Round 1: Foundation#

Part of the Project Threshold Tabletop Exercise V7.4 — Consolidated Round Materials

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Round 1: FOUNDATION#

0–6 Months from Exercise Start (~March–August 2026)#

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A. ROUND OVERVIEW#

Five to eleven industry participants converge for Round 1. The AI landscape has matured significantly—foundation models are reaching 70-80% performance on standardized exams, early automation is affecting 40% of white-collar task volume, and equity markets have rallied (S&P +12% YTD). Yet labor concerns are emerging visibly. Each industry participant must decide how to navigate this inflection point: accelerate adoption, pilot cautiously, or wait for regulatory clarity.

Time Period: 0–6 months (March–August 2026) Key Theme: Foundation and early deployment What Participants Face: Capability announcements, industry-specific productivity data, nascent regulatory signals, and early visible labor displacement

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B. SITUATION UPDATE#

(To be read aloud by facilitator at round open.)

Welcome to Spring 2026. The AI landscape has matured significantly, and economic implications are becoming impossible to ignore.

Model and Capability Announcements: The leading frontier labs have released increasingly capable copilot systems. We're seeing reliable multi-step task automation in customer service, content creation, and routine business process management. Foundation models are reaching 70-80% performance on standardized exams; competitor models are narrowing the capability gap. However, no "artificial general intelligence" milestone has been achieved—we remain in the "very capable narrow AI" era. Latency and cost have both improved, making real-time deployment feasible.

Enterprise Adoption: Pilots from 2024-2025 are graduating to production. Companies with dedicated AI teams are moving past proof-of-concept and seeing measurable productivity gains (15-25% in software and finance; more modest in traditional industries). Smaller and mid-market firms are moving cautiously, struggling with integration costs. Early instances of "AI buyer's remorse" are appearing—enterprises that rolled out copilots without proper change management are seeing limited adoption or high switching costs.

Labor Market Signals: Hiring growth in routine cognitive roles (data entry, junior analysis, claims processing) has visibly slowed. Entry-level hiring is down 40% in some industries. Conversely, demand for "AI-adjacent" roles (prompt engineers, AI training specialists, change management consultants) is booming. A few high-profile companies have announced modest headcount reductions (3-8% in specific divisions) with AI productivity explicitly cited. Unemployment remains at 4%, but job displacement is concentrated in specific geographies and demographics. Union activity around AI is emerging, particularly in consulting, law, and logistics.

Valuation and Capital Markets: AI-beneficiary stocks have rallied strongly (S&P +12% YTD). The "Magnificent AI" subset has outperformed. Industries with high displacement potential have seen selective weakness. VC deal flow for AI startups remains robust, but focus has shifted from model companies to "implementation and integration" plays. Enterprise software multiples have not collapsed but have stabilized. Regulatory conversation is nascent but clarifying: transparency, auditability, and disclosure will be demanded.

Regulatory Developments: Congress has held hearings on AI in financial services and healthcare. The SEC, FDA, and CFTC all have active AI task forces. No major binding regulation has passed, but the direction is clear: "responsible AI" frameworks are coming. State regulations (California, New York) are advancing. Regulators are concerned about concentration risk, labor displacement, and systemic financial risk.

Consumer Sentiment: Polling shows stark divergence: 68% trust AI for retail personalization; only 41% trust it for healthcare; only 35% for financial decisions. Across all industries, 72% demand transparency ("Tell me if I'm talking to an AI"). Primary concern is "job displacement"—50% of respondents worry AI will reduce job availability. When companies disclose AI use transparently and describe human oversight, trust increases 15-25 points.

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C. CORE INJECTS (2-3 Maximum)#

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R1-01: Copilot Displacement Spike#

Classification: Market Signal / Capability Announcement Time: Opening (start of round)

Narrative: A major frontier AI lab announces a new copilot generation capable of reliably executing multi-step customer service interactions with minimal human oversight. Early customer case studies show 60-70% of tier-1 and tier-2 support tickets resolved autonomously. The system handles refunds, returns, billing disputes, and escalation routing. Enterprise availability is immediate; pricing is per-interaction. A Fortune 500 retail customer is prominently featured. Stock markets react positively to beneficiary companies; some BPO and call-center stocks dip 3-5%. Within weeks, entry-level hiring is down 40% across industries—the visible labor displacement signal arrives.

