2025-07-25 01:20:00
Is Ferrari a car company?
The obvious answer is yes, but not according to its CEO, Benedetto Vigna, who recently described the company’s business model saying,
“We are not – we are not – a car company. We are a luxury company that is also doing cars.”
That’s their differentiator. Their brand. Their “schtick.” And, it works, but not because it’s a marketing ploy. It works because Ferrari backs it up with its actions.
How so?
By adhering to its founder Enzo Ferrari’s “scarcity dictum” that declares,
“Ferrari will always deliver one car fewer than the market demands.”
Delivering one fewer than the market demands —
How many businesses can say they do that?
In my experience, very few. In fact, many do precisely the opposite.
Why?
Because more is almost always considered better. Size, scale, and growth are seductive. It is what attracts new investors and fresh capital. It is what grabs attention and headlines.
The trouble is that size and growth isn’t necessarily synonymous with strong performance.
Look no further than the past five years in the auto industry.
From 2020 to 2024, Ferrari sold fewer than 60,000 cars, but for an average price of over $450,000.
Now compare that with two of the largest car companies in the world by sales and revenue. Since 2020, Toyota has sold more than 52 million cars at an average price of $32,000. Meanwhile GM sold close to 30 million at an average price of $51,000.
As a result, while these behemoths have produced revenue north of a trillion dollars versus Ferraris of less than $30 billion, Ferrari’s net margins have been significantly higher at close to 23% last year versus an average of less than 6% for Toyota and GM.
The gap is even more pronounced on a gross margin basis.
The result has been drastically different stock price performance, as seen in the chart below (RACE = Ferrari, TM = Toyota, and GM = General Motors). Margins matter.
This isn’t limited to the auto industry. In fact, we see it everywhere from retailers to banks to technology companies, among others. Each is littered with failed growth stories, from Abercrombie and Under Armour, to Lehman Brothers and Countrywide, to GoPro, Clubhouse, and Peloton.
Most recently Burberry, the once iconic British fashion company, admitted that it too had fallen victim to unbridled pursuit of global growth. In doing so, it diluted its luxury image by becoming ‘too everywhere,’ which is why its new CEO is currently unwinding many of its most ambitious detours and refocusing on what built the brand. In his words, he wants to “lean into the assets that we already have and celebrate who we are.” Said another way, he wants to return to the Ferrari model.
If so, why don’t more companies follow Ferrari’s lead? Because as J.P. Morgan CEO, Jamie Dimon, is quoted as saying,
“CEO’s feel this tremendous pressure to grow. The problem is that sometimes you can’t grow. Many times, you don’t want to grow because growth can force you to take on bad customers/clients, excess risk, or excess leverage.”
Yet today, the euphoria surrounding growth, scale, and size is palpable. It’s practically all anyone talks about.
Every company wants to be classified as hyper-growth because doing so garners the highest valuations and attracts the most attention. To achieve this, many companies are chasing markets with massive total addressable markets (aka “TAMs”) and limitless opportunities. They prioritize scale and size over everything else. The trouble though, as Bill Gurley of Benchmark Capital recently highlighted, is that this has led to a dynamic akin to a “gavage.”
Never heard of a gavage?
Neither had I, but apparently it is the tube the French use to force-feed ducks to create foie gras.
Today, venture capital firms are doing something similar, but instead of force-feeding ducks with food, they are force feeding companies with capital. In doing so, this is forcing companies to invest in areas outside their circle of competence, hire people to sell products that aren’t ready for “go to market,” and to lose discipline more broadly. In Gurley’s words on a recent podcast titled “The Gift and Curse of Staying Private,”,
“It is forcing every company to go ‘all or nothing’ and ‘swing for the fences.’ It is no longer your grandfather’s startup business or your grandfather’s venture capital fund. It is a radically different world. And if you are a founder, you would like to be able to ignore all of it and build your company the way you want to build it. But if your competitor raises $300 million and is going to 10x the size of their sales force, or 50x it, you will be dead before you know it. You won’t be around. I think it’s bad for the ecosystem because it will remove all the small and middle outcomes and force businesses to just play grand slam home runs. But that’s what it feels like to me. And it feels like we didn’t learn anything from the days of 0% interest rates.”
