AI Engineer Melbourne 2026 — Keynotes (Day 2 Morning)

You feel more productive than you’ve ever been. You put on the Iron Man suit and now you’re building things in hours that used to take weeks. And you’re exhausted by Wednesday. The craft that used to sustain you — the flow of writing code, the satisfaction of making something work — has given way to a middle loop of supervisory engineering: directing, evaluating, and correcting AI output. You’re getting more done while enjoying it less, and that’s a tension worth navigating. If the system is producing more output while eroding joy, that’s not a you problem, it’s a system design problem.

Drawing from her recently completed Masters research on AI’s impact on software engineering and conversations with practitioners and researchers at the frontier of this shift, Annie explores why this transition hits so differently for those entering the industry, those deep in it, and those who haven’t written code in years — and why who thrives most comes down to mindset, not circumstance. The good news is, that’s within your reach.

This talk offers a lens to see your own situation clearly, and a path through it. Joy and pride in work don’t happen by accident. They’re system outcomes. And we can engineer the conditions for them.

The current AI stack has a dependency most of us don’t talk about: a handful of closed models from a handful of providers, and an API call standing between every agent and every action. Mic Neale — who helped build Goose, Block’s open-source agentic coding system — thinks that’s a problem worth solving at the infrastructure level.

This talk introduces a working prototype of a decentralised mesh LLM: a system where individuals and small teams pool their spare compute to collectively run open models that none of them could run alone. When your GPU is idle, it contributes to the mesh. When you need capacity, the mesh is there. The result is access to capable open models without a cloud bill or an API dependency.

Mic will walk through how the mesh works technically — coordination, model sharding, latency management, and what happens when nodes drop out — and where it’s headed: a model where contributors earn from their idle capacity, and where the economics of running frontier-class open models shift from “data centre required” to “your neighbourhood has enough.”

This talk argues that without a deliberate focus on long horizon tasks, even the most impressive models will remain brittle and unreliable for real world applications. Short form benchmarks and isolated prompts cannot capture the complexity of extended reasoning, planning, and execution that real world problems demand. When models lack the ability to maintain coherence across hundreds or thousands of steps, they fail in subtle but critical ways: losing track of sub goals, failing to recover from errors, or drifting away from the original objective.

To address this, the talk proposes a new framework for measuring and training long horizon capabilities, including explicit mechanisms for sub goal setting, robust error recovery, and sustained persistence over extended timeframes. These are not mere incremental improvements but fundamental shifts in how we design and evaluate AI systems.