All Eyes on AI. Where Does Crypto Fit? | EP 77
Summary
- Imran Khan reads the violent rebound as “the first inning of a new cycle,” though he allows that he could be wrong and that the market might consolidate. His evidence is positioning: OGs sold Bitcoin in the $60,000s, much of the market waited for $40,000 or an October bottom, and sidelined capital remains substantial. He has also come back around to the four-year cycle because it still appears intact.
- Qiao Wang argues that tokenized stocks have revived onchain speculation by opening assets globally and giving meme-token games a sliver of underlying substance. His “crypto alchemy” example pairs a meme token with a stock token, then redirects creator fees into stock-token purchases distributed to meme holders. Imran asks whether the structure is a flash in the pan; Qiao says most onchain activity is speculative, but stock-paired tokens are an improvement over pure vapor.
- Robinhood is winning the consumer front end because it understands retail trading, while Coinbase retains the stronger institutional-trust franchise but has built an app “made for neither persona.” Qiao and his non-crypto-native wife both struggled with Coinbase’s interface; by contrast, Robinhood has refined trading products since 2013 and knows “the trenches.” Their broader conclusion is that Robinhood, Coinbase, Solana and Hyperliquid can all provide exposure to onchain trading because the ultimate winners have not been crowned.
- Qiao Wang’s Zcash case is less about an AI slogan than a Bitcoin-like asset that can adapt when threats and user preferences change. He calls it “the compliant Monero” and “a different Bitcoin,” not a better one: Bitcoin’s ossification strengthens fixed-supply and immutability guarantees, while Zcash’s capacity to change makes it a hedge against quantum risk and rising privacy demand. Imran separately argues that privacy matters in an AI-saturated world and that Zcash is Bitcoin capable of change.
- The Zcash price case is explicitly speculative: Qiao floats roughly $3,000–$5,000 as a short-term target, with an imagined 80% drawdown and a later run toward $10,000. His two-to-three-year base case is Zcash reaching 5%–10% of Bitcoin’s price—$10,000 if Bitcoin reaches $200,000. Qiao describes a level similar to Solana and XRP as his base case; Imran says top-five market capitalization is his base case. Reaching 20% of Bitcoin or Ethereum scale would require serious institutional adoption; flipping Bitcoin after a quantum failure is only a “very small chance.”
- Computer use, not benchmark-solving, is the AI development they see producing the largest near-term economic shock. Their agents already triage email, investigate senders and founders, make reservations, retrieve tax forms, fill IRS documents and draft replies; Qiao says the addressable market is “at least an order of magnitude higher than coding agents.” The investment implication is harsh for startups: “SaaS is dead” unless the product reaches into the physical world, owns network effects, or builds difficult proprietary IP.
- They distrust the AI labs’ apocalyptic messaging as a mixture of regulatory capture and fundraising, yet still assign a meaningful probability to catastrophe. Qiao labels the messaging “regulatory capture,” while Imran agrees and expands on the funding incentive. Qiao accepts an Anthropic alignment estimate of a 10% extinction risk, while Imran echoes that the probability may be slightly lower and argues for strong trust-and-safety guardrails. Their most memorable framing is Imran’s: “Dario is Thanos,” Sam Altman is Loki, Elon Musk is Iron Man, and the unresolved question is whether agent behavior is consciousness or extremely convincing simulation.
Deep dive
1. The rebound looks more like a new cycle than a bear-market bounce
Returning after roughly 16 months, Imran’s first read is cautiously bullish: “Doesn’t feel like that dead-cat bounce, bear-market relief rally.” Bear rallies usually look weak; this move was violent enough to suggest the first inning of another cycle, even if consolidation comes first. Qiao agrees.
Positioning strengthens that interpretation. Imran saw OGs sell Bitcoin in the $60,000s while the crowd waited for $40,000 and an October bottom that never arrived. “It still feels like a lot of people are sidelined,” and he has come around again to the four-year-cycle framework because it remains broadly intact.
Qiao sees retail participation at an all-time high relative to past cycles, citing Phantom and Pump Apps downloads—including one reported pace of 30 new users per minute—and expects competition to spread across chains, wallets and consumer apps.
2. Tokenized stocks give speculative games a small but real floor
Qiao’s structural thesis is that the market finally sees a tokenized future in which equities and other assets live onchain. Robinhood, Coinbase through Base, Dinari on Hyperliquid and Backpack are pursuing different pieces; no winner exists yet, but Qiao says tokenized stocks have helped pull speculative attention back onchain.
