VC Funding in a Frothy Market

The macro trend is that service based businesses gravitate towards hourly pay (outside of sales), while product businesses allow for earnings based on other statistics, like number of products shipped, while penalizing defects or returns.

Let’s start off with reasons why paying by the hour is a gold standard in the majority of todays businesses:

1. It’s easy to evaluate. An employee works 10 hours, their paycheck is 10 times their hourly rate
2. It boils individuals down into a predictable resource. This way workers can be quantized into discrete bins, to assist in estimating project costs.

   This is common for service companies which provide and charge a fee on top of labor. Project managers aren’t allowed to apply bundles of capital against individual tasks without having a body count behind it.

   Hourly rates aid in estimating cost for specific contracts and competitive bids. Potential clients can judge companies based on bids and their rates, which is the base plus overhead charged on man hours

3. Related to the above rationale, specific clients and customers have their own constraints on acceptable hourly pay. Consider an extreme case:

   Certain customers gladly pay a senior manager egomaniacal blowhard (EB) with credentials 5-10x the rate of a hard working hacker out of school. This holds true even when the young tech will end up doing the lion’s share of building a working product. In addition they’ll do so while dealing with the regular interruption of pointless status meetings to assuage leaderships’ egos. Even if the project is a smashing success the credit and earned value will go to the EB. I count myself fortunate for working with great hands on project leads, even if they are a little egomaniacal .

   I’d have no qualms paying the kid with the technical chops a lump sum, and skipping the middle management and coworker tier all together. But my line of thinking doesn’t fit well into large bureaucratic enterprise systems.

The above are just a few of the reasons why hourly income is the most prevalent form of payment, and I’m confident you could tack on many more of your own forcing functions. Now that we’ve covered reasons for hourly pay, let’s look at variations in earnings systems.

Alternative Income Systems

It’s only by peeking outside the circus tent of enterprise America that we can begin to see more rational reward systems. Far beyond BigCo negotiated executive salary wonderland, the higher pay brackets are all based on investment returns and measured company growth.

Commission
If your effort results in X% revenue growth or profits, then your income is Y% of that increase. This works fantastic in finance, affiliate marketing, and is effective for motivating and retaining business leadership. There’s limited space for the bullshit factor in this earnings systems, making it near and dear to my heart. I love bs’ing, I just don’t believe in getting paid for it.

Even for strategic leaders, determining how much growth is based on their direct efforts is fuzzy. Was the growth product based? Was it marketing? Did the market itself grow drastically faster than your business? It’s hard to judge what fraction of corporate growth is a function of individual contributions beyond the finance industry, limiting this model’s adoption outside of sales positions.

Price fixed projects
Shrewd consultants and companies with strong customer relationships take on cost fixed contracts. They are hesitant to do so with new clients due to ill defined or open ended projects and expectations. For those conditions periodic negotiated rates fit the unknown task better.

The structure of cost fixed projects falls under a product based business umbrella. It’s a single customized product, but client and company expectations are made clear before work commences. Profit margins are based on execution efficiency, a thing of beauty.

What other earning systems am I forgetting this morning (offline or I’d be stalking Wikipedia)? Consider yourself cordially invited to pitch in and elaborate in the comments below.

This story begins with a comment from sigmaalgebra, an anonymous mathematician and avc regular. I appreciated Sigma’s explanation of theory enough to reproduce it here, because the discussion gets to the heart of estimation theory and it’s relationship with knowledge about statistical distributions.

The Question: What is the performance (expected returns) of venture fund X?

Sir sigmaalgebra
Okay, we are an LP; there is venture firm A; and we want to evaluate their performance.

What we want to know is, broadly, how much money can firm A make for us over, say, the next 10 years.

Let that amount of money be X. Then with really meager assumptions, X is a real valued random variable with expectation, finite expectation, and finite variance. In just what all this means, there are succinct, elegant, and profound details in, say,

Jacques Neveu, ‘Mathematical Foundations of the Calculus of Probability’, Holden-Day, San Francisco.

Note that I didn’t claim that all the details were easy reading.

Well, we know that we can’t know the actual value of X now. So, we settle for estimating the expectation of X, that is, E[X]. For fine details on the expectation E[X], Neveu is excellent.

Why the expectation? Because in the long run in practice, from the (weak or strong) law of large numbers, also in Neveu, that is what we will see. So, we settle for the expectation.

Note: If we want to consider utility functions, then we let X be the ‘utility’ we want and maximize its expectation.

So, how are we to estimate E[X]?

Well, if we can get a ‘sample’, say, for some positive integer n, random variables Y(1), Y(2), …, Y(n), that are independent and have the same distribution as X (full details in Neveu), then we can take the average

Z = ( Y(1) + Y(2) + … + Y(n) ) / n

and use real random variable Z as our estimator of E[X].

