ELC Annual 2025

Slide deck available here: https://docs.google.com/presentation/d/15wnbqjHluu3JoecD3OCqwEu3rCwXI8BC8XPaAgVrLBE/preview?slide=id.p1 Most companies approach AI adoption backwards — adding features first, culture second. At Amplitude, we flipped the script. In just five days, we transformed 200+ product builders from AI skeptics to AI-native practitioners, achieving 40% productivity gains and cutting months-long projects to weeks. This isn't another hackathon story—it's a blueprint for how leaders can create the space, pressure, and vulnerability needed to shift entire organizations from watching AI happen to making it happen. Learn the specific tactics that worked, what surprised us most, and why the real transformation started after the week ended.

Slide deck available here: https://docs.google.com/presentation/d/1RRum-h04Bz5h044O6JoAAd7qxFN78igcQiGrryOSbXI/preview?slide=id.g388a3168856_0_404 The future of products will not be confined to a screen. We are entering an era where interfaces see, hear, speak, and imagine alongside us: powered by multimodal AI. These systems go beyond clicking and tapping, blending vision, voice, and other sensory inputs with rich, dynamic outputs like conversation, imagery, and immersive environments. The interface itself begins to dissolve, giving way to experiences that feel less like using a tool and more like collaborating with a trusted partner. In this talk, we’ll explore how to design the next generation of product interfaces where AI doesn’t just respond, it understands, anticipates, and co-creates in ways that fundamentally reshape how we interact with technology.

Traditional AI lived on simple benchmarks: accuracy, precision, BLEU scores. Generative AI broke that mold. Now, outputs are open-ended, there’s no unique gold standards, and as GenAI has been industrialized, neither datasets nor evaluation suites are shared across vendors. In this session, we’ll look at the science and strategy of evaluation in this new era: how to balance human adjudication with automated metrics, how Goodhart’s Law plays out in practice, and how evaluation itself shapes product development. Drawing on examples from healthcare, we’ll show why getting evaluation right isn’t just an academic concern, it’s the foundation for building products that customers can trust.

Everything we thought we knew about building great products is changing… or is it? Developers and designers are staying in sync and working more efficiently than ever before with tools like Figma’s Dev Mode. At the same time, new research shows that AI is blurring the boundaries between disciplines across the team. Yet as collaboration deepens and roles broaden, domain expertise isn’t disappearing—it remains essential to building great products. How can teams hone their respective skills while embracing a more collaborative product development process? Join Figma’s Marcel Weekes and Andrew Hogan for a conversation about what these shifts mean for product teams and how leaders can embrace a more collaborative, AI-enabled future.

Slide deck available here: https://docs.google.com/presentation/d/15X1X2iPJaxGp9LvjkH_os6V2FWLHvSENd8jWEixhUkU/preview?slide=id.g3887d179bda_0_0 We’ll explore: • how to mitigate excessive hallucinations in code‑generating models; • how to incorporate testing into the SDLC so it reduces (not increases) engineering time; and • common ways companies underutilize AI—and how to leverage it to accelerate delivery.

Slide deck available here: https://docs.google.com/presentation/d/1CF8RQfzika3KBv9kSCZB7ehcwVT8WEH-P3gpGDN31EU/preview?slide=id.g37b79fa8518_0_0 AI has become an operating metric—now tracked alongside revenue, user growth, and backlog in quarterly reports. This keynote examines how engineering leadership must adapt as the industry shifts from building user-facing applications to architecting systems around autonomous agents, APIs, and real-time orchestration. With compute emerging as a supply-chain constraint, regulatory frameworks lagging, and investors prioritizing measurable adoption over technical demos, the demands on engineering leaders are changing fast. This session explores the key trends driving the AI-first transition—and what they mean for the future of technical leadership.

Slide deck available here: https://docs.google.com/presentation/d/1zUYjJ-BcYMG12ZK8gmN_qj-zeofX1o_nHNV0cJpOnMg/preview?slide=id.g37b79fa8518_0_0 As AI agents enter the software development lifecycle, organizational complexity multiplies through blurred roles, overlapping responsibilities, and new accountability gaps. Having scaled developer infrastructure at Google, built collaborative data platforms at Observable, and led multi-agent systems at Jasper, Melody will share frameworks, design patterns and real-world examples for guiding teams through this change while keeping trust, culture, and execution intact.

AI has fundamentally disrupted how we hire, evaluate, and onboard engineering talent. LinkedIn is flooded with AI-generated applications. Coding interviews are compromised by invisible overlays. Meanwhile, top engineers command salaries that put them out of reach for most companies, while many teams struggle with "mercenary" talent juggling multiple clients rather than committed team members. At the same time, engineering leaders are navigating new challenges around remote team dynamics, AI tool adoption within their teams, technical debt management, and balancing innovation speed with engineering excellence. This focused panel brings together seasoned engineering leaders to discuss: The New Hiring Reality: What's broken in 2025 hiring and practical approaches that are actually working Building Committed Teams: Finding dedicated engineers vs. "mercenary" talent in a disrupted market Leading Through AI Disruption: Balancing AI tool adoption with team development and engineering excellence Scaling Smart: Managing growth, technical debt, and budget pressures in uncertain markets