Monthly Archives: July 2015

TwoSense’s Recipe for Equity Distribution

My equation for equity assignment is proportional to “risk” x “skills” x “commitment.”  I have discussed this so often with fellow entrepreneurs, and while we all seem to agree on this, I have never really found something that quantified it to my satisfaction.  This is the metric that I’ve come up with that fits how I operate.  What everyone wants is an objective function where you put in facts and get out a good split.  Unfortunately, the “facts” are almost always the subjective view of the founders.  This metric is for taking those subjective views and estimating a good distribution.

When you found a startup, there are so many blogs, books and webseries on how to assign your equity.  Vesting is a great tool that motivates founders, and safeguards for eventualities where founders leave, can’t join, or cease to get along.  Vesting is all about rewarding hard work while safeguarding against accruing “dead equity” in the cap table.  Some experts say they want to see all founders with similar equity.  Others advise that equity should be split 2-to-1 for partners working full time vs. part time.  I argue that these are all great guidelines, but don’t do the complexities of the issue justice.  Vesting is great, but how much should you be allowed to vest?  What if one partner is exponentially more valuable than another?  When you distill it down to the most essential components, there are three aspects that need to be taken into account:

Everybody wants a piece of the pie.

Risk: a startup is defined as a business operating in the face of abnormally high risk.  Imagine the total risk that the company needs to overcome is an enormous vat.  Every step the team takes reduces the risk left in the vat asymptotically (it’s never empty) until the startup becomes an established business.  But every step does not yield an equal risk reduction.  For example the work to establish initial market viability has a much higher opportunity costs than say testing product color schemes later on.  Investing energy at that level of risk is what should be rewarded, and the more risk the individual carries (or removes from the vat), the more they should be rewarded in equity.  However, if the individual is being paid at market rate, the risk they actually bear is the risk of losing their job if all goes south, which I would argue is substantially less than the opportunity cost of a founder.  This issue is usually covered by the standard pay-vs-equity tradeoff that is prevalent among startups.

Skills: what each team member brings to the table should affect how much equity they get.  Is someone the only person in the world who can fulfill a role? Does the company sink without their specific skill set or experience (max skill points)?  Can the company hire someone easily to replace them (min skill points)?  These are all tough questions that have to be addressed earnestly.  I have seen many companies where the person with the most specific and crucial skill set is undervalued (usually the executing technical co-founder), giving privilege to a non-crucial person who “had the idea.”

Commitment: there are many types of commitment: emotional, financial, legal, temporal, etc. For a startup, building a team all of these are necessary. There is plenty of work on rewarding financial commitment, that’s what Venture Capital is all about. Emotional commitment is par for the course: everyone believes in what they are doing or they wouldn’t have joined.  Time commitment is the most important and is usually handled using vesting, but needs to be addressed within the context of risk taken and skills brought to the table.

equity  “risk taken” x “skills brought to the table” x “commitment.”

The great thing about proportional equations is that absolute values are not needed, you can do all computation with relative values.  As long as everyone is judged on a uniform scale and the size of the equity pool is fixed, the rest of the math just works itself out (the basis for efficient probabilistic machine learning).  I’m not an expert in all things startup related, nor do I have all the answers, but this is what I believe and it’s how equity distribution is being modeled at TwoSense. Unfortunately, I have yet to find a dynamic vesting model that accounts for all of these aspects explicitly. Specifically, risk is the hard aspect to model.  If anyone has any feedback or ideas, please feel free to send them to us through our “info@” address on the contact page.


TwoSense helps a user get out of a speeding ticket

Max is a real person, we’ve been friends for years.  He lives in the Netherlands and works as a mechanical engineer for a large company.  He has been a TwoSense user since the first Alpha release and has helped us build the product with his feedback.  This story is about how TwoSense saved Max from having to pay an unjust speeding fine.

Max and a colleague were on a business trip and had to drive from southern Germany back up to the Netherlands after a meeting.  It was a long haul and he and his colleague split driving duties 50/50.  Halfway up the German border they stopped for a rest and a bite to eat, and he and his colleague switched places. The trip concluded without a hitch.  However, months later Max’s company received a speeding ticket forwarded by the car rental agency.  A hidden speed trap had caught their car speeding on camera.  Max’s colleague argued that Max was driving and had to pay the ticket.  Max claimed it was his colleague who was behind the wheel.  Their employer told the two they had to work out who was responsible and pay up. After a little back and forth, Max opened up his TwoSense app and looked at the map for that day.  He compared the location of the ticket with his timeline, and easily showed his colleague that the camera was located on the stretch of highway after the break where the colleague had been driving.  The colleague admitted defeat and paid the ticket.

A while back we talked about how your data could one day be used to represent you in a court of law.  While in this case it didn’t exonerate him in court, It would be an interesting case study to think about what would have happened had his colleague not fessed up.  Or, if Max could prove that the car was actually not speeding at the time that the speed trap claimed it was (in this case they were speeding).  Speeding fines can be ridiculously expensive, and notoriously difficult to dispute once you’ve been slapped with a ticket.  Either way, Max’s ownership of his location data plus his access to analytics, in this case unsupervised machine learning for venue detection, empowered him in this instance.  It also saved him a $50 fine.  With a little bit of imagination, you could see this as the first time that TwoSense earned someone money with their data: a brief preview of great things to come!


Personal Data and Constitutional Protection

In a previous post we discussed data ownership and some of the legal implications of personal data in court.  We also suggested that using TwoSense, or any other self quantifying service, could give you easy access to use your own data in case you needed it.  But that is not the whole story. Recently, a circuit court overturned a previous decision to protect location data under the 4th Amendment.  That decision required the authorities to obtain a warrant to acquire your location data form a 3rd party, such as your cell carrier.

The decision was based on the ruling that the user had no legitimate expectation of privacy for that data when it was provided to the carrier.  In theory, if that expectation existed, then a warrant is indeed required under the 4th Amendment. A cell provider that offered privacy to their users would therefore fall under 4th Amendment protection, but that would disable further data monetization strategies such as advertising and marketing, so none exists.  We can only assume that the increase in customers through the demand for privacy is worth less monetarily than the revenue generated from marketing services, which does follow trends we’ve looked at before.  But we can also assume that a privacy-preserving 3rd (or 4th) party data service (e.g. TwoSense) would be protected under the 4th Amendment.

But the 4th Amendment is not the only one that could apply here. The 5th Amendment protects the individual’s right to not incriminate themselves.  What if you have access to your personal data that incriminates you?  Do you have to surrender that data? And what is the responsibility of a 3rd party that also has access to surrender that data?  Morally these are questions with no straightforward answers.  Legally, there are some though. Precedence was created when the court protected encryption keys under the 5th Amendment, so if your data is encrypted, you do not have to surrender the password. But otherwise, as far as I can tell (see disclaimer), your data can be subpoenaed and you have to comply, wether or not that data incriminates you.

Disclaimer: I am not a lawyer, this is not legal advice.