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Alex Honnold will free solo climb the 508-meter Taipei 101 on January 24, 2026, with the entire ascent broadcast live on Netflix.

 

Prediction markets on Polymarket have attracted over $310,000 in bets, with most traders expecting a 75–90 minute completion time.

 

The event blends extreme sports, global media, and blockchain-based forecasting into a single high-profile spectacle.

Alex Honnold attempts a historic free solo climb of Taipei 101, streamed live on Netflix, as prediction markets on Polymarket wager on whether he can reach the top within 90 minutes.

 

American legendary free solo climber Alex Honnold is set to push the limits of human achievement once again — but this time, the stage is not the towering granite walls of Yosemite, but the iconic Taipei 101 rising from the heart of the city. On January 24, 2026, Honnold will attempt to free solo climb the 508-meter skyscraper’s exterior, with the entire ascent broadcast live worldwide via Netflix in a special titled Free Solo Taipei 101: Live (Skyscraper Live).

 

Prediction markets are also closely watching the event. On Polymarket, total wagers have surpassed $310,000, with the most popular betting window forecasting a completion time between 1 hour 15 minutes and 1 hour 30 minutes.

 

FREE SOLO CLIMBING 101 FLOORS: HONNOLD TAKES ON THE CITY SKYLINE

 

Alex Honnold, the legendary American free solo climber, is set to challenge the limits of human endurance once again. Best known for his historic 2017 ascent of El Capitan in Yosemite—where he climbed the nearly 900-meter Freerider route without ropes or safety gear, a feat often described as sacred ground in the climbing world—Honnold later rose to global fame through the Oscar-winning documentary Free Solo.

 

This time, the challenge shifts from natural granite walls to an urban landmark. His upcoming climb of Taipei 101 not only approaches the scale of his previous career-defining achievements, but also marks his first attempt to free solo one of Asia’s tallest skyscrapers, entirely without ropes or protective equipment.

 

According to the plan, Alex Honnold will begin his ascent from ground level, climbing upward along the skyscraper’s glass curtain walls, steel framework, and concrete exterior. The route spans all 101 floors, with no safety lines or mechanical assistance—completed solely by hand and foot from start to finish.

 

All Eyes on the Climb: Live on Netflix, with Public Screening in Xinyi

 

Titled Free Solo Taipei 101: Live, the broadcast will air at 9:00 a.m. Taiwan time on January 24, with an expected runtime of approximately two hours. In addition to the global livestream on Netflix, a large public screen will be set up at Xinyi Plaza across from Taipei 101, allowing spectators to watch the event for free without a Netflix subscription.

 

The live show will also feature commentary from five special guests, including fellow climber Emily Harrington, science YouTuber Mark Rober, and professional wrestling star Seth Rollins. Their insights will offer viewers a deeper, multi-dimensional understanding of both the technical difficulty and the broader significance of this unprecedented urban ascent.

PREDICTION MARKETS GO WILD: “HOW FAST WILL HONNOLD REACH THE TOP?”

Even before the climb begins, the challenge has already sparked intense global attention. On the blockchain-based prediction platform Polymarket, a dedicated market has emerged asking a single question: How long will Alex Honnold take to climb Taipei 101?

 

 

As of January 21, total trading volume on the market has exceeded $310,000. The most popular outcome—backed by 37% of participants—predicts that Alex Honnold will complete the climb within 1 hour 15 minutes to 1 hour 30 minutes. The second-most favored window is 1 hour 30 minutes to 1 hour 45 minutes, accounting for 20% of bets.

 

Only 4% believe he can finish in under 1 hour, while 5% are betting on a failed attempt.

 

These forecasts not only reflect market expectations around Honnold’s physical endurance and risk tolerance, but also highlight the extraordinary level of international attention surrounding this extreme urban ascent.

 

✏️ Clear Rules for Success—or Failure

 

According to the prediction market’s settlement rules, whether the climb is considered “completed” depends on whether Honnold finishes the officially designated route as defined by the broadcast.

 

If the program defines completion as reaching the 101st floor, then the climb is considered successful once Alex Honnold arrives there. If the endpoint is defined as the top of the spire, he must reach the very summit for the attempt to count as completed.

