Ed Newton-Rex joins me to discuss the issue of AI models trained on copyrighted data, and how we might develop fairer approaches that respect human creators. We talk about AI-generated music, Ed’s decision to resign from Stability AI, the industry’s attitude towards rights, authenticity in AI-generated art, and what the future holds for creators, society, and living standards in an increasingly AI-driven world.  

Learn more about Ed's work here: https://ed.newtonrex.com  

Timestamps:  

00:00:00 Preview and intro  

00:04:18 AI-generated music  

00:12:15 Resigning from Stability AI  

00:16:20 AI industry attitudes towards rights 

00:26:22 Fairly Trained  

00:37:16 Special kinds of training data  

00:50:42 The longer-term future of AI  

00:56:09 Will AI improve living standards?  

01:03:10 AI versions of artists  

01:13:28 Authenticity and art  

01:18:45 Competitive pressures in AI 

01:24:06 Priorities going forward

Will AI Companies Respect Creators' Rights? (with Ed Newton-Rex)

On this episode, Sarah Hastings-Woodhouse joins me to discuss what benchmarks actually measure, AI’s development trajectory in comparison to other technologies, tasks that AI systems can and cannot handle, capability profiles of present and future AIs, the notion of alignment by default, and the leading AI companies’ vague AGI plans. We also discuss the human psychology of AI, including the feelings of living in the "fast world" versus the "slow world", and navigating long-term projects given short timelines.  

Timestamps:  

00:00:00 Preview and intro

00:00:46 What do benchmarks measure?  

00:08:08 Will AI develop like other tech?  

00:14:13 Which tasks can AIs do? 

00:23:00 Capability profiles of AIs  

00:34:04 Timelines and social effects 

00:42:01 Alignment by default?  

00:50:36 Can vague AGI plans be useful? 

00:54:36 The fast world and the slow world 

01:08:02 Long-term projects and short timelines

AI Timelines and Human Psychology (with Sarah Hastings-Woodhouse)

On this episode, Michael Nielsen joins me to discuss how humanity's growing understanding of nature poses dual-use challenges, whether existing institutions and governance frameworks can adapt to handle advanced AI safely, and how we might recognize signs of dangerous AI. We explore the distinction between AI as agents and tools, how power is latent in the world, implications of widespread powerful hardware, and finally touch upon the philosophical perspectives of deep atheism and optimistic cosmism.

Timestamps:  

00:00:00 Preview and intro 

00:01:05 Understanding is dual-use  

00:05:17 Can we handle AI like other tech?  

00:12:08 Can institutions adapt to AI?  

00:16:50 Recognizing signs of dangerous AI 

00:22:45 Agents versus tools 

00:25:43 Power is latent in the world 

00:35:45 Widespread powerful hardware 

00:42:09 Governance mechanisms for AI 

00:53:55 Deep atheism and optimistic cosmism

Could Powerful AI Break Our Fragile World? (with Michael Nielsen)

On this episode, Ben Goertzel joins me to discuss what distinguishes the current AI boom from previous ones, important but overlooked AI research, simplicity versus complexity in the first AGI, the feasibility of alignment, benchmarks and economic impact, potential bottlenecks to superintelligence, and what humanity should do moving forward.   

Timestamps:  

00:00:00 Preview and intro  

00:01:59 Thinking about AGI in the 1970s  

00:07:28 What's different about this AI boom?  

00:16:10 Former taboos about AGI 

00:19:53 AI research worth revisiting  

00:35:53 Will the first AGI be simple?  

00:48:49 Is alignment achievable?  

01:02:40 Benchmarks and economic impact  

01:15:23 Bottlenecks to superintelligence 

01:23:09 What should we do?

Facing Superintelligence (with Ben Goertzel)

On this episode, Jeff Sebo joins me to discuss artificial consciousness, substrate-independence, possible tensions between AI risk and AI consciousness, the relationship between consciousness and cognitive complexity, and how intuitive versus intellectual approaches guide our understanding of these topics. We also discuss AI companions, AI rights, and how we might measure consciousness effectively.  

You can follow Jeff’s work here: https://jeffsebo.net/  

Timestamps:  

00:00:00 Preview and intro 

00:02:56 Imagining artificial consciousness  

00:07:51 Substrate-independence? 

00:11:26 Are we making progress?  

00:18:03 Intuitions about explanations  

00:24:43 AI risk and AI consciousness  

00:40:01 Consciousness and cognitive complexity  

00:51:20 Intuition versus intellect 

00:58:48 AIs as companions  

01:05:24 AI rights  

01:13:00 Acting under time pressure 

01:20:16 Measuring consciousness  

01:32:11 How can you help?

Will Future AIs Be Conscious? (with Jeff Sebo)

On this episode, Zvi Mowshowitz joins me to discuss sycophantic AIs, bottlenecks limiting autonomous AI agents, and the true utility of benchmarks in measuring progress. We then turn to time horizons of AI agents, the impact of automating scientific research, and constraints on scaling inference compute. Zvi also addresses humanity’s uncertain AI-driven future, the unique features setting AI apart from other technologies, and AI’s growing influence in financial trading.  

You can follow Zvi's excellent blog here: https://thezvi.substack.com  

Timestamps:  

00:00:00 Preview and introduction  

00:02:01 Sycophantic AIs  

00:07:28 Bottlenecks for AI agents  

00:21:26 Are benchmarks useful?  

00:32:39 AI agent time horizons  

00:44:18 Impact of automating research 

00:53:00 Limits to scaling inference compute  

01:02:51 Will the future go well for humanity?  

