Darwinian Demons: Climate Change and the AI Arms Race (with Kristian Rönn)
1. Shift Problem-Solving Narrative
Change the narrative from blaming individuals (corrupt politicians, greedy CEOs) to understanding and addressing the systemic “game” or root causes that incentivize harmful behaviors, as this approach is more effective for solving core problems.
2. Adopt Utilitarian Long-Termism
Care about all beings, including those in the future, and focus on global coordination to reduce global risks like climate change, AI, and nuclear threats, as this perspective is crucial for tackling complex, interconnected issues.
3. Prioritize AI for Climate Solutions
Recognize that aligned artificial intelligence could significantly simplify solving climate change by enabling breakthroughs in areas like tree planting, fusion energy, and carbon capture, making it a highly impactful area to focus on.
4. Govern AI Through Chip Controls
Advocate for and implement governance mechanisms at the compute level (AI chips/GPUs), potentially building hardware-level checks to prevent training of dangerous AI models without safety evaluations, as this offers a tractable way to control powerful AI development.
5. Develop Specialized AI Tools
Shift AI development from creating general intelligences to building specialized tools (e.g., chess engines, protein folding algorithms) that excel in specific domains, making them easier to control and predict for safety.
6. Embrace Slow Technological Evolution
For long-term survival, civilizations should adopt a strategy of slow and careful technological innovation, investing heavily in predicting and simulating the implications of new technologies before deployment, to avoid “landmines” in the fitness landscape.
7. Increase Supply Chain Transparency for Safety
Foster greater transparency in technology supply chains (e.g., AI chips) to empower every actor to “vote” against dangerous uses, leveraging interconnectedness to enforce safety standards and prevent unilateral risky actions.
8. Utilize Export Controls for AI Safety
Governments, especially those with control over advanced AI chip production, should use export controls to enforce tracking and safety evaluations for AI models trained with these chips, incentivizing safer development globally.
9. Monitor AI Chip Locations
Implement systems to track the location and access of advanced AI chips (GPUs) to prevent their use by hostile nations or terrorist groups, as this is a more tractable problem for immediate AI governance.
10. Prioritize Safety Over Profit in AI
Recognize that financial incentives can override safety concerns in AI development, as seen with OpenAI’s board drama, and advocate for structures that prioritize safety and mission over profit to mitigate existential risks.
11. Beware of Hacking Metrics
Be aware of Goodhart’s Law: when a metric (e.g., academic citations, paper count) becomes a target, it ceases to be a good measure, as people will find ways to hack it rather than genuinely improve quality.
12. Pressure Supply Chains for Net Zero
If you are a company or consumer in an economy committed to net zero, pressure suppliers in your value chain to also adopt net zero practices, as on average 90% of emissions are located in supply chains.
13. Invest in Green Technologies
Support and invest in green technologies, as international agreements signal capital flows towards them, driving innovation and cost-effectiveness, making them more competitive than fossil fuels.
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