Laura Summers
Laura is a very technical designer™️, working at Pydantic as Lead Design Engineer. Her side projects include Sweet Summer Child Score (summerchild.dev) and Ethics Litmus Tests (ethical-litmus.site). Laura is passionate about feminism, digital rights and designing for privacy. She speaks, writes and runs workshops at the intersection of design and technology.
Session
A few nights ago I was up to 2am obsessively crafting an LLM plan. ("Just one more prompt!" - famous last words). Yet it still did something inexplicably stupid. 🫠 So yeah: LLMs are both genuinely useful and genuinely destabilising. Focusing on the first and ignoring the second is how people burn out.
This talk is an honest account of what it feels like to be a developer right now, from someone inside it, and some thoughts on what might actually help. My thesis: we've been optimising for model output when we need to be optimising for human experience.
I'll share observations from my work, peers and colleagues. The peculiar fatigue of machine supervision: holding the intent in your head while the machine generates volumes of mostly-correct output that still needs your eyes, your judgement, and your taste. The way the satisfying part of the work shrank while the exhausting part grew. The isolation of pair-programming with a machine, and the loss of real human learning, interconnection and collaboration. And underneath all of it: uncertainty. About market conditions, about employability, about whether the skills we've spent years building will still matter.
The second half is about what's been working for me, and what hasn't. On the human side: encouraging pairing and teamwork even when the tools push you toward isolation, sharing the pain openly, naming the uncomfortable thing. On the technical side: structuring your environment to collaborate with LLMs more deliberately — writing plans, configuring project-specific rules. Learning when to stop prompting and just write code. And critically: rebalancing the push and pull of information so that you're directing your attention, not feeling at the mercy of the model's output. More Star Trek, less Black Mirror.
Leave with concrete strategies for recalibrating your workflow, challenges to discuss and the reassurance that if you're finding this hard, you're not broken. The feedback loop is. And we can start fixing that.