Fortune Exclusive: a16z Crypto Fund AUM Plummets 40%, Multicoin Halved, Top Crypto VCs Shrink Collectively

marsbitОпубліковано о 2026-04-17Востаннє оновлено о 2026-04-17

Анотація

According to exclusive SEC filings obtained by Fortune, leading crypto venture capital firms, including a16z crypto, Multicoin Capital, Pantera Capital, and Paradigm, experienced significant declines in their assets under management (AUM) in 2025. a16z crypto's AUM dropped nearly 40% to $9.5 billion, partly due to strategic distributions to LPs at market highs, with its first fund achieving a 5.4x DPI. Multicoin’s AUM was halved to approximately $2.7 billion, impacted by market volatility and crypto price corrections. Pantera also saw AUM shrink, though this was partly attributed to successful exits, including five portfolio company IPOs. In contrast, Haun Ventures grew its AUM by over 30% to nearly $2.5 billion, driven by a successful exit in the stablecoin sector and new fundraising. Despite the downturn, several firms are raising new funds, with a16z targeting up to $2 billion and Paradigm seeking up to $1.5 billion, indicating continued long-term confidence in the crypto market's cyclical nature.

Author: Ben Weiss

Compiled by: Deep Tide TechFlow

Deep Tide Guide: Fortune reporters obtained previously undisclosed financial disclosure documents of crypto VCs from the SEC. The data shows that the assets under management (AUM) of leading institutions such as Paradigm, Pantera, a16z crypto, and Multicoin have shrunk across the board in 2025. However, the shrinkage is not all bad news—a16z crypto returned money to LPs at the market peak, with the first fund's DPI reaching 5.4x. The only one that grew against the trend was Haun Ventures, which hit the stablecoin track by betting on BVNK's acquisition by Mastercard.

Top crypto VC players failed to escape the market crash of 2025.

Fortune reporter Ben Weiss obtained a batch of previously undisclosed investment advisor financial disclosure documents from the U.S. Securities and Exchange Commission (SEC). The data is straightforward: the AUM of top institutions like Paradigm and Pantera Capital collectively shrunk in 2025.

Caption: Changes in AUM of top crypto VCs from 2021 to 2025

Chart by: Ben Weiss / Fortune

But before listing the numbers, there's a premise to clarify: AUM is not a good indicator of VC success or failure. It does not reflect new rounds of financing, LP exit distributions, or capital calls. Crypto asset prices themselves are highly volatile—a tweet from a moody man can send prices on a roller coaster (Elon Musk, Donald Trump, Changpeng Zhao, take your pick). Veteran crypto VCs have experienced asset surges during the 2021 NFT frenzy and subsequent portfolio plunges during the "crypto winter."

Original author Ben Weiss also emphasized: Truly top investors ultimately return money to LPs. Short-term AUM changes do not equal performance quality.

With that premise clear, let's look at the specific data.

a16z crypto: AUM shrunk nearly 40%, but money returned to LPs

The combined AUM of a16z crypto's four crypto funds plummeted nearly 40% from 2024, dropping to $9.5 billion. During the same period, the parent company Andreessen Horowitz's assets under management swelled to over $100 billion.

Part of the reason for the shrinkage is that the institution began distributing returns from the first three funds back to LPs. According to informed sources, a16z crypto intentionally chose to make distributions at the 2025 crypto market peak.

How effective was it? According to Newcomer data, a16z's first crypto fund achieved a net DPI (Distributed to Paid-In capital ratio) of 5.4x. Compared to other VC funds raised in the same period in 2018 on the Carta platform, this return rate is quite impressive.

In other words, the AUM shrinkage for a16z crypto is more a result of "making money and returning it to LPs" rather than "plummeting holdings."

Multicoin: AUM halved to $2.7 billion

Multicoin Capital's fate is deeply tied to the crypto market. During the 2021 crypto frenzy, its AUM nearly tripled in a year, approaching $9 billion. After FTX collapsed, it plummeted directly, then gradually rebounded over the next two years.

But the 2025 downturn hit it again. From 2024 to 2025, Multicoin's AUM shrunk by more than half, falling to approximately $2.7 billion. Since BTC began its dive in October 2025, crypto assets have retreated across the board, and Multicoin's structure—which operates both hedge funds and VC funds—was hit hardest.

