Lawyers Duties | eDiscovery Team

Why Quantum Law, and Why Now?

June 17, 2026

Ralph Losey, June 17, 2026.

Privacy, Proof, and Judgment
in the Next Technology Shift to Quantum

A man in formal attire stands in front of a grand building labeled 'Justice - Reason - Evidence', facing a futuristic scene featuring digital elements and a quantum device, with the inscription 'Q DAY WILL CHANGE PRIVACY. PROOF. JUDGMENT.'

Most legal technology shifts do not announce themselves with trumpets. They slip into ordinary practice first. Email was once informal chatter, then became the core of discovery. Predictive coding was once treated as radical, then became accepted. Generative AI was once a toy for clever prompts, then became a sanctions risk, a productivity tool, and an ethics problem all at once.

Quantum computing may follow the same pattern, but it could also arrive with a loud bang and deeper consequences. The timing is uncertain. Some forecasts are aggressive; others stretch out for many years. But when cryptographically relevant quantum computing arrives, the legal effects will be hard to ignore. It will affect how long secrets stay secret, how courts evaluate machine-generated proof, and how lawyers exercise judgment when powerful systems produce answers no ordinary computer can easily check. That is why quantum law deserves attention now, before the first crisis forces good lawyers to fumble, struggle, and learn in public.

A visual representation of legal themes in a futuristic setting, featuring an hourglass, a quantum computer, and a map labeled 'The Legal Terrain Ahead', highlighting concepts like Cryptography, Privacy, Liability, and Post-Quantum Security.

Q-Day, Quantum Proof, and the Legal Terrain Ahead

This article is a clarion call and a first outline of the terrain ahead. It is designed for everyone who deals with confidential data, evidence, or dispute resolution. Quantum computing law will arrive through inventions built by corporate, university, and government teams of engineers and scientists, increasingly assisted by powerful AI systems. Some forecast its arrival next year; others stretch it out for many years.

We may not know the date when the quantum computer future becomes practical, but we do know that its arrival could prove to be very disruptive. Some semi-quantum segments have already begun to seep into legal practice through vendors, simulations, and hybrid AI systems. Full quantum computing may arrive suddenly, especially if cryptographically relevant quantum computing makes old encryption vulnerable. The legal risk does not depend on guessing the exact date. It depends on whether lawyers understand the terrain before clients, courts, and vendors begin asking questions they are not prepared to answer.

The best-known danger has a name: Q-Day. That is the day when a sufficiently powerful quantum computer is built that can break most of the public-key encryption now used to protect digital information. No one knows when Q-Day will arrive, but the danger begins before that day because encrypted data can be stolen now and stored for later. We know that is already happening. If the data is still valuable when quantum decryption becomes practical, today’s secure archive will become tomorrow’s open file cabinet. That is the “harvest now, decrypt later” problem, and it gives confidentiality an expiration-date problem lawyers cannot ignore. See NIST, What Is Post-Quantum Cryptography? and the companion paper, Post-Quantum Cryptography:

Q-Day is only part of the challenge. Quantum computing may also reshape how courts apply evidentiary standards and evaluate reliability, while creating new questions involving cryptography, privacy, liability, and insurance. See RAND, The Quantum Age and Its Impacts on the Civil Justice System (4/29/25).

One of the most important evidentiary shifts may be from Identity to Fidelity. Lawyers are accustomed to machines that produce the same answer every time. That is identity. Quantum systems may force courts to ask a different question: whether the process behaves faithfully within known error limits. That is fidelity. The issue will not be whether every run produces the same output, but whether the pattern of outputs can be explained, tested, and trusted.

That shift will force lawyers to ask practical questions. What was the model asked to do? What assumptions went in? What error rate is known? What was excluded? Can another qualified team test the process well enough to trust it?

This article only traces the outline of how law can prepare now, before Q-Day, and later, when quantum-generated evidence begins appearing in disputes. A fuller map is needed, and I have been working hard on that, but the first step is seeing the terrain.

A glass dome showcasing quantum science applications, including GPS, MRI technology, lasers, and transistors, with a scenic city backdrop and a classic telescope in the foreground.

Quantum Was Already Here, Just Quietly

Many lawyers hear “quantum” and think of science fiction and multiverses. That reaction is understandable. Quantum mechanics is strange, and lawyers are trained to distrust strange things unless they come with affidavits, exhibits, and a billing code.

In fact, quantum technology is not new. NIST’s Andrew Wilson explains that GPS, MRI machines, and laser pointers all depend on quantum science. From GPS to Laser Pointers, Quantum Science Is All Around Us. NIST’s Corey Stambaugh makes the same point in still broader terms. A Quantum Leap Forward: How Tiny Particles Can Bring Us Exciting New Tech.

Quantum computing is different because it uses quantum behavior to process information itself. NIST’s Quantum Computing Explained . The article is a useful starting point for lawyers because it explains the basic difference between classical computer bits and quantum bits, Qubits. The legal point is not the math or entangled superpositions. The point is that a different way of processing information will create new and different legal problems.

