Haven’t written here in over 2 years. Currently there’s 31 of you, and that may seem kind of lame…but I think it’s pretty cool. There is something pretty inspiring that for however brief period of time a synapse fired in your brain and said…”neat,” and you proceeded to click the “follow” button. While it may dwarf in comparison to some of your other favorite content channels, I just wanted to share my appreciation for your interest to whatever it was that intrigued you about our little experiment here at Dig3st. With that, I felt it was only fair to share with you why I started this project and why it is important to me so that when/if there are 10,000+ of you, I can be held accountable by our earliest contributors/readers to stay committed and consistent to the cause, which was founded on three basic premises:

1. Impoverished dialogue: While the internet has given an incredible ability to connect and converse with anyone in the world, much of it has caught an illness somewhere between the bots, trolls, fraudulent news, and unsubstantiated opinions. We are committed to maintaining a respectable and credible environment of data-driven dialogue, inspired by and thus monitored by the community.
2. People are smart: No machine, AI, robot, etc. will ever replace the human mind; and our observations are powerful. Furthermore, everyone has them, good ones at that which only each individual can uniquely possess based on their domains, skills, and experiences. Yes a career journalist may have a large repertoire of obscure metaphors just as a data scientist might have a few fancy visualization tricks up their sleeve and these are all wonderful, but it is the insight that matters. Providing a venue where these insights can be shared by anyone in a credible and to-the-point fashion bolsters the wisdom of the crowds.
3. Information overload: There is simply too much shit out there. 90% of the world’s data was generated in the last 2 years alone and the digital reader today has exponentially more access to content than ever before in human history. So the “byt3” (3-minute reads and at least one element of data viz) was designed to add a little more credibility to insights and a little more life to data for the time-strapped inquirer. Cutting through surplus with data-driven brevity is one of the primary goals of Dig3st in order to understand our world a little more efficiently, one byt3 at a time.

So where are we going from here? With the recent emergence of web3 technologies, it has become apparent that the missing piece to the Dig3st model (incentives) is now very much a real possibility for making this a reality again. At the right time, we’re going to be resurrecting Dig3st as a DAO.

The capabilities of DAO infrastructure and a community of OSINT contributors, data viz practitioners, and proliferation of independent writers creates a strong foundation for the future of Dig3st. Our evergreen ask is to contribute or help recommend a contributor who may have an interesting topic to “byt3” about. Bring them on board. Dig3st 2.0 starts today. Join the movement for data-driven independent journalism.

Dear Dig3st Community was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

It turns out the clock-gazing anticipation of a meal break affects even those who decide our civic fate, but with higher consequences than we may imagine. In a study published from the official scientific journal of the National Academy of Sciences, researchers found that judges rule more favorably either earlier in the day, or right after meals. And not just slightly more favorably —a prisoner whose case was heard immediately after a meal break could have a 65% higher chance of getting a favorable ruling than a prisoner who came right before a break, when chances could drop to 0%.

The data used consisted of 1,112 judicial rulings over a 10 month period, with the majority of decisions consisting of parole requests or changes in parole terms. Researchers controlled for crime severity (crimes ranged from embezzlement to murder), prisoner demographics, months served, previous incarcerations, and the possibility of a rehabilitation program. Judges did not know the details of the case beforehand, and took cases depending on the arrival time of the prisoner’s attorney. The judges spent approximately six minutes on each case.

A Pattern from Youth?

The drastic difference between rulings is shocking. Given how we expect seasoned judges to act — that is, unbiased, objective, rational, and without emotion — it is unnerving to think that something as seemingly inconsequential as a recess break could have such a profound effect on a judge’s decision-making capability. What would this imply for the rest of us, unseasoned as we are in keeping our biases in check?

While the study has received pushback in its chilling conclusions, the results remain relevant. Regardless of how trained we are at a certain task, factors beyond our control that affect us psychologically have a stronger grip on us than we like to admit. This is true across demographics: a 2009 study found that 8–9 year olds with more than 15 minutes of recess a day exhibited better classroom behavior, and the effects of burnout and the importance of lunch breaks are becoming more accepted in the modern workplace. We like to think that we are rational creatures when called to the task, but the opposite is more often the case.

