The Bandwagon Effect

A while back, Elon Musk himself tweeted a list of 50 cognitive biases he thinks we all should learn about at a young age. We’ve already posted several of them on the Social Leverage blog over the past year or so. And while many of us probably haven’t had the opportunity to discover them in our younger days, even learning them now can be a tremendous help to founders and investors alike. 

Today’s post covers The Bandwagon Effect.

The bandwagon effect is the phenomenon where people adopt beliefs and behaviors only because others are doing so. It’s a type of social proof. People look to what others are doing as evidence of what they should be doing. It is a self-fulling prophecy in so much as the more prevalent a belief or behavior, the more people adopt it. It is prevalent in many domains, including consumer behavior and political alignment – regardless of the evidence to support a particular belief or behavior. 

The power of the bandwagon effect comes from our desire to fit in with our peers. Fashion trends are but one example of the phenomenon. For illustration let’s switch the wagon for a train. Imagine a train station, where the train is about to depart. In the beginning only a few people get on, but as more and more people board, others begin to follow – thinking the train must be going somewhere important. Eventually the train is filled to capacity. This is similar to how the bandwagon effect works, where people adapt a position because they see everyone else doing the same thing, even if they originally may not have had any interest in it.

"I never make the mistake of arguing with people for whose opinions I have no respect." ~ Edward Gibbon

If the bandwagon effect leads people to adopt opinions or beliefs simply because they’re popular, rather than because they’re well-reasoned, the quote suggests it’s a waste of time to argue with them. They’re not likely to be swayed by logic.

What to Look for When Choosing a Venture Capital Firm

When seeking out a venture firm, founders need to look for someone with a proven track record of success, who shares their vision and values, and is willing to provide the necessary resources and support to help the venture reach its goals. Additionally, founders need to ensure that the venture capitalist is willing to provide access to their network of contacts and resources, and will be open to future collaboration.

Founders should also be aware of the venture capital firms investment criteria and approach to make sure their specific startup is a good fit. Additionally, founders need to be crystal clear on the investment terms and any legal obligations involved. It’s critical for making sure the startup can be successful in the long term.

It is important for the founder to feel comfortable with the firm and to have an open and honest relationship. This means that the founder should ensure that their expectations are clear, that they have a good understanding of the firm's expectations, and that any potential conflicts of interest are addressed at the outset, or as soon as they surface. Additionally, it can be beneficial for the founder to build a relationship with the firm prior to entering into any agreement.

The founder should also make sure that they are well-prepared to answer any questions the firm may have, and be able to provide any additional information that is requested. It is also important to be aware of any potential risks and rewards associated with the startup, and to ensure all parties involved are aware of the potential outcomes. Finally, the founder should be sure to review all relevant documents thoroughly, and to seek out any advice or assistance needed to make sure their startup is successful.

Illusory Superiority / Lake Wobegon Effect

Many people overestimate their own qualities and abilities, while simultaneously underestimating those same qualities and abilities in other people. This is known as Illusory Superiority. Researchers NW Van Yperen and BP Buunk coined the term back in 1991. This sense of relative superiority is also referred to as the Lake Wobegon effect, named after a fictional town in NPR’s Prairie Home Companion show which closed with, “That’s the news from Lake Wobegon, where all the women are strong, all the men are good looking, and all the children are above average.”

This bias leads to overconfidence and a lack of self-awareness. As a result, we tend to overestimate our capabilities as leaders, particularly the higher up the ladder we climb. Studies by organizational psychologist Tasha Eurich showed the more power a leader held, the more likely they were to overestimate their skills and abilities. One study of over 3,600 leaders across roles and industries suggested high-level leaders significantly overvalue their skills relative to low-level leaders in 19 out of 20 competencies measured.

Most of us suffer from this type of self-deception. Or is it self-delusion? Not being as attractive, capable, or creative as we think we are can be a buzzkill. And as Daniel Kahneman pointed out, being aware of your cognitive biases doesn’t necessarily mean you’ll overcome them and make better decisions. Yet the optimism of GI Joe, “Now you know, and knowing is half the battle.” can still give us a glimmer of hope.

Will AI Take Our Jobs?

There is a lot of debate about the impact of artificial intelligence (AI) on employment. Some people believe that AI will automate many jobs and lead to widespread job loss, while others believe that AI will create new types of jobs and opportunities.