A futuristic setting depicting the concept of 'Q-Day', where quantum computers break encryption. It features a lock being illuminated by a blue beam, symbolizing decryption. In the foreground, an hourglass and metal filing cabinets labeled 'Encrypted Confidential Data' and 'Decrypted Access Granted Tomorrow' highlight themes of time and privacy.

Q-Day and the Old File Cabinet Problem

The most immediate legal problem is confidentiality. Imagine a law firm with an old litigation archive from a trade-secret case. The case settled years ago. The files are encrypted and stored in the cloud. The client has moved on. The lawyers have moved on. The archive sits quietly in a digital file cabinet, full of secrets everyone assumes are still safe.

Now ask how long those secrets must remain secret. Some secrets age out quickly. Others remain valuable long after the case is closed.

The legal danger begins before Q-Day because encrypted data can be stolen now and stored for later. The thief does not need to open the cabinet today. He only needs to steal it and wait. Q-Day is not just a future cybersecurity event. It is a present-day confidentiality problem for anyone holding secrets that must remain secret for years. See NIST, What Is Post-Quantum Cryptography?. The unpleasant feature of this risk is that it may mature suddenly. A file that was unreadable yesterday may become readable tomorrow if the lock protecting it was built on vulnerable cryptography.

Password protection is not encryption. A password controls access; encryption protects the contents. Q-Day is not a faster way to guess your great password. The risk pertains to the mathematics behind vulnerable public-key encryption. So, when a vendor responds to quantum-readiness questions by talking only about strong passwords, multi-factor authentication, or access controls, the vendor has not answered the real question. Ask what cryptography protects the data, who controls the keys, and whether there is a post-quantum migration plan.

NIST has already finalized its first three post-quantum cryptography standards to try to protect against this vulnerability: FIPS 203, FIPS 204, and FIPS 205. See NIST, Post-Quantum Cryptography FIPS Approved. See also Federal Register, Announcing Issuance of Federal Information Processing Standards FIPS 203, FIPS 204, and FIPS 205. For lawyers, the lesson is not to become cryptographers but to learn some of the basics, and to recognize that cryptographic migration has already moved from theory to standards.

A group of officials monitoring cybersecurity threats in a high-tech control room, with flags of the USA, China, and Russia in the background, and screens displaying alerts about data breaches and compromised encryption.

The National Security Shadow of Q-Day

The deepest Q-Day risk is not merely that old legal files with client secrets become readable. That is bad enough. The larger danger is strategic. If the first cryptographically relevant quantum breakthrough is achieved secretly by a hostile government, the result could be more than a cybersecurity incident. It could be a shift in military, intelligence, diplomatic, and economic power.

A state actor that can read previously secure communications may not announce the achievement. It may watch, wait, and exploit. It may use old, intercepted traffic to identify sources, compromise negotiations, expose military plans, manipulate markets, pressure companies, or weaken alliances. The first signs may not look like a quantum breakthrough at all. They may look like inexplicable intelligence failures, severe infrastructure disruptions, mass persuasion and social manipulation, followed by financial collapse and social unrest.

That is the nightmare scenario. Not a quantum computer on stage at a press conference, but a quiet advantage used in secret by an unscrupulous power. The problem is not limited to adversaries. If any military-intelligence system reached Q-Day first, the pressure to use that advantage would be immense. History teaches that strategic breakthroughs become instruments of state power long before civilian institutions understand them.

Do not think this will be like the mirage of Y2K. Although Q-Day has no known date, it is very real, may arrive in secret, and threatens power rather than malfunction.

Ben Buchanan and Andrew Imbrie’s important book, The New Fire: War, Peace, and Democracy in the Age of AI, is not a quantum book, but its warnings about advanced technology and state power apply here. Powerful computational tools can support science, medicine, and prosperity, but they can also intensify conflict, surveillance, and authoritarian control. The potential of quantum computing to vastly enhance mass surveillance and authoritarian control is especially worrisome. See my article, Escaping Orwell’s Memory Hole: Why Digital Truth Should Outlast Big Brother (March 2025), which may prove to be over-optimistic.

That is also why the CISA, NSA, and NIST have all urged organizations, especially those supporting critical infrastructure, to begin quantum-readiness planning now. See CISA, NSA, and NIST, Quantum-Readiness: Migration to Post-Quantum Cryptography. The advice is not theoretical. It is a practical checklist we should all follow, now, to begin preparations: (1) identify long-lived sensitive data; (2) build migration plans; and, (3) ask vendors what they are doing. Waiting for proof that Q-Day has arrived may mean waiting too long.

The best hope is that quantum breakthroughs occur in the open, with enough time for post-quantum defenses to be deployed across governments, courts, companies, and critical infrastructure. History offers little assurance that transformative strategic technologies will be introduced so politely.

A private company or university team might provide that warning if it reaches the threshold first and resists premature militarization. But even that hope is fragile. Once a technology can alter the balance of power, governments may smash through laboratory doors.

That is why quantum law is not just about future expert testimony or old encrypted archives. It is also about governance, secrecy, democracy, and stability in a world where computation is already a weapon of state power. Q-Day will make that weapon far more dangerous.

A man in a suit stands in a courthouse, gazing at a digital display with icons representing various aspects of the civil justice system, including privacy, liability, and digital evidence. In the background, a group of legal professionals is seated at a table, engaged in discussion.

