The Sophron Network

Imran Lakha joins The Sophron® Network to discuss what it takes to run an index options book at institutional size, and what changed when he left the bank to trade his own.

We go through the 2008 volatility regime from the seat, why skew rather than gamma was the trade that made the money, and the risk that quietly kills short skew desks when the market stops behaving. We then move to the market he trades today: 0DTE dominance, dealer gamma, dispersion at single digit correlation, and how he decides when a volatility spike is worth fading.

Imran Lakha is the founder of Options Insight, the London based options education and market research business he started in 2018, where he teaches traders and asset managers to express market views and manage risk through options, and where he trades a US options book of his own.

Before founding it he spent seven months as a portfolio manager at BlueCrest Capital Management. He joined Citi in 2013, trading European index options and going on to head the desk before leaving in 2017. Earlier, at Bank of America Merrill Lynch, he traded the European index flow options book and took it over in 2009. He began his career in 2001 at Credit Suisse First Boston, taking over the FTSE vanilla flow book and trading global relative value, variance swaps and dispersion.

We cover the flow book that could not be hedged in its own market, the two ways to be bearish in 2008, and why several desk heads were annihilated when skew switched from sticky delta to sticky strike. Imran sets out the mistakes he sees most in retail option books, the three structural changes he has watched since the early 2000s, and his three VIX regimes with the one week rule that follows from them.

LinkedIn: https://www.linkedin.com/in/imran-lakha-465b4558/
Website: https://options-insight.com

00:00 - Welcome and introduction
04:20 - The 2008 book: hedging FTSE size with Eurostoxx
05:18 - Why skew, not gamma, made the money
06:45 - A million a day of theta, or a neutral book
11:03 - Sticky delta, sticky strike, and how short skew blows up
16:54 - 0DTE, dealer gamma, and reading the gamma wall
22:33 - Pay caps, and being told to stop making money
30:13 - The mistakes retail option traders repeat
38:19 - Retail, 0DTE and dispersion: three structural changes
41:41 - The three VIX regimes and the one week rule
47:28 - The mining basket hedged with free FTSE puts
51:24 - Options Insight and the free masterclass

• Running an index flow options book at size
• Skew as a position you can hold, gamma as one you cannot
• Sticky delta versus sticky strike, and short skew blow ups
• 0DTE, dealer gamma maps and the gamma wall
• Dispersion and the collapse in index correlation
• The three VIX regimes and volatility clustering
• Bank floor versus hedge fund seat
• Common mistakes in retail option books

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Guilherme Bernabe joins The Sophron® Network to discuss why hedging is bought as insurance rather than traded as a position, and what happens to the companies that get that distinction wrong.

We cover how you protect a margin in an economy where the currency moved from 2.4 to almost 6 against the dollar in a decade, the instruments that get used when a CFO has a real invoice to pay, and why the firms that go under are usually the ones that skipped the hedge or tried to profit from it.

Guilherme Bernabe is a corporate and institutional finance advisor at XP Inc, one of the largest brokerages in Brazil, working through Center Investimentos. He advises CFOs, treasurers and institutional clients on foreign exchange, commodity and interest rate exposure, across the steel industry, schools, hospitals, real estate and family holdings.

He began in 2021 at Youprime Investimentos, an XP affiliated office, building a private client book from zero to roughly 30 million Brazilian reais. He trained as a mechanical engineer: at KUHN Group he developed diesel engine systems for agricultural machinery, and at Electrolux he was a senior cost out engineer owning an EBIT improvement programme end to end. He is completing an executive Master in International Finance at the University of Amsterdam.

The conversation covers the path out of engineering, why a client book gets built on referrals rather than cold calls, and how he explains risk to clients who accept it in principle and reject it the moment a portfolio drops ten percent. He walks through a steel importer whose profit would have disappeared on a three day move in the real, and a one million euro machine invoice from Italy that had to be paid regardless of the currency.

We also discuss why the non deliverable forward covers ninety nine percent of his Forex hedging, when a farmer is right to pay an option premium or build a collar, why he hedges the commodity before the currency, what the Americanas fraud changed about his due diligence, and why Wirecard should have taught Europe the same lesson.

