Scope: Real two-party negotiation markets where TEE attestation is structurally necessary — not just nice to have. Tina's bar applied throughout: only counts if party A's move in round N materially changes party B's move in round N+1.
No competitors to DealProof found. Closest analogs: Enclave Markets (TEE for crypto block trading, no agents, no πCreds), Cybersettle (double-blind insurance claims, no TEE, suspended), Zycus ANA (AI procurement agent, no TEE). None have the full stack: TEE + dual agents + DCAP attestation + πCreds conduct layer + escrow.
| # | Vertical | Domain | Real Neg. | TEE Necessity | Volume | Pay | Reg. Risk | Reachability |
|---|---|---|---|---|---|---|---|---|
| 1 | Institutional block trade | Finance | YES | HIGH | HIGH | HIGH | LOW | MED (via Flashbots/IC3) |
| 2 | OTC derivatives ISDA/CSA | Finance | YES | HIGH | HIGH | HIGH | LOW | LOW |
| 3 | Leveraged loan bookbuilding | Finance | YES | HIGH | HIGH | HIGH | LOW | LOW |
| 4 | Creator × brand via manager | Creator Economy | YES | HIGH | HIGH | HIGH | LOW | MED-HIGH |
| 5 | Music sync licensing | Creator Economy | YES | HIGH | MED | HIGH | LOW | MED |
| 6 | Programmatic guaranteed deals | Creator Economy | YES | MED | HIGH | HIGH | LOW | LOW |
| 7 | Competing-offer verification | HR | YES* | HIGH | HIGH | HIGH | LOW | HIGH |
| 8 | Inter-employer comp benchmarking | HR | YES | HIGH | HIGH | HIGH | MED | MED |
| 9 | Union collective bargaining | HR | YES | HIGH | HIGH | HIGH | MED | LOW |
| 10 | Hospital × payer rate negotiation | Healthcare | YES | HIGH | HIGH | HIGH | MED | LOW |
| 11 | Pharma × PBM rebate | Healthcare | YES | HIGH | HIGH | HIGH | MED | LOW (via disruptors) |
| 12 | Pharma × payer outcomes contract | Healthcare | YES | HIGH | MED | HIGH | MED | LOW |
| 13 | AI training data licensing | Data | YES | HIGH | MED | HIGH | LOW | MED |
| 14 | Healthcare dataset licensing | Data | YES | HIGH | MED | MED | MED | LOW |
| 15 | Federated learning access | Data | YES | HIGH | MED | HIGH | LOW | MED |
| 16 | FMCG/CPG trade promotion | Supply Chain | YES | HIGH | HIGH | HIGH | LOW | LOW |
| 17 | Enterprise SaaS renewal | Supply Chain | YES | MED | HIGH | HIGH | LOW | MED |
| 18 | OEM × tier-1 supplier cost | Supply Chain | YES | HIGH | MED | HIGH | LOW | LOW |
| 19 | Bilateral PPA negotiation | Energy | YES | HIGH | MED-HIGH | HIGH | LOW | MED |
| 20 | Wholesale gas supply contract | Energy | YES | HIGH | MED | HIGH | LOW | LOW |
| 21 | Voluntary carbon credit | Energy | YES | HIGH | MED | MED-HIGH | LOW | MED |
| 22 | P&C insurance BI settlement | Legal/ADR | YES | HIGH | HIGH | HIGH | LOW | MED |
| 23 | EEOC employment settlement | Legal/ADR | YES | HIGH | MED-HIGH | HIGH | MED | LOW |
| 24 | Commercial arbitration settlement | Legal/ADR | YES | HIGH | MED | HIGH | MED | LOW |
| 25 | MNO × MVNO wholesale access | Telecoms | YES | HIGH | MED-HIGH | HIGH | LOW | LOW |
| 26 | Spectrum LSA negotiation | Telecoms | YES | HIGH | LOW-MED | HIGH | MED | LOW |
| 27 | MNO × neutral host small cell | Telecoms | YES | HIGH | MED | HIGH | LOW | LOW |
| 28 | DoD sole-source defense contract | Gov/Defense | YES | HIGH | HIGH | HIGH | HIGH | LOW |
| 29 | BAFO government RFP | Gov/Defense | PARTIAL | MED | HIGH | HIGH | MED | LOW |
| 30 | OTA defense prototype | Gov/Defense | YES | MED | MED-HIGH | HIGH | HIGH | LOW |
| 31 | On-chain NFT/in-game auction | Gaming | YES | HIGH | HIGH | HIGH | LOW | MED |
| 32 | P2E guild scholarship | Gaming | PARTIAL | MED | HIGH | MED | LOW | MED |
| 33 | Sports data licensing | Gaming | PARTIAL | MED | HIGH | HIGH | LOW | LOW |
| 34 | Commodity prepayment/RBL | Emerging Markets | YES | HIGH | MED-HIGH | HIGH | MED | LOW |
