Office Hours: I'm an investor at Founders Fund and was the first product manager at Scale AI. I'm Leigh Marie Braswell.Featured

Hi everyone! I’m Leigh Marie Braswell, an investor at Founders Fund. I was previously the first product manager at Scale AI. I graduated from MIT with a degree in math and computer science.Ask me anything about product, scaling companies, early stage investing, fundraising, and more!
Thanks so much for joining us @Lemur!Elphas – please ask @Lemur your questions before Friday, July 9th. @Lemur may not have time to answer every questions, so emoji upvote your favorites 🔥👍🏾➕
Whats your view on AI for SMBs. How they can adopt it and how they can take advantage of it especially in the marketing/automations side?
Had such a good time! Hope my answers were helpful.
Thank you so much for taking the time to share your experience with us, Leigh! I understand that as part of Founders Fund's thesis to invest in groundbreaking science/engineering it has invested in several hard science/tech companies (SpaceX, Solugen, etc). In your experience, how do VCs with experience investing in hard tech / deep science help or engage with scientific companies/founders differently than investors without such experience? Of these distinctions, which tend to have the biggest impact on science-based companies?(For context, I have a mechanical engineering background and am currently running a YC-backed advanced materials startup).
Ultimately when you pick an investor with a track-record of investing in a particular type of company (especially deep tech) they will have more empathy and context about the challenges you will face, and can probably give better advice on average. At FF, we are likely to ask you better questions / give better recommendations about e.g. biotech or space or robotics company-building than a VC that has no experience with companies in that area. If you're selling to the US gov, I'd strongly recommend jamming with FF given the huge amount of upfront context required to be not-unhelpful there. However, I'd caution against banking on VCs having a "big impact" on a company, regardless of their background. In general, I think of VCs as being good sounding boards for ideas, can helping with branding, and helping with hiring (my proudest angel investor moments were making good hiring matches). Any promise past that (e.g. literally helping you build your company), I'm a lot more skeptical about, especially from talking to founder friends.
Hi! Thank you for answering these questions!What is your framework for investing in early stage companies? Would love to hear your investment criteria and what you're looking for
First as a disclaimer - Investment frameworks in general change based on the firm or individual you’re talking to. My frameworks as an angel investor while I worked at Scale are different from those that I use at Founders Fund (FF), and different VCs care about a wide variety of factors when considering investment in a startup. That said, FF has one of the more straightforward “theses”-- really, the only thesis across the whole firm (for any type or stage of company from pre-seed to pre-IPO) is to invest in insanely talented founders. Founders who live and breathe the mission of their company and can inspire others to do the same. Founders who have the technical & business acumen to execute on an ambitious vision. Founders who will be able to guide their company through the inevitable challenges it will face at different stages of growth. Across all of the venture firms, FF is uniquely focused on the founder because of our own founding ethos to always be on the founder(s)’ side(s), never overruling a decision and absolutely never displacing a founder. We ultimately believe founders can run their own companies much better than any VC can- if any VC tells you differently, please run away...However, I’d be lying if I said that’s all the team at FF looks for; we each have our own unique viewpoints on what else an early stage company should have in addition to a strong founding individual or team. Most of our personal frameworks change based on stage. I’ll give some insights into my own for a general type of company. If a company is operating in an area where I have expertise (dev / data / ML tooling & applications) and I really agree with their strategy, they might be able to speed through some of these questions & diligence. At the seed stage, I’m mostly looking for founder strength, since I know there will certainly be strategy pivots in the journey to product market fit (PMF). In addition to that, I’m looking for some “unfair advantage” or “secret” (explanation here[1]) the founder/team has or knows - why are they going to be the company who creates/wins this massive market, and how will they create a moat (great reading on the possible moats here[2])? At seed it’s all about the story, since you just don’t have many data points yet (though if you have some data points, it can make the story more convincing). My advice for founders raising their seed is to practice telling that story over and over and over again; you need to have a confident & compelling pitch that answers my questions above in under 30 minutes. My husband is a startup founder & naturally a great public speaker, but he spent weeks preparing and practicing his pitch before he went out to VCs to raise. He now