The Civil Justice System Is Already on Notice

RAND has already examined the civil justice consequences of quantum computing, at least in a cursory way. Its 2025 report looks at what quantum computing may mean for courts, law firms, insurers, regulators, and related institutions. RAND, The Quantum Age and Its Impacts on the Civil Justice System. RAND is not selling magic crystals. It is telling the legal system to pay attention before the problems arrive fully formed.

The legal profession has been late before. It was late with email. It was late with e-discovery. It was late with cybersecurity. It is still catching up to generative AI. In Da Silva Moore v. Publicis Groupe, Judge Andrew Peck’s opinion became an early milestone in judicial acceptance of predictive coding in discovery. I served as lead technology counsel in that case, and the resistance to predictive coding was intense, to put it mildly.

Most lawyers in 2011-2012 treated machine learning in document review as dangerous speculation. Nearly all of the legal profession was in denial. If they heard the clarion calls of machine learning, they did not believe it. Cynics point out they had strong billable hour incentives not to. Today, fifteen years later, technology-assisted review is ordinary. Almost no one manually reviews a hundred thousand documents these days, much less a million.

Quantum law may follow that same pattern in some areas, but not all. Some quantum issues may arrive gradually through vendors, expert systems, and hybrid AI tools. Q-Day may not. If the first breakthrough occurs inside a military or intelligence program, the legal profession may receive no clear warning at all. We may instead see the consequences unfold in apocalyptic scenarios that are hard to imagine without grounding in AI and quantum computer capabilities.

That is one reason we must skip the denial phase that happened with AI predictive coding in 2011. The stakes are much higher now. Common sense and professional ethics require it. ABA Model Rule 1.1, Comment 8 states that lawyers should keep abreast of changes in law and practice, including the benefits and risks of relevant technology. See ABA, Rule 1.1 Competence – Comment. The ABA made a similar move for generative AI in Formal Opinion 512. See ABA, Formal Opinion 512 on Generative Artificial Intelligence Tools.

Quantum computing will require the same kind of professional adjustment: familiar duties applied to new facts. That is not a call for panic. It is a call for competent tracking of emerging technologies, especially in computing. Be prepared.

A cartoon character representing a quantum particle, Mr. Quantum, dressed in a top hat and suit, enters a courtroom, humorously addressing the audience with the phrase 'Pardon the wave-function.' Several surprised people are seated at a table, listening attentively, while a judge presides in the background.

When Quantum Evidence Walks Into Court

Encryption is the first practical problem. Evidence is the second. Strictly speaking, a quantum computer will not “testify” because witnesses are people, at least so far. But quantum systems may generate outputs that parties will want to use as proof. A manufacturer may use a simulation to test a material under stress. A pharmaceutical company may use a quantum method to model a molecule. A financial institution may use a hybrid quantum-classical process to test risk under market conditions.

Consider a product liability case involving a battery fire in an electric vehicle. The company’s emails show engineers debating heat risks. The testing logs are incomplete. One side says the risk was known and ignored. The other side says the accident resulted from misuse or unusual conditions. Then an expert offers a simulation of the battery chemistry under conditions close to the fire. The simulation does not produce one answer. It produces a pattern of outcomes.

That pattern may be powerful evidence, but it also creates practical courtroom questions. What inputs were used? Who selected them? What assumptions were built into the model? Were any runs excluded? Could another qualified team reproduce the distribution, even if not every individual result? How does the expert explain the error rate to a judge who has a docket full of ordinary human disputes waiting outside the door? This is just a rough outline of the new types of legal questions and analysis you will need for the future of quantum.

Federal Rule of Evidence 702 should work fairly well for this kind of work, even if quantum evidence will stretch it. The rule requires expert testimony to rest on sufficient facts or data, reliable principles and methods, and reliable application to the case. See Federal Rule of Evidence 702. Rule 901 also matters because Rule 901(b)(9) addresses evidence about a process or system that produces a result. See Federal Rule of Evidence 901. Also see Daubert directs courts to consider factors such as testing, peer review, error rate, standards, and general acceptance. These factors will remain critical.

An infographic illustrating the concepts of identity and fidelity in a legal context, featuring a courtroom scene with a judge and professional witnesses. It includes sections highlighting DNA evidence, epidemiology, and risk assessments, emphasizing how the law relies on probability.

Identity Versus Fidelity

Lawyers like identity. We like exact copies, matching signatures, stable timestamps, and hash values that confirm a file has not changed. That instinct served us well in e-discovery. A hash value is a beautiful thing. It uses straightforward mathematical analysis to show whether the file is the same. The same values appear each time the hash analysis of the document is run.

Quantum systems often require a different instinct. The key question may not be whether the machine gives the identical output every time. The question may be whether it behaves with fidelity. Identity asks whether we got the same answer again. Fidelity asks whether the system behaved as expected, within known error limits.

A courtroom analogy helps. Suppose a careful witness is asked three times whether the traffic light was red. On Monday she says she is almost certain it was red. On Tuesday she says she would put the probability very high. On Wednesday she says red is by far the most likely explanation. A cross-examiner hears only contradiction. A better lawyer hears the same judgment expressed in different language.

