Knowing this, the player walks over to the cashier and the employee asks them to hand their tickets over. We know that the player benefits because of the prizes, but why does Dave and Busters even want these tickets? Aren’t they the ones that printed them? What are they even going to spend it on? If Dave really wanted 1000 tickets that badly, couldn’t he just ask Buster to print a thousand more?

Ignoring the financial aspect of attracting people to the establishment, it really doesn’t matter how many tickets they have stored away in the back rooms. The reason they require you to spend tickets is so that you need to earn more. The gifts are there to create demand for tickets, not the other way around.

The Right to Print Money

If we think of Dave and Buster’s as a government, we’d call them a monetary sovereign - an entity that issues its own currency. The United States is in that exact position: we can print as much money as we want.

One important feature of a monetary sovereign is that they have an exclusive right to create and issue their currency. For example, the United States issues the Dollar, the United Kingdom issues the Pound, and Japan issues the Yen. If anyone else tries to print their own versions of those currencies, the nations in charge will retaliate with criminal charges. (Does anyone know how Dave and Busters deals with counterfeit tickets?)

Not every nation, however, is a monetary sovereign. Any of the 21 countries in the eurozone use the euro, so no single country in that agreement can choose to print more of their own currency at will. Other nations in this group include those that peg their currency to another nation’s — for example, the U.S. dollar is used in Ecuador and Panama.

Another limiting factor is when a country decides to guarantee an exchange rate for some kind of real life object. The United States used to constrain spending through the gold standard, which allowed dollars to be redeemed for a set amount of gold. Ever since the gold standard ended in 1971 under Nixon, we no longer have that guardrail in place.

If we no longer have the gold standard limiting us, what do we really mean when we say that we “can’t afford” something?

The Government as a Household

To build an understanding of how the government budget works, I’ll present what I take to be the “common sense” view of how the United States spends money:

1. The government wants to do something

2. They figure out how much it’s going to cost

3. They raise that amount of money through taxes and borrowing

4. They use that money to do the thing they wanted

This means that the government has two ways to do anything, neither of which sounds appealing: either we (the people) have to give up more of our hard-earned cash, or we have to run the government at a deficit by borrowing money from Wall Street or China. Our spending capabilities, therefore, are just like those of a household: we have to get the money from somewhere (like a job or a credit card) and make sure our checkbook is balanced in order to keep our finances tidy and in order.

This makes sense at first glance. After all, we’ve been conditioned through decades of rhetoric surrounding “balanced budgets” and “deficit hawks” to believe that government spending (except for our military) is either irresponsible or deeply unfair. That being said, there is one crucial hole in this household analogy: households aren’t allowed to print money.

The Government as a Government

Although analogies can be useful, they can also be misleading if we don’t understand the ways they do and don’t map on to real life. In terms of budgeting, it’s more useful to view monetarily sovereign countries like the United States as something more like a bank in Monopoly or the house in a casino. Real-life households have obligations that require them to obtain dollars from someone else, whether it’s their boss or the bank. They need those dollars to pay for food at the grocery store, property taxes on their house, fees and registrations for things like driving or operating a business, among most other parts of life to some extent.

The United States doesn’t need to do any of that. If there’s some massive expense that needs paying, the government doesn’t need to run around begging people for work or a loan. The government controls the means to bring more money into existence at any time it wants (whether physical minted or digitally transferred).

So, if the government doesn’t need to actually take our money before spending, what are taxes really doing? Rather than thinking about cash moving hands from a person, to a government, to a recipient, it’s more accurate to think about taxes as not transferring money at all. Instead, the analogy fits better if we think of cash moving from one person’s hands into a giant shredder. When money is taxed away, it’s essentially taken away from the economy — each dollar taxed is a dollar not spent on groceries, hospital bills, or mega-yachts.

Beyond taxes, understanding the United States as a monetary sovereign changes our conception of government deficits and debt. Whenever the government spends more in its budget than it decides to collect in taxes, we refer to it as running a deficit. While this conjures to mind images of being deficient in something/not having enough, all that it really means is that there’s more money flowing out of the government to us than the other way around.

Furthermore, the word “deficit” is often used interchangeably with the government “debt”. While the deficit represents the total flow of spending from the public to the private sector, the debt is the running total of all of those budgets from across the years. Therefore, we can understand the debt as the result of deficits over time.

This conversation is also made more confusing when people describe government bonds in the same language as the budgetary debt. This is reflected in how people describe the process of buying bonds: we often describe this process as the government “borrowing” money from the people. The traditional logic is that the government wants to spend money, but doesn’t have enough and doesn’t want to raise taxes. Therefore, in order to spend money on social programs, the military, and so on, the government has to beg for people’s money on a promise that they’ll be paid back with interest. With the Dave and Buster’s analogy, this would be like thinking that the establishment ran out of tickets and couldn’t afford to run their games unless they could borrow tickets from the nearby players (or nefarious ticket lenders from overseas).

So, if the United States government is a monetary sovereign (it can print its own currency) why would they ever need to borrow dollars? Rather than borrowing, we should instead think of the bonds system as a service offered to promote saving money. If we’re looking at the spending process in order, it would look as such: the government budget runs a deficit, the government spends money on programs, then money enters the economy to operate those programs. Once money enters the economy, it finds itself in the hands of people who want somewhere safe to save those dollars. The government offers bonds as a way to guarantee access to one’s money in the future. The interest rates come as a result of political/fiscal goals, whether it’s slowing down spending or encouraging saving.

We Can Always Pay for Anything

One of the roadblocks familiar to anyone who’s ever been in favor of the government spending money on anything is a question so commonplace that it operates less as a question and more as a universal rebuttal: how are we going to pay for it? The common sense here is that the government has a limited amount of dollars in its vault, so we need to go find money out there. While this logic makes sense when you’re buying something expensive for yourself, we again run into the core of the problem: you can’t print your own dollars. This focus on financial constraints not only limits our political imaginations, but deflects attention from the real, material costs that government programs actually run into.

