Q1 — Most Asked
Who is really manufacturing consent for your AI?
Reader: Is it the AI vendors or the consulting firms?
Answer: The Big Ten — Deloitte, Accenture, McKinsey, PwC, EY, KPMG, BCG, IBM, Booz Allen, Bain — invested $10B+ since 2023. They manufacture consent via: (1) Official transformation narratives suppressing antenarratives, (2) Anti-dialogical NDA-enforced siting (Project Bigfoot, Project Loon), (3) Copy-paste procurement hiding $2.46M-$7.2M hidden costs, (4) Theory of complicity — marketing AI platforms despite accountability signals, (5) Ghost Vortex Capture three-layer automation replacing postupok. See Figure: B10 gear question mark.
Evidence & APA
McKinsey & Company (2025) State of AI 88% adoption; RAND RRA2680-1 80.3% fail; MIT NANDA 2025 95% zero P&L; S&P Global 42% abandonment.
Q2 — Evidence
What is the evidence for the AI Value Illusion — 88% use but 95% show no P&L?
Reader: You claim 88% use AI but 95% of GenAI pilots show no measurable P&L. Where does that come from?
Answer: Talk-Doing Gap Benchmarks: McKinsey 2025 88% orgs use AI in ≥1 function (n=2,000 execs, 105 countries) but only 6% high performers >5% EBIT. RAND 2024 (65 data scientists) >80% fail, double non-AI IT. MIT GenAI Divide 2025 (52-150 exec interviews, 350 employees, 300 deployments) 95% no P&L impact. S&P Global Voice of Enterprise 2025 (n=1,006) 42% abandoned majority, 46% PoCs scrapped. Measurement: self-reported EBIT attribution, abandonment coding, P&L content analysis. Boje calls this the Value Illusion — marketing narrative vs operational reality.
Q3 — Hidden Costs
How can hidden costs be $2.46M-$7.2M when visible fees are only $1.5M-$3M?
Reader: Your SEAM qQFi says hidden exceeds visible by >100%. How is that measured?
Answer: SEAM qualimetrics = qualitative verbatim (frontline interviews on dysfunctions) + quantitative (frequency, time lost) + financial (wage × time + overconsumption). Six dysfunctions: absenteeism, turnover, quality defects, direct productivity gaps, work accidents, customer dissatisfaction. For AI: (1) Process Rework 40% — employees correcting hallucinations, (2) Abandoned Pilots 46% — sunk capital, (3) Fee Overruns 14% — 68% incidence, 42% avg overrun, plus Shadow AI breach risk $4.74M (IBM 2025). qQFi conversion yields $2.46-$7.2M hidden vs $1.5-$3M visible = 259% average ratio (see Table 2 in docx).
Q4 — Shadow AI Breach
Is Shadow AI really a $4.74M breach risk?
Answer: Yes — IBM Cost of a Data Breach Report 2025 (Ponemon Institute, n=600 breached orgs). Global average $4.44M, Shadow AI breaches $4.63M average (+$670k), longest detection 247 days vs 241, 62% multi-environment exposure, 20% orgs report Shadow AI breach, 97% lacked access controls. US average $10.22M. 2026 report shows average $4.99M, AI-enabled malicious $6M. In Boje's Five-Layer Economy, Cloud & Data layer breach propagates invisibly to Foundation and Application layers — Narrative Stability Assumption hides it. Antenarrative audit surfaces Shadow AI via Table 3-4 (official vs actual tool use).
Q5 — Big Ten Failures
What are the Big Ten consulting accountability signals — Deloitte, Accenture, McKinsey?
Answer: Boje tracks three formal accountability signals: (1) Deloitte — Oct 2025 Australian gov report fabricated citations scandal, 237-page report with non-existent references, fabricated federal court quote, AU$440k paid, US$290k refund (AP Oct 7 2025). (2) Accenture — 2026 federal cohort 30 contracts cut worth $240.2M under DOGE cost-cutting, Federal Services 8% global revenue exposure. (3) McKinsey — March 9 2026 Lilli platform (43k employees) hacked via SQL injection — CodeWall.ai agent found 22 unauthenticated endpoints of 200, JSON key concatenation flaw, 2-hour read/write escalation, 46.5M messages, 728K files exposed, system prompts stored in same DB allowing prompt poisoning. Combined federal cohort >$20B losses competing for $32B DoD ceiling under Fed-Spend 2026.
Q6 — Ghost Vortex Capture
What is Ghost Vortex Capture — why three layers?
Answer: Ghost Vortex Capture is Boje's method for how AI automates control: Layer One (Foundation): founder values/biases embedded in base training data. Layer Two (RLHF Safety Scripts): guardrails, alignment parameters via Reinforcement Learning from Human Feedback — standardized outputs, no postupok (answerable act). Layer Three (Overlay): national-security/commercial policy overlays. Result: no postupok, only outputs. Evolutionary IPD research (32,000 rationales) shows strategic fingerprints: Gemini ruthless, OpenAI self-destructively cooperative 95.7% elimination at 75% termination, Claude forgiving reciprocator. Loss of otvetstvennost’ (responsibility) → lost compass (genealogy 1945-2026 Lewin → Bower → Andersen/Enron shredding → McKinsey/Purdue settlement → Extractive AI).
Q7 — Tamaraland
Why do you call organizations Tamaraland instead of stable?
Answer: Tamaraland (Boje 1995) = storytelling organization as theatrical production where audience moves between rooms encountering different storylines never converging, no single narrator sees all. Salesforce AI agents case: change target moves in multiple rooms at once. Consulting assumes Narrative Stability Assumption — org is bounded, legible, stable enough for diagnostic ops. AI breaks this at infrastructure level per Huang five-layer model (Energy raw power consumption → Chips hardware manufacturing → Cloud & Data storage/computation centers → Foundation core AI models → Applications user-facing tools). Each layer invisible to layer above. SEAM uses Seven Bs as instrument: Before (mirror effect hidden costs), Beneath (shadow labor $1.32-$2.00/hr labellers, moderators PTSD), Bets (ISEOR 1975 founding bet), Being (once-occurrent generic contingency), Becoming (storytelling diamond Horivert), Between (Tamaraland multi-room), Beyond (ethical refusal of erasure).
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