LinkedIn: https://www.linkedin.com/in/guilhermebernabe

00:00 - Welcome and introductions
00:56 - From mechanical engineer to the financial market
03:40 - Building a client book from zero to 30 million reais
05:32 - Why referrals beat cold calls when the product is trust
08:19 - How to explain risk to a client who says he accepts it
10:34 - Cost out engineering and the CFO conversation
17:33 - Hedging as a trade versus hedging as insurance
19:50 - A steel importer and a three day move in the real
22:13 - The million euro machine from Italy
25:32 - Why firms in Brazil go bankrupt
27:18 - Stacking commodity, currency and interest rate risk
30:59 - Funded NDFs and short term liquidity
36:26 - Options, collars, and why farmers pay the premium
39:39 - The Selic, the leverage party, and private credit
42:25 - Americanas, the fraud that changed his due diligence
48:25 - Wirecard, governance, and not trusting a name
52:36 - The açaí franchise that COVID closed

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Euan Sinclair joins The Sophron® Network to discuss where the edge in options trading has actually gone after thirty years.

He explains why the mathematics he learned during a physics PhD mostly did not transfer, why a GARCH model that printed money in 1998 is priced in today, and why the edge now lies in finding situations rather than in building a better pricing model.

Euan Sinclair is Portfolio Manager and Senior Financial Engineer at Hull Tactical Asset Allocation in Chicago, responsible for the research and implementation of the firm's volatility strategies. He has traded options professionally since 1995.

Before Hull he traded a long and short equity volatility book at Bluefin Europe and Bluefin Trading, and was a partner at the volatility fund Talton Capital Management. He is the author of three books published by Wiley: Volatility Trading, Option Trading, and Positional Option Trading. He has sat on the editorial board of the Journal of Investment Strategies since 2012, and holds a PhD in theoretical physics from the University of Bristol, where he studied quantum chaos.

We cover the philosophy Blair Hull carried from counting cards at blackjack into the option pits, why no single indicator survives transaction costs, and why the firm's advantage lies in combining fifty small signals rather than discovering new ones. Euan draws a distinction between having a model and having a situation, illustrated by the difference between a blackjack card counter and a player who has found one sloppy dealer.

We also discuss hedging at implied against realized volatility, the Kelly criterion as an organizing principle rather than a formula, the variance premium as a consequence of risk aversion, what separates a genuine bubble from an expensive stock, and the two things he teaches practitioners first: that theta is not an edge, and that hedging is not a way of making money.

LinkedIn: https://www.linkedin.com/in/euan-sinclair
Hull Tactical Asset Allocation: https://www.hulltactical.com
Hull Tactical blog: https://www.hulltactical.com/blog/

00:00 - Welcome and introductions
01:43 - From a physics PhD to the option pits
04:42 - Blair Hull, blackjack, and find an edge, bet the edge, stay in the game
07:18 - Which parts of the physics mathematics transferred
09:51 - What the junior quants can do that he cannot
15:07 - Why the edge moved from pricing models to risk premia
18:41 - Market timing, fifty small signals, and a 2% alpha
22:51 - Why the data layer is the foundation of the house
24:45 - Hedging at implied versus realized volatility
28:59 - Two ways to find an edge: a model or a situation
34:54 - The switching option, and finding the easy game
37:53 - Kelly as an organizing principle rather than a formula
42:59 - The variance premium and what a real bubble looks like
51:36 - Theta is not an edge
58:03 - Crypto as TradFi twenty years earlier
1:01:41 - Where to follow Hull Tactical

• The variance premium as a consequence of risk aversion
• Model based edge versus situation based edge
• Bet sizing, fractional Kelly, and parameter misestimation
• Hedging at implied against realized volatility
• Why theta is not an edge and hedging is not a profit centre
• Why there are no new ideas in trading, only new places to apply them

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Alexander Levin and Daniel van Flymen join The Sophron® Network to explain how they built Plettenberg Capital, an AI-native quantitative fund in New York run like a software company.

Roughly 500 live algorithms, and no language model anywhere near the market. We take the fund apart layer by layer: the mental model they started from, the data layer everything rests on, the single engine that does both research and live execution, and the precise place where AI is allowed to sit.

Alexander Levin is Founder and Chief Investment Officer for public markets at Plettenberg Capital. He previously co-founded the AI startup Beautiful Technologies, held P&L ownership across core cloud networking at Amazon Web Services, and helped launch Cisco's $400 million Digital Alpha fund, after starting in mergers and acquisitions at Houlihan Lokey.

Daniel van Flymen is Co-Founder and Chief Technology Officer, and the engineer behind the research engine, cloud infrastructure and AI tooling. He has spent two decades as a technical co-founder and engineering leader in New York, including CTO roles at Spice and Foodwit and engineering leadership at Candid and BlinkRx.