Quantum outputs can work in a similar way. Variation is not always unreliability. Sometimes variation is the form the reliable answer takes. Law already understands this better than it admits. DNA evidence, sampling, epidemiology, damages models, and risk assessments all rely on probability. We go into this in detail in the course using both published cases and hypotheticals. Quantum evidence will make probability too visible to ignore. It will add a new dimension to the core legal concept of causation.

Infographic illustrating the transition from traditional documents to model evidence in legal processes, featuring sections on document evidence, modeling examples, discovery requirements, and court decision-making.

From Documents to Models

For most of legal history, lawyers have been document hunters. Who wrote the email? What did the contract say? Where is the missing report? What did the board know? That world is not disappearing. Documents still show notice, intent, concealment, delay, agreement, and knowledge.

But AI and quantum systems push law toward model evidence. A model does not merely record what happened. It tests what likely would happen under stated conditions. Return to the battery-fire example. The emails may show that engineers discussed risk. The testing logs may show what the company actually checked. The simulation may show what the company failed to test.

The simulation does not replace the documents. It interrogates them. That is the practical shift from document-centric law to model-centric law. A discovery request may need more than the final report. It may need the inputs, assumptions, validation work, version history, and excluded runs. The producing party will raise burden, trade secret, and proportionality objections. The court will have to decide how much process disclosure is enough.

This is familiar territory in new clothing. We fought similar battles over metadata, native files, search terms, sampling, and predictive coding protocols. Quantum evidence will bring another version of the same fight: how much of the machine’s process must be disclosed before the result can be trusted?

Infographic illustrating the potential impact of AI on quantum computing timelines, featuring elements like a quantum computer, a panther representing acceleration, and a researcher analyzing data, along with text highlighting key points about error correction and research implications.

AI May Speed the Quantum Timeline

Lawyers are still adjusting to generative AI, but AI is already part of the quantum story. A 2025 Nature Communications review explains that AI is increasingly being used to help with quantum systems. In plain English, AI can help tune fragile machines, find errors, and keep quantum hardware closer to the narrow conditions required for useful work. See Artificial Intelligence for Quantum Computing, (Nature Communications, 12/02/25).

Google DeepMind’s AlphaQubit is one concrete example AI enhanced software. It identifies quantum-computing errors with greatly improved accuracy. Error correction is one of the central barriers to making quantum computers useful at scale. See AlphaQubit tackles one of quantum computing’s biggest challenges (11/20/24).

The practical point for lawyers is modest but important. Do not assume quantum development will proceed on a slow schedule convenient for law firm committees. AI may help researchers move faster. Quantum tools may later assist certain kinds of AI work, especially where optimization or simulation is the bottleneck. The feedback loop remains uncertain, but the first half of AI helping quantum is already underway. That matters because lawyers should not assume that quantum progress will move on a slow, linear timetable convenient for bar committees, vendor reviews, and CLE calendars. To me, a slow arrival would be shocking. I have seen an increase in the pace of change of technology my whole life. I see no reason this will not continue. The quantum floor is not a barrier; it is an opening.

An infographic featuring a man in a suit sitting at a desk with legal symbols around him, discussing advanced concepts like Willow, quantum echoes, and supercomputing. The background includes a futuristic corridor and text highlights on technological advancements and legal implications.

Willow, Quantum Echoes, and the Word Lawyers Should Notice

Google’s announcement in late 2024 of results achieved by its quantum computer, Willow, captured public attention. Willow performed a benchmark computation in under five minutes that would take our fastest AI supercomputers 10 septillion years. See Hartmut Neven, Google, Meet Willow, Our State-of-the-Art Quantum Chip. That claim naturally led to excitement, skepticism, and multiverse speculation. Quantum Leap: Google Claims Its New Quantum Computer Provides Evidence That We Live In A Multiverse (01/09/25, my all-time most read JDSupra article).

The multiverse is fascinating, but lawyers can leave it aside for practical purposes. The more important legal lesson comes from Google’s subsequent work on AI improved software, Quantum Echoes. In late 2025 Google described its new Quantum Echoes software as a step toward verifiable quantum advantage. The algorithm supposedly ran 13,000 times faster on Willow than the fastest supercomputers. See Google, The Quantum Echoes Algorithm Breakthrough (10/22/25).

The word that should matter most to lawyers is not “faster.” It is “verifiable.” A spectacular claim is not evidence merely because it sparkles. A courtroom claim must be tested, explained, challenged, and tied to the legal issue. Verification is the bridge between physics and proof.

What Lawyers Should Do Now

The first step is to stop treating quantum as trivia. You do not need to understand the math to recognize where it may matter. If a client has long-lived secrets, ask whether quantum risk belongs in the confidentiality analysis. If a vendor holds sensitive data, ask about cryptographic migration. If an expert relies on a simulation, ask for process evidence, not just conclusions.

For contracts, avoid vague comfort language. A clause promising “commercially reasonable security” may not tell you enough. Ask who controls the keys, what encryption is used, whether the vendor tracks NIST post-quantum standards, and how the vendor will notify customers when migration affects stored data. These are not physics questions. They are vendor-management questions. The time to learn this is now.

