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Blame as a Service

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made a scapegoat

Instagram and Twitter were the defining cultural companies of the 2010s. These platforms created explicit status games where users compete for social capital through metrics like followers and likes, creating a world where online perception trumps real-world outcomes. The social dynamics of these platforms have been written about extensively.

Companies are now finding themselves dragged into the same status game. They are becoming increasingly obvious, with pointed X accounts and marketing stunts. In the PR-driven information age, companies have higher budgets allocated to managing their social perception. Companies are particularly interested in paying for services that reduce their chances of being associated with negative press. These social dynamics create a demand for professional blame absorption.

Just as Software as a Service lets companies rent specialized technology services rather than create them, Blame as a Service (BaaS) lets companies rent them rather than become a scapegoat. These third-party BaaS companies absorb the backlash from unpopular but profitable decisions, allowing their customers to do what really drives their bottom line without sacrificing their carefully crafted brand image.

Characteristics of a typical BaaS company include:

  1. Offers a bundle of services that hides their real value proposition of fault absorption
  2. Protects specific decision makers from decisions with negative externalities
  3. Benefit from network effects as their fault-absorbing capacity scales.

We’re starting to see more BaaS companies ushering in the Average Is Over era. The elites in all industries are small but growing in power and willing to pay for institutional deprivations that protect their interests while maintaining plausible deniability. BaaS companies engage in third derivative work. They do not directly do the work or create the equipment, but rather decide what needs to be done and take the blame for the results.

In this post, I examine the market structure of three BaaS companies operating today and the archetypes of a BaaS company of the future.

McKinsey

McKinsey is a classic example of a BaaS company. The decision to hire McKinsey is made by company executives nominally to improve the company’s bottom line.

A company might be really interested in hiring McKinsey to get an outsider’s opinion on its reasoning before taking any action, as decisions can impact billions in enterprise value. But companies usually know what needs to be done before McKinsey walks in the door. At a minimum, they hire McKinsey to execute and check financial projections. They have the deepest relevant knowledge of their field, whether the plan is to expand a product line or cut thousands of jobs.

But now it has been stamped with the authority of an “impartial third party”. When layoffs occur or unpopular restructurings are initiated, executives can point to McKinsey’s recommendations. This enables company executives to make unpopular decisions by blaming McKinsey.

Of course, McKinsey was founded in 1926 and probably didn’t see BaaS as a primary service offering at the time of its genesis. Rather, the work was very manual and having an outsourced firm run their numbers a particular way was too much of a value-add for many companies.

As McKinsey grew, it became clear that this could be one of their most attractive product lines. In many ways, the company now operates as a two-sided BaaS marketplace: ambitious graduates compete ruthlessly for the prestige of working there, while corporations compete equally ruthlessly for the political cover provided by McKinsey. Consultants get status, executives get denials, and McKinsey gets paid to facilitate the exchange.

ticketmaster

Taylor Swift and other major artists can sell out stadiums for multiple nights in a row in any city on Earth. She has extraordinary market power and can charge whatever she wants. So why doesn’t she charge the market-clearing price?

Charging a market-clearing price of $1,000+ would eliminate scalping, maximize revenue, and maximize overall consumer welfare. But this will also destroy the artist’s relationship with his fans, as he will be seen as a greedy artist who has cost his true fans.

Artists and sports leagues know their tickets have a market-clearing price of $1,000+. They know that direct pricing will eliminate scalping and maximize revenues. But they also know that charging these prices directly would destroy their carefully cultivated relationship with fans. No one wants to be seen as the greedy artist who paid off true believers.

Ticketmaster provides a platform where artists can capture premium pricing and the economic value they create without harming reputation. Side deals enable artists to capture greater economic value through the secondary market and convenience fee kickbacks.

Ticketmaster is constantly blamed for high fees, angering loyal fans, while economists take digs for writing long papers about optimal ticket pricing, while they are completely out of touch with their real customer base: high-priced artists like Taylor Swift.

Uma

Prediction market forecasters decide whether the prediction market criteria are met and which side (yes/no) should be paid.

UMA is the oracle provider for Polymarket, which has adjudicated and settled billions of dollars. On paper, UMA’s optimistic oracle allows Polymarket to claim that token holders voted on the outcome, when in reality it is a small group of whales at Polymarket that are making and influencing decisions. The protocol and market insiders get exactly what they want (votes in their favor), while avoiding liability and blame for Polymarket.

During the Zelensky suit markets saga, Polymarket was able to sidestep the bad press from people writing endless articles on UMA’s voting structure, while highlighting the fact that UMA token holders have indirect incentives and are people who live close to the Polymarket team in NYC.

When controversial solutions must be made, Polymarket may claim that they are a neutral prediction market platform that does not control the outcomes. Rather, UMA token holders make decisions through a “decentralized voting process.” Ignore the fact that voters share similar financial incentives and attend similar parties.

AI hiring platform as the next evolution of BaaS

The logical next frontier for BaaS companies is AI hiring platforms, or what Mercor is actually supposed to be. As technical talent has become an outbound endeavor, companies often know exactly what type of candidates they are targeting.

AI hiring platforms enable companies to maintain their preferred hiring practices while claiming that the AI ​​hiring black box has made objective decisions.

In reality, every single AI hiring platform from LinkedIn to Opendoor will suffer from the same adverse selection that exists in the marketplace. The best candidates never miss the market because they are recruited directly or through warm networks.

code

Man will be the preferred scapegoat in the near future. Real people can be summoned, compromised and ostracized. This shows that the optimal BaaS model is not pure AI, but humans wrapped in AI. Companies find the secret to algorithmic fairness as well as the flexibility of human operators who understand the game.

Popularity prediction hash: f1b04ab179ab686713097ebac481a0e809c98664c4fb067d9be88cb6ec700436



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