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
All content for Cloud Streaks is the property of Cloud Streaks and is served directly from their servers
with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
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
84. Should we ban social media for kids? Mentioning Jon Haidt, Seymour Skinner, Marc Andreessen...
Cloud Streaks
1 hour 3 minutes 51 seconds
1 year ago
84. Should we ban social media for kids? Mentioning Jon Haidt, Seymour Skinner, Marc Andreessen...
Jon Haidt's main points:
- No smartphones before high school. Parents should delay children’s entry into round-the-clock internet access by giving only basic phones (phones with limited apps and no internet browser) before ninth grade (roughly age 14).
- No social media before 16. Let kids get through the most vulnerable period of brain development before connecting them to a firehose of social comparison and algorithmically chosen influencers.
- Phone-free schools. In all schools from elementary through high school, students should store their phones, smartwatches, and any other personal devices that can send or receive texts in phone lockers or locked pouches during the school day. That is the only way to free up their attention for each other and for their teachers.
- Far more unsupervised play and childhood independence. That’s the way children naturally develop social skills, overcome anxiety, and become self-governing young adults.
Arguments for and Against Banning Social Media Until 16:
Arguments For Banning Social Media for 16-Year-Olds
Mental Health Issues: Social media can cause anxiety, depression, and low self-esteem due to constant comparison and social pressure.
Cyberbullying: Teenagers are vulnerable to online bullying and harassment, leading to severe emotional distress.
Privacy Concerns: Teens might not understand privacy settings, risking exposure to personal information and online predators.
Addiction and Distraction: Excessive use can lead to addiction, reducing time for studies, physical activities, and face-to-face interactions.
Sleep Disruption: Social media use before bed can disrupt sleep patterns and lead to poor-quality sleep.
Body Image Issues: Exposure to unrealistic body standards can lead to negative body image and eating disorders.
Misinformation: Teens may be susceptible to fake news, affecting their understanding of the world.
Arguments Against Banning Social Media for 16-Year-Olds
Communication: Helps teens stay connected with friends and family, fostering social bonds.
Educational Resources: Provides access to educational tools and resources.
Skill Development: Develops digital literacy and communication skills.
Self-Expression: Offers a platform for sharing interests and creativity.
Awareness and Activism: Raises awareness about social issues and encourages civic engagement.
Support Networks: Online communities provide support and a sense of belonging.
Parental Supervision: With guidance, teens can learn to use social media responsibly.
If you want to contact us please do so at info@cloudstreaks.com
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