Instead of this, the question we should be asking is this: do we have the resources? This brings our attention to the real costs and deficits we need to solve. Are there enough willing workers available to run the program? Do we have the technical capacities to achieve this? Do we have the materials and expert knowledge required in this field? What’s required to make sure this program is environmentally sustainable? Money is almost inconsequential in the broader considerations required for responsible and effective spending policies. Monetary sovereigns can always print more bills or create more money with a few keystrokes on a spreadsheet. The people, material, and infrastructures have real needs and costs. We can raise all of the money we want for a government project, but that money has nowhere to go without the real, material conditions outside the financial sphere.

This, however, doesn’t mean that money can be spent infinitely on anything for no good reason. Rather than giving us a free ticket to be financially irresponsible, recognition of these facts requires us to challenge the notion that reducing government spending is always responsible. By understanding the real constraints at play, a true responsibility goes beyond the financial constraints we naively inherited from the gold standard system. If our spending isn’t constrained by how many bars of gold we have buried underground in a vault, then our spending instead needs to be constrained by our own ability to analyze and deal with the real-life consequences of government policy.

The one guardrail that we need to be worried about is inflation. If the government prints money without removing a corresponding amount from the economy, then each individual dollar will have a lesser claim to the totality of goods and services that can be purchased with dollars. This can present real challenges to people if not adequately dealt with. For example, increased prices on certain goods like housing, food, and medical care can cause severe harm and even death if not accompanied by increased wages, social safety nets, and so on.

Thankfully, we already have the perfect tool to deal with inflation: taxes. Since the U.S. government doesn’t have a limited stock of dollars, it’s misleading to describe taxation as a process of collecting money for spending. Instead, it’s more accurate to say that taxed dollars are removed from the economy. If inflation is the result of an increased supply of dollars pursuing an unchanging stock of resources, we can counter inflation by just reducing the supply of dollars. We can think of inflation as something like a speed limit: spending too much money too fast has a risk of excess inflation, so limiting inflation through taxation keeps our economy safe.

With this view, taxation is no longer a system of the government taking money from people in order to spend it. Instead, taxation is like a society-wide agreement to all throw a portion of our dollars into the giant shredder in order to create space for the government to operate without risking excessive inflation.

What Becomes Possible?

The obvious application of this is for some of the well known social-democratic policies that have been popularized by people like Bernie Sanders and the Justice Democrats. Medicare For All and a Green New Deal are good examples. Progress on these has largely been denied by Republicans and moderate Democrats under the guise of financial responsibility. They claim that we simply don’t have enough money to do these things, even if the goals are noble. I would argue that the opposite is true: we can’t afford not to spend this money. If we look from a purely financial standpoint, there are cost savings associated with both of these policies that have been empirically demonstrated for a long time, but I think these points (while important) somewhat miss the point. If we have the people required to do the labor, the time, the knowledge, and the resources, the financial price tag need not apply. The only thing required beyond that point is the political will to bring these projects from concept to reality.

Furthermore, economists in the school of thought of Modern Monetary Theory often propose a solution to the problem of unemployment: a federal jobs guarantee. Unemployment is a condition created by capitalist market systems in which a person wishes to have a job, but does not have access to one. Their solution is to guarantee a job with a livable wage to anyone who asks. Imagine being able to walk into a federal employment office and leaving the same day with a well-paying job suited to your personal abilities, desires, and interests! First, this would be a way to create jobs which are socially desirable or necessary that might not be profitable in the private market. For example, caretaking, environmental preservation, and teaching are roles in which there isn’t much short-term market value generated. However, we can still have willing people provide these valuable services if we democratically agreed that such work was worth having in our society.

A federal jobs guarantee would reduce the power that employers have to abuse their workers. Unsafe working conditions, wage theft, and low pay are all taken as givens for the majority of the American working class since it’s often seen as nearly impossible to quit one’s job without suffering a massive financial burden. If employment at a decent wage were guaranteed, employers would actually be required to treat their workers with some amount of dignity rather than relying on keeping them captive with their reliance on wages and health insurance.

Conclusion

Our political imagination has long been captured by the neoliberal compromise. Republicans like Reagan and Bush and Democrats like Clinton and Obama have operated under the same core assumptions for decades: we have a limited supply of money to spend, and spending more than we collect is therefore inherently irresponsible. In order to respond to the enormous problems we face today, we cannot continue to abide by a false understanding of our true capabilities to respond to those challenges. To be properly equipped for the future, any political movement that hopes to harness the power of the federal government will need to adapt to the realities of today’s political economy or else risk being left behind with the gold standard.

“What’s wrong with you? You want me to break your camera?”

“Get away from me with the camera. Dude, I’ll take it, I’ll get up and take it and smash it. Get away from me. Don’t make me get up.”

“Is he with you? Did you ask for permission guy? Why are you taking photographs? (…) We’ve got some mute creep, taking pictures, stalking people.”

“Can you not take pictures of us?” “Yeah, like that’s kinda stalker-ish.”

“You’re scaring my customers, they think you’re taking pictures of them.”

“We’ve got a… photographer?” “I don’t know who this guy is.” “Hello? Why don’t you answer him? Hello? Hello! Do you talk? Do. You. Talk.”

The Panopticon

In the late 18th century, the English philosopher Jeremy Bentham proposed a revolutionary new type of prison. While prior models enforced the rules of the prison with patrols to periodically check on the prisoners, Bentham’s new idea would force prisoners to act on their best behavior without the need to move. Bentham’s panopticon was imagined as having a circular design, with a tall guard tower in the center and the holding cells surrounding it. Such a design would allow for an inspector in the tower to observe any inmate they wished without having to move. Furthermore, the vantage point meant that all of the inmates would constantly face the tower without being able to see the guards. His plan even included “conversation tubes” which would allow inspectors to remotely communicate with the individual inmates in any cell.

Therefore, Bentham believed, the uncertainty of always being watchable while never knowing if one is being watched would incentivize prisoners to regulate their own behaviors and ultimately improve themselves. This plan was meant to control and shape the minds of prisoners rather than just retaining physical control over their bodies. In Bentham’s mind, such a system embodied his progressive ideals for social reform — he genuinely hoped that the panopticon would allow prisoners to improve their behavior on their own without being subject to the harsh violence of traditional prisons.

