Unraveling the GenAI revolution: insights from big tech panel

Launchable’s Big Tech Panel delved into the intriguing world of "Building GenAI Apps (Embedded)." Moderated by Wei Gao of Madrona, the panel featured insights and experiences from Farah Ali, Electronic Arts, Ambrish Verma, Flex, and Nancy Wang, Felicis Ventures. Their discussion provided valuable perspectives on the development of AI-powered applications, making it a noteworthy part of the event. See more below:

‍Wei: How do you incorporate generative AI into your products, and what's the urgency?

‍Farah: We've made generative AI central to content creation in gaming, focusing on real-time facial animations from speech.

‍Nancy: At Amazon, we prioritize data security and privacy with generative AI, particularly in incident response and proactive protection.

‍Ambrish: Meta integrates generative AI into advertising, allowing for personalization and efficient ad creative optimization.

‍Wei: Can you share specific use cases where generative AI has driven improvements?

‍Farah: "Voice to face" technology enhances game animations and saves time.

‍Nancy: In incident response, generative AI connects alerts with contextual runbooks, improving data security.

‍Ambrish: Meta's Advantage Plus uses generative AI to personalize ad creatives, especially benefiting smaller businesses.

‍Wei: What challenges have you encountered in adopting large language models, and how did you address them?

‍Nancy: Challenges include data confidentiality, hallucinations, and forgetting. Solutions include proxies for API calls and startups tackling these issues.

‍Wei: What are the challenges when transitioning from a startup to a big company in terms of AI and technology?

‍Farah: Challenges involve the scale, varying technical awareness, and educating employees about tool usage and evolving legal and risk frameworks.

‍Wei: How can large companies stay updated with rapidly evolving technology?

‍Farah: Creating centralized programs for knowledge sharing, legal frameworks, and sandboxes for testing is essential. Prioritizing use cases and forming a long-term technology plan is crucial.

‍Wei: Why is data quality and availability important in AI initiatives, and how do companies manage these challenges?

‍Ambrish: Quality data is vital for AI projects, and managing it involves accurate tagging and permissioning, particularly for compliance and regulatory contexts.

‍Wei: How do startups and large companies partner to address AI-related challenges?

‍Nancy: Startups extend capabilities, and large companies offer resources and data. Partnerships should focus on differentiation and mutual success.

‍Wei: What should startups consider when seeking partnerships with larger companies in the AI space?

‍Nancy: Focus on extending capabilities, differentiation, dedication to integration, and addressing compliance and regulatory concerns.

‍Wei: How can startups effectively partner with large enterprises to address AI challenges?

‍Ambrish: Startups can succeed by understanding the larger company's workflow and dedicating themselves to integration and collaboration, with a focus on value addition and compliance considerations.

Special thanks to Wei and panelists.