An Anomaly in Healthcare (🚑 🙏 🙌)

Nevertheless, it is interesting to compare the performance of judges to the performance of nurses, whose daily schedules are arguably more mentally and physically draining. A 2004 study on the effects of work breaks on work staff performance found that nurses that skipped breaks and/or meal periods did not make significantly more mistakes than nurses that took these breaks. Of course, this is not an apples-to-apples comparison; there are many, many factors that could surely be studied to pinpoint why judges, students, and office workers seem to be more affected than nurses. And, as the study notes, nurses gain other benefits from breaks. Yet, it is a curious case and makes us think, is there something to learn from nurses?

Another approach is not so much what we can learn from nurses, but what we can learn from the experiences of judges, students, and office workers. The mental and physical exhaustion common in modern day to day institutions — schools, workplaces, courthouses — should imply that the schedules we’ve created for ourselves do not work. It is unnatural, and rather than adapt to the norms that were arbitrarily created (after all, “weekends” were unheard of until the last century), perhaps it’s time to consider adopting new norms.

Unlimited PTO, anyone?

The Appetite of Judgment was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

Disclaimer: This is not financial advice nor should anyone take my thoughts as financial advisements. These are mere opinions, you should consult with your financial advisor before entering into any transaction.

In what was arguably the most hyped ICO of 2017–2018, EOS and its $4B+ raise seemed to burn as bright as the sun in the crypto-verse. But it’s already becoming clear that EOS is more like a dwindling supernova…

Early Fractures

EOSIO, the open source protocol of EOS, has already experienced six distinct forks. By looking at EOS as a whole (still an early stage, overfunded startup), this is analogous to the early employees of a company splitting in six different directions…aka an outright implosion. To be clear, even if you view EOS as an unstoppable behemoth, this is not the equivalent of having a theoretical opportunity to “fork” an established tech giant like Facebook or Google for example. Both of these companies have already spent years building and extracting their mountainous value from network effects, thus fortifying their very wide moats. Early blockchain forks simply add more competitors and everything from the dApps to the user base are anyone’s game…

Mounting Tech Challenges

While UX/UI challenges remain with Ethereum-based dApps to include gas fees, one of the primary premises of EOS was to allow free services for users thus encouraging widespread adoption. But it’s naive to think crypto was exempt from basic economics in that there is no free lunch. What has been made free for the user with EOS has become costly for the developers. Between the network resources required (RAM, CPU, & NET) to develop on EOSIO, the costs to onboard a dApp user are currently $3.12. That totals to a $30M customer acquisition cost before marketing efforts just to reel in 10M users, raising some serious doubts for scalability. In order to achieve a sustainable business model for a theoretical must-have dApp (still waiting…), these costs will eventually have to shift back to the user, thereby diluting early claims of EOS. As one of the many challenges going forward, one would think that development activity would increase. But in fact, it is just the opposite with a dramatic drop-off in activity right around June 1st, which marked the end of the ICO!

Team Track Record

The team track record behind EOS is IMHO one of the most overlooked characteristics of why a current EOS HODLer should be a bit concerned. Most notably is Dan Larimer, CTO of Block.One, who is the tech lead behind EOS (we won’t even touch the CEO’s severe skills gap as a real estate SaaS founder). Just looking at Steemit alone, Larimer’s previous company, he has abandoned it in what looks to be a permanent beta phase and provides a classic example of building a product before finding product-market fit. If you haven’t already, go take a look at the content published there and then ask yourself honestly that if you could only pick one source to find quality content for the rest of your life, would you ever choose Steemit? This is not even taking into account the tokenomics…but don’t just take my criticism to see how the incentive structures are eerily similar for a select few. While a startup failure can be leveraged as a lesson for a follow-on company, a $4B+ pre-traction and easily replicable open source protocol is a bridge too far…

Between the fractured ecosystem, rising resource costs, and questionable team, the cracks of crypto’s largest ICO are already beginning to show. Despite the series of red flags from the beginning, hopefully unsuspecting investors catch themsevles before the signs turn into sirens. In reference to the $4B raise, here is a graphical reminder of what a wise man once stated…

If this byt3 elicited any response, please clap/share/comment below and explore the rest of Dig3st in the meantime. Follow us on Medium and Twitter for all the latest byt3s that are always ~ 3-minutes long and accompanied by data viz.