While some people might have heard of the panopticon or have even witnessed the construction of similar prisons, there are a few parts of Bentham’s plan that aren’t brought up as often. First, the prisoners in his imagined building weren’t just held to some standard of good behavior or kindness. Rather, the prisoners were also to be workers. The purpose of the watchtower was to keep the prisoners working at all times. Inmates would be assigned manual labor tasks, and they knew that if they stopped working they could be spotted and punished at any time. He imagined that the constant fear of punishment would cause prisoners to self-discipline themselves into completing their tasks.

Second, the scope of the panopticon went far beyond prisoners. Bentham hoped that the success of his prison model would demonstrate the benefits of constant surveillance. Such surveillance could be done in factories, schools, and in public spaces to prevent deviancy.

Flock and the New Surveillance

In 2017, American security manufacturer Flock Group Inc. began a business of selling surveillance. Whereas security cameras weren’t anything new, Flock innovated by creating a network of automated license plate readers (ALPR). Flock opened its doors to anyone who wanted to buy into their promise of safety and security — law enforcement agencies (at all levels), neighborhood associations, business owners, or anyone willing to pay could have Flock cameras installed. Recently, U.S. Border Patrol, Customers and Border Protection, and the Drug Enforcement Agency have started putting up similar ALPR cameras near highways by the border while disguising them as traffic cones or barrels.

In addition to their claim of scanning over 20 billion vehicles each month, Flock has pushed to increase their audio surveillance capacity. Flock has integrated “gunshot detection” microphones to listen for “human distress” (particularly trained to pick up human screams).

Police officers have used the data obtained through their Flock surveillance systems for what they do best: suppressing protesters (like the No Kings rallies), prosecuting activists (as with the animal rights activists at Direct Action Everywhere), and stalking their exes (this has happened multiple times).

Surveillance Camera Man

But you don’t need to be a corporation to surveil.

Some time in the early 2010s, an anonymous man in his 20s started posting videos in which he would walk around Seattle and simply point a camera at people. He wasn’t there to interview them, he wasn’t filming for a documentary — all he did was watch. He would walk into stores, restaurants, and classrooms as if nothing was wrong or out of the ordinary and just stand there, filming silently. Reactions vary from nervous laughter to violent threats, the one-way confrontations consist of a series of increasingly explicit outbursts and confused questioning.

We often tend to mentally dismiss cameras, audio recorders, and other forms of surveillance as long as we see ourselves as being in the public. We have areas of our lives (both physical and metaphorically) that we consider to be ours. We can share our private life if we wish, but no one else has privileged access to it. By defining a realm of our private life, we leave all other aspects of our lives public. We accept some level of loss of privacy when we enter the public — in a perfect world, all for some kind of common good: safety, happiness, and so on.

The reactions in the Surveillance Camera Man videos show how volatile and subjective the private/public distinction is. Regardless of if we’re on a sidewalk, in a public park, or inside a public establishment, we’re uncomfortable at the idea of someone filming us for no clear reason. Even in public, with all of the ways we voluntarily give up our privacy, the mere presence of a man with a camera pointed at us feels like an invasion of our personal lives.

While we might have social norms that hold up this distinction between private and public life, the relentless force of capital doesn’t see a difference. These lines are distorted not just as a result of drifting over time, but are now intentionally broken down in the pursuit of profit. In the workplace, we’re pressured into spending as many hours in the office as possible — our “work-life balance” can come from the ping pong table and an occasional day off. At home, the workplace isn’t gone. Instead, we’re expected to juggle unfinished tasks and urgent emails alongside our personal responsibilities. Even if we miraculously come up with some kind of free time, market pressures push us to make more money through a second job or “side hustle” or improve our employability through some form of training or education.

If our bodies and minds are already captured, our emotions aren’t safe either. Our mental health is largely defined by how well we can function within the existing social order. Our mental state, therefore, is only considered healthy insofar as it allows us to be perceived as a productive member of society. The GDP prefers a workaholic insomniac who can work all day and night over any other person who decides to devote any amount of time in their lives to their own interests, desires, and joys.

Bentham imagined prisoners never knowing if they were being watched. The mere fear of being caught deviating from protocol would prevent the need for actual punishment. Today, we know that we’re being watched and listened to 24/7, and we act in the same way. We police ourselves, avoiding social punishment by staying productive and within the narrow boundaries of what’s acceptable. We are the guard, the prisoner, and the structure in which they’re held.

These seem like opposite strategies, but they arose under the same economic moment. Post-2008 conditions - employment stagnation, low interest rates, and surplus capital seeking higher returns - created the environment in which these companies can exist. What can their diverging paths tell us about the future of 21st century capitalism?

Companies like Uber and Google aren’t anomalies - they represent a more fundamental shift. Traditional economic categories no longer suffice, so they represent a genuinely novel development. Rather than a curiosity, this new model is becoming the dominant form of contemporary capitalism. Political theorist and economist Nick Srnicek has observed these trends and given a name to this new form: the platform.

Crisis Conditions

During times of crisis, capitalism has a tendency to restructure itself around new technologies and forms of organization. In order to continue market growth, capitalism creates demand for new technology that can increase the profitability of a given firm against its competitors. Srnicek argues that there are three moments in the recent history of capitalism that have set the stage for today’s digital economy: the global profitability crisis of the 1970s, the dot-com bubble of the 1990s, and the financial crisis of 2008.

More specifically, the governmental response to these crises created the economic conditions that were necessary for the rise of platforms as we see them today. First, low interest rates were introduced to encourage investment and growth as a means of recovery following the 2008 market crash. One major effect of this was that surplus capital from wealthy people and institutions was funneled away from government bonds and savings accounts as investors turned towards speculative investments in pursuit of higher growth. Despite any of this potential for growth, however, job growth has remained largely stagnant outside of low-paying service jobs and unstable, independently contracted “gigs.” The stagnation of wages has forced many people into self-employment, where workers can “be their own boss” while still relying on large corporations for their paychecks.

