The Art of the Meta-Scam

This essay was adapted from a lecture given by Francis Tseng at Pioneerworks' 2019 Software for Artists Day.

Summer 2018 was the “summer of scam.” In April 2019, not long after the New Inquiry held a roundtable called “Scam or Die,” where they discussed then fairly recent episodes like the Fyre festival and Anna Delvey to understand the nature of scams and how the category of the "scam" has been used politically in the past. Historically, what is and isn’t recognized as a scam– or who is and isn’t called a “scammer”–is always political in some way, because essentially by naming something a scam you not only condemn it morally but also insinuate a criminality that needs to be dealt with by state intervention. Navigating this fine line, New York-based collective BUFU scamming frames scamming as a way to ameliorate injustices and inequities through subversive redistribution—redirecting resources from foundations and institutions with problematic histories towards those with connections to the harm from those histories.

For my purposes here, I define a scam as an exploitative and extractive relationship that involves deception. I use the term "relationship" because, in this context, a scam refers to something fairly continual and structural rather than to a one-off event. A scam has a beneficiary (someone who benefits from the scam), an operative (someone who is involved in the scam's execution; often but not necessarily the beneficiary), and a mark (the person or group of people being scammed).

Popularly, scams tend to be either esoteric operations like phishing the elderly, which are treated more like an unfortunate nuisance, or extravagant individual cases like Elizabeth Holmes and Adam Neumann, which are treated as anomalous. All of these are outgrowths of a broader system of normalized, naturalized, and institutionally-sanctioned scams, which include things like nuclear families, health insurance, or student loans, all of which are themselves more or less outgrowths of the most dynamic and persistent scam of the past several hundred years: capitalism.

There are a few ways that we can categorize scams. They can be characterized by the power gradient along which they flow—as oppressive scams and as resistive scams. Oppressive scams are instituted and maintained by people with power and may be instrumental in maintaining that power. Resistive scams, on the other hand, operate in the other direction. For example, an employee taking long bathroom breaks on the clock.

An oppressive scam’s primary deception is a claim to legitimacy that normalizes the scam in some way. This isn't to neglect the fact that such scams are often implemented and maintained with at least some latent threat of violence–if not outright violence–or that marks may engage in the scam with full knowledge of its illegitimacy (e.g. out of economic necessity), but more to emphasize that its continuance is facilitated by buy-in (or ignorance) among the general population. For example, the financial infeasibility of student loans is well-known by now, yet many still get them out of necessity, and the system is justified by the relative few who do manage to successfully pay them off or who can't conceive of any alternative. Scams can also be categorized as legal or illegal. Some are not merely legal, but are, in fact, constitutive of the law itself. These institutional scams are oppressive scams that are formally codified via law or policy. For example, pretrial detention and cash bail are institutional scams whose legitimacy is based on the fallacy that cash bail is necessary to keep so-called bad people off the streets. In actuality, this system puts people in such a state of desperation—they are threatened with jail, which means, for example, they can't go to work and risk losing their job, or won't be able to take care of family, unless they can pay amounts they can't afford for bail—that they have to give up their right to trial, even if they are unquestionably innocent of any crime. This is foundational to mass incarceration.

What options are there to resist or overturn any one of these deeply-rooted institutional scams? There are a few common strategies. Most widely endorsed: advocacy and electoral work which seeks to convince or elect government officials who carry out the changes. Though this strategy has been effective, it is challenged by the fact that the individuals in government are often deeply entwined in, if not direct beneficiaries of, many of these institutional scams. As well, this method has to move at the speed of the legislative/judicial/electoral systems. Another popular avenue: direct action including labor tactics focused on economic disruption–which can be hugely effective and more responsive, but require a tremendous amount of work upfront–and dual power initiatives focused on building alternative institutions such as worker cooperatives in the hopes of eventually replacing the prevailing ones.

Over the past few years I've been collecting examples of another class of tactics, which could be called "meta-scamming". These interventions disrupt a scam by turning it against itself or by using another scam against it, flipping its original power gradient. While meta-scams generally don't lead to lasting change, they can provide wiggle room within the structure to lessen, minimize, or reverse the extraction of the scam. Meta-scams, too, may be legal or illegal and; the ones that are legal are often the most interesting to me. They play with this idea of malicious compliance: you follow the letter of the law, but in a way that undermines the law itself.

