A Grand Unifying Theory: Current Case Studies

This post is one of six posts in a series on this topic. The full list of posts are linked here for convenience.

A Grand Unifying Theory: Overview
A Grand Unifying Theory: Creativity / Productivity / Specialization
A Grand Unifying Theory: Trade / Money / Banking
A Grand Unifying Theory: Groupthink / Power
A Grand Unifying Theory: Current Case Studies
A Grand Unifying Theory: Takeways

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Each of the prior sections in this series have provided limited examples along with each segment of the theory to better show how that concept behaves exponentially and interacts with related forces. However, with all of the components of the theory having been introduced for vocabulary, it is useful to take all of those elements and use them to analyze recent events to better illustrate how these forces multiply and interact. These concepts were chosen in the first place because of their applicability in so many scenarios so it isn't difficult to identify real-world examples where these factors are at work.

Investing Versus Fraud: Technology

Corporate executives often speak of "innovation" as a physical knob on the corporate dashboard that can be dialed up to spur productivity when the company begins lagging its competition or dialed down to boost profits when things appear to be going well and executives need to hit targets for bonuses. Executives for firms in "hot sectors" further emphasize this "innovation" their firm is providing merely by operating in a hot sector as justification for limiting or eliminating regulation on their business. We don't want to regulate X, that would stifle innovation, they say.

The prior section discussing creativity and productivity included a visual reflecting how overall "creativity" and learning to foster productivity might be thought of as a range of sectors all expanding outwards over time, collectively pushing out the horizon of knowledge into further frontiers. That discussion also described why growth in knowledge in any particular area faced upper limits due to shortcomings in communication and limits on the ability of any one human to absorb new information. Despite those limits, it is common to see bubbles in "investment" in particular technologies. It may be logical to assume that if research into a topic is producing improvements in some technology, that providing more funding into that topic will yield MORE improvements. That may be true but the improvement will never be linear from zero to infinity. Those upper limits kick in and cap the target area but also show other areas falling behind due to under-investment.

Even though these limitations should be obvious, bubbles are common in every economies at the onset of every new technology. Bubbles happened with steam locomotives, with oil, with telecommunications, with the Internet. And now a bubble is driving Artificial Intelligence investments. But based on all of the analysis presented here, a different question should be asked.

Do the trillions targeted for spending on Artificial Intelligence constitute investing in innovation or something else?

Based on this analysis, it is no surprise that the answer to that question is that the trillions targeted for AI do not constitute investment, they instead constitute gambling and fraud. The pattern of "investments" touted by OpenAI, Oracle, CoreWeave and Nvidia (and to a lesser extent Google and Microsoft) exhibit outcomes that should be very familiar after discussing fractional reserve lending. Any time you create a closed loop among a set of parties then allow a portion of money coming in (revenue) to go out as loans that result in more money being spent within that closed system that count as revenue which allow more lending, you are essentially operating as a bank. These firms are recording revenue by signing circular deals to provide future services and product to each other. The circular flow triggers the exact same multiplier effect as banks lending money with a reserve ratio of say 10 percent.

As a result, what might have started with $5 billion in contracts to build X amount of physical data center capacity has gone through this circular multiplier and resulted in over $1 trillion dollars in nominal contracts over the next five years to deliver data center space, power and compute. These contracts have exhausted nearly every electrical utility's ability to add power generation anywhere in the country, they have tied up supplies for not only GPU processors but basic NAND memory chips used by the entire computing industry, and driven stock prices for these firms to Price/Earnings ratios that make the 2000 Internet bubble look quaint in comparison.

Do any of these executives believe $1 trillion in infrastructure can be constructed in five years? No. Do any of them have a business model that shows they will collect enough revenue to pay for $1 trillion dollars in debt? No. So why are they doing it? Because in the short term, these circular contracts literally ARE behaving like bank loans into a closed ecosystem that is magnifying each deal and creating more fictional demand that starts the next iteration. And once the iterative loop starts, they are reluctant to publicly halt the mania because the flow cannot be stabilized to zero growth or loss, it can only GROW exponentially (making them short term money) or DECAY exponentially (costing them money if they hold their own stock).

As just ONE of many hypothetical examples of what is wrong with allowing one sector to dominate "research" and capital spending, imagine the trillion dollars being funneled to AI does NOT result in a Utopian generalized AI technology. It is equally likely that a needed breakthrough required to improve computing capabilities required progress in material sciences and semiconductors that required basic research in physics and chemistry -- research that was crippled or eliminated as every technology investing firm chased the easy play with Nvidia, OpenAI, Google, Microsoft, Amazon, Oracle and CoreWeave.

Or maybe the next best idea to advance AI technology actually involved continued research into brain physiology related to language processing to develop a different model for relationships between pieces of information and concepts. With a trillion dollars chasing chips and semiconductor fabrication plant construction, it would be very likely that sort of biomedical research did not get the funding it deserved in order to contribute to a better solution.

The Gold Standard

There might be no more loaded term in economics and general society than the term gold standard. It is used as the ultimate compliment for anything else that is thought to be the best in its field. The epitome of perfection. In actual economics, those who seriously promote a return to the gold standard as part of re-creating some prior economic utopia of stability and equity are misunderstanding (or misrepresenting) history and conveying a profound lack of understanding of all of the concepts addressed here regarding productivity, specialization, trade, money and banking.

Gold standard advocates misrepresent history when claiming that tying a given economy's currency to a fixed amount of gold avoids financial disasters. Many economies DID operate within gold standards (or silver equivalents) over the past centuries of relatively modern economic thought yet experienced crippling economic failures every ten to twenty years or so stemming from speculation, improper lending and other random events which pointed out the depth of insanity in a market at a point in time. Conforming to a gold standard didn't eliminate the speculation and it didn't ensure depositors got their prior value of money out of a bank during a period of duress. Attempting to impose a gold standard only constrained the economy from growing after a collapse or resulted in price mismatches for goods that impaired trade with other countries.

In modern economic times (the last one hundred years), industrialized economies locking their currencies together through fixed exchange rates to gold do not succeed at preventing their governments from doing stupid things that destroy their domestic economies. Instead, fixed exchange rates provide additional avenues for the ill effects of bad domestic economic strategies to spread to other economies. If a country is impairing its ability to pay back its public government debts owed to investors in other countries or private debts of its businesses to foreign investors, a floating exchange rate for its currency would result in its exchange rate DROPPING which would serve as a sign to investors that problems are looming, allowing them to more quickly adjust risk assessments and lending decisions. If the country's currency is held at a fixed rate, those problems are being masked from the wider international investment community, encouraging continued investment which then spreads those looming risks into other countries.

This is exactly what happened between 1925 and the beginning of WWII when Britain, Germany and France all resumed currency policies pegged to gold. Doing so more closely tied their economies to that of the US, which had become the largest holder of gold during WWI due to a flood of lending to Allies. When US markets tanked between 1929 and 1932, that drag was transmitted to these countries which already had distinct problems.

It is easy to recite examples from history that refute the suggestions that tying fiat currencies to a gold standard prevents fraud and economic calamity but that's not the same as explaining WHY a gold standard doesn't actually solve problems. The WHY explanation requires reiterating two of the core requirements of any currency:

3. The token or symbol must be difficult to forge so members of the community are confident the person presenting it actually performed work at some prior point representing that much value
4. The token or symbol chosen CANNOT be so difficult to create that it cannot be created at rates that keep pace with the growth of population and value being produced in the economy

As has been the case with many of these theories, they are easier to illustrate with extreme cases. Using sand as a currency would fail requirement #3 because sand exists in virtually infinite quantities throughout the world and it is difficult to distinguish one variety from another if mixed together. But imagine a society that chose to use plutonium as a currency. Plutonium certainly satisfies requirement #3 since it is impossible to synthesize out of thin air but plutonium DOESN'T satisfy requirement #4. Plutonium doesn't even exist in nature. Plutonium must be created by mining uranium then subjecting the uranium to chemical processes that require multi-billion dollar plants and millions of dollars in energy to create a single ounce and once created, it cannot be safely stored or transported for use in transactions.

But imagine an economy whose population is growing 5% yearly and whose total output (value) of goods and services is also growing exactly 5% yearly. Since output is growing in lock step with population, the ratio of value to consumers is constant so even with zero change in productivity, prices would remain stable. But remember, actual UNITS of products and services did grow 5% so to count up the value of that additional 5% of QUANTITY produced requires 5% more units of money in the economy. If the central bank issues 5% more in bills of the currency, that gap is filled and prices remain flat as expected.

Now imagine that same economy tying its currency to gold (or some other magic metal) with a fixed rate of exchange. Under a gold standard, individual currencies can only increase their total value of bills in lock step with the growth in total mined gold. If total gold increases 5% over the year, then a local economy that physically produced 5% more goods will be able to have 5% more of its bills to reflect those additional quantities and the PRICE of those goods will remain unchanged. But if total gold supply does NOT increase 5%, that local economy that created 5% more goods will have 0% more bills to reflect their value. That will drive local prices denominated in that currency up 5%. Inflation! And that inflation stems from factors that local economy cannot control. Of course, economies with gold to mine and existing gold holdings see the prices of goods from every other economy tied to a gold standard FALL making those goods cheaper for them.

