06-reference

book solve everything ch5 the mobilization 2026 04 13

Sun Apr 12 2026 20:00:00 GMT-0400 (Eastern Daylight Time) ·reference ·source: Solve Everything (solveeverything.org) ·by Alexander Wissner-Gross and Peter Diamandis

Chapter summary

Chapter 5 shifts from theory to implementation schedule, presenting a sequenced mobilization plan for industrializing intelligence across civilization. Three foundational pillars must be constructed before widespread problem-solving begins: scoring systems (blinded evaluation harnesses with Decision Records for AI Systems), data and action infrastructure (data trusts that convert messy institutional data into reusable training capital, plus action surfaces — APIs and robotic controllers — that let digital decisions reach the physical world), and energy-to-compute capacity (data centers treated as heavy industrial facilities co-located with clean power, not office buildings). The chapter then lays out a three-phase Solution Wavefront. Phase 1 (2026-2027) targets pure-information domains: mathematics reaches effective solution status with AI verification surpassing human capability, followed by computer science where models write and debug complex code at superhuman levels. Phase 2 (2028-2031) enters the physical world: chemistry and materials science operate through closed-loop labs measuring “Time-to-Property” (speed from digital concept to physical sample), and biology transitions into software via high-fidelity Virtual Cell simulation. Phase 3 (2032-2035) tackles planetary systems: energy infrastructure, intelligent grid balancing, and orbital expansion. The chapter emphasizes that evaluation must precede automation — build the scoreboards before shipping the agents.

Key frameworks or claims

RDCO strategic mapping

The mobilization schedule provides RDCO with a temporal map for content and consulting positioning. Phase 1 domains (math, CS) are where RDCO’s audience already lives — data teams writing code, building pipelines, evaluating models. The chapter’s insistence that evaluation precedes automation is the harness thesis stated as operational doctrine (see 2026-04-12-harrison-chase-harness-blog, paper-arxiv-2604-08224-agent-harness-study-2026-04-12). RoCS as a metric connects directly to Eric Weber’s outcome metrics (2026-04-04-eric-weber-data-team-roi-ai-first): Decision Velocity and Revenue Affected are enterprise-scale instantiations of RoCS — measuring cognitive spend against actual business outcomes rather than artifacts shipped. The data-trust concept maps to RDCO’s data-quality-framework work (2026-03-30-founder-data-quality-framework): trusts need quality guarantees before data becomes reusable capital, and the testing matrix is the mechanism. For phData Mode B, the mobilization schedule suggests consulting engagements should sequence as: (1) build the client’s eval harness, (2) stand up their data trust / quality layer, (3) then deploy agents against scored targets. Skipping to step 3 is the Muddle Path from Chapter 4.