Furthermore, Congress has been unwilling to use their powers to aid economic recovery efforts. Fiscal stimulus has been politically unpopular as both Democratic and Republican strategies have been centered around neoliberal austerity and fears of inflation. This has put the job of managing the health of the economy on the Federal Reserve and Central Bank, which have largely responded with a loosening of monetary policy. We’ve seen this manifest in the forms of historically low interest rates and the implementation of quantitative easing (in which a central bank buys up government bonds and other securities to inject more money into the economy). These monetary policies created a combination of excess corporate cash holdings and low returns in savings accounts and bonds, which resulted in large investments in speculative technology start-ups as an attempt to pursue higher profit margins.

It was in this environment - hungry for growth, flush with capital, and searching for new frontiers - that a particular kind of firm began to emerge.

Platforms as a Response

Many attempts have been made in the effort to come up with a name for the newest evolution of capitalism: the gig economy, the on-demand economy, the surveillance economy, etc. Although these terms all describe distinct phenomena, they all point to the same underlying economic shifts. Srnicek opts to examine these changes by focusing on the dominant organizational forms that present-day firms take.

Platforms weren’t the result of random luck and technological breakthroughs. Rather, they emerged as a direct response to the particular internal needs of companies. As more parts of the economy started to shift into the digital realm, the amount of data that became available for analysis began to increase at a faster rate than traditional business models could handle. As the models of Fordist factories and production lines weren’t able to process data at high speeds, platforms took shape as a response to rapid influx of new data. So, what mechanics allowed platforms to keep up while other firms fell behind?

Core Mechanics

Srnicek argues that at their core, platforms are all about extracting data as a raw material. Much like the raw materials of the industrial world, such as oil and minerals, data alone isn’t of much use to anyone. In order to harness the power of oil, the infrastructure of oil rigs, pipelines, and processing plants were created. So while the behaviors of users on a digital platform might generate massive amounts of data, the real value in a platform comes in its ability to analyze and translate that data into valuable insights for their clients. For example, Google doesn’t give all of the raw data they’ve collected from users to their advertising partners. Instead, they internally process that data into insights about users’ lives. Then, advertisers pay Google knowing that the ad network will show their advertising to the most receptive potential customers possible.

In order to help them extract data at a large scale, platforms position themselves as intermediaries that facilitate interactions between users. Placing themselves in this position then gives them privileged access to every user interaction, therefore enabling them to collect, process, and analyze their data as efficiently as possible.

Another defining feature of platforms is their reliance on the network effect. This takes place when having more users on a platform makes the platform more valuable for every other user. For example, Facebook uses a proprietary algorithm to match people with other users that they are likely to know or be related to. The biggest reason that people are drawn to Facebook instead of other websites that could do a functionally identical service is simple: everyone else is already on Facebook. Combined with the increased accumulation of data that comes with new users, the network effect creates a powerful reinforcing feedback loop towards further growth of the platform. In the context of capitalism, the monopolization that we see in giant tech companies isn’t just a product of greedy CEOs and consumer choices. Rather, the tendency towards monopoly is built in to the DNA of the platform model. As platforms grow larger and larger, their dominance over user data makes it ever more impossible for business competitors to arise.

The network effect also gives rise to another behavior that’s appeared puzzling through the lens of traditional business logic. To get more users and generate more extractable data, platforms often make services free (or at least very cheap) even at a cost to their profit margins. While profitability used to be the ultimate metric of business success, we’ve seen a rise of giant companies like Uber and AirB&B that are willing to remain unprofitable for a long period of time in hopes that they’ll gain a monopoly over their respective markets in the long run. In a process known as cross-subsidization, firms will often have multiple branches that aim to generate profits to subsidize the losses of giving some of their services out for free. For example, we’ve seen companies like Amazon and Google branch out into cloud computing as a way to subsidize their unprofitable programs like Amazon Prime and Google Drive.

The Taxonomy of the Platform

Srnicek categorizes the different types of platforms by looking at what data they use and how they profit from it. According to this framework, we can identify five unique groups: advertising, products, cloud, industrial, and lean platforms.

Advertising Platforms

By their nature, these platforms tend to be the most stable and easily recognizable out of the five categories. Companies like Facebook and Google provide “free” services such as social media, email addresses, or video streaming. This positioning gives them the ability to collect data generated by user behaviors. These behaviors can take the form of anything that the company is capable of tracking and storing as data. Some examples include everything you click on, every account you interact with, and how long you spend watching a video before turning it off. Once the companies collect massive amounts of this data, they’re able to construct detailed profiles of every user. Given the quantity of data, they’re often able to predict anything about you, from your age and gender to your passions and political beliefs.

Contrary to what we might initially assume, advertising platforms tend not to profit by selling our data to advertisers. Rather, they profit by selling a promise to the advertisers themselves: that they’ll find the most receptive customer possible for every ad. We can therefore see that they are materially motivated to collect as much data about you as possible to be of the most value to the advertisers. We see this take the form of ever increasing behavioral data collection in brand new domains: smart health devices (like fitness tracking watches and Bluetooth-enabled heart rate monitors), smart home devices (like 24/7 recordings from doorbell cameras and baby monitors), and smart TVs (which means that they’re allowed to remotely send images of every single thing you watch to their servers).

Product Platforms

Product platforms extract data in the form of transactions and sales. Companies like Amazon and eBay provide a digital hub where consumers can search for virtually any item they want from an endless catalogue of sellers. Being the intermediaries between the buyers and sellers, these companies can leverage their knowledge of exactly how much customers from all around the world are willing to pay for any given item.

Cloud Platforms

This type of platform extracts data through the providing of digital infrastructure to other businesses. Amazon Web Services or Microsoft Azure are examples of this, where they provide computation power, data storage, web hosting, and other services to anyone that’ll pay them. This is appealing to other businesses, because it means that they can pay for stable and expandable tools instead of having to build a solution to every technological need in-house. Cloud platforms profit by collecting data from all of the activity on their platforms, as well as collecting rent on the servers and processors they run.

