Big Technology Podcast: Generative AI 101 with Dylan Patel

Host: Alex Kantrowitz
Guest: Dylan Patel, Founder and Chief Analyst at Semianalysis
Release Date: April 23, 2025
Duration Covered: 00:00 – 40:19

Introduction to Generative AI

Alex Kantrowitz kicks off the episode by addressing listeners' curiosity about the workings and future trajectory of generative AI. He emphasizes the goal of making the episode a comprehensive yet concise guide to understanding generative AI, aiming to distill complex concepts into an accessible one-hour discussion.

“I want this to be an episode that helps people learn how generative AI works and is an episode that people will send to their friends to explain to them how generative AI works.”
[00:00]

Dylan Patel joins the conversation, bringing his expertise in semiconductor and generative AI research to break down foundational elements such as tokens, pre-training, fine-tuning, and reasoning in AI models.

Understanding Tokens in Generative AI

The discussion begins with an exploration of tokens, the fundamental units that AI models use to understand and generate language.

Dylan Patel explains that tokens are akin to syllables in human language—basic chunks of words that carry meaning.

“Tokens are in fact like chunks of words, right? In the human way you can think of like syllables, right. Syllables are often viewed as like chunks of word. They have some meaning.”
[02:23]

Alex elaborates on how tokens are represented numerically, allowing models to predict subsequent tokens effectively.

“AI models are very good at predicting patterns... assigning them a numerical value, and then basically, in its own word, in its own language, learning to predict what number comes next...”
[03:20]

Dylan further clarifies that each token is represented by multiple vectors, capturing nuanced relationships between words. For instance, "king" and "queen" share similarities but differ in specific vectors representing gender and associated historical contexts.

“These numbers are, are inefficient representation of words because you can do math on them, right. You can, you can multiply them, you can divide them...”
[04:52]

This multidimensional representation allows models to understand complex linguistic relationships beyond simple word associations.

The Pre-training Process

Pre-training involves feeding vast amounts of text data into AI models to help them learn the probabilities of token sequences.

Alex describes pre-training as exposing the model to extensive language data to predict the next token accurately.

“Pre training is the objective function, which is just to reduce loss, that is how often is the token predicted incorrectly versus correctly.”
[06:47]

Dylan expands on this by explaining that pre-training equips the model with a broad understanding of language patterns without specific directives, enabling it to handle diverse contexts.

“Pre training is defined as pre because you're still letting it do things and teaching it things and inputting things into the model that are theoretically like quite bad.”
[12:13]

He highlights the balance required during pre-training to ensure the model learns general language patterns while mitigating the risks of absorbing harmful or inappropriate content.

Fine-Tuning and Post Training

Fine-tuning, or post training, tailors the pre-trained model to specific tasks or behaviors, enhancing its utility and alignment with desired outcomes.

Alex summarizes fine-tuning as imparting personality and specific responses to the model.

“Post training is so you have a model that's good at predicting the next word. And in post training, you sort of give it a personality by inputting sample conversations...”
[18:14]

Dylan outlines various methods used in post training, including Reinforcement Learning with Human Feedback (RLHF), which involves using human-labeled data to guide the model's responses.

“Post training can be a number of different things... using reinforcement learning and other synthetic data technologies.”
[19:00]

He emphasizes that post training is critical for aligning AI outputs with human values and specific application requirements, resulting in models with distinct personalities and functionalities.

Reasoning in AI Models

The conversation shifts to reasoning, a capability that enhances AI models' problem-solving skills beyond simple pattern prediction.

Alex introduces reasoning as the model's ability to generate and evaluate multiple tokens to arrive at a coherent and accurate answer.

“Reasoning is basically instead of the model going basically predicting the next word based off of its training, it uses the tokens to spend more time basically figuring out what the right answer is...”
[25:18]

Dylan compares this to human thought processes, where individuals deliberate before answering, allowing for more accurate and contextually appropriate responses.

“Reasoning models are effectively teaching large pre-trained models to do this, right? Think through the problem. Output a lot of tokens, think about it, generate all this text...”
[26:39]

This approach significantly improves the model's performance in complex tasks such as mathematics, coding, and understanding nuanced social issues.

Model Efficiency and Data Center Scaling

Despite advancements in making models more efficient, data centers continue to expand to meet the growing computational demands.

Alex mentions the decrease in costs associated with newer models like Deep SEQ and the consequent reactions in the market, particularly with Nvidia's stock.

“...OpenAI had GPT4, then they had 4 Turbo, which was half the cost... Deep SEQ was similar... the cost fell 1200x from GPT3's initial cost to what you can get llama 3.2.3B today.”
[28:27] – [30:30]

Dylan explains that while models are becoming cheaper and more efficient due to algorithmic improvements, the overall demand for compute power drives massive investments in data center infrastructure.

“When we look across the ecosystem at data center build outs... capacity in... largest scale training supercomputers goes... billions of dollars to... next generation things.”
[32:42] – [35:54]

He underscores that increased efficiency doesn't negate the need for larger data centers; instead, it enables the scaling required to handle more complex and capable models.

The Bitter Lesson and Future Directions

Dylan Patel introduces the concept of the "Bitter Lesson", which posits that scaling up models and compute resources often yields better results than specialized, intuition-driven approaches.

“The bitter lesson because yes, you can make... but these things should also just have a lot more compute thrown behind them because if you make it more efficient, as you follow the scaling laws up, it'll also just get better...”
[35:44] – [36:18]

He discusses the ongoing trend of scaling both pre-training and post training, which is crucial for unlocking new AI capabilities and maintaining competitive advantages.

Alex reflects on the paradox of increasing model efficiency alongside expanding data centers, questioning the sustainability and future impact of such growth.

“If we are getting more efficient, why are these data centers getting so much bigger?”
[32:00]

Dylan responds by highlighting that efficiency gains alone are insufficient to address global challenges like resource scarcity and environmental concerns. He argues that continued scaling is essential for developing AI systems capable of innovative solutions.

“The earth is still going to run out of resources... unless you continue to improve AI and invent and, or just generally research new things and AI helps us research new things.”
[36:54] – [37:16]

Looking Ahead: GPT-5 and Beyond

In discussing the future, Dylan provides insights into the development of GPT-5, a model expected to integrate extensive pre-training with advanced post training.

“GPT5, as Sam calls it, is going to be a model that has huge pre training scale... and also huge post training scale like oh1 and oh3...”
[37:21] – [38:50]

He anticipates GPT-5 to offer significant improvements in capabilities, driven by both increased data and refined training techniques, setting the stage for next-generation AI applications.

Final Thoughts and Semianalysis Information

As the episode concludes, Dylan shares information about Semianalysis, his organization focused on providing in-depth analysis and consulting in the AI and semiconductor sectors.

“We have the public stuff, which is like all these reports that are pseudo free. But then most of our work is done directly for clients... roughly 30 of us across the world...”
[38:59] – [40:09]

He invites interested parties to engage with Semianalysis through their website for reports and consulting services.

Alex expresses appreciation for Dylan's insights and the informative discussion, wrapping up the episode.

“Dylan is really a thrill to get a chance to meet you and talk through these topics with you. So thanks so much for coming on the show.”
[40:17]

Note: This summary encapsulates the first 40 minutes of the episode, focusing on the core discussions about generative AI's mechanisms, training processes, efficiency trends, and future developments.