This blog is the best explanation of AI intelligence increase I've seen: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
### Defining Market Bubbles
- Traditional definition: 20%+ share price decline with economic slowdown/recession
- Alternative perspective: hype/story not matching reality over time (dot-com example)
- Duncan’s view: share prices ahead of future expectations
- Share prices predict future revenue/profit
- Decline when reality falls short of predictions
### Historical Bubble Context
- Recent cycles analyzed:
- COVID (2020) - pandemic-led, quickly reversed with government intervention
- GFC (2008) - housing bubble, financial crisis, deeper impact
- Tech bubble (1999) - NASDAQ fell 80%, expectations vs reality mismatch
- S&L crisis (1992) - mini financial crisis
- Volcker era (1980s) - interest rates raised to break inflation
### Current AI Market Dynamics
- OpenAI: fastest growing startup ever, $20B revenue run rate in 2 years
- Anthropic: grew from $1B to $9B revenue run rate this year
- Big tech revenue acceleration through AI-improved ad platform ROI
- Key concern: if growth rates plateau, valuations become unsustainable
### Nvidia as Market Bellwether
- Central position providing GPUs for data center buildout
- Recent earnings beat analyst expectations but share price fell
- Market expectations vs analyst expectations are different metrics
- 80% of market money judged on 12-month performance vs long-term value creation
### AI Technology Scaling Laws
- Intelligence capability doubling every 7 months for 6 years
- Progress from 2-second tasks to 90-minute complex programming tasks
- Cost per token declining 100x annually on frontier models
- Current trajectory: potential for year-long human-equivalent tasks by 2028
### Investment Scale and Infrastructure
- $3 trillion committed to data center construction this year
- Power becoming primary bottleneck (not chip supply)
- 500-acre solar farms being built around data centers
- 7-year backlog on gas turbines, solar+battery fastest deployment option
### Bubble vs Boom Scenarios
- Bear case: scaling laws plateau, power constraints limit growth
- Short-term revenue slowdown despite long-term potential
- Circular investment dependencies create domino effect
- Bull case: scaling laws continue, GDP growth accelerates to 5%+
- Current 100% GPU utilization indicates strong demand
- Structural productivity gains justify investment levels
### Market Structure Risks
- Foundation model layer: 4 roughly equal competitors (OpenAI, Anthropic, Google, XAI)
- No clear “winner takes all” dynamic emerging
- Private company valuations hard to access for retail investors
- Application layer: less concentrated, easier to build sustainable businesses
- Chip layer: Nvidia dominance but Google TPUs showing competitive performance
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This blog is the best explanation of AI intelligence increase I've seen: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
### Defining Market Bubbles
- Traditional definition: 20%+ share price decline with economic slowdown/recession
- Alternative perspective: hype/story not matching reality over time (dot-com example)
- Duncan’s view: share prices ahead of future expectations
- Share prices predict future revenue/profit
- Decline when reality falls short of predictions
### Historical Bubble Context
- Recent cycles analyzed:
- COVID (2020) - pandemic-led, quickly reversed with government intervention
- GFC (2008) - housing bubble, financial crisis, deeper impact
- Tech bubble (1999) - NASDAQ fell 80%, expectations vs reality mismatch
- S&L crisis (1992) - mini financial crisis
- Volcker era (1980s) - interest rates raised to break inflation
### Current AI Market Dynamics
- OpenAI: fastest growing startup ever, $20B revenue run rate in 2 years
- Anthropic: grew from $1B to $9B revenue run rate this year
- Big tech revenue acceleration through AI-improved ad platform ROI
- Key concern: if growth rates plateau, valuations become unsustainable
### Nvidia as Market Bellwether
- Central position providing GPUs for data center buildout
- Recent earnings beat analyst expectations but share price fell
- Market expectations vs analyst expectations are different metrics
- 80% of market money judged on 12-month performance vs long-term value creation
### AI Technology Scaling Laws
- Intelligence capability doubling every 7 months for 6 years
- Progress from 2-second tasks to 90-minute complex programming tasks
- Cost per token declining 100x annually on frontier models
- Current trajectory: potential for year-long human-equivalent tasks by 2028
### Investment Scale and Infrastructure
- $3 trillion committed to data center construction this year
- Power becoming primary bottleneck (not chip supply)
- 500-acre solar farms being built around data centers
- 7-year backlog on gas turbines, solar+battery fastest deployment option
### Bubble vs Boom Scenarios
- Bear case: scaling laws plateau, power constraints limit growth
- Short-term revenue slowdown despite long-term potential
- Circular investment dependencies create domino effect
- Bull case: scaling laws continue, GDP growth accelerates to 5%+
- Current 100% GPU utilization indicates strong demand
- Structural productivity gains justify investment levels
### Market Structure Risks
- Foundation model layer: 4 roughly equal competitors (OpenAI, Anthropic, Google, XAI)
- No clear “winner takes all” dynamic emerging
- Private company valuations hard to access for retail investors
- Application layer: less concentrated, easier to build sustainable businesses
- Chip layer: Nvidia dominance but Google TPUs showing competitive performance
87. Helpful Support (Independence) vs Harmful Support (Dependence). Mentioning Jon Haidt, Kim Scott,
Cloud Streaks
56 minutes 50 seconds
1 year ago
87. Helpful Support (Independence) vs Harmful Support (Dependence). Mentioning Jon Haidt, Kim Scott,
Helpful Support = 1. Increased Trajectory + 2. Increase Resilience
Harmful Support = 1. No Improvement In Trajectory + 2. Lowered Resilience
Support in Different Contexts
- Workplace: Managers should focus on developing employees' skills and independence
- Parenting: The goal is to raise independent adults, not perpetually dependent children
- Friendships: There's a delicate balance between being supportive and becoming a "coach"
- Addiction and mental health: Support should aim for long-term recovery and resilience, not enabling destructive behaviors
Jon Haidt's Three Great Untruths:
- "What doesn't kill you makes you weaker" => What doesn't kill you makes you stronger.
