Massive winners define great venture capitalists and great venture capital funds. The best investors fully internalize this power-law of venture returns and seek to back companies that can become “outliers”. Massive wins are all that matters in driving outcomes.
A key to investing behind the best companies is to identify macro market trends early and to ride the waves of growth they create. High growth companies need the wind at their back, so investing early behind emerging trends that develop quickly creates an environment for young, growing, businesses to flourish.
To be a great investor, you need to master the cycle of Seeing-Selecting-Winning-Investing-Supporting-Harvesting. “SSWISHing means you need to see many opportunities, select the best ones, win your way into hot deals, support your companies’ growth, and navigate a path to generating liquidity from your investments. Each stage feeds off the others.
The best-expected-value returns are most likely the companies in your portfolio that are killing it, so lean into your winners with more capital. Smart follow-on decisions should be married with a starting portfolio of more, rather than fewer companies, to account for the inevitable randomness in returns and performance.
I would like to thank my Flybridge partners Jeff Bussgang and Keegan Forte; all my XFactor partners, but especially Danielle Morrill, Aihui Ong, and Anna Palmer; the Columbia Business School students in Angela Lee’s class, “Foundations of VC”, that saw an early presentation on this topic; and my family for their collective input to and inspiration for these posts, although as always any mistakes and omissions are all mine.
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!
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:
Expand our investment team to address sectors and geographies in which we see significant opportunities
Support more companies
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.
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 XFactor.ventures, follow us on Twitter or reach out via email to firstname.lastname@example.org. 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.
The investing and operating partners in XFactor Ventures 2 are:
Nobel Prize-winning Economist Robert Solow once quipped that “You can see the computer age everywhere but in the productivity statistics”. This famous observation has become known as the Productivity Paradox – the conundrum that economists face when trying to explain how is it that productivity in the US slowed down just as investments in information technology exploded in the 1980s and 1990s.
Today, we face a similar paradox, the AI Paradox. Just as corporations are investing billions of dollars in AI infrastructure, the actual impact and absorption of AI in the enterprise seems relatively muted.
Earlier this month, the MIT Technology Review published an excellent article entitled “This is why AI has yet to reshape most businesses”. Some of the themes rhymed with our recent post about the potential for Applied AI companies, but also the real adoption barriers. It is striking that, although AI has been a central area of focus for may enterprises for years, actual adoption has lagged the hype. PWC recently conducted a survey of 1000 enterprises that are currently implementing or investigating AI and learned that only 20% plan for enterprise-wide adoption in 2019. Many projects are thus still stuck in pilot purgatory.
This dichotomy between promise and reality points to the power of Applied AI startup companies that focus intensely on the path to the adoption, and absorption, of AI into the enterprise. Internally, we are calling this type of startup “AAA” grade – Absorbable, Applied AI.
Here’s why the approach of AAA startups matters so much in driving AI adoption::
First, as the article points out, the “initial payoff is often modest” of AI projects. Thus, we think it is important for Applied AI companies 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”.
Second, the article mentions how if “companies don’t stop and build connections between such systems, then machine learning will work on just some of their data”, which strikes us as an approach a company focused solely on developing narrower, application-specific solutions can take as they can often derive insights gleaned from data that reflects “the real-world variance and dimensionality of the problem space” and can work aggressively with “input from domain experts to identify the logical gaps that exist and how to fill those gaps”.
Third, as “Ninety percent of the work is actually data extraction, cleansing, normalizing, wrangling”, this provides an advantage to third-party Applied AI companies, as opposed to internal DIY AI efforts, as the third party application provider–which are typically cloud-based platforms that scale across customers—is able to leverage this data wrangling cost across a wide swath of enterprises.
Finally, the article points to several aspects that impede adoption including how end users need to be “attuned to how AI works and where its blind spots are” and how “In order for them to trust its judgments, they needed to have input into how it would work” which is why we feel it is critical for Applied AI companies to combine domain expertise with technical expertise, carefully collect the domain-specific requirements and use cases, use those requirements to “instantiate the models into a broader application that fits into customer’s broadly defined workflows”, and make transparency and explainability a core feature of the application, not an afterthought.
If AI startups can apply these AAA techniques, they are going to continue to achieve superior customer traction as compared to their competitors. And, in doing, attract plenty of attention from AI-focused venture capital firms like Flybridge!
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:
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  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.
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.
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.
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.
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.
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.
This month, Crunchbase released 2018 funding stats for companies with female founders under the encouraging headline; “2018 Sets All-Time High For Investment Dollars Into Female-Founded Startups”. While that statement is factually accurate — $38.9 billion was invested in companies with a female founder in 2018, up from $19.8 billion in 2017, which represents a 3 point increase in the percent of total dollars invested from 14% to 17% — a deeper look is less encouraging.