Okay, just why would we want to use Z instead of some ‘ranking’, etc.? That is, just why is Z the ‘estimator’ we want?

First, Z is ‘unbiased’, that is, E[Z] = E[X]. That is, in the long run, Z will give us the right answer instead of something off to one side.

Second, among unbiased estimators, Z is the most accurate, that is, has the least variance. Details are in, say, the classic:

Paul R. Halmos, “The Theory of Unbiased Estimation”, ‘Annals of Mathematical Statistics’, Volume 17, Number 1, pages 34-43, 1946.

So, with Z we get the world’s only minimum variance, unbiased estimator.

And that’s why we use Z and why we use averages of past data, and some assumptions, to estimate future returns.

And that’s why we don’t take seriously ‘heuristics’ such as ad hoc ranks.

And that’s why we don’t like results that come from just ‘algorithms’ without some solid reasons to take the calculations seriously.

And that’s why we laugh at much of ‘data mining, machine learning, big data, heuristics, and artificial intelligence’.

And that’s why we want some solid reasons for any data analysis we do and not just some intuitive suggestions.

And that’s why math with theorems and proofs is so powerful.

And, Mr. P. Thiel, that’s some of why we go to college.

Thus endith the first lecture in Data Handling 101.

My response was to bring up the common fallacies of datamining, and also to discuss the limitation of estimation theory on unknown distributions. I also wanted to point sigmaalgebra at a recent find I happened upon by Jeff Jonas in support of more data over more complex statistical apparatuses.

Me
1) Unbiased estimators depend on an underlying assumption about statistical distributions. If you’re model is off, your optimization is as well. Also there are a number of documented cases where L1 (absolute value) outperforms L2 (least squares) which much of the classical theory relies on.

2) “And that’s why we laugh at much of ‘data mining, machine learning, big data, heuristics, and artificial intelligence’. “, I agree that there are common fallacies, but I think you’ll enjoy Jeff Jonas’ counter point and humorous title, “data beats math”.

Mr. Jonas’ findings:

“My gripe, if any, is that way too many people are chipping away at hard problems and making no material gains in decades (e.g., entity extraction and classification) … when what they actually need is more data. Not more of same data, by the way. No, they more likely need orthogonal data – data from a different sensor sharing some of the same domain, entities and features (e.g., name and driver’s license number).”

This morning I awoke to an in depth reply and schooling in linear algebra, which brought back fond memories of subspace theory in graduate school with Professor Zemanian. The central limit theorem crept in (justified but based on another assumption), as well as a practical statement describing statistical assumptions. In particular Sigma made it clear that the set of variables Y share the distribution of the fund in question X.

Sir SigmaAlgebra
Mark,

For your:

“Unbiased estimators depend on an underlying assumption about statistical distributions.”

but on this I assumed merely that

“random variables Y(1), Y(2), …, Y(n), … have the same distribution as X”

and that is sufficient.

Or, for algebraic details, which are easy enough, since, for i = 1, 2, …, n, each Y(i) has the same distribution as X, we have that E[Y(i)] = E[X]. Then from our estimator

Z = ( Y(1) + Y(2) + … + Y(n) ) / n

using that expectation is linear (Neveu), we have:

E[Z] = E[( Y(1) + Y(2) + ... + Y(n) ) / n]

= ( E[Y(1)] + E[Y(2)] + … + E[Y(n)] ) / n

= ( E[X] + E[X] + … + E[X] ) / n

= ( n E[X] ) / n

= E[X]

so that

E[Z] = E[X]

as desired.

The minimum variance part from the Halmos paper is tricky to see but, still, makes only meager assumptions about the distribution of the data.

For your:

“Also there are a number of documented where cases L1 (absolute value) outperforms L2 (least squares) which much of the classical theory relies on.”

Note: In L^1 and L^2, etc., the L abbreviates H. Lebesgue. He was an E. Borel student about 100 years ago and redid the integration in calculus. One result was ‘measure theory’ which via Kolmogorov became the foundations of ‘modern probability’ as in Neveu.

L^1, L^2 are ‘norms’, essentially definitions of distance, on ‘vector spaces’. In the case of a random variable such as X, the L^1 norm is

|| X ||_1 = E[ |X| ]

On the left, the double vertical bars are common norm notation (in D. Knuth’s TeX they look better). On the right, the single vertical bars are just absolute value. Here I borrow from TeX and let _1 denote a subscript.

The set of all X with finite L^1 forms a vector space. It turns out, as in Neveu, it’s ‘complete’ and, hence, a Banach space. ‘Complete’ is much as in the real numbers, that is, for a sequence that appears to converge, there is something there for it to converge to. The rationals are not complete because rationals can converge to, e.g., pi which is not a rational. One joke is, “Calculus is the elementary properties of the completeness property of the real number system.”