 

Conversely, the climb will be ruled “not completed” under any of the following conditions: the event is postponed beyond January 31, Honnold voluntarily abandons the climb, is forced to enter the building mid-ascent, or fails to reach the defined endpoint. In such cases, the outcome will be formally announced during the livestream and in subsequent coverage.

 

If you want, I can also tighten this into a shorter news brief, rewrite it in a more market-focused tone, or adapt it for a prediction-market explainer piece.

 

>>> More to read: What is Crypto Prediction Market? A Complete Beginner’s Guide

 

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〈Global prediction markets back Alex Honnold to climb Taipei 101 within 90 minutes〉這篇文章最早發佈於《CoinRank》。

Prediction Markets transform future uncertainty into tradable probabilities using market-driven incentives.

 

Blockchain-based Prediction Markets remove intermediaries, enhance transparency, and resist censorship.

 

Oracles are critical to Prediction Markets, securely bridging real-world outcomes with on-chain settlement.

Learn how Prediction Markets use blockchain to price uncertainty, aggregate collective intelligence, and enable censorship-resistant, transparent forecasting across politics, economics, and beyond.

 

When people encounter the terms “blockchain” and “markets” in the same sentence, their first instinct is often to think of cryptocurrency exchanges—platforms built around token trading, liquidity, and the rapidly expanding digital asset ecosystem. This association is understandable, as crypto markets remain the most visible and established application of blockchain technology to date.

 

However, blockchain’s potential extends far beyond facilitating crypto trading alone. At its core, blockchain is a verifiable and tamper-resistant coordination system that can support the creation of many different types of markets. Among these, one emerging category has begun to attract increasing attention heading into 2026: Prediction Markets.

 

Unlike traditional markets that revolve around the exchange of goods or financial assets, Prediction Markets focus on pricing uncertainty itself. They allow participants to trade on the outcomes of future events, transforming expectations about what will happen into market-driven probabilities. By operating on blockchain infrastructure, Prediction Markets can function without centralized intermediaries, while maintaining transparency, auditability, and open participation.

 

As Web3 infrastructure continues to mature, Prediction Markets are evolving beyond niche experiments into a distinct and meaningful use case within the blockchain ecosystem. Rather than simply offering a new form of trading, they represent a fundamentally different market model—one that uses economic incentives to aggregate information and reflect collective expectations in real time.

WHAT ARE PREDICTION MARKETS?

 

Prediction Markets are speculative platforms where participants trade on the outcomes of future events rather than traditional financial assets. Users buy and sell contracts that settle based on whether a specific event occurs.

 

For example, a Prediction Markets platform might ask: “Will a train connecting the United States and Europe be operational by 2035?” Traders can choose “Yes” or “No” contracts. If the event happens before the deadline, the “Yes” contract settles at 1 USD and the “No” contract expires worthless, and vice versa.

 

Contract prices fluctuate as new information emerges. Technological progress may push the “Yes” price higher, while delays or setbacks can increase the value of the “No” side. In this way, Prediction Markets continuously convert information and sentiment into real-time probabilities.

 

🔍 At a glance, Prediction Markets share 3 core features:

 

They trade on event outcomes rather than asset prices

Prices adjust dynamically as new information appears

Market prices aggregate collective expectations into probability signals

 

Because they pool insights from many participants, Prediction Markets often produce forecasts that are more accurate than traditional opinion-based methods. Their applications span politics, economics, sports, and weather—essentially any event with an uncertain outcome.

 

>>> More to read: What is Crypto Prediction Market? A Complete Beginner’s Guide

PREDICTION MARKETS & BLOCKCHAIN TECHNOLOGY

 

By leveraging blockchain technology, the value proposition of decentralized Prediction Markets is significantly strengthened. Traditional centralized prediction platforms rely heavily on users trusting the platform operator, which introduces structural limitations around transparency, resilience, and long-term reliability.

 

When Prediction Markets are built on blockchain infrastructure, their underlying mechanics change in fundamental ways. These advantages can be broadly summarized across 3 key dimensions:

 

✅ Censorship Resistance

 

Centralized Prediction Markets are vulnerable to shutdowns, restrictions, or external pressure from regulators, governments, or platform operators. In contrast, decentralized prediction markets governed by smart contracts eliminate single points of failure. The same code is executed across a distributed network of nodes, making it extremely difficult for any single entity to disrupt or dismantle the platform.