If you remove the $14 billion Ant Financial financing, just one investment, the dollars decline to $25 billion and the percent of total dollars declines to 13%, which is down year over year. Similarly, by the total number of investments, there was a decline from 15% to 14%. The data from Pitchbook, by way of Fortune, tells a similar story of limited progress.
These industry-wide stats are in contrast to the investment opportunities I saw in 2018:
125 of the 260 companies I met in 2018 for a focused “new investment pitch”, were with a company with at least one female founder. That’s 48%. Not quite parity, but very close.
100% of the 16 new investments made by XFactor Ventures in 2018 were in female-founded companies. XFactor Ventures was created in July 2017 in conjunction with an amazing team of women who are currently leading successful venture-backed businesses. It is our collective goal to invest in early-stage female founders pursuing billion-dollar opportunities. Of these 16 companies, 9 have all-female founding teams and in 13 of the 16 companies, the female founder is the CEO. If this data was included in the Crunchbase article linked above, XFactor would fall as the second most active venture investor in female-founded startups — below NEA but above Founders Fund, Social Capital, and Sequoia.
40% of the new investments Flybridge has made in the last 18 months (since the launch of XFactor), have had a female founder — an increase of 13% from the preceding 18 months.
While the available data shows it was not the case for the industry as a whole, for me, it’s clear that 2018 was a strong year for female founders. For new investments, venture capital has always been a network, relationship, and focus business but since the launch of XFactor, we’ve noticed a shift — our networks have expanded, driving a significant increase in the volume and diversity of the investment opportunities we’ve seen. In 2019 we expect this momentum to continue and even pick up speed. Also, stay tuned for more from XFactor as we are actively expanding the investment team and will announce a second, larger fund in the early coming months.
A month ago our portfolio company MongoDB went public. The stock priced at $24.00 per share and closed the first day of trading at $32.07 per share. While an IPO is merely a financing event in the trajectory of a successful company and, in the words of the company’s super talented CEO, Dev Ittycheria, “NOT an end but rather a new beginning for MongoDB”, it’s still an important milestone and one worth celebrating and reflecting upon.
My association with MongoDB has been an absolute pleasure, having served on the board since Flybridge led a $3.4M financing in the company at a $12M post-money valuation ($0.66 per share) in October of 2009. In that month, MongoDB had 2,563 downloads for its nascent product. Today, it has had over 30 million. When we announced the investment, I wrote about what led to our decision to support the company: a phenomenal team, a large market with trends in the company’s favor, and a great product that customers loved. What strikes me upon re-reading that post 8 years later, is how simple, but true, that analysis was.
Since the beginning, under the leadership of co-founders Eliot Horowitz and Dwight Merriman, MongoDB has had one of the most talented, creative and driven technical teams I have had the pleasure of working with. Further, throughout the company-building process, the company benefited tremendously from the advice and guidance of Kevin Ryan, the company’s third co-founder and Chairman. Our market thesis was that the database market was large, growing (at the time, $30B in annual revenue; in 2016, $44.6B) and trending towards more special purpose solutions rather than the legacy, one-size fits all relational database model. This market insight has played out broadly. In addition to MongoDB, alternative datastores like Hadoop and its derivatives in the analytics market have thrived in the market. Finally, on the product front, MongoDB has continued to be loved by developers for it’s simplicity, flexibility, scalability and the fact it can run in any environment from the company’s database as a service offering, Atlas, to the cloud, on-premise or in hybrid environments.
What we did not write about publicly, but discussed in our internal analysis, were three additional observations:
MongoDB is a classic disruption story. When we were conducting diligence on the company, many of our friends and experts with deep expertise in the relational database market were quick to point out all the product’s shortcomings and the features it lacked. These objections failed to recognize that user’s desires to (1) develop software in a more agile, iterative manner; (2) deploy databases in horizontally scalable cloud architectures; and (3) utilize a product that was easy to access and allowed for immediate productivity gains all created benefits that more than compensated for the product’s supposed shortcomings at the time. Today, this “nice toy” of a database, as one of these experts called it, sees 30% of it’s new paying customers come from applications migrating off of relational databases (the other 70% comes from net new applications).
Developers are the new King of IT. When we first invested, it becoming apparent that the proliferation of software applications across all enterprises coupled with the rise of of the cloud, and more distributed architectures, was making the developer the new “King of IT”. This allowed, and continues to allow, MongoDB to go-to-market with a very developer-centric approach and then leverage this grassroots adoption into paying customers over time. I have written much about this developer adoption strategy in general, and as it applied to subsequent investments we made in companies such as Firebase, Stackdriver and Crashlytics (all successful investments and interestingly, all now owned by Google), but the approach of building a passionate user base prior to selling into a large enterprise has proven to be a successful one.
