Reflections on the first year of Shaper Capital

One year ago, I launched Shaper Capital to build companies that solve for data fragmentation in a wide range of industries.

I’m incredibly proud of our progress. In one year, we’ve co-founded 6 companies (each with an incredible CEO and team), built a team of ~70 incredible Shapers across these companies, and announced a number of exciting milestones/launches (examples here, here, here, and here).

As we hit this anniversary, here are a few reflections on things I’ve learned in the last year.

1. This is one of the best times ever to start a company. I believe that the AI moment is real, and that we should approach startups like it’s 1995 (on the precipice of the dot-com boom): every industry is going to see radical change and major opportunities in the next five years, and the economy as a whole will see a major tailwind. Meanwhile, there’s great talent on the market, and capital markets are neither insanely frothy nor shuttered. It’s a great time to be building.
2. Learn to fail quickly. While we have 6 companies in our portfolio today, we actually launched 8 companies, and folded 2 of them within six months of launch. I saw these “failures” as extremely valuable — I learned about an industry and took a high-upside swing. By failing quickly, we didn’t waste much time or capital, and the situation could play out gracefully with everyone involved; in fact, I would repeat the experience on those two businesses, as they were valuable swings.
3. The World Wants to Put You in a Bucket. People love simple models they understand; everyone wants to categorize you and put you in a bucket. I’ve had countless people try to classify Shaper as a “VC fund,” a “venture studio,” or a “family office” — and make lots of incorrect assumptions about what we’re building. Instead of being attracted to a particular label, I’m focused on building great data fragmentation businesses, which involve a mixture of sweat leaning into a business, recruiting great co-founders, and deploying capital — something that isn’t exactly like any existing model. In general, if you’re easy to classify, you’re probably not thinking big enough; forge your own path, and let them try to find a bucket later.
4. Beware VC Dogma on Business Models. VCs can often fall into groupthink, and I’m especially skeptical of the groupthink on business models. According to most VCs, all revenue should be recurring — except that the Facebook, Google, Amazon, and Apple core business models are non-recurring; the reality is, you should price the way your clients want to buy as a first principle. According to most VCs, services revenue is nowhere near as valuable as product revenue — except at Fractional AI (a services company), I expect we’ll have better margins and more defensibility than most product companies; the reality is, you should create something that customers need that has defensibility and reasonable margins. Don’t let traditional VC dogma dictate your business model; let the problem you’re solving and the customers you’re serving guide you alongside economic first principles.
5. Beware the Siren Song of Fundraising. In the early stages, founders are hungry for signals of winning — finding product-market fit and getting the business off the ground is HARD. Raising money is often mistaken for success; headlines tout massive rounds and high valuations, and the CEO gets an ego boost. Instead, fundraising should be viewed as a tool to be used when necessary, not a trophy. Every dollar raised comes with expectations and distractions (and obviously, dilution). Entrepreneurs should spend every ounce of focus they can on what matters — solving big problems, achieving product-market fit, and building a team that can execute relentlessly.
6. Nobody Has Figured Out a Solid Location Strategy. Remote? Hybrid? In-office? The reality is, there isn’t a one-size-fits-all answer, and nobody knows yet. At Shaper, we’re experimenting across the board — it’s up to each CEO how they want to run their business. Some teams thrive by recruiting the best people wherever they reside; others need the energy of being together; it’s not clear to me that one model is inherently better than the other. The key is to have a clear strategy with intentionality, and figure out how to solve for the negatives and edge cases that come with any model.
7. Recruiting is the Most Important Determinant of Success. The most important thing I spend my time on is recruiting. From finding the right co-founders to getting the right early engineers to taking bets on high-potential generalists, talent makes or breaks all of our portfolio companies. Striving for more talent density and getting exceptional talent in the door is a never-ending pursuit, and it’s an even bigger bottleneck the more successful a company is. It’s why we’re obsessive about hiring the best and ensuring they are in roles where they will have the greatest impact. For most startup leaders, if you’re not spending at least 20% of your time recruiting, you’re not building for the long term.
8. There really is a consistent playbook for data fragmentation across industries. When I started Shaper, a big part of my thesis was that Datavant and LiveRamp followed shockingly similar playbooks in very different industries, and I thought that thesis could be applicable in many more industries. I continue to be amazed by how true this is: we’re seeing data fragmentation as one of the biggest bottlenecks in extremely different industries ranging from supply chain, to national security, or AI training; and it turns out many of the approaches to solve for this are consistent from industry to industry. I’m excited by how many great data fragmentation businesses there are to be built in the near future.
9. M&A can be a great tool in the early days. Mergers and acquisitions can get a bad rap in the startup world — sometimes seen as a desperate move to boost short-term numbers. But thoughtful M&A can be a shortcut to scale. Take Arbital Health: when we chose to acquire Santa Barbara Actuaries, it wasn’t about getting a quick win; it was about doubling down on Arbital’s core aspiration to become a neutral adjudicator in healthcare. The M&A was not about adding more to the top line; it was about strengthening the core thesis and getting our flywheel going more quickly. On the flipside, this only works if there’s a strong “bias to no” — a good startup should default to organic growth but look at M&A as a possible lever.
10. A track record of win-win-wins creates repeatability of success. I believe that the key to long term success for Shaper is to create a track record of win-wins: in ten years, my hope is that every CEO who partnered with Shaper feels like they won through working together, investors that invested in Shaper-built companies all realized great returns, employees had great personal outcomes, and obviously I hope Shaper has a successful outcome. Establishing a track record of people winning by partnering with me has been a key ethos in my career to date, and has led to many great repeated relationships: several of the early Shaper employees worked with me at LiveRamp, and several of the investors in Shaper companies have invested in me in the past. By continuing to find win-wins, we create the reputation that sets us up for our next successes.

Thank you to the Shaper team for everything we’ve accomplished in our first year, and I can only imagine what we will build in the year ahead!

The Arbital Health team offsite

The Fractional AI team bowling

A Protege team meeting