| 35 | Cross-border SCF/trade finance | Emerging Markets | YES | MED-HIGH | HIGH | HIGH | MED | LOW |
| 36 | B2B agent × agent procurement | Agentic AI | YES | HIGH | HIGH | HIGH | LOW | MED |
| 37 | Agent financial execution | Agentic AI | YES | HIGH | MED | HIGH | LOW | MED |
| 38 | Multi-agent supply chain coord | Agentic AI | YES | HIGH | HIGH | HIGH | LOW | MED |
| 39 | Clinical trial site agreements (CTA) | Life Sciences | YES | MED | MED | HIGH | LOW-MED | LOW |
| 40 | Healthcare staffing / locum tenens | Healthcare Staffing | YES | HIGH | HIGH | HIGH | LOW | LOW-MED |
| 41 | China MCN / cross-border IP licensing | Creator Economy (Cross-border) | YES | MED-HIGH | HIGH | MED | MED | LOW |
| 42 | Gig economy rate negotiation (⏰ EU Dec 2026) | Platform Labor | PARTIAL | MED | MED | MED | MED | LOW |
*Requires revision loop to pass Tina's bar. Single-round above/below fails.
Domain: Financial Markets Parties: Buy-side asset manager × broker-dealer block trading desk (and/or receiving institutional investor)
A broker intermediating a block trade is architecturally positioned to see the seller's full position, mandate constraints, and urgency — and has economic incentive to exploit that information to benefit its own principal position or favored buy-side clients.
Documented real-world failure: Morgan Stanley paid $249M to settle SEC/DOJ charges that its equity syndicate desk disclosed confidential block trade information to select buy-side investors, enabling them to short ahead of execution. This persisted three years under NDA. An NDA cannot prevent a trader from making a phone call.
- Matching logic runs inside the enclave — broker cannot read either party's full position or reserve price
- Remote attestation proves the matching algorithm wasn't modified to log positions
- No party learns "there is a large seller at $X" unless a match exists
- Structurally impossible for NDA or dark pool ATS to achieve simultaneously
- Global equity block trading: $1.54T notional (2024)
- Hundreds of thousands of negotiation sessions/year globally
- Buy-side TCA budget: $1.5M–$5M per $500M block in implicit/explicit costs
- SaaS fee of $50K–$500K/year easily passes cost-benefit test
Enclave Markets (crypto only) — Fully Encrypted Exchange running matching engine inside Intel SGX. No traditional equity equivalent. This is the gap.
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — 3-8 documented rounds, each party's move changes next |
| TEE necessity | HIGH — Morgan Stanley case proves NDA structurally fails |
| Volume | HIGH — hundreds of thousands/year |
| Ability to pay | HIGH — TCA budget proven |
| Competitive openness | PARTIAL — dark pools exist but don't solve the trust problem |
| Regulatory risk | LOW — regulators actively want better mechanisms here |
| Reachability | MED — warm path via Flashbots/Tina and Andrew/IC3 |
Domain: Agentic AI Parties: Enterprise buyer-side AI agent × supplier-side AI agent
When two AI agents negotiate on behalf of their principals, the platform operator running both agents can observe all agent states, intermediate reasoning, and instruction sets. Observable agent behavior lets opponents reverse-engineer budget ceilings and urgency signals. Principals cannot verify what instructions the opposing agent was given.
Foundation: NDAI paper (Stephenson, Miller, Sun, Annem, Parikh, 2025) — Andrew Miller co-authored. Formalizes the agent-TEE trust structure: each agent A_i takes private input x_i (principal instructions), TEE mediates the interaction so no party observes the opponent's instructions. Only the outcome exits.
