Announcing XFactor Ventures 2: Female Founders investing in the next generation of female founders


Earlier today we launched XFactor Ventures 2, a pre-seed and seed stage venture fund.  The fund, with just under $9M in commitments including a portion from Flybridge, will invest $150K each in 50+ pre-seed and seed stage companies with female founders targeting billion-dollar opportunities.  

When Anna Palmer, the rest of the Flybridge team, and I had the idea for XFactor Ventures two years ago our thesis was a simple one: putting checkbooks in the hands of existing female founders would not only help more new female founders launch and build their companies but also give the investment partners the opportunity to translate their deep networks and expertise into strong investment returns.  Despite launching with a lot of confidence, we had no idea that it would succeed to the extent it has.  As a result, we are pleased to scale up the effort – more investment partners, more companies, and more capital per company – with XFactor Ventures 2!

Kristen Watson ©
Obligatory co-founder photo in an alley

XFactor was formed out of a combination of frustration and belief.  Frustration that less than 20% of the companies receiving venture capital funding have female founders and a belief that companies with diverse founding teams will outperform in the market.  We also had a hypothesis that our unique investment team, which is comprised entirely of existing female founders of venture-backed companies would be in a preferred position to identify new high-potential, early-stage, companies with female founders and to provide not just capital, but also mentorship, guidance, and connections to help our portfolio companies succeed.

In keeping with our entrepreneurial spirit, the first fund was a true MVP that we stood up fast, learned from, iterated on quickly, and succeeded with, resulting in investments behind 28 companies, all with passionate, persistent, and talented female founders.  We thought that there was a gender gap in the venture industry, but the level of interest and the quality of investment opportunities blew us away.  This portfolio was selected from the nearly 1,500 new companies we have been introduced to and is diverse across sectors (54% B2B, 25% B2C, and the rest health-tech and FinTech) and geographies (36% Bay Area, 32% NYC, the rest are based in 9 other cities).

As we formed XFactor 2 to build on this success, we had three goals.  We wanted to:

  1. Expand our investment team to address sectors and geographies in which we see significant opportunities
  2. Support more companies
  3. Invest more capital behind each new company.   

The investment team for XFactor 2 will have 23 investing and operating partners, up from 10 in XFactor 1.  All the investment partners, other than me, are existing female-founders of venture-backed companies.  Immodestly, the team is impressive and I am regularly wowed by the depth of their expertise, their passion for our mission, and the breadth of their network. Our portfolio companies, as a result, benefit not only from the skills and mentorship of their specific sponsoring partner but also from the collective talents and experiences of the team.  The full investment team is listed below and linked here, but the high-level details include:

  • The partners have founded 28 companies that employ thousands of people and have raised over $550 million in venture capital.  The new team includes 5 YC grads, 5 Fortune 30 under 30s, 9 MBAs, 2 JDs, 2 published authors, 1 PhD, and 1 Emmy award-winning producer.
  • We added an entirely new team in Los Angeles, a vibrant and female-founder friendly market, and a partner in Seattle, Amy Nelson, who is also, excitingly, the founder of an XFactor 1 portfolio company.  As a result, we now have a strong nationwide presence in the Bay Area, Boston, Denver, Los Angeles, New York, and Seattle.
Hello LA! XFactor team members Amy Nelson, Trina Spear, Nanxi Liu, Heather Hasson, Jilliene Helman (not pictured LA partner Joanna McFarland)
  • We added expertise in the healthcare, FinTech, AgTech and frontier tech sectors.  
  • Finally, all of the partners in XFactor 1 are returning to XFactor 2.

Our investment model in XFactor 2 will remain the same.  We seek to back the most promising female-founded companies pursuing billion-dollar opportunities. We invest early, like to be the “first-check”, and will make a one-time investment of $150K, up from $100K in XFactor 1, in each company.  We do not make follow-on investments as we have found this allows us to be the “first-call”, forming open, honest, and trusting relationships with our founders. With committed capital of $8.6M, XFactor 2 will invest in 53 companies, up from 29 in XFactor 1.  

Despite these changes and growth, what remains are the frustrations and core beliefs.  Female founders are still woefully underrepresented in the ranks of companies receiving venture capital funding.  In 2018 17% of venture dollars globally went to companies with female founders, and at the seed stage, for the last five quarters, just under 20% of total funding went to companies with female founders. Depressingly, this is largely unchanged since we launched in 2017.