EOS = Empty Optimism Surfacing was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

You don’t have to look too hard to see a misalignment of vision and practice with so many blockchain projects (outright scams in the worst-case scenario). Unfortunately for Ethereum, their platform has carried the brunt of this burden due in part to lackluster dApp vetting, a premature market, and the double-edged sword of first mover advantage…

All Hat, No Cattle?

Walking through the data, we see to the left that tokens on the Ethereum platform make up a clear majority (78%) of the token market cap. It’s important to note here that the second largest platform, Omni, owes its size primarily to Tether (USDT). Now looking at the volume, there is a mirror reversal in that Omni/Tether takes up 78% of the volume amongst platforms, most likely for traders looking to dampen their volatility risk as the only stable “safe” haven in a turbulent market. With such comparatively low volume of Ethereum tokens compared to Omni/Tether, it begs the question…where are they now?

Decentralized Landfill…

In addition to having 78% of the token market cap, the Ethereum platform takes up 88% of the total number of projects equalling 691 according to CoinMarketCap (CMC). Using this as an assumption for the ecosystem and combining it with data from Dead Coins yields an estimated 791 Ethereum token failures to date. The pie chart below gives an approximate idea of the current ecosystem.

Now it may seem like this isn’t too bad, especially considering ~90% of startups fail (because realistically these are all startups). However, digging a little bit deeper into the 46% (691) of “live” ETH projects reveals that many are already on the cusp of losing steam. CMC data suggests that of these projects, 52% have volumes below $100K, 30% below $10K, and 15% below $1K. Time will tell for the remainders but you know it’s bad when there’s a crypto project dedicated to cleaning up the aftermath.

But What About “Real Use Cases?”

It’s not just trading activity that illustrates a bleak future for many projects. According to DappRadar.com, outside of exchange categories, token use often hits a peak (likely at dApp launch) and quickly dwindle thereafter, Augur being one of the most recent examples.

While we may be waiting for awhile to see a trend reversal or the one dApp that really does take off (gambling?), the evidence is clear: vetting matters. Can you imagine if the Apple Store initially filled its store with apps that nobody used (and even outright scammed their users)? The answer is easy…it wouldn’t be a store anymore.

There’s no doubt that the intellectual capital behind Ethereum is incredibly robust and at the frontier of the technology and discourse around blockchain. Furthermore, being open-source more or less insulates them from any regulatory backlash. But the reputation and network risk of a platform that hosts the majority of questionable dApps may serve as a cautionary tale for emerging platforms to vet their builders. In the meantime, the rest of us will be wondering if Ethereum has hurricane insurance for the aftermath.

If this byt3 elicited any response, please clap/share/comment below and explore the rest of Dig3st in the meantime. Follow us on Medium and Twitter for all the latest byt3s that are always ~ 3-minutes long and accompanied by data viz.

Hindsight is ERC-20/20 was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

Disclaimer: Nothing written here is financial advice nor should anyone take my thoughts as financial advisements. These are mere opinions; you should consult with your financial advisor before entering into any transaction.

Recently, the China Center for Information Industry Development (CCID) released volume 4 of what they refer to as the Global Public Chain Technology Evaluation Index. This index takes into account the core technology, applicability, and “creativity” 🤨 of 33 different cryptocurrencies they deem worthy of evaluating as seen below:

Here are some interesting (albeit questionable) takeaways:

1. Bitcoin is ranked 10th…WTF. In case this doesn’t raise concern, consider this argument for why we need Bitcoin to be number one.
2. Steem is ranked ahead of Bitcoin…if you’ve ever actually used the platform, it’s not difficult to see why this is irrational to say the least.
3. EOS ranked 1st by a large margin…few projects have generated as much distrust in what is supposed to be a trustless technology.
4. Decred ranked 32nd…stark contrast to western optimism put forth by industry leaders such as Placeholder Capital’s Decred Invesment Thesis.
5. Komodo, a fork of Zcash, is ranked 3rd (22 places ahead of its parent). That’s either an overestimation or one kick-ass toddler.

