Episode 30: Selective Disclosure

A conversation with Maggie Love, the founder of SheFi, about six years of building one of crypto's largest education communities.The idea for SheFi arrived on a run in 2019. Love had been at ConsenSys long enough to live through the 2018 bear market, and as the first DeFi teams began to form she noticed two things at once: people were earning twenty percent on their tokens through MakerDAO and Compound, and none of the people building or talking about it on stage at ETHDenver looked like her. Find more details about this episode on the Nexus blog.

Hank Korth has watched a lot of distributed systems cycles come and go. A professor in the Department of Computer Science and Engineering at Lehigh University — with an appointment in the business college and as the lead at Lehigh Blockchain — he has been thinking about networked systems since the late 1980s, when his database research examined how independently administered systems could transact across a network. On this episode of Exponential, he joined the show to talk about where his students and research team are concentrating their attention now: zero-knowledge acceleration, onchain privacy that can coexist with regulation, and the agentic era beginning to take shape beneath the headlines.

Mrudul Gole, Head of Business Development at NodeOps, joined Exponential to trace the arc of a company that started as a blockchain validator and is now building the infrastructure layer for the AI era. The conversation covered the evolution of decentralized compute, the unlikely synergies between blockchain and AI, and a bold prediction about what dev teams will look like in six months.

The most important milestone in crypto, argues Nic Roberts-Huntley, will be the one that goes completely unnoticed. In this episode of Exponential, the founder and CEO of Blueprint Finance sits down to discuss how his firm is building the infrastructure for institutional-grade DeFi yield — and why the endgame is a world where the underlying mechanics of onchain finance quietly power everything from savings accounts to credit card rewards without anyone realizing it.Blueprint Finance operates two protocols — Concrete and Glow — and positions itself as a counterweight to the degenerate, high-leverage yield strategies that proliferated during the last bull run. "We're kind of trying to be the next wave of institutional-grade yield with onchain finance," Roberts-Huntley explains. Read more on the Nexus blog.

Nexus founder and CEO Daniel Marin appeared on the Exponential podcast this week to address the company's decision to pull back from its March 17th milestone campaign and to lay out his views on AI, agentic finance, and what he sees as the defining infrastructure opportunity of the decade.The conversation covered ground ranging from how AI is reshaping startup competition to why Daniel believes blockchain infrastructure needs to be designed for autonomous agents — not just human users.

In this episode of Exponential, Laszlo Szabo, co-founder and CEO of Kiln, joins to talk about one of the most important developments quietly happening in crypto today: the evolution of yield in digital assets. As Laszlo explains, yield is becoming the bridge between traditional finance and the onchain economy, opening the door to new financial products and new participants.Kiln operates what Laszlo describes as an "onchain yield platform," providing the infrastructure that exchanges, custodians, wallets, and asset managers use to offer staking and other yield strategies to their users. "We provide them the technology, we provide them the infrastructure so that they can offer digital asset yields to their customers," he explains. What began with staking has expanded into lending, stablecoin strategies, and more advanced onchain asset management.

In this episode of Exponential, Jin Kwon, co-founder of saga.xyz, explains how blockchain infrastructure will disappear into the background.Saga began as what Jin calls “a chain to launch chains.” The original thesis was simple: make block space more accessible for builders. But while the early bottleneck in crypto was scarce block space, today the challenge is different. There is more capacity available, yet building and operating a chain remains complex.“The technical complexity is mostly solved,” Jin explains. “But the operational complexity is definitely not.”Launching a blockchain is not just about spinning up code. It requires validators, economic design, token distribution, and ongoing coordination. Saga abstracts that operational burden. Builders submit a configuration request, and Saga’s mainnet validators automatically spin up a fully connected, decentralized chain, complete with bridging and orchestration. The model works more like AWS than a traditional blockchain launch, with predictable infrastructure costs instead of open-ended gas economics.Underneath that architecture is a deeper critique of blockchain economics. “Blockchain economics is terrible for everybody involved,” Jin says bluntly. Most networks depend on gas fees for both spam prevention and revenue, but Jin argues those functions should be separated. In Saga’s model, infrastructure costs are minimized and predictable, while long-term revenue should come from the economic activity built on top of applications, not from taxing every transaction.He points to Solana as a partial step in that direction, where gas is cheap enough to fade into the background and much of the value accrues elsewhere in the stack. Saga aims to push that further by removing gas as a core revenue mechanism and focusing instead on enabling sustainable application-layer economies.The conversation also explores a broader debate in crypto: whether blockchain’s primary use case is finance or something else entirely. Jin rejects the distinction. “Finance is just one of the rails in which you create an economy,” he says. From his perspective, blockchain is fundamentally an economy-generation tool. Whether the use case is gaming, commerce, prediction markets, or payments, the underlying goal is building and coordinating new digital economies more efficiently.Looking ahead, Saga is moving upstream from infrastructure into applications, particularly at the intersection of AI and commerce. The team is exploring how crypto rails and AI tooling can simplify merchant activity, advertising, and digital payments while hiding the “gunkiness” of Web3 from end users.The long-term vision is clear: remove friction layer by layer until blockchain becomes invisible. When that happens, users won’t ask whether something is “Web3” or “finance.” They’ll simply use applications that are cheaper, faster, and more aligned with the digital economies they power.