Industrial Platforms

Industrial platforms extract data through manufacturing processes. These types of platforms aren’t as commonly recognized, since individual consumers don’t tend to interact with them as much as corporations. Examples include companies like GE and Siemens, which rent out or sell things like engines and factory parts for production and manufacturing businesses. They can then make a profit by locking those businesses into a sort of “ecosystem,” where parts from one company are incompatible with those from another company. This can also extend into providing servicing and maintenance for those industrial parts, which further incentivizes un-repairability and creates a need for platform-specialized service workers.

Lean Platforms

Lean platforms are a relatively new form that we’ve seen emerge in companies like Uber, DoorDash, and AirB&B. These types of platforms are somewhat unique among the others due to the fact that they tend to derive profits not from significant data usage, but through labor intermediation. In the case of Uber, the platform connects riders to drivers while the actual service is carried out largely without interaction from the platform. The idea of paying someone to drive you somewhere is not too different from a taxi, but the main difference is that Uber gets to decide how much each trip will cost, how much the driver will be paid for the trip, which drivers to match with each rider, and so on. These platforms are “lean” in the sense that they aim to reduce their own participation as much as possible while slashing all possible costs. Uber can label their drivers as “independent contractors” rather than employees, and can use their position of control to force drivers to pay for their own gas, repairs, cleaning costs, health insurance, car insurance, and so on.

Despite their seemingly massive rise, lean platforms have so far been largely unprofitable. Their ability to capture markets has mostly been a result of surplus capital seeking start-ups for high growth potential, and easy access to low-interest corporate debt.

Combined Platforms

We’ve also seen an increase in the number of firms willing to combine aspects of multiple types of platforms under one corporate umbrella. For example, Amazon is largely thought of along the lines of the product platform, yet a bulk of their profits tend to come from Amazon Web Services, their cloud platform wing. As the capitalist demand for profit creates a corresponding demand for increasing amounts of data, it’s natural that large firms will attempt to expand their platforms into new areas.

Srnicek’s framework gives us a powerful tool to understand the convoluted worlds of technology and capitalism. Yet, there are still some questions that I think should remain up for debate - and some ways that pushing past the framework can reveal even more about how platforms work.

What Data Tells Us About Capitalism

Data as Oil

Srnicek’s framework gives us a powerful tool to understand the convoluted worlds of technology and capitalism. Yet, there are still some questions that I think should remain up for debate.

First, we should examine Srnicek’s assertion that we should think about data as a natural resource like oil. There are a few strong points in favor of this metaphor. If we think of the capturing of data through platforms, we can draw a strong parallel to the ways in which oil and other natural resources are extracted.

In the case of digital platforms, data exists in small pockets out in the world — in the form of users. Every time a user joins a platform, a new batch of data becomes available for extraction. To make use of this data, the platform needs infrastructure that can extract, transport, and refine it into useful end-products. Much like the pumps, pipes, and plants of the oil world, user behaviors are captured, transported through fiber-optic cables, stored in server rooms, and refined through analytics into business insights.

Another similarity between data and natural resources can be found in the advantages of accumulation. Much in the same way that oil companies compete to be the first to buy (or steal) oil-rich land so that they can build their own infrastructure on it, early-movers in the digital world get their own advantages. Once they establish themselves as the dominant platform in an area, they can profit from their exclusive control over new user data. This profit then lets them reinvest into bigger databases, better algorithms, and advertising to recruit more users, which creates a natural tendency towards monopolies.

Capitalists extracting natural resources for profit will enclose and protect their territory with a threat of violence, whether it’s literal or legal. Similarly, digital platforms rely on government regulations to protect their intellectual property rights and algorithms. Furthermore, the fact that digital platforms must be run on physical servers requires a corresponding level of physical (possibly armed) security on premises.

We could even think of the natural resource status of data in terms of pollution. When the security of a platform database is compromised, we tend to see massive leaks of private user details. Things like names, addresses, financial information, and social network connections can be weaponized to harm victims of corporate data breaches, much like how oil spills and other pollution incidents cause real harm to the people nearby.

The Differences

That being said, there are clearly some ways in which data differs from natural resources. One of the most obvious is that data doesn’t deplete in the same way as a limited stock of resources. When you refine and use data, it doesn’t go away or get transformed into a new form. In fact, you can create as many copies of any dataset you want as long as you have enough digital storage.

One could argue there’s a practical limit: a finite amount of user activities can be captured, and digital storage requires Earth’s resources. But data still creates novel behaviors that finite resources don’t. You can copy a hard drive full of data to another drive — giving you two identical copies. You can’t do that with oil.

Furthermore, the massive scale of data can often create new unexpected behaviors or insights. In one form, you can combine all of the different datasets you have about one person to give you a complete picture of their life. In another, you can combine endless amounts of data in the form of books, wikis, internet posts, and so-on to create the large language models (LLMs) that power things like ChatGPT. We could say that we have a similar effect with natural resources, in that you can combine larger amounts of it to create new behaviors. While one drop of oil won’t be very useful, a huge tank of oil can power an engine that moves a vehicle. Despite that, the way that data can be extracted from so many sources and turned into a wide variety of new forms intuitively seems to be on a more complex level.

Another difference is that data doesn’t seem to be out there in the same way that some like oil or wood is. There’s a physical space where that substance would exist regardless of whether or not anyone was trying to extract it and make use of it. We can’t say the same of data. While we might have natural behaviors, those behaviors don’t get transformed into data unless there’s someone actively collecting it. In that sense we might try to conceive of behaviors as the “raw material” that must exist prior to the creation of any data. However, we run into issues when we see the ways in which data can influence and reshape our behaviors. The idea of the online trend shows this perfect: when someone is able to present data showing that a lot of people are doing the same behavior, other people might react to that data by engaging in that same thing. Data is also often collected in a motivated manner - if someone wants to prove that their thing is the best out of all of the things, they might look for reviews or focus-groups or usage rates that can enforce their narrative.

On Natural Resources

While some of the differences between data and previously existing natural resources might seem to point to the inherent properties of data being new and novel, we should also look at it from the other direction: what does data tell us about the existence and use of natural resources?

