VC Investment Trends Shift: Public Chains and AI Cool Down; Prediction and Payment Take the Lead

Odaily星球日报Published on 2025-12-27Last updated on 2025-12-27

Abstract

Venture capital investment in the crypto sector is shifting significantly, moving away from previously dominant areas like Layer 1 and Layer 2 blockchains and AI projects. According to recent data, out of 73 projects that raised over $10 million in the past three months, almost none were new public chains. Similarly, AI × Web3 sector saw only two major raises, totaling $22.8 million. Instead, prediction markets and payment systems are now attracting substantial capital. Prediction platforms Polymarket and Kalshi alone secured over $3.15 billion, driven by Polymarket’s accurate election forecasts and growing user engagement. The payment and banking sector raised nearly $1.3 billion, with companies like Ripple Labs and Rapyd leading large rounds. Stablecoin transaction volumes now rival Visa, highlighting the sector’s expansion. Real World Assets (RWA) are also gaining traction, with over $850 million raised—led by Figure’s $787.5 million IPO. Tokenized assets on-chain now exceed $36 billion. Additionally, user-friendly infrastructure projects, such as simplified wallets and onboarding tools, are receiving significant investment to attract mainstream adoption. While DeFi remains active with around $740 million in funding, it no longer dominates VC attention. The trend indicates a clear pivot toward applications with real-world use cases and revenue potential over pure infrastructure.

Original | Odaily Planet Daily (@OdailyChina)

Author | jk

The cryptocurrency venture capital market is undergoing a quiet transformation. In DefiLlama's latest funding data, among the 73 projects that completed financings exceeding $10 million in the past three months, there are almost no Layer 1 or Layer 2 public chains to be found. The public chain sector, once considered the "holy grail," has now nearly disappeared. Meanwhile, prediction markets, payment systems, RWA (Real World Assets), and infrastructure targeting ordinary users are attracting massive capital inflows.

L1, L2 Boom Ends, Large AI Financings Also Nearly Extinguished

Looking back at the peak of the 2021-2022 bull market, new public chains like Solana, Avalanche, and Fantom routinely raised hundreds of millions of dollars, with investors vying to bet on "Ethereum killers." However, three years later, the market landscape has fundamentally changed.

From Movement, Story, to this year's Berachain and Monad, the era of large public chain financings is no longer the norm.

According to data from The Block, the overall funding for blockchain networks (including L1 and L2) in 2024 was approximately $1.8 billion. While still an important sector, growth has noticeably slowed. According to statistics by user @pgreyy on X, in Q4 2025, aside from Tempo (a new public chain focused solely on payments and backed by Paradigm), no new L1 or L2 managed to secure an investment exceeding $10 million.

Can a public chain without a $10 million investment still fulfill the role of a "world computer" or an "Ethereum killer"?

Investors have realized that the market doesn't need more "high-performance public chains"; it needs applications that can bring real users and real revenue. Crypto influencer @sjdedic stated his conclusion on X: "No one cares about infrastructure anymore. The spotlight has shifted to the application layer—consumer-facing products and real use cases. Those L1s stuck in the 'medium intelligence trap,' focusing only on technology while ignoring everything else, are in trouble." He further predicted: "I wouldn't be surprised to see applications valued at tens of billions of dollars in the coming years, while L1 tokens gradually lose market share and slowly become irrelevant."

Similarly, although AI is the hottest tech concept of 2025, among the crypto projects that raised over $10 million in the past three months, only two belong to the AI sector: Inference secured an $11.8 million seed round, and TAO Synergies Inc completed a $11 million private equity round, totaling just $22.8 million. Even removing the $10 million threshold, there are only 9. This number is a drop in the bucket compared to Web2. For comparison, a mid-sized payment company, Coinflow, raised a single round of $25 million.

Who would have thought that just a year later, not only has the spectacle of Virtuals vs. ai16z become彻底的历史 (thoroughly history), but the entire AI x Web3 sector has also cooled down.

Rise of Emerging Sectors

Prediction Markets: From Fringe to Mainstream

Prediction markets are undoubtedly the dark horse of 2025 and the most prominent sector in this round of data. Polymarket and Kalshi alone attracted over $3.15 billion in funding, dominating the entire list. Polymarket generated over $3.3 billion in trading volume during the 2024 US election period, and its predictions were even more accurate than traditional polling agencies. In October 2025, the Intercontinental Exchange (ICE)—the parent company of the New York Stock Exchange—announced a massive $2 billion investment in Polymarket, pushing its valuation to $8-9 billion. Polymarket had also completed a $150 million funding round earlier.

Meanwhile, Kalshi not only completed a $1 billion Series E round but also secured a $300 million Series D round for its DeFi business, with investors including Sequoia, a16z, and Paradigm.