Additional context: Multicoin co-founder Kyle Samani left the company in February this year, turning to invest in other areas of technology.

Pantera: Five portfolio companies IPO, capital returned to LPs

Pantera Capital's AUM also shrunk, but similar to a16z, part of the reason is active distributions to LPs.

According to informed sources, Pantera had five portfolio companies go public in 2025, including Circle and BitGo. These exits brought significant cash returns.

Haun Ventures: The only one growing against the trend, AUM up over 30%

Amid the widespread shrinkage, Haun Ventures is the only exception.

The institution, founded by former a16z crypto partner Katie Haun, saw its AUM increase by over 30% year-on-year, approaching $2.5 billion. This is partly due to betting on the right track—its investment in stablecoin company BVNK was acquired by Mastercard for up to $1.8 billion. Additionally, Haun Ventures itself raised a new $1 billion fund in 2025.

New fundraising rounds have begun

Although AUM has shrunk, top institutions have not stopped:

Paradigm is raising a new fund of up to $1.5 billion. a16z crypto is raising up to $2 billion. Dragonfly just closed its $650 million fourth fund. Post-publication correction by Fortune: A Dragonfly spokesperson actually responded, confirming the data is "accurate" and stating, "We are actively deploying capital."

Spokespersons for Paradigm, Pantera, a16z crypto, Multicoin, and Haun Ventures all declined to comment.

The cyclical fate of crypto VCs

The original article ends here, but a few background points are worth adding.

Crypto VCs are fundamentally different from traditional tech VCs. Traditional VCs invest in equity, exiting through IPOs or M&A. Many crypto startups have their own tokens, and VCs' holdings are directly exposed to token price fluctuations.

Multicoin is the most extreme case: According to a previous Fortune report, its assets increased by 20,287% from 2017 to 2021, then retreated 90% in 2022. This magnitude is unimaginable in the traditional VC field.

According to Pantera Capital's outlook report earlier this year, the total crypto market cap excluding BTC (also excluding ETH and stablecoins) fell about 44% from the end of 2024 highs. But according to historical patterns, bear markets are also windows for bottom-fishing. The intensive fundraising efforts by several leading institutions at this moment are betting on the next cycle.

According to a previous Fortune exclusive report, a16z crypto's fifth fund is planned to complete fundraising in the first half of 2026, led by Chris Dixon, and will continue to fully bet on the blockchain direction. Paradigm's new fund, according to The Wall Street Journal, will expand into AI and robotics technology. The strategic divergence is clear: a16z continues all-in on crypto, while Paradigm chooses cross-sector hedging.

Пов'язані питання

QAccording to the article, which major crypto VC firm saw its AUM decrease by nearly 40% in 2025?

Aa16z crypto saw its AUM decrease by nearly 40% in 2025.

QWhat was the primary reason for the AUM decline at a16z crypto, as explained in the article?

AThe primary reason for the AUM at a16z crypto was that the firm chose to distribute returns from its first three funds back to its LPs during the 2025 crypto market peak.

QWhich crypto VC was the only one mentioned to experience AUM growth in 2025, and what was a key reason for this growth?

AHaun Ventures was the only crypto VC to experience AUM growth, increasing by over 30%. A key reason was its successful investment in the stablecoin company BVNK, which was acquired by Mastercard for up to $1.8 billion.

QWhat significant event involving five of its portfolio companies contributed to Pantera Capital's AUM decrease?

APantera Capital's AUM decrease was partly due to it distributing capital back to its LPs following the successful IPOs of five of its portfolio companies, including Circle and BitGo.

QHow does the article describe the fundamental difference between crypto VC and traditional tech VC regarding their investments?

AThe article states that traditional VCs invest in equity and exit via IPOs or acquisitions, while many crypto startups have their own tokens, meaning a crypto VC's holdings are directly exposed to the volatility of token prices.

Пов'язані матеріали

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist11 хв тому

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist11 хв тому

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.

marsbit11 хв тому

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

marsbit11 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit15 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit15 хв тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1 год тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1 год тому

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.

marsbit1 год тому

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

marsbit1 год тому

Торгівля

Спот
活动图片