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Surprise! Everyone at Your Company Suddenly Became a Developer (Kind Of)
Jason Meltzer
All Content
Scott Woody & Louis Topper · Oct 29th, 2026
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.
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Nono Guimbi, Drew McGahey, Prerna Goel & 1 more speaker · Oct 29th, 2026
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.
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Chris Lyon & Amit Gupta · Oct 29th, 2026
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.
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Sagnik Nandy · Oct 29th, 2026
Most engineering orgs have solved for AI tool adoption already. What separates the ones getting real leverage from it is shared infrastructure, workflow automation, and guardrails that survive past the champions who first pushed for it.
At Docusign, that started with a peer-led model. Engineers across teams took on responsibility for embedding AI into daily workflows, documenting what actually worked, and surfacing the friction points that tooling alone couldn't fix. That foundation now spans across all 1,100+ of the company’s engineers.
Sagnik Nandy will walk through what came next, how Docusign's engineering org moved from individual AI adoption to a connected set of internal platforms spanning the SDLC – from coding to observability to agent orchestration – and the design decisions that made those platforms reusable for engineers and product teams rather than one-off tools.
He'll cover what worked, what didn't, and how the team distinguishes AI that's actually improving outcomes and developer productivity from AI that's just increasing usage.
Attendees will leave with a practical framework for designing AI platforms that scale past the pilot stage, and an open door to keep the conversation going with Docusign's engineering team after the session.
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Martin Reynolds · Oct 29th, 2026
Every team has already made a choice about AI agents, whether they meant to or not: slow it down with human approval on everything, or let agents run fast with broad access and hope nothing breaks. Both are worse than they look - the first stalls the ROI on every coding tool you bought, the second trades a known risk for an invisible one, since general-purpose agents have no audit trail and no idea what they’re allowed to touch in production. This talk makes the case that autonomy isn’t a toggle, it’s a dial: the leaders getting real value from AI are setting risk-based autonomy per workflow, per environment, per blast radius - not asking “how much should we trust agents” but “what has to be true before we do.” Practical framework, not theory, for engineering leaders past the pilot phase and into the part where agents actually touch production.
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Brandon Waselnuk · Oct 29th, 2026
AI is in your engineering workflow. The token spend shows it, but the throughput doesn't. The human is still very much in the loop. Most software teams can't prove whether AI is actually helping, because the metrics they watch reward motion over delivery.
This is a context problem. The gains disappear between the feature branch and main because your agent doesn't fully understand how your system works. It pushes code that breaks in production, and you spend the next sprint dealing with the thrash instead of working the review backlog or shipping the next feature.
In this workshop, we build the plan to move your org from scattered adoption to an AI development lifecycle, the ADLC, where AI is a dependable part of how your team ships.
First, we place your team on a map of eight AI context maturity levels, and you compare where you landed with the people around you. You'll see the three walls that stop teams at each level, and which one is in front of you.
Second, you measure. We give you four delivery metrics that show where AI's gains leak out before production, and you run them to set a baseline for one team already leaning in. Real numbers, on your own work, so you can tell whether a team has actually moved up a level or just spent more tokens.
Third, we map the path forward. We walk through what it takes to clear each bottleneck in order: good Q&A first, then stronger code review as more PRs go up, then reliable background agents for parallel work. You leave knowing which one to tackle next and how you'll measure it.
The thread through all of it: none of this works without a context layer underneath. An agent with access to every source still can't tell which one is right. That's what turns tokens spent into work shipped.
You leave with your team's place on the maturity model, a baseline you measured yourself, and a clear next step with the numbers to watch.
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Tom Adams & Catherine Ye · Oct 29th, 2026
In this fireside chat, Catherine Ye and Tom Adams explore how Adyen has deeply embedded AI across the entire engineering lifecycle. Join us for a candid discussion on achieving 100% AI adoption, scaling developer productivity and token usage, navigating the evolving boundary between coding and engineering, and fostering team culture and junior mentorship in an AI-first organization.
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Ben Jordan · Oct 29th, 2026
Hardware innovation is full of opportunity — yet hardware engineering remains slow, expensive, and unforgiving compared to software. This talk breaks down the real mechanics of board-level electronics development: the players, dependencies, and hidden constraints that quietly dictate your timeline. Using real project data and case studies (including a startup whose hardware failure proved fatal) Quilter AI’s Staff Electrical Engineer and EDA veteran Ben Jordan pinpoints where hardware projects actually break down, from PCB layout bottlenecks to costly respins. Then he turns to what most teams get wrong about fixing it — and what actually works instead. You’ll leave with a sharper read on where your own hardware risk is hiding, and a concrete playbook for de-risking it.
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Zohar Einy · Oct 29th, 2026
Moving Beyond Coding Agents to an AI-Native SDLC Built on Shared Context, Trusted Agents, and Governed Workflows
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Anastasia Zamyshlyaeva · Oct 29th, 2026
Almost every engineering team has adopted AI, and almost every developer will tell you they feel faster. But ask whether it's actually working, where it's paying off, and what it's costing, and most leaders can't answer. Adoption has outrun our ability to measure it, and there's no playbook for leading the transformation.
Anastasia Zamyshlyaeva, VP of Engineering at GitKraken, has been closing that gap from the engineering leader's chair. When existing tools couldn't tell her whether her developers were really adopting AI, or what to do about it if they weren't, her team built a way to see it, proved it on their own engineering org first, and then put it in the hands of engineering teams everywhere. Her argument: treat your engineering workflow like a product, measured with rigor and improved deliberately instead of on gut feel.
Seeing where AI works is only half the job. The other half is scaling it, moving a developer from babysitting one agent to orchestrating several in parallel, without the cost or the chaos getting away from you. She'll get honest about what's hard: why the obvious metrics mislead, how to tell real leverage from work that just looks busy, and what it takes to move a team from experimenting to running agents at scale.
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