Unsurprisingly, if this forced feeding of capital is causing companies to be less disciplined in how they allocate capital, there will undoubtably be a negative downstream effect on the venture and private equity funds that are backing them. In fact, we may already be seeing this in the form of diminishing returns and lower liquidity.
Amazingly though, even with the private markets already awash in capital, we are about to witness an even larger inflow in the form of individual investor capital. Look no further than the recent WSJ article titled, “Why Vanguard, Champion of Low-Fee Investing Joined the ‘Private Markets’ Craze,” that highlights how the champion of passive investing has been seduced into the world of alternatives.
Vanguard is far from alone though as KKR recently announced a partnership with the mutual fund complex, The Capital Group, in order to expand its alternative offerings to “non-accredited” investors. Meanwhile, Charles Schwab announced that it is entering the alternatives world in a meaningful way by advocating that they will be incorporated into its 401k and individual investor platforms.
This is nothing new for investors though. It happened to mutual funds in the 1980’s after the advent of the 401k, hedge funds in the early 2000’s following the dot.com collapse, emerging markets after a brutal stretch for U.S. stocks, real estate funds heading into the GFC, and energy funds when commodity prices spiked more than a decade ago. In each case, too much capital proved to be a drag on performance.
Venture capital, and potentially private equity more broadly, will likely be the next victim.
So, what should an investor do?
The simple answer might be to “avoid private equity all together,” but that’s too simplistic and short-sighted. To me, that answer feels like the equivalent of giving up on investing in retail companies all together because Burberry, Under Armour, and Abercrombie overplayed their hands. Afterall, if you did this, you would have missed the Hermes, CostCo’s, Ross Store’s, and Monster Beverage’s of the world.
As a result, it feels like the better course of action is to pursue the funds that aim to achieve Ferrari status. Those that have maintained the discipline to, in Enzo Ferrari’s words, “deliver one fewer (investment or fund) than the market demands,” while avoiding those that have gotten seduced by growth at all costs.
This said, it also feels like there might be even more unique opportunities, especially in the public markets, but that is for a future post.
2025-07-10 21:52:00
Recently, I had lunch with Emmett Shine, co-founder of Gin Lane and a thoughtful voice in the conversation around AI and design.
Among other things, we talked about the future of user interfaces and experiences in an AI-driven world. He said something that really stuck with me: “humans existed without screens for hundreds of thousands of years. They will exist without screens for hundreds of thousands more.”
Increasingly, I envision a world without phones or tablets or computers. A world defined by a more immersive, primarily hands-free technological experience. A future where our children will view screens the way most of us view cigarettes. “You used to look at a screen all day?” they’ll say. “Do you know how bad that is for you?”
Meta and OpenAI clearly envision this reality, too.
Meta has invested hundreds of billions of dollars into AR/VR headsets, Ray-Ban smart glasses, a neural interface wristband, and even holographic wearables.
OpenAI announced a $6.5 billion deal with Jony Ive to create an AI device that can unseat the smartphone.
Though screens remain ubiquitous, they’re already beginning to feel outdated. It’s a clunky, poor user experience for accessing the power of AI.
What does a screenless future mean for tech? To reach a truly screenless future, we’ll likely need three things: new hardware, more natural voice dictation, and a step change in motion detection. (Or, possibly, mind reading…but let’s not get ahead of ourselves. We’ll leave that to Elon.)
In the past few months, we’ve seen a flurry of startups in all three categories.
Stealth Hardware: The dream of a magical AI-powered pin still lives on, if only in concept for now. In addition to Meta and OpenAI, a number of startups have begun to explore devices that feel as natural as jewelry or clothing, and less like a mini smartphone strapped to your chest.