Artificial Scarcity

First, the scarcity condition of a natural resource is one of the prerequisites of being able to make a profit off of them. Just as was the case with early forms of capitalism, platforms are constructed in a way that creates scarcity artificially. Prior to capitalism, peasants controlled their means of subsistence (in the form of farm lands) until an outside force violently enforced the private ownership of that land. Once the peasants were separated from their common control over the food they grew to survive, they were forced into a state of general market dependence in which they only had two choices: wage labor or starvation. In Capital Volume One, Marx described this process of primitive accumulation as such:

The process, therefore, that clears the way for the capitalist system, can be none other than the process which takes away from the laborer the possession of his means of production; a process that transforms, on the one hand, the social means of subsistence and of production into capital, on the other, the immediate producers into wage laborers. The so-called primitive accumulation, therefore, is nothing else than the historical process of divorcing the producer from the means of production. It appears as primitive, because it forms the prehistoric stage of capital and of the mode of production corresponding with it.

- Capital Volume One, Part VII

In other words, capitalism needed to separate people from their means of survival — forcing them into wage labor as the only alternative to starvation. The parallel to today is clear: just as peasants were pushed off their land, users are now pushed off their own data.

Digital platforms generate their value through the extraction and processing of user data. Clicks, posts, and messages are stored in their private databases where they can then profile their users and profit however they see fit, whether their customer is a data broker, an advertiser, or a government (e.g. the data ICE and CBP use to fuel their mass deportations). Users produce the data, yet they have no control or ownership over it. Mirroring the early capitalist mythologies of virtuous accumulators and the lazy masses, digital platforms increasingly encourage an ideology that worships capital owners over everyone else. Companies like Apple and Facebook Meta make big shows of their CEOs through keynote speeches and public stunts to convince the public that the owners are visionaries, geniuses, and trail-blazers. This worship can even be imposed on us algorithmically as Elon Musk showed us when he threatened his engineers at Twitter into boosting his own posts to rank 1000 times higher than anyone else’s.

Activity Generation

As platforms have evolved, we’ve seen that pre-existing behaviors aren’t just used as data. Rather, those behaviors are actively transformed into new forms that are easier to extract from. Every social event becomes an Instagram-able “moment” to be shared. Every bad social interaction becomes a viral video on Reddit or Facebook (the amount of death threats you get from strangers on the internet will vary heavily depending on your gender and socioeconomic status). Every night out at the bars (combined with a lack of public transportation options) becomes a necessary Uber trip or a drunk driving fine - your money is funneled to capital owners either way.

Domain Expansion

Just as is the case with oil, the limits of easily available surface-level data must be eventually be overcome to keep up with the capitalist demand for growth. As oil reserves in some areas have depleted, technology evolved in order to extract from more difficult areas - fracking to extract from deep rock formations and deep-sea drilling to move off-shore. The push for research and products to create the Internet of Things parallel those evolutions. Products like smart watches, smart TVs, health-trackers, and doorbell surveillance cameras reflect this tendency. This can also take the form of new capabilities for products you already use, like smartphones. Every new data point is a new inference about your personal profile, which means it’s profitable to track you using everything your device has to offer. Every user action, no matter how obscure or how private, is seen as open space to be captured by an algorithm. In this way, the ever-increasing capitalist demand for heavier surveillance of users echoes the “discovery” rhetoric of colonialism.

Some might wonder why we should even bother with trying to categorize data as a natural resource or otherwise. While just making these connections on it’s own won’t change anything, our conceptual understandings and framings of the things in our lives shape what’s considered to be politically possible and “realistic”. For example, if we were to conceptualize data as a mere product generated by companies, we would naturally have to center our focus on the distribution and effects of data after it’s created. If we instead think of data as something that must be extracted from our lives and then refined out of its natural state, we can better understand the ways in which data extraction itself can change our lives before it’s even processed. It’s an open question as to what solutions for data ownership, usage, and privacy will produce the best outcomes. If the current situation entails the sole ownership of data under one corporate entity that can charge rent for access, we should look to imagine and construct ways in which data can be accessed and harnessed for the common good.

Convergence and Fragmentation

One of the more puzzling aspects of platform growth are the ways in which platforms try to protect their control over data while expanding into new areas. As platforms have grown, they’ve shown a tendency to occupy very similar spaces - each company can have their own versions of social media, cloud storage, and advertising networks. One possible explanation is that they’ve all identified a limited subset of platform types and data sources that can remain profitable in the long term. Unlike the horizontal and vertical integrations and mergers of traditional business models, the ways platforms expand tend to be driven by their need to occupy key positions in data flows to remain profitable against competition. Rather than combining to create larger or more efficient unified platforms, this has mostly taken the form of every company trying to do a bit of everything.

In response to this tendency towards convergence, platforms have responded by building walled “ecosystems” through enclosure. Users get locked in through dependency and the inconvenience of switching away. Where there are alternatives, platforms make it as difficult as possible by making sure that none of your data is compatible with anything else. A lack of data portability means that any of the convenience you’ve gotten at the cost of your privacy will be lost when you try to leave the platform. Think of social media or email providers - it feels impossible to “migrate” your social connections, followers, group chats, or anywhere else. We’ve also seen this in the form of operating systems and app stores. Our digital lives increasingly rely on closed-source, proprietary software. Unlike open-source programs, where you’re free to look at all of the code that runs in the background and modify it however you like, our interactions with software are increasingly dominated by subscription models, surveillance, and incompatibility.

Looking Forward

Conceptually understanding platforms as profit-seeking firms that are built to extract, refine, and use data can help us understand what’s at stake politically and what responses might be required. While this framework can’t answer everything, it can certainly give us a more clear understanding of how platforms will respond to various interventions.

The first and most obvious strategy is through regulations and antitrust enforcement. The U.S. government could implement short-term fixes like cracking down on tax avoidance and holding companies accountable for privacy violations. Local regulations could help with lean platforms like Uber and AirB&B that exploit workers and make cities less affordable. While these are desirable and likely necessary, Srnicek argues they remain unimaginative — bandaid fixes that neglect structural conditions like network effects and tendencies toward monopoly. That said, they could raise consciousness and build political capacity. An FTC that aggressively fought platform power could bring specific wrongdoings into the public eye, and organizing around short-term demands could build coalitions for more radical demands.

