Link to podcast. Link to transcript (organised by me). 27th September 2023

Sajith: Fascinating glimpse into an unusually-designed venture firm, structured like a company more than a fund, and one that is designed to look at sourcing and support not like the traditional venture fund, but more via analysing datasets and extracting signals from them, to both find (source), and then mind (support) these companies. Think Bridgewater but for venture. Reminded me a bit of 776 (Fund) founded by Alexis Ohanian, though the difference is that 776 doesn’t source the way a SignalFire does, though 776 does build products, like SignalFire. The podcast episode has some fascinating passage. One which stood out was how, if YC = Stanford U, then SignalFire is like Stanford Hospital, which is both a treatment + teaching centre. Another one was how it is difficult for analog funds to transform themselves into quant-style funds like Signalfire, and how SignalFire designed principles such as equal status for everyone (both investors and tech), no deal attribution, etc., to reinforce their strategic approach.  Finally, a fascinating tidbit: the data-led approach meant high costs on items that you don’t seen in a traditional venture fund such as AWS (which was half its management fee on its $50m fund; likely $500k, given management fee of $1m, usually 2% of fund size) and credit card data sets (which was more than the management fee of $1m)!


SignalFire’s origin story

Chris: “We interviewed about 170 different funds and fund types, everything from accelerators to incubators to venture funds to quant hedge funds to corporate venture groups etc., and looked at how do you build systematic advantage through data, workflow software, etc. No different than an operating company would benefit their sales team, and we actually took a lot more pages out of the corporate playbook than we did out of the Venture playbook in the initial ideation. “

“…so we looked at it from a systems approach where if we have deal team members that are out hunting and trying to find the right entrepreneurs, why don’t you give them qualified leads as a much more efficient way to focus their time rather than set up shop in this local Starbucks and say, I’m open for office hours, which just is insane to me.And we wanted to build a firm with structural advantage. My vision was to build the absolute best platform for the next generation of great investment talent to have the most leverage and maximise their odds of success. And if you could do that, you’d attract the best talent because why would you start somewhere else that just had brand, historical network, etc. “

“…we broke the venture process into five core functions: sourcing, picking, winning, portfolio support and portfolio construction, and then systematically applied the first principles of how would you apply data, workflow collaboration, networks to each of those functions to do them sustainably in a better way than the venture industry had done historically.”

“And then we have put the team members into swim lanes and they’re getting fire hose of leads alerts that are tracking from these millions of companies…we’re giving highly qualified SDR-type leads to those deal team members to then go pursue the companies in their swim lanes. “

How they structured SignalFire

Chris: “If you think of Y Combinator’s design like Stanford, you apply through an application and then run it through an algorithm, they then pull out GPA and test score equivalent characteristics of founders, put a human loop and interview them, buy a very cheap option, put them through a one to many support program, cherry-pick their best companies, bring in an optimised fundraising environment, and then let hundreds of companies go at that point to in a Darwinian type fashion with the venture industry supporting the strongest ones over long periods of time. They’re designed like a university to be one to in many ways we’re designed. If they’re Stanford, we’re Stanford Hospital. We’re a part teaching institution, part care institution. So we do hundreds of events a year for our portfolio companies as, but it’s the grad school version. We have a sales mastery series, a market mastery series, a talent mastery series, A people mastery series, which is more the HR function and recruiting functions, etc. And then we have founder development programs and all sorts of things for scale, and then we have a network of a hundred plus advisors that are specialists. So, as a GP, I’m a general practitioner. I’m not the Swiss army knife to use to play that hospital analogy, but then I refer to a phlebotomist, pediatrist, echo cardiologist. We just have it all in network.”

“If you look at the top quant hedge funds, because of the way that they approach the business, the way that they recruit, the way that they construct a portfolio is radically different than a traditional analog hedge fund or mutual fund. There’s only so many companies a human can analyse deeply in order to have conviction, and so they build much more concentrated portfolios and have a very different approach. The reason the hedge funds that are quantitative tend to persist over much longer periods of time is they’re taking many more micro risks and taking advantage of many more arbitrages over a much larger portfolio because they’re rules and data-driven as opposed to manual. So they may be in and out of a position in seconds, let alone by the end of the day, whereas many of the hedge funds are much longer duration because the opportunity cost of analysing that company deeply takes a long time.”

“If you look at the quant hedge fund world, not a single one of the top quant hedge funds started as an analog fund and converted. They all started de novo as quant funds because Fidelity is not thinking about how to run fibre optics from Chicago to New York to shave milliseconds off a trade, or how do I co-locate to the closest server to the exchange to shave another millisecond or how do I ingest that Goldman analyst report using NLP? So I can trade off sentiment before you can read the first sentence as a human. You build different structural advantages as a result of that it’s very hard for companies to start transforming.