Ambiguity/Unknowns:

• Real-world performance on complex edge cases remains untested at scale.
• True cost of ownership is non-obvious.

Industry Impact:

Facilitator Guidance:

• Early deployments show ~15-20% silent failure rate on edge cases.
• Pricing is lower than traditional BPO but requires upfront infrastructure; total cost of ownership is non-obvious.
• The flagship customer experienced 30% headcount reduction in pilot divisions but also faced 5% increase in escalations due to customer frustration with AI limitations.
• Within 8-10 weeks, competitors will announce similar capabilities. First-mover window is short.

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R1-02: Regulatory Signal#

Classification: Regulatory Signal / Policy Coordination Time: Mid-round (Day 1 afternoon or Day 2 morning)

Narrative: Three regulatory bodies issue concurrent signals. The SEC proposes guidance on AI-related disclosure and risk management for public companies. A Senate Finance Committee hearing features testimony from both banking advocates (citing efficiency gains) and consumer advocates (citing algorithmic discrimination and labor displacement). The FDA publishes draft guidance on AI-assisted clinical decision support, requiring manufacturers to demonstrate safety, transparency, and clinician oversight mechanisms. The message is clear: transparency, auditability, and disclosure will be demanded—but the timeline and enforcement intensity remain uncertain. Healthcare Provider, Healthcare Payer, and Finance participants face immediate pressure to assess compliance gaps.

Ambiguity/Unknowns:

• Timeline for finalization: months or years?
• Enforcement intensity: advisory or binding?

Industry Impact:

Facilitator Guidance:

• SEC guidance will likely require disclosure of model performance, training data provenance, and bias risks. Enforcement bar has risen.
• Senate hearing will attract media. Two senators will call for a "financial services AI transparency act." Industry will testify against "innovation-killing regulation."
• FDA guidance is non-binding but is expected to become de facto standard. Any manufacturer deviating will face skepticism.
• One large healthcare system is lobbying FDA behind the scenes for looser requirements; this will leak within 2-3 months.

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R1-03: Market Signal#

Classification: Market Signal / Competitive Dynamics Time: Mid-round (concurrent with or slightly after R1-02)

Narrative: Q1 2026 earnings season and venture funding data converge to send a clear signal. Large, well-capitalized enterprises that deployed AI in late 2025 are reporting visible productivity gains (15-25% in software and finance). Cloud providers cite 3-4 percentage points of margin expansion from AI infrastructure optimization. Consulting firms report 22% increase in billable hours per consultant year-over-year. Financial institutions cite 150 bps of operating margin improvement. However, one major software company misses guidance—attributed to slower enterprise AI adoption than expected; the stock drops 15%. The divergence is acute: proven AI adopters are beating expectations; others are disappointing. Wall Street begins explicitly ranking companies by "AI execution capability" and incorporating AI adoption maturity into valuation models. Venture capital is consolidating into mega-funds; smaller VC firms are struggling to raise capital.

Ambiguity/Unknowns:

• Is the productivity improvement sustainable, or does it plateau as low-hanging fruit is exhausted?
• Are valuation premiums for "AI winners" justified by fundamentals, or is a bubble forming?

Industry Impact:

Facilitator Guidance:

• This inject validates the scenario's "S&P +12%" figure.
• Participants that chose aggressive R1 strategies should prepare to show earnings aligned with announced productivity in Round 2.
• Participants that chose conservative approaches should show minimal AI impact in Round 2 earnings (neutral or slightly negative).
• The "AI execution capability" ranking becomes a real analyst talking point. Market rotates toward winners.
• One company that "missed guidance" signals that not all organizations can execute transformation equally. Market rewards execution; punishes stumbles.

