"Compute is the New Oil", Leaving Google, Founding Groq, Agents, Bias/Control (Jonathan Ross)

Matthew Berman
4 Apr 202424:22

TLDRJonathan Ross, founder and CEO of Groq, discusses his journey from Google, where he invented the TPU, to founding Groq, a company focused on creating high-speed AI chips. Ross emphasizes the importance of compute power, likening it to the new oil, and addresses the challenges of starting a startup within a large company. He details Groq's unique approach to chip design, which prioritizes efficiency and low memory usage, and how it contrasts with traditional GPU designs. Ross also talks about the potential of generative AI, the future of the industry, and the societal implications of AI. He highlights the need for AI to enhance human decision-making rather than replacing it, and stresses the importance of subtlety and nuance in AI's role in society.

Takeaways

  • ๐Ÿš€ **Innovation within Large Companies**: Jonathan Ross highlights the constraints of innovation within large corporations like Google, which led him to found Groq to pursue more ambitious projects.
  • ๐Ÿ’ก **The Groq Architecture**: The unique Groq architecture was not conceived while Ross was at Google. It was the interaction with VCs post his departure that led to the idea of an AI chip with easier-to-use software.
  • ๐Ÿ”‹ **Memory and Efficiency Trade-offs**: Groq chips are designed with lower memory per chip, which, despite requiring more units, leads to higher efficiency and faster inference speeds.
  • ๐Ÿ’ป **Cloud vs. On-Prem Hardware**: Ross advises starting with Groq's cloud service for ease of use and scalability, and only considering on-prem hardware for very large-scale operations.
  • ๐ŸŒŸ **The Future of Compute**: Compute power is likened to the new oil, with generative AI requiring significant computational resources to create new content in real-time.
  • ๐Ÿ“ˆ **Market Opportunities in AI**: Ross sees opportunities in the infrastructure layer of AI, where handling the 'drudgery' can lead to substantial and lasting businesses.
  • ๐Ÿค– **Agents and AI Collaboration**: Ross is particularly bullish on agents, believing that Groq's high inference speeds will enable more sophisticated and interactive AI applications.
  • โš™๏ธ **Optimizing for Groq Hardware**: Model builders are encouraged to take advantage of Groq's low-latency architecture and automated compiler to optimize their models for Groq's hardware.
  • ๐ŸŒ **The Role of AI in Society**: Ross is hopeful that AI will add nuance to human discourse, while also acknowledging the fear that comes with the vast potential of generative AI.
  • ๐Ÿ›ก๏ธ **Control and Bias in AI**: Groq aims to empower human decision-making rather than replace it, ensuring that AI models assist without controlling the choices people make.
  • ๐Ÿ“š **Educational Impact**: Ross envisions a future where children growing up with generative AI will have more curiosity and a nuanced view of the world due to the subtlety that AI can provide.

Q & A

  • Why did Jonathan Ross decide to leave Google?

    -Jonathan Ross decided to leave Google because he felt constrained by the corporate environment. He realized that to pursue his innovative ideas within Google, he needed to get approval from many people across different departments, which was limiting. Outside of Google, he could approach any of the thousands of venture capitalists for funding, allowing him to be more ambitious and bold.

  • What was the initial focus of Groq when the company was founded?

    -The initial focus of Groq was on developing a compiler that would make software much easier to use, which was a challenge Jonathan Ross identified with existing AI chips. They spent the first six months working solely on the compiler before starting the chip design, which provided Groq with a unique advantage.

  • How does Groq's chip architecture differ from traditional chips in terms of memory and efficiency?

    -Groq's chip architecture is designed to be more efficient by reducing the reliance on external memory, which is a common bottleneck in traditional chips like GPUs. Groq's chips have lower memory per chip, but they are part of a larger system that allows for faster processing without the need for constant memory access, similar to an assembly line concept.

  • What is Groq's approach to cloud services and when should a company consider acquiring their own Groq hardware?

    -Groq offers cloud services where developers can start using their technology immediately through an API, without any initial cost. Companies should consider acquiring their own Groq hardware when they reach a significant scale, doing millions of tokens per second, and require a large amount of computational power that might not be efficiently met through cloud services alone.

  • What is the potential business model for Groq hardware in terms of leasing or providing access to their chips?

    -Groq is not planning to rent individual chips but will allow users to upload their own models and run them on Groq's infrastructure. This approach is due to the automated nature of their hardware, which reduces the need for manual management and enables better utilization of the hardware resources.

  • How does Jonathan Ross view the future of compute in the context of AI and generative AI?

