// technology adoption

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The AI Model Glut Has Outpaced Our Ability to Learn

The relentless release cycle of large language models and AI systems has created a saturation problem: organizations and individuals now face more capable tools than they can evaluate, integrate, or master within realistic timeframes. This inverts the traditional tech adoption curve where scarcity drives competition and urgency. Abundance instead breeds decision paralysis and waste, as teams abandon half-implemented systems to chase the next release while vendors compete on novelty rather than solving actual user problems.

Healthcare Leaders Must Redesign Work Before AI Can Help

The healthcare industry layers technology onto broken workflows—EHRs that created administrative bloat, now AI tools expected to patch systemic inefficiency. The root problem isn't computational power but organizational design. Until hospital systems, insurance companies, and clinics restructure how work moves between departments and who owns which tasks, AI will accelerate the existing mess, concentrating gains among some providers while leaving others drowning in new data management burdens. Competitive advantage over the next five years goes not to organizations buying the fanciest AI, but to those willing to do the unglamorous work of eliminating redundant handoffs and decision points first.

Schools adopt Big Tech's manipulative growth tactics

Educational institutions are now mimicking the engagement-maximization strategies that made social platforms and consumer tech products dominant—creating artificial urgency, manufacturing FOMO, and designing curriculum around tool adoption rather than foundational skills. This shifts the burden of tech literacy from companies to public schools while inadvertently training students to accept and normalize the exact behavioral mechanisms that tech companies use to capture attention and data. Students learn how to use specific platforms before learning why to think critically about them.