Document-driven
The deliverable is a document: a contract, a filing, a due diligence report.
ABL 51st Conference·Mexico City·October 2026
Leading Through
Disruption
How technology is changing business, leadership and decision-making.
Adolfo Rangel Prado · Independent Consultant & Associate Professor, Universidad Panamericana
Today · 35 minutes
01Intelligence is becoming abundant
02Why most firms don’t see the return
03The business of law, repriced
04Deciding with machines
05The firm that learns
06Leading through it
01Section
01Intelligence is becoming abundant
02Why most firms don’t see the return
03The business of law, repriced
04Deciding with machines
05The firm that learns
06Leading through it
01Intelligence is becoming abundant
Navier–Stokes: a 90-year-old open problem
The equations that describe how fluids move: water, air, blood.
≈10,000agents working in parallel · 88 hours
≈3 hper result, one model, one month later
OpenAI · 8 Sep and 6 Oct 2026 · 370+ new results published, with different levels of verification
01Intelligence is becoming abundant
From answering questions to doing the work
Chat
What should I look for in a change-of-control clause?
The model explains it.
The lawyer carries the answer to the work.
Agent · when we delegate an objective
“Review this SPA against our playbook and leave it ready for partner review.”
01Intelligence is becoming abundant
Why law is first in line
Rules-bound
The rules are written down: statutes, regulation, the firm’s own playbook.
Verifiable
The output can be checked against a source. That is what makes errors catchable at scale.
The fields where AI moves fastest are the ones where work can be read, ruled and checked.
Framework: Mark Ajzenstadt, “The AI-native services firm”, Aug 2026. He sells AI services: read it as a lens, not a verdict.
01Intelligence is becoming abundant
What has already happened in legal
OpenAI · Sep 2026
Astra for Law
54% fully correct on hard legal research questions, vs 38.7% for the general model.
OpenAI’s own 200-question test
Harvey · Aug 2026
Tenet
A legal model built on an open base model. Top score on contract tasks at under ¼ of the cost.
Reported by Harvey on the LAB benchmark
ABC Legal · Aug 2026
50+ agents in production
About 50% lower cost on certain legal tasks. Fifteen non-engineers had agents running in a week.
Published by Anthropic about its customer
Read vendor numbers the way you read the other side’s exhibits: useful, and never neutral.
01Intelligence is becoming abundant
When intelligence is abundant, what becomes scarce?
A first-year associate and a senior partner can use the same model from the same phone.
“If you value intelligence above all other human qualities, you’re gonna have a bad time.”Ilya Sutskever co-founder of OpenAI · 2023
What gains weight: what we want to achieve, our judgment, and our ability to make it real.
02Section
01Intelligence is becoming abundant
02Why most firms don’t see the return
03The business of law, repriced
04Deciding with machines
05The firm that learns
06Leading through it
02ROI · Return on investment
95%
of organizations in the study reported no return on their generative AI investment.
MIT NANDA, 2025 · preliminary findings: 52 organizations interviewed, 153 leaders surveyed, 300+ public initiatives. Not a universal rate.
Productive individuals don’t automatically make productive firms.
Central idea · Hebbia
02Why most firms don’t see the return
We have seen this before: electricity
The bottleneck isn’t the model. It’s the org chart.
Paul A. David, “The Dynamo and the Computer”, American Economic Review, 1990.
02Why most firms don’t see the return
A faster lawyer is not a better firm
The 10× lawyer
- Each lawyer with their own chat
- Faster first drafts, same process
- Prompts and know-how live in one head
Gains stay with the person, and leave with them
The productive firm
- Workflows redesigned around the outcome
- Shared playbooks, context and permissions
- Every correction improves the system
Gains compound across matters and people
The question for leaders: what breaks if this person leaves?
03Section
01Intelligence is becoming abundant
02Why most firms don’t see the return
03The business of law, repriced
04Deciding with machines
05The firm that learns
06Leading through it
03The business of law, repriced
For every $1 spent on software,
about $6 is spent on services
Software companies went after the $1. AI companies are going after the $6, at software margins. The pitch is no longer “AI for lawyers”. It is the AI law firm. Sequoia Capital thesis, as summarized in July 2026
Your clients’ legal spend is now a target for venture capital.
03The business of law, repriced
AI doesn’t replace the lawyer. It reprices the first draft.
Selling hours
- Revenue grows with time spent
- Efficiency shrinks the invoice
- The client pays for the process
AI is a threat to the business model
Selling outcomes and assets
- Revenue grows with judgment and results
- Efficiency widens the margin
- Playbooks become firm assets
Every model improvement works in your favor
One boutique firm in Mexico set its goal as “six people with the capacity of thirty”. That only pays if the pricing changes too.
03The business of law, repriced
The pyramid has a hole at the bottom
If agents do the first draft, how do you train the next partner?
An open question, not a forecast. “Replacing the pyramid” is Luke Drago’s phrase (The Intelligence Curse), Oct 2026.
04Section
01Intelligence is becoming abundant
02Why most firms don’t see the return
03The business of law, repriced
04Deciding with machines
05The firm that learns
06Leading through it
04Deciding with machines
Separate the intelligence
from the authority
The most trustworthy system won’t be the one built on the model we trust most. It will be the one that lets us trust the model the least. Satya Nadella, CEO of Microsoft · essay, 10 Oct 2026 · paraphrased
Treat the model like a capable new hire: identity, least privilege, logs and approvals.