 

Once a Prediction Markets protocol is deployed on a blockchain, it can operate independently without reliance on centralized control. This decentralized structure ensures that no organization can easily censor market activity or manipulate outcomes.

 

For example, in politically sensitive regions, traditional prediction markets may be forced to shut down to prevent certain information from spreading. Blockchain-based Prediction Markets, however, are inherently more resistant to such censorship, offering a more open and reliable environment for information exchange.

✅ Removal of Intermediaries

 

Blockchain technology enables Prediction Markets to function through direct interaction between users and smart contracts, effectively removing the need for intermediaries. This reduces costs, minimizes counterparty risk, and eliminates the requirement to trust a centralized operator or pay additional platform fees.

 

Smart contracts automatically execute trades, settlements, and payouts according to predefined rules. This automation significantly reduces the risk of human error, misconduct, or fraud.

 

In traditional Prediction Markets, participants must trust that the platform operator will fairly resolve outcomes and distribute payouts. In blockchain-based markets, these processes are handled transparently by code, improving both trust and verifiability across the system.

✅ Improved Accessibility

 

Decentralized Prediction Markets are typically permissionless, allowing anyone with an internet connection to participate regardless of location. This dramatically lowers entry barriers and increases diversity among participants.

 

By contrast, traditional prediction platforms often impose high fees, account restrictions, or geographic limitations. Blockchain-based Prediction Markets are globally accessible, enabling broader participation from individuals with diverse backgrounds and perspectives.

 

This inclusivity not only democratizes access but also enriches the quality of market predictions. Participants from remote or underrepresented regions—often possessing valuable local knowledge—can contribute insights that might otherwise be overlooked, ultimately enhancing the collective intelligence of Prediction Markets.

 

>>> More to read: Is Polymarket Legal? Gambling or Finance?

THE ROLE OF BLOCKCHAIN ORACLES

 

One of the key challenges facing decentralized Prediction Markets is determining event outcomes without relying on a central authority. Blockchain oracles address this problem by providing mechanisms to verify real-world results and relay them on-chain. There are several common approaches to implementing oracles in Prediction Markets:

 

📌 Third-Party Data Sources

 

This approach is relatively straightforward, relying on external data providers to supply outcome information. However, because the data is ultimately controlled by a third party, it reintroduces a degree of centralization and weakens the trustless nature of decentralized Prediction Markets.

 

📌 Incentivized Reporting

 

Another approach uses financial incentives to encourage honest reporting by participants. In some Prediction Markets, users stake tokens to report event outcomes. Accurate reporting is rewarded, while false reporting results in the loss of staked tokens. This mechanism aligns economic incentives with truthful behavior and reduces reliance on a single authority.

 

Blockchain oracles are essential for ensuring both accuracy and trust minimization in Prediction Markets. They act as a bridge between on-chain systems and real-world information, enabling markets to settle based on verifiable external data. For example, in weather-related Prediction Markets, oracles can aggregate data from multiple reputable meteorological sources to validate outcomes.

 

A wide range of oracle solutions is currently being developed to improve the reliability and security of Prediction Markets. Some designs rely on decentralized networks of reporters who verify data through consensus mechanisms, while others adopt hybrid models that combine decentralized validation with trusted data sources. As blockchain technology continues to evolve, oracle systems are expected to become more sophisticated—providing stronger guarantees of accuracy, resistance to manipulation, and overall robustness for Prediction Markets.

 

>>> More to read: What is Oracle in Crypto?

CONCLUSION

 

For forecasting future outcomes, Prediction Markets are more than just an exciting speculative tool—they are increasingly recognized as advanced mechanisms for gathering reliable information across multiple domains. By using financial incentives to encourage individuals to share their knowledge, Prediction Markets can generate valuable insights into social, industrial, and political trends.