uses the same pitch/slides recruiting his early team. Practice makes perfect and will be helpful down the road!At Series A, I’m usually looking for quantitative signs of PMF. There’s great recent posts here[3] and here[4] on what that looks like. For enterprise software businesses in particular, you need evidence on the pain point your product solves & willingness of customers to pay to solve that pain point. For consumer, marketplace, or hardware businesses, the milestones are likely different & around engagement, demand/supply liquidity, or technical progress (special case here for a pre-launch product). This is also the stage where I might start doing/reading customer calls through a third party (GLG, Alphasights, Tegus) to talk to your customers & hear how “mission critical” they consider your solution, especially in a competitive market. I might even involve my technical network for sanity checking assumptions about the industry.Post Series A, you enter the world of deeper financial diligence. I’m definitely asking for a “data room” at this stage which is a folder where I can look at your margins, LTV / CAC, and customer data to calculate cohort retention. I need to be convinced that we can “pour fuel on the fire” (give you lots of $$$) and see expected results. I also need to be fully convinced that the market isn't just "massive" but quantifiably so (TAM/SAM). This is the area that I personally-- coming from a PM/eng background-- had to learn the most about (thanks FF team!), but it’s extremely important to make sure FF is investing in truly venture-scalable businesses.If you have any questions about the above or want to jam on pitches in a low stakes environment, more than happy to help you prep! My email is [email protected]. Pitching can be so much harder & more intimidating than it needs to be, and I’d love to help you put your best foot forward.[1] [2][3][4]
Thank you for this detailed response! I learned so much in one thread! I'll definitely reach out to chat more!
Thanks for your time, @LemurWhat are some things your Scale AI team did in the early days to sell to companies when your product was brand new and you were just getting started? Would love to hear about some of the tactics that both scale and don't.Second, what are some of the successful B2B selling tactics your founders are utilizing?
Great question! Not a comprehensive list but a few good examples -Things Scale did in early days that did scale...* Invested a ton of time in designing and implementing a really sleek website. Customers thought we were a bigger team than we were, which definitely helped us win early pilots! This is very important when you’re 10 people selling to customers 100x your size…* Hired technical salespeople. When your first salesperson can do API calls to create live demos themselves, that frees up a lot of technical bandwidth to do other things. Their credibility also helps win deals!* Attended ML conferences. This was the cheat code for getting feedback on our products from a wider group of the ML community, improving our brand, & helped us start so many conversations. Once we realized how important these conferences were to the community, we began to become more involved, and eventually started to help with chats & submitted research papers. Things Scale did in early days that didn’t scale. Caveat - all of this was SO NECESSARY to get us to the next stage and make all of these things scalable! I mostly mention all of these as examples that the “do things that don’t scale” advice is correct & thriving at small startups.* We did labelling tasks ourselves (e.g. drawing bounding boxes) in the earliest days. I am a LiDAR (3D data) annotation expert because if we had a tasker shortage, I’d step in and get the tasks done! This has great scalable consequences of giving us more insight into the labelling tools, operational infrastructure, and how best to train other taskers.* We manually trained and acquired taskers. Insights here helped us create the systems that did a lot of this automatically. This ultimately become one of the core moats of Scale, and as an investor I love to find companies that address challenging operational problems head-on (because they are hard to copy!)* We spent a ton of time with each new hire both interviewing & convincing after the offer (I was no exception myself!). Alex (Scale CEO) writes about this a lot -- the earlier the company, the more time needed to evaluate if someone’s a fit (through technical and importantly comprehensive behavioral interviews) and the more time needed to get people to buy into the vision. As you grow, there are more external data points for validation as well as more structure around onboarding, so this scales better.B2B selling tactics our founders are using - there are many! Some of the above, and a lot of traditional ones (ads, account based marketing, etc.) One trend I’ve been seeing a lot lately is creating a ton of super high quality content to educate customers about a category and to build your own personal brand as an expert. I think a lot of people in the data tooling space in particular- especially given how crowded the ecosystem seems- do this super effectively.
Hi @Lemur! Awesome that you’re doing this AMA. How did you evaluate Scale when you were deciding on opportunities/roles to pursue?What caused you to make the switch to PM?