The takeaway is that adoption of a gold standard can unduly benefit economies that control outsized shares of total gold reserves but adoption of a gold standard by economies lacking gold can not only IMPAIR their growth but actually trigger CONTRACTION regardless of how productive and fiscally prudent that economy is.

The prior discussion of inflation already addressed the macroeconomic impact of DECLINING prices -- they encourage saving (which can be good for individuals) but increased saving across all members of an economy SHRINKS the economy and tips the operation of the economy from the upward spiral to the downward spiral. In the context of gold standard debates, there isn't some rule of natural law and physics that says confirming to a gold standard is bad for an economy. However, in the real world, the availability of new gold is not predictable year to year and is not evenly distributed across all economies participating in trade. When the costs of physically mining gold vary or the total of new gold fluctuates wildly due to local conditions or global politics, any economy tying its currency to a gold standard is subjecting its local economy to fluctuations that have nothing to do with its local productivity, the actual WORK value of its goods and services produced, or any other policy choices made by that society.

Cryptocurrencies

The thinking driving the popularity of cryptocurrencies since Bitcoin emerged as the first notable example in 2009 reflects some of the same faulty logic as that used to support implementation of a gold standard, with some other unique political and technical twists. As with the rationales provided for a gold standard, it is worth developing a basic understanding of the similar fallacies behind cryptocurrencies to avoid being misled in policy debates about the role of cryptocurrencies in an economy that is both modern and secure.

Debunking support for cryptocurrencies again starts with re-stating the capabilities that any currency must provide in order to serve as money in a modern economy.

1. It must be widely accepted as a means of facilitating a transaction ("legal tender")
2. It must hold value predictably over an arbitrary period of time -- not forever but hopefully for days, weeks or months ("store of value")
3. The token or symbol must be difficult to forge so members of the community are confident the person presenting it actually performed work at some prior point representing that much value
4. The token or symbol chosen CANNOT be so difficult to create that it cannot be created at rates that keep pace with the growth of population and value being produced in the economy
5. The token should be physically convenient to use in commerce -- money using stones that weigh 3 tons a piece are not convenient for commerce between communities

Right off the bat, existing cryptocurrencies fail for requirements #1 and #5 because they not only require a computer or smartphone to interact with the "ledger" scattered across the globe, the persons attempting to spend or collect them as part of a transaction require Internet connectivity. Internet access is NOT a given in many authoritarian regimes as a matter of protecting the regime but Internet access cannot be assumed as a given purely for a variety of technical reasons. An unexpected software fault could cascade across multiple networks or multiple data centers and prevent access to servers providing ledger functions. A lower level, more catastrophic, fault in a power grid could completely cripple all connectivity, preventing the use of cryptocurrencies for transactions even in emergency situations.

Cryptocurrencies also exist in a unique state that poses another challenge acting as legal tender. The concept of "legal tender" is a very abstract phrase for a more direct, crass term -- coercion. As stated in the prior analysis about money, adoption of any arbitrary money within a community likely requires some amount of coercion within that society in order to get a suitable share of the entire community to USE the money for daily transactions. There's no magic percentage that has to be reached for a given choice of money to become viable but it seems intuitive that that share cannot be only 5 or 10 or 15 percent of the population or even 5/10/15 of the portion of the population engaging in trade. Social coercion can help but government coercion is far more effective. Officially stating that a given choice of money is "legal tender" for all debts requires sellers to accept the money for purchases. If the money is refused, the government can pursue civil or criminal charges to encourage adoption.

Cryptocurrencies have a unique problem in that they are, for the moment, stateless. They don't exist in any particular physical location, they aren't issued by any particular government and no particular government can deterministically alter its fiscal decisions in any way that would influence the worth of a cryptocurrency. So if a dispute about the integrity of a cryptocurrency arises or its value as expressed in terms of other traditional currency suddenly fluctuates drastically and suspiciously, which government has any motivation to back that cryptocurrency and essentially coerce people into continuing to accept it? NONE.

Cryptocurrency fans like to focus on the theory that a decentralized, stateless currency is superior to any traditional fiat money issued by a government or a central bank because no one can just "fire up the printing presses" for a cryptocurrency like they can paper bills. The problem with the lack of any centralized authority to manipulate a cryptocurrency is that there's a flip side to that supposed benefit. A cryptocurrency with no central AUTHORITY controlling it means no authority has any RESPONSIBILITY to protect it amid turmoil. Cryptocurrencies are instant orphans in any true financial meltdown. When the next global market failure occurs and the real purchasing power of currencies across the globe plummets in lock step, NO GOVERNMENT will be focusing first on protecting the value of any cryptocurrency or even ensuring online access to them. It's somebody else's problem, by definition, which means it's nobody's problem to fix.

So if cryptocurrencies are incapable of satisfying all of the demands of a legitimate currency and acting as money, what do cryptocurrencies actually represent? To a large extent, they represent a target for use in purely speculative gambling, hoping on a continued rise as more people are attracted to the rising price (not the underlying utility). Cryptocurrencies are literally the poster child of an asset driven by the perpetual hope of a greater fool coming around the corner.

Investing Versus Fraud: Derivatives and Private Equity

The multiplier effect created by fractional reserve lending isn't the only means available in the financial sector for capturing exponential wealth while also magnifying potential losses to multiples of an original investment. Buying stocks on margin, purchasing options on commodities and stocks and synthesizing new bets on other people's financial contracts (derivatives) are all means of potentially making large amounts of money or incurring losses that vastly exceed original investments.

Unfortunately for the larger population, "creativity" within the financial sector seems preoccupied with devising schemes to exploit leverage for making money and there appears to be no "OFF" position to this particular switch. Beginning in the summer of 2025, quarterly financial filings of public firms and unexpected implosions of privately held firms demonstrated that a new form of fraud has been perfected since roughly 2015 and involves banks, hedge funds and the third member of the unholy trinity of finance, private equity firms.

The easiest way to convey the nature of the fraud is first think of a pay-day lender and an individual needing credit to afford an urgent car repair. In a pay-day lending scheme, the lender takes a huge risk by supplying the loan amount but balances it by charging a very high interest rate. Even if the borrower cannot pay the principal back, as long as they can pay the interest, the payday lender will roll over the loan to the next week or month. But the payday lender IS still exposed to non-payment risk of the original principal.

Instead of the original payday lender taking that risk and holding it for an extended period, imagine the borrower goes to ANOTHER payday lender and borrows a larger amount from them to pay off the original payday lender (interest + principal). With this scheme, no single payday lender holds the risk of that particular borrower for a very long time and since it might appear that the borrower paid off the prior balance, their credit might not reflect the fact they still cannot afford the car repair. The borrower keeps paying higher loan origination fees each time but never makes progress retiring the debt and all of the payday lenders are making money.

This simplistic round-robin payday lender example is very akin to financial dealings that have been taking place between banks, hedge funds and private equity firms for the past decade, possibly longer. The existence of this process became visible beginning in the summer of 2025 after surprising bankruptcies of large companies (parts conglomerate First Brands, used auto conglomerate Tricolor) controlled by private equity firms brought to light that these firms had recently landed new loans sometimes only WEEKS before filing for bankruptcy. The private equity firms involved in some of these deals -- Blackrock, The Carlyle Group, Bain Capital, KKR -- and banks involved in the loans -- JPMorgan Chase, Jeffries, Fifth Third -- are some of the biggest institutions in their field. Where was the due diligence?

The apparent answer is that as this model evolved and began increasing short term profits, all of the banks and PE firms adopting the model just began assuming any particular risk would be shuffled off to the next "payday" lender so risks were small. What's the point of completing due diligence if the loan will roll over to another firm in twelve months?

What none of these parties understood was that this practice was extracting profits for them but bleeding the target dry over a period where large economic forces were undermining the entire business model of the borrower, magnifying their cash drain. But the PE investors didn't understand that. They don't really care about cash drain while executing their core business model. Their goal is to BE that cash drain. They're not trying to save the borrower and turn it around, they are simply trying to extract its cash.

The banks didn't think about that either because they didn't look at the cash flows, they expected to offload the loan in a short period of time, either by it being re-financed somewhere else or being bundled into a derivative and sold off like another McMansion mortgage. And hedge funds who invested in the bonds backing these loans were looking for high interest rate returns and assumed they could hedge any risks through derivative bets. All of this provided liquidity to businesses whose ACTUAL financial performance merited no such liquidity, all because other parties had perfected a means to extract profit from it.

While being distracted by all of those rationalizations, the banks, PE firms and hedge funds involved also failed to realize that keeping these zombie companies alive was also extending the life of failing management teams at these firms who also had personal interest in maintaining executive roles while also potentially looting the companies as well with lavish expenses. Since no one was properly auditing the books of these firms before lending, the rogue behavior of the management team added to the unpredictability around the firm's ultimate failure, surprising everyone.