01:12:22 A good plan for safe AI  

01:26:03 What makes AI different?  

01:31:29 AI in trading

Understanding AI Agents: Time Horizons, Sycophancy, and Future Risks (with Zvi Mowshowitz)

On this episode, Jeffrey Ding joins me to discuss diffusion of AI versus AI innovation, how US-China dynamics shape AI’s global trajectory, and whether there is an AI arms race between the two powers. We explore Chinese attitudes toward AI safety, the level of concentration of AI development, and lessons from historical technology diffusion. Jeffrey also shares insights from translating Chinese AI writings and the potential of automating translations to bridge knowledge gaps.  

You can learn more about Jeffrey’s work at: https://jeffreyjding.github.io  

Timestamps:  

00:00:00 Preview and introduction  

00:01:36 A US-China AI arms race?  

00:10:58 Attitudes to AI safety in China  

00:17:53 Diffusion of AI  

00:25:13 Innovation without diffusion  

00:34:29 AI development concentration  

00:41:40 Learning from the history of technology  

00:47:48 Translating Chinese AI writings  

00:55:36 Automating translation of AI writings

Inside China’s AI Strategy: Innovation, Diffusion, and US Relations (with Jeffrey Ding)

On this episode, Allison Duettmann joins me to discuss centralized versus decentralized AI, how international governance could shape AI’s trajectory, how we might cooperate with future AIs, and the role of AI in improving human decision-making. We also explore which lessons from history apply to AI, the future of space law and property rights, whether technology is invented or discovered, and how AI will impact children. 

You can learn more about Allison's work at: https://foresight.org  

Timestamps:  

00:00:00 Preview 

00:01:07 Centralized AI versus decentralized AI  

00:13:02 Risks from decentralized AI  

00:25:39 International AI governance  

00:39:52 Cooperation with future AIs  

00:53:51 AI for decision-making  

01:05:58 Capital intensity of AI 

01:09:11 Lessons from history  

01:15:50 Future space law and property rights  

01:27:28 Is technology invented or discovered?  

01:32:34 Children in the age of AI

How Will We Cooperate with AIs? (with Allison Duettmann)

On this episode, Steven Byrnes joins me to discuss brain-like AGI safety. We discuss learning versus steering systems in the brain, the distinction between controlled AGI and social-instinct AGI, why brain-inspired approaches might be our most plausible route to AGI, and honesty in AI models. We also talk about how people can contribute to brain-like AGI safety and compare various AI safety strategies.  

You can learn more about Steven's work at: https://sjbyrnes.com/agi.html  

Timestamps:  

00:00 Preview  

00:54 Brain-like AGI Safety 

13:16 Controlled AGI versus Social-instinct AGI  

19:12 Learning from the brain  

28:36 Why is brain-like AI the most likely path to AGI?  

39:23 Honesty in AI models  

44:02 How to help with brain-like AGI safety  

53:36 AI traits with both positive and negative effects  

01:02:44 Different AI safety strategies

Brain-like AGI and why it's Dangerous (with Steven Byrnes)

On this episode, Ege Erdil from Epoch AI joins me to discuss their new GATE model of AI development, what evolution and brain efficiency tell us about AGI requirements, how AI might impact wages and labor markets, and what it takes to train models with long-term planning. Toward the end, we dig into Moravec’s Paradox, which jobs are most at risk of automation, and what could change Ege's current AI timelines.  

You can learn more about Ege's work at https://epoch.ai  

Timestamps:  00:00:00 – Preview and introduction 

00:02:59 – Compute scaling and automation - GATE model 

00:13:12 – Evolution, Brain Efficiency, and AGI Compute Requirements 

00:29:49 – Broad Automation vs. R&D-Focused AI Deployment 

00:47:19 – AI, Wages, and Labor Market Transitions 

00:59:54 – Training Agentic Models and Long-Term Planning Capabilities 

01:06:56 – Moravec’s Paradox and Automation of Human Skills 

01:13:59 – Which Jobs Are Most Vulnerable to AI? 

01:33:00 – Timeline Extremes: What Could Change AI Forecasts?

How Close Are We to AGI? Inside Epoch’s GATE Model (with Ege Erdil)

The Future of Life Institute (FLI) is a nonprofit working to reduce global catastrophic and existential risk from powerful technologies. In particular, FLI focuses on risks from artificial intelligence (AI), biotechnology, nuclear weapons and climate change. The Institute's work is made up of three main strands: grantmaking for risk reduction, educational outreach, and advocacy within the United Nations, US government and European Union institutions. FLI has become one of the world's leading voices on the governance of AI having created one of the earliest and most influential sets of governance principles: the Asilomar AI Principles.

Future of Life Institute Podcast

Gus Docker's Show

The year is 2045. Humanity is not extinct, nor living in a dystopia. It has averted climate disaster, major wars and the risks from advanced Artificial Intelligence. How? This was what we asked the entrants of our Worldbuilding Contest to imagine last year.

Our new podcast series digs deeper into the eight winning entries, their ideas and solutions, the diverse teams behind them and the challenges they faced. You might love some; others you might not choose to inhabit. FLI is not endorsing any one idea. Rather, we hope to grow the conversation about what futures people get excited about.

Ask yourself, with each episode, is this a world you’d want to live in? And if not, what would you prefer?

Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts.

Explore the worldbuilds discussed in these episodes here: https://worldbuild.ai/winners

This podcast was produced by the Future of Life Institute. FLI is a non-profit that works to reduce large-scale risks from transformative technologies and promote the development and use of these technologies to benefit all life on Earth.

Gus Docker

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