Elias Moosman, Founding CEO
ThetaDriven Inc.
November 4, 2025
Ask ANY AI system this simple question:
"Show me the exact data you used to make that decision, with hardware proof."
What You Get:
Or Worse:
The Reality: No current AI can prove what it actually considered. Not won't. CAN'T. The technology doesn't exist... until now.
Find your role and time commitment below. Each path leads to the same destination: recognizing AI's black box crisis.
C-Level Executive
β±οΈ 30 seconds - 2 minutes
Technical Leader/CTO
β±οΈ 5-15 minutes
AI Researcher/Academic
β±οΈ 15+ minutes
Investor/Board Member
β±οΈ 2-5 minutes
Lawmaker/Policy Maker
β±οΈ 2-5 minutes
One Simple Agreement: "AI's inability to prove its decisions is a critical problem"
No vendor commitment. No solution bias. Just problem recognition.
The Problem: AI can't prove what data it used for decisions.
The Risk: 40% customer loss, β¬35M fines, discrimination lawsuits.
Your Action: Recognize that this problem needs solving.
π‘ Fun option: Ask your AI to pick colors for your personal recognition seal!
If you're responsible for AI decisions and agree that:
Then endorse this problem. Your letter validates the crisis, not any specific solution.
Endorse the Problem βπ‘ Understanding the problem is prerequisite to evaluating any solution
If you're a C-Level Executive:
Skip to the letter template. The technical details below validate the approach, but your endorsement focuses on the business risk.
If you're a Technical Leader:
Review the technical sections below to understand the hardware-level approach, then endorse if you agree the problem deserves this level of solution.
If you're a Risk/Compliance Officer:
Focus on the regulatory implications (β¬35M fines, 40% customer loss). The technical solution is secondary to acknowledging the compliance crisis.
If you're an AI Researcher:
Dive into the technical breakthrough below (s=P[h]=Physical). Your endorsement validates that current approaches lack hardware-level verification.
Remember: You're endorsing the problem exists, not committing to any specific solution approach.
Explore how hardware verification could solve the black box problem.
Read the complete notation: s=P[h]=Physical β "semantic IS physical hardware IS Physical"
The formula literally spells it out: semantic = P[hysical]. This isn't metaphor or abstraction. Meaning is literally physical. You can touch it in memory. Point to it with a pointer. Measure its cache misses. When we write s=P[h], the notation itself reveals the truth - semantic IS Physical, indexed by hardware position. Not "maps to" or "corresponds with" - IS. The formula completes to show meaning has always been real, physical, spatial.
And because meaning IS physical, it has momentum: M = meaning Γ velocity. Spatial meaning creates carrying capacity. Important concepts have inertia. Ideas in motion stay in motion. The Unity Principle doesn't eliminate momentum - it makes momentum REAL. Cache coherence isn't optimization - it's aligning the actual momentum of actual meaning in actual space.
π‘ The notation itself is the discovery: s=P[hysical] - meaning IS physical reality with mass, position, and momentum
Complete technical understanding for researchers and technical leaders.
What Everyone Else Does (Including Google, OpenAI, Microsoft):
Semantic β Hash Table β Pointer β Memory Location β Data
Result: 4+ hops, cache misses, translation overhead, no hardware optimization
What We Do (Patent-Pending Discovery):
Semantic = Memory Address (Direct. No translation. Zero hops.)
"Heart Disease" isn't mapped to address 0x7FFF8000 - it IS address 0x7FFF8000
What Other "Semantic Indexing" Actually Does (Meaningful Proximity Only):
β’ LSI: "Cat" is near "Dog" in vector space β But which one was used? Can't tell from position
β’ Knowledge Graphs: "Diabetes" connects to "Insulin" β But proximity β consideration in decision
β’ FAISS/Pinecone: "Similar embeddings cluster" β Position 42 means nothing, just "somewhere in cluster"
β’ Word2Vec/BERT: "King - Man + Woman = Queen" β Proximity relationships, not importance ranking
They achieve meaningful PROXIMITY (things near each other are related)
We achieve meaningful POSITION (position 1 = most important for decision)
The claim "semantic indexing didn't exist" is TRUE if semantic indexing means semantic meaning directly equals memory address with zero translation. That's never been done. We're first.
