Stablecoins hit $300B market cap: Why Tether’s $10B profit is just the beginning

ambcryptoPublished on 2026-01-26Last updated on 2026-01-26

Abstract

Stablecoins have become the dominant revenue engine in crypto, with the market cap reaching $300 billion. Tether exemplifies this by generating over $10 billion in profit in 2025. Ethereum serves as the primary settlement layer, with stablecoin supply on it growing to over $160 billion and generating roughly $5 billion in revenue that year. Concurrently, Ondo Finance has emerged as a leading real-world asset (RWA) platform, with its Total Value Locked (TVL) surging to $2.5 billion, driven by tokenized U.S. Treasuries and equities. The growth in RWA TVL is closely linked to the expansion of the stablecoin market, as stablecoins provide the essential liquidity and settlement rails. This trend highlights a shift towards structural, yield-based growth rather than speculative narratives.

While L1s ecosystems continue to chase hype through narrative cycles and speculative throughput claims, crypto’s most reliable profits have accrued elsewhere.

Stablecoins have quietly evolved into the sector’s dominant revenue engine, driven by their scale, ubiquity, and control over on-chain settlement.

As a result, issuers have been able to convert this structural advantage into sustained cash flows, exemplified by Tether [USDT] generating over $10 billion in profit in 2025.

Stablecoin issuers have evolved into large-scale revenue generators, with Ethereum serving as the dominant settlement layer, anchoring that growth.

In 2025 alone, issuers generated roughly $5 billion in revenue tied to Ethereum-based supply. Quarterly revenue expanded from near $1.2 billion early in the year to about $1.4 billion by Q4.

At the same time, stablecoin supply on Ethereum [ETH] grew by nearly $50 billion, surpassing $160 billion.

As reserves expanded, yield-based income scaled predictably. This dynamic reinforces Ethereum’s financial gravity, deepens liquidity, and strengthens its role as core on-chain monetary infrastructure.

ONDO emerges as a core liquidity hub for tokenized RWAs

ONDO Finance is rapidly consolidating its position as a leading real-world asset platform, with Ondo Finance [ONDO] pushing total value locked (TVL) to roughly $2.5 billion by January 2026.

Earlier in 2025, TVL hovered just above $1 billion. Since then, capital has accelerated sharply, driven by tokenized yield products.

Tokenized U.S. Treasuries account for nearly $2 billion, led by OUSG and Ondo US Dollar Yield [USDY]. Meanwhile, tokenized stocks and ETFs exceed $500 million across more than 200 assets.

As ONDO expands across multiple chains, its scale signals growing institutional confidence in on-chain RWAs.

Is RWA TVL growth following stablecoin liquidity cycles?

RWA TVL growth increasingly tracks stablecoin supply expansion, revealing a clear liquidity-driven relationship.

As the stablecoin market cap climbed toward $280–300 billion by late 2025, RWA TVL simultaneously expanded to roughly $16–19 billion.

This side-by-side growth reflects function, not coincidence. Stablecoins act as settlement rails and yield-bearing inputs for tokenized treasuries and equities.

Consequently, platforms like Ondo doubled TVL beyond $2.5 billion as stablecoin-backed demand intensified.

Therefore, the trend signals structural conviction, though short-term stalls in stablecoin issuance can temporarily cap RWA momentum.


Final Thoughts

  • Stablecoins have become crypto’s most reliable profit engine, translating settlement scale into recurring cash flows while Ethereum reinforces its role as the dominant on-chain monetary layer.
  • Concurrently, RWA growth remains liquidity-driven, with TVL expansion closely following stablecoin supply, positioning platforms like ONDO as beneficiaries of stablecoin-backed demand rather than speculative cycles.

Related Questions

QWhat was Tether's profit in 2025 according to the article?

ATether generated over $10 billion in profit in 2025.

QWhat is the total value locked (TVL) in Ondo Finance by January 2026?

AThe total value locked (TVL) in Ondo Finance reached roughly $2.5 billion by January 2026.

QHow much revenue did stablecoin issuers generate from Ethereum-based supply in 2025?

AStablecoin issuers generated roughly $5 billion in revenue tied to Ethereum-based supply in 2025.

QWhat is the relationship between RWA TVL growth and stablecoin supply, as described in the article?

ARWA TVL growth increasingly tracks stablecoin supply expansion, revealing a clear liquidity-driven relationship where stablecoins act as settlement rails and yield-bearing inputs.

QWhat role does Ethereum play in the stablecoin ecosystem, according to the final thoughts?

AEthereum reinforces its role as the dominant on-chain monetary layer for stablecoins.

Related Reads

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

NVIDIA's Rubin Ultra, the top-tier variant of the newly announced Rubin AI accelerators, has reportedly seen significant specification downgrades, according to an industry report from SemiAnalysis. Initially designed with four compute dies (4-die), the Rubin Ultra is now said to be reduced to a 2-die design. Key changes highlighted in the report include: * **No increase in peak theoretical compute performance**, remaining at 35 PFLOPs like the standard Rubin. * **Severe reduction in memory capacity** to 192GB using 8-Hi HBM stacks, which is less than the standard Rubin's 288GB using 12-Hi stacks. * **Negligible memory bandwidth improvement** of only 1 TB/s. * **Slightly higher chip-level power consumption**. * The **primary upgrade is a massive increase in scale-up interconnect capacity**, supporting connections for up to 576 GPUs via NVLink, compared to 72 for the standard Rubin. The report suggests the redesign is primarily a cost-optimization move driven by the sharp rise in HBM (High-Bandwidth Memory) prices. By reducing the expensive HBM content and shifting investment towards enhanced system-scale networking, NVIDIA aims to maintain the platform's value for large-scale AI training clusters while managing soaring material costs. The news reportedly triggered a sell-off in South Korean memory stocks, with SK Hynix and Samsung shares falling around 8%, as markets grew concerned that NVIDIA—a major HBM buyer—might be reducing its reliance on high-capacity memory, potentially capping future pricing power for memory makers.

Odaily星球日报11m ago

Rubin Ultra Makes Major Cuts, Even Nvidia Can't Handle Memory Price Hikes?

Odaily星球日报11m ago

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.

marsbit1h ago

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

marsbit1h ago

Trading

Spot
活动图片