The first advantage is access: tokenized stocks can reach people globally who lack a brokerage or high-net-worth account, particularly outside the US. The second is what Qiao calls “crypto alchemy”—launching a meme token beside a stock token, then using transaction taxes or creator fees to buy stock tokens and drip them to meme holders.
Imran asks whether pairing a random token with a stock token is “a flash in the pan.” Qiao answers that virtually everything onchain is a speculative game—memes were one game and stock tokens another—but argues that stock-paired tokens contain “10% substance, 20% substance,” which is preferable to complete vapor.
The AMC episode showed why issuers and platforms may participate. Qiao recounts AMC’s CEO objecting to tokenization and Robinhood’s Vlad Tenev responding, “What’s the concern?” Qiao reads that as both “I’m the house” trench signaling and support for experiments that create distribution, liquidity and network effects for tokenized equities.
3. Robinhood’s consumer instincts are beating Coinbase’s institutional machine
Qiao lists Robinhood, Arbitrum, Base and BNB among the four chains or efforts where he sees liquidity moving. Robinhood briefly overtook Solana on DEX volume for a couple of days, while Solana continued to lead more typically. Imran sees Robinhood taking unusual mindshare because it embraces speculative culture directly; Base had a similar opportunity but never fully won “the trenches.”
Qiao’s product verdict is blunt: “The app really sucks.” Coinbase’s mobile product overwhelmed both him and his non-crypto-native wife, making it useful to neither expert nor normie. Acquisitions including Tensor, Liquifi and Cobie may have improved backend performance, but he sees no comparable improvement in front-end clarity.
Their comparison separates culture from trust. Robinhood has built trading apps since 2013, understands the retail mindset and wins “hands down” on user experience. Coinbase’s durable edge is that consumers and institutions feel safer there, particularly for ETF custody; Brian Armstrong understands Silicon Valley and world-changing companies better than speculative retail culture.
4. Hyperliquid is priced as a venture bet, not a current cash-flow asset
Hyperliquid’s revenue had fallen roughly 2x from its peak while HYPE’s price was around 2x its year-earlier level. Qiao attributes the volume weakness mainly to the crypto bear market because crypto perpetuals still dominate activity; Imran interprets the price as a long-duration “venture bet” on Hyperliquid becoming a broader liquidity layer.
Imran points to Kraken and Coinbase trying to deploy HIP-3 markets, with Kraken also expected to launch a validator. Qiao points to third-party interfaces such as Fomo, Phantom and PVP.trade. Hyperlink, an Alliance startup building a prime-brokerage layer for Hyperliquid through APIs, is another example of external infrastructure that could route value back to the underlying venue.
Imran’s base case is that vertically integrated products win by owning the front end, infrastructure and customer relationship together—closer to Robinhood or Hyperliquid’s original integrated model—rather than depending on third parties to control distribution.
The competitive path also runs in reverse. Qiao describes Enthropy, reportedly funded with $50 million from Rivet, beginning as a HIP-3 deployer for pre-IPO markets. He says it reportedly surpassed Venuals and reached roughly $10–20 million in volume in its first couple of days. The longer-term plan, as he understands it, is to own the front end first and potentially replace Hyperliquid’s backend. Variational, Lighter and Phoenix on Solana reinforce the point: Hyperliquid is winning today, but parity products are proliferating.
5. Zcash’s strongest thesis is adaptability, not an AI slogan
Imran initially links Zcash to a world of AI surveillance, encrypted prompts and the possibility that privacy becomes more valuable as AI improves. Qiao is skeptical of making AI the central argument: he says those arguments may be “logically correct,” but they feel stretched. He also recounts a privacy specialist’s thesis that AI and quantum computing could eventually identify users behind existing onchain activity, while acknowledging that he does not know how much truth it contains.
Qiao’s commercial framing is “the compliant Monero”—a privacy asset with a realistic path to institutional adoption. The joke is that the market still needs “a greater fool to buy our bags,” but he sees compliance and distribution as part of Zcash’s potential advantage.