At The New Inquiry we published a few projects under a similar rubric that we called "rhetorical software." Following the logic of the meta-scam, they are meant to exploit an oppressive system against itself, with an emphasis on technological interventions in addition to an eye towards critique and awareness-raising. Projects like Bail Bloc could be considered a meta-scam. In that project, we exploited the arguably oppressive scam of cryptocurrency and the frenzy of wealth it was generating to make a dent in the bail system.

But meta-scams don't always require a technological component; they can rely on legal loopholes or other exploits and contradictions. Here are some other examples of oppressive scams and the meta-scams that seek to undermine them.

Student Debt

Student debt is an institutional, oppressive scam that many people have experience with. Education is extremely expensive in the U.S. and is only getting more expensive. If you want to educate yourself you resign yourself to a life where someone else is entitled to a chunk of your wealth. All of the risk is pushed onto the student. You have no idea what the job market is like when you graduate, and you have very little latitude to pursue a field you're interested in (unless you're drawn to STEM majors). But as we saw with the recent admissions scandals, higher education's function in its current form may just be to buy a particular kind of (expensive) legitimacy.

In total, there is about $1.6 trillion in student debt in the US, across roughly 45 million people. This debt is growing faster than any other household debt, seeing about 160% growth since the Great Recession, and the highest 90+ day delinquency rate of all household debt (over 10%). Loans also look like they're getting more expensive: after a drop about 5 years ago, interest rates are increasing again. The average debt for the class of 2018 was $29,200, and the average US household owes about $45,000[^1]. Legislation that would relieve this debt burden is combated by interests who want student debt to remain a reliable asset.

Of course, debtors are interested in minimizing their losses; if a debt looks like it'll default, they'd rather sell it to a debt collector at less than its value rather than mark it as a complete loss. Then it's the debt collectors problem to get the person to pay up. Basically the lenders' desire to minimize loss creates a market where debt can be bought for less than it's worth.

This was taken advantage of by a project that came out of Occupy in 2012, Rolling Jubilee. Rolling Jubilee buys up this cheap debt and then cancels it. The debtor effectively gets a discount on their debt. Over their active period they cancelled about $32 million of debt with $700k, i.e. at a cost of about 1/45 of the debt.

Rolling Jubilee is a meta-scam because it uses these dynamics of debt and profit against itself. But of course there are limits. People still make money off this system - it doesn't fundamentally change it, butut it reduces pressure and gives breathing room. The project is no longer active, but its creators continue their fight to overturn the debt system as The Debt Collective.


Uber is a scam, shifting as much financial and legal risk as possible to drivers and using legal tactics to avoid responsibility. For example, the company classifies  drivers as independent contractors (or in their insidious lingo, "partners") so they don't have to be paid minimum wage, benefits, overtime pay, sick leave, etc; Uber also requires drivers to cover their own costs such as maintenance and fuel (this is of course a common practice among other ride-sharing companies; this year a California law challenging this practice went into effect—Uber is trying to circumvent it).

Another tactic that Uber uses, which is by no means unique to them, is the inclusion of arbitration clauses in their driver contracts. As a driver you give up your right to sue the company in open court and instead agree that Uber will mediate disputes through a third-party of their choosing, and such disputes can only be carried out on an individual basis (i.e. no collective bargaining). Crucially, Uber covers the arbitrator's and arbitration fees. This allows them greater control over the process than an independent court would offer, but it comes at some cost.

Drivers organized and turned this against Uber. According to Gizmodo, “A group of 12,501 drivers opted to take Uber at its word, individually bringing their cases up for arbitration, overwhelming the infrastructure that’s meant to divide and conquer.” The arbitration service requires a non-refundable $1,500 fee for each demand. When this article was published, Uber had paid for only 296 of them. The total cost would be about $18.7 million. I'm not a lawyer, but from what I've read it sounds like if Uber fails to carry through the arbitration (i.e. the terms of their own contract), they may be in breach which then allows drivers to sue outside of arbitration. There is a case now attempting to compel arbitration, such that Uber can't delay the filing fee in the hopes that the plaintiffs drop their claims. Since that story was published, the number of drivers filing arbitration demands increased to 60,000 and Uber ended up settling for somewhere between $146 and $170 million[^2]. This strategy is clearly effective. Workers at Lyft, Chipotle, and Buffalo Wild Wings have also used a similar approach.

Another Uber-related example of an effective meta-scam is collusion among drivers to induce surge pricing, detailed in "POTs: Protective Optimization Technologies". Drivers coordinate to collectively turn off their app to trigger a drop in supply, thus inducing surge pricing.