Levin argues that most people analyse the market zoomed all the way in, and that the useful move is the opposite: treat it as one lake of capital moving between sectors, where almost everything reduces to momentum or mean reversion. Van Flymen then takes the system apart: the data layer, where a missed corporate action invalidates every backtest above it; the simulation engine, which is the same engine that runs in production; and the execution stack, all built in house.

We also discuss deliberately brittle code, two minute deploys, the Swiss cheese model of aviation safety applied to a trading system, why they use no volume or fundamentals or alternative data, whether sentiment is already in the price, and what happens to research teams once the tooling that compresses weeks of work into seconds is available to everyone.

LinkedIn: https://www.linkedin.com/in/levinalexander/
LinkedIn: https://www.linkedin.com/in/danielvanflymen/
Plettenberg Capital: https://plettenbergcapital.com
Enquiries, as given on the episode: [email protected]

00:00 - Welcome and introductions
02:58 - Two seats: the investment side and the engineering side
11:36 - The lake: liquidity, sectors and how hot money moves
18:43 - Regimes, and why an eight month downtrend is not a random walk
24:17 - A flock of birds: why they run around 500 algorithms
27:42 - The data layer, and why getting it wrong invalidates everything
32:49 - Pragmatism, brittle code and a two minute deploy
36:16 - What they deliberately do not use
43:08 - Where the AI actually sits: compression, not prediction
48:54 - The Swiss cheese model, applied to a trading system
50:41 - Is everything already in the price?
55:40 - Twenty strategies you understand, or 500 you do not
59:31 - The five year view: leaner teams, more autonomous systems

• Markets as a lake of liquidity moving between sectors
• Momentum and mean reversion as the two underlying behaviours
• Regime identification across 500 decision trees
• Point in time data, corporate actions and backtest integrity
• One engine for research, validation and live execution
• Where AI belongs in a quant fund, and where it does not
• Price only research, and sector exposure through ETFs

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Marc Nunes joins The Sophron® Network to argue that a weaker trading signal nobody else has is worth more than a stronger one that every fund already runs.

He has sat on essentially every side of the quantitative trade, and now runs open competitions claiming a global crowd can out-signal the very research teams he used to lead. This episode puts that to him as a research question rather than a pitch.

Marc Nunes is the co-founder and CEO of AlphaNova, a Singapore based, AI-native asset management platform that crowdsources trading signals through open competitions, evaluates them with proprietary methods in geometry and statistics, and deploys the survivors into a live deep learning trading system.

He was previously Chief Risk Officer at BFAM Partners in Hong Kong, and for five years chief executive of Shell Street Labs, where he led a team of PhD researchers building AI trading models. He spent seventeen years at Societe Generale Corporate and Investment Banking, latterly as a Managing Director, having started as a financial engineer at C*ATS Software in New York. He holds a PhD in Mathematics from UC Berkeley.

We cover why alpha decays and how it travels between funds inside people's heads, why he pays for signals no more than 50% correlated with his existing book, and how AlphaNova separates a real signal from an overfit one using leakage checks, submission limits and a live period.

We also discuss the Gaussian copula that priced super senior protection at zero before 2008, correlation read as points on a sphere, who actually wins these contests, agentic AI as table stakes, and why an agentic hedge fund is going backwards.

LinkedIn: https://www.linkedin.com/in/marcdacostanunes/

00:00 - Welcome and introductions
01:37 - From running a signal lab to crowdsourcing alpha
04:13 - Testing for data leakage and overfitting
07:09 - Why hiring the smartest person is not enough
09:02 - C*ATS, FiCAD and knowing what is under the hood
13:17 - LTCM, 2008 and the copula that priced risk at zero
18:56 - Learning more about markets away from the trading desk
21:33 - Moving from an investment bank to a hedge fund
24:35 - Why alpha decays and how it travels between funds
26:25 - Designing a contest that demands decorrelated signals
30:22 - How far to take AI in the research workflow
39:43 - Who actually wins these competitions
45:43 - Why an agentic hedge fund is going backwards
57:35 - Catching a competitor who deobfuscated the data
59:44 - Correlation as geometry: points on a sphere
1:02:05 - Weak and orthogonal versus strong and crowded
1:11:40 - What surprised him about running competitions
1:20:33 - Advice for quants and founders

• Crowdsourced alpha versus hiring a research team
• Overfitting, data leakage and competition integrity
• Alpha decay and why signals travel between funds
• Decorrelation as the product, not raw Sharpe
• Correlation as geometry, and signal compression
• Models, assumptions and liquidity in 1998 and 2008
• Agentic AI in quant research, and its limits
• Who wins quant competitions, and why not the obvious people

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Robert Carver joins The Sophron® Network to explain what changes, and what does not, when you go from running a $5 billion fixed income book at a systematic hedge fund to trading your own futures portfolio from home.