Genuine Voice: The days of “Sorry, I didn’t catch that” are numbered. Berlin’s Synthflow just raised $20 million to power AI agents that hold real-time conversations with sub-400 millisecond response times. Wispr Flow just announced a $30M round for their AI-dictation app (it works like a dream). During WWDC, Apple focused on their new Apple Intelligence features and highlighted improvements in voice. And, YC put out an RFP for voice technologies ahead of their Spring 2025 batch. It might seem like the die has already been cast, but there is still plenty of space for winners who focus on nuance like intonation, turn-taking, and empathy. Because the moment you think “voice is solved,” a better product will win.
Human-Level Motion Detection: Motion sensing has matured past rudimentary gesture controls. Ambient.ai has raised north of $50 million to deliver near-human perception in physical spaces. Imagine a watch that can sense gestures, like a wave, to lock your front door.
Together, these innovations form the skeleton of a screenless UX. Design is what will flesh out that vision.
What does a screenless future mean for design? Take the screen away and what’s left is tone: cadence, empathy, restraint. Personality becomes the new product. In a market where models and APIs commoditize by the quarter, trust still compounds the old-fashioned way: one human reaction at a time. Brand DNA becomes a moat not because it looks pretty, but because it behaves: saying sorry before it’s asked, cracking the joke that defuses anxiety, staying silent when silence feels like respect.
What does a screenless future mean for you? Our collective sci-fi vision of the future often features a sea of blinking lights and fluorescent screens in classics from Blade Runner to The Jetsons.
But there’s a peaceful quality to technology fading into the background. Counterintuitively, even if technology becomes ubiquitous, if it is also virtually invisible, it gives us space to get back to a life that is a little more, well, human.
Mark Weiser once wrote, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.”
As AI makes that reality possible, the most powerful technology won’t demand our attention. It will give it back to us.
2025-06-27 21:05:00
Outrunning Giants: Building in OpenAI’s Shadow
From 2016-2021, a flurry of writing assistants, conversational chatbots, travel booking platforms, and meeting transcription tools popped up on the AI scene, many growing at breakneck paces.
And then along came ChatGPT’s steady rollout of new features: from freemium chatbots and writing tools in 2021 to, the release of Operator for booking in January 2025, and Record mode for meeting transcription in June 2025 — putting real strain on startups trying to compete.
This is a refrain I hear over and over in consumer AI investing: how can you build consumer applications when OpenAI has such a lion’s share of user attention (and data) to build any tool it wants.
When a single company begins to dominate a category, the instinct is to copy or cave. But the correct move is to find the spaces where the behemoth can’t compete. Where its own size might work against it. During Web 2.0, Google was that behemoth, bulldozing everything from Yahoo and MapQuest to AltaVista and AskJeeves. Still, e-commerce and social carved out their own ground beyond Google’s grasp.
Now OpenAI towers in consumer AI with hundreds of millions of weekly users and vast compute at its back. So where might value accrue outside its shadow?
The Gravity of the Giant
OpenAI’s ChatGPT has become a default destination for anyone exploring what consumer AI can do. Its freemium launch hit 1 million users in days and reached a record breaking 100 million MAUs in just two months.
What’s even more remarkable is the retention: over 80% of active users return regularly, and paid subscribers show retention north of 70% after six months.
That level of attention fuels personalization: the more you use it, the more it learns your patterns, preferences, and shortcuts. It’s human nature to stick with what feels familiar, but the ever-improving feedback loop of AI only magnifies that tendency. Consumer apps face both OpenAI’s head start and this natural stickiness. But just as Google could not put up a fight against Amazon and Facebook, there are areas where startups have the edge. Here are the three that I’m paying close attention to:
1. Vertical Trust and Domain Expertise
General-purpose chatbots struggle with high-stakes, regulated domains. Imagine asking for tax advice or mental health guidance from a generic model: liability concerns and nuanced regulations demand specialized expertise. In personal finance, healthcare, taxes, real estate and other fields built on trust, users need evidence of domain credibility. A startup that embeds clinicians, registered accountants, or licensed advisors into an AI workflow can differentiate.