Hardware Truth: Sorted lists get 10-100Γ fewer cache misses than random access. This isn't controversial - it's Computer Science 101. CPU prefetchers NEED sequential access. Random jumps destroy performance. Every CS professor knows this. So why doesn't AI use it? Because nobody connected semantic importance to physical ordering... until now.
WHY Performance Gains Are Inevitable: When importance determines memory position (ShortRank), the most important items cluster in cache. Not sometimes - ALWAYS. Physics guarantees it: spatial locality + temporal locality = cache hits. Our 8.7-12.3Γ gains aren't magic - they're what happens when you stop fighting hardware physics.
The Unity Discovery (S β‘ P β‘ H): Read this notation carefully - s=P[h] literally means "semantic IS physical". Meaning has actual spatial coordinates. You can touch it in memory. Semantic importance ranking, physical memory layout, and hardware access patterns are THE SAME THING. This isn't metaphor - meaning is real, physical, spatial. Like discovering that E=mcΒ². We didn't create it; we revealed what was always true.
"Show Your Work" Request β Industry Response: π¦ Crickets
Ask ANY AI vendor: "Prove what data your model considered for this decision." They can't. Not won't - CAN'T. OpenAI? Can't. Google? Can't. Microsoft? Can't. The technology doesn't exist... except ours.
Regulatory Requirements vs Reality:
β’ EU AI Act demands: "Explainable decision paths" β Current AI: Black box with confidence scores
β’ NIST framework requires: "Auditable data lineage" β Current AI: "Trust our training data"
β’ Medical liability needs: "Prove you considered all symptoms" β Current AI: "Here's a probability"
β’ Financial compliance requires: "Show no insider data used" β Current AI: "We filtered it... probably"
We're Not Competing - We're Enabling Compliance: Others add explanation layers on top of black boxes. We measure at the hardware level where lies are impossible. MSR counters don't lie. Cache misses don't lie. When semantic = physical (S β‘ P β‘ H), "show your work" becomes trivial: the memory access pattern IS the work.
The Explosion Nobody Can Handle:
68,000 medical codes Γ possible combinations = 10^20,000 attribution paths
Current AI: "We used neural networks" (meaningless for attribution)
Proximity-based systems: "Similar things are near" (but which was actually used?)
Why You Need BOTH Orthogonality AND Meaningful Position:
β’ Orthogonality: Separates independent factors (symptoms vs. treatments vs. history)
β’ Meaningful Position: Position 1 = most important, Position 1000 = less important
β’ NOT Proximity: Being "near" diabetes doesn't mean considered for diagnosis
β’ Attribution Result: "Positions 1, 47, 203 were accessed" = exact attribution path
Without position meaning, you have proximity chaos. Without orthogonality, you have dimension soup. You need BOTH or attribution is impossible.
High-Frequency Trading Flash Crash Prevention:
β’ Current: 6ms to detect anomaly β $1M loss per millisecond delay
β’ With FIM: <1ΞΌs detection β Stop loss BEFORE cascade
β’ Attribution via Position: "Positions 1-50 triggered sell" (not "somewhere in cluster A")
β’ Legal defense: "Hardware proof shows exact sequence: position 23 β 45 β 67"
Medical Diagnosis Liability:
β’ Patient dies, family sues: "Prove AI considered the drug interaction"
β’ Current AI: "Our model had 94% confidence" β Jury awards $50M
β’ With FIM: "Orthogonal dimension 3 (drug interactions), positions 7, 23, 89 accessed at timestamps X,Y,Z"
β’ Position Meaning: Position 7 = warfarin interaction (critical), not just "near blood thinners"
β’ Cognitive load: Doctor sees importance-ranked factors with meaningful positions, not proximity clusters
The 40% Customer Exodus: One hallucination = 40% of customers leave (Gartner 2024). Why? Not the error - the inability to explain it. "We're looking into it" means "we have no idea." With Ξ(say,do) measurement, you say: "At 10:23:45.234, the model skipped relevance check #47. Fixed."