For the L^2 norm of X, that is

|| X ||_2 = E[ X^2 ]^(1/2)

Similarly the set of all X with finite L^2 forms a vector space. It turns out (Neveu again), it’s ‘complete’ and, hence, a Banach space.

But also important is, for this vector space, we can define the ‘inner product’

(X,Y) = E[ XY ]

Then the norm is just from the inner product:

|| X ||_2 = (X,X)^(1/2)

With this inner product, we have a Hilbert space, which is a good thing because we can do projections.

Completeness for the random variables under the L^1 and L^2 norms is a bit amazing.

It appears that Hilbert space is an abstraction of several important examples from the 19th and 20th centuries, especially Fourier theory, various orthogonal polynomials, spherical harmonics, various parts of differential equations, etc. So, get to do the derivations once, just from the axioms, and apply them many times.

Apparently Hilbert space was a von Neumann idea; there’s a claim that once he had to explain what he meant to Hilbert!

Back to our estimator Z: When we have n samples, let’s call our estimator Z(n). Then, as n grows large, we would like Z(n) to be a better estimator of E[X].

Well, in the L^2 norm, our error squared is:

|| Z(n) – E[X] ||_2^2 = E[ (Z(n) - E[X])^2 ]

= E[ (Z(n) - E[Z(n)])^2 ]

= Var( Z(n) )

or the variance of Z(n). Such math looks MUCH better in TeX!

But from the definition of Z(n) and our assumptions and our use of Y(1), Y(2), …, Y(n),

Var( Z(n) ) = Var( ( Y(1) + Y(2) + … + Y(n) ) / n )

= ( 1/n^2 ) Var( ( Y(1) + Y(2) + … + Y(n) ) )

= ( 1/n^2 ) Var( Y(1) ) n

= Var( Z(1) ) / n

So, as n grows, our error

|| Z(n) – E[X] ||_2 = Std( Z(1) ) / n^(1/2)

where Std is the standard deviation, that is, the square root of the variance.

So for large n, our L^2 error converges to 0. So, our estimator Z(n) converges to E[X] in the L^2 norm. Then (Neveu) some subsequence of our sequence Z(n) must converge to E[X] with probability 1, that is, ‘almost surely’, and that’s the best we can hope for. In practice, or with more analysis or assumptions, Z(n) will converge to E[X} almost surely without the issue of subsequences.

For practice, convergence in L^2 is good enough to take to the bank.

If we were to work with L^1 instead, then things would be more difficult. Also we might end up estimating the median of X and, thus, have a biased estimator of the expectation of X.

Also, we can know more than just that Z(n) converges to E[X}; in addition we can find 'confidence' intervals. From the sum of the Y(i) and the central limit theorem, for n over a few dozen, the distribution of Z(n) will be close to Gaussian. So, we can use a t-test to get a confidence interval. If we want to be still more careful, then there are some simple, somewhat 'computer intensive', 'distribution-free' (where we make no assumptions about the distribution of X) methods for getting a confidence interval on our estimate.

On the value of 'big data', that's questionable. I would recommend being careful about what we know about the data we seek and use. The criterion of "orthogonal" is not so good; likely he meant independent.

One warning about 'big data' is in our:

|| Z(n) - E[X] ||_2 = Std( Z(1) ) / n^(1/2)

So if we multiply n by 100, then we divide the right side by the square root of 100, that is, 10 and, thus, get just one more significant digit of accuracy in our estimate. So, getting three significant digits is common; getting four is usually a strain; getting eight or more is nearly absurd.

Commonly getting three significant digits is so easy that struggling with ‘big data’ to get, say, six significant digits is not worth the effort.

One of the problems in practice, e.g., in ‘data mining’ and ‘machine learning’, is just ‘fitting models’. There can be some value there, but there are also some serious pitfalls: E.g., for some positive integer n and for numerical data (x(i), y(i)), for i = 1, 2, …, n, if the x(i) are distinct then we can just write down a polynomial p of degree n-1 so that, for all i, p(x(i)) = y(i). This is called Lagrange interpolation and has a richly deserved reputation for just absurd results. Fit? Yes. Useful? No! For something less absurd, can also use various splines, least-squares splines, and for functions of several variables. Still, getting ‘causality’, ‘meaning’, or good predictions from such efforts is not easy.

My estimator of the coveted E[X] was from samples Y(i), but the thread assumed some additional data about first rounds and follow on rounds. So, with this additional data, might a more accurate estimate be possible? In principle, yes.