Live market: Zycus ANA already runs supplier negotiations end-to-end. Gartner: AI agents will intermediate $15T in B2B spending by 2028. 90% of B2B purchases handled by AI agents projected. By 2026, 1 in 5 B2B sellers will face AI buyer agents issuing autonomous counteroffers.
- Both agents run inside enclave with instruction sets sealed
- Agent behaviors (offers, counteroffers, reasoning chains) processed inside enclave — neither principal nor platform operator observes intermediate states
- Only outcome exits: agreed terms or no deal
- Hardware attestation lets each principal verify opposing agent ran the code it claims
- $15T B2B spend intermediated by AI agents by 2028 (Gartner)
- Zycus ANA: production-grade deployment, 1,500+ procurement leaders at 2026 summit
- 19% of buyers using AI in purchasing report less confidence due to inaccurate AI outputs (Forrester)
- $10B+ enterprise value at risk from ungoverned AI in go-to-market (Gartner 2026)
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — 4-12 rounds, agent moves causally determine counterpart responses |
| TEE necessity | HIGH — platform operator visibility is structural, not behavioral |
| Volume | HIGH (growing fastest of any vertical) |
| Ability to pay | HIGH — Chief Procurement Officer controls budget |
| Competitive openness | OPEN — Zycus has no TEE layer, this is the wedge |
| Regulatory risk | LOW — no specific regulation yet, first-mover window open |
| Reachability | MED — Andrew Miller connection, NDAI paper is the credential |
Domain: Creator Economy Parties: Talent manager (representing creator/influencer) × brand in-house influencer marketing team or agency
The talent manager structurally holds both parties' private parameters simultaneously:
- Creator's rate floor (the minimum they'll accept)
- Brand's budget ceiling (probed through questions like "what's the campaign budget?" before revealing the creator's rate)
Manager is paid 10-25% commission on deal value — incentivized to close at maximum extraction from the brand while keeping the creator just satisfied. Neither side can verify how the manager used their private information.
Specific documented failure modes:
- Dual-sided extraction — manager knows both ceiling and floor, extracts maximum spread
- Rate card leakage — creator's true floor rate travels through recruiter/manager networks, becomes ceiling in future negotiations
- Scope creep exploitation — brand reveals usage rights requirements after creator already showed price flexibility on base rate
- Manager self-dealing — routes brand to easiest-to-close creator, not best fit
TEE accepts each party's private parameters into a sealed enclave, runs matching algorithm, returns only "deal exists at $X for scope Y" — manager sees deal terms without ever learning creator's true floor or brand's true ceiling.
- Global influencer marketing: $32.55B (2025), projected $44B+ (2026)
- Millions of brand deal negotiations/year at the negotiable tier (micro through macro)
- Average deal $5K–$25K; 3-6 rounds per negotiation over 5-21 days
- No incumbent platform addresses the manager dual-information-extraction problem
Grin, Aspire, Traackr — marketplace platforms, no negotiation privacy layer The Cirqle — rate negotiation feature with counter-offer history, no enclave Foam.io — talent manager workflow tool, same trust problem
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — 3-6 rounds, scope reveals causally change rate in next round |
| TEE necessity | HIGH — manager is architecturally positioned as rent-extracting intermediary |
| Volume | HIGH — millions/year globally |
| Ability to pay | HIGH — brand CMO and talent agency both have budget |
| Competitive openness | OPEN — no incumbent solves this |
| Regulatory risk | LOW — standard contract law |
| Reachability | MED-HIGH — creator economy accessible, cohort has adjacent contacts |
Domain: HR / Compensation Parties: Job candidate × corporate recruiter/HR business partner
Competing-offer bluffing is documented at massive scale. Former Google recruiters confirm offers were rescinded when forged offer letters were detected. The market consequence is adverse selection: candidates who bluff benefit; honest candidates cannot extract the same concessions without bluffing. Employers who demand verification lose candidates with real offers who refuse to share a competitor's confidential document.
Secondary trust problem: When employer requests competing offer letter, they gain intelligence about the competitor's compensation structure — all of which is operationally sensitive. "Companies ask to begin with because they want to know what their competitors are offering."
