Note: I've only included below resources I myself have fully consumed/subscribed & felt helpful in building foundational understanding.

 

I hope to include paste my summaries/notes to the site with time, but for now here's the list;  Indicates Strong Recommendation

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1. AI's Trillion-Dollar Opportunity: Sequoia AI Ascent 2025 Keynote (Link) **

2. Trends - Artificial Intelligence Bondcap (Link) **

3. Ilya Sutskever’s Speech at NeurIPS 12/2024 (Link) **​

   • Helpful to understand scaling laws + why Ilya left OpenAI (a desire to solve AGI, not build a consumer wrapper)

4. Dario Amodei essay, Machines of Loving Grace 10/2024 (Link) **

5. Leopold Aschenbrenner’s essay, Situational Awareness: The Decade Ahead 07/2024 (Link) **

6. Andrej Karpathy’s General Audience deep-dives on LLMs from ~L4M (Link) **

7. Kelvin Mu’s essay, 2024 Backward Pass: The Definitive Guide to AI in 2024 (Link)

8. Dwarkesh Patel:

   • Satya Nadella Interview 02/2025 (Link) **

   • Dario Amodei Interview 08/2023 (Link)

   • Will Scaling work? Article 12/2023 (Link)

   • Ilya Sutskever Interview 03/2023 (Link)

   • Highly recommend subscribing to his substack as well

9. Sam Altman Interviews:

   • FreePress

   • DealBook

   • Lex

   • YC (How To Build The Future; 11/8/2024) **

   • YC (The Future of OpenAI, ChatGPT's Origin, and Building AI Hardware) **

   • HBS Fireside Chat

   • How to Start a Startup

   • ReThinking with Adam Grant

10. Jensen interviews:

    • View From the Top**

    • CES 2025

    • Cleo Abram’s Show

11. Articles from:

    • Benedict Evans  **

    • Every **

    • Stratechery **

    • Less Wrong

    • Gwern

    • Shtetl-Optimized

    • Interconnects

12. Ted Talks:

    • Mustafa Suleyman (“New Specifies”)

    • Surya Ganguliv (“Neuromorphic Computing”)

    • Aravind Srinivas (Perplexity Commercial)

13. VC Papers / Interviews

    • Bessemer’s Investment Strategies for the AI Revolution (Link)

    • Sequoia’s Interview with OpenAI’s Deep Research Team (Link)

14. Classes:

    • Dair.ai (Link)

    • Professor Roger Grosse’s (Link)

    • Andrew Ng’s on DeepLearning.AI (Link)

    • Coursera: AI for Everyone (Link)

15. A handful of academic papers from arXiv

    • Do Massively Pretrained Language Models Make Better Storytellers? (2019) (Link)

    • Model Evaluation for Extreme Risks (2023) (Link)

    • Are Emergent Abilities of Large Language Models a Mirage (2023) (Link)

    • Sparks of Artificial General Intelligence Early Experiments with GPT-4 (2023) (Link) 

    • Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models (2021) (Link)

    • Scaling Laws for Neural Language Models (2020) (Link)

    • Scaling Laws for Autoregressive Generative Modeling (2020) (Link)

    • Attention is All You Need (NIPS 2017) (Link)

    • ImageNet Classification with Deep Convolutional Neural Networks (2012) (Link) **

    • Technological Singularity, Vernor Vinge (1993) (Link) **

    • AlphaEvolve: A coding agent for scientific and algorithmic discovery (June 2025) (Link) **

    • The Era of Experience (2025) (Link) **

    • Link to download **

16. Books:

    • SuperIntelligence (Bostrom) **

    • Engines that Move Markets (Nairn) **

    • Quantum Computing for Everyone (Bernhardt)

    • Quantum Computing Since Democritus (Aaronson)

    • Society of the Mind (MInksy)

    • Deep Learning (Goodfellow) **

    • Deep Learning (Bishop)

    • Artificial Intelligence, A Modern Approach (Norvig) **

    • Hands on Machine Learning with Scikit-Learn, Keras, & TensorFlow (Geron)

    • The Coming Wave (Suleyman)

    • The Singularity is Nearer (Kurzweil) **

    • Genesis (Kissinger, Schmidt, Mundie)

    • Abundance (Kotler)

    • Co-Intelligence (Mollick)

    • The Soul of A New Machine (Kidder)

    • Unmasking AI (Dr. Buolamwini)

    • Advanced Analytics and AI: Impact, Implementation, and the Future of Work (Tony Boobier)

    • Advances in Financial Machine Learning (Marcos Prado)

17. YouTube Playlist (Link)