"Compute is the New Oil", Leaving Google, Founding Groq, Agents, Bias/Control (Jonathan Ross)
TLDRJonathan Ross, founder and CEO of Groq, discusses his journey from Google, where he developed the Tensor Processing Unit (TPU), to founding Groq, a company specializing in AI chips. Ross explains the decision to leave Google stemmed from a desire for more ambition and the freedom to pursue innovative ideas without corporate constraints. He emphasizes Groq's unique approach to chip design, focusing on efficiency and lower memory per chip, which allows for faster inference speeds. Ross also addresses the potential business models for utilizing Groq's technology, suggesting that companies start with Groq Cloud before considering on-premises hardware deployment. He shares his optimism about AI's role in enhancing human discourse through subtlety and nuance, while also acknowledging concerns about decision-making and control. Ross highlights the importance of preserving human agency in AI and ensuring that models assist rather than dictate human choices.
Takeaways
- 🚀 **Founding Groq**: Jonathan Ross, previously at Google, invented the TPU and later founded Groq to pursue more ambitious goals outside of the constraints of a large corporation.
- 💡 **Innovation within Google**: Ross realized the limitations of innovation within a large company, comparing the TPU project to a startup within Google, highlighting the need for multiple approvals versus the freedom of a startup.
- 🏗️ **Groq's Architectural Focus**: Initially, Groq spent significant time developing its compiler, which provided a unique advantage and laid the foundation for their chip design.
- ⚡ **Inference Speed**: Groq is known for its high inference speed, capable of processing hundreds of tokens per second, which is significantly faster than human reading speed.
- 💾 **Memory Trade-offs**: Groq chips have lower memory per chip, which means businesses need to purchase multiple units for high-volume inference tasks, but this design choice enables higher efficiency and throughput.
- 🤖 **Agents and AI**: Ross is bullish on the potential of AI agents and sees Groq's inference speed as a key enabler for these applications, allowing for rapid interaction and decision-making.
- 🌐 **Cloud vs. On-Prem Hardware**: Groq recommends starting with their cloud service for ease of use and scalability, and only considers on-prem hardware deployment for businesses with extremely large-scale needs.
- 💹 **Market Opportunities in AI**: Ross suggests that while model layers may be commoditized, there are opportunities in infrastructure and silicon layers, though starting a new silicon company is challenging due to market saturation.
- 🛠️ **Optimizing for Groq Hardware**: Model builders can optimize for Groq's hardware by utilizing low-latency architectures and taking advantage of Groq's automated compiler and quantization capabilities.
- 🌟 **The Future of AI**: Ross is hopeful that AI will bring subtlety and nuance to human discourse, while also acknowledging concerns about decision-making and control as AI becomes more integrated into daily life.
- 🔍 **Curating AI Models**: Groq aims to curate models that assist human decision-making rather than replacing it, focusing on preserving human agency in the age of AI.
Q & A
What was Jonathan Ross' role at Google before founding Groq?
-Jonathan Ross was at Google where he invented the Tensor Processing Unit (TPU), which is the custom silicon that powers much of Google's AI capabilities.
Why did Jonathan Ross decide to leave Google and start his own company?
-Ross felt constrained by the corporate structure at Google, which required many approvals to pursue new ideas. He saw an opportunity to be more ambitious and bold outside of Google, where he could pitch his ideas to numerous venture capitalists for funding.
What was the initial focus of Groq during its first six months?
-Groq initially focused on developing a compiler that would make software easier to use, before they started designing their chip. This focus provided them with a unique advantage.
How does Groq's chip architecture differ from traditional approaches, and why?
-Groq's architecture is designed for efficiency and to avoid memory limitations, allowing for faster processing speeds. It uses a larger system where data 'shoots through' as opposed to the 'assembly line' approach of GPUs that are slower due to external memory dependencies.
What is the advantage of Groq's cloud service for businesses looking to utilize AI?
-Groq's cloud service allows businesses to start using their technology immediately without any initial investment. It is scalable and handles the complexity of hardware management, offering high utilization rates and lower energy consumption.
What are some of the future challenges and opportunities that Jonathan Ross sees in the AI industry?
-Ross sees compute power as the new limiting factor, often referred to as the 'new oil.' He believes that the ability to create new information in the moment, rather than just copying it, will be crucial. He also emphasizes the importance of subtlety and nuance in human discourse, which he hopes AI will enhance.
What is Jonathan Ross' perspective on the role of generative AI in future human interactions?
-Ross is hopeful that generative AI will bring subtlety and nuance to human discourse, provoking curiosity and helping people understand different points of view. He believes that it will be a tool that enhances human decision-making rather than replacing it.
How does Groq's approach to hardware design benefit from its focus on a larger number of chips?
-By designing for more chips, Groq can achieve higher efficiency and better use of the chips for shorter periods, similar to an assembly line. This results in lower costs per operation and faster processing speeds.
What is the significance of Groq's inference speed for the use of AI agents?
