Trade War & The Markets | EP 74

This episode unpacks the escalating US-China trade war's market impact, OpenAI's aggressive acquisition strategy, and persistent transparency challenges within crypto, offering critical insights for investors navigating uncertainty.

Market Banter and On-Chain Observations

• The conversation kicks off with observations on recent market events and on-chain activity.
• The hosts note Eric Trump's poorly timed ETH trade, selling near the bottom and potentially rotating into another token, highlighting the speculative and sometimes irrational behavior even among well-connected individuals. This serves as an anecdotal lead-in to broader market discussions.

Crypto Project Controversy: Movement Labs

• The discussion shifts to recent turmoil surrounding Movement Labs and its co-founder Rushi.
• Allegations surfaced on social media regarding insider trading, token price manipulation, and issues with market makers.
• An event disturbance in China involving disgruntled airdrop farmers further fueled negative sentiment.
• Rushi subsequently announced a temporary leave of absence, leaving the situation unresolved and underscoring the operational and reputational risks inherent in early-stage crypto projects.

Market Manipulation and Transparency Gaps

• The hosts delve into a specific token incident (identified as M) that experienced a 90% price drop rapidly, allegedly due to a large token holder dumping their position.
• While the exact party is unconfirmed (Laser Digital was accused but denied involvement), the event highlights severe transparency issues.
• Market makers, token listings, and founder disclosures operate in opaque environments, potentially misleading investors about a token's true market value. "A lot of those areas are quite dark right now and so people get manipulated into buying prices of tokens that may or may not be actual real value," one host notes.
• Market Makers: Entities that provide liquidity to trading pairs on exchanges, aiming to profit from the bid-ask spread. Their actions can significantly impact price but often lack public transparency.
• Actionable Insight: Investors must critically evaluate token price action, recognizing that significant portions may be influenced by undisclosed market maker activity or large holder sell-offs rather than organic demand.

The Unsolved Problem of Crypto Transparency

• The conversation broadens to the systemic lack of transparency in crypto regarding token ownership and insider sales.
• This is contrasted sharply with TradFi (Traditional Finance) – the established financial system – where regulations like SEC filings mandate disclosure from major shareholders.
• The hosts recall Masari's 2017 attempt to address this, concluding it's a problem requiring collective action from major exchanges (Binance, Coinbase) and top players, which hasn't materialized.
• The potential for real-time, on-chain disclosure exists but requires linking addresses to identities, a step the industry has resisted.
• Strategic Implication: The persistent lack of transparency remains a fundamental risk. Researchers should track initiatives aimed at improving disclosure, while investors must factor this opacity into their risk assessments.

Debating Regulation and Operational Discipline in Crypto

• The hosts contemplate whether crypto's "permissionless" nature is entirely beneficial, suggesting some level of regulation might be necessary, perhaps akin to a 90% free market with 10% centralized rules.
• They reference Snapchat founder Evan Spiegel's experience, where the IPO process enforced greater operational efficiency and long-term thinking.
• This suggests that some regulatory frameworks or standardized processes (like quarterly updates, though crypto could do this daily via on-chain data) might foster more sustainable projects.
• Key Consideration: While crypto prizes decentralization, the lack of standardized reporting and operational frameworks can hinder maturity and investor confidence.

AI Landscape: OpenAI's Acquisition Strategy

• The discussion pivots to the AI sector, focusing on OpenAI's reported $3 billion acquisition of Windsurf after failing to acquire Cursor twice.
• The hosts analyze the motivations:
• Data Acquisition: Acquiring coding assistants like Cursor or Windsurf provides invaluable data (user prompts, accepted code suggestions) for training better coding models, an area where OpenAI currently lags behind competitors like Anthropic. This data acts as Reinforcement Learning with Human Feedback (RLHF) – a technique using human feedback to fine-tune AI models.
• App Layer Control: OpenAI likely anticipates the commoditization of foundational models (large, general-purpose AI models like GPT-4) and aims to capture value at the application layer. Acquiring popular apps like Windsurf solidifies their user base beyond ChatGPT.
• Investor Takeaway: OpenAI's strategy signals the increasing importance of proprietary data and user distribution in the AI race. Investors should monitor how foundational model providers move up the stack into applications.

AI Competition and Startup Cannibalization

• OpenAI's expansion is framed as potentially creating a "super app" for various AI modalities (coding, content generation, general intelligence).
• However, this aggressive expansion risks cannibalizing numerous startups building niche AI tools. One host notes, "for every category that OpenAI... introduces a product for, it cannibalizes like hundreds of startups." This highlights the intense competitive pressure in the AI space.
• Strategic Risk: Startups building thin wrappers around existing large language models (LLMs) face significant platform risk from incumbents like OpenAI.

