GENERAL
Curated Resources
Curated Resources for AI Leadership
Internal document for tracking high-quality resources across AI, technology, and leadership domains
🎓 Essential Learning Resources
Standalone resources for building fundamental understanding - start here
LLMs & Generative AI Understanding
Note: While AI encompasses many technologies (machine learning, computer vision, robotics, etc.), this section focuses specifically on Large Language Models and generative AI - the transformative technologies behind ChatGPT, Claude, and similar systems.
- Author: Brit Cruise (Art of the Problem channel, Former Khan Academy educator, X in a Box co-founder)
- Content: Historical development and breakthrough moments leading to ChatGPT
- Why Essential:
- Context for understanding current AI capabilities and trajectory
- Non-technical but deeply insightful storytelling
- Shows progression from research breakthroughs to commercial products
- Perfect introduction for leaders who need the big picture
- Best For: Strategic understanding of AI development trajectory and business implications
- Duration: ~25 minutes
How LLMs Work Explained Briefly
- Author: 3Blue1Brown (Grant Sanderson)
- Content: Mathematical intuition behind LLM functionality with visual explanations
- Why Essential:
- Exceptional visual explanations of complex mathematics
- Builds intuitive understanding of transformer architecture
- Perfect balance of mathematical rigor and accessibility
- 3Blue1Brown's signature visual style makes complex concepts clear
- Best For: Understanding the mathematical foundations without getting lost in details
- Duration: Brief technical overview
Deep Dive into LLMs like ChatGPT
- Author: Andrej Karpathy
- Content: Comprehensive general audience explanation of Large Language Models
- Why Essential:
- Covers full training stack of how LLMs are developed
- Provides mental models for LLM "psychology" and behavior
- Practical guidance for getting the best use from LLMs
- Most comprehensive public explanation from a leading AI researcher
- Best For: Leaders who need deep understanding of what LLMs actually are and how they work
- Duration: ~1 hour comprehensive dive
- Author: Andrej Karpathy
- Content: Example-driven practical walkthrough of LLM applications
- Why Essential:
- Real-world usage patterns from AI expert
- Demonstrates practical applications beyond hype
- Shows how to integrate LLMs into actual workflows
- Bridges theory to practice
- Best For: Understanding practical implementation and workflow integration
- Duration: Extended practical examples
📡 Following & Staying Updated
Resources to subscribe to for ongoing insights and staying current with AI developments
Frontier AI Leadership
Note: These are the leaders of the primary frontier AI labs. Following them provides direct insight into the strategic direction of the entire field.
- Author: Sam Altman (CEO of OpenAI)
- Focus: Future of AI, AGI development, societal impact, long-term AI strategy
- Why Follow:
- Direct insights from the leader of the world's most influential AI company
- His posts often signal major strategic shifts and future product directions
- Provides a high-level vision for where AI is headed and its potential impact on humanity
- Articulates the long-term thinking behind OpenAI's mission
- Content Style: Strategic, visionary, long-form essays. Infrequent but high-impact.
- Key Topics: AGI, superintelligence, AI safety & alignment, economic impact of AI, future of work
- Resources: Blog, X/Twitter for more frequent updates
- Author: Dario Amodei (CEO of Anthropic)
- Focus: AI safety, interpretability, steerability, and the development of responsible AI
- Why Follow:
- Leader of Anthropic, a key competitor to OpenAI with a strong focus on AI safety
- Co-inventor of Reinforcement Learning from Human Feedback (RLHF)
- Provides a critical perspective on building safe and reliable AI systems
- Former VP of Research at OpenAI, led development of GPT-2 and GPT-3
- Content Style: Thoughtful essays, interviews, and op-eds on AI strategy and safety.
- Key Topics: AI safety, constitutional AI, interpretability, responsible scaling, AI policy
- Resources: Website, X/Twitter
- Author: Demis Hassabis (CEO and Co-Founder, Google DeepMind)
- Focus: AGI, using AI for scientific breakthroughs (AlphaFold), neuroscience-inspired AI
- Why Follow:
- Leader of Google's flagship AI research lab, a key player in the AGI race
- Responsible for historic breakthroughs like AlphaGo and AlphaFold
- Represents a long-term, research-heavy approach to AGI
- Provides a view into how AI is being used to solve fundamental scientific problems
- Content Style: Official announcements, high-level vision posts, research updates.
- Key Topics: AGI, scientific discovery, AlphaFold, Gemini, computational neuroscience
- Resources: Google Blog, X/Twitter
- Author: Ilya Sutskever (Co-Founder, Safe Superintelligence Inc.; former Co-Founder & Chief Scientist, OpenAI)
- Focus: Singularly focused on building Safe Superintelligence (SSI) in a dedicated research lab.