This pattern is not merely financially ill-advised, it is undoubtedly criminal fraud. With most lending, a debtor and creditor have significant latitude to renegotiate loans amounts, terms and interest rates as circumstances evolve. However, this lending model hinges upon deferring recognition of core insolvency so some of the particpants can continue extracting fees from loans which absolutely cannot be paid back. It's another variant of fractional reserve lending with one extra dangerous twist. Here, the multiplier effect originates from the PE firm's choice to re-finance the "payday" loan for the operating business. If the loan is funded through "investors" in the PE firm, no bank is involved so there is no minimum "reserve" the PE firm must satisfy, as long as they can attract capital. Banks may participate and provide some of the loans but bank participation in this lending becomes only a fraction of the multiplier that ultimately results. The extent of the danger from these loans is INVISIBLE to the larger market because most of these PE firms are privately held and don't publish quarterly results.

When the bank lender, the PE firm and the operating entity X have all made horrendous decisions, their primary motivation may become one of simply deferring recognition of reality or to dump the bad asset (the individual loan or the company stock) on an unsuspecting "investor."

The criminality behind this pattern stems from the fact that the model is purposely blurring a distinction between "lending" and "investing." In most contexts, these terms involve money and risk and are blurred together in most minds. Someone deciding between buying corporate bonds and Treasury bonds treats both of those alternatives as investments just like stock purchases. For discussion here, there is a crucial distinction to be made.

When a party makes a LENDING decision, a risk is being accepted in exchange for a future profit. However, the lender expects to be taking a relatively small risk and demands a smaller fee (interest) for that risk. But the lender is going to minimize the risk by learning as much as possible about the risk by understanding the borrower's raw income, existing debts, cash flow from their business, market risks for that business, etc.

When a party makes an INVESTING decision, a risk is also being accepted in exchange for a future profit. But the magnitude of that risk is vastly larger than that of a loan and the source of the profit made by the investor doesn't come from fixed payments (ignoring dividends) from the investment, the profits come from OWNING a share of the actual business. The amount of profit possible for the investor isn't capped at the initial investment, it can accumulate to be many times the original investment. But in exchange for that opportunity, the investor accepts that the value of their investment could fall to zero unless they sell it to someone else first.

This PE lending scheme purposely blurs this distinction by

• trying to capture income from higher interest rate loans to marginal borrowers
• while trying to take advantage of the assumption of due diligence associated with credit evaluations for loans
• while expanding the source of funds for these loans via equity sales
• while avoiding actual audits to validate the underlying integrity of borrowers

In short, this scheme is injecting derivative levels of risk into a sector of finance that normally expects very little volatility and far higher predictability. Once this mechanism began iterating on itself with its "multiplier" effect, all of the parties participating develop a shared interest in propping up the bubble. They all hope they can shuffle a bad loan to one more party before the music stops but they're all unwilling to give up the short term profits being pocketed from the scam. In other words, this is the mortgage and derivatives failure of 2008 all over again.

Prior to the 1980s, the "investing" vehicles posing the highest risk for the destruction of value were commodities / futures and margin trading. Trading of commodities futures dates back millennia but even America's modern commodity trading dates the establishment of the Chicago Board of Trade in 1848. Commodities trading stemmed from a legitimate need within economies to spread the risk associated with weather-induced fluctuations in crops and stockyard supplies to ensure farmers and ranchers had steadier incomes year to year and ensure food manufacturers could smooth out supply issues to continue delivering food products to consumers. Futures trading thus wasn't an attempt to CREATE new forms of risk then gamble on the outcome, it was created to QUANTIFY existing risk to allow a larger market to balance it across a wide number of participants for the benefit of all participants.

As of 2026, the landscape is vastly different. The exact paper value of all derivative swaps in the US market alone is unknown but thought to be around (gulp...) $400 trillion dollars. This in an economy of about $32 trillion dollars GDP, government debt of roughly $36 trillion, consumer debt of $19 trillion and corporate debt of $14 trillion. Essentially, the financial sector ran out of ways of extracting income from the dollars required to create $32 trillion in REAL value so it invented new types of financial gambles to attract "investment" that essentially amount to BETS on existing stocks and bonds or even BETS ON BETS on existing stocks and bonds and the "value" of those primary and secondary bets now exceeds the value of the REAL economy by a factor of 12.5.

The private equity lending debacle unfolding right now is just the latest financial scam tied to runaway, exponential forces stemming from poor financial regulation and fraud. Like every similar exponential bubble in financial history, it won't end with a polite announcement and a slow, multi-year contraction to some prior level of sanity. Once everyone realizes the bubble is out of raw material, it will collapse nearly overnight and trigger massive disruptions throughout the economy.

WTH

A Grand Unifying Theory: Groupthink / Power

This post is one of six posts in a series on this topic. The full list of posts are linked here for convenience.

A Grand Unifying Theory: Overview
A Grand Unifying Theory: Creativity / Productivity / Specialization
A Grand Unifying Theory: Trade / Money / Banking
A Grand Unifying Theory: Groupthink / Power
A Grand Unifying Theory: Current Case Studies
A Grand Unifying Theory: Takeways

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It seems logical to assume the ultimate behavior of a collective of humans would be some sort of weighted average of the behaviors and goals of the individuals in that collective. If a group of one hundred people split 52/48 between tendency A and tendency B, it seems logical that the collective would "tend" towards A a majority of the time, maybe a super-majority of the time depending on decision making rules. If there is uncertainty about the exact split of those in that collective, it seems obvious that modeling the behavior of those individuals more accurately somehow would improve the accuracy of predictions about the behavior of the collective.

Reality seems to support vastly different conclusions. A collective with an arbitrary split of 52/48 between A and B might actually exhibit a significant skew in direction C, in a third dimension not predicted by merely analyzing A and B. This divergence has nothing to do with the stated purpose of the organization, be it business, political, social or legal. It stems from innate aspects of humans working within ANY sort of hierarchy, regardless of why that hierarchy has been adopted or imposed. Understanding this divergence is vital to understanding the nature of solutions that must be pursued when attempting to correct for bad behavior after calamity strikes or correcting behavior before calamities are created.

Groupthink: None of Us Is As Dumb As All Of Us

The author of this entire series hereby stipulates that this portion of the analysis has the least grounding in any concrete scientific or quantifiable measurement. It is thus the section most guilty of hand-waving and "pop-psychology" methodology. However, any time that a complex problem can be simplified to fit on a Despair poster and sell thousands of copies, there's a nugget of truth to be unearthed and polished up. In this case, these observations are based on over thirty years watching managers at all levels in their native habitat, the corporate organization chart. Those observations make it clear that the divergence between INDIVIDUAL tendencies and GROUP tendencies stems from these key factors:

• differences in human affinity to hierarchy and power
• the complexities made possible through specialization and its resulting hierarchy - complexities that grow exponentially with the size of the organization
• differences in financial and social incentives offered to people based on position and status

In prior sections of this analysis, discussions about productivity and human creativity stated that human interaction with others accelerates learning by sharing experiences which can improve rote productivity or lead to improved processes that improve productivity. Here, an argument is being outlined that assumes working with others IMPAIRS productivity by triggering unexpected interactions that drain effort away from formal goals and expend it in directions none of the individuals supposedly want individually. Three factors make this possible.

Affinity to Hierarchy -- This parasitic drain stems from imposing hierarchy upon a collection of people who will have different individual tolerances for and interest in such hierarchy. Managing one's relationship with that hierarchy and attempting to influence one's role in that hierarchy becomes another task in the day of each member of the collective. Those who seek a sense of structure will devote time to syncing themselves to whatever direction can be gleaned from the hierarchy. Those that seek power and status will devote time attempting to advance their position. Those turned off by hierarchy will attempt to minimize their interaction with the collective and perform as much of their work independently as possible.

Hierarchy and Complexity -- Even for people with no particular aversion to hierarchy, larger organizations with more complex hierarchies make extreme complexity more manageable, but the complexity can never be completely eliminated. Large organizations creating complex goods and services require processes unique to the organization that have nothing to do with the service or product being delivered. This becomes its own area of specialization that can be VERY abstract, making information about such work prone to misinterpretation. Unfortunately, this complexity can also make it easier for bad actors to hide bad actions behind the complexity, delaying recognition of actions that require correction due to quality issues or outright malfeasance.

Incentives -- If the range of individual feelings toward hierarchy is wide, the range of incentives influencing the BEHAVIOR of people at different levels of a hierarchy is stupendous. Arguably, these widely varying incentives drive most of the unpredictable variance between goals of individuals and actual outcomes from an organization. Those familiar with the work environment in Corporate America are familiar with the trope about "executive hair" and how those in "Mahogany Row" tend to conform to certain stereotypes about appearance and dress. These sartorial similarities absolutely PALE in importance to the similarities in more critical areas of communication tendencies and risk-taking.

