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Accelerators vs Incubators vs Coworking vs Venture Capital

Note: I already have all the solutions and plans figured out, so this isn’t like a brainstorming session article.  This also isn’t a professional article, it’s my personal blog.  Trying to communicate some of the beginner stuff, but I’m a horrible teacher due to a subpar Broca that regulates the transition from ideas to the formation of comprehensible verbal words expressing those ideas. At least you can trust that my understanding of these ideas are actually a millions times higher then what I’m capable of telling you about.  

This area is the focus of my discovery.  So I don’t talk about it often, but wanted to highlight some differences and existing models in the market:

While both may raise money and invest, startup accelerators and incubators are run more like businesses themselves (often under the guise of “preparing companies for venture capital”). A venture capitalist is supposed to be a specialist- an investor that invests in something very special, like the next Google. Technically, these two shouldn’t even be investing in the same companies.

Most founders I’ve seen don’t understand this difference. One founder recently, for example, tweeted about their shock when they saw an incubator using their company as a PR strategy to raise money for the incubator, instead of for the company. This happens all the time, especially when the accelerator or incubator is new. Because it’s a business, and most founders don’t realize they don’t have the next Google– that means it costs money to help them and they can’t make up for it with equity.   

Equity-Based Models (In Search of the Googles)

There’s an interesting conundrum here- the question of who has the next Google and therefore actually has valuable equity to give (almost no one).

Silicon Valley is always afraid to say the ‘emperor has no clothes’.   They have witnessed something special enough to know it exists, so everything is considered special.  And because they kept all the opportunity isolated to one area- they could afford to assume everyone was special.

The rest of the World had the exact opposite problem- they aren’t tainted by their experience and similar group beliefs.  But they have never seen something special, so nothing is ever automatically assumed to be special.  In fact trying to do anything special, was usually completely hopeless because of Silicon Valley’s high concentration of venture capital.

The disparity between these two, allow both to remain ignorant in their own worlds.  Resolving that isn’t the entire solution, it’s actually only a very tiny portion, but it is the first thing that must happen.

Incubators

Most incubator models are actually just selling desk space and throw in some mentors; who then evolved into YCombinator like models propositioning themselves on a little bit of cash and resources in exchange for equity to launch a company.  The YCombinator models breaks here- the “next Google” doesn’t need to be prepared with “mentors’ and launch resources” for venture capital to begin with.  Because this REALLY just lowers value and the focus on launching takes the focus away from discovery (where Google stayed for 4 years and Standard Oil for 7).  And venture capital shouldn’t be investing in incubator companies.  But because the equity/mentor incubator biz model falls apart unless it raises follow-on VC funding for it’s companies, the ruse continues.  There is perhaps a question over the value of “middle ground” companies (what are basically “successful” lean startups), but I won’t get into that because then I can write 100 more pages.

Co-Working

If they don’t do the money and mentoring component, we also might call them co-working spaces. Again it’s the same thing- ie desk space. Coworking spaces are like incubators, but decided that mentors were difficult to get to volunteer, so peers could mentor each other.  WeWork for example fit into this real estate category, because it’s value proposition was redesigning the office space and reducing the lease terms.  We call it desk space because actual commercial leasing still involves retail storefronts, medical, warehousing, etc.

Commercial Leasing

The reason these real estate models evolved into incubators in the first place- was because commercial real estate leasing was very different then residential: commercial uses the income approach for valuation of the property. So the more successful the tenant was- the higher $ for the landlord. Instead of focusing on other metrics like vacancy rates. Most people didn’t realize that commercial leasing was always similar to investing too, because it was about picking the right businesses. Often landlords invested upfront as well in their companies to “build-to-suit”, and would loose too much money if 90% of them failed. Hense why the lease terms were often so long to begin with. But there’s a critical difference- one picks for returns because the LP is the customer, and the other is going to focus on short-term metrics to increase rents because you are the customer.

So all these models: incubators, startup accelerators, and coworking spaces are actually very unrevolutionary. They’re all pretty much the same thing. It’s easy to see why people are getting them confused, as venture capitalists are also trying to compete with each other now and have started offering more services and increasingly are loosing sight of being a specialist.

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“Fail Safe”

“A fail-safe in engineering is a design feature or practice that in the event of a specific type of failure, inherently responds in a way that will cause no or minimal harm to other equipment, the environment or to people.”

Unlike inherent safety to a particular hazard, a system being “fail-safe” does not mean that failure is impossible or improbable, but rather that the system’s design prevents or mitigates unsafe consequences of the system’s failure. That is, if and when a “fail-safe” system “fails”, it is “safe” or at least no less safe than when it was operating correctly.[1][2]

Since many types of failure are possible, failure mode and effects analysis is used to examine failure situations and recommend safety design and procedures.

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No Kill Zone

Someone had mentioned I will kill entrepreneurs. 

