Ai

There is that old joke told by Woody Allen describing his experience as a university student: “I was thrown out of college for cheating on the metaphysics exam; I looked into the soul of the boy sitting next to me.” If he’d managed to pass his course, Woody Allen might have been ahead of his time. Metaphysics is becoming a popular topic today, and it seems now everyone desperately wants to look for souls in large language models (LLMs). A recent conference last April was held by Anthropic with religious scholars and philosophical thinkers to discuss the nature of the soul when it came to their latest Claude models. It seems they needed guidance that Dario Amodei was unable to provide. ...

In his recent post on X, Andrej Karpathy uses the funnel as an analogy for the AI understanding gap — that gap being largely one of scale in terms of population exposure to the models. The same analogy also helps illustrate the cost and benefit gaps, the inverted nature of which are colliding with popular acceptance of AI. As shown in the exhibits below, this same model can be applied to the benefit and cost gaps experienced by the same populations. ...

Stocks, Flows & Impediments to Rapid Diffusion · 2022–present ↓ Click any stock or resistance node for detail  |  Flows (dashed orange) show rates of change  |  Resistance nodes (red) act as governors limiting flow rates STOCK FLOW RESISTANCE REINFORCING LOOP AI Capability Models, APIs, Tools, Research ▲ High & Accelerating Workforce Readiness Skills, Habits, Comfort Institutional Trust Authorization & Confidence Regulatory Clarity Legal Frameworks & Standards ▼ Currently Low AI Adoption Real Workflow Integration ◈ Primary Output Stock Infrastructure Compute, APIs, Tooling ▲ Growing (uneven) capability exposure → training inflow API/service buildout demonstrated competence use cases define rules authorized deployment legal clearance Job Threat Perception ⊖ slows Publicized AI Failures ⊖ erodes trust Legal / Liability Uncertainty ⊖ chills adoption Legacy System Complexity ⊖ slows R+ Early adopter success builds proof cases → trust SYSTEM BOUNDARY: Enterprise AI Adoption B− Balancing loops (resistance nodes) create natural governor on adoption rate — — —

Based on Gartner Hype Cycle for Cloud Platform Services, 2025 (Hover over the dots to see details) Technology Positioning Innovation Trigger Peak of Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity EXPECTATIONS TIME Technology Analysis All Positions Innovation Trigger Peak of Expectations Trough of Disillusionment Slope of Enlightenment All Timeframes <2 years 2-5 years 5-10 years >10 years Obsolete Technology ↕ Position ↕ Time to Plateau ↕ Analysis ↕