Who Leads AI in 2029? Five Scenarios, Not One Prediction
Explore five scenarios for AI leadership in 2029, with evidence triggers, counterarguments and a practical method for testing enterprise platform commitments.
AI evaluation
AI Dark Horses: Who Could Change the Competitive Balance?
Assess AI dark horses including Thinking Machines, SSI, Mistral, World Labs and robotics labs, with evidence gates for enterprise pilots and supplier choices.
AI Feedback Loops: Building Systems That Stay Correctable
Keep AI systems correctable as feedback accumulates. Govern knowledge promotion and withdrawal, protect authorization boundaries, reconcile unknown execution outcomes, and test the conditions that should stop the workflow.
AI Uncertainty: Why Confidence Scores Are Not Enough
Give token entropy, semantic uncertainty, calibration, and abstention distinct jobs. Evaluate how uncertainty changes the next workflow step instead of relying on a single confidence score.
AI Output Verification: Check Claims Before Acting
Evaluate infrastructure claims against scoped evidence. Use a two-cluster example to separate supportability, capacity, isolation, and resilience, including the difference between normal and failure-state capacity.
AI-Assisted Decisions: When Repetition Becomes False Confidence
Prevent a tentative AI diagnosis from becoming accepted knowledge through repetition. Preserve source lineage, separate observations from interpretations, and make changed evidence trigger reassessment.
AI Decision Controls: From Learned Patterns to Authorized Actions
Turn model recommendations into bounded, authorized actions. Define execution contracts, recheck approvals, reconcile uncertain outcomes, and test the full workflow before expanding an agent’s production authority.
The Double-Slit Experiment and AI: Why Context Changes the Answer
Use the double-slit experiment as a bounded analogy for AI context and evaluation. Distinguish presentation effects from changed evidence, and turn the comparison into a controlled testing approach.
Schrödinger’s Cat and AI: Plausible Is Not Proven
Separate a plausible AI answer from a verified claim and an authorized action. Use the Schrödinger’s cat analogy carefully, then apply evidence gates to infrastructure decisions.
AI Generalization: Did Your Model Learn the Right Pattern?
Test whether an AI model responds to the evidence that matters. Use controlled incident variations to expose shortcuts, leakage, brittle recommendations, and failures hidden by aggregate scores.
Entropy in AI and Human Thinking: Why Certainty Is Not Accuracy
Distinguish physical, information-theoretic, and psychological uses of entropy. Learn why predictable AI responses and repeated human beliefs still need independent evidence and practical validation.
AI Context Governance: From Test Results to Production Controls
Turn context-evaluation findings into production controls. Preserve critical evidence through retrieval and summarization, assign ownership, and keep release evidence separate from runtime execution authority.
Rehearse Before You Automate: Why AI Agents Should Practice Before They Act
Rehearse agent behavior in controlled environments before granting production authority. Test realistic failures, policy conflicts, recovery, and repeated trials against the scope the agent will actually receive.
Feedback Is the Control Plane: Preventing AI Systems From Repeating the Wrong Behavior
Decide which production experiences may influence future AI behavior. Qualify observations, route corrections to the right layer, evaluate changes, and control their promotion into durable knowledge or policy.