- "always trust your feelings" => Feelings should be examined, sometimes immediate responses are counter productive and one should 'think slow, not think fast'.
- "life is a battle between good people and evil people" => The world is not zero sum, most things are 'win-win'.
Defining Effective Support
- Support done well leads to independence and growth, not dependence
- The goal is to "teach someone to fish" rather than continuously "giving them fish"
- Good support maximizes the trajectory of someone's improvement over time
- Effective support may involve allowing someone to struggle or fail in order to learn and grow
Challenges in Providing Support
- It can be difficult to let someone struggle or fail, especially in personal relationships
- There's a balance between intervening and allowing natural consequences
- The recipient's mindset (growth vs. fixed) impacts the effectiveness of support
- Clear communication about the intention and reasoning behind support is crucial
Reframing Support
- Support should be viewed as increasing resilience and ability to handle future challenges
- It's about being on the same "team" and working together for mutual growth and success
- Good support acknowledges feelings without necessarily endorsing them
- Support should aim for win-win outcomes rather than reinforcing a zero-sum mentality
Cloud Streaks
This blog is the best explanation of AI intelligence increase I've seen: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
### Defining Market Bubbles
- Traditional definition: 20%+ share price decline with economic slowdown/recession
- Alternative perspective: hype/story not matching reality over time (dot-com example)
- Duncan’s view: share prices ahead of future expectations
- Share prices predict future revenue/profit
- Decline when reality falls short of predictions
### Historical Bubble Context
- Recent cycles analyzed:
- COVID (2020) - pandemic-led, quickly reversed with government intervention
- GFC (2008) - housing bubble, financial crisis, deeper impact
- Tech bubble (1999) - NASDAQ fell 80%, expectations vs reality mismatch
- S&L crisis (1992) - mini financial crisis
- Volcker era (1980s) - interest rates raised to break inflation
### Current AI Market Dynamics
- OpenAI: fastest growing startup ever, $20B revenue run rate in 2 years
- Anthropic: grew from $1B to $9B revenue run rate this year
- Big tech revenue acceleration through AI-improved ad platform ROI
- Key concern: if growth rates plateau, valuations become unsustainable
### Nvidia as Market Bellwether
- Central position providing GPUs for data center buildout
- Recent earnings beat analyst expectations but share price fell
- Market expectations vs analyst expectations are different metrics
- 80% of market money judged on 12-month performance vs long-term value creation
### AI Technology Scaling Laws
- Intelligence capability doubling every 7 months for 6 years
- Progress from 2-second tasks to 90-minute complex programming tasks
- Cost per token declining 100x annually on frontier models
- Current trajectory: potential for year-long human-equivalent tasks by 2028
### Investment Scale and Infrastructure
- $3 trillion committed to data center construction this year
- Power becoming primary bottleneck (not chip supply)
- 500-acre solar farms being built around data centers
- 7-year backlog on gas turbines, solar+battery fastest deployment option
### Bubble vs Boom Scenarios
- Bear case: scaling laws plateau, power constraints limit growth
- Short-term revenue slowdown despite long-term potential
- Circular investment dependencies create domino effect
- Bull case: scaling laws continue, GDP growth accelerates to 5%+
- Current 100% GPU utilization indicates strong demand
- Structural productivity gains justify investment levels
### Market Structure Risks
- Foundation model layer: 4 roughly equal competitors (OpenAI, Anthropic, Google, XAI)
- No clear “winner takes all” dynamic emerging
- Private company valuations hard to access for retail investors
- Application layer: less concentrated, easier to build sustainable businesses
- Chip layer: Nvidia dominance but Google TPUs showing competitive performance