    -Jonathan Ross views compute as the new oil and believes it will be a limiting factor in the future. He suggests that generative AI represents a shift from the Information Age, where data is copied and distributed, to a new era where new information is created on the fly, requiring substantial computational power.

  • What advice does Jonathan Ross have for entrepreneurs looking to start a company in the AI space?

    -Ross advises entrepreneurs to focus on areas where they can add value, such as infrastructure or building physical products. He suggests that while the model layer is becoming commoditized, there's potential for higher expected value in creating AI models, albeit with higher variance and uncertainty.

  • What are Jonathan Ross's thoughts on the use of Groq's high inference speed for powering agents?

    -Ross believes that Groq's high inference speed is extremely beneficial for agents, as it allows for rapid interactions and decision-making. He compares the speed to the transition from dial-up to broadband, emphasizing that users prefer faster responses even if they can't process the information as quickly.

  • How does Groq ensure that the models available on its platform are of high quality?

    -Groq focuses on making the best of the best models available on its platform. They do not aim to host a large number of models but rather curate a selection of high-quality models that provide significant value to users.

  • What is the main concern of Groq regarding the control and bias of the models they run?

    -Groq's mission is to preserve human agency in the age of AI. They aim to ensure that the models they run do not make decisions for users but instead help users understand and make their own decisions, thereby maintaining human control and reducing bias.

  • What is the potential impact of generative AI on human discourse and understanding?

    -Ross is hopeful that generative AI will bring subtlety and nuance to human discourse, encouraging curiosity and a more in-depth understanding of different perspectives. He believes that it can help people to explore complex ideas and viewpoints more thoroughly.

  • How does Jonathan Ross perceive the future role of AI in society, considering the fears and concerns about job displacement and AI becoming sentient?

    -Ross is optimistic about the role of AI in society. He sees it as a tool that can enhance human capabilities, provoke curiosity, and improve decision-making. He acknowledges the fears but emphasizes the importance of understanding and controlling the use of AI to ensure it serves as a beneficial addition to human intelligence rather than a replacement.

Outlines

00:00

๐Ÿš€ Founding GROCK and the Journey from Google

Jonathan Ross, the founder and CEO of GROCK, discusses his departure from Google where he invented the Tensor Processing Unit (TPU). He highlights the limitations he faced within a large corporation and the freedom he sought in starting his own venture. Ross emphasizes the importance of ambition and boldness in entrepreneurship and shares how GROCK's focus on compiler development before chip design gave them a unique advantage.

05:01

๐Ÿ’ก GROCK's Chip Design and Business Model

The conversation delves into GROCK's chip design, known for its high inference speed but lower memory per chip. Ross explains the strategic decision behind this design, comparing it to an assembly line process to emphasize efficiency. He discusses the company's business approach, recommending businesses start with GROCK Cloud and consider on-premises hardware for large-scale operations. Ross also touches on the potential for GROCK hardware to be used in a cloud service model, improving hardware utilization and reducing costs.

10:03

๐ŸŒ The Future of Compute and AI

Ross and the interviewer explore the future of the AI industry, considering compute as the new limiting factor, akin to oil. They discuss the challenges of deploying AI models and the costs associated with inference. Ross provides advice for new AI entrepreneurs, suggesting that while model layers may offer high potential rewards, they also come with higher risks. He advocates for focusing on infrastructure or 'picks and shovels' level businesses, which are less glamorous but can be more sustainable and less competitive.

15:03

โš™๏ธ Utilizing GROCK's Inference Speed and Optimizing Models

The discussion highlights GROCK's inference speed and its implications for various applications, including interactive speech and real-time data generation. Ross shares the rapid growth of GROCK's user base and developer interest due to their speed advantages. He also addresses how model builders can optimize their models for GROCK hardware, mentioning the benefits of architectures that leverage low latency and the potential for numeric quantization.

20:03

๐ŸŒŸ Hopes and Fears for AI's Impact on Society

Jonathan Ross expresses his optimism about AI enhancing human discourse by adding subtlety and nuance, fostering curiosity, and improving understanding. He draws a parallel between the initial fear of the telescope's revelations and the current apprehension towards AI. Ross acknowledges the concerns about AI's potential to control human decisions but asserts GROCK's mission to empower human agency. He stresses the importance of curating AI models to ensure they assist rather than dictate human choices.

Mindmap

Keywords

๐Ÿ’กGroq

Groq is a company founded by Jonathan Ross, which specializes in developing custom silicon for artificial intelligence applications. The company's focus is on creating high-speed, efficient hardware that can handle the complex computations required for AI, particularly in the area of machine learning and deep learning. In the video, Ross discusses Groq's unique approach to chip design and its advantages in terms of inference speed and memory usage.