It is important to note that AI has the potential to automate certain tasks within a job, but it is unlikely to completely replace the need for human workers in most cases. Instead, AI is more likely to change the nature of work and the skills that are required for various jobs.

For example, AI might be used to automate routine tasks, allowing workers to focus on more complex, higher-value tasks that require human judgment and creativity. In other cases, AI might be used to augment human workers, providing them with tools and capabilities that allow them to be more productive and efficient.

Overall, it is difficult to predict exactly how AI will impact employment in the future, but it is important for individuals and society as a whole to be proactive in preparing for and adapting to these changes. This may involve investing in education and training to develop the skills that will be in demand in the future, as well as considering how to address potential challenges such as job loss and income inequality.

*this post was written entirely by ChatGPT…

Founder in Focus: Chuhan Wang - Founder and CEO of Surfboard

I had the opportunity to interview Surfboard founder (Fund IV) Chuhan Wang before her appearance on Panic with Friends to share her founder story. 

Chuhan spent the last decade working in investment banking at Morgan Stanley, as a Venture Capitalist, and growing a company from Series B to initial public offering (IPO). Having worked closely with many founders from both the board side and the company operations side, Chuhan realized three things:

  • No one likes board meetings – they’re time consuming and useless.

  • Nearly all first-time founders have zero board experience. It’s a by-product of founding a company; and isn’t what they signed up for.

  • There’s tremendous variance. A few boards are highly functional, but most boards are ineffective. Boards that do a good job either have someone with a lot of experience to guide the process, or have pieced together several existing tools and built a workflow around them.

Of all the successful companies Chuhan’s worked with, an engaged board and helpful investors are the common themes. She realized if it’s something no one likes, but it’s important, and only a few people do it well, then she’d have the opportunity to productize the experience and build the interface for founders and their boards to work effectively together. “Building a company is hard,” Chuhan says. “Managing a board and investors shouldn’t be.” This is more true now than ever as founders are increasingly coming from more diverse backgrounds and need to learn to cultivate relationships with their board. These insights led her to found Surfboard. She wants to empower all founders to communicate confidently and efficiently with their board, regardless of their comfort and experience levels.

Founding Surfboard led Chuhan to Social Leverage as a seed investor. She found them through her college friend Scott Law. Scott runs Meridian Street Capital, which focuses on early-stage healthcare investments. When she mentioned she was looking to raise seed capital, Scott immediately recommended Social Leverage. To her delight, she found out later Scott was a Limited Partner as well. 

Chuhan says working with Gary, Howard, Matt, Tom, and the rest of the team as Social Leverage has been a great experience. She says Gary’s experience as a founder who’s ‘been there and done that’ provides practical advice on product strategy and go-to-market. She’s enjoyed having Gary on the board. “There’s a common misconception among early stage founders that taking on board members means giving up control of their company and putting their CEO job at risk,” says Chuhan. “The stories of Uber and WeWork certainly played a role in shaping these impressions.” But she says it’s most helpful to get a board as early as you can. The more years founders have running the board, keeping records and engaging in corporate governance, the more trusted their company becomes – which can be huge for the M&A or IPO opportunities down the road. Founders gain so much more from the expertise, time, and network of their board and investors, and this should outweigh any other concerns they may have.

Some of the best advice Chuhan’s received as a founder was to ask for help when you need it. She says being a founder can be incredibly lonely. It may seem like you’re fighting alone and no one can help you, but you should remember there are people, like your board members and investors, who have faith in you. Rather than providing them one-sided updates, engage them by asking for help. You’ll build stronger relationships from these interactions. 

On the flip side, “Ship fast and iterate” is popular entrepreneurial advice Chuhan doesn’t agree with. She says it over emphasizes speed and downplays the importance of being thoughtful when developing a product. Users are busy, skeptical about your product, and don’t have the time to learn things. Shipping too fast can introduce friction and noise into the user experience before you get any signal on whether or not a new feature is working – you end up switching gears and moving on to the next thing. Being thoughtful about what you’re building and shipping can minimize this friction and save you money in the long run. 

Founders all need motivation and on that front, Chuhan says she loves the movie “About Time”. It makes her rethink the concept of second chances and what we should do to really live our lives to the fullest. She looks at everything she does, every conversation she has, every decision she makes, and asks herself if this is what she’d really want to do if she never got a second chance? It’s about being conscious throughout daily life. When you’re more aware of what you’re committing to, you become more motivated to do it well. 

