Structuring a modern AI team — Denys Linkov, Wisedocs

AI Team Anatomy - Companies should recognize that technology is not always the limitation to success, but rather how technology is used [1] - Companies need to identify their bottlenecks, such as shipping features, acquiring/retaining users, monetization, scalability, and reliability, to prioritize hiring accordingly [3][4] - Companies should consider whether to trade their existing team with domain knowledge for AI researchers from top labs, weighing the value of domain expertise against specialized AI skills [1] Generalists vs Specialists - Companies should structure AI teams comprehensively, recognizing that success isn't tied to a single role [2] - Companies should prioritize building a comprehensive AI team with skills in model training, model serving, and business acumen, balancing budget constraints [7] - Companies should understand the trade-offs between hiring generalists and specialists, with generalists being adaptable and specialists pushing for extra performance [18][19] Upskilling and Hiring - Companies should focus on upskilling employees in building, domain expertise, and human interaction [19] - Companies should hire based on the need to hold context and act on context, ensuring accountability for AI systems [23][24][25] - Companies should verify trends and think from first principles when hiring, considering new grads, experienced professionals, and retraining opportunities [27]