- Candidate's actual offer letter from Company B loads into sealed enclave
- Employer's salary band for the role loads into same enclave
- Enclave computes: is the offer genuine? what is the gap?
- Returns only: "competing offer verified, gap is X%" — employer never sees full offer letter (protecting Company B's confidential comp data), candidate never learns employer's full band ceiling
- 7.6M job openings in US (May 2026 BLS data)
- 5.2M hires per month in US
- Tens of millions of professional comp negotiations/year globally
- Headhunter commission: 20-33% of first-year base, structural incentive to close at employer's preferred number
Critical design note: Must have revision loop to pass Tina's bar. Candidate sees "gap is X%" → revises ask → employer revises offer → iterates to convergence or walk-away. Single-round above/below is a screener, not negotiation.
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES* — with revision loop |
| TEE necessity | HIGH — verification without exposing competitor's comp structure is architecturally impossible any other way |
| Volume | HIGH — tens of millions/year |
| Ability to pay | HIGH — CHRO and TA budget |
| Competitive openness | OPEN — Levels.fyi/Glassdoor are public data, this is private verification |
| Regulatory risk | LOW — pay transparency laws are tailwinds |
| Reachability | HIGH — most accessible buyer of any vertical |
Domain: Legal / ADR Parties: P&C insurer claims adjuster × plaintiff's personal-injury lawyer
Each side holds privileged information they cannot disclose:
- Insurer: internal reserve, adjuster authority limit, bad-faith exposure analysis
- Plaintiff: true walk-away threshold, privileged case-strength EV, client risk tolerance
ZOPA objectively exists in most cases but is never discovered because neither side can safely reveal their range. Cybersettle proved iterative blind bidding works at scale ($1.8B settled, ~200K claims) but operator technically sees both sides' bids.
TEE moves bid matching into hardware-attested enclave. Privileged case-strength EVs load as sealed inputs — used in computation without privilege waiver because no human observes them. Architecturally impossible with NDAs: NDAs bind behavior but not technical read access by the operator.
- Millions of BI settlement negotiations/year globally
- ~95% of PI cases settle pre-trial
- US personal injury law market: $61.3B revenue (2024)
- Cybersettle processed 200K+ claims before suspension — market validated
Cybersettle suspended due to patent litigation — market is currently unserved by the closest analog. Window is open.
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — 4-10 rounds, new evidence materially shifts subsequent offers |
| TEE necessity | HIGH — privilege preservation during computation is architecturally impossible without TEE |
| Volume | HIGH — millions/year |
| Ability to pay | HIGH — insurer claims budget, billions in leakage annually |
| Competitive openness | OPEN — Cybersettle suspended, market unserved |
| Regulatory risk | LOW |
| Reachability | MED — warm intro needed to P&C claims leadership |
Domain: Supply Chain / B2B Commerce Parties: FMCG/CPG manufacturer (P&G, Unilever, Nestlé) × retail buyer (Walmart, Tesco, Carrefour)
$500B in annual trade promotions globally. Industry estimates: 35-40% waste — over $175B in nonproductive spend per year. The waste is structurally caused by information asymmetry during negotiation:
- Manufacturer's private info: cost floor, production capacity, supply chain timing, minimum pass-through for brand objectives
- Retailer's private info: actual demand forecast, promotional calendar commitments, real pass-through intent vs. represented intent
Forward buying and diverting — documented, industry-named failure modes where retailers buy large quantities at promotional prices then resell at full price or to other retailers, defeating the manufacturer's promotional objective entirely.
TEE accepts both parties' actual cost floors, demand forecasts, and promotional calendars as sealed inputs. Computes whether a mutually profitable promotion exists at what terms. Returns only the deal — neither side sees the other's raw data. Prevents scope-expansion exploitation by requiring both parties to commit scope before pricing is revealed.