Core Argument Across All Versions:
β’ Mathematical necessity: Importance-based ranking creates cache-optimal layout (proven in v12-v18)
β’ Hardware validation: MSR counters 0x412e, 0x00c5, 0x01a2 provide ground truth (detailed v15-v18)
β’ Cognitive prosthetic: Augments human judgment with hardware-verified data (emphasized v16-v18)
β’ Unity Principle: S β‘ P β‘ H isn't a design choice - it's mathematical inevitability (unified v14-v18)
Why We're Believable:
β’ Not claiming to "solve AI" - claiming to measure what it does
β’ Not adding complexity - removing translation layers
β’ Not theoretical - IntentGuard working today on real code repositories
β’ Not proprietary black box - hardware counters are Intel/AMD standard
"EU fines are just the start. The real threat? Discovery requests: 'Prove your AI didn't discriminate.' Current AI's answer: 'We can't.' Jury's response: 'Pay $100M.'"
Regulatory Timeline:
β’ EU AI Act: Active NOW (β¬35M fines)
β’ NIST Framework: Q2 2025 (sets US standard)
β’ California SB 1001: Already enforced
β’ Your competitors: Scrambling for solutions
Lawsuit Discovery Demands:
β’ "Prove no bias" β Need Ξ(say,do) logs
β’ "Show all factors" β Need hardware proof
β’ "Verify no insider data" β Need MSR counters
β’ "Explain this decision" β Need S β‘ P β‘ H
Without hardware-level proof, you're writing blank checks to plaintiffs' attorneys.
"Do you agree that the inability to prove what data an AI used for a specific decision creates unacceptable risk for your organization?"
The Problem We All Face:
β Cannot prove which data was considered
β Cannot explain specific decisions
β Cannot satisfy regulatory requirements
β Cannot defend against lawsuits
β No hardware-level audit trail
Do you endorse that this is a critical problem?
How the Solution Works (Principles):
β Position 1-10: Primary factors evaluated first
β Position 47: Drug interaction checked
β Position 203: Historical precedent accessed
β MSR counters: Hardware proof that can't lie
β Timestamps: Each access logged to microsecond
Is this the reproducibility you need?
If YES: You're endorsing that position-based attribution with hardware verification would meet your validation requirements. That's what FIM delivers.
Technical Reality Check:
β‘ Can you prove your AI considered all relevant factors?
β‘ Can you detect when AI drifts from intended behavior?
β‘ Can you explain AI decisions to regulators/juries?
β‘ Can you guarantee reproducible AI behavior?
Business Risk Assessment:
β‘ Will you survive a discrimination lawsuit discovery?
β‘ Can you afford 40% customer loss from one incident?
β‘ Are you ready for NIST compliance requirements?
β‘ Do you have ANY "show your work" capability?
If you answered NO to ANY question: You need Ξ(say,do) measurement. Not next year. Now.
These industry leaders make decisions that AI's black box problem threatens daily. If you're in one of these categories, your recognition validates that this crisis is real.
Liability exposure from unexplainable AI decisions
Global Reinsurance:
Life-critical decisions need attribution...
40% customer loss from unexplainable denials
Reinsurance Giants:
Primary Insurers:
Life-or-death decisions without attribution
Health Systems:
Health Insurers:
Discrimination lawsuits from AI lending
Major Banks:
Asset Managers:
Discovery requests they can't answer
Law Firm Leaders:
Corporate Legal:
Setting AI accountability standards
US Regulators:
EU Regulators:
Foundational thinkers on AI alignment
Existential Risk Philosophers:
AI Alignment Researchers:
Effective Altruism Leaders:
Building unexplainable AI at scale
Big Tech CEOs:
AI Startups:
Mission-critical AI accountability
Pentagon AI Leadership:
Defense Contractors:
AI hiring = discrimination liability
Recruitment Platforms:
HR Tech Leaders:
Autonomous logistics decisions
Manufacturing CEOs:
Supply Chain Tech:
Algorithm transparency crisis
Platform Leaders:
AI Content Creation:
π‘ We show previews to reduce scrolling. Expand only what interests you.