His system covers roughly 250 futures markets, runs fully automatically, and needs about five minutes of his attention a month. This conversation is about what those five minutes are actually spent on, and about the parts of systematic trading that rarely get discussed: contract rolls, reconciliation, broker APIs and the discipline of not intervening.

Robert Carver is an independent systematic trader, investor and writer, and the author of Systematic Trading, Smart Portfolios, Leveraged Trading and Advanced Futures Trading Strategies, with a fifth book, The Art and Science of Trading, due in December 2026. He trades his own portfolio using pysystemtrade, the open source Python framework he built and publishes alongside his live positions, and is a visiting lecturer at Queen Mary, University of London.

He previously spent seven years at AHL, part of Man Group, most recently as Head of Fixed Income, where he managed a $5 billion portfolio across roughly $1.5 billion of annualized active risk. Earlier he was a research manager at the Centre for Economic Policy Research and an exotic derivatives trader at Barclays Capital.

We examine why 999,000 of his million lines of Python have nothing to do with signals, why he argues 99% of a trader's success is implementation, and why he refuses to retire strategies that appear to have stopped working.

We also discuss capacity and execution at different account sizes, regime models, AI in research, the architecture of pysystemtrade, broker reconciliation, counterparty risk, and what a systematic fund actually worried about in 2008.

LinkedIn: https://www.linkedin.com/in/robert-stuart-carver/
Books and blog: https://systematicmoney.org

00:00 - Welcome and introductions
02:03 - Leaving institutional trading to build his own stack
04:31 - Anatomy of a trading system
06:36 - Why it is five minutes a month
09:06 - Simple signals inside a complicated system
12:31 - Why 99% of success is implementation
14:21 - Running $5 billion versus your own account
20:24 - Backtests and forward looking information
22:24 - Has a strategy really stopped working
28:17 - Return expectations, not return targets
36:07 - Leverage, overtrading, overfitting
37:24 - AI in research
38:42 - Regime models and the one finding he uses
48:13 - Inside pysystemtrade
56:47 - Reconciliation and broker APIs
1:04:08 - Build it or borrow it
1:08:17 - 2008, and counterparty risk
1:14:40 - The three lessons, and the woman in the red dress

• Building an institutional grade trading stack as an individual
• Why implementation, not signal quality, drives the outcome
• Capacity, execution and contract size at different account sizes
• Backtesting without forward looking information
• Break tests, structural change and holding period
• Regime models and where they break down
• AI in quantitative research and overfitting risk
• pysystemtrade: simulation, production and process modularity
• Broker integration, reconciliation and operational risk
• Counterparty risk in futures versus crypto

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Luciano Boudjoukian França joins The Sophron® Network to answer a question most quants never get to ask: is Brazil a hidden gem, or a trap dressed up as one?

A factor strategy backtested on Brazilian equities can show roughly three times the alpha of the same strategy in the United States. Most of it never reaches the investor. This is a conversation about the gap between the backtest and the live book, and why that gap is the entire business in an emerging market.

Luciano Boudjoukian França is a founding partner, Chief Investment Officer and portfolio manager at Avantgarde Asset Management, the São Paulo firm he founded in 2015 and one of the pioneers of factor investing in Brazil. Avantgarde runs a data-driven, systematic process across a family of multifactor equity and multi-asset funds, designed to remove emotion from investment decisions. The approach traces directly to his MSc dissertation at Insper on the low-volatility anomaly in Brazilian equities.

Before asset management, Luciano spent his early career in derivatives and structured products for corporate clients at Banco BBM, and in corporate and real estate finance at ABN AMRO (formerly Banco CR2), Banco Pine and Even, where he structured capital-markets instruments such as CRIs, CCIs and REITs. He has served on the boards of Inepar and Resale, where he led the first Brazilian crowdfunding exit through a strategic acquisition. He holds an MSc in Economics from Insper, a Bachelor's in Economics from the University of São Paulo, and the CFP® certification.