๐Ÿ’กTensor Processing Unit (TPU)

A Tensor Processing Unit (TPU) is a type of application-specific integrated circuit (ASIC) developed by Google, designed to accelerate machine learning workloads. Jonathan Ross, while at Google, was instrumental in creating the TPU, which has since become a critical component in running various AI software. The TPU is mentioned in the context of Ross's previous work and how it influenced his decision to found Groq.

๐Ÿ’กInference Speed

Inference speed in the context of AI refers to how quickly a machine learning model can process input data and produce an output. Groq's chips are highlighted for their exceptionally fast inference speeds, which can reach 5, 6, 7, or more tokens per second. This speed is crucial for real-time applications and enhances user experience by providing rapid responses.

๐Ÿ’กMemory per Chip

Memory per chip denotes the amount of memory available on a single chip or processing unit. Groq's chips are noted to have a lower memory per chip compared to other solutions, which necessitates the use of multiple chips to handle large-scale AI tasks. This design choice is a trade-off for higher efficiency and speed, as explained by Ross in the discussion.

๐Ÿ’กCompiler

A compiler is a program that translates code written in one programming language into another language. In the context of Groq, the company has developed an automated compiler that works with their chips to optimize the performance of AI models. The compiler's role is emphasized in the video as a key factor in Groq's unique advantage.

๐Ÿ’กAI Chip

An AI chip is a type of processor designed specifically to handle the mathematical computations associated with artificial intelligence applications, such as neural networks. Ross discusses the development of AI chips at Groq, focusing on their design philosophy and the trade-offs they made regarding memory and processing power.

๐Ÿ’กCloud Provider

A cloud provider is a company that offers resources and services through the internet, typically on a subscription basis. Groq provides cloud services where developers can leverage Groq's hardware without having to purchase it. This approach allows for easy access to powerful AI processing capabilities via an API, as mentioned by Ross.

๐Ÿ’กUtilization Rate

Utilization rate refers to the extent to which a resource, in this case, computing hardware, is being used. Ross points out that the utilization rate of GPUs is relatively low, with a significant portion of their time wasted. Groq aims to improve this by offering a service that maximizes the use of their hardware, providing better efficiency and cost-effectiveness.

๐Ÿ’กSilicon

In the tech industry, 'silicon' often refers to the material used in semiconductors and, by extension, to the chips or the hardware itself. Ross discusses the challenges and opportunities in the silicon market, particularly regarding AI chips, and how Groq differentiates itself in this space.

๐Ÿ’กAgents

In the context of AI, an agent is an autonomous entity that can perceive its environment and take actions to achieve specific goals. Ross expresses optimism about the future role of AI agents, suggesting that they will work together in sophisticated ways, potentially powered by Groq's high-speed inference capabilities.

๐Ÿ’กQuantization

Quantization in the context of AI hardware refers to the process of reducing the precision of the numerical values used in computations. This can lead to faster processing speeds and lower power consumption. Groq's hardware supports quantization, allowing for efficient execution of AI models, as highlighted in the video.

Highlights

Jonathan Ross, founder and CEO of Groq, discusses his transition from Google to founding his own company.

Ross invented the Tensor Processing Unit (TPU) at Google, which powers AI software.

He left Google to pursue more ambitious projects unconstrained by corporate limitations.

Groq's unique architecture focuses on ease of use and high inference speed, with speeds of 5-7+ tokens per second.

Groq chips have lower memory per chip, requiring businesses to purchase multiple units for high-demand applications.

The design choice for lower memory was made to increase efficiency and avoid memory bottlenecks.

Groq provides a cloud service that allows developers to start using their technology immediately without any cost.

For companies needing massive scale, Groq can discuss on-premise hardware deployments.

Groq's business model does not include renting individual chips but may allow users to upload their models for Groq to run.

Groq aims for high utilization rates of their hardware, unlike GPUs which are often underutilized.

Compute power is seen as the new oil and will be a limiting factor for AI in the future.

Ross advises startups in the AI space to focus on infrastructure or model layers for the best chance of success.

Groq is particularly interested in the potential of AI agents and how their high inference speed can power collaborative agents.

Groq's compiler is automated, simplifying the optimization process for their hardware.

Model builders are encouraged to optimize for Groq's hardware by utilizing low-latency architectures and quantized numerics.

Groq is selective about the models they host, aiming to provide only the most interesting and useful models on their platform.

Ross is hopeful that generative AI will bring more nuance and curiosity to human discourse.

He expresses concern about the potential for AI to centralize decision-making and the importance of preserving human agency.

Groq's mission is to ensure AI aids human decision-making without taking control, promoting an informed and nuanced perspective.