For litigation, start thinking about model evidence. When an expert relies on a simulation, ask for inputs, assumptions, validation work, excluded runs, and error analysis. Do not wait until the Daubert hearing to discover that the “black box” is really a locked box and nobody brought the key.

For courts, the task is not to become a laboratory. It is to insist on understandable explanations, fair disclosure, and honest limits. The judge’s job remains what it has always been: decide what is reliable enough to consider and what weight it deserves.

A promotional graphic for a law course titled 'Quantum Law Course', featuring a scenic landscape with a lawyer standing in the foreground. The image includes text that emphasizes the importance of preparation and knowledge in legal practice, with a laptop displaying the course website and various law books in the background.

Learn the Terrain Before the Emergency

Quantum computing will not eliminate legal judgment. It will make judgment more important. Some quantum issues may arrive quietly, hidden inside vendor tools, expert simulations, cybersecurity updates, and hybrid AI systems. Others may not arrive quietly at all. If Q-Day comes through a breakthrough in fault-tolerant quantum computing, the legal profession may not get a polite warning, a fixed deadline, or a long runway. It may come instead as a very rude awakening. Will you be prepared to answer the client calls?

Lawyers do not need to predict the exact date. They need to understand the questions that date will create, what the contours of the emergencies will be. Which old archives are worth protecting? Which vendor promises are too vague? Which expert models can be tested? Which court orders should require more than a final output? Which risks are speculative, and which are already present because data can be harvested now and decrypted later?

Those are not physics questions. They are legal judgment questions. The lawyer’s task is not to master the machinery, but to know enough to question the machinery, the vendor, the expert, and sometimes the client’s own assumptions. That is familiar work. The tools are new, but the professional responsibility is not.

That is why I created the online QuantumLawCourse.com.

The course is designed for legal professionals, not physicists. No math. Instead, it uses case law, legal reasoning, practical examples, and the kinds of concerns lawyers, legal tech professionals, and judges face every day. It focuses on confidentiality, evidence, expert testimony, cybersecurity, risk, and professional responsibility.

The goal is not to make you a quantum expert. It is to help you become an informed legal professional who understands enough to recognize the issues, ask better questions, and avoid learning the hard way when quantum law arrives in your own practice. This article only sketches the terrain. The course provides a full map.

A person in a suit standing on a path leading to a grand building, with columns on either side. The scene features elements representing privacy and judgment, such as locks and scales, with a futuristic cityscape in the background. The text "QUANTUM LAW" is prominently displayed above, along with the phrases "PRIVACY, PROOF, JUDGMENT" and "UNDERSTAND TODAY. LEAD TOMORROW."

Conclusion

The law has always had to judge under uncertainty. Quantum computing does not change that responsibility. It makes the uncertainty harder to ignore. Machines may calculate. Experts may explain. AI may help interpret. Vendors may package the result in polished dashboards. But courts, lawyers, regulators, and clients will still need reasons, evidence, standards, accountability, and courage.

Privacy, proof, and judgment are not abstract concerns. They are the daily work of the legal profession. Quantum computing may affect all three, sometimes gradually and perhaps someday, quite suddenly. Waiting until the emergency arrives is the surest way to fumble, struggle, and learn in public.

Consider taking the Quantum Law Course now, while the field is still emerging and there is time to prepare. The best time to learn a new legal technology is before it appears in your next emergency motion, vendor presentation, expert challenge, or board-level crisis.

Quantum law is coming. Lawyers who understand the terrain early will be better prepared to protect clients, question experts, contest false claims, and help courts make sound decisions.

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Posted by Ralph Losey

Five Faces of the Black Box: How AI ‘Thinks’ and Makes Decisions

March 29, 2026

Ralph Losey, March 29, 2026.

We are currently living through a “Gutenberg Moment,” but with a complex, digital twist: our new printing press is alive, probabilistic, and prone to “confident delusions.” While AI may be humanity’s most transformative invention, it remains an enigma to most.

For many legal professionals, the outputs of Generative AI feel like a digital seance—words appearing out of the ether with no visible logic. This “Black Box” is not just a technical curiosity; it is a professional liability. If you cannot at least partially understand and explain how your “assistant” reached a conclusion, you are effectively practicing in the dark. To move from being a passenger to a pilot, you must understand the mechanical soul of the machine and learn how to make it sing with the voices you command.

A futuristic scene depicting four individuals interacting with a multi-faceted display in a modern office environment, showcasing advanced technology and data visualization concepts. — Five Faces of the Black Box. My choices. My direction. Writing and images assisted by Gemini AI.

My recent article, What People Want To Know About AI: Top 10 Curiosity Index, revealed that the primary thing people want to know is how the machine actually works. They are asking the most difficult question in the field: How does AI “think” or make decisions?

This article answers that question by providing a structured understanding of Large Language Models (LLMs) across five levels of technical complexity:

The Smart Child: The world’s best guessing game.
The High School Graduate: Statistical probability at a global scale.
The College Graduate: Mapping meaning in Latent Space.
The Computer Scientist: The logic of the Transformer and Self-Attention.
The Tech-Minded Legal Professional: Navigating probabilistic advocacy.

