Meta Follows the Trend into Prediction Markets: Can It Avoid Repeating the Failure of the Metaverse?

Foresight NewsPublicado em 2026-06-25Última atualização em 2026-06-25

Resumo

Meta, the tech giant behind Facebook, has reportedly formed a team to develop "Arena," a new application focused on prediction markets. Users would use platform points to place bets on outcomes in politics, sports, and global events. This move follows Meta's massive, nearly $900 billion, losses from its heavily-invested metaverse division, Reality Labs. The prediction market industry is already showing strong demand, with leading platforms like Kalshi and Polymarket facilitating hundreds of billions in annual volume. Meta, with its 3.56 billion daily active users across its apps, possesses the unprecedented scale to bring this niche activity to a mainstream audience, similar to its past success in cloning features like Stories and Reels. However, Arena faces significant hurdles. Meta plans to start with a points-based system to avoid strict financial regulations, but this may dilute the core incentive of accurate prediction that real-money markets provide. More critically, Meta enters the space with a major trust deficit stemming from its past regulatory battles, notably the failed Libra/Diem stablecoin project, and its controversial history with political content and misinformation. The prediction market sector itself is under increasing regulatory scrutiny, with recent CFTC actions including fines and the first-ever insider trading case. While Meta's vast user base offers a unique opportunity to expand the market, its success hinges on navigating complex regulations and ...


Author: Gino Matos

Compilation: Luffy, Foresight News


TL;DR:


  • The New York Times reports that Meta has formed a small team to develop an internal project codenamed Arena, a point-based prediction app allowing users to wager on outcomes of politics, sports, and global events.
  • Prediction markets have demonstrated genuine demand. With 3.56 billion daily active users, Meta has the potential to bring this niche sector to the mass market.
  • However, Meta's trust crisis, combined with election and misinformation scrutiny, could make Arena a regulatory target even before it scales.


On June 23, The New York Times reported that Mark Zuckerberg is leading a dedicated team to develop a prediction market application called Arena. Users will use platform points to place bets on outcomes like political elections, sporting events, and international affairs.


This is the same company that rebranded for the metaverse and whose Reality Labs division has accumulated nearly $90 billion in losses. Now turning to prediction markets, a sector with strong real demand and an established user base but fraught with complex regulatory rules, this pivot could be Meta's smartest strategic adjustment or a repeat of its past costly failures.


The Metaverse's Staggering Bill


In October 2021, Facebook officially changed its name to Meta. Zuckerberg stated the company's core goal was "to bring the metaverse to life" and predicted the metaverse would reach one billion users within a decade.


Losses for the Reality Labs division, which carries this vision, have continued to swell: $17.7 billion in operating losses in 2024, $19.2 billion in 2025, with cumulative losses nearing $90 billion. Meta has indicated to investors that losses for this segment in 2026 could be on par with 2025.


Its flagship social VR platform, Horizon Worlds, saw monthly active users drop below 200,000 in 2022, far short of the initial 500,000 target. Meta subsequently revised expectations downward again and plans to wind down the VR version by 2026.



Why Prediction Markets Are a Completely Different Game


In 2026, the combined monthly trading volume of the two leading platforms, Kalshi and Polymarket, was approximately $24 billion, with industry projections for full-year prediction market trading volume exceeding $130 billion.


Robinhood launched a prediction markets section in 2025, Interactive Brokers integrated event contracts into its trading platform, and the Golden Globe Awards even incorporated prediction market interaction segments. A Bernstein research report in April estimated the sector's annual trading volume could reach $1 trillion by 2030.


Meta has always been adept at replicating popular products and leveraging its massive traffic to overtake: Instagram launched Stories after Snapchat's ephemeral posts; Meta launched Threads after Twitter dominated social text for a decade; Reels followed TikTok's short-video craze. As of April, Meta's family of apps boasts 3.56 billion daily active users, a traffic volume that dwarfs all existing prediction market platforms.


Arena's points-based design continues Meta's consistent strategy: capturing existing user behaviors and demand, embedding them within its own traffic ecosystem, and relying on massive distribution to compensate for a lack of product originality.


Building a prediction market primarily requires software, an information feed, an account system, content moderation, and compliance, with some scenarios possibly involving licensed partners. In contrast, the metaverse requires custom hardware, immersive content, avatars, dedicated runtime environments, and years spent cultivating user habits. The massive losses at Reality Labs prove the extreme cost of creating a completely new paradigm from scratch.