Another approach involves publicly owned platforms — where the state invests in platforms that serve the public rather than capital. Run as public utilities, they could enhance our lives alongside infrastructure projects like rural internet access and sustainable electricity. This way, network effects could be harnessed for the common good instead of enriching shareholders.

The most ambitious approach is developing alternatives that overturn the current status quo. If platform mechanics could give people alternatives to market dependence and wage labor, the possibilities could be endless. Decentralized networking — like Monero, Peer-to-Peer torrents, and Tor — has shown ways to transfer information anonymously without a central authority. Newer technologies like Meshtastic and Meshcore offer off-grid communication that escapes privately owned cell towers. If scaled beyond niche hobbyist projects, we could overturn reliance on corporate-controlled communication. What if platforms could be harnessed not as a means to silo off and exploit data for oneself, but instead as a way to share art, knowledge, and the other great parts of human life that have been out of reach for the vast majority of people around the world?

I believe that Srnicek gives us the right framework. The harder question is what we do with it.

1. The Dutch Dilemma

In the 1970s, a combination of oil embargoes and broadly increasing energy costs led to a widespread energy crisis across the Western world. People were forced to accept this new reality, meaning that they would have to start paying closer attention to how much energy their households were using. One particular suburb in the outskirts of Amsterdam ran into a perplexing issue. Even though all of the houses in the area were built at the same time, with the same structure and the same materials, they noticed that some households were using one-third less electricity without needing to change their own personal behaviors. All of the households got their energy from one provider that charged identical prices, and the families living in them were relatively similar. No one could come up with an explanation for this sharp divide between high-electricity and low-electricity households.

The true difference came down to one small quirk of the construction process: some houses had electric meters displayed in the front hall, whereas others had their meters displayed in the basement. The low-electricity households turned out to be the ones with meters in their front halls. Because the meter was in a spot that people had to walk past constantly, the members of these households saw the meter ticking up all day. In contrast, the households with meters in their basements rarely saw the information that could’ve reminded them about their electricity bills1.

2. One More Lane Will Solve Everything!

In the urbanized world, almost nothing can compare to the rage induced by the highway traffic of rush hour. No one likes being stuck in traffic, so we naturally look to an obvious solution: add another lane. If our roads are congested, then we can reduce that congestion by creating more space for cars to pass each other.

For an example of this fix, we can look to none other than the glorious Katy Freeway in the common-sense state of Texas, USA. In 2008 they decided to go big: who needs ‘just one more lane’ when you can instead have 26 of them? As we all know, this solution permanently fixed their traffic problems and Texans no longer need to sit stuck in traffic for hours. Right?

As it turns out, having 26 lanes doesn’t actually solve anything. Traffic jams continue to haunt these drivers just as much as anywhere else. But it’s not like anyone involved in this process is consciously choosing to make traffic worse. Individual drivers obviously don’t want this to happen, and if anything city planners are trying their best to fix it. Everyone agrees that there’s a problem here — so why is it that our so-called solutions seem to always make things worse? How is it that rational people with good intentions can produce outcomes that benefit no one?

Despite our best intentions, we see this pattern everywhere in our lives. Everyone sees the same problem, everyone agrees to implement a solution, yet the same old problems continue to persist while the problem-solution-problem cycle continues for generations. In fact, one of the great frustrations of politics is that it often seems like our most persistent issues only exist because of the failed fixes of the past. Shop inventories and prices swing wildly in response to steady demand, once-stable systems seem to collapse out of nowhere, and major pieces of legislation don’t have any effects until years down the line. We blame bad luck, disruptive events, and incompetent bureaucrats, yet dealing with these individual elements is almost never enough.

The problems aren’t the fault of bad people, bad luck, and bad actions. The true culprit is the hidden architecture of systems — the relationships between parts within systems that amplify or dampen change over time. Understanding this architecture is the difference between lasting solutions and billion-dollar highways that make traffic worse.

3. The Structure of a System

The Three Parts

Not every group of things is necessarily a system. In Thinking in Systems, Donella Meadows defines a system as “an interconnected set of elements that is coherently organized in a way that achieves something.” We can see that the parts of every system must consist of three things: elements, interconnections, and functions2.

Elements are the visible parts of a system — trees in a forest, or cars on a highway, or even intangible things like the collective pride of a nation. Although they’re often the easiest parts to spot, they’re usually the least important. Interconnections are the relationships that connect the elements, and are oftentimes built on a flow of information. The students in a school are interconnected by social rules, rumors on social media, and the knowledge they share with each other. A system will tend not to change even if you completely replace all of the elements. The individual cells in a human body are constantly replaced, yet the interconnections keep the system intact.

Functions are often the most important part of a system, yet they’re also the hardest to spot. Almost every system has a function of ensuring its own survival and continuation into the future. Functions don’t have to be intentional — in fact, they can directly contradict the goals set out by a system’s creator. The example of Texas’ Katy Highway shows us that the functions of human-made systems aren’t necessarily as obvious as we would like them to be. As Meadows puts it:

“A system’s function or purpose is not necessarily spoken, written, or expressed explicitly, except through the operation of the system. The best way to deduce the system’s purpose is to watch for a while to see how the system behaves.”

Stocks and Flows

Stocks are a specific type of element within a system. Meadows defines stocks as “the elements of the system that you can see, feel, count, or measure at any given time.” A stock can be physical (oil in a tank, items in a store), but they don’t have to be (your reserves of motivation or your self-confidence). Flows are interconnections that act to change the levels of a stock over time, whether it causes that stock to rise or fall. Because of this, a stock can act as a “present memory of the history of changing flows within the system.” We can measure these flows by keeping track of how various stock levels change over time.

Stocks can oftentimes take a long time to change because flows take time. You can’t drain a full bathtub instantly — the time it takes is dictated by how fast the water can flow down the drain. Although this can be detrimental, stocks can be harnessed in a positive way by intentionally using them as delays, buffers, and stabilizers. For example, a large stock of saved-up money can make a sudden emergency expense much less harmful.

“Systems thinkers see the world as a collection of stocks along with the mechanisms for regulating the levels in the stocks by manipulating flows.”