When it comes to books that have inspired Chuhan, her favorite is “The Three-Body Problem” trilogy. She’s a big fiction reader in her limited spare time. And “The Three-Body Problem” is a reminder that startup life is not everything. There are bigger unanswered questions out there. The books are a way to smooth out the everyday ups and downs while making challenges appear more manageable. 

When I asked Chuhan how being an entrepreneur has turned her into a better person, she says, “Since I started Surfboard, I’ve become more aware of my health, as I want to make sure I have a strong body to work hard and make Surfboard successful. I used to never be able to find time to work out, and now I purposely carve out time 2 or 3 times a week to do it. I’ve lost 20 pounds since I started working out more regularly, and despite working longer hours, I’m feeling the healthiest since maybe college days.”

Finally, when it comes to defining success, Chuhan says when she was on the investing side, she defined it as buying optionality to the upside and keeping a diverse portfolio to hedge risk. This required her to go broad and make educated guesses quickly. But as an entrepreneur, her success is tied to her ability to find the riskiest assumption in the business and remove that risk. Rather than stay on top of every major trend and news headline, her focus has shifted to diving deep into problems and solving them. 

Seeds Investor (Fund IV) Closes $2.7M Seed Funding Round

Social Leverage led the seed round for Seeds Investor (Fund IV), a fintech firm providing financial advisers the technology to deliver a personalized and engaging investing experience to their clients. Social Leverage GP Matt Ober joins the board of directors. The seed round saw additional participation from The Compound Capital Fund I, LP, the affiliated venture capital fund of Ritholtz Wealth Management, and other financial industry veterans.  

“We’re thrilled to welcome investors who harbor a deep understanding of the advisory space and the distinct opportunity that lies ahead,” founder and CEO Zach Conway said in a company release.

The firm, founded in 2019 by financial advisors Zach and Michael Conway, has been quietly building out its platform to efficiently deliver tailored portfolio solutions to advisors looking to align their clients personal values and financial priorities. 

“As asset management becomes increasingly commoditized, advisors need to pivot their value proposition toward a more engaging, digital, and personalized experience,” said Social Leverage founder and GP Howard Lindzon. “I’m delighted to back this visionary team as they help advisors make the transition.”

The Rise of AI: Understanding Machine Learning

The rise of Artificial Intelligence (AI) is a trend that will have significant implications for your business. Machine Learning (ML) is ground zero for unleashing the potential of AI for businesses, governments, and consumers.

Industries and Institutions vary, but the fundamentals stay the same. We’ll look to the Joint Artificial Intelligence Center (JAIC) publication, “Understanding AI Technology” by Greg Allen to get a better understanding of Machine Learning.

“AI is not an elixir. It is an enabler.” Lt Gen John N.T. “Jack” Shanahan.

BASICS

Investors need to understand: What is AI? How does it work? What are the types of Machine Learning, and how do they differ? What are the risks and limitations of AI?

MACHINE LEARNING

Artificial Intelligence is an umbrella term covering a broad swath of technology. When people say “they’re using AI” at work, they usually mean systems that use Machine Learning to automate processes. ML is a few years away from being an autonomous Intelligent machine able to make decisions on its own.

The important point is, while machine learning systems program themselves, human intervention is critical to the learning process. This human intervention includes choosing data and algorithms, setting the learning parameters, and troubleshooting problems.

WHY IS ML IMPORTANT?

The big reason? ML lends itself to automation tasks too complex for human programmers. The number of rules required is impractical, if not impossible, to accomplish with a human coder. ML is well suited for content generation, language translations, pattern recognition, and speech transcription.

WHY NOW?

There are multiple reasons the pause in the Fourth Industrial Revolution is now accelerating. I’ll highlight two of the big ones:

Big data

When machine learning was being developed decades ago, few applications had enough training data to be useful. Fast forward to today, and we’ve got more data than we can shake a stick at. We’re drowning in data.

Computational Power

Computer hardware is finally cheap and powerful enough to process Big Data. The switch from CPUs to GPUs, originally meant for graphics and video games, means we now have the ability to crunch calculations at blazing speeds. GPUs can speed up the algorithm training process 10 to 20x.

TYPES OF MACHINE LEARNING

Supervised

Supervised learning requires a human accurately label input data. Training data has images compared with the correct classification labels. Companies can purchase pre-labeled data to run an generate labels on their own data.

