AI DevSummit 2025 + DeveloperWeek Leadership 2025

Nalini Garg, Deloitte, Senior Manager

As artificial intelligence (AI) rapidly transforms the landscape of clinical trials, the need for responsible implementation has never been more critical. This session will explore how AI can be harnessed to enhance clinical trial design, patient recruitment, data analysis, and outcome prediction, while ensuring ethical considerations are front and center.

We'll delve into the potential of AI to streamline trial processes, reduce costs, and accelerate the development of new therapies. However, with great power comes great responsibility. We'll discuss the challenges of bias, data privacy, transparency, and regulatory compliance, and how to address them to build trust in AI-driven methodologies.

Join us as we navigate the delicate balance between innovation and ethics, and discover best practices for integrating AI into clinical trials in a way that enhances accuracy, efficiency, and fairness. Whether you're a researcher, clinician, or industry professional, this session will provide valuable insights into the responsible advancement of AI in clinical trials, ensuring that technological progress benefits both science and society.

Debo Dutta, Nutanix, Chief AI Officer

Generative AI is reshaping industries, from healthcare to finance and beyond, offering transformative opportunities for innovation. However, deploying it effectively in enterprise settings requires navigating complex challenges around scalability and security. Debo Dutta, VP of Engineering & AI at Nutanix and founding member of MLCommons—an AI consortium advancing technology for global impact– shares his insights on integrating GenAI seamlessly while ensuring robust infrastructure and safeguarding model integrity. Debo delves into the tech stack essentials, including foundational models, fine-tuning frameworks, deployment tools, and governance systems needed to tailor to diverse business needs across industries. 

David Campbell, Scale AI, AI Risk Security Platform Lead

In this talk, we will explore the journey of Red Teaming from its origins to its transformation into AI Red Teaming, highlighting its pivotal role in shaping the future of Large Language Models (LLMs) and beyond. Drawing from my firsthand experiences developing and deploying the largest generative red teaming platform to date, I will share insightful antidotes and real-world examples. We will explore how adversarial red teaming fortifies AI applications at every layer—protecting platforms, businesses, and consumers. This includes safeguarding the external application interface, reinforcing LLM guardrails, and enhancing the security of the LLMs' internal algorithms. Join me as we uncover the critical importance of adversarial strategies in securing the AI landscape. 

Rob Whiteley, Coder, CEO

With great power comes great responsibility—and yes, coding agents can backfire if they’re not thoughtfully deployed. Instead of boosting productivity, they can frustrate developers, slow down progress, and even diminish your competitive edge.

Join Coder CEO, Rob Whiteley, as he unpacks three essential strategies organizations should embrace when scaling agentic coding agents enterprise-wide. Drawing from firsthand lessons and internal experiments—where agents effectively resolved nearly half the tasks we threw at them—we’ll share real-world scenarios showcasing engineering work AI agents excelled at. We’ll also highlight where they fell short, detailing what went wrong and what we’d do
differently next time.

You’ll walk away understanding:

* How realistic expectations ensure AI boosts rather than hinders productivity.
* Why effective human-agent collaboration is critical for developer satisfaction and innovation.
* How clear governance frameworks can prevent technical debt and keep productivity flowing.

Learn from our experiences and ensure your organization’s deployment of AI coding agents empowers your developers, ignites innovation, and solidifies your market leadership.

Shomron Jacob, Iterate.ai, Head of Applied Machine Learning & Platform

This AIDev Summit session will navigate an increasingly pivotal crossroads: the decision between investing in proprietary, custom-tailored Large Language Models (LLM) or capitalizing on the versatility and ease of public generative AI services.

The session will begin by demystifying the complexities of private LLMs. With domain-specific capabilities and enhanced data security, these models have faster customization and compliance with industry-specific regulations. Yet, they also pose challenges: a bigger investment, infrastructure requirements, and ongoing maintenance. These elements necessitate a thorough examination.

Next, the session will scrutinize public generative AI services, exploring the inherent benefits of these ready-to-use solutions. With their scalability, diverse applications, and lower upfront costs, they hold significant appeal. But they also come with their own set of considerations, such as data privacy, standardized performance, and reduced control over the model’s behavior.
With real-world examples, we will walk through how various organizations have approached this decision, the results they achieved, and the invaluable lessons learned.

The session will then go into a decision-making framework, with the purpose of enabling attendees to assess their options between private LLMs and public generative AI services more effectively.