Here, the drivers that are exploited by Uber find a way to increase their income, which is more than justified considering how much Uber takes—recently, Uber cut the per-mile rate for drivers in LA by 25% and reduced the minimum fare payout by 30%; overall Uber takes almost 40% commission on average. In some cities this can soar as high as 54%, all before additional costs like maintenance that the driver is responsible for. This is an interesting example because this scam actually benefits Uber but harms riders. So, we might also want to consider the collateral damage of a meta-scam. On one end, we have high precision meta-scams, where only intended targets are effected, and on the other we have scorched earth meta-scams, where nearly everyone else is impacted in some way.


Another institutional, oppressive scam is the pharmaceutical industry, which is worth about $1.1 trillion. If you're not personally familiar with how expensive prescription medications are in the U.S., you may have heard one of the many horrifying stories of people crowdfunding their continued existence or flying elsewhere to access more reasonable prices.

The patent system is a big contributor to the pharmaceutical industry's extortionate pricing, providing temporary monopolies on drugs that are easily extended through minor tweaks to the drug such as changing how the drug is delivered (a practice called "evergreening").

Part of the justification for this system of drug patents is that drug development is expensive. The space of possible compounds is massive--there are more potential drugs than there are stars in the universe--and so it's a slow and costly process to find new useful drugs.

Pharmaceutical companies are always looking for ways to bring the costs of drug discovery down. So far, the primary methods include making slight changes to the compound to see if something useful results and "bioprospecting," which means looking at plants or medical traditions (the latter is known as "biopiracy"). With AI and machine learning, drug companies have been developing computational drug discovery systems as another avenue. The success of these systems would bring down development costs, but there's no reason to believe that drugs will be any cheaper as a result nor affect the existing patent regime in any way.  

For Rhizome’s 7x7 conference last year in Beijing, Sean Raspet and I created a drug discovery system called It’s a machine learning system that generates chemical compounds that could be useful, tries to guess what they might be useful for, and publishes them to a website.

By publicly publishing these compounds, we take advantage of a part of patent law called "prior art"—if someone has come up with your invention before you, you can't get a patent (at least, it's more complicated to get one). If just happens upon a drug that ends up being useful in the future and publishes it on the site, this could function as prior art, which could make it more difficult for a pharmaceutical company to patent it. In the ideal scenario, it keeps useful compounds in the public domain—where they should be.

The project uses technology in a way that violates some of the assumptions under which the patent law and pharmaceutical industry operate. Prior art was meant to protect the profits of companies that were spending billions on drug development, but with we try to use it against that original intent.

The Limits of Scamming

Meta-scams as a political strategy, like many other such strategies, have their limits. In the case of Rolling Jubilee, the scam still involves paying money into the system, and so lenders and parties in that system still benefit. It doesn't fundamentally change it, even if it does give debtors breathing room—although that alone can be pretty valuable—and calls into question the validity of the larger debt system. While Rolling Jubilee itself did not end the student loan system, it no doubt had an impact on other movements and initiatives that continue to challenge it, and likely helped make student loan forgiveness a more palpable position for political candidates.

Likewise, the Uber drivers’ collective surge price triggering doesn’t harm Uber, and in fact potentially benefits them because it increases their own revenue. There’s also a bit of collateral damage: Riders have to eat up the extra cost that Uber is refusing to pay. But at the very least it contributed to a foundation for further collective action among Uber drivers.

The arbitration exploit, on the other hand, might see more impact. If arbitration clauses come to be a liability for companies, what will replace it? Or how will they tweak it to "patch out" that exploit? Or will they abandon it entirely? This last one seems unlikely, but at least that action makes it a little more feasible than before.

So maybe these meta-scams can lay a bit of groundwork for future progress. But there's also a deeper concern here: meta-scams are parasitic in a sense. They, by their nature of turning a scam against itself, rely on the dynamics of the target scam (e.g. debt collection) to work. Meta-scams mitigate the harm of their target scams, but do not necessarily actively dismantle them. Does this mean that they, in some way, perpetuate it?

Still, the time that these meta-scams can buy people can provide some maneuverability or opportunity for more substantial change, or they might call into question the terms of the oppressive, institutional scams they target. But ultimately, we need new kinds of infrastructure rather than just meta-scams—infrastructure that makes it harder for these oppressive scams to operate and take root in the first place. We need infrastructures that increase autonomy and the decisions that affect us so one day we can live scam free. But for now, happy scamming.

This article was commissioned in conjunction with Rhizome's forthcoming Money as Medium research season, supported by Iterative Capital.