Morgan Stanley

Leadership in AI Assisted Engineering – Justin Reock, DX (acq. Atlassian)

Dec 19, 2025 · 18:11

Justin Reock, Deputy CTO at DX (acquired by Atlassian), argues that AI's impact on engineering productivity varies wildly and that leaders must move beyond top-down mandates to focus on psychological safety, measurement of actual outcomes, and targeted integration across the SDLC. He presents data showing a 2.6% average increase in change confidence but extreme variability across companies, with some seeing 20% drops. Emphasizes that writing code is rarely the bottleneck; instead, leaders should identify and fix bottlenecks like context switching, citing Morgan Stanley's DevGenAI saving 300,000 hours annually by converting legacy code specs and Zapier reducing engineer onboarding to two weeks via AI agents. Introduces DX's AI Measurement Framework covering utilization, impact, and cost, and stresses trust-building through system prompt feedback loops and temperature settings. The episode delivers actionable guidance on measuring AI's true impact and enabling engineers through education, time to learn, and creative unblocking of usage.

Reinforcement Learning for Agents - Will Brown, ML Researcher at Morgan Stanley

Mar 7, 2025 · 18:17

Will Brown, a machine learning researcher at Morgan Stanley, argues that reinforcement learning (RL) is the essential path to unlocking autonomous AI agents, citing DeepSeek's R1 and OpenAI's Deep Research as proof: R1 used GRPO to make models learn chain-of-thought reasoning without manual data, and Deep Research applies end-to-end RL for up to 100 tool calls. He shares his own open-source single-file GRPO script—a 1B-parameter LLaMA model trained on math questions that demonstrated self-correction and improved accuracy, sparking community forks and blog posts. Brown introduces 'rubric engineering' as a new practice akin to prompt engineering, where reward rules (e.g., XML structure, integer answers) guide model improvement, and warns about reward hacking. He previews his current work: a framework for RL inside multi-step environments, letting developers reuse existing agent code for training. The talk concludes that fine-tuning and RL remain relevant as open-source catches up, and that skills like building evals and prompts translate directly to the RL era.

OpenAI for VP's of AI + Advice for Building Agents

Mar 5, 2025 · 16:52

OpenAI's Toki Sherbakov and Prashant Mital explain how enterprises adopt AI through a three-phase journey: building an AI-enabled workforce with ChatGPT, automating operations with APIs, and infusing AI into end products. They detail a Morgan Stanley case study where retrieval methods improved an internal knowledge assistant's accuracy from 45% to 98%. The pair define agents as models with instructions, tools, and self-terminating execution loops, then share four field lessons: build with primitives before frameworks, start with a single purpose-built agent, graduate to a network of specialized agents with handoffs for complex tasks, and keep prompt instructions simple while running guardrails in parallel using fast models like GPT-4o mini for safety and reliability.