-Groq's high inference speed allows AI agents to work together more effectively, enabling real-time interactions and decision-making processes that would not be possible with slower speeds.
How does Groq ensure that the models used on its cloud platform are of high quality?
-Groq focuses on making the best models available on its platform, curating them to ensure they provide value and do not make decisions for users, but rather assist them in understanding and making their own decisions.
What is the potential business model for companies that are not using Groq's hardware but are inspired by its approach?
-Companies could potentially lease out hardware resources, similar to how some organizations lease out Nvidia cards. However, Groq itself is not focused on renting individual chips but rather providing a service that maximizes hardware utilization.
What advice does Jonathan Ross have for entrepreneurs looking to start a company in the AI space?
-Ross advises focusing on areas where there is less competition, such as infrastructure, where handling 'drudgery' can lead to significant value creation. He also emphasizes the importance of starting with a clear understanding of the problem you aim to solve and pursuing it with passion.
Outlines
🚀 Founding GroQ and the Journey from Google
Jonathan Ross, CEO of GroQ, discusses his departure from Google, where he was instrumental in creating the Tensor Processing Unit (TPU). He reflects on the limitations of working within a large corporation and the decision-making process that led him to start his own venture. Ross emphasizes the importance of ambition and boldness in innovation, contrasting the need for internal consensus at Google with the potential for external funding and support as an independent entrepreneur. He also shares how the initial focus on software development before creating the chip gave GroQ a unique advantage.
💡 GroQ's Chip Design Philosophy and Business Considerations
The discussion shifts to GroQ's chip design, which prioritizes efficiency and avoids memory constraints, leading to a design that uses more chips but offers significant advantages in performance. Ross explains the rationale behind this approach, comparing it to an assembly line and emphasizing the cost and efficiency benefits. He addresses concerns about the investment required for businesses to acquire GroQ hardware and suggests starting with GroQ Cloud for ease of use and scalability. The conversation also touches on the potential for GroQ to offer a service where users can upload their models for execution, leveraging GroQ's efficient hardware utilization.
🌐 The Future of Compute and AI Entrepreneurship
Ross and the interviewer explore the future of the AI industry, considering the value of compute power and the potential bottlenecks that may arise in the coming years. Ross shares his insights on the different layers of the AI ecosystem, from silicon to applications, and where he sees the most value being created. He advises aspiring AI entrepreneurs to focus on areas where they can add unique value, whether in hardware, infrastructure, or model development. Ross also expresses optimism about the role of agents in the future of AI, highlighting the potential for GroQ's high-speed inference to enable new applications.
⚙️ Optimizing for GroQ Hardware and the Selection Process for Models
The conversation delves into technical aspects, with Ross explaining how model builders can optimize their work for GroQ's hardware. He discusses the advantages of certain architectures that benefit from low latency and the potential for quantization to enhance performance. Ross also outlines GroQ's approach to selecting models for its cloud platform, emphasizing a focus on quality and the company's commitment to providing users with the best models available.
🌟 Hopes and Fears for the Future of AI
In the final part of the discussion, Ross shares his hopes and fears regarding the future of AI. He is hopeful that AI will bring subtlety and nuance to human discourse, fostering curiosity and understanding. However, he also acknowledges the fear that AI could lead to a loss of human agency, as people may become overly reliant on AI for decision-making. Ross stresses GroQ's mission to empower human decision-making rather than replace it, ensuring that AI serves as a tool to enhance human capabilities rather than control them.
Mindmap
Keywords
💡Groq
💡Tensor Processing Unit (TPU)
💡Inference Speed
💡Memory Constraints
💡Cloud Provider
💡null
💡Compute
💡Agents
💡Hardware Lottery
💡Quantization
💡Control and Bias
Highlights
Jonathan Ross, founder and CEO of Groq, discusses the founding story of Groq and his departure from Google.
Ross shares his experience of building Google TPU and the constraints he felt within a large company.
He emphasizes the importance of ambition and boldness when starting a new venture.
Groq's unique approach to chip design, focusing on efficiency and avoiding memory limitations.
The decision behind Groq's lower memory per chip and its implications for businesses.
Groq's inference speed is unparalleled, reaching 5-7+ 100 tokens per second.
The counterintuitive design choice of using more chips for better efficiency and cost-effectiveness.
Groq's cloud service offers immediate access to their technology for developers.
The potential business model of leasing out hardware like Nvidia cards and its applicability to Groq.
Ross's perspective on the future of compute power and its comparison to the new oil.
Advice for entrepreneurs looking to start a startup in the AI space, focusing on different layers of opportunity.
The potential of agents and how Groq's inference speed can power their collaboration.
Optimizing models for Groq hardware by taking advantage of low latency and the automated compiler.
Groq's focus on providing the best models on their platform, rather than a large quantity of models.
Ross's hopeful outlook on AI bringing subtlety and nuance to human discourse.
Concerns about control and bias in AI models and Groq's mission to preserve human agency.
The challenge of curating models and information to ensure AI aids human decision-making.