These are just a few of the many anomalies one can point out with this index that may not show up too frequently in your local English-speaking crypto circles, subreddits, meetups, etc. But by no means should this be reason to scoff at the index for a couple of reasons:

1. Government Influence: The Chinese crackdown on cryptocurrency trading is big, very big. They have given every reason to believe that the crackdown has been effective and yet blockchain innovation seems to find its way of circumventing this crackdown. Furthermore, the very notion of ranking cryptocurrencies could suggest they aren’t trying to ban cryptocurrency at all. Instead, they look to be seeking a government-moderated crypto rollout in the best way they know how: using heavy-handed tactics to make the crypto market malleable enough to shape it the way they want. So when a government-backed index illustrates their own valuation methods, it’s probably not just for shits and giggles.
2. Industry 4.0 Powerhouse: One doesn’t need to look much further than China-based Bitmain to see how a blockchain behemoth takes shape. In such a hyped space, Bitmain simply sees their crypto mining dominance as a means to an end for their ultimate ambitions in AI/ML. If successful, many see Jihan Wu (Bitmain CEO) poised to be one of the greatest entrepreneurs of our lifetime. And if one giant wasn’t enough, Binance is arguably the fastest profitable startup to achieve unicorn status (< 6 months) despite being forced to move outside of China’s borders. While these private successes may have nothing to do with the CCID index, it’s not unreasonable to assume they have some leverage when it comes to government alignment and vice versa.

Whether this index is based in sound/universal valuation methods or simply a subjective Chinese interpretation, at the very least they test our assumptions and keep our perspectives in a nascent market fresh and flexible.

If this byt3 elicited a response, please clap/share/comment below and explore the rest of Dig3st in the meantime. Follow us on Medium and Twitter for all the latest byt3s that are always ~ 3-minutes long and accompanied by data viz.

Asymmetric Lens: How is China Valuing Cryptocurrencies? was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

Disclaimer: Nothing written here is financial advice nor should anyone take my thoughts as financial advisements. These are mere opinions; you should consult with your financial advisor before entering into any transaction.

To premise this byt3, I prefer to invest in projects that will generate value over time (and view the technology as only a fraction of the total equation) so day traders need not read any further. Secondly, I only put in what I could afford to lose completely in the crypto market in order to alleviate any and all emotional decisions. This amount is always going to be different based on a person’s risk profile and personal circumstances. With that out of the way, here are some arguments as to why overweighting certain crypto assets is logical:

High Market Risk

In the 24 hours that followed the singular SEC announcement to delay their decision on the VanEck-SolidX Bitcoin ETF until September, Bitcoin shed 10% of its value and the total crypto market cap shed a whopping 14%. To appreciate this perspective, consider that the largest single-day drop of the Dow Jones Industrial Average during the Wall Street Crash of 1929 was “only” 12%. It’s no secret that the market is still nascent with projects that were all formed in roughly the same decade and thus rising and falling on the same tide. Until projects start to measurably demonstrate their value through both utility/network effects and regulations progressively facilitate adoption, it’s going to be a wild ride for a bit…

Low Diversifiable Risk

While diversification is tried and trued for equity investments, there is one key ingredient missing in crypto: low correlation. Anyone who has been in the market for more than a week has probably experienced the crimson or evergreen tides (and rarely in-between) on their favorite market cap site. Without the ability to diversify away risk made possible by low correlation between coins, the efforts are arguably futile.

Insufficient Data

While pricing, network, and GitHub data are readily available, real traction data is lackluster at best. Equity markets benefit from a plethora of tools such as Bloomberg Terminals, Gartner, CBInsights, etc. to make the best investment decisions and portfolios based largely on the measurable business traction a company is achieving. The fog is still heavy in crypto markets further fueling the speculative discourse that dominates the majority of your broader Telegram feed. This makes diversification more of a spray-and-pray than a sound strategy and dilutes time better allotted to conducting deep research and performance tracking of a few promising projects.