Our conviction for the investment opportunity has only increased over the past 2 years.  We are constantly wowed by the tenacity, resilience, and talents of the female founders we see starting companies and, of course, particularly love the 28 founding teams we have supported with our first fund.  The opportunities being pursued by female founders are diverse and don’t conform to stereotypes. We have in our portfolio AI companies, FinTech companies, SaaS companies, Healthcare and medical companies, Future of Work platforms, Consumer apps, and E-commerce brands, and all are seeking to build businesses of significant scale.  We are excited and thrilled to have the opportunity to back an additional 50+ such inspirational teams from XFactor 2 in the coming years.

Are you a female founder with the XFactor looking for funding? If so — we want to talk to you! Please find us online at, follow us on Twitter or reach out via email to  As we believe in diversity and inclusion of all people, of all genders, races, ethnicities, sexual orientations, educational backgrounds, religions, abilities, socioeconomic backgrounds, immigration statuses, and more, we will review and consider all opportunities that meet our investment criteria.

Front (L to R): Nicole Sanchez, Amanda Bradford, Allison Kopf, Anna Palmer, Danielle Morrill, Aihiui Ong. Back (L to R): Jules Pieri, Jessica Mah, Mada Seghete, Joanna McFarland, Aubrie Pagano, Natalya Bailey

The investing and operating partners in XFactor Ventures 2 are:

Bay Area

  • Erica Brescia, Co-Founder & COO, Bitnami (B2B tech expertise) 

  • Ooshma Garg, Co-Founder & CEO, Gobble (B2C expertise)
  • Aihui Ong, Founder & CEO, EdgiLife (B2B2C and CPG expertise)
  • Mada Seghete, Co-Founder & CMO, (B2B and mobile expertise)
  • Amanda Bradford, Founder & CEO, The League (B2C and mobile expertise)
  • Liz Whitman, Co-Founder & CEO, Manicube, President Red Door (B2C expertise)


  • Anna Palmer, Co-Founder & CEO, Dough and Fashion Project (B2C expertise)
  • Chip Hazard, Co-Founder & General Partner, Flybridge Capital  (B2B expertise)
  • Jules Pieri, Co-Founder & CEO, The Grommet (B2C expertise)
  • Natalya Bailey, Co-Founder & CEO, Accion Systems (Deep tech expertise)


  • Danielle Morrill, Co-Founder & CEO, Mattermark; GM, Meltano at GitLab Inc. (B2B expertise)

Los Angeles

  • Jilliene Helman, Co-Founder & CEO, Realty Mogul (FinTech expertise)
  • Joanna McFarland, Co-Founder & CEO, HopSkipDrive (B2G and B2C expertise)
  • Nanxi Liu, Founder & CEO, Enplug (Deep tech expertise)
  • Trina Spear & Heather Hasson, Co-Founders & Co-CEOs, FIGS (B2C and Healthcare expertise)
  • Jessica Mah, Co-Founder & CEO, InDinero (B2B and Fintech expertise)

New York

  • Allison Kopf, Founder & CEO, Artemis (AgTech expertise)
  • Aubrie Pagano, Co-Founder & CEO, Bow and Drape (B2C expertise) 

  • Kate Ryder, Founder & CEO, Maven (Healthcare expertise)
  • Nicole Sanchez, Founder & CEO, CreditHero (FinTech expertise)
  • Kathryn Minshew, Co-Founder & CEO, The Muse (B2C and B2B expertise) 


  • Amy Nelson, Founder & CEO, The Riveter (B2C and B2B expertise)

Applied AI: Beyond The Algorithms

One of the primary areas of focus for Flybridge over the years has been to be the first institutional investor behind companies looking to transform the enterprise technology landscape with modern software.  Given the explosion in the volume of data being generated globally, this theme has led to investments in companies such as MongoDB (databases) and Nasuni (storage) that operate at the data infrastructure layer of the enterprise tech stack.

More recently, we have been investing in further advances in data management, analytics, machine learning, and artificial intelligence.  While the potential for artificial intelligence has been written about extensively, what is less well understood is that the algorithms and underlying tools are only a fraction of the value and are unlikely to be a source of long-term differentiation.  Fully realizing the power of AI requires a deep understanding of the domain and the specific workflows that AI will seek to improve and optimize. In other words, the application layer of AI ultimately drives the business value. And we believe the window of opportunity for the AI application layer is now.

