My First Million
by @myfirstmillionpod
1 videos
Recent Posts
Brainstorming business ideas with a billion-dollar founder
Focus on AI infrastructure leaders like NVIDIA (NVDA) and Micron (MU), which currently trade at attractive PEG ratios between 0.25 and 0.3, suggesting they are undervalued relative to their massive growth. To manage the high volatility of these AI positions, consider using "collars"—an options strategy that limits downside risk while maintaining exposure to the long-term trend. Shift your macro strategy away from bonds and fixed income toward Gold and Bitcoin (BTC) to hedge against the inevitable devaluation of fiat currency. In the private markets, look for consumer platforms like Revolut or Polymarket that demonstrate a "lightning in a bottle" metric of 60% daily active users (DAU/MAU). Prioritize investments in "human curation" services and AI-driven creative tools that move beyond passive scrolling toward active user generation.
Top assets covered by My First Million
The 9 most-discussed assets across My First Million’s content on Kazuha.
My First Million’s sentiment — last 30 days
Aggregate of all sentiment-scored insights from My First Million in the last 30 days.
Frequently asked about My First Million
What does My First Million talk about on Kazuha?
Kazuha indexes 1 post from My First Million, with AI-extracted insights covering 9 distinct assets (stocks, ETFs, cryptocurrencies, and other investable assets).
Which assets does My First Million cover the most?
My First Million's most-discussed assets on Kazuha are PRIVATE, PRIVATE, PRIVATE, PRIVATE, MU. See the "Top assets covered" section above for the full breakdown with sentiment.
Is My First Million bullish or bearish right now?
Mostly bullish. In the last 30 days, My First Million had 8 bullish, 0 bearish, and 1 neutral takes across all assets they discussed (per AI-extracted sentiment scoring on Kazuha).
Where does Kazuha get My First Million's insights?
My First Million's publicly available content (podcast episodes, YouTube videos, or X/Twitter posts) is transcribed and analyzed by an LLM that extracts the assets discussed and the speaker's sentiment toward each one. Each insight links back to the original source.