On the surface, senior leaders usually APPEAR to be very calm, deliberative and conservative in their interactions with nearly ANYONE in the organization. This veneer of inscrutability is thought to reflect confidence, leadership and decisiveness (if not infallibility). In reality, in many corporate settings, pay incentives are so extreme that leaders exist in a vastly different bubble than average people in the same organization. The stakes for the next "win" are so high that leaders certainly have an incentive to swing for the fences to achieve that goal but their pay is already so high at its minimum that their worst case scenario (failing, getting fired, getting a golden parachute) is still one hundred times better than the life outcome of nearly anyone else. Leaders in this situation do NOT have the same incentives to avoid DOWNSIDES as everyone else and consequently, such leaders are predisposed to accepting higher risks with extreme "personal NPV" for them, even if the "collective NPV" for the business is much worse.

The real problem with this incentive structure is it affects the promotion process because risk takers don't want a layer of management beneath them constantly reminding them of potential downsides. They want people to take orders, rally the troops and take the hill. This creates a self-selecting feedback loop in the organization that filters downward, layer after layer, hire after hire and begins altering the culture of the entire organization.

An Anecdote

The prior analysis made a case that the goals and outcomes from an organization of individuals cannot be accurately predicted by knowing or controlling the goals and motivations of those individuals. There is an even more important corollary to this initial claim. The corollary is that if the behavior of an entire organization cannot be PREDICTED by knowing and controlling the goals of its individuals, it can also be assumed that the behavior of the organization cannot be CHANGED by even conscious attempts to "change the culture" by hiring new members, even at senior levels. The organization itself isn't human and doesn't care WHO is in charge. The organization itself has its own "inertia" beyond the control of the humans within it.

Here's a possibly long but hopefully enlightening anecdote to illustrate this concept.

A third of the way through my career, at the end of the 2000-era Internet bubble, my employer imploded and I landed a new role at another firm in town in roughly the same industry as before. The new job started in October of 2001 and I had two weeks time off to decompress from the prior firm that ended with a whimper and get dialed in for the new role. I had worked previously for a large telecom firm then the small Internet firm that tried to operate as a creative, entrepreneurial startup but nothing prepared me for the culture shock I encountered from the first day at employer #3.

I arrived on my start date at the HR office at 8:00am, assuming the new boss would be there to escort me to my office and begin doing on-boarding paperwork, getting a laptop, etc. Nope. The HR person didn't even show up until 8:40. The new VP boss didn't show up until 8:54am. He escorted me to his section of the building but conducted three other conversations with people he encountered in the halls and elevator along the way, treating me like an annoying runaway dog being dragged back to the family yard. No sense of professionalism towards a new hire and rude to not only me but everyone he encountered in that six minutes.

Within an hour on that first day, I was pulled into a CRUCIAL meeting. It seems the company had partnered with another company to actually operate the gear delivering services to roughly forty percent of the entire company but that company was declaring bankruptcy and had posed an ultimatum. Give us some bridge money to keep your stuff alive while you try to migrate it to your own stuff or don't pay us anything, "go dark" on December 1 and lose all of your customer revenue until you can turn up your own network with whatever customers are still willing to stay with you after dropping their service.

The "leaders" in this meeting including my boss were collectively flummoxed as to which alternative to take. They were completely at a loss as to how to even model the problem from a financial and calendar perspective. Being unsure of how input from a first-day employee would be received among ten corporate VPs, I said nothing in the meeting. As I walked out with the new boss, I outlined how the problem could be modeled to clarify the decision for execs:

• identify all key locations
• identify cost of new OC3 or larger circuits
• identify equipment intervals for new gear
• identify time required to pre-build new network configurations for new year
• identify revenue/sub and subscriber counts at each locations
• calculate revenue lost between go-dark and our expected turn-up date
• identify keep-alive bridge costs between go-dark and our expected turn-up date
• calculate the difference: if revenueloss > bridgecost, pay bridge cost

He and a peer VP working the problem with him heard this and said, great, mock it up and we'll review that in the next meeting. It took me about 20 minutes to model the structure of the model and sanity check it with mock figures and another day to find people with access to appropriate figures and get those numbers plugged in (remember, I'm a Day One employee who knows no one).

On the day of the next CRUCIAL meeting, I expected to email one of the VPs the spreadsheet and expected one of THEM would actually drive the projector in the boardroom and talk through the explanation. Nope. As I provided the quick summary to them on which variables to flip back and forth to better highlight the contrast between the alternatives and how they varied in different markets, they said we don't know how to do that, you bring your laptop to the meeting and drive the screen and talk through the analysis.

So on day three or four of my job, I sat in the boardroom with the CEO, a few SVPs of operations and finance and probably ten other VPs talking through the model and explaining to them that given uncertain delivery intervals of replacement gear, uncertain intervals for delivery of new circuits to the new gear and the likely churn of customers to competitors if a go-dark approach was chosen, it made sense in most markets to pay the bridge costs, keep the customers lit while expediting our internal network turn-up.

In reality, ANY of the executives in that room should have possessed the basic "feel" of that business to think through those options verbally and come to the best decision without a meeting. But even with a spreadsheet prepared to outline that train of thought, none of them had the basic Excel literacy to scroll up and down in the spreadsheet, much less the acumen to make a multi-million dollar decision based upon the content without being spoon-fed the analysis.

Only a few days after that, work began to plan the connection of our gear to new backbone circuits to other carriers. Engineers in one region were concerned about how to incorporate the new circuit while keeping the prior circuit connected for an overlap period. In network circles, this is a relatively straightforward process of configuring "peer" connections using Border Gateway Protocol (BGP) and configuring ranges of your IP space to "advertise" as available to that upstream provider while keeping other ranges internal to your network. They seemed stumped so I told them, "I know BGP pretty well, export your current configuration file, hide the current passwords and mail me the file and I'll take a look."

They did. I took a look. The name of the router was fw1 (it should have been something like frtwtxbb01). The current configuration had no BGP configuration whatsoever. It had a static "default route" entry in another routing protocol (OSPF) not normally used on peer connections between Internet carriers that pointed all traffic out one port on the router. This was a router serving (at that time) about 120,000 customers. With no dynamic rerouting of traffic. No filters preventing leaks of private IP ranges to other networks. No policies to reject accidental advertisements of private IP ranges from upstream providers. Astonishingly amateurish.

I was with that firm for twenty plus years. Over that time, the CEO office changed hands five times. The SVP positions probably churned out every six or seven years on average. VP slots often changed every three to four years. Yet despite that turnover at the senior leadership levels, there are elements of that original 2001 culture I encountered my first day -- hitting me like walking into an invisible wall -- that persisted to the day I left. And many others who hired on over this same period reported the EXACT same jarring Day One experience upon joining the firm. The rudeness of leaders. The ignorance of leaders. The arrogance. The surprising lack of proficiency among technical roles. Home-grown tools and spreadsheets for tracking projects, costs and future budgets involving BILLIONS of dollars that changed every year but never proved more suitable for their stated purpose for anyone who had to populate them or make decisions off them.

The company remains the corporate management equivalent of the Salvador Dali painting, The Persistence of Memory. Things look normal after a quick glance but then you notice the melting clocks dripping off the table and hanging in the trees.

Power and the Persistence of (Bad) Culture

The mix of high churn in leadership roles with stagnant (often toxic) culture described above seems completely incongruous. The odds would seem extremely low that a multi-billion dollar firm could encounter THAT much leadership churn without any of them making ANY dent in ANY of its systemic cultural problems. One might think that purely based on averages, half of those leaders would have brought in some "best practice" and maybe HALF of those would have taken root and become reflected in the culture twenty plus years later. In fact, those working in the company that entire time would come to the exact opposite conclusion. Long-timers would conclude the company as an organization essentially developed an immunity that killed off any new directive or process that conflicted with established patterns and the inertia of those toxic patterns exceeded the power and influence of even the CEO of the company. (And this is assuming each of those new leaders entered with the absolute best of intentions and competence. Reality suggests otherwise.)

How does this organizational immunity to improvement develop?

Here are likely contributors to this phenomena:

• Flavor of the Month -- Churn at senior levels is nearly as toxic as stagnation with weak talent. Frequent role changes convince lower tiers to ignore the next new thing and wait for the new guy to churn out rather than adopting requested changes.
• C-Grade Players Don't Attract A-Grade Players -- "Leaders" who swap senior positions every three years might be doing so to avoid looming accountability for major bad decisions. They're not "A" players, they're "C" players. When these mercenaries quit one firm and join another, they often hire their C-tier buddies and bring them in as well, further lowering the quality curve within the new company.
• Slouching Towards the Mean -- The sheer size of large organizations guarantees their cross-section of employees / members will tend towards societal averages in every measure. A firm employing ten people specializing in a unique technology can hire above the norm and keep certain character traits out of the firm. A firm operating 200 retail locations open 16 hours a day across forty states is going to have an employee base that much more closely mirrors all of the pathologies found in the overall population. These pathologies WILL find occasional expression within the company and often trigger many of the odd outcomes that weren't formally stated by anyone as a goal.
• Hidden Truces Thwarting Accountability -- Senior leaders often declare informal truces with competing leaders whose organizations SHOULD act as checks on each other to preserve autonomy. Let me do what I want with my financial systems and I won't second guess your IT spending plan in front of the CEO.