- Why Follow:
- A legendary figure in deep learning, pivotal to many of OpenAI's breakthroughs.
- His new company, SSI, approaches AGI development with safety as its primary, non-commercial goal.
- While he posts infrequently, his communications are highly significant for the long-term future of AI.
- Represents the purist, safety-first research track for AGI development.
- Content Style: Highly infrequent, mission-focused, and deeply impactful announcements.
- Key Topics: Safe Superintelligence (SSI), AGI safety, long-term AI research, AI consciousness.
- Resources: X/Twitter
Strategic & Academic Perspectives
- Author: Prof. Ethan Mollick (Wharton Associate Professor, Co-Director of Generative AI Labs, TIME's Most Influential People in AI)
- Focus: AI implications for work, education, and life from academic research perspective
- Why Follow:
- Research-based insights (not speculation) from leading AI academic researcher
- Practical applications of AI for leaders and organizations
- Author of "Co-Intelligence" (NY Times bestseller) and numerous academic papers
- 325,000+ subscribers demonstrates broad appeal and value
- Balances AI opportunities with realistic risk assessment
- Extensive experience implementing AI in educational settings
- Content Style: Academic rigor made accessible, research-backed analysis, regular updates
- Key Topics: AI strategy, workplace transformation, educational AI, entrepreneurship innovation, practical AI implementation
- Subscription: Free newsletter, also provides free AI resources and prompts at "More Useful Things"
Seizing the Agentic AI Advantage
- Author: McKinsey & Company
- Focus: Strategic adoption of AI agents to overcome the "gen AI paradox" where widespread tool adoption doesn't translate to bottom-line impact.
- Why Follow:
- Provides a CEO-level playbook for moving from scattered AI experiments to strategic, process-oriented transformation.
- Introduces the "agentic AI mesh" as a new architectural paradigm for scalable and governed AI.
- Articulates the shift from merely automating tasks to reinventing entire business processes with AI agents at the core.
- While not a technical guide, it offers a clear strategic vision for how to achieve scalable impact with agentic AI, making it essential for leadership.
- Content Style: Strategic report, executive summary, case studies.
- Key Topics: Agentic AI, Gen AI paradox, process reinvention, AI strategy, CEO playbook, organizational transformation.
Technical & Engineering Updates
- Author: Andrej Karpathy (Founder of Eureka Labs, Former OpenAI/Tesla AI Director, Stanford CS231n Creator)
- Focus: Deep learning fundamentals, neural networks, AI education, computer vision
- Why Follow:
- Legendary figure in deep learning with hands-on experience at OpenAI, Tesla, and Stanford
- Creator of CS231n - one of the most influential deep learning courses ever
- Exceptional ability to explain complex AI concepts from first principles
- Educational YouTube channel with "Zero to Hero" neural networks series
- Built foundational projects (micrograd, char-rnn, ConvNetJS) that taught thousands
- Real-world AI deployment experience (Tesla Autopilot, GPT-4 improvements)
- Content Style: Deep technical education, from-scratch implementations, visual explanations
- Key Topics: Neural network fundamentals, computer vision, LLM internals, AI education, deep learning mathematics
- Resources: YouTube channel, blog posts, open-source educational projects, course materials
- Author: Simon Willison (Co-creator of Django, Creator of Datasette, PSF Board Member)
- Focus: AI/LLMs, Python/Django, Data Tools, Security
- Why Follow:
- Deep technical expertise with 20+ years web development experience
- Practical AI implementation guidance with working code examples
- Early insights on AI trends and emerging technologies
- Balanced perspective on AI opportunities and risks
- Strong focus on AI security (prompt injection, ethical considerations)
- Content Style: Technical but accessible, daily updates since 2002
- Key Topics: AI-assisted programming, Django/Python patterns, data visualization, web security, open source project management
- Subscription: Free weekly newsletter, RSS feeds, paid monthly digest ($10+ GitHub sponsors)
📚 Reference & Research Resources
To be populated
🛠️ Tools & Platforms
To be populated
🤝 Communities & Networks
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📖 Books & Long-form Content
To be populated
🎙️ Podcasts & Audio Content
To be populated
📺 Conferences & Events
To be populated
Document Maintenance Notes:
- Review and update quarterly
- Add new resources as they are discovered and vetted
- Remove or archive resources that become outdated or inactive
- Following & Staying Updated: Format includes subscription info, content style, update frequency
- Deep Dive Learning: Format includes duration, best use case, essential learning value
- Maintain consistent format within each section type
Thank you for reading.
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