More Like Venture than Equity Investing

Lastly, crypto investments are far more akin to seed-stage investing (with the exception of maybe BTC/ETH given there longer history & scale) making sound valuation techniques something to aspire to, but always to question in the current environment. On the bright side, unlike startup investing, at least we get stop protection in a 24-hour market for any major scandals/bad news/etc.

In conclusion, while some diversification is certainly not a bad thing (especially when sleep is at stake), conducting thorough due diligence with the time you have on projects that you have unique insight or expertise into can yield a higher payoff than applying a broad brush.

This video is always a good reminder that there is no way around the direct correlation of quality and time. Crypto projects are no exception.

If this byt3 elicited any response, please clap/share/comment below and explore the rest of Dig3st in the meantime. Follow us on Medium and Twitter for all the latest byt3s that are always ~ 3-minutes long and accompanied by data viz.

A Case Against Diversifying Crypto Portfolios (For Now) was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s no secret that Silicon Valley is the established venture behemoth of the US, followed by its distant cousin in NYC (Silicon Alley). But whether it be slippery echo chambers, decreasing marginal benefit of startups catering to elites (cool story, Snapchat), or just a desire for some edgier climate, it’s also no secret that the “venture” in VC may be taking on a more literal meaning. So what does capital access look like to a budding entrepreneur in the U.S. in 2018? And more importantly, what does it look like outside of the coastal powerhouses (CA/NY)?

Money Tree or Cambridge, Massachusetts?

With 28% of the local population enrolled at two of the world’s most prestigious intellectual powerhouses (Harvard/MIT), this actually should not come as too much of a surprise. Furthermore, 62% of seed funding in the area went to the capital-intensive healthcare industry further amplifying the outlier status of Cambridge, Massachusetts. But beyond Cambridge, it’s again easy to see why you don’t even need to throw the stick to hit a VC in Silicon Valley. Constituting the next 5/6 spots are regions all originating from the Bay Area to include SF proper, Berkeley, San Mateo, Sunnyvale, and Santa Clara. After the Bay Area are the quiet(-ish?) rumblings of Austin, Texas and some pleasant surprises in Columbus and Kansas City. Of note that in spite of being the second-largest ecosystem, NYC’s seed funding per capita is significantly diluted by its dense population and amplified by what could culturally be considered by some founders to be old money & tight fists.

So Where Does that Leave the Rest of “U.S.”?

First, some fun references. Below is a list of what some other U.S. regions call their respective hubs of innovation. Maybe we should just start a new prefix altogether? Personally looking forward to Crypto- “insert term here”…

1. Silicon Beach — Los Angeles
2. Silicon Prairie — Midwest USA
3. Silicon Harbor — Charleston, South Carolina
4. Silicon Forest — Portland, OR
5. Silicon Slopes — Utah
6. Silicon Bayou — New Orleans, LA
7. Silicon Hills — Austin, TX

Now, back to the data. For the rest of the country (outside of CA & NY), the graph below lists every city’s total seed funding that closed at least $1M in the first half of 2018 and broken down by sector (see legend to right).

While the above graph constitutes $1.05B in seed funding, it still only represents 37% of the total $2.83B in funding that occurred when you include CA & NY. Considering 81% of the population lives outside of these regions, there is a bit of a disconnect. While it does not necessarily hold a candle to personal wealth inequality, it begs the question of whether this is a symptom or a cause? And while there are certainly substantial benefits to having concentrated centers of industry (ex. LA/entertainment, NYC/finance, Las Vegas/vice, etc.), perhaps the venture landscape of tomorrow can continue to quite literally look a bit broader…

If this byt3 elicited any response, please clap/share/comment below and explore the rest of Dig3st in the meantime. Follow us on Medium and Twitter for all the latest byt3s that are always ~ 3-minutes long and accompanied by data viz.

Putting the “Venture” into VC was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

There is zero-margin for error in integrating drones into the airspace and the current FAA regulations fittingly demands a more mature market. On the same beat, it doesn’t take much more than a look at current street congestion, last-mile efforts, and the ability to save puppies to conclude that drones are here to stay. Currently, the $1.8B U.S. market is dominated by the capital-intensive hardware manufacturing of the drones themselves. But the supporting ecosystem and applied services of low-altitude aerial tech are exemplified by startups such as:

Airmap — Airspace management platform integrating low-altitude drone operations with existing airspace authorities.