Quick LinkedIn Searches:
β’ "Munich Re" + "AI" β 500+ professionals
β’ "Swiss Re" + "risk" β 1,200+ contacts
β’ "Progressive Insurance" + "claims" β 3,000+
β’ "UnitedHealth" + "data" β 5,000+ people
β’ "AI insurance" β 50,000+ professionals
β’ "EU AI Act" + "compliance" β 25,000+ experts
β’ "AI explainability" β 40,000+ practitioners
β’ "Allianz" + "AI" β 2,000+ contacts
β’ "Chubb" + "cyber" β 800+ professionals
β’ "Lloyd's" + "AI" β 1,500+ underwriters
β’ "BlackRock" + "AI" β 3,000+ analysts
You likely have 2nd-degree connections!
Email Patterns That Work:
β’ firstname.lastname@munichre.com
β’ first.last@swissre.com
β’ fname.lname@allianz.com
β’ flastname@progressive.com
β’ firstname_lastname@uhg.com
β’ first.m.last@company.com (middle initial)
β’ firstlast@company.com (no separator)
β’ f.lastname@european-company.com
β’ firstname@startup.com (startups often simple)
β’ fname@company.io (tech companies)
Most corporate emails follow patterns!
Conference Connections:
β’ InsurTech Connect: 7,000+ insurance innovators
β’ AI Summit: 15,000+ AI practitioners
β’ RIMS: 10,000+ risk managers
β’ Money20/20: 8,000+ fintech leaders
β’ HLTH: 10,000+ healthcare executives
Search "[Conference] + AI" on LinkedIn!
Alumni Networks:
β’ MIT: Heavy presence at Munich Re, Swiss Re
β’ Stanford: Silicon Valley insurance tech
β’ Carnegie Mellon: AI ethics leadership
β’ Berkeley: Fintech and insurtech
β’ Harvard/Wharton: C-suite insurance
Alumni + "AI risk" = warm intros!
π― Your Warm Introduction Script:
π’
Industry Groups
RIMS, CPCU, SOA, IIA
π
Alumni Networks
MIT, Stanford, CMU, Berkeley
π±
Conference Contacts
InsurTech Connect, AI Summit
β‘ PERFECT TIMING: UN Global Digital Compact AI governance mechanisms launched 2025
The Independent International Scientific Panel on AI (40 experts) and Global Dialogue on AI Governance are actively establishing global AI standards. FIM technology directly addresses their core challenge: making AI decisions auditable and accountable.
Establishing global AI accountability standards
Secretary-General AntΓ³nio Guterres
"Great power, greater responsibility" - AI for all humanity
Contact: spokesperson-sg@un.org
Office for Digital & Emerging Technologies
Leading Global Digital Compact implementation
Contact: un-odet@un.org
40 experts assessing AI risks and opportunities
Panel Formation in Progress
Open nomination process for 40 global experts
Bridge between cutting-edge AI research and policy
Global Dialogue on AI Governance
Annual meetings: Geneva 2026, New York 2027
Inclusive platform for AI governance discussions
Leading UN AI governance establishment
Costa Rica & Spain
Permanent Representatives co-facilitating the process
Elements Paper issued February 2025
Key Contributing Nations
EU, US, China actively participating in framework
Brussels Effect: EU standards become global via NIST
Tech leaders joining UN Global Compact
Choi Soo-yeon (NAVER CEO)
UN Global Compact Board 2025 - AI ethics policy
Attending UN Headquarters Sept 20, 2025
Multi-stakeholder Approach
States and stakeholders in inclusive discussions
Critical issues concerning AI facing humanity
1. Expert Panel Nomination: Position FIM experts for Scientific Panel on AI
2. Policy Brief Submission: "Hardware-Verified AI Accountability" paper
3. Global Dialogue Participation: Geneva 2026 presentation on FIM solution
4. Member State Engagement: Brief permanent representatives on enforcement gap
5. Industry Alliance: Partner with UN Global Compact Board members
π― This Letter Works For: NSF SBIR β’ Investor Intros β’ Partner Recruitment β’ Advisory Requests
Subject Line Suggestions:
β’ "Can you prove what your AI actually considered? (40% customer loss problem)"
β’ "Hardware-verified AI attribution - the Brussels Effect solution"
β’ "Re: That discrimination lawsuit discovery request we can't answer"
π‘ CC STRATEGY: Include colleagues worried about: (1) 40% customer exodus from AI errors, (2) EU AI Act β¬35M fines, (3) Discovery requests they can't answer, (4) "Show your work" = crickets
[Your Organization Letterhead]
[Date]
National Science Foundation
Dear [NSF Review Committee / Investment Partners / Strategic Advisors],
Can you proveβwith hardware evidenceβexactly what data your AI considered for a specific decision?