In this episode of Exponential, Joao Reginatto joins to discuss the future of digital money and the role M0 is playing in making stablecoins more customizable, composable, and interoperable. As Reginatto explains, M0 is building “an operating system for digital money” — a platform that allows institutions and application developers to create their own tailored forms of stablecoins, all while inheriting a shared set of interoperable standards.“The premise is that money is plural,” Reginatto says. “In the traditional world, every commercial bank issues a slightly different version of the same currency. We accept that because there’s strong interoperability. In crypto, that’s missing — and that’s what M0 is working to fix.”

In this episode of Exponential, Kevin Lehtiniitty, CEO of Borderless, shares how his team is building a new kind of financial network — one where stablecoins move across global corridors just as easily as traditional money, but with far less friction.Lehtiniitty brings deep experience to the problem. He helped launch TrueUSD, one of the earliest U.S.-based fully reserved stablecoins, and now leads Borderless, which he describes as the first stablecoin payments network. Unlike individual stablecoins or isolated on/off ramps, Borderless acts as a connectivity layer across more than 77 countries, abstracting the complexity of moving money across jurisdictions, banking systems, and rails.“We want to live in a world where blockchain-based finance powers everyday experiences — but does so in the background, where people don’t notice,” he explains. The goal is to support global fintech builders who want to launch neobank-like apps using stablecoins without needing to integrate dozens of local providers or manage fragmented APIs.He emphasizes that payments isn’t just a technology problem. “If you aren’t able to also solve for compliance and the risk elements, it doesn’t really go anywhere.”A recurring theme throughout the conversation is credible neutrality. “Circle has no incentive — in fact, a disincentive — for their payments network to support or promote their competitive products,” Lehtiniitty says. Borderless, by contrast, aims to be an open, interoperable network similar to Visa or SWIFT — not another walled garden with “blockchain lipstick.”That philosophy also applies to infrastructure. “We need to really focus on two things,” he says. “One, the underlying blockchain infrastructure needs to be permissionless and open. And two, stablecoin networks can’t be side projects — they need to be credibly neutral.”In addition to infrastructure, Borderless is also building visibility. Lehtiniitty reveals they’ve assembled what they believe is the world’s largest dataset of stablecoin-to-fiat FX prices and now publish a public benchmark to help the ecosystem understand where stablecoins outperform traditional remittance rails.

In this episode of Exponential, Zohar Bronfman, co-founder and CEO of Pecan AI, unpacks the role of predictive AI in today’s business landscape — and why it’s quickly becoming indispensable.At its core, predictive AI estimates the likelihood of future events using massive amounts of data. Zohar explains: “In very general terms, it’s the ability to make some form of a statistical likelihood estimation of any type of future event.” He contrasts this with the limitations of human decision-making: “Our mind can take into consideration three or four pieces of information for a given task… Predictive AI would basically take 1500 data points.” Zohar draws a line between generative AI, which mimics human-like reasoning, and predictive AI, which is optimized for decision-making at scale. This capability makes predictive AI critical for business operations, particularly when it comes to modeling customer behavior, forecasting, and operational decisions. As Zohar puts it: “The most successful businesses… Amazon, Meta, Google… they all use predictive AI extensively.”  But predictive AI still faces major hurdles. One is data normalization — unlike standardized text used in language models, proprietary business data is highly idiosyncratic. “It’s like different languages… it’s extremely hard to transfer learning that happened on one business’s data.”Zohar also points to an under-discussed challenge: causality. “AI today… is correlational analysis, not causal analysis. The fact that something is going to happen doesn’t mean that you can affect it.”