That is how a COLLECTION of individuals operating as a single entity can exhibit a specific behavior that no INDIVIDUAL member of the collective claims to support, even if some members of the collective hold positions explicitly designed to THWART that behavior. The other forces at work within the collective jointly exert more inertia allowing the behavior through indirect influence than any individual can identify and counteract.

This clash between these collective secondary forces versus individual actors holds true beyond corporate environments. It holds true in any sufficiently large organization -- charities, educational institutions. governmental bodies, religions, unions...

Organization Power and Society

If the strange immunity of organizations to the exercise of power within them by their leaders makes sense, it has profound impacts for society. First, once an organization begins exhibiting patterns of abusive behavior (monopolistic practices for businesses, wasteful extravagance for charities and universities, captured gerrymandered-to-death political bodies, abusive treatment of believers in the case of religions, etc.), the INSTITUTION will take actions to defend and perpetuate ITSELF despite what any member or leader claims to do to correct the issue.

Because of that first point, organizations that begin fighting off external efforts to correct perceived internal problems will devote increasing shares of internal resources to the defensive battle rather than whatever might have been the original "mission" of the organization. This dilution of focus can materially alter the glide path of the entity, with dire consequences in the longer term.

Even if punitive financial or criminal penalties are assigned to the entity or specific members, the culture that allowed those actions to take place will likely persist, The explicit form of offense targeted by the correction might never happen again, but those lower level innate characteristics that allowed it to occur are still in existence and may lead to different actions with similar negative consequences to still occur.

This pattern also means that no outside entities attempting to coerce an offending organization into altering its behavior can take solace in having personal relationships with that organization's leaders as a lever for forcing change. The leaders you know at that organization may be as upstanding as you could ever wish for and as well-intentioned as you could hope for but if their organization has systematically engaged in bad behavior, you cannot confuse the HUMANS with the ENTITY. The ENTITY is likely the root problem and deserves no mulligan or do-over.

It's also important to point out that this pattern is in no way based on the original intended purpose or original actual behavior of an organization. The pattern defined here is driven solely by the size of an organization, the resources it grows to control and these "ricochet" effects of human reactions to operating within hierarchies.

The real conclusion from these observations is that for some organization offenses, NO amount of penalty can eliminate the "organizational DNA" that permitted a fault to occur and might allow similar faults in the future to occur. For some organization problems, no single change in leadership will be capable of changing the culture. The inter-related incentives and secondary impacts are too complex to understand and unwind and the "muscle memory" of the organization itself is far longer than the muscle memory of any subset of employees.

That means that for some problems faced by some organizations, those problems are unsolvable as long as the organization continues to exist in its current form. In the case of a corporation that gambles heavily and triggers a market collapse, that means that firm should be literally eliminated from existence. For a monopoly abusing market share to cheat customers or stifle competition, that means breaking the company up, not simply hitting it with a billion dollar fine. For a government whose "control levers" have become completely controlled by uber-wealthy private interests or by the parties themselves rather than the public...?

That's a topic for the concluding entry in this series.

WTH

A Grand Unifying Theory: Trade, Money and Banking

This post is one of six posts in a series on this topic. The full list of posts are linked here for convenience.

A Grand Unifying Theory: Overview
A Grand Unifying Theory: Creativity / Productivity / Specialization
A Grand Unifying Theory: Trade / Money / Banking
A Grand Unifying Theory: Groupthink / Power
A Grand Unifying Theory: Current Case Studies
A Grand Unifying Theory: Takeways

---

Trade, money and banking are the concrete, water and aggregate in the foundation of all modern economies. They improve the overall efficiency and productivity of an economy by

• aiding in the specialization of labor
• facilitating the exchange of goods and services produced through that specialization using agreed-upon symbols of prior labor exerted
• allowing those symbols of prior labor exerted to be accumulated over time to ensure ready access when needed.

Understanding how these factors work independently and in concert with one another iteratively over time is vital to understanding how current policies claiming to solve perceived problems in these areas actually impact these elements and -- in many cases -- are magnifying those problems rather than correcting them.

While complex, these three topics are the easiest to map to physical activity in the real world and the easiest to illustrate with numerical examples. Some of the analysis will include some math to supply the more crucial formulas used when discussing these concepts. However, like everything in economics and social sciences, actual world behavior can never be predicted or even analyzed after the fact with 100% accuracy using equations. The key to the equations is understanding the sensitivity / volatility they reflect when making small or large changes to their inputs.

Specialization Leads to Trade

The prior installment in this analysis series addressed the nature of creativity and productivity and stated that the most obvious method for improving productivity at a task is practice through sheer repetition. Any sufficiently complex or physically challenging task will require both processing memory and muscle memory to improve the quality and speed with which the task can be performed. Human societies figured this out early on and quickly learned that five people could get fifty tasks done more rapidly if each person specialized in ten tasks rather than all five having to learn fifty tasks. Why?

Learning usually lessens production rates for the teacher of a task as they slow down to explain the process being taught and obviously the student is nowhere near as productive as the teacher to make up for the lost input. Also, as tasks become more complex, the student must usually be taught predecessor skills A, B and C before being taught D. If every worker must progress through the learning curves of fifty tasks, the collective is deferring off into the future the point where anyone will be optimally productive at all fifty tasks or the workers may never reach ultimate productivity rates on any task ("jack of all trades, master of none").

Specialization of labor allows a group to avoid spending time teaching everyone every tasks and allows each person to get further up the mastery curve on the tasks they retain. If all of the people involved are in the same family unit, it's clear the collective output will benefit the group so the value of specialization is immediately apparent. Everyone knows the amount of labor that went into each unit of each type of output so if

• Grog was able to produce 3 units of squirrel in 8 hours of hunting
• Og was able to obtain 3 units of clean water in 8 hours of searching
• Thag was able to 3 three pairs of moccasins in 8 hours
• each of them needs exactly one of those other units

then specialization allowed the three to produce everything they needed in 8 hours instead of presumably more hours with each doing all three tasks less efficiently. The three are better off keeping 1 unit of their work and trading the remaining two units for the other things. When everyone can see all of the work being done and the degree of specialization is relatively small, this form of pairwise bartering A for B and B for C or even different ratios such as 2A for B, etc. can suffice.

But specialization also works across larger groups that span family units or even communities. However, as the degree of specialization increases and more work is done out of sight, greater uncertainty must be accommodated as part of the trading process. It becomes very difficult for everyone in the system to understand all of the pairwise trade exchange rates. Mathematically, an economy with N unique products would require N(N-1)/2 distinct exchange rates. For example, a world of A, B and C requires A:B, A:C and A:C rates (3). A world with products A, B, C and D requires A:B, A:C: A:D, B:C, B:D and C:D or 6 rates. A world with 20 products would require 20(20-1)/2 or 190 prices.

Clearly in an economy of even modest complexity, attempting to determine HUNDREDS of exchange rates and use them fairly during trades becomes unworkable for all participants. Attempting to do so either wastes time as participants attempt to master the exchange rates or it requires them to trust many different people to appropriately exchange A for B then B for C into (eventually) product Q. It also becomes readily apparent to Grog that carrying his stash of dead squirrels (A) around while trying to trade them into units of Q becomes very, ummmm, unpleasant.

Trade Leads to Money

As communities increased the number of distinct goods and services they were willing to trade within the community or between communities for other goods and services, the idea of generalizing "prices" away from ratios between two direct "goods" into a single "universal" good probably took relatively little time to take root. It is likely that people realized that everyone seemed to know the "value" of their particular product expressed in bushels of corn or square yard of fabric or hunting tool. From that point, traders likely made the connection that prices are not required to always be expressed in ratios involving two ACTUAL products. One of the elements can by arbitrarily chosen because it always gets converted back to something of value at the conclusion of every trade. This is the concept of money. It is a "common denominator" to the price of any product that allows that product's value to be compared against any other product.

