Hook
In June 2025, Lazard’s private equity secondary market survey dropped a quiet bomb. Of the institutional investors surveyed, 91% declared that “proprietary data + network effects” are the only moat in the AI era. Only 4% said they haven’t changed their investment approach. That’s not a debate—it’s a consensus. And in markets, consensus is a leading indicator of where capital flows next.
As a digital asset fund manager in Nairobi, I’ve seen this pattern before. In 2020, when DeFi Summer peaked, the consensus was that liquidity mining was the new moat. By 2022, that consensus was dead. Today, the AI consensus is reshaping software valuations—and crypto software is not immune. The ledger remembers what the algorithm forgets, but the algorithm is now rewriting the ledger.
Context
Lazard’s survey, conducted before August 2025, targeted PE secondary market investors—those who buy and sell stakes in private software companies. The finding: 91% see “proprietary data + network effects” as the core moat against AI disruption. Another 5% are “wait-and-see,” and only 4% unchanged. The report implies that investors are shifting capital away from traditional software into other opportunities, citing AI threat.
This is a macro signal. The same forces apply to crypto software: protocols, dApps, and infrastructure projects. The value anchor is moving from “code” to “data + network.” In crypto, code is open source, but data—like order flow, user behavior, and compliance logs—can be proprietary. Network effects are the heartbeat of every DEX, L2, and social platform. So how does this consensus translate to our asset class?
Core: The AI Revaluation of Crypto Software
I’ve been in this industry since 2017, auditing Gnosis Safe’s multisig contracts. Back then, the moat was code quality. Today, code is commoditized. The real moat is the data that flows through the code and the network that holds it together.
Let me break this down using the Lazard framework applied to crypto.
1. The “Code Value” to “Data/Network Value” Shift
Traditional software valued functionality. Crypto software valued trustlessness. But AI is making pure functionality obsolete. If a LLM can generate a smart contract, what’s the value of a developer? 91% of investors now say the moat is data and network effects. In crypto, that means:
- Proprietary data: Order books, MEV flows, identity verification logs, compliance data. These are not on-chain; they are off-chain databases controlled by protocols. The most valuable data is not shared—it’s hoarded.
- Network effects: User base, liquidity depth, composability. A DEX with 10x the liquidity of a competitor has a moat that AI can’t replicate overnight.
Based on my experience modeling DeFi liquidity stress in 2020, I saw that smallholder farmers using stablecoins for remittances were vulnerable to liquidity gaps. The moat wasn’t the code—it was the network of users and the data of their transaction patterns. The same principle applies now at scale.
2. The Valuation Vacuum
The survey shows that only 4% of investors haven’t changed their method. That means 96% are actively trying to price AI risk. But in crypto, there is no standardized framework. Existing multiples (TVL, fees, active users) don’t capture AI exposure. We are in a valuation vacuum: old metrics are dying, new ones aren’t born. This is where alpha lives.
Consider a protocol like Uniswap. Its code is open source, but its network effect (liquidity, user habit) is a moat. An AI model could replicate the code, but it can’t replicate the existing liquidity. However, an AI agent could route users to the cheapest liquidity, potentially bypassing the Uniswap interface. The moat is not the code—it’s the data of who uses it and why.
In my 2024 ETF integration work, I found that on-chain data lags institutional flows by 14 days. That lag is a data advantage. The Lazard survey confirms that investors are now paying for that advantage.
3. AI Agent Risk
In 2026, I modeled how 10,000 AI agents executing 1 million transactions could impact market depth. The result: increased efficiency but higher systemic fragility. AI agents can drain liquidity from a protocol faster than any human. The Lazard survey’s “wait-and-see” stance reflects this fear. Investors are hesitant to fund software that can be disrupted by autonomous agents.
But here’s the nuance: AI agents also create new data moats. The agent’s own behavior becomes a dataset. The protocol that can capture and analyze agent activity—like a DEX tracking MEV bots—builds a moat. The ledger remembers what the algorithm forgets, but the algorithm can also be trained on the ledger.
Contrarian: The Consensus Is Overrated
91% agreement is a red flag. In markets, when everyone agrees on a moat, the moat is already priced in. The real edge is finding the companies that are wrongly classified.
First, “proprietary data” in crypto is often illusory. On-chain data is public. Off-chain data can be bought. The true moat is not the data itself, but the trust to hold it. “Trust is borrowed; trust is never owned.” Circle’s USDC compliance-first strategy is a risk—it can freeze any address. But that trust is also a moat for regulated investors. The data moat is a function of regulatory trust, not technology.
Second, network effects in crypto are fragile. A better UX or lower fees can flip a network. Look at the L2 wars: Arbitrum had network effects, but Base grew faster by leveraging Coinbase’s user base. The moat is not the network—it’s the distribution channel. AI can optimize distribution, not replace it.
Third, the survey ignores the reliability moat. In B2B software, trust and uptime matter. The same applies to crypto infrastructure. A DeFi protocol that has never been hacked, with audited code and a proven uptime record, has a moat that AI cannot replicate. The “4% unchanged” investors may be the ones who understand that.
Takeaway
Lazard’s survey is a mirror for crypto. The valuation vacuum is real, but it’s also an opportunity. The next 12 months will see the emergence of a new framework: AI Exposure Score (how vulnerable is the protocol to AI disruption?) and Data Moat Quality (how unique is the data?).
We build walls not to keep out, but to keep safe. The wall is no longer code—it’s the data and the network. But safety is the only yield that compounds over time. In a sideways market, positioning for the macro shift is the only trade.
Are you still valuing software by its code, or by its data?