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New York • September 8 & 9, 2027
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“Do I fall behind and risk obsolescence, or keep up and risk burnout?” This is how I felt as new AI models, frameworks, editors, and existential proclamations arrived daily, all while I was leading multiple projects and raising a young child. I eventually recognized this false dichotomy: obsessively following AI news is not required to be effective.
This epiphany was rooted in the Diffusion of Innovations model. Innovators and Early Adopters exhaustively study every new development, and we need them. But being an Early Adopter doesn’t have to be your job! You can belong to the pragmatic Early Majority and capture most of the benefits by learning from your Early Adopter peers. And these roles aren’t fixed: you might upshift to Early Adopter for the right opportunity, or downshift to Early Majority when personal circumstances arise.
Your superpower as a leader is determining which advancements do matter – through experience and institutional knowledge – and fast-tracking their adoption. At my company, we cultivated a culture of experimentation grounded in the scientific method.
Three tactics accelerated the diffusion of learnings through the organization:
- (1) gave Early Adopters a dedicated space to share and discuss headlines
- (2) ran show-and-tells to bridge Early Adopters and the Early Majority
- (3) facilitated mob programming sessions for hands-on practice with each team’s tech stack. Based on DX metrics, our True Throughput has increased 2.5x.
This talk is for overwhelmed engineering leaders and senior ICs who feel quietly guilty about not experimenting with every AI trend. I’ll share how I recalibrated my relationship with the AI news cycle – by curating my sources and ignoring low quality content – and became more deliberate about how I deliver value to my organization.
Key takeaways:
- Why you won’t miss the boat: transformative AI advances gain enough mindshare to become standard practice without following every headline.
- Why being in the Early Majority is a legitimate baseline for AI adoption, and when it’s worth shifting to Early Adopter instead.
- How to curate a personal AI news diet, and spot the red and green flags that separate noise from signal.
- How to build a culture of experimentation and diffuse Early Adopter insights into real productivity gains.
- How to use your institutional knowledge to identify the AI technologies that matter, without becoming an “AI obsessive” yourself.”
