Building Deep Tech Beyond the SaaS Playbook with Naveen Rao, VP of AI at Databricks
Dec 4, 2024
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Naveen Rao, VP of AI at Databricks, has a rich background in AI hardware and software, having previously founded Nervana and MosaicML. He shares his journey from a childhood fascination with science fiction to innovating in AI. The conversation dives into the distinct playbooks required for deep tech versus SaaS, emphasizing a deeper understanding of problems. He discusses the shifting landscape of AI post-ChatGPT, the synergy between hardware and software, and the future of AI training and decision-making, advocating for breakthrough innovations to navigate evolving demands.
Deep tech innovation requires a unique approach distinct from SaaS, focusing first on understanding complex problem spaces before product development.
Naveen Rao's background in neuroscience and engineering emphasizes the importance of interdisciplinary knowledge for advancements in AI hardware development.
Successful deep tech startups must build skilled research teams and adapt continuously to evolving customer needs, particularly in AI technology.
Deep dives
Understanding Deep Tech
Innovating in deep tech requires a distinct approach compared to traditional SaaS products. Emphasis should be placed on deeply understanding the problem space before engaging in customer interviews or product development. It's essential to prioritize technological innovation and assemble a capable team that can build solutions tailored to complex problems. This foundational understanding is crucial before transitioning into customer-centric strategies, which only becomes relevant once a viable product exists.
A Childhood Fascination with AI
An early interest in artificial intelligence is rooted in childhood experiences and a passion for technology. Starting from middle school, exposure to science fiction literature sparked a fascination with the complexities of the brain and machine learning. A background in engineering, programming, and electronic circuitry laid the groundwork for a career devoted to building innovative products. This unique blend of creativity, curiosity, and technical skill set the stage for significant future accomplishments in the tech industry.
Navigating the Challenges of Hardware Startup
Launching a hardware startup amidst skepticism from venture capitalists presents considerable challenges. Understanding the evolving landscape of AI neural networks required innovative thinking about chip architecture, ensuring they could effectively handle new computing demands. Building a skilled research team with expertise in deep learning and machine learning was pivotal in shaping the direction of technology development. Successfully navigating initial barriers led to the creation of foundational technology that has significantly impacted the AI computing landscape.
Mosaic’s Response to Industry Trends
Mosaic ML has capitalized on the growing demand for scalable and customizable model training solutions, particularly in the wake of developments in large language models. By engaging with early adopters and gathering insights into their needs, the company established a framework that could streamline building and deploying AI systems. The acquisition of users in the enterprise sector surged post-ChatGPT, highlighting a newfound urgency to adopt advanced AI technologies. This rapid growth underscores the importance of continuously adapting to shifting customer priorities in a fast-evolving technological landscape.
Future Directions in AI Research and Application
The exploration of compound AI systems, counterfactual reasoning, and intention-based decision-making highlights exciting research frontiers in artificial intelligence. By breaking down complex AI models into manageable components, these systems aim to optimize how machines understand and respond to human intent. There remains a significant need to refine computing substrates to enhance AI performance, especially in rapidly changing environments. Integrating innovative solutions with practical applications will be key to shaping the future trajectory of AI development.
Naveen Rao discovered artificial intelligence through science fiction in middle school, devouring the works of Asimov while his peers in Kentucky were doing whatever kids in Kentucky normally did. Growing up in a family of doctors, he chose a less conventional path, following his early interests in programming and circuit building.
After establishing himself as an engineer, he stepped away to pursue a neuroscience PhD at Brown, finishing in record time. This combination of engineering expertise and biological understanding shaped his approach to AI hardware development at his companies Nervana (acquired by Intel) and MosaicML (acquired by Databricks).
In this conversation, the VP of AI at Databricks breaks down what's actually needed for meaningful progress in machine reasoning (hint: it's not just bigger models), and why deep tech development needs a different playbook than what we're used to.
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