All technology has the potential to kill many things.  Technically, yes someone can build a system to do that.

Already found all the risks and the system will be built with fail-safes; for many different scenarios.  I would have to go into technical details, but it can’t eliminate entrepreneurs for example because this is a guiding Law that it’s built on.  If anything I’d say we’re fortunate we had enough time to identify those risks before building it.  Not unusual to start focusing on the larger implications in Year 10.

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Maybe schizo

I swear sometimes I don’t know how I am here, is anything real, or if I am even real. Even if I try to do something normal for once, it just gets weird.

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Single founders +

Told you, you, you, and you.

“We show that companies started by solo founders survive longer than those started by teams. Further, organizations started by solo founders generate more revenue than organizations started by founder pairs, and do not perform significantly different than larger teams. This suggests that the taken-for-granted assumption among scholars that entrepreneurship is best performed by teams should be reevaluated, with implications for theories of team performance and entrepreneurial strategy.”

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3107898

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Reorganizing the Workday.

I will confirm reorganization of the workday is necessary in the plans.  It is a mistake to believe you will just double productivity in the workforce by adding women, and that something else will not be sacrificed.  We are now seeing a dramatic decrease in the number of young men working.  Women are now the sole or primary providers of 40% of households with dependents.  I emphasize again- sole or primary income providers.  If both genders are to be in the workforce (because I assume men exiting the workforce is not the goal either), the organization of the work day will have to be changed.

Men are also now making headway in the family courts. For many states, the court now begins with 50/50 timesharing of children.  Unless there is an issue, the majority of men I have met in their 20′s have their kids 50% of the time now.  Courts have even rid themselves of the term “custody”.  These are recent changes in the past 5 years, and most of the population this will affect haven’t even had children yet.  About 45% of marriages currently end in divorce, this is also the age group that employment income peaks.  That’s a significant portion of the workforce that can’t move anymore.  People with 50/50 don’t move away from each other because of school districts.

So to recap- significant portion of plans, but we are headed that direction anyway.  Not realistic to have employees pulling 12 hour days anymore between 8 hours office time + networking/company events in evenings + 2 hour commutes.

I don’t know what to do about this female vs VC thing.  Spent all that time increasing female VCs, and they give us a makeup unicorn.  Not that impressive.  Women already have more then enough clothes and makeup.  Just stop that.

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Book Deal

There will be time later for the book.

“Queen of Capitalism”, poor unfortunate souls.

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Experimental VC

VC experiences and perspectives are very tainted.  At one point I was making a VC bio, and then benchmarking how it changed the results of the same experiments.  It changed the results so dramatically, I had to take it off quickly.  Basically my ass was coddled/kissed far too much, and everything I was told was a lie.

A few other things…

  • Measuring how information would travel through networks. I planted different documents and stories with the help of different entrepreneurs; then tracked where it went.
  • What was most ironic, was we think of Silicon Valley as being a place that everyone else in the world copies.  Perhaps it was once like that.  But at the time I did my research, it was the opposite.  It was just that the resources hadn’t been spread out.
  • In other words, Silicon Valley is taking everyone within a geographical location- and making them successful.  Then claiming innovation only happens there because this is where all the smart people live. That is actually a lie, and one of the reasons why everyone there will defend it.  Because there is an underlying knowledge that if we now removed that location factor, many of these same people would probably not be successful.  Definitely not once they are put back up against entrepreneurs outside of SV, who have been facing high levels of adversity.
  • I actually found that there was innovation happening everywhere, but there is also a deep knowing that there is no “way out” building that type of company.  So the entrepreneurs are often forced, instead, to work on something smaller.
  • Combating how networks evolve into what Silicon Valley now has, was another large area of discovery for me.
  • Experimented alot with the social gullibility of investors in the earlier years (these were some of my first tests).  I would go into forums with different personas and sway the outcomes.  The usual. Both positive and negative.
  • My personas had different age groups, genders, credentials, etc.

Very very very small portion of what I’ve been doing.

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The Dangers of Brown-Nosing

I think the VC brown-nosing from entrepreneurs has reached unsustainable levels. How would a VC ever know if they’re incorrect, because they’re never challenged. Every single blog post and comments are just praise and agreement. Occasionally one will add information to the VCs comment, but never actually challenge it.

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Quid AI isn’t that good.

Quid’s AI reported that over 50% of venture capital winners will now be outside of Silicon Valley, 20% outside of the US.  It says it also “predicted” a few success 8 years ago here in this article.

From an artificial intelligence perspective, it’s actually not new information.  What it’s really doing is analyzing and inferring a function from labeled training data, in other words…Quid’s AI is not actually a real “prediction”.  It’s something that already exists.

The labeled training data is based off companies that have already raised venture capital.  Which 80% of Venture Capital is injected during the Infrastructure Building Phase (known as Series B; however what it’s called in the press may be different). Most of the VCs already know the outcome of their companies by this stage.