This document discusses the rise of legal AI and its implications for the legal profession. It begins by explaining that lawyers serve as "transaction cost engineers" who help facilitate transactions by reducing costs. New technologies can now help automate and supplement some of lawyers' work in reducing transaction costs. The document then provides examples of how legal AI could impact different areas of law like e-discovery, criminal justice, patents, and more. It argues that building AI to mimic lawyers' cognitive processes may allow for faster, cheaper, and more accurate legal work. Finally, it considers questions around whether and when a "legal singularity" could occur where AI replaces most or all lawyers.

1. Lawyering in the AI Age

2. My goal is to answer these 3 questions:
1. From a law practice
standpoint, why should we
care about legal AI?
2. How does one build AI,
generally?
3. Where to find Legal AI?

3. 1937 Ronald Coase: transaction costs are a central determinant
of how economic activity is organized.
1997 Ronald Gilson: Imperfect markets give rise to
intermediaries to lift the wedge between parties. “Lawyers are
transaction cost engineers.”
2015 Nicole Shanahan (at Stanford CodeX): Technology
supplements lawyers as transaction cost engineers. Technology
is the ultimate transaction cost economizer.
Origins: I wanted to understand what my job as a lawyer was

5. What the article actually says is this:
When we shift focus from thinking about legal
technology in terms of a lawyer’s efficiency, to
viewing these advancements within the context
of socioeconomic organization, we can begin to
realize its true significance.

6. Borrowing from transaction cost theory,
there should be 3 core tenets of legal technology:
 1. Optimizing for the exchange of information.
 2. Setting consistent expectations between parties.
 3. Mitigating risks.

7. Our job as modern legal technologists is to build
software that mimics the cognitive processes of
lawyers. We expect that we can produce faster,
cheaper and more accurate legal work products.

8. In the context of E-Discovery/Federal Rules, for instance:
 Proportionality
(b) Discovery Scope and Limits.
(1) Scope in General. Unless otherwise limited by court order, the
scope of discovery is as follows: Parties may obtain discovery
regarding any nonprivileged matter that is relevant to any party's
claim or defense and proportional to the needs of the case,
considering the importance of the issues at stake in the action, the
amount in controversy, the parties’ relative access to relevant
information, the parties’ resources, the importance of the discovery in
resolving the issues, and whether the burden or expense of the
proposed discovery outweighs its likely benefit. Information within this
scope of discovery need not be admissible in evidence to be
discoverable..

9. How can the tech community help with the
Federal Rule of Civil Procedure 26?
One top of the head proposal….
Create a computational weighting system based on
Judge Laporte’s Proportionality Matrix

11. In the context of Criminal Justice
 “Predictive Policing”
 Prosecutor Discretion Tools

14. In the context of Patents
 Practice Management
 Valuation Analysis
 Licensing Strategy

16. FOR THE FIRST TIME EVER
THIS IS ALL TECHNICALLY FEASIBLE
SO, WHAT DO YOU NEED TO
UNDERSTAND ABOUT LEGAL AI?

17. General AI
Machine
Learning
Logic/Rules
Automation

18. General AI
Machine
Learning
Logic/Rules
Automation
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA
DATA

19. General AI
Machine
Learning
Logic/Rules
Automation
Computational
Logic
(1) the representation of facts and
regulations as formal logic and
(2) the use of mechanical reasoning
techniques to derive consequences of
the facts and laws so represented.
Computational
Law

20. General AI
Machine
Learning
Logic/Rules
Automation
Supervised
Learning
Unsupervise
d Learning
 Training Data
 Hand-Labels “These e-mails
exemplify willful infringement”
 Clustering “these e-mails have
similar expressions of willfulnes
s”
 Dimensionality Reduction

21. General AI
Machine
Learning
Logic/Rules
Automation
Supervised
Learning
Unsupervise
d Learning
(Deep) Neural Networks

22. Supervised
Learning
Unsupervise
d Learning
 30 Million Positions from previously playe
d Go matches used as training data
 It then began to play
itself, creating more da
ta for “reinforcement”
learning.

23. General AI
Machine
Learning
Logic/Rules
Automation
Painful and Slo
w

24. General AI
Machine
Learning
Logic/Rules
Automation
SUDDEN

26. IS IT POSSIBLE TO PREDICT
THE TRANSITION TO LEGAL AI?

28. “Coasean Mapping”
Forms &
E-Filing
Client
Intake
E-Discovery
Drafting
Briefs
Client
E-mails
COST
Transaction cost economizing function
COMPLEXITY

29. “Coasean Mapping”
Forms &
E-Filing
Client
Intake
E-Discovery
Drafting
Briefs
Client
E-mails
COST
Transaction cost economizing function
COMPLEXITY
Legal Singularity?

30. www.legaltechlist.com

32. WILL GENERAL AI REPLACE LAWYERS?