In January 2021, a loosely affiliated digital subculture on Reddit’s r/wallstreetbets managed to drive the share price of the video game retailer GameStop, whose physical store estate had been steadily shrinking, up by more than 1,700% between the final close of 2020 and the close on 27 January.1 The immediate media reaction framed this as a story of small retail investors challenging powerful institutional hedge funds.2 The episode indicated a structural transformation in the modern political economy, where there are strong links between algorithmic attention networks, price discovery, and capital allocation.

Interpretation caveat: The short squeeze framing of the GameStop episode is contested. The Securities and Exchange Commission’s own staff review concluded that neither a short squeeze nor a gamma squeeze was the primary cause of the price rise, pointing instead to sustained buying by positive sentiment investors.

The New Market Infrastructure

To trace how information alters economic behaviour, one must examine the shift in the operating system of the internet over the past decade. The early commercial web was structured around intent and search. This slowly developed into social media. You saw what your friends posted or what the figures you chose to follow published. But the current era, pioneered by TikTok and subsequently adopted across Meta, X, and YouTube, dismantled the social graph entirely in favour of the engagement graph.

Today, content is no longer distributed based on who you know or who you explicitly follow, but on what an opacity-wrapped neural network predicts will hold your gaze for longer.3 The primary metric of digital media shifted from connection to retention.

Traditional financial economics rests on the assumption that the market price of an asset represents the present discounted value of its expected future cash flows. Information enters the market, analysis evaluates its balance-sheet implications, and rational stakeholders bid prices up or down towards the equilibrium. However, the algorithms that dictate the modern information environment do not optimise for long-term value; they optimise mainly for emotional resonance.4 Asset prices frequently cease to track earnings, reports or macroeconomic data. Instead, they track the rate at which an idea, narrative, or meme can circulate through an algorithmic feedback loop. In this sense, modern financial markets increasingly resemble epidemiological systems, where ideas spread with characteristics similar to infectious diseases. Narratives become economically significant not because they are immediately true, but because widespread belief in them alters investment decisions.5 Furthermore, traders no longer merely analyse corporate SEC filings; they deploy natural language processing models to attempt to scrape people’s feeds.6

The macroeconomic consequence is a distortion in the allocation of capital. Capital markets exist fundamentally to route society’s savings into their most productive uses, funding infrastructure, technological innovation, and growth. When asset valuations are governed by the mechanics of today’s algorithms, viral videos and memes can raise equity capital cheaply despite failing business models, while quiet, cash-flow-generative businesses in less glamorous sectors face inflated capital costs.5 The sums involved are substantial. GameStop raised roughly 1.7 billion dollars through two at-the-market equity offerings in 2021, and AMC Entertainment raised more than 2 billion over the same year, in both cases selling stock into prices driven by retail enthusiasm rather than earnings.7

The phenomenon itself isn’t new. The conviction that "House prices always rise" fuelled excessive borrowing before the 2008 financial crisis, while beliefs such as "The internet changes everything" inflated the dot-com bubble two decades earlier. What distinguishes the contemporary economy is not the existence of economic narratives, but the unprecedented speed and scale with which algorithms can manufacture and amplify them. Whereas earlier narratives spread over months through newspapers and television, TikTok, X, Reddit and YouTube can propagate them globally within hours.

Political Currency

Consider the ongoing political phenomenon in India surrounding the "Cockroach Janata Party" (CJP). When the Chief Justice of India, Surya Kant, said during an open court hearing that "parasites" were attacking the system and spoke of "youngsters like cockroaches, who don’t get any employment or have any place in the profession," the comment fed directly into algorithmic recommendation systems optimised for moral outrage.8 The insult was instantly packaged into satirical memes, short-form videos, and digital manifestos. Within three days a one-line joke posted on X had coalesced into a digital political movement whose Instagram account had passed three million followers, with more than 350,000 people signing up for membership through a Google form.8

Kant later said the remark was directed at people acquiring fraudulent degrees rather than at India’s youth, but the clarification arrived long after the recommendation systems had done their work.9

What began as an algorithmically boosted piece of internet satire acquired a website, a manifesto and a visual identity within twenty four hours, assembled with the help of generative AI tools, and drew sign-ups from sitting and former parliamentarians. It remains a satirical party with no formal political standing, which is precisely the point. Social media mechanics can bypass traditional institutional political channels entirely to generate instantaneous civil momentum, without any of the organisational apparatus that such momentum once required.

