Easing Monetary Policy Is the Real Exit: When Crypto VCs Stake Their Claims in the Agent Network Effect
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Easing Monetary Policy Is the Real Exit: When Crypto VCs Stake Their Claims in the Agent Network Effect
In the high-value domain of finance, blockchain serves as an open finance testing ground, while stablecoins act as credentials for agent-optimized market processes—a matter not of scale or resource investment, but of mechanism design and expansion.
Navigating Web3 tides with focused insightsAuthor: @zuoyeweb3
AI Is an Opportunity for Nerds; Agents Are an Opportunity for Money
Venture capital—mega-funds like a16z—have long told us their story is about cycles and exits. But from a Solo GP’s perspective, it feels more like harmonic resonance between signals and structure: you must uncover the unspoken, underlying patterns they omit.
In 2021, a16z returned $12.5B in gains to its LPs, achieving a DPI higher than the sum of the prior decade. Yet 2021 also marked the beginning of disaster for the U.S. VC industry—stripping away the tangible DPI, what remained was merely paper gains.
In other words, 2021 was the golden era of exits: LPs actually received real cash. But if those LPs reinvested, they’d face ongoing pain that persists to this day.
Caption: Liquidity injection *is* the real exit.
Image source: @jasonlk @PeterJ_Walker
All this tells a contradictory narrative—and crypto market volatility has tracked it closely. In 2022, the metaverse craze supercharged Web3, even artificially extending the bull market. It wasn’t until early 2025 that Binance’s “girlfriend token” farce finally put a period on the VC-token era.
Today, most VCs have fallen silent. Economies of scale have been dragged into capital-intensive models centered on compute and data—delaying ROI indefinitely. Network effects remain elusive on-chain, pushing firms toward institutionalization and SaaS channel fees just to survive.
Yet looking across VC history, every interest-rate cycle—each round of monetary easing or tightening—has nurtured a distinct VC model. We repeatedly reinvent risk valuation logic. And crypto’s relative freedom allows astute participants to unearth the most profitable signal mechanisms.
When VC Stops Taking Risks
“Every passion originates from external objects impacting the sense organs, setting animal spirits in motion through the nerves.”
Recall: In March and April 2021, Roblox and @coinbase opted for Direct Listings—unlike conventional IPOs, direct listings sell only existing shares, require no underwriters, and impose no lock-up periods.
Interestingly, both were led by a16z. Beneath dazzling DPI figures, a16z raised $2.2B for its third crypto fund in June 2021—and followed up with a $9B fund in January 2022.
So what was the cost?
Coinbase’s stock price fell 90% from its peak by 2023. It’s clear: a16z’s role in U.S. equities mirrors that of crypto VCs. Yet the puzzle remains: a16z still raised $7.2B in 2024—and $15.1B in 2026.
Even in May 2026, its fifth crypto fund exceeded $2.2B—bringing its cumulative crypto fund total near $10B.
The market presents a binary choice: become an a16z LP and wait for liquidity injection to deliver astonishing DPI—or become the cost behind that DPI.
But a new problem arises: a16z isn’t particularly sharp at detecting market signals. Put differently, every VC “king” of a given cycle eventually suffers from the “curse of scale”: excessive size drains motivation to discover ultra-early paradigms—especially revolutionary, not incremental, mechanisms.
- Arthur Rock, the father of modern venture capital, launched at the peak: Fairchild and Intel inaugurated Silicon Valley’s VC model;
- KP and Sequoia formalized institutionalized VC—but led alternately during PC and mobile internet eras;
- YC transformed VC into a probabilistic, volume-driven mechanism—mass-producing sub-unicorns beneath the power law;
- Masayoshi Son and SoftBank turned VC into a near-Ponzi game of colossal scale via Alibaba and China-listed stocks.
Thus, as old giants bask in past glory, ambitious newcomers disrupt via institutional innovation—proving unique insight and securing cheap capital to launch their own era of adventure.
Caption: Shifting VC cycles
Image source: @zuoyeweb3
Even reputation itself can be monetized: Paradigm co-founder Matt Huang backed ByteDance. Though ByteDance couldn’t go public, Paradigm pivoted to “crypto ByteDance”—and now, latest reports indicate it’s shifting further into AI and robotics.
Let’s revise our answer: If you can’t become an a16z LP—and don’t wish to be trampled as its cost—then you must identify nascent, under-amplified signals and use novel mechanisms to outcompete incumbents.
Cracks are already visible: In 2021, a16z wasn’t “allowed” to participate in Anthropic’s financing. Instead, early bets came from individual investors—Skype co-founder Jaan Tallinn and former Google CEO Eric Schmidt led the Series A; FTX’s SBF joined in 2022, giving us another enduring vision of Crypto × AI.