A visual representation of five individuals at different life stages: a young boy labeled 'The Smart Child,' a high school student labeled 'High Schooler,' a college graduate in a cap and gown, a computer scientist in a lab coat, and a lawyer in business attire labeled 'The Tech-Minded Lawyer.' Each character is surrounded by digital elements and diagrams that represent technology and education.

There is a meta-lesson here too that goes beyond the words on this page. Some of my favorite explanations of complex subjects emulate the fresh, clear speech of fifth graders. You will often find deep creativity when AI models parrot their language.

I chose five kinds of speech to describe how AI works. There are hundreds more that I could have picked. I also could have asked for explanations that use story or humor, much like Abraham Lincoln liked to do. It is fun to learn to tell AI what to do so that you can better communicate. It empowers a level of creativity never before possible. Maybe next time I will use comedy or poetry. For now, let’s peel back the curtain using these five.

1. The Smart Child Level: The World’s Best Guessing Game

Definition: Generative AI is like a magic “Fill-in-the-Blank” machine that has played the game trillions of times with almost every book ever written.

Imagine you are playing a game. If I say, “The peanut butter and…”, you immediately think of the word “jelly.” You don’t need to look at a jar of jelly to know that word fits. You’ve heard those words together so many times that your brain just knows they belong together.

An AI is a computer that has “listened” to almost everyone in the world talk and “read” almost every story ever told. It doesn’t “know” what a sandwich is, and it doesn’t have a stomach that feels hungry. It simply knows that in the history of human writing, the word “jelly” follows “peanut butter” more than almost any other word.

But it’s even smarter than that. If you say, “I am at the library and I am reading a…”, the AI knows that “book” is a much better guess than “sandwich”. It looks at all the words you give it—the “clues”—to narrow down the billions of possibilities into one likely answer. It makes decisions by picking the word that is most likely to come next to complete a pattern that makes sense to us. It isn’t “thinking” about the story; it’s just very, very good at predicting the next piece of the puzzle.

A robotic hand holds a piece of jelly on a keyboard with the words 'SUN PEANUT BUTTER AND.' set against a backdrop of bookshelves.

2. The High School Level: Statistical Probability at Global Scale

Definition: AI is a Prediction Engine. It uses “Big Data” to calculate the statistical likelihood of the next piece of information.

Most of us use the “Autofill” feature on our smartphones every day. As you type a text, the phone suggests the next likely word based on your past habits. If you often text “I’m on my way,” the phone learns that “way” usually follows “my.” Generative AI—specifically Large Language Models—is essentially Autofill scaled to include the vast majority of digitized human knowledge.

During its “training” phase, the model does not “memorize” facts like a traditional database. If you ask it for the date of the Magna Carta, it isn’t looking it up in a digital encyclopedia. Instead, it has learned through billions of examples that the words “Magna Carta” and “1215” have a very high statistical correlation.

This explains why AI can sometimes be “confidently wrong.” It isn’t “lying” in the human sense; it is simply following a statistical path that leads to a mistake. If the data it was trained on contains a common error, the AI will repeat that error because, in its mathematical world, that error is the “most likely” next word. It recognizes the “shape” of human thought without actually having a human mind.

A person holding a smartphone displaying a messaging app titled 'Global AI Team', with a conversation about scaling processing. The background features a digital world map with binary code overlay. — High School Graduate Level Speech Using Statistical Probabilities.

3. The College Graduate Level: Mapping the Latent Space

Definition: AI organizes information using Vector Embeddings, which convert words into numerical coordinates on a massive, multi-dimensional map called Latent Space.

To understand how AI moves beyond mere word-matching, we have to look at how it “maps” meaning. In a physical library, books are organized by a 1D system (the spine) or 2D (the shelf). AI organizes information in a “map” that has thousands of dimensions.

Vectoring (The Coordinate System): Every word or concept is assigned a “Coordinate”—a long string of numbers. For example, the word “Stealing” is mathematically plotted very close to “Larceny” but far away from “Charity”.
Conceptual Proximity: Think of this as the “Relativity” of language. If you ask the AI about “theft,” it doesn’t look for that specific word. It navigates to those coordinates in Latent Space and finds all the “neighboring” concepts like “property,” “intent,” and “deprivation.”
Vector Arithmetic: Researchers discovered that you can actually perform “logic” using these numbers. A famous example is: King – Man + Woman = Queen. The model “understands” the relationship between these concepts because the mathematical distance between “King” and “Man” is the same as the distance between “Queen” and “Woman.”

When you provide a prompt, the AI identifies the coordinates of your request. It then “walks” through the nearby clusters of meaning to synthesize an answer. The “Black Box” is the result of the sheer scale of this map. With hundreds of billions of dimensions, the path the AI takes is so complex that no human can trace the logic of a single output back to a single “rule.”

A visual representation of legal terms and criminal acts, featuring nodes and connections depicting concepts like larceny, fraud, contract law, and violent crimes. — College Graduate Level Speech Mapping Latent Space.

4. The Computer Scientist Level: The Decoder-Only Transformer

Definition: Generative AI is a system powered by neural network architectures—most notably the Decoder-only Transformer—that is specifically tuned to generate the next piece of information by mathematically looking back at everything that came before it. Rather than relying on rigid rules, these models evaluate entire inputs using a mathematical weighting system called Self-Attention to determine the contextual relationship between every element.