Unsupervised

These algorithms extract features and group them based on their similarity. Because there’s more unlabeled data than labeled data floating around, unsupervised learning is useful for companies who want to explore their own data for insights.

Semi-Supervised

Like the name implies, you take the best (or worst) of Supervised and Unsupervised learning. Often a small set of labeled data is combined with a larger set of unlabeled data. 

Reinforcement Learning

Data is autonomously collected by AI agents from within the perceiving environment. It works well with games because it generates its own training data. AlphaGO is a Reinforcement Learning systems that iterated 4.9 million games in 3 days. (Consider the average game is about an hour for humans.) These algorithms explore all possible actions and learn to determine the most optimal actions what will maximize rewards.

CONCLUSION

As Greg Allen states in the Guide, organizations should not pursue AI and ML for its own sake. Allen recommends identifying specific metrics for performance and productivity. Adopting AI will require some changes to existing business processes. If companies don’t make those changes, most AI projects will deliver a fraction of the value sought.

Even Financial, Inc (Fund II) Acquired by MoneyLion

MoneyLion ($ML) acquired Even Financial, Inc. (Fund II). The acquisition opens up MoneyLion platform users to a network of over 400 financial firms and 500 channel partners who provide a wide-array of financial services and products.

Even revolutionized the customer experience for financial services recommendations through machine learning and a trusted user experience. The first phase of product integration is complete, with over 60 solutions providers from Even now available on MoneyLion's Marketplace.

Even will operate as an independent subsidiary of MoneyLion, and Even's current management team will continue to lead. You can learn more here.

What Dunbar’s Number Means to Your Network

They say your “network is your net worth” — social leverage, if you will. But how big should your network be? Dunbar’s number, and what it means in the social age, is a good place to start.

Dunbar’s number is defined as the number of people in our network we can have stable social relationships with. It’s the limit where an individual knows who each person in the network is, and how each person relates to every other person in the network. British anthropologist Robin Dunbar, for whom the number is named, proposed humans can comfortably maintain 150 people in their network. He explains the degree of social intimacy as: “the number of people you would not feel embarrassed about joining uninvited for a drink if you happened to bump into them in a bar.”

One example of the theory in action in the business world comes from GORE-TEX founder Bill Gore. He found once a building had more than a certain number of people they were less likely to work together as a team. So, he would build another building. And another. And another. At GORE-TEX, no business units were allowed to grow beyond a certain size. Bill believed “you have to divide so you can multiply”.

In the age of social networks it seems daunting to create relationships that matter. It’s not unheard for users to have thousands of ‘connections’ on LinkedIn, Facebook, Twitter, Instagram etc.

The Network of Networks

Before you run off and lock down your social to just 150 of your nearest and dearest, I recommend reading a blog post titled Maintaining Relationships: The Fallacy of Dunbar’s Number by Brad McCarty.

Brad expands on the theory by using what he calls ‘Buckets’. Like Gore’s buildings, Brad is able to maintain better relationships by breaking his contacts into smaller buckets. After reading about Bill Gore, Brad reorganized the 3,160 contacts in his network based on tags with titles like CEOs, VCs, Angel Investors, Media etc.

Brad’s network is less stove-piped than the GORE-TEX factory example. He noticed people in the network often crossed into multiple tags with some people belonging to up to five different tags. However, lending credence to Dunbar’s Number, Brad found there wasn’t any one tag with more than 150 people assigned to it.

Brad acknowledges he might not be at the ‘have an uninvited drink at the bar’ level with his network, but by using his tag system, he’s got almost 2,500 people he knows well and keeps in touch with on a regular basis. He says if your handle your buckets correctly, your networks can be sustainable and add value.

Fundamental Attribution Error

Our tendency to perceive other people’s behavior as a reflection of their personality, rather than attribute those behaviors to situational or environmental conditions, is known as the Fundamental Attribution Error.

For example, the man who cut us off in traffic did so because he is rude, not because he may have been on the way to the emergency room after learning his daughter was in a car accident. It’s a pitfall we should be wary of — we often find ourselves justifying our own behavior based on our own circumstances while judging others’ actions based solely on their personality.

Succumbing to the Fundamental Attribution Error can sometimes reinforce a positive opinion of someone, but more often than not, it only reinforces negative opinions of those around us. To combat this bias, think of an iceberg; we’re only seeing what’s above the water, yet a multitude of contributing factors surrounding our behavior are lurking below the surface.