The model may handle surface-level queries, but the human-vetted pipeline or validated data sources create a barrier OpenAI alone cannot clear without replicating specialized teams. It’s a classic dyanmic, but deep verticals reward focused players more than horizontal giants.
2. Bridging to the Physical World
As powerful as AI is, many high-value consumer experiences still benefit from (or even require) real-world integration. Startups like Doctronic show how AI triage leads to a telehealth session, then perhaps to in-person care. Travel can be reimagined: an AI plans an itinerary, but it also books local experiences, arranges guides, or curates surprise pop-ups — and then follows you there. Real estate might not just match listings and orchestrating visits, but guide you in real time through inspections, paperwork, and move-in services.
These require logistics, partnerships, and on-the-ground networks. The moat isn’t the model; it’s the operational backbone that connects AI suggestions to tangible outcomes. That’s a hard feat for a digital-first (and only) company like OpenAI.
3. Closing the Creativity Gap
A June 2025 study in Nature Human Behaviour investigated brainstorming tasks where participants used tools like ChatGPT versus relying on their own ideation plus web searches. It found that while AI can boost idea quantity, it often reduces variety: many AI-assisted participants produced very similar concepts, suggesting a “creativity gap” in generative models compared to unfettered human thinking. This echoes concerns that AI’s training on large but finite datasets drives convergence toward statistically common patterns, limiting novelty despite fluent output. Enhancing human creativity, mimicking human creativity will be tremendous efforts requiring specialized teams that think creatively themselves, and not just technically.
This isn’t to dismiss LLMs’ value (accelerating first drafts can meaningfully improve the creative process) but genuine creativity demands human curation, oddball connections, and serendipity. Startups that build tools to surface unexpected prompts, aggregate diverse human inputs, or structure hybrid workshops can capture value where vanilla AI falters.
Final Thoughts
It’s easy to feel outgunned by OpenAI’s budget and reach when single new feature release can eclipse months of effort. But success isn’t’ about trying to outscale OpenAI. It’s about finding places where AI is a means to something bigger. Giants excel at horizontal scale, but stumble on deep trust, real-world execution, or genuine novelty. That’s where value will continue to accrue. In the gaps that AI can’t fill.
Checklist for founders:
Trust gap: Which regulated domain do you know deeply? How will you embed vetted experts and compliance into your workflow?
Physical integration: What real-world services or partnerships can you build that a digital-first giant would find expensive to replicate?
Creative differentiation: How will you inject human serendipity or domain-specific inputs before or alongside AI to surface truly novel ideas?
Operational moat: What processes, partnerships, or data pipelines can you lock in early to defend against copycats?
User habit loops: How will you create feedback loops (e.g., personalized data, rewards) that cement engagement beyond a generic model’s appeal?
2025-06-26 01:31:00
Lately, it feels like every corner of my internet bubble is talking about venture returns—Carta charts one day, leaked DPI tables the next. I’ve seen posts on lagging vintages, mega-fund bloat, the “Venture Arrogance Score”, the rising bar for 99th‑percentile exits, and the PE-ification of VC.
But for all that noise, I haven’t seen much that actually walks through how returns metrics evolve over time in an early-stage fund. That’s a crucial gap. Many analyses focus on funds that are still mid-vintage, where paper markups can tell an incomplete or even misleading story.
So I pulled the numbers on Collaborative’s first fund, a 2011 vintage that’s now nearly fully realized. It offers a concrete look at how venture performance can unfold across a fund’s full lifecycle.
Fund 1 was small: $8M deployed across 50 investments. Check sizes ranged from ~$10K to ~$400K, averaging around $100K for both initial and follow-on rounds. It was US-focused, sector-agnostic, and mostly pre-seed through Series A.
Without further ado, here’s how the fund performed over its lifetime across a few core metrics:
Note: Distributions are net to LPs; contributions reflect paid-in capital. IRR data was not meaningful (“NM”) for years 1–5.