04Deciding with machines
A plea is not a control
A plea
“Please be careful with confidential information.”
- Lives in the prompt
- Depends on the model behaving
- Cannot be audited
A lock
The agent cannot send, sign or file. A human button does.
- Lives outside the model
- Works even when the model is wrong
- Can be explained to the client
For every agent: what must it never do without someone authorizing it?
04Deciding with machines
Human in the loop, by design
Trigger
Counterparty sends a draft
Interprets
Reads every clause
AI
Compares
Against our playbook
cites each source
Proposes
Redline and issues list
Decides
Lawyer approves
the send button is human
Escalation Caps above threshold, unknown clauses or missing sources go straight to a partner.
The part that interprets cannot act on its own.
Illustrative workflow · software for fixed rules, AI to interpret, people for decisions and exceptions.
04Deciding with machines
Confidentiality is now a contract term
Data
Zero retention is a pricing tier, not a feature
Selected firms get a version where nothing is stored. Everyone else gets default settings. Read the terms before you type a client’s name.
Reporting on Astra for Law, Sep 2026
Learning
Who owns the learning loop?
The question is not only who owns the data. It is who keeps the corrections your lawyers make every day.
“AI sovereignty is your alpha” · Palantir, Jul 2026
The model is something you buy. The system around it is something you build and own.
05Section
01Intelligence is becoming abundant
02Why most firms don’t see the return
03The business of law, repriced
04Deciding with machines
05The firm that learns
06Leading through it
05The firm that learns
A firm learns when every error changes the system
If the correction stays in one person’s head, the firm didn’t learn. The person did.
This is Toyota’s continuous improvement. What’s new: the system can now draft its own correction, and a human approves it.
05The firm that learns
The partner’s redlines are the firm’s most valuable asset
Works
Agent drafts
with an audit trail
Corrects
Lawyer reviews
accepts, edits, rejects
Learns
Proposes a rule change
every week
Approves
Partner signs off
the playbook updates
Earned autonomy The agent may automate only after consistent agreement with human decisions.
Today those corrections live in tracked changes and email. They evaporate.
Loop based on ABC Legal’s published setup (Anthropic, Aug 2026).
05The firm that learns
Lawyers already know how to do this: it’s called drafting
A wish
“Be careful with indemnities.”
- Can’t be tested
- Means something different to each reader
A rule
“Indemnity caps above 1× fees go to a partner, with the clause cited.”
- Observable and testable
- The same for every reader, human or machine
“A company’s eval suite will be its most valuable resource.” Your precedents and partner reviews are yours.
George Sivulka, Hebbia · Jul 2026
06Leading through it
First principles
Start from the outcome the client needs and the real constraints, not from today’s process.
01
What has to happen?
02
What conditions must it meet?
03
How would we solve it if we started today?
Then decide what software does, where an agent helps, and where a lawyer’s judgment is required.
06Leading through it
Three ways to see the same due diligence
Today
Manual review
Associates read the data room, fill the review table by hand, and a partner reviews at the end.
Weeks · findings depend on who read
AI as a tool
Assisted review
Each associate uses a chat to summarize documents. The process stays the same.
Faster · same layout, same risks
Redesign
Designed from the outcome
Agents fill the table against the firm’s playbook, citing every source. Lawyers handle exceptions and judgment calls. Each correction updates the playbook.
Measure: turnaround · first-pass acceptance · issues caught
Illustrative example · designs to be measured, not observed results.
06Leading through it
Five decisions for Monday morning
01
Pick one workflow, not an “AI strategy”: document-driven, rules-bound, verifiable.
02
Define authority: what the agent may never do without a human.
03
Capture corrections as written, testable rules in a shared playbook.
04
Measure it: turnaround, first-pass acceptance, exceptions escalated.
05
Rethink the price: charge for the outcome and the judgment, not the hours the machine saved.
06Leading through it
The advantage isn’t knowing more.
It’s knowing again.
The blueprint of the AI-native firm is public. Every competitor can read it. What we know about these tools today is worth about ninety days.
What can’t be copied: how fast your firm learns, and who owns that learning.
Intelligence is becoming a utility.
Judgment, authority and trust are still a leadership job.
Pick one workflow. Decide what the machine may never do alone. Make every correction count.
Adolfo Rangel Prado
Independent Consultant · Associate Professor, Universidad Panamericana
AAppendix
Sources
- OpenAI · Navier–Stokes, 8 Sep 2026 · mathematical results, 6 Oct 2026
- Anthropic · Building effective agents
- Mark Ajzenstadt · the AI-native services firm, 27 Aug 2026
- OpenAI · Astra for Law, Sep 2026 · summary on privacy tiers
- Harvey · Tenet, Aug 2026
- Anthropic · ABC Legal case, Aug 2026
- Ilya Sutskever · on intelligence and human qualities, 2023
- MIT NANDA · The GenAI Divide, 2025 (preliminary findings)
- Hebbia · Productive individuals don’t make productive firms
- Paul A. David · The Dynamo and the Computer, American Economic Review, 1990
- Sequoia thesis · selling work, not software (summary), Jul 2026
- Luke Drago · on replacing the pyramid, Oct 2026
- Satya Nadella · separating intelligence from authority, 10 Oct 2026
- Palantir · AI sovereignty is your alpha, Jul 2026
- George Sivulka (Hebbia) via Marc Andreessen · evals as a company’s key resource, Jul 2026
Vendor-reported figures are labeled as such. Workflows marked “illustrative” are designs, not observed results.