A subtle but CRUCIAL idea underpinning this origin story of money is that after all of the abstractions of trade between two workers and exchanges of the products they make, the use of money in a transaction acts as a generic representation of prior value being provided or work being performed. After thinking through the evolution of how Grog and Og reached a point of trading A for B and B for A by direct bartering, the ratio at which both are willing to execute that trade is based upon three factors ALONE:

• how much work PartyA must exert to create his units
• how much work PartyB must exert to create his units
• the competitive advantage of each at doing their single task versus the other

As one example, if both PartyA and PartyB must exert 8 hours of labor to create their units and both are equally competent at both types of work, neither has a competitive advantage so the price for A and B will essentially be 1 A for 1 B. However, if PartyA only takes 4 hours to create his units, that doesn't mean B pays half as much when buying units of A. If PartyB is not equally as productive at making A as making B, PartyA holds a competitive advantage and the price might still remain 1 A for 1 B. But regardless of those considerations, at the core of it, those PRICES always reflect a perception of WORK having been performed. Nothing changes when the parties agree to price their work in terms of units of an arbitrary form of money so... MONEY MUST ALWAYS REFLECT THE VALUE OF PRIOR WORK TO BE ACCEPTED AND USED IN AN ECONOMY.

Beyond that basic tenet, there are additional traits that ANYTHING used as money must possess in order to facilitate trade within an economy.

1. It must be widely accepted as a means of facilitating a transaction ("legal tender")
2. It must hold value predictably over an arbitrary period of time -- not forever but hopefully for days, weeks or months ("store of value")
3. The token or symbol must be difficult to forge so members of the community are confident the person presenting it actually performed work at some prior point representing that much value
4. The token or symbol chosen CANNOT be so difficult to create that it cannot be created at rates that keep pace with the growth of population and value being produced in the economy
5. The token should be physically convenient to use in commerce -- money using stones that weigh 3 tons a piece are not convenient for commerce between communities

In this analysis, the term "money" will generally be used to refer to these underlying concepts while "currency" will be used to refer to a specific symbol chosen to embody these traits. Conceptually, coins and currency are interchangeable within this framework so coins won't be referenced here much if at all.

The subtle point between all of these requirements is that they all influence each other but in some cases they contradict each other as well. For example, ensuring acceptance of a currency as legal tender among a sufficient portion of the community may require some collective coercion by that community's government. If half of the community doesn't trust the valuation of the currency and won't accept it, that conceptually halves the trading scenarios that can utilize the currency, making it less convenient and valuable for the remainder.

The store of value requirement can conflict with means of making the currency easy to use. If the community decides slips of paper with Grog's likeness are more amenable to transactions than 3-ton stones, that's great but what if everyone is able to easily draw Grog's likeness on a piece of paper? Everyone has an incentive to forge units of currency which breaks the trust users require between a unit of currency and a unit of value ("prior work"). If you operate in a community with total currency of 1000 and you hold 50 slips worth 50 hours then you find someone has forged 1000 new slips of paper into existence, all of that currency is worth HALF what it used to be worth. Your share of 50 now only has the buying power of 25.

This risk of forgery and limited technologies to counteract forgery in other materials that might be used for currency led to the adoption of what we now term precious metals as units of money. Historically, "precious metals" had value in large part because they were both beautiful to humans for jewelry AND they were relatively hard to find in nature and required special skills to refine and form into useful shapes. That made them natural fits to address concerns about forgery so gold and silver coins became widely used.

The coercion point becomes particularly important BETWEEN communities that might adopt different currencies. It is still possible to facilitate trade between currency Y and Z but that requires the two communities to agree upon their relative exchange rate. As hundreds of years of history and later portions of this analysis will address, trading between economies with different currencies requires trust which can be broken by a variety of internal factors. If community Z somehow doubles the amount of its currency without actually doing WORK, attempting to sell products with a prior Y:Z exchange rate is essentially cheating anyone accepting Z currency for goods made in community Y priced in Y currency. If members of community Y not only accept payment with Zs but hold those Zs for use later in other transactions, they are losing value because of actions within community Z. As one can imagine, these scenarios lead to conflict between the communities. What are the options in this scenario for community Y?

• immediately adjust pricing to the new rate and eat prior losses?
• cease trade with community Z going forward?
• retaliate with alternate economic measures to recoup the loss or force Z to revert to the prior state?
• attempt to enforce the prior exchange rate through military force?

The ultimate, albeit cynical, point here is that coercion of one type or another (social or physical) and of some level (mild or extreme) is an inherent requirement for any currency to achieve broad adoption and provide value to an economy. Any object (physical or virtual) suggested for use as a currency is worthless if it can be repudiated and rejected without consequence.

Money Leads to Banking

So far, an attempt has been made to link creativity and productivity, then link those factors to greater wealth, then link those factors to greater trade which then accelerated all of those factors leading to the adoption of money. It will now be argued that the evolution of money made it easier to identify the creation of greater wealth which led to centralizing services related to housing and protecting wealth - the essence of banking.

Banking services evolved because continued specialization and wealth creation highlighted timing issues associated with aggregating wealth. With farming as an example, a farmer's "work" is exerted over months of time, resulting in a single big "payoff" when a crop can be harvested, then stored in a silo for use and sale over the next year until the next crop is ready for harvest. Farmers quickly realized that creating a barn or silo to house a crop and keep it out of the weather and reasonably free of vermin allowed them to break this feast or famine cycle and enjoy more economic security over longer periods. As the use of money took hold to denominate transactions and wealth was represented in money units instead of bushels or bales, the same concepts were applied to money and incorporated into banking.

After seeing wealth creation accelerate via trade, economies quickly identified the housing and protection of money as a new service of great value to a community. The means for providing these services obviously evolved over time but the idea of building a facility with specially secured "vaults" to guard against theft or fire and protecting it with armed guards eliminated the need for each worker to worry 24x7 about their own wealth. The bank operator could charge a small fee to the depositors to cover the cost of the building, the vault, and the labor to guard the contents and provide a small profit for the bank and still protect the assets more effectively than individual workers. Specialization of labor at work again.

History and archaeological artifacts reflect variations across the planet over thousands of years but "banking" of assets in centralized locations and "lending" evolved hand in hand over history. It takes little imagination to understand why. As specialization of labor facilitated the accumulation of wealth and that value was easier to observe when abstracted into money, workers recognized certain problems with the timing of "cash flows". At some point, the same type of "cash flow" timing problems that led to deposit accounts as a banking service led to the concept of lending money. In our example, Grog harvested his crops and converted his silo worth of corn into 100 units of currency good for spending down over the next year. His neighbor did the same thing with the cattle brought to slaughter. Now the bank has 200 units of currency sitting in two accounts... That will be spent down fairly evenly / predictably over the next twelve months... That are doing NOTHING for anyone until the depositor withdraws the currency.

That's not efficient.

The owner of the bank can see someone else moving into the community who needs to set up shop as a blacksmith but needs money for a building, a forge and an anvil. The total cost of those items is 50 currency units. The new arrival doesn't have the 50 units. The bank has 200 units in its accounts but they belong to two other people. But the banker is reasonably sure they won't be withdrawing all of that 200 over the next few months. Banking thus evolved into combining deposit accounts with lending to essentially arbitrage these cash flow timing differences in ways that a) kept the money "at work" in the economy and b) collected additional fees for the banker as they sat in the middle between depositors and borrowers and managed the flows.

As this framework was adopted, it aided higher productivity in the community. Bankers saw more depositors with balances that didn't just vary between zero and X over months or years, they saw balances that certainly varied but overall, tended upwards over long periods as sustained wealth was being created. Bankers eventually realized that a larger share of deposits could be lent out without risking getting caught short for redemptions and that increased lending would further increase lending profits and grow the economy. Rather than lending 25% of deposits, the bank could lend 50%. Or 60%. Or 75%.

This practice is called fractional reserve lending. The economic consequences of fractional reserve lending might be a bit difficult to visualize but the mathematical result is simple to express as an equation. For a bank holding D units of original deposits, if the bank retains r percent (e.g. 20% is r=0.2) of those deposits on hand to handle daily withdrawals and lends out the rest to other customers, the ACTUAL value of total "money" that will come into existence from that initial deposit D is calculated by this formula:

M = D / r

For example, for a reserve ratio r = 20%, initial deposits of 1000 currency units will result in a total of M = 1000 / 0.20 or 5000. That money is created over the following iterations:

• initial deposit of 1000
• retain 20% of 1000 or 200, lend the other 800
• 800 goes out but re-enters the banking system when spent, returning 800
• retain 20% of 800 or 160, lend the other 640
• 640 goes out but re-enters the banking system when spent, returning 640
• retain 20% of 640 or 128, lend the other 512

If this cycle is repeated, the numbers get smaller but add up to 5000 units on deposit in the bank. This fractional reserve lending literally created money out of thin air.

To REALLY understand what banking entails, it is crucial to explicitly describe the work being performed by bankers in an economy. In this simple example, the first function the bank had to master was operating a physically secure facility and hiring personnel that members of the community could trust with their money. Without lending in the picture and no other way to make a profit, the bank owner had to set a price for the cost of handling deposit accounts and depositors had to agree that cost was LESS than their internal cost of trying to house and protect their money at their home. At that point, the banker is specializing in physical risk security and "retail" procedures for handling deposits and withdrawals.

Once the bank identifies lending as a profit opportunity, the bank must develop skills at evaluating risks associated with would-be borrowers. Where did you come from? Is your face on a wanted poster three towns away? Do you owe any money to someone else? How is your business going to operate? How much do you think you'll be able to earn per year? How much will you be able to afford for a loan payment? If I lend you 50 and you skip town after making 10 in payments, how can I get my other 40 back?