One approach, the most powerful possible in the L^2 sense given sufficient data, is just old cross tabulation. That is, and an easy exercise starting with Neveu, if we have some data Y and want to estimate X, then we can take the conditional expectation of X given Y, that is, E[X|Y]. Then there is some function f so that E[X|Y} = f(Y), and this function f minimizes

|| X - f(Y) ||_2

Well, cross tabulation can be used to give an estimation of a discrete approximation to f.

Another approach, that can work with less data, is to assume a 'model' with some parameters and then determine the parameters by fitting to the available real data. Pursuing this direction is beyond the scope of this post now!

This would be, what, a Q&A session after the first lecture in Data Handling 101?

Neveu? An elegant, powerful, crown jewel of civilization.

I found an hour this morning to carefully read through and think about Sigma's reply:

Me
Thank you for the in depth reply and review of subspace theory. Your careful description brought back fond memories of graduate school, and your passion for the subject is obvious.

Let's focus on a couple of assumptions that appear minor but could lead to surprise and unexpected results.

"random variables Y(1), Y(2), ..., Y(n), ... have the same distribution as X"

Do you believe Would you wager that there is a distribution for X that bounds the behavior of the venture fund in question? Even if there is such a distribution and X is historically well behaved how do you account for singularities?

----------------------------------------------------------------------

Also in this step:

|| Z(n) - E[X] ||_2^2 = E[ (Z(n) - E[X])^2 ]
= E[ (Z(n) - E[Z(n)])^2 ]
= Var( Z(n) )

you replace E[X] with sequence Z(n) in order to later describe how Z(n) converges to E[X] for large n.

|| Z(n) – E[X] ||_2 = Std( Z(1) ) / n^(1/2)

That appears to be using the result of the derivation to prove the derivation.

———————————————————————-

With regards to the central limit theorem:
“From the sum of the Y(i) and the central limit theorem, for n over a few dozen, the distribution of Z(n) will be close to Gaussian.”

With this statement are you pegging E[X] as Gaussian? That can’t be correct. Once you assume E[X] is Gaussian you’re working on a different problem, and no longer trying to predict an individual venture fund’s returns. Are you stating Fred and the team at USV are properly characterized by a Gaussian random variable ? Of course you could fit a Gaussian curve through their portfolio returns, but doing so doesn’t make company returns adhere to the distribution. Fred has described fund performance as a power law curve in the past here.

———————————————————————-

On big data: (I wish I could annotate your comment to split off better side discussions). The limitation you describe on significant digits is predicated on your model: ||Z(n) – E[X]||_2 = Std( Z(1) ) / n^(1/2). What Jeff described was by using simpler models and more data he achieved better results than by applying more complex models to far less data.

Is there a large difference between model fitting (agreed there are good and poor techniques), and assumed distributions? I could represent unknown spaces of measured data with a placeholder distribution that describes local region behavior instead of generating presumed values (splines/interpolation/least squares).

I didn’t have time to dig in and understand your mention of cross tabulation, but the relationship has me interested E[X|Y] = f(Y) where f minimizes || X – f(Y) ||_2. The best estimator for current fund performance should weight previous performance, I just don’t know how that would look (environment and macro economic trends impact performance as much as the selection of the investors).

I’ve relied on Taylor series expansions a number of times in Physics while chasing dominant relationships. I feel comfortable pursuing dominant relationships with limited parameter spaces. Allow parameter number to grow and your fits keep on improving (always penalize additional degrees of freedom when scoring).

Here’s the principle. As your company grows, you tend to add people in “layers”.

1. The top layer is the first founder or founders. There may be 1, 2, 3, or more of you, but you all start working about the same time, and you all take the same risk… quitting your jobs to go work for a new and unproven company.
2. The second layer is the first real employees. By the time you hire this layer, you’ve got cash coming in from somewhere (investors or customers–doesn’t matter). These people didn’t take as much risk because they got a salary from day one, and honestly, they didn’t start the company, they joined it as a job.
3. The third layer are later employees. By the time they joined the company, it was going pretty well.

What happens if you raise an investment? The investment can come from anywhere… an angel, a VC, or someone’s dad. Basically, the answer is simple: the investment just dilutes everyone.

Using the example from above… we’re two founders, we gave ourselves 2500 shares each, so we each own 50%, and now we go to a VC and he offers to give us a million dollars in exchange for 1/3rd of the company.

1/3rd of the company is 2500 shares. So you make another 2500 shares and give them to the VC. He owns 1/3rd and you each own 1/3rd. That’s all there is to it.

What if one of the founders doesn’t work full time on the company? Then they’re not a founder. In my book nobody who is not working full time counts as a founder. Anyone who holds on to their day job gets a salary or IOUs, but not equity. If they hang onto that day job until the VC puts in funding and then comes to work for the company full time, they didn’t take nearly as much risk and they deserve to receive equity along with the first layer of employees.