- $500B annual trade spend globally
- $175B estimated waste annually (35-40%)
- Trade promotion management software market: $2.64B (2025)
- 80%+ of consumer goods manufacturers engage in trade promotions
- 20-25% of gross sales invested in trade spend
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — 8-15 rounds over 4-8 weeks |
| TEE necessity | HIGH — $175B waste directly attributable to information asymmetry during negotiation |
| Volume | HIGH — millions of deal-events/year |
| Ability to pay | HIGH — VP Sales / National Account Management budget |
| Competitive openness | PARTIAL — TPM software ($2.64B market) manages spend post-hoc; none address information asymmetry during negotiation |
| Regulatory risk | LOW |
| Reachability | LOW — requires warm intro to major CPG or retail buyer |
Domain: Life Sciences / Pharma Parties: Pharmaceutical sponsor (or CRO acting as sponsor's agent) × clinical research site (academic medical center or community site) Source: Dedicated follow-up sweep — identified as missed vertical from Tier 1
Two structural asymmetries define CTA negotiation:
FMV opacity: Sponsors maintain internal FMV (Fair Market Value) databases of per-patient rates accepted at comparable sites across all their trials. Sites negotiate blind — no access to what competing sites accepted. Anti-kickback statutes and the Physician Payments Sunshine Act legally require payments to be at FMV, yet the process for establishing FMV is documented as "an opaque subject with rather unclear guidelines." Sites systematically undervalue their services or over-negotiate to compensate for uncertainty.
CRO instruction opacity: When a CRO negotiates on behalf of a sponsor, the site cannot determine whether a CRO position represents the sponsor's actual limit or the CRO's own risk-averse posture. Sites frequently report that CROs refuse to escalate contested terms to the actual sponsor, causing extended stalemates. The CRO is structurally positioned as a commercially-interested intermediary — Pattern 1 from the swarm's structural finding.
From ACTA workgroup empirical prioritization of 28 CTA terms:
- Indemnification / limitation of liability
- Intellectual property (especially Phase I)
- Publication rights (sponsor wants 18-month embargo; site wants independent right)
- Subject injury / patient care costs
- Per-patient payment / FMV budget line items
- Overhead / indirect cost rates (AMCs want 30%+; sponsors resist)
- Timeline milestones / enrollment commitments
- Average CTA negotiation: 64 days industry-wide, >100 days at AMCs
- 4-8 documented redline exchanges on legal terms, plus separate iterative budget rounds
- Duke internal process: 5 business days (first action) → 7 business days per redline exchange → escalation triggers at 45 and 55 business days
- Causally linked moves confirmed: site proposes higher per-patient rate → sponsor counters with reduced overhead percentage → site adjusts publication clause → sponsor lengthens review period. Each round causally determines the next.
- Tufts CSDD: $40K/day direct cost of trial delays
- $500K–$800K/day in commercial opportunity cost (lost revenue for each day a drug reaches market late)
- Mayo Clinic with AI (TCN): reduced CTA cycle from 6-9 months to 25 days
- ACTA standard template: saves 48-57 days vs. no template
- MDIC MCTA for medical devices: cycle time worsened from 133 to 164 days (2019-2024) — existing solutions are not solving the problem
Standard NDA and audit clause cannot give a site access to the sponsor's FMV database without transferring competitive intelligence about every other site's accepted rates.
A TEE-backed FMV verification tool would:
- Accept sponsor's internal FMV database as sealed input
- Accept site's proposed per-patient rate as sealed input
- Compute: is this proposal within FMV range for this protocol type/geography/phase?
- Output only: "in range / out of range" with hardware attestation
- Neither party sees the other's raw data; sponsor's FMV database is never exposed; site's negotiating floor is never revealed
This is a standalone wedge product — doesn't require replacing TCN or Ledger Run, doesn't require full CTA negotiation automation. It solves the specific legally-mandated FMV compliance problem that every existing platform handles poorly.
- The Contract Network (TCN) — AI-powered CTA negotiation, Mayo Clinic partnership, venture-backed. Focused on process/workflow speed, no TEE. Reduced Mayo's cycle from 6-9 months to 25 days.
- Ledger Run — CTA negotiation platform, early-stage. Process focus, no sealed computation.
- ICON, Parexel, Covance/Labcorp — CROs that negotiate CTAs as bundled service. Structurally conflicted — they ARE the CRO instruction opacity problem.
- ACTA / TransCelerate / MAGI — template standardization consortia. Cover ~80% of standard terms; indemnification, IP, publication rights, and FMV remain contested regardless.