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D. OPTIONAL INJECTS (Use Only If Table Finishes Early)#

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R1-OPT-01: Insider Trading Indictment#

Classification: Legal / Regulatory / Reputational Time: Late round (Day 2 afternoon)

Narrative: The SEC announces an indictment against a former executive of a major AI company for insider trading. The executive, based on non-public knowledge of model capability delays, sold shares before a public announcement that disappointed investors. The indictment is widely reported and raises questions about information asymmetry in AI development and the reliability of company guidance. The incident reflects growing investor attention to AI capability announcements and the risk of overhyped claims. Market reaction is modest, but the reputational damage to the company is material. Insurance companies and governance advisors begin offering "AI disclosure risk" insurance.

Ambiguity/Unknowns:

• Will this become a precedent for other prosecutions?
• How will it affect investor confidence in AI company guidance?

Industry Impact:

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E. COLLECTIVE BONUS (Optional, within Cross-Industry Discussion)#

Facilitator Script (Read Aloud):

"Does anyone want to recognize an especially strong strategy this round, or flag one that seems particularly risky?"

Procedure:

Scoring:

• If 3+ participants agree an industry has a strong strategy: +2 cumulative score bonus
• If 3+ participants agree an industry has a risky strategy: -2 cumulative score penalty
• Maximum one +2 and one -2 per round
• If no consensus or no one speaks up, no bonus applied

Facilitator Guidance:

• The Collective Bonus creates lightweight peer accountability without forcing participation.
• Encourage participants to cite specific reasoning if they do nominate.
• Announce results openly — nominations are public, not anonymous.
• In Round 1, nominations often reflect initial impressions. Later rounds will reflect track record.

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F. PRIVATE CARD DISTRIBUTION#

Facilitator Note: Distribute the appropriate Private Information Card from Private Cards to each participant face-down. Round 1 uses Private Card 1. Card 1 is shared across related industries (e.g., Retail and CPG receive the same card; Healthcare Provider and Healthcare Payer receive the same card; Finance, Consulting, and Law receive the same card; Manufacturing and Logistics receive the same card; Big Tech and B2B/B2C SaaS receive the same card). Private cards contain industry-specific data, internal metrics, or confidential intelligence relevant to each participant's decisions in this round. These cards are intentionally asymmetric—participants have incomplete information about other industries, forcing reliance on public injects and cross-industry inference.

Private cards are used in Rounds 1-3 only. Round 4 operates on symmetric information.

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G. INDIVIDUAL INDUSTRY DECISION SUBMISSION REQUIREMENTS — V7.4#

Each industry participant submits:

1. Industry Decision (1 REQUIRED):
   • Each participant must submit an explicit decision for their industry per round.
   • Use banded framework: avoid invented precision, use directional language.
   • Participant states their choice, rationale using bands, and expected outcome.
   • The decision reflects the industry's strategic response to the injects and market conditions.

Submission Format:

[INDUSTRY NAME] — ROUND 1 DECISION

DECISION TITLE: [Title]

RATIONALE:
  [Why this choice? How does it respond to injects and your private card?]

BANDED ASSESSMENT:
  - Spend/Commitment: [Band]
  - Time-to-Impact: [Band]
  - Execution Complexity: [Band]
  - Dependency: [Band]
  - Scale: [Band]

EXPECTED OUTCOME:
  [What do you expect in Round 2 earnings and industry conditions?]

INDUSTRY REPRESENTATIVE: [Name]

Time to Submit: 18 minutes after injects conclude.

Key Notes:

• One decision per participant per round.
• Use banded framework to avoid false precision.
• The decision should reflect the industry's response to the injects and cross-industry dynamics you anticipate.

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H. INDUSTRY HEALTH BASELINE — ROUND 1#

All industries start at cumulative score 0 (Steady condition). Industry Health conditions will be announced for the first time at the start of Round 2, based on Round 1 scoring outcomes.

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End of Round 1 Materials