I've been asking this question to every AI vendor, and the silence is deafening. OpenAI can't. Google can't. Microsoft can't. And neither can we. This is why I need your help evaluating ThetaDriven's breakthrough claim.
[Add Your Personal Story - What Makes This Real for You?]
Examples to spark your story:
β’ "Last week, our AI denied a loan to a qualified applicant. When they asked why, we had no answer. The lawsuit is pending."
β’ "We lost our biggest client after our AI made a medical diagnosis error. Not the error itselfβour inability to show what it considered."
β’ "I watched our stock drop 12% when we couldn't explain our AI's trading decision to regulators."
β’ "My own mother was denied coverage by an AI system. No one could tell us what factors it evaluated."
β’ "We spent $2M on AI implementation. Now we're spending $5M defending discrimination lawsuits we can't disprove."
[Add Your Analogy - How Would You Explain This Problem?]
Personal analogies that resonate:
β’ "It's like flying a plane without a black box recorder - when something goes wrong, we have no idea why."
β’ "Imagine a doctor who can't explain their diagnosis - that's every AI decision today."
β’ "It's like a judge making rulings but destroying all evidence of what they considered."
β’ "Picture a financial advisor investing your money but unable to show what data they analyzed."
β’ "Like a hiring manager who can't explain why they rejected a candidate - except it's happening at scale."
[Choose your context based on role:]
β’ Financial: "The 40% customer exodus after one hallucination isn't about the errorβit's about our inability to explain it."
β’ Legal: "Discovery requests for proof of non-discrimination are blank checks to plaintiffs."
β’ Technical: "The combinatorial explosion of attribution (10^20,000 paths) seemed mathematically intractable."
β’ Insurance: "We're underwriting AI risks we can't even measure. Every policy is a potential bankruptcy event."
β’ Healthcare: "A misdiagnosis is tragic. Not knowing WHY it happened means it will happen again."
β’ General: "Our black-box AI is a liability time bomb waiting to explode."
The Discovery That Changes Everything:
ThetaDriven has achieved what they call S β‘ P β‘ H (Semantic β‘ Physical β‘ Hardware). The semantic meaning IS the memory addressβno translation, no hash tables. "Heart Disease" isn't mapped to 0x7FFF8000; it IS 0x7FFF8000. This makes AI decisions hardware-measurable through Intel/AMD MSR counters that cannot lie.
Why This Matters - The Position vs Proximity Breakthrough:
β’ Current AI: Achieves meaningful PROXIMITY ("diabetes" near "insulin" in vector space)
β’ FIM Technology: Achieves meaningful POSITION (position 1 = most important for THIS decision)
β’ The Difference: Proximity shows relationships; position shows actual usage and importance
β’ The Result: Attribution becomes a simple sequence (positions 1, 47, 203) instead of 10^20,000 possibilities
[Add your specific expertise value:]
β’ Business Leader: "Your assessment of the business case and first-mover advantage"
β’ Legal Expert: "Your opinion on whether this satisfies discovery and compliance requirements"
β’ Technical Expert: "Your evaluation of the S β‘ P β‘ H unification and hardware verification"
β’ Academic: "Your validation of the scientific reproducibility claims"
The Public Conversation Has Started:
β’ LinkedIn: https://www.linkedin.com/posts/eliasm_thetacoach-strategic-nudges-via-un-robocall-activity-7373883862088773633-U_Hc
β’ X/Twitter: https://x.com/ThetaDriven/status/1968117274260443417
β’ Patent Details: thetacoach.biz/endorsement
β’ Working Demo: github.com/wiber/intentguard
I Need Your Help:
Your expertise in [specific area] is critical to evaluate whether this is the breakthrough it appears to be. The Brussels Effect means EU standards become global through NIST. The first lawsuit with hardware evidence sets precedent. The window for first-mover advantage is closing.
