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6. Regulatory & Macro Environment

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United States Regulatory Landscape

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STIR/SHAKEN (Call Authentication)

What it is: FCC-mandated cryptographic attestation for caller ID, combating robocalls and spoofing. Status:
  • Full implementation deadline: June 30, 2023 (extended from 2021)
  • Non-compliance = $10k-230k per violation per day
  • Required for all VoIP/SIP providers
Impact on Market:
  • ✅ Raises barriers to entry (compliance costs $200k-2M per carrier)
  • ✅ Favors established players (Twilio, Bandwidth) with infrastructure
  • ❌ Penalizes small resellers unable to afford attestation
  • 🔄 Creates demand for attestation-as-a-service (Bandwidth leads here)
Voice AI Implications: Outbound AI agents face higher scrutiny. Legitimate use cases (appointment reminders, payment alerts) now need:
  • Valid business relationship attestation (full “A” level)
  • CRM integration proving prior consent
  • Fallback to SMS if calls blocked

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TCPA (Telephone Consumer Protection Act)

What it governs: Automated calling, pre-recorded messages, SMS marketing. Key requirements:
  • Express written consent for auto-dialed calls/texts
  • Do-Not-Call registry compliance
  • Opt-out mechanisms in every interaction
  • 9pm-8am calling restrictions
Penalties: $500-1,500 per violation; class-action friendly Voice AI Considerations:
  • “Automated call” includes AI agents, even with LLM-generated speech
  • Consent management systems (CRM flags) now table-stakes
  • “Press 1 to opt out” must trigger instant suppression
  • Industry push for AI-specific clarifications (pending FCC rulemaking)

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State Privacy Laws (CCPA, CPRA, VBPA, etc.)

Core requirements:
  • Right to know what data is collected during calls
  • Right to deletion of call recordings/transcripts
  • Opt-out of “sale” (includes 3rd-party analytics vendors)
  • Biometric data consent for voice-printing (Illinois BIPA, Texas CUBI)
Voice AI Exposure:
  • LLM training on customer call data = potential “sale” under CCPA
  • Voice cloning/emotion detection = biometric data (requires explicit consent)
  • Retention policies: Many states limit recording storage to 90 days

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India Regulatory Landscape

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TRAI (Telecom Regulatory Authority)

Key regulations affecting voice AI:
  1. DLT (Distributed Ledger Technology) for Spam:
    • All commercial calls/SMS must be registered on blockchain
    • ₹5,000 fine per unregistered interaction
    • Impact: Creates compliance moat for established players (Tanla, Route Mobile); startups struggle with multi-operator integrations
  2. Interconnection charges:
    • IUC (Interconnect Usage Charge) reduced to ₹0 in 2020
    • Shift to “bill and keep” model (sender pays network costs)
    • Impact: Lowers voice termination costs, accelerating VoIP adoption
  3. Numbering plan:
    • DID allocation fragmented by 22 telecom circles
    • Virtual number providers need separate licenses per state
    • Impact: Slows national SIP rollout; benefits incumbents with existing allocations

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IT Act 2000 & Aadhaar Act

Data localization:
  • Payment data must be stored in India (RBI mandate)
  • “Critical personal data” (inc. financial, health) may require localization (pending)
  • Voice AI Impact: International SIP trunking okay, but transcripts/analytics data may need in-country hosting
KYC for voice bots:
  • Aadhaar-based eKYC required for financial transactions
  • Voice biometrics proposed as authentication method
  • Opportunity: Unified voice-ID for banking (Uniphore, Skit pursuing)

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DoT (Department of Telecom) Compliance

Lawful Intercept (LI) requirements:
  • All VoIP/CPaaS providers must build interception capability
  • 24/7 monitoring portal for law enforcement
  • Cost: ₹2-5 crore capex per provider; ongoing audit costs
  • Impact: High barrier for startups; acquired players (Airtel IQ, Jio CX) absorb into telco infrastructure

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European Union (GDPR, ePrivacy, AI Act)

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GDPR (Voice-Specific Considerations)

Consent for recording:
  • “Explicit, informed, freely given” consent before recording
  • Cannot be bundled with T&Cs (must be separate checkbox)
  • Withdrawal must be as easy as granting
Right to erasure:
  • Customers can demand call recording deletion
  • Must honor within 30 days
  • LLM training data = extra complexity (can you “untrain” a model?)
Data minimization:
  • Transcript only what’s necessary
  • Delete after retention purpose satisfied (e.g., 90 days for QA)
Voice AI Implications:
  • Real-time processing (no recording) preferred to minimize liability
  • Anonymization pipelines (stripping PII) before analytics
  • On-prem deployments growing in EU to keep data local

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EU AI Act (Voice-Specific Classifications)

High-Risk Systems (includes many voice AI use cases):
  • Emotion detection in employment/education (banned in some contexts)
  • Biometric categorization (accent, dialect inferencing)
  • Requirements: Conformity assessment, human oversight, transparency
General-Purpose AI (LLMs):
  • If >10^25 FLOPs (e.g., GPT-4 class): Systemic risk evaluation
  • Voice AI using frontier models = inherits compliance burden
Voice AI Strategy:
  • Build “human-in-loop” into high-stakes interactions (insurance claims, loan approvals)
  • Transparency: Disclose when customer is talking to AI (no “passing as human”)
  • Document training data provenance (GDPR Article 22: automated decision-making)

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Macro-Economic Factors

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Labor Cost Arbitrage Erosion

The Problem:
  • US contact center agent: 15−25/hour→15-25/hour → 3,750-6,250/month loaded
  • India contact center agent: 3−6/hour→3-6/hour → 750-1,500/month loaded
  • Historic arbitrage: 75-80% savings drove offshoring
The Shift:
  • AI agent: 0.15−0.40/minute→0.15-0.40/minute → 1,800-4,800/month for 24/7 coverage (10k min/month)
  • New arbitrage: 70-90% savings vs. India labor
Implication: India’s BPO advantage compressed from 75% to 25-50% vs. AI. Voice AI adoption in India is faster than US because wage delta no longer justifies human-only model.

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Cloud Infrastructure Pricing

Trend:
  • Compute costs (GPU inference): -30%/year (NVIDIA volume, competition)
  • Network egress: -15%/year (AWS/GCP pricing pressure)
  • Storage: Effectively $0 marginal cost now
Impact on Voice AI:
  • 2022: LLM inference = $0.20/minute → made AI uneconomical
  • 2025: $0.03-0.05/minute → AI now cheaper than human
  • 2027 (projected): $0.01-0.02/minute → AI 100× cheaper than human
Strategic Implication: Business models based on “AI as cost center” flip to “human agents as luxury.” Pricing power shifts to whoever controls quality (vertical models, compliance) not infrastructure.

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5G & Edge Computing

Opportunity:
  • 5G network slicing: Guaranteed <50ms latency for voice packets
  • Edge POPs: Process STT/TTS locally (reduce cloud round-trip)
India Advantage:
  • Jio/Airtel 5G rollout fastest globally (400M+ subscribers by 2025)
  • Network-adjacent AI inference (Airtel IQ, Jio CX) = structural latency advantage
Challenge:
  • Edge deployment requires CapEx (POPs in 50+ cities)
  • ROI unclear until 5G SA (standalone) core deployed