What I really appreciated about Joel’s equity split:

• It cuts quickly past the immeasurable contribution arguments and focuses on a balanced split
• It has tiered layers of dilution as the company grows
• Investors act as just another dilution layer

But there was one detail of Joel’s split I couldn’t fully comprehend. He labels any early team member with alternative income creating responsibilities as a non cofounder. Instead of an equity split, he suggests these contributions be rewarded with IOUs, or the promise of future compensation when the company is capable of paying. I described why I took issue with this arbitrary time boundary in my comment on AVC which is copied below:

Joel mentions other sources of income as a decision boundary for rewarding effort with IOUs or the possibility of future cash, instead of equity. What about cofounders with other responsibilities like family, dependents, an active social life, a longer sleep cycle or health issues that require time. Isn’t Joel stating that all early team member’s time should be valued equally? If so then almost no one qualifies as a founder and we should all write each other IOUs for cash.

How many early startup investors take IOUs for the possibility of future fixed percent gains? They buy equity with their dollars, why should the risk of contributing time be rewarded any less so?

If perceived fairness is the bullseye, we must agree on what it means to be fair. Experience shows we tend to shift fairness in favor of the guy or gal with control or capital.

Related posts:

1. Joel Spolsky’s answer
2. Fred’s post

What’s broken

• Divergence of GDP growth and Service Industry productivity:
  
  Our socio-economic system is experiencing a growing disparity between GDP and service industries, which is a product of the fixed hourly cost of public services versus the exponential growth of product based markets. The phenomenon is labeled Baumol’s cost diease:

  In a range of businesses, such as the car manufacturing sector and the retail sector, workers are continually getting more productive due to technological innovations to their tools and equipment. In contrast, in some labor-intensive sectors that rely heavily on human interaction or activities, such as nursing, education, or the performing arts there is little or no growth in productivity over time. As with the string quartet example, it takes nurses the same amount of time to change a bandage, or college professors the same amount of time to mark an essay, in 2006 as it did in 1966.

  Baumol’s cost disease is often used to describe the lack of growth in productivity in public services such as public hospitals and state colleges. Since many public administration activities are heavily labor-intensive there is little growth in productivity over time because productivity gains come essentially from a better capital technology. As a result growth in the GDP will generate little more resources to be spent in public sector. Thus public sector production is more dependent on taxation level than growth in the GDP. (source)

• Ballooning Government Spending:
  

  It seems abundantly clear that the cost structure of the U.S. economy has ballooned to unsustainable levels. By cost structure I mean all the fundamental inputs: energy, healthcare, etc.

  For instance: the budget of every city, every county, every agency and every department of every government in the U.S. is under pressure from two basic sources: pension costs and the inexorable rise in mandated healthcare employee costs. What was once a relatively modest share of total employee compensation–healthcare insurance, drug coverage, and eye care–has swollen into a huge percentage of total employee compensation.

  If you wonder why building permits now cost more, and parking tickets have tripled, and why garbage fees are skyrocketed, look no further than the crushing burden of ever-rising pension and healthcare costs on every level of government.

  These skyrocketing costs act as a huge economy-wide tax, raising the input costs of every good and service in every nook and cranny of the economy. Healthcare (what I call “sick-care” because our collective health continues to slide despite ever-larger sums spent on “curing” us) consumes $2.7 trillion of the $14 trillion U.S. economy–almost 20%. That’s equivalent to a 20% VAT (value-added tax) on every purchase.

  The bubble mentality of the past decade has also constructed an economy-wide tax. Here is an example. A famed local independent bookstore which once had 3 outlets in the SF Bay Area recently closed its last store. The local newspaper reported it owed two months rent of about $6,600.

  How many books would the store have to sell to net $3,300 for basic rent? Add in utlities, employee wages and benefits, cash flow needed to maintain inventory, etc. and you come up with a stupendous number of books which would have to be sold.

  Compare that to a cost structure in which rent was $500 per month.
  (source)

• Government is incapable of simplifying: Our national government is incapable of simplifying, improving public service efficiency, or rational cost cutting in areas which have grown without check.
  

  Bureaucracies and institutions may suffer from Baumol’s Disease, but they also suffer from the Ratchet Effect: they only know how to add expenses and scale up. Productivity, Baumol’s Disease and the Cliff Just Ahead (December 8, 2010) Baumol’s Disease describes the rising costs of sectors whose productivity gains lag behind more productive sectors. Thus education costs more even as manufactured goods fall in price, as labor-intensive education doesn’t lend itself to leaps in productivity.

