Most companies resolve the tension between AI usefulness and data risk by locking the data down, quietly killing the use case. Handshake couldn't: it supplies training data to frontier labs moving at an insane speed while running agents across its own stack. Matt walks through the internal products they're building (Mordor, Heimdall, Charon, Tartarus, and Citadel) that make both possible — column-level masking so models query broadly without broad exposure, a single surface where risky agent actions stage for human approval, a proxy metering every model call that turns the cost record into a training corpus, and two forms of sandboxing for safe agent code execution.

Onboarding a new Carta customer meant eight manual steps: emails, spreadsheets, re-uploads, and an implementation manager stitching a cap table together by hand from dozens of legal PDFs. It is now one drop-in flow, at 97% precision and 94% recall on extraction from unstructured documents. A firm arriving with 8,000 documents across hundreds of portfolio companies now onboards in weeks instead of months. Ruchita Chandhok uses that build to work through four decisions any team putting an LLM in front of real data will face: Where the contract between platform and product lives. Typed schemas, versioned like APIs, not prompts. It is what lets the two teams stop blocking each other. What a reviewer needs to trust a machine-extracted value. Provenance on every field, down to the page and sentence. Verification in seconds beats confidence scores. When to build evals. Before the second feature, not after the first incident. You cannot reconstruct a regression from logs. What the model should never be allowed to do. Arithmetic, dedup, and date math belong in tested code. Reserve the model for judgment.

Most AI ROI conversations collapse into one question: did developers ship faster? That is an incomplete balance sheet. Enterprise AI also changes risk exposure, infrastructure and token spend, software quality, and how reliably value reaches customers. Engineering leaders from 1Password, Auth0 (Okta), and Anthropic will pressure-test the same deployment through three lenses: defense, dollars, and delivery. They will explore where these goals reinforce one another, where they conflict, and what leaders should measure before deciding to fund, scale, or stop an AI investment.

Last year we discussed how a single AI Week moved 200+ builders to be 40% faster. This is the part that matters more: what happened in the 51 weeks after. The spike faded, the skeptics got louder, the token bills arrived, and the agents we built failed in ways we could not explain. A year later, Amplitude ships three times the pull requests with the same headcount, cycle time is under an hour, and our second AI Week pulled in the entire company, from sales reps opening a terminal for the first time to the CEO shipping his own demo. None of it came from the tools. It came from leaders willing to build live in front of 600 people and fail, from treating AI spend as leverage instead of cost, and from accepting that adoption is a treadmill, not a project. This is the sequel: what stuck, what didn't, and what it takes from the person at the top.

Every engineering leader putting AI into production is making a set of decisions that will outlast any single model release. How much to build internally vs. buy, how much control to trade for convenience, who owns the model, the data or the token cost curve as the AI market shifts. Each of these decisions shapes team structure, vendor dependencies, and how quickly an organization can adapt. Drawing on his work helping teams at companies like Uber and Shopify deploy AI at scale, Benny will walk through the tradeoffs behind these calls, including where a general-purpose model makes sense vs. when customizing open models wins, and how to build an architecture and a team that keeps evolving instead of locking into one choice. Attendees will leave with a framework to reason through these decisions for their own organization, weighing flexibility against operational load and how to lead a team through the daily decision-making of AI.

AI uniquely enables technical founders to operate the entire business, even if you're not yet a world-class expert in every business function. Hear how Scott Woody - founder, CTO, and CEO of Metronome - navigated the transition from CTO to CEO, scaled up revenue, fundraising, and board management without prior experience, and why he believes AI uniquely advantages technical founders to become world-class CEOs.

AI has changed the way engineering teams build software. It helps engineers move faster than ever before. But while the technology has evolved rapidly, one thing hasn't changed: people still need leaders. As engineering leaders, our role is becoming less about having all the technical answers and more about creating an environment where people can do their best work. Trust, coaching, difficult conversations, strategic thinking, growth and helping teams navigate uncertainty are still at the heart of great leadership, and they're becoming even more important as AI becomes part of our daily work. In this panel, engineering leaders will share how their role is evolving in the AI era, what leadership skills have become more valuable, and what lessons they've learned while leading teams through constant change. We'll discuss where AI can make managers more effective, where human leadership remains irreplaceable, and how to prepare the next generation of engineering leaders for a future where technical excellence alone won't be enough. Whether you're a new manager, an experienced leader, or thinking about making the transition into management, you'll leave with practical ideas and fresh perspectives on what it means to lead people in a world where AI has changed almost everything except the need for great leadership.

Leading through periods of high ambiguity requires more than just technical oversight; it demands a fundamental shift in mindset. In this session, Chris Lyon, SVP of Engineering at Twilio, pulls back the curtain on one of the most challenging yet transformative periods of his career. From navigating the roughest times marked by high attrition, leadership transitions, and product strategy limbo, to the technical debt of finishing the M in M&A, Chris shares a raw, honest look at what it takes to steer an engineering organization through a storm. Attendees will gain insights into why the hardest challenges often lead to the most rewarding professional moments, including: The M&A Finish Line: how to drive integration while maintaining innovation velocity. Navigating the Human Side of Volatility: Lessons learned from managing high attrition and the ride that isn’t for everyone. Strategic Resilience: How to maintain mental focus on moving the needle, rather than getting distracted by the external noise of rumors and organizational restructuring.