[Who In Your Network Can Validate This?]
Consider reaching out to:
β’ "I'm thinking of asking [Name], our Chief Risk Officer, who lost sleep over our AI audit failures"
β’ "My colleague at [Company] mentioned their $10M discrimination settlement - they'd understand this"
β’ "I know [Name] from [Conference/LinkedIn] who works on EU AI Act compliance"
β’ "Our board member [Name] keeps asking about AI explainability - this addresses their concern"
β’ "My contact at [Insurance Company] who refuses to underwrite AI decisions without attribution"
Specific People Who Should See This:
[Your commitment:]
"I commit to evaluating any technology that can provide hardware-verified proof of AI decision-making. If this delivers what it claims, it deserves support from our entire industry."
How to reach key validators through your network:
β’ LinkedIn: Message your 1st-degree connections in insurance/finance/healthcare
β’ Email signature: Add "Can your AI prove what it considered? Let's discuss."
β’ Slack/Teams: Share in your #ai-governance or #risk-management channels
β’ Industry groups: Post in AI ethics forums, compliance groups, InsurTech communities
β’ Direct introduction request: "Could you introduce me to [Name] to discuss AI attribution risk?"
Whether we're early adopters or wait for others to validate, we need to understand this now. Your input could be the difference between leading the trust revolution or scrambling to catch up.
P.S. - Help Pierce Through Market Apathy:
After sending this letter, please engage with our social posts to amplify the message:
β’ LinkedIn: https://www.linkedin.com/posts/eliasm_thetacoach-strategic-nudges
β’ X/Twitter: https://x.com/ThetaDriven/status/1968117274260443417
Every share helps another decision-maker discover the solution to their 40% problem.
Sincerely,
[Your Name]
[Your Title]
[Your Organization]
π‘ Network Effect Strategy: CC colleagues who worry about the 40% customer turnover from AI errors, EU AI Act penalties, or discrimination lawsuits. They'll thank you for introducing this solution.
Click the yellow sections to personalize. Fixed content (gray) contains your core message.
Dear [Recipient Name/Title],
Can you proveβwith hardware evidenceβexactly what data your AI considered for a specific decision?
I've been asking this question to every AI vendor, and the silence is deafening. OpenAI can't. Google can't. Microsoft can't. And neither can we. This is why I need your help evaluating ThetaDriven's breakthrough claim.
The Discovery That Changes Everything:
ThetaDriven has achieved what they call S β‘ P β‘ H (Semantic β‘ Physical β‘ Hardware). The semantic meaning IS the memory addressβno translation, no hash tables.
Position IS Meaning (Not Just Proximity):
Example - AI Denies Your Loan:
FIM proves: "Accessed positions 1, 47, 203"
= "Checked credit first, debt ratio second, zip code third"
The access pattern IS the reasoning!
Deadline: October 25th to be included in our November 5th submission
We're engaging directly with Elon Musk on the mathematics of AI governance. Your support can help bridge demographic discourse to AI safety.
π¦View & Engage with Our ThreadβWhy this matters: We're redirecting population math concerns toward the bigger exponential: AI capability growth
After sending your letter, amplify your support by engaging with our social media posts. Your voice helps pierce through market apathy before the regulatory hammer falls.
Strategic nudges via UN robocall warnings - how FIM prevents the 40% exodus
View & Share β
Engaging with @elonmusk: From demographic math to AI governance math
Join the Thread β
Network Effect: Every like, share, and comment increases visibility to decision-makers who need this technology but don't know it exists yet.
