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S2-E5: Understanding the AI Value Chain: From GPUs to Applications
In this episode, Piyush and Nikhil explore the AI value chain. We explore the different components that go into delivering an AI application from start to finish, including the compute layer, the foundational model layer, and the application layer. Drawing analogies to traditional supply chains, we discuss how companies like NVIDIA, OpenAI, Google, Microsoft, etc. fit into this ecosystem. Tune in to understand the intricacies of AI development and the roles various players have in this rapidly evolving field.
=== ⏰ CHAPTERS ===
00:00: Introducing the concept of a Value Chain
08:37: The Value Chain of traditional software applications
25:41: Value Chain of AI: compute, models, and applications
44:04: Broader considerations in the value chain: environmental impact, closed-source vs. open-source, etc.
=== 🧠 KEY CONCEPTS ===
- The AI Value Chain: The value chain for AI includes three main layers: compute (hardware and cloud), foundational models, and applications. Each layer involves different companies and technologies that add value to the end product.
- Compute Layer: NVIDIA GPUs play a crucial role in AI model training due to their efficiency in neural computing. Cloud companies like AWS, Google Cloud, and Microsoft Azure provide the infrastructure for training and deploying these models.
- Foundational Models: Training AI models is resource-intensive, requiring vast amounts of data and computational power. There is a distinction between open source and closed source models, impacting how developers can use and customize them.
- Application Layer: Applications built on AI can either integrate their own models or rely on existing ones. The value of AI in applications depends on the balance between computational cost and the value added to the user experience.
- Energy Consumption: AI's growing energy demands pose environmental challenges, potentially driving innovations in sustainable energy solutions, including nuclear energy.
=== 🔗 LINKS ===
- Homepage: https://artscienceai.substack.com/about
- YouTube: https://www.youtube.com/@ArtScience-AI
- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai
- Linkedin: https://www.linkedin.com/company/art-science-ai
- Facebook: https://www.facebook.com/ArtScienceAI
- Instagram: http://instagram.com/artscienceai/
- Threads: https://www.threads.net/@artscienceai
- Twitter / X: https://x.com/ArtScienceAI
=== 💬 KEYWORDS ===
#AI #GenAI #LLM #ChatGPT #podcast
View all episodes
By Nikhil Maddirala and Piyush AgarwalIn this episode, Piyush and Nikhil explore the AI value chain. We explore the different components that go into delivering an AI application from start to finish, including the compute layer, the foundational model layer, and the application layer. Drawing analogies to traditional supply chains, we discuss how companies like NVIDIA, OpenAI, Google, Microsoft, etc. fit into this ecosystem. Tune in to understand the intricacies of AI development and the roles various players have in this rapidly evolving field.
=== ⏰ CHAPTERS ===
00:00: Introducing the concept of a Value Chain
08:37: The Value Chain of traditional software applications
25:41: Value Chain of AI: compute, models, and applications
44:04: Broader considerations in the value chain: environmental impact, closed-source vs. open-source, etc.
=== 🧠 KEY CONCEPTS ===
- The AI Value Chain: The value chain for AI includes three main layers: compute (hardware and cloud), foundational models, and applications. Each layer involves different companies and technologies that add value to the end product.
- Compute Layer: NVIDIA GPUs play a crucial role in AI model training due to their efficiency in neural computing. Cloud companies like AWS, Google Cloud, and Microsoft Azure provide the infrastructure for training and deploying these models.
- Foundational Models: Training AI models is resource-intensive, requiring vast amounts of data and computational power. There is a distinction between open source and closed source models, impacting how developers can use and customize them.
- Application Layer: Applications built on AI can either integrate their own models or rely on existing ones. The value of AI in applications depends on the balance between computational cost and the value added to the user experience.
- Energy Consumption: AI's growing energy demands pose environmental challenges, potentially driving innovations in sustainable energy solutions, including nuclear energy.
=== 🔗 LINKS ===
- Homepage: https://artscienceai.substack.com/about
- YouTube: https://www.youtube.com/@ArtScience-AI
- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai
- Linkedin: https://www.linkedin.com/company/art-science-ai
- Facebook: https://www.facebook.com/ArtScienceAI
- Instagram: http://instagram.com/artscienceai/
- Threads: https://www.threads.net/@artscienceai
- Twitter / X: https://x.com/ArtScienceAI
=== 💬 KEYWORDS ===
#AI #GenAI #LLM #ChatGPT #podcast