Land and Expand is a powerful business model. The flip side of a grassroots adoption first model is that when you do land a paying customer, you often land them for relatively small dollars. But, with a recurring revenue model and a product that delivers on its promises, over time these small customers renew and expand and this can build a large and growing recurring revenue base as shown in the chart below. In the case of MongoDB, this led to annual revenues that grew from $40.8M in FY 2015 (Jan) to $101.4M in FY 2017; in almost 300 customers that now spend in excess of $100K per year, up from 110 in early 2015; net ARR expansion rate of over 120% for each of the last ten quarters; and, annual cohorts that show, in the case of 2013 for example, 4.1x expansion over 4 years (i.e $5.3 million in FY13 grew to $22.1 million in FY17).
Of course getting the investment thesis right only matters if the company is able to execute. And the team at MongoDB has executed exceptionally well. Along the way, the founders were joined by Dev Ittycheria, who to no one’s surprise given his track record of success, has proven to be a remarkable, strategic, focused and results oriented, leader. He is also an exceptional recruiter and under his guidance the company has added well over 400 employees, including Michael Gordon, an extremely adept CFO (who after the IPO process is in need of a good night’s sleep), Carlos Delatorre, CRO who has built a world-class sales team, and Megan Eisenberg, CMO who has the unique talent in a marketing executive of being to drive both high level corporate marketing and a demand generation machine. Under Eliot’s leadership as CTO the company has also built a world-class, deeply technical, enterprise software team in New York City (which many thought was not possible, but it turns out to be a distinct advantage), including Cailin Nelson, SVP Cloud Engineering, Dan Passette, SVP Core Engineering, and Richard Kreuter, SVP Field Engineering. A hat tip to all of these executives, plus the 800+ other employees at MongoDB, on what they have built together.
It has been my distinct pleasure to work with such a talented team for the last 8 years. But, again, the IPO is just a financing. The company feels like it’s just getting started in its quest to disrupt this massive database market. I look forward to remaining on the company’s Board and continuing the journey for many years to come, building an important, anchor public technology company in New York City.
Last Wednesday marked the two-week point since the launch of XFactor Ventures. We are grateful for, and a bit overwhelmed by, the support and feedback. We thought there was an unmet market need for female founders investing in female founders, but you amazed us with the volume, quality, and breadth of opportunities that have come into XFactor.
Over our first two weeks, we received 200 female-founded companies to review. Obviously, there is a huge pool of talented and creative female founders! Equally obviously, not all of these investment opportunities are going to fit with our expertise, passion or capacity, and we know that will mean we will end up missing out on some fabulous opportunities. Given we can’t do it alone, we want to make sure amazing female founders have access to as many resources as possible. As a result, we are developing an Allies list with whom we can make introductions. If you’re an angel interested in backing seed stage founders or a fund that is committed to backing women and mixed gender teams, drop us a note to email@example.com letting us know what areas (however you define it) are most of interest to you!
In reviewing these opportunities we are struck by the fact that, apart from the high-quality level, there is no such thing as a normative female founded company. While the stereotypical beauty/fashion company is a segment within our deal flow, it is nowhere near the most common. The companies we are seeing are diverse, broadly reflective of the venture industry, tech-driven and blow away the myth that female founders only start female-focused companies. Specifically, 50% are deep tech companies (Software, AI, VR/AR, Networking, IoT, Robotics, Wearables, Other hardware), 20% are e-commerce (mostly B2C, some B2B, in the pet, clothing, beauty, food and home furnishings markets among others), 12% are Biotech (even though it’s not an area of focus or expertise for us at this time) and the remaining 18% are scattered across a variety of categories including fin-tech, ed-tech, marketplaces, content and tech-enabled services.
Nor do female founders always have a female co-founder. Of the companies with co-founders, 68% have mixed gender founding teams. This reinforces our belief that diverse founding teams will have a better perspective on market opportunities, how to define and market products for the widest possible audience, will make better decisions, and be more successful in attracting and retaining talent. Interestingly, almost half of the companies have a solo-founder, and while this has always struck me as a harder path, some of the best female-founded companies (StitchFix, LearnVest and The Real Real are three examples that come immediately to mind) have followed this path, so it’s clearly not a determining factor one way or another.
On the XFactor front, since our launch we have closed 3 investments and committed to 2 others. We are extremely impressed with the quality of the first 5 female founders we are fortunate enough to support. We will follow up with details in future posts, once these companies announce their financings and plans, but the companies are in the AI (2x), Data, Cloud Platforms and Content/Community fields and we look forward to working with these teams as they take on their respective billion dollar market opportunities.
Thank you all again for the support and introductions and for all the female founders we have met for your talent, perseverance, and grit. As always, if you are interested in speaking with our team, please email us at firstname.lastname@example.org.