Caption: The positioning race has just begun
Image source: @zuoyeweb3
a16z doesn’t need to take risks; SBF deployed retail money to “effectively arbitrage A\.” If we seek the most rational starting point for a Solo GP, Claude’s venture history stands out as exemplary.
Unlike personal angels, a Solo GP runs the entire VC process solely on self-directed research. The Agent era is easy to grasp in theory—but humans pioneered it first. Unlike YC’s broad-spectrum approach, Solo GPs still demand deep engagement per investment—every single check matters for DPI.
a16z has become a market indicator itself. As new tech trends emerge, newer players scramble to act just a hair faster than a16z. Beyond large AI models, they’re now fixated on Agents.
Here lies a dangerous leap: economies of scale fail to materialize in large AI models. Each additional human user raises server costs—unlike software, costs cannot be amortized. Thus, network effects haven’t emerged in Agents as expected; inter-Agent invocation remains aspirational.
Inhuman Network Effects
“In 1784, Watt improved the rotary steam engine; in 1824, Carnot, a Frenchman, completed its theoretical foundation.”
Everything about AI is a black box. Scaling Laws were observed by “Adi Wang” at Baidu; the math required for Transformers barely exceeds graduate-level rigor—yet why they surpass graduate-level math remains unknown.
AI is an opportunity for nerds: simply fund the most cutting-edge researchers and wait for brute-force miracles. Talent acquisition—the Silicon Valley norm—is the ultimate proof: Researcher > Data > Model.
But large AI models struggle to recoup costs. Reiterating: scale economics work *against* them—even shifting from training to inference, or from dialogue to task execution, won’t reverse this trend.
The sole path forward for LLMs is becoming traffic hubs like AWS or Cloudflare. If production-side costs are unavoidably high, consumption-side growth must be infinite.
Agents are an opportunity for money: Agents must become consumers themselves. Infinite agents × infinite consumption—that’s why inter-Agent invocation has become mainstream discourse.
Yet, to a large extent, Agents and Bots remain indistinguishable—no one can clearly define what makes an Agent, nor distinguish it from pre-existing Bots.
Caption: Bots ≠ Agents
Image source: @Cloudflare
If forced to define Agents, reinforcement learning’s “evaluator agent” serves as the origin point of this tech wave. DeepMind’s vision hinges on agents autonomously evaluating training success—a critical step toward next-generation intelligence.
This differs sharply from Claude’s coding-centric framing. An Agent viewed through programming is merely a human programmer’s role mapped onto code. When we speak of “Agentic Coding,” we’ve strayed far from AlphaZero’s conception of Agents.
Caption: High-value Agent use cases
Image source: @zuoyeweb3
Only from this vantage does “Agent-as-replacement” hold water—and Claude’s disruption of SaaS becomes plausible: it’s simply the next iteration of human outsourcing:
- Advance into higher-value roles: after programmers, accountants and analysts;
- Move toward fewer full-time employees: after outsourcing, pay per Agent invocation.
Yet a persistent issue remains: Agents show no human-like social relationships. Real business interactions don’t become smoother because Agents are applied—humans still prefer dealing with other humans.
We *have* created more Agent scenarios—internal coordination works well (e.g., big tech layoffs + GPU swaps).
Caption: High-value scenarios need no humans
Image source: @trueupio
But externally—collaboration *with others*—caution is warranted: it remains unproven. In May 2026, U.S. employment surged strongly—nonfarm payrolls rose by 172,000, concentrated in leisure/hospitality and healthcare (blue-collar sectors), while finance shed 22,000 jobs.
Human anxiety over Agents is real—but severely overestimated.
Of course, like asking whether the Sahara needs shoes, this could signal continued investment in boosting model intelligence, expanding Agent capabilities, and funding Robotics.
In short: Agent economics holds only theoretically. Infinite consumer-side growth hasn’t materialized. So how do we make inter-Agent invocation happen—and generate network effects?
Crypto Positions Itself for the Agent Era
“Evolution doesn’t always increase complexity; evolution isn’t always upward-trending.”
Let’s summarize knowns—to warn against unknown dangers ahead.
Venture capital no longer reliably detects technological signals—it’s become a game for the few brave;
Agents are being mass-produced in hopes of reducing LLM production costs—but natural invocation relationships between them don’t spontaneously arise.
These two seemingly contradictory statements contain a subtle coordination: find the signal mechanism that *stimulates* Agent invocation.
Simply issuing Agent assets—or “Agent-ifying” DeFi protocols—is meaningless. On-chain, there are already few humans and many bots; layering smart contract calls only increases technical risk. This path is not smooth.