This evolution from platform to applications is not an uncommon one: when a new technology platform is immature and not well understood, there is a lot of room for innovation at the underlying technology layer, but as the platforms mature the application layer is where value accrues.  For example, in the PC era, once Windows and its associated tools were well developed, apps accumulated a huge amount of value; and similarly in the early Internet era, once the browser, server, and app server infrastructure were well established, apps became super valuable.

This “Applied AI” investment thesis resulted in four new investments by Flybridge in 2018: Aiera, which is using AI to drive fundamental equity analysis; Kebotix, which is using AI to discover and create advanced chemicals and materials; Looka, which is an AI-powered graphic design platform; and Proscia, an AI-powered digital pathology solution.  We also made follow-on investments in our existing, scaling, Applied AI portfolio companies Bitsight, Bowery Farming, Datalogue, and DataXu.  

Given the breadth of this emerging portfolio, we thought it would be helpful to expand on what we look for in Applied AI companies and some of the keys to success in building a company in this exciting field.  We believe that it is important to:

  1. Go Old School. Opportunities for Applied AI companies lie outside of the markets targeted by traditional web-scale companies.  As Andrew Ng recently observed, “a lot of the stories to be told next year [2019] will be in AI applications outside the software industry”.  Many of these markets, such as Real Estate, Finance, Healthcare, Oil & Gas, Agriculture, Manufacturing, and Logistics, have the advantages of being A) extremely large, B) where innovative AI driven approaches can drive massive levels of improvement versus the status quo, C) where potential customers may not be able to access AI talent on their own, such that build versus buy is less attractive, and D) not being an area of focus of the Googles and Baidus of the world where their massive troves of data can be a source of significant competitive advantage.  
  2. Combine Talents. The most successful teams in Applied AI will have a unique combination of an understanding of the domain and the technical capabilities to realize the vision.  Even more specifically, it is doubly helpful if one of the founders was formerly a practitioner in the field. For example, Dawson Whitfield, the founder of LogoJoy, was previously a top-notch graphic designer himself; Ken Sena, was a top-ranked equity analyst before founding Aiera, and Kebotix co-founder Professor Alan Aspuru-Guzik holds a Ph.D. in Chemistry and is a leader in the field of computational chemistry. In other words, AI experts will do better when teamed up with someone that comes from the field in which they are seeking to operate.
  3. Drive continued technical innovation.  Given how quickly the field is advancing, a deeply technical co-founder who is up to speed on, and willing to continually learn about the latest advances in AI, and see the application of new approaches to the problems their company is seeking to solve is essential.  Whether it is “few-shot” learning approaches, ensemble models, GANs, CNNs, transfer learning, explainability, and a myriad of other developing techniques, knowing and understanding the strengths, weaknesses, and applicability of different approaches is critical. We often see the domain expert mentioned in point 2) renting or borrowing their AI expertise in the form of advisors and part-time experts, but this approach is not good enough given the need to have a tight feedback loop between market-driven customer needs and the AI-driven technology insights and art of the possible.
  4. Create Data Network Effects. The most successful companies will have a clear understanding and angle on how to start and continue to spin the data network effects flywheel.  Generally, this requires having access to initial datasets that can begin the model building process, and a well thought out and focused strategy on how to increase the quantities of data available for analysis.  The initial data sets, which Proscia refers to as “inorganic data”, might be acquired, and are used to overcome the cold start problem of training a new model from scratch. In contrast, “organic data” that comes from the ongoing use of the platform can help hone and refine the algorithms over time.  Taken together, this means the cost of data should decline over time because organic data is typically free (or even negative if you can get users to pay you for the service delivered while the data is collected). In the pursuit of data, it is important to remember that the sheer volume is not always inherently better. Yes, size matters, but quality matters more.  The data should reflect the real-world variance and dimensionality of the problem space and a data strategy should incorporate input from domain experts to identify the logical gaps that exist and how to fill those gaps. Further, when assessing a technical team (per point 3), we believe it is important that they know how to build an AI infrastructure that can be monitored for changing performance and updated accordingly as the scale and scope of the datasets increase.  For example, when Aiera first started making buy-sell calls on stocks, they only did so on 16 companies based on a model that analyzed 10,000 documents a week from 300 data sources. Today, they cover nearly 2,000 securities with a model that analyzes 500,000 documents a week from 22,000 data sources. Perhaps not surprisingly, the accuracy, breadth, and duration of their buy-sell calls increased significantly over this time.
  5. Absorb The Algorithm. The specific algorithms and AI techniques themselves are not the sources of defensible value so the most successful AI companies will instantiate the models into a broader application that fits into customer’s broadly defined workflows.  We call this “Absorbable AI”, which means customers can incorporate the AI into their business and realize the benefits of the operational insights. Successful AI Applications need to not only explain why the model is generating certain results (and, importantly, explainability also helps understand and highlight bias such as gender-based or racial bias), but also integrate into the customer’s business in a logical and systematic way.  It’s also important to manage expectations along the way, as many Applied AI use cases falter based on overselling the potential and customers expecting too much when in reality improvements come incrementally over time. These application and process skills are often found coming from the more traditional application development space in fields such as UX, visualization, workflow/BPM, and integrations.
  6. Craft the Business Model. Thinking through the business model of a company is critically important.  Depending on the domain, the openness of customers to new approaches, their willingness to pay for innovation, and the scale of the level of AI being incorporated, the best way to realize the value of an Applied AI company could be by selling an application that makes human work more efficient and accurate–an end-to-end automation stack that replaces humans–or it could be by selling a complete product.  For example, our indoor farming portfolio company, Bowery, leverages a significant amount of AI to drive efficiency and quality in their operation, but they decided the best way to realize the value of that AI was to sell incredibly tasty, locally grown, pesticide-free green vegetables versus selling an AI-powered Farm Operating System to other growers. A similar example would be Tesla, where the vision is the sell a complete, AI-powered, autonomous vehicle, as opposed to say Cruise, which chose to realize the value in selling the application (and the company) to other automobile producers.  