VC Sentiment: Caution Amidst the AI Hype

• The hosts discuss venture capital perspectives on AI, citing Vinod Khosla's comment about YC batches being rendered obsolete by new foundational models and Peter Thiel's (via Founders Fund) cautious approach.
• Thiel reportedly views the current AI landscape as reminiscent of the dot-com bubble, warning against over-investment due to extreme competition and potential cannibalization.
• This contrasts with the general VC frenzy around AI.
• Investor Perspective: Experienced VCs like Thiel express significant skepticism about the long-term viability of many current AI startups due to hype and competition, suggesting a need for careful due diligence.

Shifting Narratives: Crypto vs. Stablecoins

• The conversation touches on the intense competition in AI, suggesting it's currently "one or two orders of magnitude higher" than in crypto.
• Crypto, conversely, is seen as becoming more "non-consensus." A notable narrative shift involves reframing parts of the industry around stablecoins (cryptocurrencies pegged to stable assets like the US dollar) rather than "crypto" itself, potentially to attract talent and users wary of crypto's speculative reputation.
• This echoes previous attempts to rebrand (Web3) or differentiate (Bitcoin maximalism).
• Trend to Watch: The "stablecoin, not crypto" narrative aims to make blockchain technology more palatable to mainstream and TradFi audiences by focusing on practical payment/fintech use cases.

Trade War Analysis: Market Impact and Investor Sentiment

• The discussion dives deep into the US-China trade war, with one host stating, "I think the trade war is worse than what people expect it to be." Key points include:
• Market Confusion: Powell's recent comments suggest the market is still digesting information, leading to uncertainty.
• Retail Behavior: Anecdotally, retail investors still seem to be in "buy the dip" mode, expecting Fed intervention, with no signs of capitulation.
• Valuations: US stocks remain expensive (forward P/E around 20) even before fully accounting for tariff impacts.
• Economic Impact: Tariffs (e.g., 10%) hit the cost of production, severely impacting profit margins. Examples cited include significant price increases for toys (80%), vehicles ($7,600 avg.), and average household costs ($3,800 annually).
• Bearish Outlook: The hosts express a bearish view on US stocks over the medium term (3-6 months), expecting the market to eventually price in the negative impacts.
• P/E Ratio (Price-to-Earnings Ratio): A valuation metric comparing a company's stock price to its earnings per share. A forward P/E uses estimated future earnings.

Geopolitical Risks: Trade War Strategy and Conflict Potential

• The hosts debate the strategy behind the trade war. One perspective suggests it's a deliberate attempt led by Treasury Secretary Scott Bessent to curb China's growth by isolating it economically.
• However, the execution (alienating allies) appears inconsistent with this goal.
• The alternative view is that Trump is acting impulsively ("yoloing") without a clear strategy, using negotiation tactics like bluffing and starting with extreme demands.
• The most concerning outcome discussed is an increased risk of military conflict, particularly over Taiwan, with one host estimating the probability has risen from 25% to 50% over the next three years due to the trade tensions.
• Investor Alert: Heightened geopolitical tension, especially regarding Taiwan, poses a significant systemic risk to global markets and supply chains, impacting both crypto and AI sectors reliant on semiconductor manufacturing.

Startup Ecosystem Impact: Trade War and Venture Funding

• The trade war's economic fallout is expected to impact venture investing, which typically lags public markets.
• While AI remains hot (evidenced by high valuations like $22M for pre-product AI startups post-demo day), a broader economic slowdown could cool funding across the board.
• There's also concern that talent flow (e.g., founders from China, India) might be affected.
• Strategic Consideration: Crypto AI startups should prepare for potential tightening in the funding environment if economic conditions worsen due to trade conflicts.

Supply Chains and Manufacturing: US vs. China Dynamics

• A potential military conflict could severely disrupt semiconductor supply chains, hindering AI and robotics progress.
• The discussion highlights China's dominance in manufacturing key hardware, including industrial robots (51% global share vs. US 5%), EVs (despite safety concerns mentioned), and solar panels (around 80% global share).
• This manufacturing prowess is seen as a strategic advantage, particularly in conflict scenarios.
• The hosts argue tariffs are ineffective for reshoring manufacturing compared to direct investment in domestic industries.
• Key Insight: Dependence on China-centric supply chains for hardware crucial to AI and robotics represents a major vulnerability, amplified by trade tensions.