Scale was definitely a tough decision to make at the time, as it seemed super risky in my current judgement. At the time, many Scale critics thought deep learning - the type of ML that needs a ton of different pieces of human-reviewed data to learn about all possible edge cases - would go away and not be necessary for self driving and other complicated ML problems. In addition, Scale was a tiny team in a small office without the luxuries of a big tech company. However, few main reasons ultimately convinced me to join Scale -- * Faith in Alex (the founder/CEO) & early team - Alex has one of the highest combinations of IQ + EQ that I'd ever seen, and the early team that he assembled was stellar. When you feel like the dumbest person in the room, you should stay in that room.* FOMO - Scale was serving the customer I was most smitten with - the machine learning engineer. The problems I'd be working on were literally creating the standards for part of the ML stack in industry. I knew that bigger company options are less time sensitive & I could likely always go back if things didn't work out. Ultimately, I realized joining Scale wasn't as risky as I thought it was, & I'd always regret passing up such an interesting & educational opportunity. Switching to PM from engineering was motivated by wanting to understand a lot more about how to build a business from all fronts, not just the technical one. Being a PM, you get exposure to sales, marketing, analytics, legal, etc. which was so interesting to me. Also, you usually get closer interactions with the customer, which was my favorite part of being an engineer (understanding what you really need to build).
Thank you for joining us! What were the biggest challenges to scaling Scale? How did you make early decisions to set yourself up for effective scaling?
Touched on many of these in an answer above! Best early decision we made was spending a ton of time on hiring & making sure the people we hired really "gave a shit" about solving insane challenges :)
Thank you!!!
You have such an incredible background! What's the most surprising thing you've learned since switching from operating to VC? And how did you know it was time to transition?
No huge surprises other than how much fun it is! If you love going down intellectual rabbit holes learning about wildly different companies & meeting a ton of new people every day, VC is such an awesome job. I got really into angel investing especially in my friends but also through sourcing during Covid. I was having such an awesome time finding & working with early stage companies that I realized I wanted to become an investor full-time.
Hi @Lemur first off your alias is the name of my home country's most famous animal so big bond already :-) And second how did you transition to venture being in ops? Would love to hear how you connected with the team etc.?
Great question! In my opinion, best way into VC is developing a brand as an angel investor that leads to conversations with VCs at firms you might want to work at. VCs love chatting with operator angels on a regular cadence to share notes and that can lead to them keeping you in mind for potential opportunities at their firm.
@Lemur wonderful, this confirms a lot of the assumptions/feedback I had gotten before :)
Thanks for hosting Office Hours Leigh! Wondering if being a PM at Scale had taught you anything that you've found to be helpful as an investor now!
So many things! Alluded to some in other posts, but ultimately learning firsthand how to grow a startup & develop a moat. My technical background helps me every day for looking at tools for eng / PM as well as providing a perspective on enterprise software at large. I also like to think I'm more helpful / pleasant to talk to given the empathy I have from being at a super early stage startup & knowing the challenges firsthand.
Thank you so much for offering to chat and answer our questions! What decisions do you think made scaling Scale AI more effective? E.g. choice of data infrastructure, team structure and etc. What factors do you think make such decisions effective? While evaluating AI companies for investment at Founders Fund, what do you think are important factors to look at, especially related to data sources, machine learning technology, infrastructure and etc.?
Re: Scale - Alluded to a lot of these in a post below. I think one of the most impactful things is not just team structure but empowering people to make decisions outside of their nominal role - this enables high velocity / iteration which can be life or death for a startup in early days. Re: AI company eval - Important factors I consider for AI companies include: strong & uniquely suited team, scalable product (not too much customization needed, good margins after infra cost factored in), data is available and better yet has exclusive / flywheel characteristics, strategy is sound (if you execute, will create/win the market). I also have some opinions about how the ML ecosystem will shake out and which important companies still need to be built, but I'm saving that for a blog post one of these days. Bonus points if you're in agreement with my thoughts there, but if not my opinions are strong but loosely held :)
Thank you so much for the detailed response! Looking forward to the post about your view on ML ecosystem.
How do you balance/budget training of new behavior with developing for easy adoption in order to create traction?
Seems relatively company-specific, but in general my advice is to watch & listen to your customers and then... do it again and again, and involve your whole team. 0 to 1 (the book) gives a lot of great strategy advice at a higher level about product decisions as well!