In practice, humanity’s first principles won’t be replaced by Agents—because role mapping depends on business relationships. Domestic IT procurement doesn’t buy 4090s; the U.S. and China won’t bail each other out. Technology’s boundaries are narrower than we imagine.
Caption: Agent economy positioning race
Image source: @zuoyeweb3
Exa targets Agents’ demand for real-time + high-quality data—clean once, invoke many times. This is genuine scale economics—but unlikely to trigger invocation between Claude and Codex.
Catena serves B2B Agent compliance needs in finance—even seeking OCC licensing for regulatory legitimacy. This is a specialized network effect—but won’t lower per-unit usage costs.
Stablecoins and related payment protocols aim to capture C2C entry points + settlement exits: lightweight protocols reduce usage costs; micropayments reduce collaboration friction.
But it’s still insufficient. To achieve daily A2A (Agent-to-Agent) communication, humans must willingly “surrender their souls”—akin to TrueNorth’s three-step roadmap:
- Get humans using Agents to assist transactions;
- Train Agents to learn from human transaction participation;
- Let Agents autonomously execute on-chain transactions.
Compared to Claude’s integration with IBKR—constrained by policy and legal hurdles—TrueNorth’s live trading on Hyperliquid faces no such barriers.
Yet getting humans to willingly accept Agent guidance remains distant—far more distant than VC imaginations.
Caption: Payments + Trading > Yield
Image source: @zuoyeweb3
In attempts to fuse Agents with finance, “invest primarily in payments, secondarily in trading” dominates the structural consensus.
Payments are certain: PayPal and Stripe’s market share will be stablecoin-ized—and stablecoins themselves will be Agent-ized.
Trading holds vast potential: from Simons to Jane Street, and from Liang Sheng’en’s perpetually unpaid Fantasia, VC imagination runs wild.
Yet none of this matches our imagined reality of Agents fully taking over payments and trading.
Quant strategies build “compute hegemony”—still a speed advantage *over humans*. Trading builds “channel advantages”—still fee discounts *relative to banks*.
A chasm thus emerges: VCs aim to catalyze human willingness to be actively replaced by Agents. a16z is powerless here—throwing money didn’t save Clubhouse or Towns Protocol. For the far more complex financial Agent scenario, it can only lie flat.
If we borrow DeFi’s success playbook: let Agents touch money first—low-frequency, small-value validation—before scaling to high-frequency, large-value daily use.
Imagine roads filled with Tesla Robotaxis running Full Self-Driving: safety might actually *increase* versus mixed human/AI driving. But to make this happen, humans must serve as test subjects:
- A minority uses AI-assisted driving—establishing technological parity with human drivers;
- Reduce accident rates among this minority—and establish compensation frameworks.
In other words: building mechanisms for Agents to *handle money* converts users more easily than mechanisms for Agents to *earn money*. Only after Agents accumulate sufficient real-money experience will humans stop thinking—and click “confirm” reflexively.
Only when Agents actively participate in markets can efficiency and safety improve. View it this way: the Agent’s pursuit of yield *is* the market’s efficiency-enhancement process—bootstrapping progressively, writing C++ with C++, optimizing Agents with Agents.
Trading is the endpoint for Agents—but before that, you must run a long elliptical track.
In this high-value domain of finance, blockchain is the open finance testbed; stablecoins are the vouchers documenting how Agents optimize markets. This has nothing to do with scale or resource input—it’s about mechanism design and expansion.
Conclusion
“Life is all about cycles—without cycles, there would be no era-specific windfalls; each generation inevitably supersedes the last.”
VC is growing smaller and more personal: Solo GPs, OPCs—no dominant trend yet of Solo GPs investing in OPCs. Amid the unpredictable surge of tech waves, we don’t yet know which paradigm will prevail.
“Software is eating the world”—after the dot-com bubble burst in the early 2000s, it delivered over two decades of sustained tailwinds. Now we enter the new era: “Agents are eating software.”
Agents are development tools—and symbols of productivity evolution—but no Agent-built software has yet become a mass-market application. That’s factual. After the anticipated IPOs of @SpaceX, OpenAI, and @AnthropicAI, the foundational LLM positioning race has concluded.
If this heralds a new era of sustained tailwinds, then newly fundraising crypto VCs—including @dragonfly_xyz, ParaFi, Haun, @paradigm, and a16z—will either continue scaling up—or new funds like 5cc, targeting specific prediction-market verticals, will rise to prominence amid the next deployment frenzy.
Even the entire DeFi industry will undergo paradigm renewal. Across the past two Kondratiev cycles, financial-system innovation has continuously evolved—and this time, Agents and stablecoins will jointly ignite a dual revolution.
Crypto is small; the world is vast. Let’s witness it together!
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