 

Blockchain-powered decentralized alternatives directly address the limitations of centralized platforms. By reducing reliance on trusted intermediaries and increasing transparency, blockchain-based Prediction Markets create a more resilient and trust-minimized market structure. As more sophisticated oracle systems are developed, these platforms are expected to become even more accurate, transparent, and reliable—unlocking the true potential of Prediction Markets.

 

Looking ahead, the convergence of Prediction Markets and blockchain technology points to a future where collective intelligence is harnessed more effectively, markets are more open and fair, and information flows more freely and securely.

 

 

 

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〈How Prediction Markets Work on Blockchain?〉這篇文章最早發佈於《CoinRank》。

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X’s API crackdown is not a minor policy tweak but a clear denial of the InfoFi incentive model, as external rewards for posting are deemed incompatible with platform content governance.

 

The sharp reactions from InfoFi tokens and projects show that the change disrupts the economic foundations of posting-driven incentives, forcing teams to shut down, pause, or fundamentally redesign their products.

 

While InfoFi itself is not disappearing, the “talking-to-earn” era built on permissionless, API-driven incentives is effectively over as platforms reclaim sovereignty over their information flows.

 

X’s decision to revoke API access for InfoFi applications marks a decisive rejection of posting-based incentive models, triggering market fallout and signaling a broader reassertion of platform control over content production.

On January 15 at 22:39, X announced the revocation of API access for InfoFi applications. Multiple apps that relied on “posting-based incentives” were immediately affected. As APIs were cut off, some projects announced the suspension of related features or adjustments to their business direction. Tokens associated with InfoFi saw sharp declines, with several InfoFi-related tokens (KAITO, COOKIE) recording double-digit drops within a short period. Community members offered a blunt summary: “the talking-to-earn era is over.”

 

 

The violent reactions across InfoFi applications and tokens indicate that this change went far beyond a routine rules update. It altered the operational foundations of related apps and triggered cascading market effects. This was not a minor tweak, but a clear statement by X against a specific application model.

 

WHAT HAPPENED: X FORMALLY REJECTS THE INFOFI INCENTIVE MODEL

 

This time, X left little room for interpretation.

 

X’s product lead Nikita Bier stated in a post that X is revising its developer API policies and will no longer allow any applications that “reward users for posting on X” to continue accessing the API. In his description, such apps were explicitly labeled as “infofi” and identified as one of the primary sources of recent AI spam and reply pollution on the platform.

 

 

Unlike past platform governance approaches that emphasized advance notice and observation, X acted decisively this time: API access for relevant InfoFi apps had already been revoked. The official rationale was straightforward—external incentive mechanisms were driving a flood of task-based, templated content into the feed, severely degrading the user experience. Once bots realized that “posting no longer pays,” X believes the content environment would quickly self-correct.

 

Notably, Nikita Bier added a pointed remark: InfoFi applications had previously paid millions of dollars in API access fees, but X does not need that revenue.

 

That single sentence effectively delivered a verdict on the InfoFi business model. Judging from the execution and official wording, this adjustment was not aimed at isolated cases of API abuse, but represented X’s unequivocal rejection of InfoFi’s core model—external incentives directly intervening in platform content production.

 

For teams whose developer accounts were terminated as a result, X’s proposed “transition plan” was equally telling: the platform would assist them in migrating their businesses to Threads and Bluesky. In other words, X chose not to reform or absorb this incentive mechanism, but to remove it entirely from its ecosystem.

 

WHAT WAS REJECTED IS NOT CONTENT, BUT INFOFI’S INCENTIVE PATH

 

Taken at face value, the official explanation frames this as a routine cleanup of AI spam. In the context of InfoFi, however, that rationale alone cannot explain X’s firm stance.

 

The crux of the issue may not be whether content has value, but who produces it and for what reason. InfoFi’s core logic uses external tokens or points to directly incentivize users to post, reply, and interact on the platform. In the short term, this does boost activity. But it quickly turns content creation into “task execution”—posting is no longer about expressing views, but a prerequisite for claiming rewards.

 

Once incentives exist outside the platform’s governance system, the platform inevitably loses control over content motivation and quality. InfoFi apps do not care whether a reply adds informational value, only whether it meets “settlement” criteria. For X, this means the feed is effectively being taken over by an external economic system.

