PodcastBanklessMo Jaleel

The Biggest Barrier to Wall Street Using Ethereum

Institutional adoption of Ethereum is constrained less by fundamental research than by engineering challenges around privacy, security, scalability, censorship resistance, and compliance. The discussion examines selective disclosure, private payments, interdealer compression, and the effort to turn institution-specific work into open-source infrastructure, while highlighting demand for compliant DeFi products. Speakers expect adoption to begin in private or siloed ecosystems, with venture-scale businesses potentially emerging within 24 months and most financial infrastructure possibly moving onto blockchains within seven years.

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Key Takeaways

Regulatory clarity, including the Genius Act, is associated with greater institutional interest in Ethereum, but adoption also depends on translating blockchain capabilities into the legal and compliance frameworks used by financial organizations. Institutions may select jurisdictions that provide the legal properties they require.

Ethereum's lack of default privacy is a central institutional barrier because organizations want public-chain liquidity and business opportunities without exposing account-level information or trading histories. Selective disclosure could preserve verifiable aggregate metrics such as trading volume, market capitalization, TVL, and AUM while restricting detailed account access.

Institutional privacy cannot be solved with a single standard design: requirements vary by business use case, legal constraints, throughput, and security needs, and privacy L2s or other tools may involve unacceptable trade-offs. The speakers describe the remaining challenge for these use cases as primarily an engineering problem rather than a fundamental research problem.

Moving systems that already handle billions of dollars onto Ethereum requires confidence in the full system's security, privacy, censorship resistance, and scalability. A nearly nationwide confidential-payments project illustrates the complexity, involving the sender, receiver, auditor, and government.

The speakers argue that institution-specific implementations and privacy built into Ethereum's core can advance together: ETH Systems converts business and legal requirements into technical work, then generalizes lessons from architecture reviews, proofs of concept, and production environments into open-source components. Open investigations and permissive licensing are presented as ways to build institutional trust.

GEO-POLITICS

Regulatory Arbitrage — Regulatory clarity, including the Genius Act, is associated with greater institutional interest in Ethereum. Institutions may choose jurisdictions that provide the legal properties they need.

“So up until recently, we've been through Clarity of the coming, and like Genius Act, they were looking at it almost like, oh my God, this would be amazing if…”—

TECHNOLOGY

Ethereum Privacy — Ethereum lacks privacy by default, creating a barrier for institutions that need both the liquidity and business opportunities of public Ethereum and control over their information.

“One is their business, and the second is that they're compliant. And because of those two things, they kind of tend towards places that they can control…”—

Customized Privacy — Institutional privacy solutions must reflect specific business requirements, legal constraints, and use cases. Privacy L2s and other tools can involve trade-offs that institutions may reject.

“And then basically doing POCs and write -ups to show things that are missing, like how you can combine these building blocks to create protocols and solutions that will fit…”—

Performance Tradeoffs — Institutional requirements range from retail-scale transaction throughput to higher-assurance security use cases. Solutions must therefore be evaluated against the target application's requirements.

“So you see this wide sort of spectrum in terms of what these users are looking for, and then also different timelines and different sort of targets…”—

ECONOMY

For-Profit Structure — ETH Systems chose a for-profit structure because its work is expected to take multiple years, public-goods funding is difficult to sustain, and some institutional counterparties require a commercial entity for procurement.

“For the work that we're doing it's gonna be a multi -year journey and the most i think cleanest way sustainably funded it is to do as a for -profit…”—

MARKETS

Interdealer Compression — Interdealer compression is presented as a potential multibillion-dollar Ethereum opportunity. Shared state could reduce reliance on trusted third parties, capital requirements, and transaction volumes, but public visibility creates a privacy problem.

“And this is the perfect example where a kind of like, I would say, foundational new technology like Ethereum, a shared state, could give them this without this particular third…”—

FORECASTS

Venture-Scale Businesses — The speaker predicts that a number of venture-scale businesses focused on institutional blockchain use cases could appear within 24 months. Institutional engagements often take at least 18 months.

“I'll make a prediction over the next 24 months you can see a whole number of businesses pop up that are like that and the reason why I say 24…”—

Siloed Ecosystem — The speaker expects institutional Ethereum adoption to begin in a distinct private or siloed ecosystem. Compliant connections to core DeFi systems such as Morpho could develop over time.

“First, there's going to be a very distinct parallel, dare I say even siloed ecosystem that gets built out for institutions that is private and fits their form factor of…”—

ETH Systems

Mo Jaleel and Oscar Thorne introduce ETH Systems as an Ethereum Foundation spinout focused on helping institutions access Ethereum through privacy-preserving cryptography.

The Founders

Mo describes his traditional-finance and Ethereum Foundation experience, while Oscar explains his long involvement in privacy protocols, zero-knowledge research, and institutional privacy strategy.

Institutional Privacy

The speakers distinguish institutional privacy from individual privacy and explain how ETH Systems maps legal, business, and technical requirements to available Ethereum protocols and vendors.

Real-World Use Cases

ETH Systems discusses institutional use cases including private payments, tokenized deposits, national-scale payment systems, and interdealer compression, where privacy must coexist with security, scalability, and censorship resistance.

From Bespoke to Scalable

The founders argue that bespoke institutional work can become scalable when specific requirements are generalized into reusable protocols, libraries, technical specifications, and open-source infrastructure.

Building the Product

ETH Systems operates as a for-profit company so it can sustain a multiyear institutional engagement model, pass procurement requirements, and fund open-source work informed by commercial deployments.

Institutions Are Coming

The company intends to use forward-deployed engineering and institutional collaborations to discover product opportunities, with venture-scale businesses likely emerging as long sales cycles mature.

The Future of Institutional DeFi

Institutional demand is arriving largely inbound, driven by interest in compliant versions of DeFi products such as Aave, Uniswap, Lido, and other Ethereum applications.

Privacy vs Transparency

Adoption will likely begin in private or siloed institutional ecosystems before compliant integration with Ethereum’s core DeFi protocols becomes practical and widespread.

What Ethereum Can Do?

Selective disclosure can preserve transparency, auditability, and aggregate metrics such as TVL and trading volume while hiding individual identities, balances, and transaction histories.

Cypherpunks and Institutions

ETH Systems asks Ethereum’s ecosystem to collaborate on institution-ready DeFi products and open-source components, framing cypherpunk principles and institutional needs as increasingly compatible.

Important Disclaimers

Not Investment Advice: For education only — not financial, tax or legal advice.

AI-Generated: Made by AI from source transcripts and may contain errors.

No Guarantees: Past performance does not predict future results; investing carries risk.

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