Below is a line chart of the returns data:
Note: IRR is shown as a dashed line corresponding to the secondary axis.
Contributions wrapped up by year 4, which is typical for early-stage funds. Distributions didn’t start until year 4 and peaked around year 10:
Returns were highly concentrated. Eight companies—Upstart, Lyft, Scopely, Blue Bottle Coffee, Maker Studios, Gumroad, Reddit, and Kickstarter—drove nearly all distributions.
Note: “Cost” is cumulative capital actually invested. “Cash Back” is cumulative proceeds received by the fund from realization events, and excludes (i) any remaining unrealized value and (ii) fund-level fees and expenses. “Multiple” is the quotient of Cash Back divided by Cost.
Together, these accounted for just $0.8M of invested capital but returned $37.6M—an average multiple of 45x. The remaining 42 investments, representing $7.2M, returned only $2.0M (a 0.3x multiple).
Within those eight, outcomes varied widely: one company alone delivered 73% of all cash returned. Adding the next three brought the cumulative share past 90%. Multiples ranged from 1.4x to 115x—illustrating just how concentrated and variable even a “winning” subset can be.
We believe the biggest risk in early-stage VC isn’t failure. It’s missing (or mis-sizing) the outlier. In Fund I, eight companies drove nearly all distributions. One check alone accounted for more than 70% of DPI. This is the power law at work.
Collaborative’s story now spans 15 years with four early funds in harvest mode. Three rank in PitchBook’s top quartile with two in the top decile by DPI. In each, a small number of companies drove the bulk of returns. Perhaps these will make for future posts.
Until then, I hope this serves as a reminder to take venture performance narratives based on unrealized funds with a grain of salt. Some trends are real and worth watching. But many of the loudest signals may fade or reverse as funds mature. Until they’re fully played out, their stories are still being written.
2025-06-18 21:38:00
Collaborative Fund recently launched AIR, a new kind of accelerator for design-led AI products. It draws inspiration from the institutions that reshaped creative possibility in their time, places that brought together unlikely collaborators at key moments of technological and cultural inflection.
As we recruit for the first cohort, we’re talking to people who had a hand in creating those lighting rod moments. A few weeks ago we asked Nicholas Negroponte, founder of the MIT Media Lab, to reflect on what happens when culture and technology collide to create new ways of thinking.
Today we’re talking to Tom McMurray, former General Partner at Sequoia, who helped shape Silicon Valley’s first golden age. Tom was an early investor in Yahoo, Redback Networks, C-Cube, NetApp—and, importantly, Nvidia. He now serves on multiple boards focused on science and impact. We spoke to him about pattern recognition, capital discipline, and why he’s an investor in AIR.
Tom McMurray: It was very clear where to invest in the networking space—bandwidth was in high demand. It was less clear in the pure Internet space. Our diligence process was pretty established so we continually developed more refined filters and leveraged off our core capability in chips and enterprise software. In the case of Nvidia we had what I call a Sequoia moment—that wonderful nonlinear diligence process where after parsing through the business, we reached a point where there’s only a single question left to decide. Our secret sauce was that we often knew many times more than the founders did about their business. We understood where the real risks were.
For companies like Nvidia, our expertise in semiconductors from investments in Cypress, Microchip, LSI Logic, and Cadence, plus our experience with gaming companies, meant the market risks were very low. We could easily do due diligence on the founders because many worked for friends of Sequoia Partners—this was our sweet spot in the 1990s. And in the Internet wave, we had special insight through our investments in Cisco and Yahoo. They were market pioneers who saw the world 3-5 years ahead of us. They pointed the way many times, and we just jumped on it.
Wilf Corrigan, a Sequoia Technology Partner and CEO at LSI Logic (where Jensen worked before starting Nvidia), told him to talk to Don Valentine at Sequoia about his “chip idea.” Jensen pitched the evolving game world and the need for more performance. Honestly, I had no idea at that point why we should invest in the company. But we asked harder and harder questions about team, competition, distribution, and got solid answers.