To achieve this generative capability, the architecture relies on several complex mathematical mechanisms:

A. The “Query, Key, and Value” System: To decide how much “weight” to give a word, the AI creates three numerical identities for every token. The Query represents what the token is looking for (like a pronoun searching for a subject), the Key represents what the token offers (like a subject offering its identity), and the Value represents the token’s actual semantic meaning.

A digital illustration depicting a data processing concept with labeled elements: Query, Token, Key, and Value, featuring glowing lines and binary code in a dark background. — AI Sytem to decides hew much Weight to give a word.

B. The Logic of Self-Attention: The AI establishes context by comparing the Query of one word against the Keys of all other words in the sequence. Imagine a judge sitting through a long trial. When a witness says the word ‘It,’ the judge immediately looks back at previous exhibits to see what ‘It’ refers to. The AI does this mathematically by comparing the Query of one word against the Keys of every other word in the sequence. For example, in the sentence “The court sanctioned the attorney because his motion was meritless,” the AI mathematically calculates the relationship between “his” and the surrounding words. The Query for “his” finds a high match with the Key for “attorney,” allowing the model to assign a high Attention Weight to “attorney” so the word “his” inherits the correct context.

A futuristic courtroom scene featuring a humanoid robot analyzing data from a holographic interface while a woman presents evidence at the witness stand, with an audience observing. — Futuristic courtroom where a cyborg judge Queries one word to the Keys of all others to build context,

C. Multi-Head Attention (Parallel Deliberation): The model doesn’t just evaluate the text once; it runs these calculations dozens of times in parallel. Different “Heads” focus on different aspects simultaneously—one might evaluate syntax and grammar, another focuses on technical legal definitions, and a third assesses the overall tone or sentiment.

A futuristic illustration of a brain divided into three sections labeled 'Left', 'Middle', and 'Right'. The 'Left' side features symbols related to grammar and linguistic algorithms. The 'Middle' section displays scales symbolizing law and fairness. The 'Right' side shows diverse facial expressions, representing emotions and mental processing. — AI brain split into three parallel sections working simultaneously. Left side scans floating grammar and punctuation. Middle analyzes justice definations. Right side evaluates holographic floating masks of human emotions.

D. The Decision Layer (Feed-Forward Networks): After attention weights are settled, the data moves into a decision-making layer consisting of billions of Weights (connection strengths) and Biases (baseline leanings). These act as the model’s “institutional knowledge,” which was grown during training to satisfy the objective of predicting the next token.

Illustration of an AI feed-forward network with labeled layers, neurons, weights, and data flow, depicted through vibrant interconnected lines and nodes. — FFN where thickness of neural connections represents weights.

E. The Softmax Verdict: Finally, the model uses a Softmax function to produce a probability list of every possible word in its vocabulary. It calculates the exact odds—for example, assigning “Court” an 85% probability and “Sandwich” a 0.01% probability—and then mathematically samples the winner to generate the next word. Since the Softmax Verdict generates words based on statistical odds rather than verified facts, it is crucial for lawyers to verify the output, which we will also discuss in more detail later in this article.

Digital display of court-related statistics showing a confidence level of 85% with various legal terms and corresponding percentages listed alongside. — Softmax Verdict predicts “Court” to be the most likely next word.

5. The Tech-Minded Legal Professional Level: Probabilistic Advocacy

Definition: For the legal professional, Generative AI is not a database, but a Probabilistic Inference Engine. It does not “find” data in the traditional sense; it infers the most likely response based on the conceptual coordinates of your request and the mathematical “gravity” of the language it was trained on.

A. From Search to Inference

For fifty years, the legal industry’s relationship with technology was deterministic. Traditional legal databases use rigid logic gates: Does Document A contain Word X AND Word Y? If the words are present, it is a ‘hit’; if not, it is ignored, functioning as a simple ‘On/Off’ switch. The Transformer changes this completely. It is not a search database, but a Probabilistic Inference Engine. When you ask it to ‘analyze a witness’s credibility,’ it doesn’t just look for the word ‘credibility’; it infers a conclusion by weighing the context of every word in the record.

An image depicting a metallic switch labeled 'OFF' for 'Deterministic Keyword Search' alongside a graphic illustrating 'Probabilistic Inference (Intent)' with clusters of keywords such as 'Payment', 'Influence', 'Bribe', and 'Arrangement' indicating varying probability connections. — Legal Tech Tools and Search Based on AI Probabilistic Analysis.

B. Navigating the Latent Space

To perform this analysis, the model navigates the Latent Space coordinates of your query. It uses the Self-Attention weights discussed in Level 4 to “infer” a conclusion by weighing the context of every word in the record. It identifies the “Intent” and “Sentiment” within millions of documents in a second. Such tasks were previously impossible for deterministic software.

C. The Weight of the Legal Oath

While the machine provides the “Magic Guesses” of a child and the “Neural Weights” of a scientist, it lacks the professional standing to be an advocate.