Comparison of Core Dimensions: Metaverse vs. Prediction Market Arena


Arena Is Not Meta's First Foray into Prediction Markets; Its Previous Product Was Shut Down Long Ago


As early as 2020, during the initial pandemic, Meta launched Forecast, a points-based public prediction app focused on current events, which was shut down in 2022. At that time, Polymarket had not yet exploded with the 2024 U.S. presidential election, Kalshi had not won its lawsuit with the Commodity Futures Trading Commission (CFTC) over election contracts, and annual industry trading volume had not yet surpassed $50 billion.


The sector Meta is about to enter is rife with regulatory penalty cases:


  • In 2022, the CFTC determined Polymarket was offering off-exchange event derivative contracts without registration and fined it $1.4 million.
  • Kalshi spent years in federal litigation fighting for the right to offer election contracts. A district court issued a favorable ruling in September 2024, and the CFTC dropped its appeal in May 2025, opening a compliance space for election event contracts, but debates over political trading and market integrity persist.
  • In April 2026, the CFTC brought the first-ever insider trading lawsuit in prediction markets, accusing an active-duty U.S. military officer of profiting from trades on Polymarket using confidential information about Venezuelan operations.


Meta's past forays into financial products have long made regulators highly wary of its financial ambitions. The digital stablecoin project Diem (originally Libra), spearheaded by Facebook, was ultimately sold at a low price to Silvergate Bank in 2022 after regulators concluded that Meta controlling a payment network for billions of users would concentrate excessive financial and social power. During the Libra hearings, Meta's model combining social identity, political content, financial incentives, and market data faced fierce opposition from regulators.


It's precisely because points-based prediction gaming can avoid stringent financial regulations in the early stages that Meta has chosen it as Arena's starting point.


What Advantages Does Massive Traffic Bring?


The most viable form for Arena's initial product is building mass prediction features leveraging social scale: Instagram creators launching prediction markets for award shows, Facebook groups discussing sports odds, WhatsApp communities sharing collective prediction views, and Meta AI aggregating mainstream expectations across the network.


This version would temporarily avoid the cash-settled event contracts that have previously drawn regulatory penalties, operating solely within Meta's social graph of 3.56 billion daily active users.


However, the core logic of prediction markets relies on real monetary stakes to discipline prediction behavior and form fair prices. Once replaced with points-based interaction incentives, the product will prioritize reach and user engagement time over the accuracy of prediction outcomes.


Meta's poor track record of handling political content and combating misinformation means regulators and media will naturally scrutinize every controversy Arena triggers.


Meta's traffic advantage is sufficient to support the sector's scale. The success logic of Stories and Reels is identical: capture existing user preferences and amplify their spread through a billion-user platform. If Arena builds lightweight social prediction features, controls financial barriers to entry, and makes prediction markets easily accessible to the average Facebook user while platforms like Kalshi maintain their professional trading positioning, Meta could grow the overall industry pie, benefiting existing leading platforms.


Crypto-native users with financial awareness have built the trillion-dollar prediction market sector. Meta's 3.56 billion daily active users represent a massive, previously untapped mainstream user base, which is also the greatest opportunity in this move.


But just two months before news of Meta's entry broke, the CFTC brought the first-ever insider trading lawsuit in prediction markets, indicating ongoing tightening of regulatory scrutiny. Meta's platforms covering prediction markets related to elections, sports events, and public figures are highly susceptible to regulatory intervention. Coupled with the company's negative history of handling sensitive political content, Meta enters the arena with an inherent credibility deficit, where its massive traffic could amplify all kinds of negative controversies.


Four Potential Development Scenarios for Arena


Several of Meta's previous financial products have failed outright because regulators deemed trust issues unsolvable.


Arena has inherent advantages: the prediction market sector is established, and a real user base exists. But the operator, Meta, carries the same negative reputation that doomed Libra. Once elections and monetary transactions are involved, trust becomes a core asset Meta must earn through long-term stewardship; traffic scale alone cannot compensate for a lack of credibility.

Perguntas relacionadas

QAccording to the article, what is the name of Meta's new prediction market application project and what is its key feature?