What Happens Next?

Whether in the context of capital markets or state governance, these episodes highlight a critical measurement gap. Central banks and financial regulators use stress tests and statutory access to monitor systemic risk; state institutions use traditional intelligence and political polling to gauge public sentiment. Yet, the algorithmic ranking systems that accelerate sentiment propagation operate entirely outside regulatory scrutiny. Regulators can observe the resulting price fluctuations on stock exchanges or mass gatherings in public squares, but without access to backend platform interaction data, they possess no quantitative model to track how engagement algorithms convert viral attention into real-time capital allocation or political disruption.

Furthermore, researchers, central banks and regulators cannot meaningfully analyse systems they cannot observe. The recommendation models that determine what billions of people see each day are proprietary assets, guarded by technology firms as closely as financial institutions protect their trading strategies. The EU introduced the Digital Services Act to increase algorithmic transparency, yet researchers report that accessing the very data needed to study recommender systems remains difficult in practice.10 The interaction data used to train and optimise these systems, including how long users pause on a video, which posts they skip, and what prompts them to comment or share, is rarely made available to independent researchers. Companies argue that releasing such information would expose commercially valuable intellectual property, weaken their competitive advantage, and create privacy risks for users. As a result, economists attempting to understand how digital attention influences markets are often left studying only the downstream outcomes such as share-price volatility or consumer behaviour rather than the algorithmic mechanisms that generated them.

As social media platforms integrate generative AI and autonomous recommendation engines deeper into the average person’s life, the boundary between the information economy, political stability, and financial markets will continue to dissolve. Economists may need to reconsider what constitutes market information itself. The defining scarce resource of the twenty-first century is no longer information, but attention, and the markets of the future will be shaped by whoever controls its distribution.

Footnotes

  1. U.S. Securities and Exchange Commission. Staff Report on Equity and Options Market Structure Conditions in Early 2021 (opens in a new tab). Washington, D.C.: SEC, 18 October 2021. ↩

  2. Chohan, Usman W. Counter-Hegemonic Finance: The GameStop Short Squeeze (opens in a new tab). SSRN working paper, 28 January 2021. Later published in Chohan, Usman W. and Van Kerckhoven, Sven (eds.), Activist Retail Investors and the Future of Financial Markets, Routledge, 2023. ↩

  3. Smith, Ben. How TikTok Reads Your Mind (opens in a new tab). The New York Times, 5 December 2021. ↩

  4. Merrill, Jeremy B. and Oremus, Will. Five points for anger, one for a 'like': How Facebook's formula fostered rage and misinformation (opens in a new tab). The Washington Post, 26 October 2021. ↩

  5. Shiller, Robert J. Narrative Economics: How Stories Go Viral and Drive Major Economic Events. Princeton: Princeton University Press, 2019. ↩ ↩2

  6. Hansen, Kristian Bondo and Borch, Christian. Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance (opens in a new tab). Big Data and Society, 2022. ↩

  7. GameStop Corp. Form 10-Q for the quarterly period ended 31 July 2021 (opens in a new tab). U.S. Securities and Exchange CommissionSen, Anirban. AMC sets unusual shareholder vote for meme stock sale approval (opens in a new tab). Reuters, 1 February 2023. ↩

  8. Sharma, Yashraj. 'Cockroach Janata Party': Top Indian judge's comment sparks satire, protest (opens in a new tab). Al Jazeera, 20 May 2026. ↩ ↩2

  9. Sappal, Gurdeep Singh. The Chief Justice Called Them Cockroaches. History Knows Where That Language Leads (opens in a new tab). The Wire, 16 May 2026. ↩

  10. Desmarais, Anna. European Researchers Say Big Tech Is Blocking Access to Their Data (opens in a new tab). WIRED, 24 July 2026. ↩