• Consider Venice for exposure to privacy-focused AI: the guest cited revenue above a $100 million run rate and roughly 10% monthly growth, but these claims are not a guarantee of continued growth.
• Treat Zcash (ZEC) as a potential, risk-sized hedge against concerns about Bitcoin’s (BTC) future privacy and cryptographic security; no allocation or price target was provided.
• NEAR offers a longer-term team-and-adaptability thesis, but the discussion gives no near-term catalyst, timeframe, or price target.
• Be cautious about chasing Hyperliquid (HYPE) after its reported 33–35x rise; the on-chain derivatives opportunity is long term, but no valuation case supports buying at current prices.

Bitcoin (BTC)

• The guest views Bitcoin as a non-sovereign store of value, but argues that its privacy and long-term security are not guaranteed.
• He pointed to possible risks from future changes in cryptography, the post-quantum transition, and Bitcoin’s declining block rewards, which are scheduled to end by 2140.
• He described Zcash as a potential insurance policy against these risks, rather than as a replacement for Bitcoin.

Takeaways

• The discussion frames Bitcoin’s long-term case around both its monetary role and the resilience of its underlying security. The guest’s view is that greater concern about those risks could justify holding more Zcash relative to Bitcoin, but he did not give a specific allocation.

Zcash (ZEC)

• The guest said his firm owns Zcash and described it as Bitcoin with privacy.
• He sees it as a possible hedge against Bitcoin losing privacy or failing to adapt to advances in cryptography, including a potential post-quantum transition.
• He argued that Zcash’s role is like an insurance policy: the amount to hold would depend on how much risk an investor sees in Bitcoin.
• He also said that a Zcash ETF or institutional ownership would not, by itself, prevent individuals from using the network privately.

Takeaways

• The investment case presented is defensive and conditional: Zcash may be more relevant if an investor is concerned about Bitcoin’s privacy or long-term cryptographic security.
• The guest did not specify an allocation or price target, and the insurance thesis depends on the risks to Bitcoin materializing.

NEAR (NEAR)

• Dragonfly co-led NEAR’s seed round in 2018, when it was called NEAR AI, and the guest said the firm still holds the asset.
• The initial investment case was that the founders were unusually strong engineers who could build a better blockchain. The guest said the founders revised their protocol after receiving critical feedback, which impressed him.
• NEAR’s strategy changed over time. It originally competed as an Ethereum alternative, with sharding and scalability as central ideas. The guest emphasized that strong teams need to adapt when markets change.
• He said the firm continues to hold because it trusts the team and believes the project’s work is unfinished, adding that NEAR could become “a lot bigger” without providing a target.

Takeaways

• The discussion highlights team quality and adaptability as key parts of the investment thesis—not just an early technical promise.
• The guest’s continued holding reflects his firm’s conviction, but the transcript offers no price target or timeline.

Ethena (referred to as “Athena” in the transcript; ticker not stated)

• Dragonfly led Ethena’s seed round after the collapse of Terra, when many investors were skeptical of on-chain stablecoins.
• The guest said Ethena’s model addressed a problem that had held back its predecessor, Lemma Finance: insufficient on-chain perpetual-futures activity. Ethena could use centralized exchanges while reducing counterparty exposure through third-party custody arrangements.
• He described Ethena primarily as a yield product and argued that demand for dollar yield could support growth, particularly if regulated U.S. stablecoins are not allowed to pay yield under the GENIUS Act.
• Dragonfly still holds its investment.

Takeaways

• The thesis depends on continued demand for dollar yield and on Ethena’s ability to manage the market and counterparty risks involved in its strategy.
• The guest did not provide a price target or a specific investment recommendation.

Hyperliquid (HYPE)

• Dragonfly owns HYPE. The guest said the firm tried to invest in Hyperliquid before its token launch, but founder Jeff declined outside capital and chose to self-fund.
• The guest views the founder’s willingness to fund the project himself as a strong signal of conviction.
• He argued that on-chain perpetual trading could grow beyond crypto into a much larger market for derivatives on traditional assets. He compared the opportunity with the global contract-for-difference market, while noting that on-chain platforms could offer broader access and transparency.
• The interviewer noted that HYPE had risen roughly 33–35x; the guest did not give a price target or argue that the token was cheap at its current price.

