You Think You Understand Network Effects, but You Probably Don’t
What exactly are network effects and the ‘moat’ that every company desires? Uber built a ride-sharing empire around the local network effects between drivers and riders – how so? What types of moats exist and who fooled investors with a moat that happened to be nothing more than a mirage?
Why it matters: Economic moats. Competitive advantages. Network effects. One of these is not like the other. While this may not necessarily be fair, to understand network effects, you need to differentiate how they are generated and the type of competitive advantage (i.e. moat) they can establish – and why it’s easy to misinterpret business models that benefit from more connected users from those that have true network effects.
Matt Ward dissects Uber’s and Blue Apron’s initial success and recent struggles through the lens of their perceived (and ultimately lack of sustainable) networks effects as a competitive advantage. In an era where SaaS startups are growing quickly by claiming network effects as their primary moat-making advantage, VC’s need to be able to differentiate between those companies that truly have acquired this advantage and those that have not.
Investor View: Explainable AI
What is ‘Explainability 2.0’? Kenn So outlines how these advances in AI can better uncover the decision making process that is often hidden in the black box of machine learning algorithms, known as Explainable AI.
Why it Matters: A perceived pre-requisite to any startup these days, machine learning and artificial intelligence are still in their nascent stage when it comes to intelligently digesting output in many regards. At this point, with the scalability of ML programming libraries and capabilities, often lost is the art of interpreting the output. More challenging yet is looking into the black box to better understand the decision making process within the algorithms. This has serious positive implications for industries where in-depth understanding of the risks (insurance, banking), trends (consumer spending), and sentiment (journalism) require developing models with inputs and decision making processes that are often subjective and/or hard to explain. While ML concepts in and of themselves appear challenging at the outset to digest, ironically the application of AI to these model-dependent tasks can shed light on potential biases and improvements to model inputs.
What about VC? Many VC’s are developing in-house ML-based programs to more efficiently identify promising startups, and being able to better understand how these models decide – given the set of parameters and assumptions – the outcomes, can help build better models – and inform decisions. This of course applies the next level down when being able to better assess the validity and potential differentiation between startups who rely on ML techniques, for example.
Addressing the Diversity Deficit in VC
This quantitative study seeks to answer key questions to the value-add of diversity in venture capital. How much more successful are companies who benefit from diversity in leadership? What is the current state of diversity in the London VC landscape and what actionable insights can we draw from these studies?
Why it Matters: While this study is focused on the London VC market, the principles and benefits are ubiquitous across not only VC markets but businesses in general. When considering the impact venture capital has on markets, drawing insights from diverse resources and talents – much as we do with GoingVC – is not only important, but can create a competitive advantage. This report quantitatively proves this thesis – from differences in the success of IPOs when considering diversity of the ventures to the value-add when it comes to generating startup ideas.
IPO Crashes Send Chills from Wall Street to Silicon Valley (subscription may be required)
Have we reached a turning point in valuations for private companies seeking IPO exits? With the no-go for WeWork and questions swirling about Pelaton’s valuation, the public markets may be souring on Silicon Valley’s sweetest deals.
Why It Matters: As we alluded to last month, the WeWork IPO and company valuation stood on rocky grounds from the perspective of valuation (my personal thesis was this was due to the imbalance between the tech-driven startup valuation and the true nature of their business model – a real estate operating company). It does not take much to spook public markets – and given the current uncertainty at a macroeconomic level – the IPO market is likely to be much less receptive to all-growth, no-profit companies going forward. Those next up in line need to take notice.
A startup factory? $1.2B-exit team launches $65M super{set}
Tom Chavez, the founder of Rapt (acquired by Microsoft) and Krux (acquired by Salesforce) can make building and exiting a startup look easy – and is now trying to develop a scientific approach to do so with the launch of super{set}, an enterprise startup studio with a half-dozen companies already incubating.
Sound familiar? Sure – but Chavez argues the difference comes from the combination of execution playbook and technology that create the building blocks for which companies can launch and grow.
Why It Matters: Every founder and VC knows the challenges that exist when building a company. It’s always extraordinarily harder than most could ever anticipate – and even with the right mentors, team, and timing, there’s no straight path to success. But that shouldn’t stop innovators from trying to mix the best recipe for success. In an industry where a winning percentage of 30%+ is considered wildly successful, the bar is low for generating a “successful” approach. As these programs continue to sprout up, how they choose to specialize and carve niches will be an interesting pattern to follow.
Pitch, a presentation startup from Wunderlist’s founders, raises $30M more to take on PowerPoint
Perhaps following on the successful adoption of inbox-zero app Superhuman, Pitch has raised $19M from Thrive Capital and others to improve presentation software.
Why It Matters: Given software is a rapidly maturing industry at this point, many of the industry leaders have acquired an impressive amount of features. So much so, that many startups are carving out specific functions, improving on them by 10x, and building a business on said single-function. Executed well means a unique and differentiated product, while done poorly means developing a feature (at best) instead of a business.
This happened with Craigslist – as the segments and verticals got ‘spun-out’ into successful startups (think Zillow for the housing section, AirBnB for rentals, ZipRecruiter for jobs, Shiftgig for gigs, Reddit for discussion forums, Tinder for personals, and Nextdoor for community) – and could happen with many mega apps (Slack, Microsoft Office, among others), and the dynamic between market share leaders and upstarts is certainly an interesting one to monitor. Can there be multiple winners in a space? Are the sum of the parts more valuable than the whole or not?
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