Blame as a Service

Productized scapegoating

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

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

Just as Software as a Service lets companies rent specialized technology services instead of building it, Blame as a Service (BaaS) lets companies rent scapegoats instead of becoming them. These third-party BaaS firms absorb the backlash from unpopular but profitable decisions, allowing their clients to pursue what actually drives their bottom line without sacrificing their carefully cultivated brand image.

The characteristics of a typical BaaS company includes:

1. Offers a bundle of services that conceal their true value proposition of blame absorption
2. Shields elite decision-makers from decisions with negative externalities
3. Benefits from network effects as their blame-absorption capacity scales

We are beginning to see more BaaS companies in the Average is Over era. The elite class across industries is smaller but growing in power and increasingly willing to pay for institutional lackeys that protect their interests while maintaining plausible deniability. BaaS companies engage in third derivative work. They don't do the work directly or build the tools, instead deciding what should be done and absorbing the blame for the consequences.

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

McKinsey

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

A company may be genuinely interested in hiring McKinsey to get an outsider's view on their reasoning before undergoing an action, as decisions could affect billions in enterprise value. But companies usually know what needs to be done before McKinsey walks through the door. At minimum, they hire McKinsey to execute and check financial projections. They have the deepest contextual knowledge of their field, whether the plan is expanding product lines or cutting thousands of jobs.

But now it comes stamped with the authority of an "unbiased third party." When layoffs hit or unpopular restructuring begins, executives can point to McKinsey's recommendations. This enables company executives to take unpopular decisions by outsourcing blame to McKinsey.

To be sure, McKinsey was founded in 1926 and likely did not foresee BaaS as a primary service offering at genesis. Rather, work was very manual and an outsourced firm to run their numbers in a specialized way was very high value add to many companies.

As McKinsey developed, it became clear that this could be one of their most lucrative product lines. In many ways, the firm now operates as a two-sided BaaS marketplace: ambitious graduates compete ruthlessly for the prestige of working there, while corporations compete just as ruthlessly for the political cover McKinsey provides. The consultants get status, the executives get deniability, and McKinsey gets paid for facilitating the exchange.

Ticketmaster

Taylor Swift and other marquee performers can sell out stadiums for multiple consecutive nights 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 the $1,000+ market-clearing price would eliminate scalping, maximize revenue, and maximize aggregate consumer welfare. But it would also destroy the artist's relationship with their fans, as they would be seen as the greedy artist who priced out their true fans.

Artists and sports leagues know the market-clearing price for their tickets is $1,000+. They know that direct pricing would eliminate scalping and maximize revenue. But they also know that directly charging these prices would destroy their carefully cultivated relationship with fans. Nobody wants to be seen as the greedy artist who priced out the true believers.

Ticketmaster offers a platform where artists can capture premium pricing and the economic value they create without taking the reputational hit. Side deals via secondary market and convenience fee kickbacks enable artists to capture more economic value.

Ticketmaster gets perpetually blamed for high fees, angering loyal fans while sniping economists to write lengthy papers about optimal ticket pricing, while they are fully aligned with their actual customer base: high-value artists like Taylor Swift.

UMA

Prediction market oracles adjudicate whether or not the criteria for a prediction market was met and which side (YES/NO) should be paid out.

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

During the Zelensky suit market saga, Polymarket was able to sidestep much of the bad press by people writing endless articles on the voting structure of UMA while glossing over the fact that UMA token holders have ulterior incentives and are people who live in close proximity to the Polymarket team in NYC.

When controversial resolutions must be made, Polymarket can claim they are a neutral prediction market platform that doesn't control the outcomes. Rather, the UMA token holders decided via a "decentralized voting process". Ignore the fact that the voters share the same financial incentives and attend the same parties.

AI hiring platforms 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. With technical talent becoming more of an outbound endeavor, companies often know the types of candidates they are targeting.

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

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

Coda

Humans will be the preferred scapegoats for the foreseeable future. Real people can be subpoenaed, negotiate settlements, and be ostracized from society. This suggests the optimal BaaS model is not pure AI but humans wrapped in an AI veneer. Companies get the mystique of algorithmic objectivity plus the flexibility of human operators who understand the game.

Popularity prediction hash: f1b04ab179ab686713097ebac481a0e809c98664c4fb067d9be88cb6ec700436