Crypto Innovation: The "Content Coin" Experiment

• The hosts discuss the recent Base/Zora token launch ("Base is for everyone"), framed as an experiment in tokenizing content ("content coins").
• The launch saw initial hype followed by a crash, partly due to a sniper buying and dumping a large portion of the supply.
• While criticized by many as just another memecoin launch plagued by familiar issues (bonding curves, snipers), the underlying narrative explores whether creators can earn revenue directly from on-chain content.
• The hosts remain skeptical about the "content coin" terminology catching on compared to the widely understood "memecoin."
• Bonding Curve: An automated market maker mechanism where token price is algorithmically determined by supply. Early buyers get lower prices, later buyers pay more. Often susceptible to manipulation by bots (snipers).
• Research Note: This experiment tests new models for content monetization on-chain, but faces challenges with token distribution mechanics and market perception.

AI Model Wars and App Layer Defensibility

• The rapid pace of AI model development is highlighted by OpenAI's 03 model reportedly surpassing Google's Gemini 2.5 shortly after the latter claimed the top spot.
• This constant flux reinforces the idea that foundational models might become commoditized. The defensibility for AI apps, therefore, may lie in distribution (user base) and the ability to flexibly integrate the best underlying model for a specific task (like Cursor using Anthropic for coding), rather than being tied to a single provider's potentially inferior model.
• However, achieving distribution first is key.
• AI Investment Angle: App-layer AI startups that achieve significant distribution may have leverage, but the underlying model layer remains fiercely competitive and prone to rapid shifts.

AI User Experience: Memory and Human-Like Interaction

• The discussion touches on OpenAI's memory upgrade for ChatGPT. While the exact technical improvement is unclear, the goal is to provide more personalized, context-aware responses based on past interactions.
• One host notes appreciating the tailored answers and experiencing fleeting moments where the AI feels more "human-like" or empathetic, suggesting a potential "moat" if users develop stronger connections due to personalization.
• The other host remains less convinced about this emotional connection forming a strong defense.
• Future Trend: Enhanced memory and personalization in AI assistants could increase user stickiness, potentially creating a switching cost, though the depth of this "moat" is debatable.

Market Outlook and Portfolio Adjustments

• Revisiting their market positions, the hosts confirm a bearish stance on US stocks, actively selling into rallies.
• Holdings mentioned include Google, TSMC (though reducing due to war risk), Tesla, and Bitcoin. Bitcoin is viewed as trading like a mix of Nasdaq and gold.
• Due to a low cost basis and high tax implications, the primary Bitcoin holdings remain untouched. One host has sold all equities and holds crypto assets like Bitcoin, Solana, and "Fartcoin," viewing the latter as a potential "safe haven" within the memecoin space during uncertain times.
• Investor Action: The hosts are actively de-risking from US equities due to trade war concerns and high valuations, while maintaining core Bitcoin positions and selective crypto holdings.

Revisiting Market Forecasts

• The hosts reflect on their previous bullishness on US equities (driven by strong economic data pre-trade war escalation) and crypto earlier in the year.
• They acknowledge feeling "exhausted" even when bullish, a potential sell signal they didn't fully act on as "crypto natives."
• The current outlook is uncertain short-term, bearish medium-term (US stocks), with potential for a better 2025 for risk assets like Bitcoin. Trump's pressure on Fed Chair Powell adds another layer of instability.

Spotlight on Emerging Startups

• The episode concludes by highlighting promising startups from their current accelerator batch:
• Stablecoins: A significant portion (one-third) focuses on specific regions or use cases like remittance and yield.
• Niche AI: Startups targeting specific verticals, like AI for scientific research (Cursor alternative), AI-driven video ad creation, and Poofne (Vibe coding for crypto apps, strong team from Phantom/Coinbase).
• Video Generation: Slop.com (referred to as slop.club in transcript) leverages models like Kling/Vø for rapid video creation and remixing, aiming to build a community ("4chan for video models"). The core insight is reduced latency enabling new user behaviors.
• Decentralized Exchange: 01.exchange aims to build an improved trading platform, capitalizing on perceived stagnation from competitors like Photon and Bullex, whose founders may be less incentivized after initial success.
• Key Theme: Promising areas include regionally-focused stablecoins, vertically-integrated AI applications, novel content creation platforms leveraging new AI capabilities, and next-generation DeFi infrastructure.

The episode underscores heightened market uncertainty driven by the trade war and intense AI competition. Crypto AI investors and researchers must prioritize risk management, closely monitor geopolitical shifts and AI platform strategies, and continue to scrutinize the persistent transparency issues within the crypto market itself.