Feedback Loops

A feedback loop occurs when a stock affects the flows into or out of itself. These structures are oftentimes behind the appearances of sudden spikes or falls in a stock, but they also keep stocks within a certain range despite efforts to the contrary. We can broadly categorize feedback loops into two types: balancing and reinforcing.

Balancing feedback loops counteract change by pushing a stock in the opposite direction of any external force. The thermostat is a classic example: when the temperature drops below the setpoint, heating turns on; when it rises above, heating turns off. When we add highway lanes, congestion initially drops — but the balancing loop brings more drivers onto the road until traffic returns to its previous gridlock. This is why reshaping a system’s structure matters more than pushing harder against it — balancing loops will simply absorb whatever force we throw at it.

Reinforcing feedback loops are the opposite: they amplify change rather than counteracting it. Consider compound interest: the more money in your account, the more interest you earn, which increases your balance, which earns even more interest. The rich get richer through the same mechanism that causes spiraling debt, bank runs, and melting ice caps.

Once you start to recognize both types of feedback loops your understanding of systems can be greatly enhanced. As Meadows puts it: “instead of seeing only how A causes B, you’ll begin to wonder how B may also influence A — and how A might reinforce or reverse itself.” Furthermore, multiple feedback loops can intersect and influence each other. When one feedback loop starts to have a stronger impact on the behavior of a system than another, we can describe that change as shifting dominance. Systems can change radically and rapidly once the dominance is shifted — whether those shifts result in prosperity or crisis depend on the system’s structure prior to those changes.

4. Tricks, Traps, and Tragedies

The interactions of all of the various feedback loops we find in systems can oftentimes lead us to fall into the same pitfalls over and over again. Even when we’re in seemingly distinct scenarios, there are certain structures that tend to give us trouble. There are many such traps, but here we can look at some of the most common and destructive ones.

The first structure we see is known as the tragedy of the commons. Although this problem is oftentimes framed in terms of individual greedy people, a systems lens can show us that this occurs due to rational and predictable behaviors. This trap occurs when there’s a commonly shared resource that individuals can benefit from while spreading the consequences out among everyone. For example, overfishing can occur when individual people or entities don’t see a significant consequence for extracting fish from a body of water faster than the rate at which the fish can repopulate. This results in an overuse of the shared stock until eventually the entire stock is depleted and no one can benefit from it anymore. The way out of this situation is to reduce the delay of feedback and increase the severity of punishments for taking more than one’s fair share.

The second structure is known as shifting the burden to the intervenor. This trap can also be characterized as dependence or addiction depending on the situation. This occurs when a solution is implemented to reduce the symptoms of a problem without actually addressing the underlying systemic issues. Over time, more and more of the so-called solution is needed to preserve the appearance of a fixed system. A great example of this can be found in the actions of 1950s logging companies. In parts of North America, loggers noticed that budworms were destroying valuable spruce and fir trees and therefore cutting into company profits. To combat this, they started to spray insecticides to kill those budworms. While this gave the illusion of solving the problem, the insecticides had the unintended effect of killing the natural predators of the budworm — birds, spiders, parasitic wasps, and diseases. This weakened the balancing feedback loop that kept budworms alive without letting them take over the entire forest. With no more natural predators left, the logging companies were forced to spray ever-increasing amounts of insecticides to take over the gap left by the disappearing predators. The way to escape this trap is to understand and enhance a system’s capacity to address problems on its own.

The third structure is known as the drift to low performance. This happens when we let our performance goals be influenced by our past performances. Especially in cases where there’s a negative bias in perceptions of the past, the perceived worsening of performances leads to a lowering of standards, which then leads to further declines. We often see these lowering standards expressed through comments like “this is how it’s always been” or “that’s just how things are.” Even though each small decline in performance standards seems completely reasonable, the slow erosion over time results in a reinforcing feedback loop that pushes us towards the worst possible outcomes. To solve this issue, we can keep our performance standards independent from our outcomes. Alternatively, we can set our goals based on our best historical performances rather than our worst ones.

The commonality between these three traps are that feedback loops lead to important information being missing, delayed, or distorted. In order to avoid the undesirable outcomes associated with these structures, the solutions we implement have to be focused on changing the structures underlying our systems rather than just shifting around individual elements within those systems. We can’t fix these problems by throwing more and more ‘common-sense’ solutions at the symptoms — we need to examine and reshape the underlying structures.

5. A Double-Edged Dance

Why is it that something as simple as an electric meter’s location (hallway versus basement) can produce substantial energy savings, yet a project as ambitious as a 26-lane highway can spend billions of dollars while just making a problem worse? The meter placement changed the information flow in the system—residents could see their electricity consumption in real time, creating a balancing feedback loop where high usage triggered immediate awareness and behavior adjustments. The highway solution ignored feedback entirely. Planners assumed static demand, missing the reinforcing loop where new lanes reduce travel time and attract nearby development, which attracts more drivers to fill the lanes. Accidental architectural choices can reshape behavior more effectively than billion-dollar projects — when one works with the system’s feedback structure and the other fights against it.

This pattern repeats everywhere once you start looking for it. We blame incompetent bureaucrats for policy failures, greedy individuals for tragedies of the commons, or bad luck for economic crashes—when the real culprit is often the feedback structure itself. A traffic jam isn’t caused by too many individual drivers; it’s caused by the reinforcing loop between road capacity and demand. Poverty isn’t caused by individual poor choices; it’s maintained by balancing loops that resist intervention.

When we try to rationally solve problems, we often get stuck in a linear mindset: find a cause and effect, then manipulate the cause to change the effect. Rather than asking ‘who’s to blame?’ or ‘what single fix will work?’, we need to ask different questions: What feedback structures are at play? What information is missing, delayed, or distorted? Which loops amplify harm, and which promote wellbeing? This shift in thinking becomes ever more important as we tackle climate change, global health, and wealth inequality—problems that resist simple solutions precisely because they’re systemic.

As Donella Meadows puts it:

“We can’t impose our will on a system. We can listen to what the system tells us, and discover how its properties and our values can work together to bring forth something much better than could ever be produced by our will alone. We can’t control systems or figure them out. But we can dance with them!”