Speed of adoption of new technology #3
Description
Detailed Summary with Additional Examples from Text
Historical Context of Technological Advancements
- Major Shifts:
- Apple and tech giants surpassing oil companies.
- Advancements in deep learning and data-meets-compute approaches.
- Smartphone becoming a ubiquitous computing device.
- Solar-generated electricity becoming cheaper than fossil fuels.
- Cost of sequencing a human genome falling below $1000.
- CRISPR and CAR-T therapies development.
- Impact: These technological milestones question if we are experiencing short-term effects or a long-term trend.
Data and Compute in Machine Learning
- Key Insights:
- The significance of data and compute power is emphasized over traditional algorithmic methods.
- References to Peter Norvig's paper on data's effectiveness and Rich Sutton's preference for computation over heuristics.
- Deep Learning’s Moment: Started after the recognition of data and compute's role in sophisticated machine learning systems.
Economic and Technological Impacts
- Significant Reductions in Costs:
- Genome sequencing becoming 10x cheaper.
- Solar-generated electricity's price dropping by a factor of four.
- 80% reduction in the cost of lithium-ion batteries.
- Sectoral Impacts: These reductions impact not just technology, but also energy, transport, and healthcare sectors.
Acceleration Due to Feedback Loops
- AI and System-Wide Feedback Loop: Improvement in AI performance increases its utility, triggering demand and investment, especially in semiconductors.
- Example: AI's advancements leading to a cascade in demand, further driving investment down the stack.
Transition Time and Technology Adoption
- Historical and Current Transitions:
- Technology transitions usually taking 8-14 years.
- Current transitions may be faster due to consumer choices and feedback loops.
- Electricity Price Impact: Stable and declining electricity prices will have profound implications for under-electrified sectors.
Dealing with Knightian Uncertainty
- Types of Uncertainty:
- Knightian uncertainty involves unknowns where probabilities can't be assigned.
- Contrast with Bayesian risk, which relies on known probabilities.
- Strategies for Handling Uncertainty: Includes scenario thinking, flexibility, adaptability, resilience, and preparedness.
Themes for 2024
- Key Trends and Implications:
- Emphasis on electrification, AI, and decentralized systems.
- Potential rapid deployment of these technologies into economies, surpassing science and engineering hurdles.
Corporate AI Agenda and Business Models
- Corporate Adoption of AI:
- Significant increase in AI use in corporations.
- Legal challenges like copyright issues and the shaping of AI business models.
- Economic Implications: AI's impact on job markets and corporate strategies.
Scientific Advancements Through AI
- AI's Acceleration of Science:
- Examples like DeepMind’s Alphafold increasing speed of discovering protein structures.
- AI's role in unlocking new applications in biotech and healthcare.
Decentralization and Democratization of Technology
- Movement Toward Decentralization:
- Decentralization in AI and energy systems.
- Potential for decentralized AI systems to operate independently on devices like smartphones.
- Governance and Regulation: The need for new governance models and regulations for decentralized systems.
Global Challenges and Geopolitical Dynamics
- Influencing Factors:
- Climate change, demographic shifts, and geopolitical changes affecting technology trends.
- Interaction between technological advancements and geopolitical-economic realities.
Conclusion
The summary encapsulates the transformative technological developments of the past decade, highlighting critical shifts in sectors like AI, renewable energy, and genomics. It emphasizes the shift from data and compute over traditional algorithms in machine learning, the economic and sectoral impacts of cost reductions in key technologies, and the role of feedback loops in accelerating AI advancements. The summary also addresses the challenges of Knightian uncertainty in forecasting future trends, outlines significant themes for 2024, and discusses the corporate adoption of AI and its implications. The impact of decentralization in technology and the broader context of global challenges and geopolitical dynamics shaping these trends are also explored.