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$GOOGL · The Future of AI Agents | Jesse Zhang Interview
now playing · $GOOGL
$GOOGL···bullish· highjesse zhang
Jesse Zhang bullish on Google: consumer data moat critical for AI
Google's massive consumer touchpoints generate the data flywheel essential for long-term AI model improvement, giving it a structural advantage over pure B2B model providers.
$META···neutral· lowjesse zhang
Meta has consumer data advantage but execution uncertain
Meta possesses similar consumer data scale to Google, but its new superintelligence lab's ability to capitalize remains unproven.
$ANTHROPICbearish· mediumjesse zhang
Anthropic lacks consumer data flywheel vs ChatGPT
Without a strong consumer-facing product like ChatGPT, Anthropic misses the continuous real-world data loop needed to improve models long-term.
$COGNITIONbullish· mediumjesse zhang
Cognition in hypothetical AI portfolio: bet on coding agents
Cognition's team and traction in AI coding agents make it a core holding in a concentrated AI portfolio.
$CURSORbullish· mediumjesse zhang
Cursor in hypothetical portfolio: coding agent leader
Cursor's strong team and product in AI-assisted coding warrant a portfolio slot alongside Cognition.
$ETCHEDbullish· mediumjesse zhang
Etched represents high-variance hardware layer bet
Specialized AI hardware startups like Etched offer asymmetric upside despite early-stage risk.
$PIKAbullish· mediumjesse zhang
Pika in portfolio: video generation model bet
Exceptional team building foundational video models justifies inclusion in a concentrated AI portfolio.
$CHAIbullish· mediumjesse zhang
Chai in portfolio: vertical foundation models for healthcare
Domain-specific foundation models (e.g., healthcare) are a compelling diversification within AI model layer bets.
$PHYSICAL-INTELLIGENCEbullish· mediumjesse zhang
Physical Intelligence in portfolio: robotics foundation models
Embodied AI / robotics foundation models represent a high-potential frontier worth a portfolio slot.
$OPENAIbearish· mediumjesse zhang
OpenAI API business model threatened by low switching costs
Model APIs face intense competition and near-zero switching costs (one line of code), pushing labs toward applications where they can capture more value.