At scale, those are vastly different skills than running a deposit-only institution. The bank needs to have loan officers who understand different types of businesses and their revenues and costs. The bank needs loan offices with "people reading skills" to guard against fraudsters. The bank likely needs employees with relationships with law enforcement to help research the background of would-be borrowers who may have skipped out on prior lenders in other communities. Because of these inherent risks of lending, the bank realizes a fee should be paid by the borrower to balance out these extra risks being taken by the lender.

The extra premium collected from a borrower through the series of loan payments is termed interest but, as the examples above make clear, that interest charge isn't a one-dimensional reflection of a single risk. It reflects ALL of the risks being taken by the lender between the time of the initial loan and final payment of the loan months or years later. But the interest charge also reflects something called the time value of money. Even in a risk-free world where the lender somehow knows the borrower will pay back the loan exactly as expected, giving X units of money for Y years is costing the lender something. What, exactly? The opportunity to use the money for something else over that time. If the lender could identify something to invest the money in that earned 9% interest, lending the money to someone else to earn a 7% return would represent a 2% reduction in the lender's potential income.

Lending Leads to Cycles

This concept of the time value of money is INTRINSIC to the adoption of trade, money and lending with fractional reserve lending. When banks adopt fractional reserve lending, they are literally providing extra fuel for commerce out of thin air because of the multiplier effect where a 20% reserve ratio turns a single 100 unit deposit into 500 total units. Fractional reserve banking puts bankers in an incredibly powerful and lucrative position. Bankers can literally grow an economy by lending money which adds to the bankers' profits which begets more lending which grows profits which begets more lending... ad infinitum.

If bankers want to make even MORE money, they can lower the reserve ratio they are trying to follow. If 20% with a multiplier of 5, why not 15% with a multiplier of 6.67? Or 5% with a multiplier of 20? (As a real-world aside, the Federal Reserve previously set minimum reserve ratios that its member banks had to meet but that Federal Reserve reserve ratio minimum was eliminated in March of 2020 amid the COVID financial contraction. Separately, the FDIC still enforces a minimum reserve ratio on banks that is around 2%, a multiplier of 50. Capital requirements also rein in lending as well but the key point here is that the multiplier factor in US banking is quite high.))

Over time, bankers realized this model for lending worked and had enough predictability to make it profitable even if some share of lenders failed to pay back loans. The interest paid by all borrowers was enough to cover the cost of the portion that defaulted. In fact, banks realized that if they ran low on money to lend, they could relax fees charged to depositors or even pay them a small percentage on deposits to attract new money to then lend out at higher rates. This is the core dynamic at work in banking today. Historically, banks have paid low rates in the 2% range on deposit accounts while charging 7-9% on loans, allowing themselves to pocket a 5-7% return without much difficulty or risk. (Over the past decade, rates on deposit accounts have gotten much lower and rates on loans are often in the 12-16% range due to poor regulation but that's another topic.)

Stop and consider the larger pattern and incentives from this cascade of productivity growth, growth in wealth, use of money, adoption of lending and fractional reserve lending that synthesizes new money for use in creating new wealth. It sounds magical. It can seemingly produce wealth out of nothing. But the recursive cycle still requires raw ingredients to contribute to the cycle -- labor and creativity. When the supply of those resources is exhausted, growth is not possible and loans made in the last stages of this expansive cycle start defaulting, bankers start losing money on loans and begin restricting new loans which slows growth which slows requests for new loans which lowers bank income which further lowers lending... ad infinitum

The same seemingly infinite virtuous loop becomes a death spiral when certain market limits are reached and all of these behaviors become reductive and the economy contracts. Sometimes cataclysmically.

Does this boom / bust cycle sound familiar?

It should. These self-reinforcing economic and psychological mechanisms do not just drive the basic business cycle. They drive every economic bubble and resulting economic collapse in history save for those directly attributable to natural disasters.

An Optimal Level of Inflation?

The economic and psychological factors that create typical business cycle patterns seen over centuries and more acute periods of extreme growth and sudden collapse from fraud also lead to other lessons that are absolutely not intuitive to average people without a background in mathematics, finance, and government regulation. Even those with expertise in those areas are likely to disagree on specifics, if for no other reason than their particular occupation or employer provides them incentives to manipulate policies in other directions.

The first crucial lesson stemming from business cycles and their contributing factors involves distinctions between interest rates and inflation. Both metrics are expressed as percentages and are frequently mentioned as though they are identical forces. In reality, they DO influence each other but they ARE distinct factors. To explain the difference between the two, it's useful to examine two extreme examples, then move towards the center for a more nuanced perspective.

In an economy with high inflation, say 20% per year, a product that costs 1.00 units of currency today will cost 1.20 units in a year. Or stated in reciprocal terms, 1.0 units of purchasing power today will have only 0.83 units of purchasing power in a year. In an economy facing hyper-inflation of 20% per MONTH, prices rise by a factor of 8.92 (792 percent) on a yearly basis. 1.0 units of purchasing power today will only have 0.11 units in a year. In that environment, that "money" is NOT meeting its requirement of serving as a reliable store of value and no one in the economy will want to hold units of that money for even a few hours or days. Thus, extreme INFLATION creates a harmful incentive to spend nearly everything, which drives up prices more which feeds inflation, perpetuating the harmful cycle.

Because of that, virtually everyone understands that high inflation is explicitly BAD for any economy. However, that understanding is often reversed into a second "understanding" that is entirely wrong. If high inflation is bad, surely after a period of high inflation, having a period of DEFLATION to return prices to prior "good" levels would be beneficial, correct?

Wrong.

In an economy with trade, money and fractional reserve lending, DEFLATION is just as undesirable as high INFLATION. Again, imagine a more extreme scenario. If prices in an economy are dropping 5% yearly, a product that is just beyond affordability NOW might become affordable in a year. This encourages saving so money is deposited rather than spent. While this provides more resources for the bank to lend out, other businesses are seeing less spending (cuz things will be cheaper a year from now) so borrowing to grow businesses declines, and the chain of recursive factors starts working in reverse towards CONTRACTION. If the rate of inflation is significant, the economic contraction can be severe and trigger massive job losses and business failures.

Between these two extremes of high INFLATION and high DEFLATION, there must be a sweet spot for price levels. It might seem that optimal level of inflation in an economy with fractional reserve lending would be ZERO percent inflation. In reality, this is NOT the case. No two economists have been identified on the planet who agree on what a single best level of inflation might be but the general consensus is around 2 percent. There's no equivalent of some physical or chemical process in the natural world that makes that number optimal. However, across dozens of economies over nearly one hundred years of "modern" theory regarding economics and finance, anything below that range seems to trigger contraction and anything above it seems to stimulate the host economy for two to three years before forces begin snowballing and prices skyrocket ahead of income triggering a decline on a different path.

More importantly, as mention earlier, inflation rates and interest rates both involve the same mathematical impact on financial calculations but they reflect different risks. In a world where prices are optimally stable and only growing at 2 percent yearly, it is very logical for a bank to charge an interest rate of 7% on a loan to a business. Why? Because the extra 5% between 2% and 7% is reflecting actual business risks the bank is accepting by loaning money to the business. This risk is distinct from changing price levels in the economy reflected by current inflation statistics. Obviously, high inflation rates might make a firm's product less affordable and thus increase its risk of defaulting on a loan. That could trigger a higher interest rate on a loan but in that case, a risky loan in an economy with 7% inflation might require an interest rate of 12% for the bank to lend.

WTH

A Grand Unifying Theory: Creativity, Productivity and Specialization

This post is one of six posts in a series on this topic. The full list of posts are linked here for convenience.

A Grand Unifying Theory: Overview
A Grand Unifying Theory: Creativity / Productivity / Specialization
A Grand Unifying Theory: Trade / Money / Banking
A Grand Unifying Theory: Groupthink / Power
A Grand Unifying Theory: Current Case Studies
A Grand Unifying Theory: Takeways

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Any philosophical analysis of the arc of history, economics and politics and their purposes has to be grounded in an assumption about whether it is possible to name a single goal which can accurately reflect a goal of the entire population. The easy answer is NO, doing so is impossible because people can have multiple goals and prioritize them differently, based on which goals have already been met to some minimal threshold. For analysis here, it will be assumed the universal goal driving most human behavior is providing the proper necessities for basic life and health. Adding one additional assumption to that initial goal -- that "sustaining" human life results in a growing human population -- sets the conditions in place for everything to be analyzed over the paragraphs and posts to come.

