Oct 15, 2026 · 48 min
AI leaders debate AGI, adoption and the cost of intelligence
#246: AI Answers - Live from MAICON 2026: AGI, AI Costs, and the End of GPTs
The discussion connects fast-moving model advances to the harder questions organizations and workers face about trust, purpose, ownership, and change.
- 1AI adoption depends less on demonstrations than on personalized learning, cross-functional trust, and workable organizational permissions.
- 2Falling model costs may hide increasing abstraction, as users interact with agents without choosing the systems underneath.
- 3AGI remains difficult to define, while its implications for employment, portable workflows, and social responsibility are already being debated.
Don't miss
The hosts distinguish AGI from isolated benchmark performance by imagining a system that handles broad ordinary tasks across contexts.
The brief
Recorded live at MAICON 2026, Paul Roetzer and Mike Kaput move from the conference’s big ideas to the practical problem of keeping people informed without exhausting them.
Their adoption argument is organizational, not merely technical: personalized learning must meet people where they are, while legal, procurement, IT, and trust barriers determine what gets used.
As models change faster than roadmaps can keep up, the hosts expect costs to fall and AI to become more abstract, with agents choosing the underlying systems for users.
The sharpest turn comes with AGI: rather than treating it as a benchmark triumph, they ask what broad competence across ordinary contexts would actually look like in daily life.
The final questions make the stakes concrete, examining whether AI workflows belong to workers or employers and what replaces custom GPTs as platforms evolve.
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