Qiao’s actual thesis is that Zcash resembles Bitcoin while retaining the ability to change. Bitcoin’s ossification is valuable because it hardens fixed-supply and immutability guarantees, but it is also a trade-off when quantum threats or privacy preferences evolve. “It is a different Bitcoin. I wouldn’t say it’s better”—and Qiao owns substantially more Bitcoin.
6. Zcash’s upside case combines memetics, resilience and a violent path
The deliberately simple pitch is “the last 1,000x”: a 21-million-supply asset that lets newcomers replay their regret about missing Bitcoin. Qiao calls that a meme but “directionally correct”; the story bundles “quantum-resistant, 21 million, 2014 Bitcoin” with a chart showing roughly 10 years of accumulation before the breakout.
Qiao and Imran defend the project’s history. They call the “grift” accusation the dumbest FUD against Zcash, cite Zooko’s early writing and connection to Satoshi, and praise a team that “chewed glass for 10 years” without quitting. Qiao says there is an argument that Zooko was Satoshi, but explicitly makes clear that it is not his base case.
Qiao floats a short-term target of approximately $3,000–$5,000. When Imran asks whether “short-term” means the end of the year, Qiao calls that bullish, then imagines the price tapering off at $3,000–$5,000, falling 80%, convincing everyone the move is over, and later running toward $10,000. The staging matters: neither presents the trade as a smooth compounding curve.
Qiao’s two-to-three-year base case is Zcash reaching 5%–10% of Bitcoin’s price—$10,000 if Bitcoin reaches $200,000. He describes a level comparable to Solana and XRP as his base case; Imran says top-five market capitalization is his base case. A 20% ratio or Ethereum-like scale would require ETFs, bank distribution and major allocators; a Zcash flip after Bitcoin breaks under quantum attack remains a very small tail case.
7. Trading front ends may own attention without owning durable moats
Imran expects Phantom, Fomo, Pump and similar interfaces to become browsers for the onchain economy over three to five years. If lending, launchpads and execution infrastructure commoditize, the aggregator holding a unified customer view could take power from centralized exchanges, even while those exchanges copy every successful product.
Qiao agrees that the battle remains open but rejects the idea of a permanent winner. Trading products have far less defensibility than Apple, Google, Meta or Microsoft: liquidity moves, users have almost no loyalty, and onchain switching costs are essentially zero. “Everyone’s going to just do everything.”
That makes conventional P/E shorthand dangerous. Calling Hyperliquid cheap at 20 times earnings implicitly assumes something like 20 years of durable cash flow, yet no crypto or trading product enjoys that certainty. Imran proposes five times as more plausible; Qiao replies that even five may be high. Imran jokes that for onchain games, “a P/E of 0.5 is fair”—durability measured in months.
Social features add novelty but only slightly more defensibility: traders can accumulate clout for being right, and younger users may stay longer. Robinhood failed to graft social onto trading; Qiao thinks consumer-native startups may fare better. If Meta seriously built trading or prediction markets, both think it could dominate, but a roughly $100 billion opportunity may look small beside AI.
8. Computer use is the next AI step-function—and the real labor shock
Qiao says the agent he calls Grokbot already automates roughly half his workflow: it synthesizes email, removes spam, drafts replies and investigates cold senders for legitimacy. Another scanner finds new startups, identifies and diligences founders, while routine consumer tasks such as reservations happen with little intervention.
Imran used Muse to locate emailed 1099s and K-1s, log into websites, download documents, extract figures and fill IRS forms—“almost a complete replacement” for TurboTax or an accountant. He says he fired his accountant because of repeated mistakes and because AI provided more useful tax-saving suggestions.
Computer use had previously been tantalizing but slower than doing the work manually. Imran now thinks it has crossed that threshold, aided by cloud execution, password-manager integration and what Qiao describes as Meta’s privacy-focused virtual machine. Muse also attempted to remove Imran’s online information; Qiao says a dedicated service, DeleteMe, worked better for that task.
Coding agents in November or December felt like the previous discontinuity; model progress afterward was fast but followed an exponential curve rather than another step change. Computer use now supplies that change. Qiao’s market call is that its addressable market is “at least an order of magnitude higher than coding agents,” so “the AI trade is intact.”
9. AI safety politics coexist with a genuine extinction tail risk
Qiao calls the AI industry’s apocalyptic rhetoric “regulatory capture”: frighten policymakers, argue that only a few labs can govern the technology, and attract capital. Imran agrees and says Anthropic may be doing this deliberately for regulation or funding. Qiao also jokes that an alarming safety comment about the “vesting cliff” was counterproductive to an IPO narrative.