Iris Automation — Collision avoidance for commercial drones utilizing computer vision and deep learning technology to safely guide drones beyond visual line of sight.

Mars and Indemnis — Drone recovery systems utilizing automated parachute deployment to minimize hazard of flying over uninvolved persons.

Zipline — A fully active aerial logistics company using drones to deliver life-saving medical supplies to rural areas in Rwanda & Tanzania (regulatory arbitrage?) through hub and spoke distribution.

JD.com — Though far from a startup, Alibaba competitor JD.com is already using drones to deliver packages to rural areas in China.

We’ve Been Ready…

While these startups all provide evidence of what is possible with aerial drones, looking at a few other key trends show that the technology and market is already far better positioned than autonomous cars:

Autopilot — Dating all the way back to 1912, autopilot on manned aircraft has evolved over the last 100+ years to automate all aspects of aircraft control with the exception of taxi and takeoff.

Consumer Drone Capabilities — Consumer drones such as the ones found in DJI’s fleets can already automate an entire GPS-guided path with sensors to prevent collisions with buildings or trees using the bulk of technology found in a common smartphone and costing no more than $1K.

Clear Skies — With the exception of airspace deconfliction and inclement weather, autonomous flight has arguably far fewer variables than autonomous driving which may be why autopilot has already been in development over the last 100 years. Icy roads, deer run-outs, unruly drivers/pedestrians, debris, poorly marked signs, construction…the list goes on for variables that autonomous cars need to account for that just aren’t there when you look up.

Navigating Regulations

Currently, the two largest FAA regulations that companies are chomping at the bit to get past are beyond-visual-line-of-sight (BVLOS) restrictions and prohibited flight over uninvolved persons (i.e. all urban areas).

But beyond mere regulatory enforcement, the FAA has already been proactive in facilitating drone operations in class B/C/D airspace. Previously, drone pilots had to request authorization up to 90 days in advance; understandably cumbersome. With the FAA roll-out of the Low Altitude Authorization and Notification Capability (LAANC), pilots can now obtain authorization in near real-time to conduct operations.

While the ecosystem is still far from achieving full maturity, with the blank slate of an open sky and the incredibly evolved technology already powering drones today, there is always 400 feet of opportunity above us.

If you like this byt3, please clap below and explore the rest of Dig3st in the meantime. Follow us on Medium and Twitter for all the latest byt3s that are always ~ 3-minutes long and accompanied by data viz.

For the Future of Autonomous Vehicles: Look Up… was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

My brother and I are big fans of Shark Tank. After watching a few episodes together on vacation recently, I started to wonder whether it’d be possible to identify any patterns in the way Sharks make deals on the show. For instance — are male Sharks more likely to make deals with men than women? Or, for that matter, are male entrepreneurs more likely to make it on the show in the first place? With a few of these questions in mind, I started looking for the data I’d need to come up with answers.

Collecting the data

As I suspected, I wasn’t the first to ask these sorts of questions about Shark Tank. In a post on Quora, Halle Tecco described her own detailed analysis of the show. Amongst other insights, Halle found that across the first six seasons of the show, the two female Sharks (Barbara Corcoran and Lori Greiner) made a larger percentage of their deals with women than their male counterparts.

Halle made the data she collected available online. Due to a lack of information in the dataset on entrepreneurs with non-binary gender identity, I only used male and female labels in my analysis.

Posing the question

It’s worth noting that the number of men and women appearing on the show is not equal: the ratio of all-male teams to all-female teams is roughly 70/30.

I was curious to see how each Shark’s funding probabilities might look after accounting for this skewed ratio. As I thought through different ways to measure this effect, I kept asking myself the same question — what percentage of pitches from either gender did each Shark make a deal with?

I needed a bit more data to be able to answer my question. Luckily, there is a Wikipedia page that provides short descriptions of each Shark Tank episode. I used Beautiful Soup to scrape the attendance records for each of the main Sharks on the show. By combining Halle’s deal data with the attendance records I collected, I was able to determine how often each Shark made deals with the male and female entrepreneurs they heard pitches from.