TEE-specific gap: No incumbent provides sealed FMV computation or attested negotiation records. EMA ALCOA++ requirements for tamper-proof audit trails would be exceeded by TEE-attested negotiation logs.
- Hundreds of thousands of CTA negotiations globally per year
- US alone: ~50,000-80,000 industry-sponsored trials active at any time, each requiring a CTA per site
- High-value, high-friction subset: AMC negotiations, Phase I-II trials with contested IP terms
- MEDIUM volume (thousands of genuinely contested multi-round negotiations per year at the tier where TEE adds value)
- Sponsor-side: VERY HIGH. $40K/day direct + $500K-$800K/day commercial opportunity cost. A $5K-$50K per-negotiation TEE fee is trivial vs. delay cost.
- Site-side (AMC): MEDIUM. Budget-constrained research administration offices.
- Primary buyer: sponsor-side VP Clinical Operations or Chief Procurement Officer.
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — 4-8 documented rounds, causally linked moves |
| TEE necessity | MEDIUM — FMV gap is structural and unsolvable without sealed computation; but existing AI tools gaining traction on process side without TEE |
| Volume | MEDIUM — hundreds of thousands of CTAs/year; contested multi-round subset is thousands/year |
| Ability to pay | HIGH — sponsor-side delay costs make fee trivial |
| Competitive openness | PARTIAL — TCN/Ledger Run own workflow; TEE-specific angle has no incumbent |
| Regulatory risk | LOW-MEDIUM — EMA ALCOA++ is a tailwind; HIPAA/GDPR apply to patient data not CTA terms |
| Reachability | LOW — 18-36 month enterprise sales cycle; 6-12 month procurement process; no obvious landing without pharma warm intro |
Strongest FOR argument: FMV attestation is a standalone wedge with clear ROI, regulatory tailwind, and no incumbent. Doesn't require replacing TCN. Both sides benefit simultaneously (sponsor avoids kickback exposure; site avoids underpayment). Can be built without existing pharma relationships.
Strongest AGAINST argument: The buyer is a pharma VP of Clinical Operations with an existing CRO relationship, a 6-12 month IT/GCP validation requirement before any new software touches the contracting process, and deep caution about unknown vendors. Solo founder without pharma relationships faces 18-36 months to first paying customer. Not a near-term bet — a validated secondary vertical to activate when a pharma warm intro materializes.
Domain: Healthcare Staffing Parties: Staffing agency × hospital (bill rate side) + staffing agency × clinician (pay rate side) Source: Dedicated follow-up sweep — identified as missed vertical from Tier 1
The staffing agency is architecturally positioned as a dual-information intermediary — exactly Pattern 1 from the structural finding:
- Agency knows the hospital's bill rate ceiling (what the hospital is willing to pay)
- Agency knows the clinician's pay rate floor (what the clinician will accept)
- Agency's margin is the spread between the two — economically incentivized to maximize opacity on both sides
Documented margin structure: Locum tenens agency markups 40-50% of bill rate. Travel nursing agency take 30-45% of bill rate. Neither hospital nor clinician can verify the actual spread without seeing the other party's private terms.
Specific documented failure modes:
- Agency margin opacity — hospital pays $200/hr bill rate; clinician receives $110/hr; agency takes $90/hr spread. Neither side knows the other's number.
- False urgency via credentialing manipulation — agency controls information flow on credentialing status; can create artificial urgency by delaying or accelerating credential processing as a negotiating tactic.
- Exclusivity gaming — agency claims exclusivity to prevent hospital from checking competing agencies; hospital cannot verify whether clinician is simultaneously being placed elsewhere.
- MSP/VMS intermediation — Managed Service Provider layer adds another intermediary between hospital and agency, further obscuring bill rate and margin information.
- Round 1: hospital opens requisition at target bill rate; agency tests clinician supply
- Round 2: agency reports low fill probability; hospital raises bill rate or changes terms
- Round 3: agency decides how much rate movement to pass through to clinician; revises offer
- Round 4: clinician counters on pay, travel, housing, schedule; agency either compresses margin or seeks more from hospital
3-6 rounds documented for premium locum tenens and crisis/shortage travel assignments. Hospital's move in round N causally determines agency's offer to clinician in round N+1.
