Seven Powers in the AI Era
Source-grounded pack with Run AI Power Audit, Test AI Operating Advantage, and Test AI Scale and Network Power, plus 1 more.
Get this packGrounded in the complete Seven Powers in the AI Era series.
Open the pack, choose a workflow, and follow its setup and input instructions. You can also open each workflow below.
10 source essays · 4 usable skills · reviewed Jul 2026
Use this pack when
Choose this pack when the work touches Seven Powers in the AI Era and you need a bounded workflow with explicit inputs, a defined output, and human review gates.
Choose the job in front of you.
4 source-grounded skills. Load one workflow at a time.
Run AI Power Audit
Use this workflow when founders or executives must separate AI adoption and near-term impact from durable competitive power, test all Seven Powers, identify decaying fake moats, and choose evidence-bound operating bets.
Output specification
Return an AI power audit with workflow_map, seven_power_tests, impact_vs_defensibility, fake_moat_decay, evidence_and_confidence, operating_bets, owners_metrics_and_tests, and kill_or_strengthen_conditions.
Test AI Operating Advantage
Use this workflow when a company claims AI-era power from brand and trust, proprietary data or rights, distribution or talent, or an accumulated process of evaluation, review, learning, and recovery.
Output specification
Return an AI operating advantage test with causal_claims, trust_economics, privileged_resource, process_learning_loop, imitability_and_decay, classification, falsification_tests, and compounding_investments.
Test AI Scale and Network Power
Use this workflow when a company claims AI scale economies or network effects and must prove that volume lowers unit cost or participation increases per-user workflow value in a way competitors cannot readily reproduce.
Output specification
Return a scale and network power test with causal_loops, unit_economics_by_volume, workflow_participant_value, customer_behavior, rights_and_trust, competitor_replication, classification, and falsification_test.
Test Counter-Positioning and Switching
Use this workflow when a company claims incumbents cannot copy its AI business model or customers cannot leave, and must test incumbent self-harm, response paths, valuable operating memory, performance loss, and AI-assisted migration.
Output specification
Return a counter-positioning and switching test with entrant_move, incumbent_self_harm, response_paths, residual_advantage, customer_loss_map, migration_counterfactual, classification, and falsification_tests.
Keep consequential decisions explicit.
These workflows structure analysis and artifacts; they do not authorize autonomous external, personnel, financial, legal, security, or other high-impact action.