 

From this perspective, AI spam is a symptom, not the cause. What truly crossed X’s red line was the structural issue of “third-party incentive layers embedding directly into the platform’s content distribution system.” If such a model were tolerated, content order, recommendation logic, and even user relationships would gradually be shaped by incentive designers.

 

This explains why X left almost no room for InfoFi to reform. In X’s judgment, InfoFi was not an ecosystem participant in need of correction, but a content production pathway no longer permitted to exist.

 

Accordingly, this API purge represents X’s proactive reclaiming of content sovereignty: when external incentives conflict with platform experience, X chose to sever the former rather than relinquish control of the feed.

 

FROM “SHUTDOWN” TO “RECONSTRUCTION”: INFOFI PROJECTS COLLECTIVELY SHIFT DIRECTION

 

X’s API crackdown did not remain a policy announcement; it quickly triggered chain reactions among InfoFi projects.

 

According to Odaily Planet Daily, the first clear response came from Cookie DAO. After communicating with X regarding API usage policies, the team announced the formal shutdown of the Snaps platform and the termination of all ongoing creator incentive programs. Cookie acknowledged in its announcement that this was a “difficult and sudden” decision, but emphasized that the goal was not to abandon InfoFi, rather to ensure its data layer and core products remain compliant.

 

 

Judging from the wording, Snaps’ shutdown appears more like a defensive move to limit losses under sudden impact. On one hand, Cookie stressed that it always used official data sources and remains an enterprise-level API client of X. On the other, the team openly stated that InfoFi is undergoing structural change, and whether Snaps can exist in a “new form” depends on further guidance from X. This language itself signals deep uncertainty about the sustainability of the original incentive model.

 

By contrast, Kaito’s adjustment was more proactive. Kaito announced the shutdown of Yaps and its incentive leaderboards, while launching Kaito Studio, explicitly bidding farewell to the “open, permissionless incentive distribution” path. According to official statements, Kaito Studio will resemble a more traditional tiered marketing platform, where brands select creators based on predefined standards. Platform coverage will also expand beyond X to include YouTube, TikTok, and other social channels.

 

 

In explaining the shift, Kaito did not avoid the inherent issues of the InfoFi model. It noted that even with higher thresholds and screening mechanisms, low-quality content and farming behavior remained difficult to eliminate. After discussions with X, the team agreed that a “fully permissionless incentive distribution system” no longer aligns with the shared needs of platforms, brands, and creators. Reading between the lines, the end of Yaps appears to be a deliberate abandonment of the original InfoFi route.

 

Taken together, these cases point to a clear trend: once platforms tighten API access and incentive boundaries, InfoFi projects must either pause aggressive incentive strategies and revert to data and tooling roles, or fully reconstruct their business logic toward models closer to traditional marketing and content partnerships.

 

For now, while token prices fluctuate, a collective collapse of InfoFi projects has not occurred. What is clear, however, is that the model of relying on platform APIs and using external incentives to directly drive posting and interaction has become extremely difficult to sustain.

 

CONCLUSION: THE TALKING-TO-EARN ERA IS OVER, BUT INFOFI’S QUESTIONS REMAIN

 

The responses from InfoFi projects suggest this shift is not simply a “ban” or a failure. Whether Cookie’s return to a data-layer focus or Kaito’s move toward a Studio model closer to traditional marketing, both indicate that InfoFi has not disappeared—it simply can no longer exist as “in-platform incentive arbitrage.”

 

When people say “the talking-to-earn era is over,” what has ended is not the quantification of content or the pricing of influence, but the open incentive pathway that relies on APIs and treats posting and replying themselves as settlement objects. As platforms reassert sovereignty, the room for this model is rapidly shrinking.

 

As for migration to Threads or Bluesky, that appears more like a temporary buffer than a real solution. The deeper question is whether InfoFi can still find an irreplaceable role without taking over platforms’ content production rights.

 

X may be the first platform to press the button, but the signal it has sent is unmistakable: content sovereignty is returning to the platforms themselves.

 

Original Article

〈X Ends the InfoFi Incentive Model by Its Own Hand, Marking the End of the “Talking-to-Earn” Era〉這篇文章最早發佈於《CoinRank》。