This episode features Olivier Godement, Head of Product for Business Products at OpenAI, discussing the current state and future of AI adoption in enterprises, with a particular focus on the recent releases of GPT 5.1 and Codex. The conversation explores how these models are achieving meaningful automation in specific domains like coding, customer support, and life sciences: where companies like Amgen are using AI to accelerate drug development timelines from months to weeks through automated regulatory documentation. Olivier reveals that while complete job automation remains challenging and requires substantial scaffolding, harnesses, and evaluation frameworks, certain use cases like coding are reaching a tipping point where engineers would "riot" if AI tools were taken away. The discussion covers the importance of cost reduction in unlocking new use cases, the emerging significance of reinforcement fine-tuning (RFT) for frontier customers, and OpenAI's philosophy of providing not just models but reference architectures and harnesses to maximize developer success.

(0:00) Intro
(1:46) Discussing GPT-5.1
(2:57) Adoption and Impact of Codex
(4:09) Scientific Community's Use of GPT-5.1
(6:37) Challenges in AI Automation
(8:19) AI in Life Sciences and Pharma
(11:48) Enterprise AI Adoption and Ecosystem
(16:04) Future of AI Models and Continuous Learning
(24:20) Cost and Efficiency in AI Deployment
(27:10) Reinforcement Learning and Enterprise Use Cases
(31:17) Key Factors Influencing Model Choice
(34:21) Challenges in Model Deployment and Adaptation
(38:29) Voice Technology: The Next Frontier
(41:08) The Rise of AI in Software Engineering
(52:09) Quickfire

With your co-hosts: 

@jacobeffron 

- Partner at Redpoint, Former PM Flatiron Health 

@patrickachase 

- Partner at Redpoint, Former ML Engineer LinkedIn 

@ericabrescia 

- Former COO Github, Founder Bitnami (acq’d by VMWare) 

@jordan_segall 

- Partner at Redpoint