Abstract
Once you figure out how to use AI to make your organization productive, you will soon find you have a whole new set of problems. While some of those problems will be novel thanks to LLMs, many of them are just the result of there being "more stuff". Good engineering practices can help you manage all that new code, but you may need to look to larger systems and organizations to learn how to deal with your found new scale.
Interview
The changes happening as a result of AI use in Software Engineering all appear to be pointing in one direction right now: More. More code, more changes, more speed. At first it feels like everyone is having a productivity renaissance, but eventually you will have to work out how to handle more of everything. It turns out that "more" is a problem that large companies have already. Looking at how large scale code bases, and specifically Google's codebase, are managed today provides some insight into the coming wave of problems likely to impact developers everywhere soon.
If you aren't already drowning in PRs, code reviews, expensive tests, and more, you will be soon.
One of the weird things about problems of scale is that they are all somewhat unique, owing to the constellation of factors that emerge when you make things really big and really fast in an organization. Despite that, there are likely to be common smells - for example dealing with resource exhaustion (AI or CPU) or increasingly complex social & engineering contracts among the people in the organization. In fact a lot of what is commonly called "bureaucracy" in large technical organizations is simply the result of humans trying to get a handle on the complexity of scale. Find me someone who hasn't complained about bureaucracy at work 😊.
Recognize how systemic the problems are around them. Scale doesn't affect one thing, it affects everything.
The quality of the content. You have some of the most interesting speakers and some of the highest quality content anywhere.
I've never attended but I have been following InfoQ QCon and watching your videos since 2007 or 2008, whenever you started posting them. That's the real testament if you ask me - nearly 20 years of being entertained and informed.
Topics
$2,995, Conference (3 days). Current pricing ends November 10th. All pass options.
QCon San Francisco 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Engineering the Developer Experience Hosted by Ankit Jain Co-Founder & CEO @AviatorFrom the same track
Wednesday 18 November
10:35 Ballroom A Session Features vs Futures Kent Beck Original Signer of the Agile Manifesto, Author of the Extreme Programming Book Series, Rediscoverer of Test-Driven Development Details coming soon. 11:45 Ballroom A Session Software Engineering No More Small Systems, How AI May Bring Large System Problems to Everyone Adam Bender Technical Lead for Enterprise Engineering @Google Once you figure out how to use AI to make your organization productive, you will soon find you have a whole new set of problems. While some of those problems will be novel thanks to LLMs, many of them are just the result of there being "more stuff". 13:35 Ballroom A Session Growing Engineers in the Era of Agentic Coding Adam Berry Staff Engineer @Netflix Early-career engineers are learning the craft in an environment where the surface they touch is often an agent, not a codebase. The instinct is to treat this as a rupture in how engineers grow. It isn't. 14:45 Ballroom A Session Software Understanding Shipping Faster, Understanding Less Margaret-Anne Storey Professor of Computer Science @University of Victoria, Co-Creator of the SPACE Framework and the Triple Debt Model for Software Health, a Canada Research Chair in Human and Social Aspects of Software Engineering AI is changing software development faster than our engineering practices are evolving, and yet many teams are facing growing pressure to ship features faster. But moving faster can create three related threats to software health: technical debt, cognitive debt, and intent debt. 15:55 Ballroom A Session The Diary of an AI Adopter: How I Went From Skeptical to Enthusiastic in Our Organization’s AI Migration Efforts Tara Hernandez Vice President @MongoDB & Lecturer at the UC Santa Cruz Center for Innovation and Entrepreneurial Development From market research reporting questionable success of AI adoption in enterprise, to CEOs announcing layoffs due to AI, to companies announcing they’re cutting back or eliminating AI due to prohibitive costs or technical missteps, it can be hard to know what the path to success should look like.