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
91. Techno Optimism Vs Socialism. Mentioning Marc Andreessen, Sam Altman & More
Cloud Streaks
1 hour 45 seconds
4 months ago
91. Techno Optimism Vs Socialism. Mentioning Marc Andreessen, Sam Altman & More
https://a16z.com/the-techno-optimist-manifesto/
"
Techno-optimism is the belief that rapid technological progress is the main driver of human prosperity and should be pursued as a moral imperative. It argues that:
Growth = Good: Innovation creates abundance, longer lives, and better living standards.
Barriers = Bad: Regulation, caution, and pessimism slow down progress and should be resisted.
Technology as Solution: Challenges like poverty, disease, and climate change are best solved by accelerating science and technology rather than restricting them.
In short: Techno-optimism sees faster innovation as the surest path to human flourishing — and treats resistance to technological progress as harmful.
"
Here’s a structured overview of the major schools of economic thought, mapped across time, followed by an estimate of which views dominate public and policy thinking today.
📜 Timeline of Major Economic Schools
1. Mercantilism (1500s–1700s)
Core idea: National wealth measured by gold/silver; exports > imports; heavy state control.
Influence: Guided colonial empires, trade monopolies, and tariffs.
2. Classical Economics (1776–1870)
Key figures: Adam Smith, David Ricardo, John Stuart Mill.
Core idea: Free markets, division of labour, “invisible hand”; emphasis on production and growth.
Impact: Industrial Revolution policy, laissez-faire capitalism.
3. Marxism & Socialist Economics (mid-1800s–present)
Key figure: Karl Marx.
Core idea: Critique of capitalism, labour theory of value, class struggle, state ownership.
Impact: Inspired communist revolutions, socialist policies, labour movements.
4. Marginalism & Neoclassical Economics (1870s–present)
Key figures: Jevons, Walras, Marshall.
Core idea: Value determined by marginal utility; equilibrium analysis; rational individuals.
Impact: Foundation of modern mainstream economics, microeconomics.
5. Keynesian Economics (1930s–present)
Key figure: John Maynard Keynes.
Core idea: Markets can fail (esp. in depressions); governments should manage demand using fiscal & monetary policy.
Impact: Guided post–WWII Western economies, welfare state expansion.
6. Monetarism & Chicago School (1950s–1980s)
Key figure: Milton Friedman.
Core idea: Control money supply to manage inflation; limit government intervention.
Impact: Reaganomics, Thatcherism, central bank independence.
7. Austrian School (late 1800s–present, revived 1970s)
Key figures: Carl Menger, Ludwig von Mises, Friedrich Hayek.
Core idea: Importance of entrepreneurship, spontaneous order, critique of central planning.
Impact: Free-market think tanks, libertarian movements.
8. Development Economics (1940s–present)
Core idea: Structural transformation, role of institutions, tackling poverty in Global South.
Impact: World Bank, UN development policy, debates on aid.
9. New Keynesian & New Classical Synthesis (1980s–present)
Core idea: Rational expectations (New Classical) + sticky wages/prices (New Keynesian).
Impact: Dominant academic framework; forms the basis of central bank models today.
10. Modern Schools (1990s–present)
Behavioural Economics: Psychology meets economics (Kahneman, Thaler).
Post-Keynesian / MMT (Modern Monetary Theory): Governments with sovereign currencies can run large deficits to ensure employment.
Ecological Economics: Sustainability, climate change, “beyond GDP”.
Techno-Optimist / Data-driven Economics: Big data, market design, platform economies.
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