Details
An app that works perfectly for 10 users often falls apart at 100 and completely breaks at 10,000. That gap between "I built something that works" and "I built something that scales" is where most of the real engineering happens, and it's almost never what gets talked about at a typical AI meetup.
Breaking Point is a technical session built around that exact gap. Builders and engineers walk through real systems that hit a wall at scale the tech decisions, the rebuilds, the architecture calls that had to change once real load hit framed around one core question: was the bottleneck the technology, or how hard it was pushed?
What to expect
- Technical walkthroughs of real systems that broke under scale, and what changed to fix them
- Honest post-mortems: what was over-engineered too early, what was under-built and paid for it later
- A room skewed toward builders and engineers who want the technical depth, not the surface-level pitch
Who this is for
- Engineers and technical founders who've hit (or are worried about hitting) their own scaling wall
- Enterprise engineering leads evaluating what "AI at scale" actually requires versus a working prototype
- Builders who want technical war stories, not another "AI use cases" overview
Selection note
This session is intentionally technical. Attendees and speakers are selected for people actually building and scaling systems not a general audience session.
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