In this episode of Professional Punters, we sit down with Sebastian Gallic, founder of Indexify. Sebastian interviews me about Freeport for the first 45 minutes; then we turn the microphones around and I ask him the questions.
Sebastian spent the first part of his career in securitization at Fitch Ratings, working on credit structures including aircraft leases and asset-backed securities. He later joined restructuring firm M3 Partners, where his creditor-side work covered Celsius, BlockFi, Voyager, and FTX. He now builds Indexify, a self-custodial social-investing platform where creators assemble and rebalance onchain portfolios called Stacks.
This conversation is about decentralized financial architecture as well as how two different consumer builders (one focused on investing using spot tokens, the other focused on trading using derivatives) think about product building. How trading and investing targets distinct user groups and thus require different approaches.
Perpetual Futures
When Freeport first started, it was a mobile interface that routed perpetual trades to onchain venues including Hyperliquid, Ostium, Aster, and Lighter, with an information layer above it that followed the news, people, filings, and tweets that traders cared about.
At the time, most of the listed perps on these venues were crypto rather than stocks since the natural users of these platforms were crypto natives. The reason that the DEXs decided to cover more than crypto is simple: attention rotates. One month traders care about Bitcoin, another month chips, oil, or gold.
The reason that users love perps, in addition to leverage, is that they can trade around the clock and can create price exposure to an asset that a trader cannot buy directly. If news breaks about SK hynix while the U.S. market is closed, waiting for an American ADR to reopen can mean waiting until after the move.
Fundamentally, a perp lets you “trade a number.” A funding payment between longs and shorts helps keep the perp near its reference price: when the perp is rich, longs generally pay shorts; when it is cheap, shorts generally pay longs.
Losing positions are liquidated as their collateral runs out. If a violent move leaves an account or isolated position with negative equity after liquidation, a venue can auto-deleverage ranked positions on the profitable side to prevent bad debt and preserve solvency. The structure creates leverage and 24/7 exposure.
Trading 13F Filings
A filing can be accurate and still be too stale to trade. A Form 13F can arrive up to 45 days after quarter-end. Political and corporate-insider disclosures also typically arrives late. Extracting the position is easy. Deciding whether the position still contains information is not.
A potentially useful AI workflow is to rebuild the timeline. What kind of investor reported the position? What thesis or informational edge does that investor usually have? What catalyst would have made it work? Had that catalyst already happened, or is it still unrealized? A model can consider hundreds of filings against those questions and put a select number in front of a human.
A particular example is the list of bitcoin miner turned neoclouds that Situational Awareness held. Because Leopold Aschenbrenner and Avital Balwit (Anthropic Chief of Staff) are very well-connected, one could naturally assume that they have edge in guessing which neocloud was going to get large enterprise deals. At the time of the 13F only some of his picks had large deals announced, suggesting that there was perhaps still some edge in the other symbols.
Hyperliquid Deployment
An outside deployer can define and operate a perpetual market on Hyperliquid. The deployer chooses the contract and oracle specifications, sets operating parameters, and shares in trading fees. It also has to stake 500,000 HYPE which can be slashed. Because the stake is denominated in HYPE, its dollar value moves with the token (around 40M at $80/Hype).
A longer-term goal was for Freeport itself to deploy perps on assets people want to trade but cannot easily access, particularly pre-IPO companies. Private companies make the oracle problem much harder. Ventuals previously offered valuation perps on companies including OpenAI and Anthropic before sunsetting those markets. Its methodology combined offchain valuation data with market-price discovery, but the valuation component was unpredictable and difficult to hedge.
Structurally, we believe that demand will only increase for pre-IPO exposure because companies stay private much longer and reach enormous valuations before ordinary investors can buy them. Accredited-investor rules and public-company disclosure regimes reflect a real investor-protection concern: a public company has regular reporting obligations, while a private issuer may tell an outside investor much less.
But the cost of excluding most people from many private offerings and years of potential upside seems to outweigh any protection benefits judging by the spectacular returns that these firms have delivered.
Broker Diversification
Brokers and exchanges are cyclical. When customers make money they have more capital and more appetite to trade; when every position is red, many stop opening the app. A trading business cannot remove that cycle, but it can diversify where attention comes from.