AAccording to the article, Meta's new prediction market application project has the internal codename 'Arena'. Its key feature is that users can use platform points (a non-cash, points-based system) to place bets on the outcomes of political elections, sports events, and international affairs.

QWhat significant financial loss did Meta's Reality Labs division incur as part of its metaverse strategy, and what happened to its Horizon Worlds platform?

AMeta's Reality Labs division, which spearheaded the metaverse strategy, has accumulated losses nearing $90 billion. Its flagship social VR platform, Horizon Worlds, saw its monthly active users drop below 200,000 in 2022, failing to meet its initial 500,000 target. Meta subsequently lowered expectations further and plans to gradually shut down the VR version by 2026.

QHow does the article contrast the business model and challenges of building a metaverse versus building a prediction market like Arena?

AThe article contrasts the two by stating that building a prediction market primarily requires software, information flow, account systems, content moderation, and compliance systems, which can sometimes rely on licensed partners. In contrast, building a metaverse requires custom hardware, immersive content, virtual avatars, specialized operating environments, and takes years to cultivate user habits. Reality Labs' massive losses demonstrate the extremely high cost of creating a completely new market category from scratch.

QWhat are the main regulatory challenges and historical baggage that Meta faces in launching Arena, according to the article?

AThe main regulatory challenges and historical baggage include: 1) Meta's previous failed digital currency project, Diem (formerly Libra), was blocked due to regulators' concerns about Meta's control over a potential payment network for billions of users. 2) There is ongoing industry scrutiny, including the first-ever insider trading lawsuit in the prediction market sector by the CFTC just two months before Meta's announcement. 3) Meta's past poor record in handling political content and combating misinformation means regulators and the media will scrutinize any Arena-related controversy, especially around elections and financial incentives. This trust deficit is identified as a core weakness.

QWhat is the article's view on the potential impact of Meta's massive user base on the prediction market industry?

AThe article sees Meta's massive daily active user base of 3.56 billion as both its biggest opportunity and a potential risk. The opportunity lies in introducing the concept of prediction markets to a vast audience of mainstream users that existing niche, crypto-native platforms have never reached, potentially growing the overall industry 'pie' to the benefit of all players. However, the risk is that Meta's inherent 'trust crisis' and negative reputation, combined with its enormous scale, could amplify any controversies or regulatory issues Arena faces, potentially drawing intense scrutiny before the product can even scale.

Leituras Relacionadas

Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

Goldman Sachs Report: China's AI Models at an Inflection Point China's open-source/open-weight large language models (LLMs) have reached performance parity with top global proprietary models, according to a Goldman Sachs report. This is driven by architectural innovations and higher parameter efficiency, allowing Chinese models to achieve comparable capabilities at 2%-10% the parameter size and significantly lower cost. The market is evolving into a two-tiered structure: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with premium pricing and a low-end, price-sensitive segment for global SMEs and individual users. Key points: * **Cost & Performance:** Innovations like Mixture of Experts (MoE) enable high performance with smaller models. Projects like Meituan's LongCat 2.0, trained on domestic hardware, highlight progress in tech self-sufficiency. * **Open-Source Strategy:** Most Chinese players use open-source/open-weight models for flexibility and ecosystem growth. However, Goldman notes this may underreport actual deployment and revenue. A shift toward "open-weight + community license" models with revenue sharing (e.g., MiniMax) could improve monetization. * **Market Shift & Global Expansion:** Enterprise AI adoption is shifting from "token maximization" to "ROI-first." International expansion, especially in non-US markets, is a major growth driver. Chinese models are increasingly available on global platforms like AWS Bedrock and Microsoft Copilot. * **Competitive Landscape:** Using a framework based on pricing power, cost advantage, and financial strength, Goldman identifies **Zhipu AI and DeepSeek** as the strongest in foundational text models, and **ByteDance** as the leader in multimodal/video generation. The report maintains Buy ratings on MiniMax and Kuaishou. * **Market Growth:** China's AI model API and subscription revenue is projected to grow from an estimated ¥35 billion in 2026 to ¥879 billion by 2030.

marsbitHá 10m

Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

marsbitHá 10m

Goldman Sachs Deep Dive Report: Who Will Become the Long-Term Winners in China's AI Large Model Industry?

