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165: "What Caused AI to Skyrocket in 2026?" ft. Justin Coats

165: "What Caused AI to Skyrocket in 2026?" ft. Justin Coats

Episode 165 Published 5 months, 1 week ago
Description

Erik and Justin dig into what it actually means for AI to “become real,” arguing that consumer usage does not equal workplace adoption. The conversation lands on a “pause moment” where organizations are finally forced to address governance, security, policy, and measurement because the tools are now powerful enough to create real operational risk and real operational leverage.

🧭 Conversation Highlights

  • Justin distinguishes massive user counts from meaningful adoption, emphasizing that most people use AI on free tiers or inside existing apps without realizing it is AI.
  • Both agree the real shift is B2B and workplace implementation, where adoption breaks down into training, governance, governance-adjacent policy, and data access safeguards.
  • They compare possible “adoption metrics” like tokens per user versus prompts per week, and weigh what better reflects ongoing, valuable use.
  • Justin describes where the governance battles are emerging now: permissions, agent access patterns, AI clauses in contracts, and how to build an internal org chart that can manage AI agents like a new

💡 Key Takeaways

  • “AI is real” is not the same thing as “AI is widely used.” Real adoption shows up when an organization can safely incorporate it into workflows and data boundaries.
  • The pause is partly rational: once AI is embedded, the limiting factor becomes governance, not novelty or access.
  • Token usage is a tempting metric, but it can reward inefficiency and does not necessarily correlate with value, especially in consumer scenarios.
  • The biggest operational bottleneck is org-wide alignment: you can token-max development, but ROI still collapses if the rest of the company cannot keep up.

❓ Questions That Mattered

  • How do we differentiate early adoption by curious consumers from sustained, workplace-relevant adoption inside organizations?
  • Which measurement is most honest: tokens per user, prompts per week, time-in-platform, or something else that reflects real value over time?
  • What does “success” even mean after the novelty phase, when policy, governance, security, and data access are now the gating factors?
  • Are there governance solutions that can unlock cross-silo collaboration without creating new unacceptable risk?

🗣️ Notable Quotes

  • “99 % of those 1.3 billion individuals that are using AI currently are just using AI through a free feature, a free account.”
  • “it’s more than just buy a license and tell people to use it.”
  • “we’re kind of in this pause moment where organizations, leaders, boards, managers, directors, employees are all identifying, holy cow, okay, the tool's really powerful.”
  • “You’re as fast as your slowest team.”

🔗 Links & Resources

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