Payments & Banking: The Stablecoin "Super Cycle"

So who picked up the heat from public chain funding? The answer is undoubtedly the payments/banking sector.

Judging by the funding data, the payments sector raised nearly $1.3 billion, covering a complete ecosystem from underlying infrastructure to consumer applications. In 2025, stablecoin circulation grew by approximately $30 billion since the start of the year, with monthly transaction volumes exceeding $1 trillion, now comparable to Visa's scale.

Ripple Labs secured a $50 million strategic investment, and Rapyd completed a $50 million Series F round. These two companies alone raised $100 million, accounting for the majority of the payments sector. While giant players continue to lead, new digital banks, interbank B2B services, and financial services are also active. Singapore's Pave Bank, France's digital bank Deblock, Switzerland's Future Holdings, and the Netherlands' Amdax all received over $20 million in funding.

It's hard to imagine that in 2019, VC investment in the stablecoin field alone was less than $50 million.

RWA: Bridging Virtual and Real

Real World Asset tokenization (RWA) is moving from the experimental phase to scaled application. Looking at the funding data, Figure led the entire RWA sector with an IPO size of $787.5 million. Combined with its additional $25 million funding, one company contributed over 95% of the RWA sector's funding. RWA compliance company Satschel secured a $15 million equity round, on-chain stock trading infrastructure company Block Street completed an $11.5 million round, bringing the total RWA sector funding to over $85 million.

According to data from RWA.xyz, on-chain tokenized assets exceed $36 billion. Among them, the market cap of tokenized gold grew from $1 billion to over $3.27 billion in 2025, a 227% increase.

Simultaneously, traditional asset management giants like BlackRock, Apollo, and Franklin Templeton are actively tokenizing institutional-grade products. Private equity fund managers are beginning to adopt blockchain to represent ownership of traditional assets, enabling fractional ownership and instant settlement.

Another focus for VCs is infrastructure projects that can reach ordinary users. The characteristic of these projects is lowering the barrier to using cryptocurrency, allowing non-crypto-native users to easily access blockchain services. Directions like wallet abstraction, social logins, and fiat on/off ramps are attracting investment. The U card RedotPay received a $47 million strategic investment, investment company Finary completed a $29.4 million Series A, and self-custody company Bron secured a $15 million seed round. These projects are lowering the threshold for users to use cryptocurrency, paving the way for the next wave of user growth.

DeFi: Steady Recovery

DeFi showed steady performance in this funding round, raising a total of approximately $74 million. However, compared to the prediction markets and payments sectors, the scale of individual financings was significantly smaller. Decentralized exchange Flying Tulip became the funding champion among pure DeFi projects with a $20 million seed round. Lighter secured $68 million, and Jito received a $50 million strategic investment. The 2025 funding data reflects a more cautious valuation attitude from VCs towards the DeFi sector. According to The Block's data, the DeFi sector completed over 530 financings in 2024; 2025 has clearly not reached that scale.

Trending Cryptos

Related Questions

QAccording to the article, which sectors have seen a significant decline in VC funding for large-scale projects (over $10 million) in the past three months?

AThe article states that Layer 1 and Layer 2 public blockchains have nearly disappeared from large funding rounds. The AI x Web3 sector has also seen a significant cooling, with only two AI projects raising over $10 million in that period.

QWhat are the two prediction market companies mentioned that collectively raised over $3.15 billion, and what was a key event that demonstrated their utility?

AThe two prediction market companies are Polymarket and Kalshi. A key event demonstrating their utility was the 2024 U.S. presidential election, during which Polymarket processed over $3.3 billion in trading volume and its predictions were more accurate than traditional polling agencies.

QHow does the current scale of stablecoin transaction volume compare to a major traditional payment network, and which payment companies received the largest investments?

AThe monthly transaction volume of stablecoins has surpassed $1 trillion, which is comparable to the transaction scale of Visa. The payment companies that received the largest investments were Ripple Labs ($500 million strategic investment) and Rapyd ($500 million Series F round).

QWhat does RWA stand for, and which company in this sector had a major liquidity event mentioned in the article?

ARWA stands for Real World Assets. The company Figure had a major liquidity event, leading the sector with an IPO that raised $787.5 million, in addition to another $25 million in funding.

QWhat shift in investor focus does the crypto influencer @sjdedic describe, and what is the 'medium IQ trap' he references?

A@sjdedic describes a shift in investor focus from infrastructure to the application layer—consumer-facing products and real-use cases. The 'medium IQ trap' refers to L1 blockchains that are overly focused on technical aspects while ignoring other crucial elements like user adoption and real revenue, causing them to fall into困境 (difficulties).

Related Reads

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit9m ago

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit9m ago

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit1h ago

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbit1h ago

Trading

Spot

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of AI (AI) are presented below.

活动图片