  But Baumol’s Disease doesn’t explain why fighter aircraft now cost $300 million each when the “best of the best” five years ago cost $56 million, or how Medicare has leaped from $52 billion a year to $600 billion a year in a decade. Nor does it explain why property taxes have risen 60% above inflation in the past 10 years.

  What does explain these gigantic increases is monopoly powers granted to cartels by unaccountable State fiefdoms. With the Federal government able to borrow and spend without any visible limits, then the sky’s the limit on everything from MRI tests to Medicaid to foreign wars. With the public unable to opt out of local government, then local government expands and passes the costs onto the private-sector tax donkeys. (source)

• Real wages are contracting: The purchasing power of an average American is dropping while at the same time our nations overall productivity (GDP) is growing. Where’s the additional revenue going? The solution is that wealth is compressing into the hands of fewer people, yielding flat averages but overall GDP growth.
  

What can be done to remedy the situation

When large complex systems such as our economy become broken, there are two broad strategies for remedying the situation.

• Adapt laws to patch it: The first strategy is to make subtle changes in law which promote natural competition which distributes the problem. For software developers this is the equivalent of patching bugs in a library. The only limitation of legal patches is that they are unable to fix fundamental flaws in design without a complete overhaul
• Adopt alternative systems: Another method to repair our economy is to adopt a brand new economic system. We could reinvent capitalism, with an understanding of it’s connection to public services. A variety of different socio-economic systems could compete in parallel on small scales, as they do in our states and counties, allowing us to identify which best works for our federal government. There’s no reason for a drastic shift, merely allowing the existence of alternatives is enough to propel our nation down a path of parallel economic evolution. Folks will self select the systems which best serve their needs. Imagine the possibility of dynamic socio-economic systems.

Related articles:

1. The Devolution of the Consumer Economy, Part II: Rising Costs, Declining Wages
2. Lowering the Cost Structure of the U.S. Economy
3. Productivity, Baumol’s Disease and the Cliff Just Ahead and Baumol’s cost disease
4. Who will demand reductions in public employee benefits?
5. Bureau of Labor Statistics, all types of charts can be created on the data page

US Federal Government Shutdown

I can comprehend the looming presence of a government shutdown. It’s the rational consequence of irrational spending without sufficient revenue. There simply isn’t enough monopoly money to go around, and legislators don’t want to face the necessary cutbacks which will result in a sustainable budget. Each and every year since the 1990s the US government has spent more per year than it generates in tax revenue. Healthy economic growth and inflationary forces diminish the relative debt, while deflation or economic setbacks can lead to catastrophic feedback loops. Fiduciary failure from US leadership has resulted in deficits and bloating debt with each passing year.

The strongest vote is the one we make with our wallets. Dramatic expenditure reduction or massive increases to tax revenue are necessary if we want our national government to continue to exist as we know it. Allowing our federal government to go bankrupt is a strong statement that we believe it’s impossible to fix. How did we find ourselves in such a predicament?

Federal Reserve

“I sincerely believe… that the principle of spending money to be paid by posterity under the name of funding is but swindling futurity on a large scale.”
— Thomas Jefferson

The Federal Reserve sells off Treasury bills, issuing debt to access immediate capital. The deficits our federal government run at consistently adds to the US Federal Debt. The income generated from selling off bills is used to prop up the government and keep it running, a titanic mockery of Weekend at Bernies.

The following is a living reminder of the estimated federal debt:

The approximate breakdown of US debt is available live at usdebtclock.org³, linked through the image below. Caution the site is flakey and ties up browsers (Flash based).

The numerical size and rate of growth of US national debt is disturbing. If citizens spent like this in our personal lives, we’d quickly converge on both bankruptcy and poverty. What’s even more sickening is what happens when the cash pipes stop flowing.

Elected officials, including Boehner, Reid and Obama, all would be paid during a shutdown, unless Congress changes the law. Soldiers, law enforcement officers and other government employees whose jobs are deemed “essential” would keep working yet wouldn’t get paychecks until the standoff is resolved. (source¹)

The government shutdown would result in essential personal continuing to work but without compensation. The most optimistic characterization of this practice is unfree labour, the more sinister label is slavery⁴.

References:

1. Long Government Shutdown Would Harm U.S. Economy, Hit Washington Hardest
2. Federal Reserver System
3. US federal budget
4. Good debt clock: US Debt Clock (javascript) and buggy debt clock: US Debt Clock (flash)
5. Unfree Labor and Slavery

It does when Virgin Galactic takes wealthy folks into space

Today’s post is from a series of emails a few friends and I are having about the current state of wealthy distribution.