About 90 minutes into the pitch, Pierre Lamond asked Jensen how big the chip was. Jensen said “12 mm.” Pierre looked at Don and Mark; they nodded, and we committed to the investment on the spot. We led the Series A and Mark joined the board. The rest is history.
The critical question wasn’t about market size or vision—it was “can they build the chip, get the performance, and the price point?” That’s why the chip size question was the deciding factor. The semiconductor partners at Sequoia—Don, Pierre, and Mark—understood the significance immediately.
Because you’re reproducing the conditions that made Sequoia’s hallways electric in the ’90s—cross-pollination of builders, researchers, and designers who argue, prototype, and iterate in the same room. Great companies are rarely solo acts; they’re jazz ensembles riffing toward a common groove.
The secret is to lean on your wins, learn from them, apply it as you expand, iterate, and keep going.
“Stay cheap until it hurts.” Capital efficiency forces clarity. The companies that survived the dot-com crash had burn rates lower than their Series A checks.
Every wave starts messy. But history says two truths persist:
Jobs evolve faster than they evaporate. Cisco killed some circuit-switch jobs yet birthed the entire network-engineer class.
Bias follows data, not silicon. Fix the training data, and you fix 80 percent of the problem. That’s human homework, not machine destiny.
The “force for good” part kicks in when entrepreneurs bake guardrails into the business model, not just the codebase.
I’d look for that “Sequoia Moment”—where after all the questions about team, market, and technology, we identify the single critical factor that determines success. For Nvidia, it was “how many millimeters wide is the chip?” Sometimes, it’s these seemingly simple technical questions that reveal whether a company can execute on its vision.
And to founders who remember: progress isn’t inevitable—people make it so.
2025-06-13 02:54:00
A boy once asked Charlie Munger, “What advice do you have for someone like me to succeed in life?” Munger replied: “Don’t do cocaine. Don’t race trains to the track. And avoid all AIDS situations.”
It’s often hard to know what will bring joy but easy to spot what will bring misery. Building a house is complex; destroying one is simple, and I think you’ll find a similar analogy in most areas of life. When trying to get ahead it can be helpful to flip things around, focusing on how to not fall back.
Here are a few pieces of very bad advice.
Allow your expectations to grow faster than your income
Envy others’ success without having a full picture of their lives.
Pursue status at the expense of independence.
Associate net worth with self-worth (for you and others).
Mimic the strategy of people who want something different than you do.
Choose who to trust based on follower count.
Associate engagement with insight.
Let envy guide your goals.
Automatically associate wealth with wisdom.
Assume a new dopamine hit is a good indication of long-term joy.
View every conversation as a competition to win.
Assume people care where you went to school after age 25.
Assume the solution to all your problems is more money.
Maximize efficiency in a way that leaves no room for error.
Be transactional vs. relationship driven.
Prioritize defending what you already believe over learning something new.
Assume that what people can communicate is 100% of what they know or believe.
Believe that the past was golden, the present is crazy, and the future is destined for decline.
Assume that all your success is due to hard work and all your failure is due to bad luck.
Forecast with precision, certainty, and confidence.
Maximize for immediate applause over long-term reputation.
Value the appearance of looking busy.
Never doubt your tribe but be skeptical of everyone else’s.
Assume effort is rewarded more than results.
Believe that your nostalgia is accurate.
Compare your behind-the-scenes life to others’ curated highlight reel.
Discount adaptation, assuming every problem will persist and every advantage will remain
Use uncertainty as an excuse for inaction.
Judge other people at their worst and yourself at your best.
Assume learning is complete upon your last day of school.
View patience as laziness.
Use money as a scorecard instead of a tool.
View loyalty (to those who deserve it) as servitude.
Adjust your willingness to believe something by how much you want and need it to be true.
Be tribal, view everything as a battle for social hierarchy.
Have no sense of your own tendency to regret.
Only learn from your own experiences.
Make friends with people whose morals you know are beneath your own.
