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Bell Curve

The Next Bull Market | Roundup

Friday, 17 April 2026 · 4 min read · Listen to the episode ↗

The hosts open by placing the current bear market at roughly six months deep, with Dave citing historical precedent of approximately 12-month bear cycles and flagging 2027 as the stronger year for crypto. The panel argues Bitcoin has become a fully institutional asset that will no longer generate the wealth effect feeding altcoin rotations, making thesis-driven investing essential.

The hosts opened by stating the bull market has not started yet, placing the current bear market at approximately six months deep. Dave said bear markets have historically lasted around 12 months before activity recovers, with conditions looking a little better by end of 2025 but not out of the woods, and 2027 shaping up to be quite a good year for crypto. Current bear market sentiment was compared to 2018, when conditions were bearish enough that many participants left the space entirely.

Miles argued each crypto cycle has gotten harder and more narrow, requiring thesis-driven investing rather than throwing darts at the board. The four-year cycle is playing out as expected, including Bitcoin leading in spot and rotation into lower-quality assets, with the historical pattern being that lower-quality assets pump hardest until everything goes sideways four years later. The expected rotation from Bitcoin to ETH to Solana to altcoins did not fully materialize last cycle, and this cycle the rotation may stop at Bitcoin itself without flowing down to altcoins. Bitcoin has become a fully institutional asset class and will behave like institutional assets rather than offering 20x return potential, making the wealth effect that historically fed the rest of crypto less pronounced.

DeFi and RWA are expected to be the dominant themes of the next cycle rather than AI narratives, which speakers described as still too ahead of their time. Protocols named as fundamental compounders of the next cycle include Aave, Morpho, Sky, Maple, and Kamino. Hyper Liquid was specifically cited as having a good chance of leading the cycle, with the argument that trading protocols generate significantly more revenue than lending protocols and therefore deserve a higher multiple. Morpho was described as needing to position itself as more defensible than mid-to-large cap SaaS stocks, with liquidity and regulatory moats cited as the basis. A caveat was raised that blue chip DeFi protocols face a higher hurdle for institutional bids because more data is available and valuations get compared to public fintech comps rather than being hand-wavy.

AI agents operating on crypto rails doing autonomous investing were predicted as a potential next capital formation mechanism, analogous to ICOs, NFTs, DeFi, and layer ones in prior cycles. The critical unsolved bottleneck is trust, with security risks making it unclear how to trust agents with money given ongoing smart contract hacks, though speakers suggested the trust and security problem could be solved technologically within the next six to twelve months. Dave predicted AI will become a back-end technology in crypto rather than a front-end narrative by the next bull cycle. AI agents using crypto are also identified as a potential cultural normalization pathway for onboarding new users, though one speaker believes adoption will take two to three times longer than another expects.

Three categories were predicted to drive distribution flows in the next cycle: ETFs via financial advisors, institutional buyers of blue chips, and narrow narrative-driven speculative plays. Boomer money entering via ETFs and RIAs is predicted to go through the same discovery process earlier participants did, eventually landing on DeFi. Solana is expected to catch a bigger bid from ETF flows than in previous cycles. Retail never came back in the last couple of cycles despite meme coin activity, and meme coins are expected to die out the way NFTs did after their cycle peak. Retail is predicted to return next cycle but will concentrate on recognizable brands and assets, with Asian retail flagged as a significant variable given its outsized role in prior cycles. Approximately one trillion dollars or more of wealth liquidity is expected to flow from companies including Anthropic, OpenAI, SpaceX, and xAI, with some portion potentially entering crypto markets.

Dave predicted prediction markets are massively underestimated in total addressable market and have outperformed almost any other crypto project during the bear market, with Polymarket and Kalshi identified as the two category winners. Social crypto applications were identified as a sleeper sector likely to emerge in the next 12 months. Dave also flagged network states, on-chain IDs, privacy, and quantum computing as continuing themes, noting Zcash has been pumping even during the bear cycle as a signal of growing value placed on monetary privacy. The Genius Act has passed but cannot be fully implemented until November of this year, meaning no Genius-compliant stablecoin can currently be issued, and a trillion dollars of new stablecoins from implementation is predicted to primarily benefit DeFi protocols. Speakers cautioned that regulation has historically been a fade in terms of driving near-term price movement.

There is a disagreement among speakers about what retail actually wants from crypto. One speaker argues retail comes for leapfrog returns far beyond normal financial outcomes, while another contends the largest pools of capital will consolidate around reliable 10 to 15 percent equity returns or guaranteed 5 percent returns. One speaker argued that framing crypto purely as a venue for 1000x speculation is damaging to the space and causes it to lose credibility over time. Crypto culture was described as shifting from cypherpunk counterculture toward something more mainstream, with one speaker observing that many early participants were performing ideological beliefs rather than genuinely holding them, primarily motivated by financial returns while presenting cypherpunk values as their rationale.

This summary was generated from the episode transcript and can contain mistakes.