A rich neighborhood next to a poor one isn’t likely to send their kids to the same school, or the same colleges, or have intros to the same business colleagues. But the communities share a border, and have equal say in local government*. Roads go between the neighborhoods, and wealthy folks don’t want their kids hanging out in the poor parts of town. The servants and laborers for the wealthy neighborhood are very likely to come from the poorer area. This is our reality.

If a wealthy acquirer purchases and moves to their own private island and abandons society to shun it’s high taxation, wealth doesn’t afford them anything beyond what they leave with. Any group which doesn’t obey the system where the buyer earned their wealth (pirates!) could claim the island. The island buyer would have little recourse but to negotiate with the visitors, hire their own army, or demand the government in which the property was purchased to enforce ownership rights by force.

Are we born into a type of subservience to wealth? What about societies which function nearly outside of the flow of our currency? Or is the contrary true, where we are born into a type of social debt to the people around us. Our peers can legitimately vote to increase taxes on our former earnings at any time. There is little room for the right to enormous reserves of wealth in a desperate society. The rules will continue to slide in favor of the many, at the cost of minorities. Mass inflation or the toppling of governments can leave currency valueless. Wealthy businesses and individuals constantly barter with the governments which protect them, to maintain their ongoing control of resources.

Money doesn’t exist or function unless most of society believes in it, and continues to hold it sacred as a system of fair exchange. Consider the uprisings around the globe recently. The right to survive and claim more than their government legally sanctioned was enough to cause drastic political upheaval. In nations where a tiny fraction of people control the money and government, social unrest lit up like a raging inferno. Even when a majority suppresses a large minority, it’s enough to cause revolutions. Some don’t end well for those who rebel, as those in control have tanks and bombs. Those in power enforce their right to wealth and control by spending some of that wealth on weapons.

It’s clear that wealth doesn’t exist in a vacuum. I believe the right to earn and own wealthy is fundamental to capitalism and free societies. Capitalism and currency is an interesting system. Either you have it and deal with uncontrolled whacky distributions brought on by folks that work harder and/or game the system, or you get rid of it and force everyone to work like slaves or starve. Without financial incentives, who decides who does what job best or who even gets a job? I wouldn’t mind a forcing function (beyond inflation) to motivate continuous reinvestment of capital. When too much money sits stagnant in one entity’s control, it transforms into lost social potential. Perhaps static reserves should face higher taxation, easily avoidable through reinvestment. Money put to work tends to produce greater social wealth (healthy, somewhat stable communities).

It’s a fine balancing act, and I’m not sure even our sharpest economic pros should be making large systematic changes. If there’s a better way to distribute resources we haven’t found it yet.

Notes:
*= The idea is every citizen gets equal representation, but the reality is it’s anything but equal (lobbying).

Marc Andreessen suggests the most important characteristic to predicting successful startups is market.

In a great market — a market with lots of real potential customers — the market pulls product out of the startup.

The market needs to be fulfilled and the market will be fulfilled, by the first viable product that comes along.

The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.

Minimum Viable market is a continuation of the ideas I discussed in the elusive nature of success. What market is sufficient to sustain a business, and how can an entrepreneur or content creator build on that market once equilibrium is achieved. Kevin Kelly has revisited his postulate that in today’s market, a content creator, artist, or small company needs only 1000 true fans.

A true fan is someone who would buy whatever you produced during the year, and would spend say at least $50 on your stuff, go to every one of your shows, or signings, purchase anything you produced. If the independent artist dealt directly with these fans, getting most of what they paid (unlike the deal under publishers, labels, studios, galleries) then the arithmetic suggested an artist could, in theory, need no more than 1,000 fans to make a $50,000 living.

That was the theory. The question was, were there any stars of 1,000 true fans? Was anyone actually doing this? My friend Jaron Lanier threw up an even more realistic challenge: were there any examples of artists surviving on direct fans who did not migrate from some kind of success with “old media” first? In other words, do we see any a true-fan supported indie artist going digital first?

Back then in 2008, I could only find three artists who might have qualified. I felt that it was still a little earlier because this process would almost by definition, take time to cultivate.

Now several years later I can point to a whole pile of creative people who are making a good living independent of traditional media mediators, who are living directly off their fans.

The biggest star these days is Amanda Hocking, who is selling 100,000 ebooks on Kindle per month, and keeping most of that money ($3-5 retail). (FYI, her books are low-brow vampire Twilight knockoffs.) As one publisher whispered online: “there is no traditional publisher in the world right now that can offer Amanda Hocking terms that are better than what she’s currently getting, right now on the Kindle store, all on her own.”

A minimal sustainable market can range from one client to hundreds of millions of customers depending on margins and the duration of the relationship. An individual can survive easily with a single true fan which nets the content creator $50k per year, but the content creator or business is highly dependent on having at least one true fan all year.

