Is finance crypto’s first chapter or its final form? VCs weigh in…

ambcryptoPublished on 2026-02-10Last updated on 2026-02-10

Abstract

The article explores whether finance represents crypto's foundational use case or its ultimate application, featuring perspectives from venture capitalists. Chris Dixon of a16z Crypto argues that non-financial applications are still emerging, with finance developing first due to infrastructure needs. Haseeb Qureshi of Dragonfly counters that consumer crypto failed due to weak demand and poor products, not regulation, asserting finance is crypto's only proven product-market fit. Capital flows in 2025 support this, with venture funding exceeding $20 billion, heavily focused on DeFi and infrastructure. TVL recovered to ~$99 billion, and stablecoin supply surpassed $307 billion, demonstrating finance's dominance. Revenue data further reinforces this: top DeFi protocols like PancakeSwap and Aave generated significant fees, while non-financial sectors like gaming struggled with thin revenue and low user monetization. The conclusion is that finance remains crypto's primary value layer, with strong capital and payment flows, whereas utility sectors face challenges in converting engagement into sustainable revenue.

Has the future of cryptocurrency in finance been firmly established, or is the potential for non-financial applications only beginning to unfold?

Chris Dixon, Managing Partner at a16z Crypto, rejected the narrative, stating,

“It’s fashionable right now to declare that non-financial use cases of crypto are dead.”

He explained that blockchains introduced a coordination primitive, with finance emerging first because infrastructure had to develop before other sectors.

Haseeb Qureshi, Managing Partner at Dragonfly, responded directly by challenging the idea that regulation was to blame. He asked why finance thrived despite facing even stricter scrutiny.

In his view, consumer crypto failed because of weak demand rather than policy barriers, arguing that the products themselves were poor and ultimately failed the market test.

Dixon pointed to internet-era sequencing and supportive policy shifts, while Qureshi cites adoption history, concluding finance remains crypto’s only proven product-market fit.

Capital flows reveal crypto’s true product-market fit

Venture funding rose sharply in 2025, surpassing $20 billion, the highest level since 2022 and more than double 2023 totals. Growth accelerated into Q4, where $8.5 billion flowed across 425 deals, up 84% quarter over quarter.

Capital focused on later-stage rounds, infrastructure, and DeFi, signaling institutional conviction rather than retail hype. This expansion aligned with DeFi’s stabilization phase.

Total value locked recovered to about $99.07 billion, rebounding from the $50 billion bear-market floor, while stablecoin supply exceeded $307 billion.

Lending platforms such as Morpho maintained deep liquidity, reinforcing finance as crypto’s product-market fit layer. Meanwhile, stablecoin settlement reached trillions annually, with adjusted volumes rivaling traditional rails in throughput.

Together, funding inflows and payment growth supported finance-led adoption, validating Haseeb’s demand view while still reflecting Dixon’s sequencing logic.

Revenue density is still anchored

Earnings concentration across top protocols reinforces the value-accrual divide.

Financial platforms led profitability through the year, with PancakeSwap [CAKE] generating about $15.8 million in 30-day earnings and Aave [AAVE] $10.4 million, signaling fee-driven sustainability.

As emissions declined, retained value strengthened through burns and staking, which supported net profitability. In contrast, non-financial sectors relied heavily on token rewards to drive usage. Gaming and social activity spiked during airdrops and play-to-earn phases; however, retention weakened once subsidies faded.

Revenue density remained thin, with top blockchain games producing roughly $4.2 million daily and ARPU often below $10–$30. Thus, the utility attracted users but struggled to convert engagement into a durable cash flow.

All this together, from a revenue and venture‐returns perspective, Haseeb’s demand argument appears stronger, while Dixon’s view remains structurally long‐term.


Final Thoughts

  • Finance remains crypto’s dominant value-accrual layer, with capital, revenue, and payment flows consolidating around DeFi and stablecoin rails.
  • Utility sectors drive engagement but fail to convert usage into durable cash flow, reinforcing venture skepticism despite long-term expansion potential.

Related Questions

QAccording to the article, what is the main point of disagreement between Chris Dixon and Haseeb Qureshi regarding non-financial crypto applications?

AChris Dixon believes that non-financial use cases are not dead and that finance emerged first due to necessary infrastructure development, while Haseeb Qureshi argues that consumer crypto failed primarily due to weak demand and poor product quality, not regulatory barriers.

QWhat do the 2025 venture funding trends, as cited in the article, indicate about the crypto market's focus?

AThe 2025 venture funding, which surpassed $20 billion and saw an 84% quarter-over-quarter increase in Q4, was focused on later-stage rounds, infrastructure, and DeFi, signaling strong institutional conviction in financial applications rather than retail hype.

QHow does the revenue performance of financial DeFi protocols like PancakeSwap compare to non-financial sectors like gaming?

AFinancial protocols like PancakeSwap generated significant earnings ($15.8 million in 30 days) demonstrating fee-driven sustainability, while non-financial sectors like gaming produced much lower revenue (roughly $4.2 million daily) and struggled to convert user engagement into durable cash flow.

QWhat key metric is used to show the recovery and strength of the DeFi ecosystem in the article?

AThe article cites the recovery of the Total Value Locked (TVL) to about $99.07 billion, rebounding from a bear-market low of $50 billion, and the growth of stablecoin supply exceeding $307 billion as key metrics demonstrating DeFi's strength and stabilization.

QWhat is the article's final conclusion regarding crypto's product-market fit?

AThe article concludes that finance remains crypto's dominant value-accrual layer, with capital, revenue, and payment flows consolidating around DeFi and stablecoins, while non-financial utility sectors drive engagement but fail to create sustainable revenue models.

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.

marsbit49m ago

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

marsbit49m 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.

marsbit2h ago

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

marsbit2h ago

Trading

Spot
活动图片