.
“Big funds are starting to look a lot more like Blackstone,” he observes. “They’re looking a lot less like Benchmark.” When you manage $10B at 3-and-30, you need companies that consume hundreds of millions in capital. Physical AI, deep tech, defense, nuclear, autonomous vehicles: these are plays that require the deployment of billions. “It’s not [just] convenient,” Rick says of this alignment. “It’s actually extremely strategic.”
Taken to an extreme, the result is a market that systematically favors capex-intensive plays and penalizes capital efficiency. Veeva Systems, which raised $9M before its IPO and has been compounding cash ever since, “would not exist in this market,” Rick argues. Not because the opportunity isn’t there, but because no large fund can write a meaningful check into a company that doesn’t need one. As we explored in The Fundability Trap, the market increasingly rewards fundability over investability. Rick extends that critique to capital structure itself: funds that need to deploy billions will find theses that require billions, whether or not those theses create durable value.
Physical AI is the latest expression. Rick acknowledges that cornered physical resources (cameras in warehouses, sensors on shipping routes) can create genuine moats. He likes how Steel Atlas put it in their “data gusher” thesis: limited providers, expensive-to-replicate infrastructure, sensors that throw off valuable data, software that compounds on top. But the broader category gives him pause. Most physical AI companies pitching AI-optimized mining, energy services, or manufacturing face incumbents with lower cost of capital, deeper distribution, and decades of vendor relationships. A truly defensible, proprietary hardware wedge is one thing. Off-the-shelf hardware plus compute is another. GPU capex, unlike railroads or transmission lines, has a useful life of two to three years. The analogy to durable infrastructure is questionable.
At Euclid, we’ve discussed hardware as a moat in Vertical AI, and why unique datasets have become more important than ever to the AI application layer. We do find it interesting, however, that deep tech proponents point to data generation as a key source of defensibility for the theme. Even the Data Gusher piece above lists Palantir as the canonical success story of a “data factory” — a Vertical AI business that is 100% software, eschewing both proprietary hardware and infrastructure by design. Deep tech or physical AI may be a compelling way to deploy capital at high velocities, but its unit economics and data advantages largely remain to be seen.
The Quiet Compounder
Rick’s answer is his portfolio. Between a third and half of Equal’s Fund I companies are free cash flow positive while growing at rates above 100%. These aren’t companies in the venture zeitgeist. Most aren’t raising $100M rounds or getting profiled in The Information. They’re vertical businesses that found defensible positions in their markets and are compounding quietly.
Equal’s internal filter centers on one question: “Can this company hit $100M of gross margin within the next five to seven years?” Very much in the vein of the Bling Capital underwriting framework we explored with Kyle Lui a few weeks back. The GM filter alone eliminates most AI services companies raising at astronomical multiples, because it forces you to confront the margin structure rather than assume it away.
And it all comes back to fund economics. Rick’s “dragon number,” the exit value needed to return the fund at average ownership, is roughly $570M. A $1B outcome still returns 2-3x the fund. That is a fundamentally different game than a megafund that needs $100B outcomes to move the needle. Founders Fund, by comparison, has invested in 600 companies, and six really matter to them, at average entry points well above $1B on a dollar-cost-average basis.
There are many ways to draw up a 10x fund in venture capital: 4 vs. 40 investments a year, $5M vs. $500M fund size, 20% vs. 2% ownership. They can all work. But the math has to hold together — and that math determines what the managers have to prioritize, and therefore, founder alignment. We laid out all the numbers in our essay, The Quiet Death of Founder-VC Alignment.
Hope is not a strategy. Yet VC fund strategies that assume $100B exits or capturing double-digit percentages of all venture-backed exits abound. Rick sees a landscape in which most AI services companies have zero chance of monopolizing their sector, and most physical AI plays face incumbents with structural advantages. The contrarian position might be the most old-fashioned one: invest in models that have actually been proven to make money.
The Takeaway for Vertical Founders
If you’re building in Vertical AI and everything seems to be working — revenue is growing, customers are signing, VCs are circling — first of all, congrats. But second, take a beat. Beta-driven product market fit means the rising tide is lifting your boat alongside everyone else’s. The question, to paraphrase the Buffet quote we all know and love, is what happens when the tide goes out.
Most high velocity-startups prioritize early commercial success, and more than ever, we feel that’s the right move. Vertical AI adoption is still nascent, economy wide. You’ve got to get in, grow, and figure it out. But along the way, the founders who build durable companies in this cycle will work just as hard to answer the long-term questions. Here are Rick’s top three:
What is your path to not simply a market-leading position, but a monopoly?
What does your net margin look like at scale, not just gross margin?
What’s defensible when every incumbent in your sector is also implementing AI?
As we’ve explored at The Verticalist many times before, workflow depth and data gravity are earned. Early market pull is critical, and the right path to product-market fit. But don’t mistake it for a moat. If you’re not leveraging traction to build something structurally defensible, you’re running a race that gets harder every quarter. Assume you have more competition than is visible, because beyond startups, incumbents will take their shots too, even if they’re slower to materialize.
On the capital side, we also agree with Rick on this: pick aligned investors. Beyond personal simpatico, a fund’s economics should match your company’s trajectory, stage, and vision. If you’re building a capital-efficient Vertical AI compounder, the partner who needs you to raise $200M and exit for $20B is not your partner. The one who will celebrate a $500M exit at 15% ownership is.
Key Moments from this Episode
00:00 — Why something feels broken in venture capital
05:17 — The hidden problem with AI-native services
10:01 — Why competition kills AI margins
15:19 — Is physical AI the next gold rush?
21:10 — Which AI businesses actually build lasting moats?
28:03 — Why robotics and autonomy are different bets
35:39 — The return of capital-efficient startups
40:21 — Why today’s biggest VC funds play a different game
44:53 — How Rick evaluates billion-dollar opportunities
46:58 — The vertical AI markets Rick is most excited about
Beta measures how much an investment moves with the broader market (a beta of 1 tracks it, above 1 amplifies it). Alpha is the excess return earned above what that beta exposure alone would predict — the value added by skill, rather than just the underlying market movement.