LinkedIn: https://www.linkedin.com/in/lucianobfranca/

Avantgarde Asset Management: https://www.avantgardeam.com.br/

00:00 - Welcome and introductions
02:04 - Pioneering systematic factor investing in Brazil
05:34 - Why Brazil behaves like an island market
06:59 - Vale, Petrobras and a value market of 150 names
10:38 - From options market making to systematic factors
15:42 - Why active ETFs are banned in Brazil
20:07 - Why discretionary firms struggle to become systematic
23:58 - Competing with AQR for the same Brazilian spread
29:13 - Factor crowding and what breaks in emerging markets
34:59 - Three times the backtested alpha, eaten by costs
41:11 - Is the model ever overridden?
43:56 - The 2020 crash and a 30% drawdown
50:16 - Why he would rather play in the minors league
53:56 - What AI changed in the research process
1:03:39 - Rapid fire: overrated and underrated factors
1:12:34 - São Paulo, Brazil and Dutch DJs

• Factor investing and the low-volatility anomaly in Brazilian equities
• Why implementation quality, not signal discovery, drives emerging-market alpha
• Liquidity, spreads and hidden trading costs as the binding constraint
• Data hygiene: point-in-time data, survivorship controls, corporate actions
• Portfolio construction in a market of roughly 150 listed names
• Why discretionary managers fail to transition to systematic processes
• Model autonomy and the limits of human override
• Drawdown management and low-beta factor exposure through the 2020 crash
• Capacity constraints of a boutique systematic manager
• AI in the quantitative research workflow
• Scaling a factor process into other emerging markets

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Rob de Rozario joins The Sophron® Network to explain why the gap between a sub 3 trillion dollar digital asset market and the roughly 200 trillion dollar world of traditional finance gets closed on risk and education, not on returns.

After two decades structuring exotic and FICC derivatives, Rob moved into digital assets because most participants could describe extraordinary returns but could not talk about risk. This conversation is about the bridge between those two worlds.

Rob de Rozario is the Founder, CEO and Chief Trading Officer of Alphaparty, an Amsterdam based asset management and trading firm running alpha rich strategies across digital asset markets. Before Alphaparty he led trading at a digital asset firm across DeFi, derivatives and market making.

His earlier career spans Lehman Brothers in Tokyo and Singapore (commodity exotics and hybrids), Nomura in Singapore (FX structured products across Asia ex Japan), and Leonteq in Zurich as Head of FICC Structuring. He holds a BSc with Honours in Applied Mathematics and Astrophysics from Monash University and pursued doctoral research in Financial Mathematics at UNSW.

We get into counterparty credit risk and the FTX lesson, the education gap that keeps allocators on the sidelines, tokenized real world assets and stablecoin payments, structured products settled on chain, where alpha still hides for small funds, running risk in a 24/7 market, and how AI is reshaping who wins.

We also discuss the Citi tokenization report, crypto volatility trading and Deribit, the reality of building companies, which tokens have genuine utility, and where derivatives volume sits five years out.

LinkedIn: https://www.linkedin.com/in/rob-d-93a43316/

00:00 - Welcome and introductions
02:54 - From FICC structuring to digital assets
05:27 - Counterparty credit risk and the FTX lesson
07:17 - A sub 3 trillion asset class next to 200 trillion in equities
08:48 - The education gap and the FX University story
13:00 - The Citi tokenization report and equities on chain by 2030
15:10 - Structured products settled by smart contract
22:15 - Where alpha hides: prediction markets, Hyperliquid, smaller venues
25:55 - How mature is crypto volatility trading
34:19 - Running risk in a 24/7 market and three lines of defense
36:30 - Inside Alphaparty: team, structure and returns
42:40 - From astrophysics to markets, and where AI actually helps
51:46 - Which tokens have real utility, and which go to zero
57:16 - Building companies before finance
65:40 - Rapid fire and the future of on chain settlement

• The trillion dollar gap between digital assets and global equities
• Counterparty credit risk as the most underpriced danger in crypto
• Translating exotic and FICC derivatives experience into digital assets
• Institutional adoption, education and the risk committee
• Tokenized real world assets and stablecoin payments
• Structured products settled on chain
• Where durable alpha still hides for small funds
• Risk management in 24/7, venue fragmented markets
• AI as a tool that favors small, fast teams
• Utility as the test for which tokens survive

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Jeremy Kadouch, Portfolio Manager at MN Fund, joins The Sophron® Network to answer a deceptively simple question: how close are we to fully autonomous trading?

MN Fund runs a 24/7 systematic volatility engine that reads the market, reasons about it, and executes on its own. But the trade decision is deliberately kept away from AI, and the honest answer on autonomy is fewer decision makers rather than none.