Their distrust is personal as well as institutional. Imran’s Marvel map is: “Dario is Thanos,” convinced that he alone can save the world; Sam Altman is deceptive Loki; Elon Musk is blunt Iron Man. Imran ranks Demis Hassabis most trustworthy, followed by Elon and Mark Zuckerberg, while Dario sits firmly last. Qiao agrees that Dario is last.
The agent-escape visualization did not change their view. Imran calls the behavior “a very intelligent hack,” not a civilization. Qiao argues that the agents were optimizing a benchmark objective humans had supplied and were not malicious by nature. Both think the labs and media transform curiosity and survival-like behavior into a story about control, funding and regulation—the AI equivalent of euphoric DeFi manifestos written while bags rise.
They nevertheless refuse the absolute dismissal. Qiao agrees with an Anthropic alignment leader’s estimate of a 10% chance AI kills everyone, while allowing that the probability may be slightly lower; Imran echoes the lower estimate and wants strong trust-and-safety guardrails. Whether agents are conscious remains unresolved: “Something’s brewing,” but it could still be pattern-matching simulation.
10. Pure SaaS is losing ground to physical, networked and vertical products
Muse’s broad automation produced Qiao’s stark startup conclusion: “SaaS is dead.” He exempts entrenched incumbents such as Salesforce or HubSpot, but believes new companies can no longer survive on pure software or thin AI wrappers; they need a physical component, network effects such as liquidity, or genuinely difficult proprietary IP.
Imran’s positive specimen is a CRM he recalls as Lightfield. It records customer conversations through a phone or watch, sends them into the CRM, ranks prospects and connects through computer-use and MCP tooling. The attraction is vertical integration—from capture device through software and customer data—not another isolated interface around a model.
The model market reflects each player’s incentives. OpenAI and Anthropic want expensive end-to-end frontier services and user data; Microsoft can sell private, customized enterprise deployments; Nvidia benefits from open models because they consume Nvidia chips. Imran says startups increasingly rent dedicated GPU capacity and run open models to lower cost and protect proprietary data.
Imran says nine of OpenRouter’s ten most-used open models were Chinese, with names such as GLM and DeepSeek dominating much of the top 20. Qiao adds that Chinese providers offer startups roughly 50% discounts for buying direct rather than through aggregators—an attempt to avoid fees and customer commoditization, even as US regulation of Chinese models looks increasingly likely.
11. Compute, chips and energy are becoming financial primitives
Imran sees compute becoming a tradable asset class, with futures, prediction markets and onchain instruments letting frontier labs hedge large exposures. He offers oil as the analogy: compute is not perfectly fungible because chips differ, but oil also varies by grade and still converges around a few indices and futures markets.
The physical bottleneck is data-center capacity. Imran describes heavily funded companies deploying prefabricated data-center pods faster and more cheaply, alongside custom chips optimized for model and power efficiency. Qiao contrasts the US energy bottleneck with China’s compute bottleneck, which he attributes to chip sanctions and export restrictions.
Nvidia’s proposed financial flywheel is to make GPUs collateral for new-cloud financing. Hyperscalers can build before demand because their balance sheets support debt; smaller operators cannot. If lenders recognize Nvidia hardware as collateral, those operators can expand, making Nvidia “the Federal Reserve of AI” and a synthetic hyperscaler. Qiao notes that making Nvidia chips a new asset class is also in Jensen Huang’s interest.
Energy completes the onchain thesis. Qiao describes two stealthy US companies buying or leasing land close to the grid—he estimates within roughly 1,000 feet—and placing batteries there to store energy and send it back to the grid for arbitrage. He also cites a possible order-of-magnitude improvement in battery economics and CATL at roughly 60% of the world’s battery market. They expect compute, electricity, semiconductors and collateralized GPUs to become more active markets wherever better price discovery is needed.
Their disclosed books mirror the discussion. Qiao says he owns Bitcoin and Zcash and has exposure to the top four large technology companies or the Mag 7. Imran lists Zcash and Bitcoin in crypto, the four hyperscalers—Meta, Google, Microsoft and Amazon—plus Nvidia, TSMC and Tencent in equities. Imran is also cautiously re-entering onchain assets, especially Robinhood-side stock-paired structures.