This situation is similar to employment with the difference being the direction of work coming from the individual instead of an employer. Consultants serve a number of clients to generate enough income to comfortably survive, and have moderate flexibility (depending on market and reputation) in the types of jobs they select. Alternatively a content creator could be a smashing success for only a couple of months and generate enough profit to live comfortably indefinitely, but this is the exception not the rule.

As founders or self sufficient content creators we need to understand who composes our minimum viable market, and whether we can reasonably expect to grow a profitable business in that area. Market sizing is essential for entrepreneurs who wish to disrupt and navigate large dynamic markets. Even a small or solitary business’ survival is incumbent on understanding the values of the market they serve.

Perhaps a more appropriate term for right sizing a market is minimax viable market.

Minimax (sometimes minmax) is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain. Alternatively, it can be thought of as maximizing the minimum gain (maximin).

Related posts on marketing:

• Marketing Oneself is a Distraction
• Marketing Posts

I’ve had the good fortune of enjoying many day trips into New York City’s museums, but I’ve always wondered how they function as non-profit organizations. I know the major revenue generated by museums is comprised of large private endowments, visitor donations, grants for public institutions, and special fund raider parties. But it’s got to be very expensive to upkeep a building like the Metropolitan Museum of Art.

Some quick back of the envelop estimation lends me to believe the MET takes on $300,000 dollar per week from visitor donations alone*. That yields revenue of $15 million per year. On top of that small amount add cafeterias and gift shops with high margins, and supercharge that revenue with large donations from wealthy private supporters, fund raisers that boost the endowment fund, member fees, and investment returns on the endowment fund. I suspect the MET has $200-220 million dollars each year in revenue to operate or add to its fund.

An article from 2009 describes one third of the museum’s operating budget comes from the endowment fund, and places staff levels at 2200 (full and part time). Given the employee head count we can estimate a cost in salaries and benefits at approximately $100-150 million. Assuming a reasonable cost for building upkeep gives us a cost of doing business is $200+ million dollars per year which is in the same ballpark as revenue, which we’d expect for a non-profit.

One interesting takeaway from the speculation (and 2009 article) is that on boon investment years the museum can operate at a much higher level due to the extraordinary value generated from it’s endowment fund which is somewhere between $2-3 billion dollars.

Notes:
*= Assuming 10k visitors per day on the weekends and 2k visitors per week day (low balling that guess) and each donating on average $10 (more for adults, less for folks short on cash).

The financial outlook for Social Security is little changed from last year. The short term outlook is worsened by a deeper recession than was projected last year, but the overall 75-year outlook is nevertheless somewhat improved primarily because a provision of the ACA is expected to cause a higher share of labor compensation to be paid in the form of wages that are subject to the Social Security payroll tax than would occur in the absence of the legislation. The Disability Insurance (DI) Trust Fund, however, is now projected to become exhausted in 2018, two years earlier than in last year’s report. Thus, changes to improve the financial status of the DI program are needed soon.

Social Security expenditures are expected to exceed tax receipts this year for the first time since 1983. The projected deficit of $41 billion this year (excluding interest income) is attributable to the recession and to an expected $25 billion downward adjustment to 2010 income that corrects for excess payroll tax revenue credited to the trust funds in earlier years. This deficit is expected to shrink substantially for 2011 and to return to small surpluses for years 2012-2014 due to the improving economy. After 2014 deficits are expected to grow rapidly as the baby boom generation’s retirement causes the number of beneficiaries to grow substantially more rapidly than the number of covered workers. The annual deficits will be made up by redeeming trust fund assets in amounts less than interest earnings through 2024, and then by redeeming trust fund assets until reserves are exhausted in 2037, at which point tax income would be sufficient to pay about 75 percent of scheduled benefits through 2084. The projected exhaustion date for the combined OASI and DI Trust Funds is unchanged from last year’s report.

The long-run financial challenges facing Social Security and those that remain for Medicare should be addressed soon. If action is taken sooner rather than later, more options will be available and more time will be available to phase in changes so that those affected have adequate time to prepare.

There are couple of important takeaways from the report. The disability insurance fund lost two years of longevity, which is a rapid drop. Much like large insurance funds, social security funds are subject to a heavy dose of speculation. The report authors expect the economy to recover in 2012-2014, which is both optimistic and fairly uncertain. Many more baby boomers are expected to retire and begin collecting benefits, which highly probable. If there’s one thing I’ve learned observing economic shifts is that its anything but predictable beyond today. As much as folks try to project national and global markets onto systematic processes, highly volatile periods reveal the unpredictable nature of large financial systems (see complex adaptive systems). Since accurately modeling the future is damn near impossible, I humbly suggest falling back on the old proverb for retirement preparation, hope for the best, but prepare for the worst.