Jeremy Kadouch is Portfolio Manager at MN Fund, an Amsterdam liquid digital asset fund, where he runs the fund's trading strategies and capital deployment alongside founders Michaël van de Poppe and Esmee Sikkens. He brings more than eight years in digital assets across trading, tokenization and product development.

He also built Quorum Index, an autonomous, LLM powered market intelligence platform, and founded Cask Capital, which tokenizes cask aged spirits. He began as a retail crypto trader and has completed Yale University's Financial Markets course.

We go from MN Fund's three pillar book to the move from discretionary to systematic trading, where AI does and does not belong in execution, why the fund benchmarks against Bitcoin, and how to think about risk and drawdown. We close on tokenizing real world assets and building a Bloomberg for retail out of AI agents.

We also discuss realized volatility harvesting, leverage and tail risk, the maturation of crypto markets over eight years, and the discipline of not tokenizing everything.

LinkedIn: https://linkedin.com/in/jeremy-kadouch

00:00 - Welcome and introductions
03:32 - What MN Fund is, and the multi strategy single fund model
06:40 - The three pillar system and allocation by regime
08:39 - From manual trading to a systematic engine
11:37 - The intelligence layer, and why it is not fully autonomous yet
12:35 - Trading realized volatility, and why not Bitcoin or meme coins
16:41 - Systematizing discretion, and AI's real role
21:15 - An algorithm that reads and reasons, with no AI in execution
23:52 - Eight years in crypto: maturation and regulation
36:04 - Why MN Fund benchmarks against Bitcoin
39:42 - Cask Capital and tokenizing cask aged spirits
49:24 - The discipline of not tokenizing everything
52:18 - Quorum Index and how close we are to full autonomy

• Systematic vs discretionary trading
• Algorithmic volatility harvesting in crypto
• Risk, drawdown control, and R multiples
• Where AI belongs in a trading stack, and where it does not
• Agentic systems and autonomous market intelligence
• Bitcoin as a benchmark
• Tokenization and real world assets
• Regulation, liquidity, and how crypto markets have matured

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Aditya Shetty joins The Sophron® Network to talk through "internet capital markets": the idea that with a phone and an internet connection you can access any asset, any market, regardless of your location, your politics or your investment size. Recorded the same week tokenized SpaceX went live, the conversation is grounded in a concrete example and an honest distinction every finance listener needs.Aditya Shetty is Lead at Superteam Global, the talent and community network for the Solana ecosystem, which runs programs such as Superteam Earn, Idea Bank and Instagrants to help builders ship and get funded. He is based in Mumbai and works across the global Solana community.We start with Solana framed as technical and financial infrastructure, an internet capital market positioned as a successor to NASDAQ. From there we get concrete with SpaceX on Solana: how Backpack built a full-stack wallet, exchange and regulated securities brokerage, why the launch partner Sunrise mattered for liquidity, and how that creates a single liquid market where price arbitrage is close to risk free. We draw the line between owning the underlying stock and holding a synthetic price tracker, separate the three layers of Solana, and close on composability, fees, Superteam's community model and the bridge between TradFi and DeFi.Follow Aditya Shetty on LinkedIn: https://www.linkedin.com/in/aditya-shetty-97ab5258/

Core Timestamps

00:00 - Welcome and introductions: Superteam and internet capital markets

01:02 - Buying SpaceX 24/7: ownership versus synthetics

02:37 - What SpaceX on Solana did differently

02:57 - Internet capital markets versus the US capital markets

03:31 - Backpack: wallet, exchange and a regulated securities brokerage

05:05 - One liquid market and risk-free arbitrage

05:17 - Decentralization and the case for investor education

06:43 - Education, KYC and DeFi trust assumptions

07:54 - Synthetics and mimicking: what you actually own

10:02 - The three layers of Solana: token, network and infrastructure

12:18 - Limited inventory, one-to-one minting and fees

14:08 - Composability: trading the weekend and borrowing against shares

15:40 - The Superteam model and community-driven investing

18:42 - Closing vision: reimagining access and the TradFi to DeFi bridge

Main Topics Covered

Internet capital markets as a successor to NASDAQ

Tokenized equities and buying SpaceX on Solana

Real ownership versus synthetic price exposure

Backpack's wallet, exchange and regulated brokerage stack

Single liquid markets and risk-free arbitrage

Investor protection, education and KYC in an open-access model

The three layers of Solana: token, network, infrastructure

Composability: borrowing against and staking tokenized shares

Fees across wallets, DEXs and aggregators

Superteam's bottoms-up community model

The bridge between TradFi and DeFi

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