Points 5 and 6 can be better visualized in the following matrix:


With the continued explosion of data availability, advances in AI techniques, and the accessibility and performance of computing (GPU) cycles, we believe the trend of AI as the next great application enabler will continue for some time, and we look forward to finding more Applied AI companies with passionate domain experts and technical founders to invest behind in the coming year.

Thanks to my partner Jeff Bussgang, our advisor Harini Suresh, David West of Proscia, and Bryan Healey of Aiera for their input and insights in developing these thoughts.

Fired Up By a Flybridge Family Reunion


Last week there was an interesting piece of news in the tech world when Firebase/Google acquired the Fabric product line and team from Twitter. Over 20+ years in the venture industry and hundreds of companies, this was a first for me: two companies we had invested in merged post their respective acquisitions by larger players. While it was unusual, nothing could make me happier than to see Crashlytics, which was acquired by Twitter in 2013, and Firebase, which was acquired by Google in 2014, join forces and continue the missions they held from their founding, and our original seed investments, of improving the lives and effectiveness of mobile developers. Huge congrats to Andrew, James, Jeff and Wayne and many thanks to both Twitter and Google for their support of both companies and allowing them to deliver on their common goals.

Rewind the clock to 2011. At the time my core investment focus was on developer-driven cloud platforms and an insight that companies that went to market with products that delighted developers could achieve significant adoption and break down many of the barriers seen by companies that focused on more traditional enterprise sales models. Within this broader theme (that also led to our investments in companies such as MongoDB, Stormpath and Apiary), it was clear at that time that the mobile developer was the new rock-star and thought leader and that most new application development spend was for mobile apps. But while mobile developers were leading the way, it was still too hard and technically challenging to quickly and easily get high quality apps into the market. Over the next year, this thesis led us to make seed investments behind two phenomenal teams. Both companies started out focused on very different markets – Crashlytics on crash reporting and Firebase on a platform to allow developers to easily build serverless back-end platforms. But both had a common goal of creating innovative technologies to help developers create amazing apps.

It’s always interesting to look back on your investment successes to see what if any common traits they shared. For Firebase and Crashlytics, there were many:

  • Founded by young, passionate entrepreneurs[i] who had strong technology backgrounds, startup experience and an innate understanding of their target customer who were
  • Creating platforms that addressed large and expanding markets with a
  • Special “developer first” approach to working with the developer community that led to rapid adoption of their platforms which led them to
  • Blow away their seed round metrics within less than a year and in turn raise Series A Rounds led by their initial investors and ultimately to being
  • Acquired by leading technology companies less than a year after Series A rounds and these
  • Acquirers provided significant incremental resources, let the teams run independently and continue to innovate under their own brands post the acquisitions

I could not be more proud of both of these teams. With millions of apps and hundreds of thousands of developers now using their technologies, what each has built is exceptional and I am 100% sure, will only get better as they join forces.

[i] It should also be noted in this time of anti-immigrant sentiment that in each company one of founders was born outside the US