Defining Terms: Creativity, Productivity and Specialization

This first phase of analysis focuses on three key factors that may often be considered synonyms of each other but merit a few key distinctions. For purposes here, creativity involves the ability of humans to formulate unique ideas from the world around them and use those ideas to alter the impact of the environment upon them or their impact on the environment. Productivity is a measure of the rate at which humans can complete tasks required for baseline survival or for satisfying more intangible goals higher up on the Maslow pyramid such as esteem and self-actualization. Specialization is the practice of purposely reducing the VARIETY of tasks performed by a single person or group in order to allow that person or group to become far more productive at a smaller set of tasks.

It is crucial to caveat that the relationships BETWEEN creativity, productivity and specialization are not mathematically precise nor will all people view them the same way. Caveman Og may have found a new source of pigment that allows him to double the number of cave paintings completed per week but cavewoman Thog may likely conclude that has done nothing for Og's productivity in slaying wooly mammoth for tonight's dinner. If the family is starving, cave art is worth considerably less than food.

Creativity Leads to Productivity

Since humans do not emerge from the womb exhibiting adult levels of understanding and physical abilities, even sustaining any existing human population with a 1:1 replacement ratio between parent and child unavoidably creates an addition burden on existing adults as children are raised. The longer dependency period for human children REQUIRES increasing levels of productivity at least for SOME period of time (12-18 years?) from adults. If a couple required 2.0 units of food daily for sustenance, once a child arrives, those same parents require SOME incremental volume of food per day for a child. If 2.0 units of food required 2.0 units of work in their local environment, that couple is going to have to work some additional amount, say 0.X units, to exchange for 0.X units of additional food for a child. Have a second kid? Add another 0.X to the load. And don't forget a bed, additional clothes, daycare while Og and Grog work at the rock wheel quarry, etc.

Where are the extra units going to come from? Certainly, the parents can attempt to work more hours every day but that likely becomes counter-productive after about sixteen hours of work per day and some work might require daylight which is further limited. If Value = Hours x Productivity and Hours are already maxed out, the only other way to get more Value is to increase Productivity. There are only so many ways to improve productivity:

• continued mastery of a physical task as originally devised ("practice")
• identification of easier / more productive sources of materials to utilize ("better inputs")
• devising a better process for accomplishing a result that yields the desired outcome ("a new and improved buggy whip")
• devising a different solution for the underlying need that requires less work / resources ("a car instead of a horse-drawn carriage")

All of these approaches for increasing productivity can occur even with a single human operating alone. Once the system involves multiple people, increased productivity inevitably leads to specialization.

Productivity Leads to Specialization

In a system with multiple people, workers quickly realize that if tasks A and B are required every day and everyone currently is working at tasks A and B individually for their own needs, everyone would increase their productivity by choosing one of the two, getting even MORE productive at that task, then swapping with others for units from the other task. Everyone gets more total units of A and B generated in a fixed amount of time, giving everyone a share of a bigger pie. Or everyone gets the same amount as before with less total time spent working. An improvement either way.

At a higher level of abstraction, specialization is just a narrower form of trade which will be addressed in the next installment in this series. However, one distinction between specialization and trade is worth making that involves the level of trust required and the degree of formality among the parties. If two parties are each creating 1 unit of A and 1 unit of B independently then agree to specialize so that one creates 2.2 units of A, the other creates 2.2 units of B and they both wind up with 1.1 units of A and B, it is easy for both to conclude the specialization is worthwhile.

However, if the B worker only produces 2.1 units, will the A worker still produce 2.2 units of A and accept 1.05 units of B? If the two workers are related or from the same community, these variations can still be worked out without complex rules and specialization will still provide value. As the universe of workers gets larger, more formality is likely to be expected to ensure some workers are not shirking and free-riding on the higher productivity of others without increasing their own as well.

This is a key point early on... Notice how quickly concerns about actual work come into play among people pondering specialization and productivity? Whether people understand the linkage or not, EVERYTHING with people's perceptions of the value of THINGS comes down to their grasp of the amount of WORK involved to create those things. If someone is going to trade you something from thin air for something you worked eight hours for, you can get that other thing out of thin air as well and keep your object and be better off. A trade only makes sense if approximately equal value derived from work is perceived in both objects involved with the trade.

Funding Creativity / Productivity

So far, the analysis of creativity and productivity have essentially assumed that all creativity just emerges from a random "spark" of an idea in a human that is pursued, experimented with and eventually results in increased productivity that creates extra value. But the spark phase of that process is not totally random. There are things that can be done to increase the likelihood of a new "spark" occurring to a worker and resulting in an improvement. The first involves purposely allocating time to workers to rethink existing tools and processes. To the extent that enough time is set aside for this purpose, this work can be assigned to specific workers as another form of specialization. When a group formalizes work to this degree, this approach acts as an "investment" in the collective system.

Modern business obviously implement this exact strategy and attempt to optimize the amount of resources (money, people, time) to this "research" to maximize productivity gains without spending all incoming cash on research at the expense of profits. In reality, the link between a specific level of resource commitment and growth in productivity is virtually impossible to model with mathematical precision. These factors are all

• hard to quantify,
• they have exponential, compounding effects over time,
• and those effects are spread across many other disciplines

The fact that no one can present an accurate numeric model with a straight face does not mean it is not worth modeling the process to think through what-if scenarios to appreciate out results change from inputs, funding and priorities that vary -- randomly or intentionally. As a first attempt, imagine the entire world of knowledge being neatly divided into eight sectors. All of the sectors are related and start off adjacent to each other with a specific amount of existing knowledge represented by the area of a circle.

Over time, each area of knowledge grows in total area and expands its "frontier" ever outwards. Collectively, all of the sectors do the same thing and the overall scope of human knowledge is represented by the total area and the outer "frontier" of all of those expanding circles.

In reality, all sectors do not expand at the same rate over time. Progress may seem to wax and wane over years / decades, just due to the random interactions between all of the disciplines. But what if interests associated with one sector attempt to boost spending on their sector at the expense of other sectors? In the visual below, the growth rate was changed from 4% yearly for all sectors to 12% for Computer Science while Geology and Education were lowered to 0.5%.

After 20 years, the compounding effect on Computer Science is readily apparent but even this modeling approach is flawed. What if over that twenty year period, progress in computer science actually required discovery of new formations of rare earth metals required to manufacture high density semiconductors? In this example, the extra funding for computer science "research" came at the expense of Geology and Education so it is possible less resources in those areas will "starve" computer science of a needed innovation that will impair future advancements in computer science.

Special interests routinely lobby for explicit boosts on spending in their sector or relaxed regulation that artificially stimulates spending in their sector not just because they think it will someone improve society but because they are uniquely suited to personally benefit from that boosted spending. A corollary to this is that special interests will lobby for boosting spending in their sector AT THE EXPENSE of other sectors. Giving them the benefit of the doubt, they may do so because they don't understand the cascading long term effects of "starving" other sectors. But that doesn't mean these relationships can be ignored. The larger public needs the vocabulary and analytical tools to identify these situations to more closely examine stories being told to decide what is best. This is an underlying theme of this entire analysis.

Impairments to Productivity

So why are creativity and productivity not linearly related to "spend" in any particular sector? In any modern society, there is an upper bound to the rate at which new knowledge can be shared and adopted. Until the internet reached critical mass in the 1990s, it was virtually impossible for a single person to instantly communicate an idea to millions of others without coordination and consent from a multitude of parties (publishers, broadcast media, etc.) Even with the Internet, not every inventor or expert is adept at packaging information about a discovery in a format that is suitable for educating thousands / millions of others about their new invention. It takes time and effort to develop materials to train other people who will train even more to eventually communicate the discovery to the masses. This acts as friction which slows down adoption.

Events over the past four years provide a different, more concerning example of why progress cannot be instantly communicated and adopted. The primary AI technologies in use today based upon large language models were created by ingesting petabytes of existing online content, analyzing the text of that content and calculation trillions upon trillions of statistics about the next most likely "token" to follow a prior "context" of tokens previously seen. This flow is illustrated below.

As illustrated, the universe of online content was mostly human generated (green) until about 2014 when the use of "bots" to fake likes and comments on social media became obvious. By 2022, the launch of competing AI platforms led to an explosion in AI generated content (in red) which has already swamped many platforms. This red content is being fed back into AI training flows and becomes indistinguishable when converted to trillions of statistical probabilities of character sequences. This means AI systems are contaminating their own well with increasing amounts of slop which permanently impairs the ability to separate human-curated content from slop forever which slows down progress rather than speed it up.

This is not hypothetical. Generative AI technology has taken an already flawed publish-or-perish model in academia and swamped it with even higher volumes of suspect content. This cascade of machine generated plagiarism and noise has tarnished the reputation of many online publications found to have released "research" with plagiarized content or synthesized data. It has also overwhelmed the ability of qualified peers to conduct reviews of such papers without taking away more of their time from their research. As a result, many of those publications are REDUCING the number of papers being published. They cannot provide a qualified human review to five times the volume being created with one fifth of the prior effort and care being given by those submitting the papers. The net effect? Tools that should assist in IMPROVING the quality of research are instead being used in ways which are significantly REDUCING the quality of new research in nearly every field.

WTH