Nalini Garg, Deloitte, Senior Manager

As artificial intelligence (AI) rapidly transforms the landscape of clinical trials, the need for responsible implementation has never been more critical. This session will explore how AI can be harnessed to enhance clinical trial design, patient recruitment, data analysis, and outcome prediction, while ensuring ethical considerations are front and center.

We'll delve into the potential of AI to streamline trial processes, reduce costs, and accelerate the development of new therapies. However, with great power comes great responsibility. We'll discuss the challenges of bias, data privacy, transparency, and regulatory compliance, and how to address them to build trust in AI-driven methodologies.

Join us as we navigate the delicate balance between innovation and ethics, and discover best practices for integrating AI into clinical trials in a way that enhances accuracy, efficiency, and fairness. Whether you're a researcher, clinician, or industry professional, this session will provide valuable insights into the responsible advancement of AI in clinical trials, ensuring that technological progress benefits both science and society.

Debo Dutta, Nutanix, Chief AI Officer

Generative AI is reshaping industries, from healthcare to finance and beyond, offering transformative opportunities for innovation. However, deploying it effectively in enterprise settings requires navigating complex challenges around scalability and security. Debo Dutta, VP of Engineering & AI at Nutanix and founding member of MLCommons—an AI consortium advancing technology for global impact– shares his insights on integrating GenAI seamlessly while ensuring robust infrastructure and safeguarding model integrity. Debo delves into the tech stack essentials, including foundational models, fine-tuning frameworks, deployment tools, and governance systems needed to tailor to diverse business needs across industries. 

David Campbell, Scale AI, AI Risk Security Platform Lead

In this talk, we will explore the journey of Red Teaming from its origins to its transformation into AI Red Teaming, highlighting its pivotal role in shaping the future of Large Language Models (LLMs) and beyond. Drawing from my firsthand experiences developing and deploying the largest generative red teaming platform to date, I will share insightful antidotes and real-world examples. We will explore how adversarial red teaming fortifies AI applications at every layer—protecting platforms, businesses, and consumers. This includes safeguarding the external application interface, reinforcing LLM guardrails, and enhancing the security of the LLMs' internal algorithms. Join me as we uncover the critical importance of adversarial strategies in securing the AI landscape. 

Rob Whiteley, Coder, CEO

With great power comes great responsibility—and yes, coding agents can backfire if they’re not thoughtfully deployed. Instead of boosting productivity, they can frustrate developers, slow down progress, and even diminish your competitive edge.

Join Coder CEO, Rob Whiteley, as he unpacks three essential strategies organizations should embrace when scaling agentic coding agents enterprise-wide. Drawing from firsthand lessons and internal experiments—where agents effectively resolved nearly half the tasks we threw at them—we’ll share real-world scenarios showcasing engineering work AI agents excelled at. We’ll also highlight where they fell short, detailing what went wrong and what we’d do
differently next time.

You’ll walk away understanding:

* How realistic expectations ensure AI boosts rather than hinders productivity.
* Why effective human-agent collaboration is critical for developer satisfaction and innovation.
* How clear governance frameworks can prevent technical debt and keep productivity flowing.

Learn from our experiences and ensure your organization’s deployment of AI coding agents empowers your developers, ignites innovation, and solidifies your market leadership.

Shomron Jacob, Iterate.ai, Head of Applied Machine Learning & Platform

This AIDev Summit session will navigate an increasingly pivotal crossroads: the decision between investing in proprietary, custom-tailored Large Language Models (LLM) or capitalizing on the versatility and ease of public generative AI services.

The session will begin by demystifying the complexities of private LLMs. With domain-specific capabilities and enhanced data security, these models have faster customization and compliance with industry-specific regulations. Yet, they also pose challenges: a bigger investment, infrastructure requirements, and ongoing maintenance. These elements necessitate a thorough examination.

Next, the session will scrutinize public generative AI services, exploring the inherent benefits of these ready-to-use solutions. With their scalability, diverse applications, and lower upfront costs, they hold significant appeal. But they also come with their own set of considerations, such as data privacy, standardized performance, and reduced control over the model’s behavior.
With real-world examples, we will walk through how various organizations have approached this decision, the results they achieved, and the invaluable lessons learned.

The session will then go into a decision-making framework, with the purpose of enabling attendees to assess their options between private LLMs and public generative AI services more effectively.