Takeaways

• The upside case is tied to growth in on-chain derivatives and expansion beyond crypto assets.
• The large prior run-up makes valuation an important consideration; the guest’s long-term market thesis does not establish that the token is attractively priced today.

Venice (token ticker not stated)

• The guest identified Venice as the crypto asset he currently had the most belief in. He described it as a privacy-focused AI inference platform that offers private, unrestricted AI use and has a token.
• He said the business had grown to more than $100 million in revenue run rate, up from about $15 million when he first spoke with the team, with growth of roughly 10% month over month. These figures were the guest’s claims in the interview.
• His core thesis is that people will want privacy and control over their AI conversations, while current legal frameworks may treat conversations with AI services as information shared with a third party.
• He sees Venice as positioned for a broader debate over AI, privacy, and civil liberties.

Takeaways

• Venice combines two themes discussed in the episode: rapid AI growth and demand for privacy. The investment case depends on whether that demand translates into durable customer growth and whether the business can maintain its position as AI regulation and competition evolve.
• The revenue figures are not a price target or a guarantee of future growth.

Variational (platform; no token ticker stated)

• Dragonfly invested in Variational, initially as a smaller seed investor and later as the lead in a subsequent round.
• The guest described its model as request-for-quote (RFQ) trading: users request a price and market makers compete to provide one. He argued that this approach could suit real-world assets better than building a separate on-chain order book for every asset.
• The investment thesis is that RFQ could make a wide range of derivatives more practical to trade on-chain, including assets with less consistent trading volume.
• The guest described the potential market as very large, but did not provide a specific valuation target.

Takeaways

• The opportunity, as presented, is exposure to on-chain trading infrastructure rather than a specific token.
• The thesis depends on the platform attracting liquidity and users across a broad range of markets; the transcript does not establish that this adoption is assured.

Polymarket (company; no ticker stated)

• The guest said his firm invested early at a valuation of $175 million and cited a later round reportedly planned at $21 billion.
• He used the investment to illustrate venture capital’s power-law returns: a small number of winners can drive overall fund performance.
• He cautioned that selling winners simply because they have risen many times can limit the potential returns from successful investments.

Takeaways

• The discussion is a lesson about venture investing and holding periods, not a public-market recommendation. The reported later valuation does not establish what the investment is worth today or whether it is accessible to ordinary investors.

Solana (SOL)

• The guest recalled passing on Solana’s seed round, reportedly priced at $0.04, after focusing heavily on technical claims in the project’s materials.
• He said his analysis identified problems with individual claims but failed to recognize the broader investment opportunity. He characterized it as an example of being technically thorough yet reaching the wrong investment conclusion.

Takeaways

• The anecdote cautions against treating a detailed technical critique as a complete investment analysis. It does not amount to a current endorsement or price view on SOL.

Ethereum (ETH)

• Ethereum was discussed mainly as a comparison with NEAR. The guest said NEAR aimed to build sharding and scalability features ahead of Ethereum’s planned upgrades, which took longer than expected.
• He also argued that institutional products such as ETFs do not automatically undermine permissionless use or crypto’s original values; institutions, in his view, are using the same underlying networks as other users.

Takeaways

• The comparison emphasizes the importance of execution and adaptability in blockchain competition. The transcript provides no specific view on ETH’s valuation or a price target.

Crypto and venture investment themes

• The guest compared today’s AI investment excitement with the 2020–2021 crypto boom: real technological change can coexist with weak projects, high valuations, and investors acting from fear of missing out.
• He said the AI investment cycle could become “crazy” before turning difficult. Specifically, he warned that the scale of data-center construction and related debt would require substantial revenue; a downturn or dip in demand could make the economics hard to support.
• He cited Anthropic’s large compute commitments as an example of the spending involved, but gave no investment recommendation on Anthropic.
• He described a core crypto risk as the loss of the sector’s values and willingness to experiment. He did not consider institutional participation alone a reason to be bearish.
• He said investors are more likely to make mistakes when buying assets they do not understand or investing out of fear of missing out.

Takeaways

• Separate genuine technological progress from speculative activity: the guest’s view is that both can occur in the same cycle.
• For private or early-stage investments, the discussion favors understanding the business and its risks over following other investors into a crowded deal.
• The specific AI risks cited were heavy infrastructure spending, debt, and the possibility of weaker demand; no timing for a downturn was given.