技术革命周期诊断（佩雷斯框架 / Perez Tech-Cycle）

Overview

This skill operationalizes Carlota Perez's model from Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages (2002). It turns the book's historical narrative into a repeatable phase-diagnosis procedure: identify the technological revolution in question, score observable evidence against the recurring phase signatures, determine the dominant phase (and its runner-up), then map the phase to the correct financial-capital vs production-capital logic and to concrete investment / strategy implications.

The core insight to carry into every analysis: the crash is a scheduled phase of the revolution, not a refutation of the technology. The deciding question is never "is it real?" but "which phase are we standing in?"

When To Use

• User asks "现在的 AI / 半导体 / 新能源 / 某个产业处在技术革命的哪个阶段？"
• User asks "这是不是泡沫？" / "现在是不是转折点？" / "会不会有黄金时代？"
• User references Perez, 技术革命与金融资本, 金融资本, 泡沫, 黄金时代, 康德拉季耶夫长波, 创造性破坏.
• User wants a tech-/industry-cycle read to inform asset allocation, sector rotation, or corporate strategy.
• User wants to stress-test a "this time is different" thesis against 240 years of precedent.

Workflow

Step 1 — Identify the revolution and paradigm

Determine which technological revolution / techno-economic paradigm is under analysis. Load references/framework.md for the canonical list of five revolutions and the definition of a techno-economic paradigm (key low-cost input + infrastructure + organizing model). If the subject is newer than ~1971 (e.g. AI, bio-tech, energy transition), treat it as a candidate 6th revolution and note the analogy explicitly. Always state the paradigm's "best-practice common sense" before diagnosing phases.

Step 2 — Load the framework reference

Read references/framework.md to anchor:

• The two-period / four-phase model (Installation: Irruption → Frenzy; Turning Point; Deployment: Synergy → Maturity).
• Financial capital vs production capital behaviour per period.
• The state's role and the inequality reversal at the turning point.
• The three historical golden ages as calibration anchors.

Step 3 — Run the phase diagnosis (scorecard)

Apply the 10-criterion observable scorecard in references/scorecard.md. For each criterion, record the current evidence and score how strongly it matches each phase signature (0–3). Two ways to execute:

• Script (preferred, auditable): generate a blank matrix with python scripts/phase_scorecard.py --template scores.csv, fill scores.csv from evidence, then run python scripts/phase_scorecard.py --input scores.csv --name "<主题>". The script sums per-phase columns, reports the dominant phase and its runner-up, and appends a dated row to history.csv so the phase judgment becomes a time series.
• Manual: tally the 10 criteria by hand, keep the runner-up visible, never declare the phase "settled" — hold the frenzy case and the synergy case side by side.

Step 4 — Map phase → implications

Use this decision rule, drawn directly from the framework:

• Irruption / early Installation: own the builders of the new infrastructure; accept bubble volatility; financial capital leads, profits from building capacity.
• Frenzy / late Installation: watch for decoupling of paper from earnings, valuation dispersion collapse inside the theme, issuance flood, retail euphoria — these are frenzy signatures, not confirmations.
• Turning Point (crash): survival and balance-sheet strength dominate; the crash transfers the installed base to stronger hands at rational prices — the precondition for the golden age, not its negation.
• Synergy / Deployment: the winners are the users of the installed base — companies applying the new paradigm to take margin in ordinary industries; production capital leads, profits from using it; inequality reverses; state sets stable, credible rules.
• Maturity: paradigm exhaustion; returns migrate to finding the next "big bang".

Step 5 — Add the state & inequality lens

State which actor must intervene at the turning point (proactive state: regulation, public investment, income redistribution). Flag whether the current setup risks a "gilded age" (false prosperity) versus a true golden age. Note the inequality signature: top-1% share rises through installation/frenzy and falls through deployment.

Step 6 — Emit a structured diagnosis report

Output: (1) the revolution/paradigm identified; (2) the phase read with score and runner-up; (3) the financial-capital vs production-capital logic in play; (4) the three most decision- relevant implications; (5) the key risk if the read is wrong (the "fighting the wrong war" error: selling a real revolution because it crashed, or holding builder-phase exposure on deployment-phase assumptions).

Guardrails

• The model is a heuristic, not a mechanical predictor. Each occurrence is unique; overlaps and irregularities are normal. State this caveat in every diagnosis.
• Score against evidence, not mood. The 10-criterion scorecard exists precisely to force evidence-based scoring instead of vibes.
• Always keep the runner-up phase visible — allocation should hold both the frenzy and the synergy case until evidence resolves them.

Resources

• references/framework.md — canonical framework: five revolutions, two-period/four-phase model, financial vs production capital, state role, golden ages, theoretical lineage.
• references/scorecard.md — the 10 observable criteria, what evidence to collect per criterion, and the phase signature each points to.
• references/examples.md — worked examples: trigger phrases, a fully filled Frenzy-read scorecard (AI 2026), its mapped implications, a contrasting Synergy-read scorecard (internet ~2012), a sample output report, and a no-script manual tally. Read this first when unsure how to operate the skill.
• scripts/phase_scorecard.py — stdlib-only scorecard: generates a blank matrix, scores a filled CSV, reports dominant phase + runner-up, and logs a dated history row.