Each bar represents the percent of entrepreneurs of a given gender that the Shark made deals with. For example, Mark Cuban made deals with 20.2% of the women and 17.1% of the men he heard pitches from.

Discussion

The main conclusion to draw from the graph is that certain Sharks do not make deals with men and women at equal rates. The next step is to ask what might be contributing to this imbalance. The wrong step is to jump to the conclusion that the Sharks are acting on sexist motivations.

Clearly, there are many factors at play here. For one, Barbara Corcoran and Robert Herjavec — the Sharks with the largest funding biases — tend to invest in completely different industries (food/beverage and tech, respectively). Additionally, the data I collected do not contain information on which Sharks made offers that were rejected, meaning my graph does not account for potential biases on behalf of the entrepreneurs.

Season 10 of Shark Tank premiers this October. ABC’s promo for the show claims that “[The Sharks] will once again give people from all walks of life the chance to chase the American dream”. I’m looking forward to the premiere as much as anyone else. But with this analysis in mind, I’ll think a bit more about who gets to walk onto the show — and leave with a deal.

Swimming or Sinking in the Shark Tank…Does Gender Matter? was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.

As a lifelong cyclist/motorcyclist, commuting has always been more of a joy than a chore. I’ve been fortunate to travel through seven countries & eight states via motorcycle and I rarely ever had to feel the angst of “traffic” in my local residence. Even after having moved to New York City, despite the fair share of close calls, pedaling would be the most convenient for me if it weren’t for high theft rates (and ∴ lugging around a 10-lb. lock). In 2016, I had the good fortune of traveling to Europe, where after a lifetime on two wheels, found myself envious of their culture of practical urban mobility. Scooters, bikes, mopeds, motorcycles…all making the most efficient use of limited road space for solo-commuters to complement the substantial public transport options and still have plenty of room for those carpooling with family and friends. So after seeing the recent surge in bike/scooter sharing services in the U.S. via companies such as Lime, Bird, Ofo, and Spin, it gave a renewed sense of confidence that we too could adopt a more practical form of transportation that carries a minimal footprint and some reasons why we all should embrace it:

1. Because they’re already here…

The white squall of bike/scooter share programs surged into major urban cities over the last two years. Outside of China’s early moves into the space, the U.S. stands to benefit immensely from these ventures. As indicated in the data above, the moves are still ramping up, quickly accelerating companies such as Lime and Bird into unicorn valuations in just under two years due in no small part to their impressive execution and “forgiveness rather than permission” mentality. Now, to think that these scooters/bikes are just going to go away because one or two of them were inconveniently placed on “your” sidewalk is a losing battle. Instead, it may be better to use them more frequently (and be pleasantly surprised by the results) and/or help change the dialogue for regulators to embrace and facilitate a new form of urban mobility.

2. Decongestion

It doesn’t take a Ph.D in urban planning to rationalize that a well-scaled network of bikes and scooters will take up a substantially smaller physical footprint than the current state of vehicles on the road. While little data has been supplied to show the degree of which scooter/bike trips replace car trips, any data interpretations are going to be skewed based on the network availability at the time of analysis. The question is more cultural in that can/will users adopt at the rate of which these companies can provide higher accessibility. For the sake of minimizing congestion, we should.

3. Clean Transport

Seeing the number of complaints about bike/scooter share programs in San Francisco (and elsewhere) from people who also probably claim to be “concerned” with climate change is a textbook case of missing the forest for the trees. This same group of grumblers (you know who you are) are painfully analogous to the equally pretentious British Royal Society for the Protection of Birds, who went on to obstruct phase 2 of a planned 1000-MW London Array offshore wind farm due to the perceived “threat” to the red-throated loon. Newsflash: There are plenty of loons…and for the very few of them who ended up actually striking a giant moving blade in the air, Darwinism was working. If we’re taking a utilitarian approach towards environmental protection, then minimizing fuel-burning vehicle traffic is always a net positive. Consider the bigger picture for the common roof we call Earth…and bring on the two-wheeled (e-)transport.

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Micromobility 2.0: Embracing the Scooter/Bike Revolution… was originally published in Dig3st on Medium, where people are continuing the conversation by highlighting and responding to this story.