Goldman Sachs Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry? China's AI large model sector is at a historic inflection point. Goldman Sachs argues that the intelligence of Chinese open-source/open-weight models is approaching top global proprietary models. Rapid adoption by domestic enterprises and global SMEs is creating a data flywheel effect that will further drive model iteration. The evolution is summarized as moving from "DeepSeek's cost-efficiency moment last year to GLM's model-intelligence moment this year." Chinese models achieve near-state-of-the-art performance at significantly lower cost, primarily due to architectural innovations like Mixture of Experts (MoE) and higher parameter efficiency. Models like DeepSeek V4 Pro (1.6T params), GLM5.2 (0.7T), and MiniMax M3 (0.4T) are much smaller than global leaders. Recent advancements in coding capability are attributed to better data curation and RLHF. Landmarks like Meituan's LongCat 2.0, trained fully on domestic AI chips, demonstrate progress in hardware stack independence. The market is forming a "two-tiered structure." The high-end tier (e.g., GLM5.2, Alibaba's Qwen3.7 Max) prices around $1 per million tokens, about 10-25% of US top models, with estimated inference gross margins of 10-20%. The low-end tier (priced as low as $0.06-$0.2 per million tokens) targets price-sensitive global SMEs and individuals. MiniMax derives 60-70% of revenue overseas. Goldman forecasts China's AI model API/subscription revenue to grow from an estimated RMB 35bn in 2026 to RMB 879bn by 2030. Most Chinese players adopt open-source/open-weight strategies for deployment flexibility and community feedback, though this limits monetization as deployments on third-party platforms (e.g., Alibaba Cloud) may not generate direct revenue. A shift towards "open-weight + community license" models with revenue-sharing agreements (like MiniMax's approach) could improve unit economics. International expansion, particularly in non-US markets, is the key growth driver. The global enterprise AI paradigm is shifting from "token maximization" to "ROI prioritization." Chinese models are already hosted on major global platforms like AWS Bedrock and are under consideration for integration into Microsoft Copilot. Using a competitive framework based on pricing power, cost advantage, and financial strength, Goldman identifies the strongest players: In foundational text models, Zhipu AI (initiated coverage) and DeepSeek lead. In multimodal/video generation, ByteDance's Seed is the frontrunner, with Kuaishou's Kling and MiniMax's Hailuo also well-positioned. Goldman maintains a Buy rating on MiniMax, citing its attractive valuation.

链捕手Há 14m

Goldman Sachs Deep Dive Report: Who Will Become the Long-Term Winners in China's AI Large Model Industry?

链捕手Há 14m

Is Ethereum Truly a "World Computer"?

Title: Is Ethereum Really a "World Computer"? Ethereum, envisioned as a "world computer" by its founder Vitalik Buterin, aims to be a decentralized platform for global applications. However, a recent analysis by Four Pillars raises questions about whether it is more accurately a "Western computer," based on the geographical distribution of its validators. Currently, the United States dominates with 38.19% of all validators, followed by Germany at 13.04%. Combined, these two countries account for over half of the network. In contrast, Asian representation is minimal, with Singapore holding only 3.15%. The concentration is partly due to affordable cloud hosting services like Hetzner and OVH in Europe and North America, as well as the prevalence of residential validators in the U.S., where individuals run nodes via home internet connections. When examining professionally operated validators, the distribution becomes more balanced. The U.S. share drops to 25.81%, while Asian countries like Singapore (7.28%), Hong Kong (6.44%), Japan (6.38%), and South Korea (4.59%) collectively approach the U.S. level. This shift reflects strategic deployments by institutions to meet regulatory requirements and reduce latency for local users. However, regions like South America, the Middle East, and Africa remain underrepresented. Ethereum's peer-to-peer network mechanisms, such as gossipsub, disadvantage areas with low node density, creating a feedback loop where delayed message propagation reduces validator performance and rewards. This imbalance challenges Ethereum's promises of censorship resistance and global accessibility. Despite these issues, opportunities exist for growth in underrepresented regions. As demand for localized staking infrastructure rises, early entrants in areas like the Middle East could establish dominant positions by offering compliant, low-latency solutions. The evolving validator landscape highlights both the structural challenges and the potential for Ethereum to move closer to its "world computer" ideal.

Foresight NewsHá 2h

Is Ethereum Truly a "World Computer"?

Foresight NewsHá 2h

Trading

Spot
活动图片