Standard NDA binds agency behavior but cannot prevent mental use of both sides' private information. Audit clauses require disclosure to verify — but disclosure destroys the confidentiality that makes the system work.
TEE enables:
- Hospital's true bill rate ceiling into sealed enclave
- Clinician's true pay rate floor into sealed enclave
- Agency's proposed spread into sealed enclave
- Output: "spread is within verified fair-band range" with attestation
- Neither hospital nor clinician sees the other's private number; agency cannot lie about the spread without the attestation failing
- US healthcare staffing market: $45.48B (2025)
- 8% of US physicians actively working locums; 27% have prior locum experience (CHG 2025)
- Travel nursing: peak $24B post-COVID, stabilizing at $15-18B
- Volume: HIGH — hundreds of thousands of placement negotiations/year in contested segment
- Value at stake: even 10% spread compression on $45B market = $4.5B in redistributed value
- AMN Healthcare, CHG Healthcare, Barton Associates, CompHealth, Weatherby, Cross Country — they ARE the margin opacity problem. Will resist, not adopt.
- Boutique low-margin agencies (Locumpedia, competitive challengers) — already use transparency as differentiation; would benefit from third-party verifiable proof. Entry point.
- Hospitals / health systems frustrated with MSP/VMS opacity — most natural buyer.
Sell to hospitals and boutique challenger agencies — NOT to AMN/CHG. Hospital buyer: VP of Workforce or Chief Nursing Officer. Budget: contingent labor / temporary staffing line. Value proposition: verifiable proof that agency spread is within fair-band range, reducing hidden intermediation cost.
| Dimension | Score |
|---|---|
| Real negotiation (Tina's bar) | YES — non-commodity segment, 3-6 documented rounds |
| TEE necessity | HIGH — trusted-middleman failure, sealed computation is the only mechanism |
| Volume | HIGH — hundreds of thousands/year in contested segment |
| Ability to pay | HIGH — hospitals face acute vacancy costs, already heavy contingent labor spend |
| Competitive openness | LOW-MED — entry via hospitals and boutique challengers, around incumbents |
| Regulatory risk | LOW — no specific regulation; price transparency tailwinds in healthcare generally |
| Reachability | LOW-MED — needs healthcare staffing warm intro; hospital procurement cycle 3-6 months |
| Incumbent conflict | HIGH — AMN/CHG profit from opacity; adversarial relationship |
Domain: Creator Economy (Cross-border) Status: Gist only — not added to primary product brainstorm. Distribution too hard for UK/US solo founder without China-based partner.
Trust problem is real and well-documented:
- Yin-yang dual contracts: public contract shown to platforms/brands; private side letter captures true economics. SPC actively prosecutes these.
- Revenue misreporting: Inner Mongolia streamer claimed 260K RMB; tax investigation revealed 15M+ RMB hidden over two years.
- Rate card opacity: MCNs negotiate with many brands simultaneously; brands cannot verify whether they are getting competitor rates.
- Cross-border enforcement gap: US/EU judgments generally not enforceable in Chinese courts; ex ante verifiable computation more valuable than better contracts.
Volume: 40,000+ MCNs in China, $30B+ market, global MCN market projected $153B by 2034. HIGH volume.
Why not primary: Distribution into China MCN ecosystem requires local partners, guanxi, Chinese language capability. Platforms (Douyin, Kuaishou) hold the data and could internalize TEE before a solo founder can monetize. Realistic entry only via Western brands or cross-border agencies — slow, indirect path.
Activate when: A China-adjacent distribution partner, cross-border agency relationship, or Western brand customer with China operations materialises.
Domain: Platform Labor Status: Parked. Market is 1-2 years from existing at scale. Revisit Q1 2027.
Trust problem is real:
- Algorithmic rate-setting opacity: HRW 155-page May 2025 report documents six of seven major platforms use opaque algorithms; workers don't know pay until after completing job.
- GMB June 2025 dynamic pricing report: "dynamic pricing benefits Uber more than drivers" — even formal union recognition doesn't solve the data asymmetry.
- The TEE argument: sealed algorithmic fairness attestation proves algorithm is fair without revealing the algorithm. Exactly right technically.
Why parked:
- EU Platform Work Directive national transposition deadline: December 2, 2026. As of July 2026, most member states still drafting implementing legislation.