Crypto can be weak while AI is strong. Oil can move while technology stalls. And the AI trade is no longer just the Magnificent Seven. Korean, Taiwanese, Japanese, and Chinese companies now sit throughout the compute supply chain, while many remain difficult to access through an ordinary U.S. brokerage account. A product can follow all active markets instead of betting on one asset class or one country.
Credit Securitization
Sebastian’s route into crypto began with aircraft. At Fitch, he worked on securitization and the off-balance-sheet obligations of capital-intensive businesses: in particular aircraft leases and the financing structures that let an airline operate a fleet without buying every plane outright.
A published rating can look like another piece of public research. Sebastian emphasized that it comes from a deeper information process. Rating analysts speak directly with management and operating teams, receive confidential information, and combine it with public filings. However, the issuer-paid model creates an obvious incentive conflict.
Sebastian then got closer to operations and to the people on the other side of a failed balance sheet. At M3 Partners, he worked on creditor-side restructuring and forensic assignments across the major crypto bankruptcies. In his own account of the work, the recurring problem was opacity; customers thought they owned assets, then learned in court that they held merely claims against a bankrupt company.
CHIP, USDAI, & SUSDAI
An example of onchain credit securitization was USD.AI, a protocol financing AI infrastructure. The names matter because USDai, sUSDai, and CHIP represent three different claims.
USD.AI is the protocol’s liquid, PYUSD-backed synthetic dollar and carries minimal GPU-loan exposure. A holder can stake it into sUSD.AI, a floating, yield-bearing vault share whose assets include GPU-backed lending positions and Treasury bills. CHIPis separate again; being the protocol’s governance and utility token.
Compute operators need capital to acquire GPUs, and USD.AI’s permanent loans are secured by installed, revenue-generating hardware. Bridge financing or equipment-vendor terms can cover the procurement gap; after installation and verification, a USD.AI facility can take out that bridge and amortize over roughly three years. sUSDai gives holders exposure to the resulting GPU-credit yield.
Pre-IPO Tokens
One of Indexify’s best product examples was a pre-IPO technology Stack. Sebastian said it had included instruments tracking Anthropic, OpenAI, Kalshi, Polymarket, and Anduril. The instruments came from PreStocks, which issues tokens designed to track the prices of pre-IPO companies.
That is a different structure from the valuation perps discussed in the first half, and it uses a different pricing process. PreStocks notes that its tokens are backed by SPV exposure and provide economic exposure. For a private-company token, the issuer terms, SPV structure, transfer restrictions, and redemption path matter at least as much as the ticker. There were times where the price of PreStocks tokens dropped due to concerns about SPV risk.
Decentralized Margin Loans
Near the end, we discuss Morpho and the typical lending rules in smart contracts. In a simple overcollateralized market, lenders supply one asset, borrowers post collateral and borrow another, and interest flows back to suppliers. The protocol applies the collateral and liquidation rules without a bank approving each loan manually.
The innovation that differs from traditional finance is the utilization curve. Morpho’s interest-rate model responds primarily to the share of supplied assets that have been borrowed. Sustained utilization below its target pushes rates down; utilization above the target pushes them up to encourage repayment and attract more supply. The curve and market state are public rather than being repriced opaquely.
Sebastian’s hypothetical was a bank customer who owned ETH and wanted to borrow against it. Rather than hold and underwrite the volatile collateral on its own balance sheet, the bank or another interface could direct the customer to an onchain lending market whose code handled collateral and liquidation.
Professional Punters is presented by Freeport Markets: Freeport lets users trade 24/7, weekends included, with no KYC and up to 200x leverage across stocks, indices, commodities, crypto, rates, and more, with access to pre-IPO exposure in companies like OpenAI & Anthropic. For traders who already follow sharp sources across Twitter, Substack, hedge fund filings, corporate insider activity, and political trading disclosures, Freeport uses AI to read the sources they trust, connect the dots across markets, and surface trade ideas as narratives develop.
About the Host: Lihong Wang is the founder and CEO of Freeport Markets. Before starting Freeport, he traded discretionary semiconductor names at quantitative market-making firms including Jane Street and IMC Trading, building both systematic and discretionary strategies. Lihong graduated from Duke with a degree in mathematics and statistics, and has raised over $2.5M to build Freeport from investors including Y Combinator, Alliance DAO, and Informed Ventures. For more of his long-form research, visit freeportlogbook.substack.com.
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