We see in the technology industry all the time to move from one paradigm to a modern technology stack or an AI driven approach versus a sort of traditional SaaS approach, whatever it may be, it’s very hard to reinvent yourself because in the quant hedge funds, the quants are the kings. In the traditional analog world it is the portfolio managers or the analyst or whatever it is, and if you look at the venture world, it’s the founder facing deal partner gets all the heat and light.

And the data guys or the IT guy is in the broom closet. And they’re looking at getting any light, let alone free lunch. This is why from day one, we made everyone at SignalFire on the investment team. Everyone on the entire firm gets carry. We tell everyone on portfolio support, you are a feedback Luke on these companies and can help inform investment decisions. They may not be deeply involved in the first investment, but they’re very deeply involved in follow-up investments in those decisions. How are they building their team? How are they converting customer leads? What is their learning trajectory? Because those are all leading indicators of ultimately how a company will thrive and the engineers and data scientists are involved in the entire process. They source leads, they can help with diligence and they do consulting advisory work to the portfolio to keep it interesting, but also to elevate them to their deserved stature as equals within these firms.

From day one, we had no deal attribution. We did a lot of things structurally to make sure that they understood that this is a firm that deeply appreciated and cared about equally what they were doing on the engineering side as the deal team was doing on the founder-facing side. “

“On the sourcing side, you’re looking at not necessarily the outcomes, but are these leads of companies that are high fidelity with companies that get invested by high-quality investors upstream? If they are, I want to see that lead. Even if we choose not to invest, I want to see that lead in a window of time that I could have invested and led the deal. It doesn’t help me to get an alert after one of my peers has led the investment. That’s a tombstone of a missed opportunity. That’s not a helpful signal. 

On the portfolio support side are what’s the engagement? It’s very much like a consumer enterprise app. Over 65% of our companies are on our platform on a weekly basis, so you can look at utilisation rates, this is product market fit. The same things that we look at for a SaaS company apply to ourselves and what is the output of this. We use humans to train our founders and their teams on how to recruit and best practices and interviewing and comm studies and all this kind of stuff and techniques of sourcing, but then we also give them sonar to make sure they’re fishing in the right waters and give them leads on who’s actionable right now, who’s in market, who’s showing signals of that, who’s high quality, those types of things.”

“So, there are very proven playbooks that we can use to make sure that we’re making efficient higher ROI investments and we sunset products if it’s not getting traction no differently than a company would and making sure that we then reallocate resources on things that are higher impact for our portfolio companies and then the world changes. It was all recruiting before the financial correction, but now it’s on sales efficiency and the other types of things is companies have to make better use of the resources that they have. We have to adapt with the times and constantly be challenging ourselves and asking the hard questions. Are we getting the ROI? “

Why more VCs haven’t adopted the SignalFire model

Chris: “…on our first fund of $50 million, our AWS bill was half our management fee. Our credit card data set was more than the entire management fee and we hadn’t hired anyone yet. And so we had to do corporate advisory and recruiting and all sorts of different things to have other sources of revenue outside of management fee and we had to raise money from day one.”

“In the early years at a five x or so multiple of management fee in order to take these losses on this bet that we were making, that we could build a platform that would be much more systematic and institutionalised than your typical venture fund. But then you have a chicken the egg problem because no LP wants to jump into some new quantitative approach to venture if you haven’t built the platform yet, but you can’t pay for the platform if you haven’t raised the capital yet. And so how do you balance these two things? And so we had to be really creative and scrappy and the team did an exceptional job of having multiple jobs, VCs by day and management consultants by night to the Fortune 100 on. We did everything from merger and acquisition research to post-merger integration work to competitive intelligence to hedge funds, quant hedge funds, operating companies, etc.

We had many of the Fortune 100 as clients in the early days in order to keep the lights on in order to amortise the expense of the data. And most people that come into venture want to be venture capitalists. They don’t want to do all those things. It’s a really hard chicken and egg problem, but we somehow managed to get through it and I think a lot of other people haven’t done that. You’ve seen Google Ventures or Coatue or people that were existing entities that had a quantitative orientation to them do permutations of this, but it’s really hard to bootstrap from nothing. But then the irony is that if you don’t start it from nothing and you try to append it to an existing organisation, as I’ve mentioned with hedge funds, it’s very hard to make that transformation. And so the incumbents have really struggled to do it and unless you had other revenue streams that could deficit spend for a substantial period of time, it’s hard to bootstrap off the ground. And that’s what’s constrained the amount of competition.”

Hiring / Recruiting

Chris: “One of my early learnings in recruiting was looking for people who were already doing the job on their own as a side hustle or whatever but didn’t realise that this even existed. And when they found that this existed, they were all in, they were predisposed to do it even before I had to sell it…The hard work was finding them not convincing them that the data could give them advantage in this world. And the same was true of LPs.”