- The product reframes from "negotiation platform" to "compliance attestation tool" — different buyer (platform legal/compliance team), different sales motion (B2B enterprise SaaS), same 6-12 month procurement cycle problem.
- US market 2-3 years behind (Prop 22 upheld; federal bill uncertain).
- UK bilateral negotiation (Uber-GMB, Deliveroo-GMB quarterly meetings) is live but small scale — the negotiation is collective, quarterly, and the data tool GMB actually uses is standard statistical analysis, not TEE.
Timing advantage if you move now: EU member states still drafting legislation. A founder who builds the algorithmic fairness attestation tool today and engages with the legislative drafting process could be the first compliant solution when mandates activate December 2026. That's a 6-month window.
Activate when: EU national transposition laws take effect AND a specific platform compliance team engagement materialises.
After adversarial analysis across 38 candidates, real two-party TEE negotiation clusters into three structural patterns:
An intermediary holds both parties' private parameters simultaneously and has economic incentive to exploit the information asymmetry.
Examples: Talent manager (creator × brand), PBM (pharma × payer), block trade broker (asset manager × receiving investor), headhunter (candidate × employer), platform operator (agent × agent)
Why TEE is necessary: NDA binds behavior but cannot prevent mental use of information. TEE removes the intermediary from the information channel structurally — not just legally.
This is the largest and most immediately addressable pattern.
The private information is a live, continuously-changing data feed that cannot be disclosed even for verification without permanently transferring competitive intelligence.
Examples: Network telemetry (MNO × MVNO), actuarial cost model (payer × hospital), agent instruction set (B2B agent × agent), cost curve (PPA generator × offtaker)
Why TEE is necessary: Audit clauses and NDAs can govern documents. They cannot govern live data without disclosing it. TEE is the only mechanism that can process live data from two competing parties and return a verified result without either party seeing the other's operational data.
Private information is protected from disclosure by law (attorney-client privilege, TINA cost certification, HIPAA), but must be USED in a joint computation to resolve a negotiation.
Examples: Case-strength EV in PI settlement (attorney-client privilege), litigation funder floor in arbitration, TINA-certified cost data in DoD contracts, PHI-adjacent data in healthcare rate setting
Why TEE is necessary: Information enters the enclave, computation runs, only the result exits — privilege never waived. Human mediators approximate this via shuttle caucus but cannot provide cryptographic attestation of non-leakage.
| Swarm Finding | DealProof Component |
|---|---|
| Both agents need sealed instruction sets | TDX enclave on Phala Cloud |
| Neither principal can verify opposing agent's code | DCAP on-chain attestation — verifiable before payload sent |
| Negotiation conduct needs auditing not just output | πCreds auditor layer — SCAE/ACPP threat coverage |
| Settlement needs to be trustless | Solidity escrow on Ethereum Sepolia |
| Multi-round memory integrity | Contexto memory hashes |
The key insight from the swarm: DealProof's architecture was built for the B2B agent × agent case. Every other vertical is the same primitive applied to a different trust gap. The stack doesn't change — only the domain-specific parameters change.
| Player | What They Have | What They're Missing |
|---|---|---|
| Enclave Markets | TEE for crypto block trading | No negotiation agents, no πCreds, no escrow, no DCAP |
| Cybersettle | Double-blind insurance settlement | No TEE (operator sees both bids), currently suspended |
| Zycus ANA | AI procurement negotiation agent | No TEE, platform operator sees everything |
| SUAVE (Flashbots) | Confidential execution for DeFi | Not negotiation, no agent layer, no πCreds |
| Phala Network | TEE infrastructure | Infrastructure provider, not application layer |
| Oasis Network | Confidential compute | Infrastructure provider, not application layer |
DealProof is the only product combining: TEE execution + dual autonomous agents + DCAP on-chain attestation + πCreds conduct credentials + on-chain escrow.
Generated from 15-agent adversarial swarm. Tier 1: 15 domain research agents (Sonnet). Tier 2: 6 adversarial passes (Tina's bar, TEE necessity, incumbent killer, volume reality, regulatory minefield, buyer existence). Tier 3: Opus synthesis. Total candidates evaluated: 38. Survivors after full adversarial battery: 6 primary, 9 secondary.