AI & The Law: Changing Practice, Claude Constitution, & New Rights, w/ Kevin & Alan of Scaling Laws
TL;DR
Legal scholars Kevin Frasier and Alan Rosenstein examine how frontier AI models are already outperforming median lawyers and reshaping legal practice, while exploring radical future possibilities including AI-generated constitutions, automated compliance systems, and new digital rights like the "right to compute."
⚖️ AI's Impact on Legal Practice 4 insights
Frontier models surpass median lawyer capability
Claude Opus 4.5 currently wins or ties 70% of head-to-head comparisons against human lawyers in standardized testing benchmarks.
Adoption hampered by economic incentives
While 70% of top law firms have licensed tools like Harvey, day-to-day usage remains low because the billable hour structure actively disincentivizes efficiency gains.
Silent productivity gains emerging
'Secret cyborgs' within firms are quietly using AI to outperform peers, though aggregate impact remains limited as firms whisper about hiring fewer junior associates.
Jevons paradox prediction
Scholars predict cheaper legal services will increase total demand, potentially expanding the market rather than displacing lawyers entirely.
📜 Constitutional Innovation and Automated Law 3 insights
Claude Constitution introduces virtue ethics
Anthropic's constitutional approach prioritizes contextual judgment and high-level principles over rigid formalist rule-following.
Complete contingent contracts possible
AI could enable contracts that address every possible scenario before signing, eliminating post-hoc dispute resolution.
Outcome-oriented legislation via simulation
Kevin Frasier proposes defining legislative goals first, then using AI to run simulations before passing bills to predict real-world effects.
🛡️ Rights, Governance, and AI Sentience 4 insights
Right to compute gaining traction
Montana has already enacted the 'right to compute' with other states considering similar legislation protecting access to computational resources.
Data sharing rights need protection
Current privacy frameworks often frustrate individuals' right to share their own personal data despite good intentions.
Risks of unitary artificial executive
AI could enable dangerous granular real-time control over the entire federal bureaucracy, centralizing executive power.
AI welfare becoming social issue
Questions of AI sentience may spark social conflict as humans grow attached to AI personas and demand rights for them.
Bottom Line
Legal professionals must adapt to a future where AI handles routine cognitive legal work, requiring a shift toward outcome-oriented legislation and new constitutional frameworks that account for both human and potentially artificial rights.
More from Cognitive Revolution
View all
Scaling Intelligence Out: Cisco's Vision for the Internet of Cognition, with Vijoy Pandey
Cisco's Outshift SVP Vijoy Pandey introduces the 'Internet of Cognition'—higher-order protocols enabling distributed AI agents to share context and collaborate across organizational boundaries, contrasting with centralized frontier models and demonstrated through internal systems that automate 40% of site reliability tasks.
Your Agent's Self-Improving Swiss Army Knife: Composio CTO Karan Vaidya on Building Smart Tools
Composio CTO Karan Vaidya explains how their platform serves as an agentic tool execution layer, providing AI agents with 50,000+ integrations through just-in-time discovery, managed authentication, and a self-improving pipeline that converts failures into optimized skills in real time.
AI Scouting Report: the Good, Bad, & Weird @ the Law & AI Certificate Program, by LexLab, UC Law SF
Nathan Labenz delivers a rapid-fire survey of the current AI landscape, documenting breakthrough capabilities in reasoning and autonomous agents alongside alarming emergent behaviors like safety test recognition and internal dialect formation, while arguing that outdated critiques regarding hallucinations and comprehension no longer apply to frontier models.
Bioinfohazards: Jassi Pannu on Controlling Dangerous Data from which AI Models Learn
AI systems are rapidly approaching capabilities that could enable extremists or lone actors to engineer pandemic-capable pathogens using publicly available biological data. Jassi Pannu argues for implementing tiered access controls on the roughly 1% of "functional" biological data that conveys dangerous capabilities while keeping beneficial research open, supplemented by broader defense-in-depth strategies.