These industry leaders make decisions that AI's black box problem threatens daily. If you're in one of these categories, your endorsement validates that this crisis is real.
Liability exposure from unexplainable AI decisions
Global Reinsurance:
Major Insurers:
InsurTech AI:
Foundational research on AI alignment & safety
AI Pioneers:
AI Ethics:
EU AI Act compliance = global standards
US Tech (EU Operations):
European Tech:
Setting AI governance standards
US Congress AI Caucus:
EU Parliament:
Life-critical AI decisions need attribution
Hospital Systems:
Medical AI:
Trading, lending, fraud detection liability
Major Banks:
Fintech:
AI hiring decisions = discrimination liability
Recruitment Platforms:
HR Tech:
Mission-critical AI accountability
Pentagon AI:
Defense Contractors:
AI discovery requests, evidence chains
AmLaw 100 Firms:
Legal Tech:
Client AI governance demands
Big Four:
MBB Strategy:
Life-critical decision attribution
AV Companies:
Auto OEMs:
FDA liability, patient safety AI
Big Pharma:
Clinical Research:
π’ Are You One of These Stakeholders?
If you're in any of these categories, you face the 40% customer loss risk, β¬35M EU fines, and discrimination lawsuit liability daily. Your endorsement validates that this problem is real and urgent for your industry.
π Find Your Connection Path:
β’ LinkedIn: Search "[Company Name] + AI risk" or "[Company Name] + compliance"
β’ Mutual connections: Check who you know at Munich Re, Swiss Re, Allianz, Progressive
β’ Alumni networks: MIT, Stanford, Berkeley alumni work at these companies
β’ Conference contacts: Anyone from AI Summit, InsurTech Connect, RIMS
β’ Direct emails: firstname.lastname@[company].com often works
Test our trust debt measurement on any codebase
npm install intentguardComprehensive articles on FIM technology and trust measurement
See the technology in action
Help us understand how severely this problem affects your organization (0 = not at all, 10 = existential threat)
βΉοΈ This survey is completely independent from the endorsement. Submit anonymously without signing in.
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Your Risk Score: 5.0/10
β οΈ Significant - Consider prevention before crisis
Threat: 5/10
Urgency: 5/10
Exposure: 5/10
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Copy this prompt for ChatGPT/Claude/Gemini:
Example AI responses:
β’ "Red (#DC2626) for danger, Blue (#2563EB) for trust"
β’ "Orange (#F97316) for warning, Green (#16A34A) for solution"
β’ "Purple (#9333EA) for mystery, Gold (#FACC15) for clarity"
Share Your Seal:
Examples of AI-chosen color combinations:
Action
Memory
Decision
Trust
Innovation
Breakthrough
Each seal represents someone's AI-generated color choice for the problem/solution duality
Why this matters: When thousands create and share their seals, we visualize the collective recognition that AI's black box problem demands immediate attention. Your colors become part of the movement.
π― Make It Viral: The #AIAccountabilitySeal Challenge
1. Ask your AI for colors representing the problem/solution
2. Create your seal at thetacoach.biz/heraldik
3. Share with #AIAccountabilitySeal
4. Challenge 3 colleagues to create theirs
The Policy Challenge:
You're being asked to regulate AI, but current technology cannot comply with your laws. The EU AI Act demands "explainable AI" - but no vendor can actually prove what their AI considered. You're legislating requirements that are technically impossible with today's systems.
Local & State Level:
Federal & International:
What You Need to Know:
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Problem Validation
We'll confirm you understand the $35M fines, 40% customer loss, and legal liability risks
Principle Exploration
Deep dive into HOW hardware verification works (Sβ‘Pβ‘H unity discovery)
Strategic Discussion
Only after understanding both problem and principles do we explore partnership
CTO & Co-Founder, The Whisper Company
Ret. Professor, UT Austin (30 years Applied Intelligence)
"I see FIM and hardware-based trusted execution environments as a match made in secure computing heavenβa software blueprint for transparency paired with hardware's ironclad enforcement."
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ThetaDriven Inc. | Patent Pending Technology | Building Trust in AI