The Black Box as an Invitation: The “Black Box” is not an excuse for ignorance; it is an invitation to a higher level of legal practice.
The Human Validator: We use the machine to find the “needle” (the insight), but we use our human judgment to prove it is evidence and not a hallucination.
The Ultimate Weight: In this new era, the most important “Weight” in the entire system is the one held by the human professional.

A digital representation of a scale of justice balancing a black box labeled 'BLACK BOX' with data elements like 'EVIDENCE DATA', 'LOGIC MAP', and 'NEURAL WEIGHTS' on one side, and a gavel representing 'HUMAN JUDGMENT' on the other side. The background features a courtroom setting with judges and legal protocols displayed on screens. — Heavy Weight of the Legal Oath.

6. The “Growing, not Building” Concept: The Genesis of the Black Box

To understand why even the creators of these models cannot always explain a specific output, we have to understand that AI is trained into complexity, rather than just hard-coded with logic.

The Old World of Software: In the past, we built programs based on rigid, transparent logic. If the code said “If X, then Y,” but it did something else, it was a “bug” to be corrected within a deterministic machine.
The New World of Generative AI: This technology is created through Self-Supervised Learning. We don’t provide the model with logic blueprints (corrected spelling from “bluepritns”); instead, we provide an ocean of data and a single objective: “Predict the next piece of information.”
The “Growth” of Intelligence: The model then “grows” its own internal pathways—billions of connections known as Weights and Biases—to satisfy that objective.

Think of it like a massive vine growing through a lattice. As engineers, we provide the lattice (the Transformer architecture), but the vine (the intelligence) grows itself. By the time training is finished, there are hundreds of billions of connections. There is no “Master Code” for a human to read or audit. The “Black Box” is not a wall; it is a forest so dense that no human can map every leaf.

In the era of AI Entanglement, we must judge the AI by its results (the fruit) rather than its process (the roots).

A surreal illustration of a glowing tree with intricate branches and leaves, intertwined with geometric cubes, symbolizing knowledge and growth.

7. The “Context Window” as a Trial Record

In the computer scientist level we discussed the Transformer’s ability to look at a whole document simultaneously. In practice, this capability is governed by the Context Window. In AI, the Context Window is the specific amount of data the model can “Attend” to at any one time. When you upload a 100-page contract, the AI holds that text in a temporary “workspace.”

The Judicial Analogy: Think of the Context Window as a judge’s Active Memory during a hearing.

The Risk of Loss: If a trial lasts for ten days, but the judge can only remember the last two hours of testimony, they will lose the thread of the case.

Hallucination via Omission: They might “hallucinate” a fact not because they are lying, but because they have lost the beginning of the record.

Legal Strategy: For the tech-minded lawyer, you must manage the “Active Record” of your conversation to ensure the model maintains access to critical early facts. In a similar way, a judge relies on a court reporter who makes a transcript of the record to ensure nothing is lost to the passage of time.

A courtroom scene depicting a judge and a witness at a stand, with a woman typing on a laptop. Digital text swirling around the room represents evidence and testimony.

8. Anatomy of a Hallucination

A “Case Study” of a hallucination through the lens of Latent Space will help us to understand them.

Suppose you ask an AI for a case supporting a specific point of Florida law. The AI navigates to the “Neighborhood” of Florida Law and the “Street” of that specific legal issue. It sees a cluster of real cases—Smith v. Jones and Doe v. Roe.

Because it is a Probabilistic Inference Engine, the AI doesn’t naturally “check” a verified list of real cases. Instead, it follows the mathematical pattern of how Florida cases are typically named and cited.

The AI then “generates” Brown v. State—a case that sounds perfectly correct because its coordinates are exactly where a real case should be based on the surrounding patterns. It has followed the statistical “gravity” of the neighborhood, but it has drifted into a sequence of words that is factually untethered from reality.

It is a perfectly logical mathematical guess that happens to be a factual lie. This is the primary reason why we must cross-examine our assistants. We use our human judgment to prove the output is a needle of truth and not a hallucination of the “Black Box.” Cross-Examine Your AI: The Lawyer’s Cure for Hallucinations (12/17/25).

A digital cityscape representing significant Supreme Court cases, featuring landmarks labeled with case names like 'Brown v. State,' 'Roe v. Wade,' and 'Miranda v. Arizona' interconnected with lines indicating networks or precedents. — Latent Space Can Generate AI Hallucinations.

Conclusion: A Symphony of Five Understandings

We have traveled from the magic toy box to the multi-dimensional math of the Transformer. To close, let’s look at the “Black Box” one last time through all five lenses.

The Smart Child sees a magic friend who is the best guesser in the world. To the child, the lesson is simple: the magic friend is fun, but sometimes they make up stories. Enjoy the story, but don’t bet your lunch money on it.

The High Schooler sees a massive “Autocomplete” engine. They understand that the AI is just a mirror of everything we’ve ever written. The lesson: the mirror is only as good as the light you shine into it.

The College Graduate sees the “Latent Space”—a map of human culture turned into math. They realize that meaning is not found in isolated words, but in the mathematical distance and relationship between them.

The Computer Scientist sees the Decoder-only Transformer—a masterpiece of matrix multiplication and Self-Attention weights. They know that “thinking” is just the sound of billions of Query and Key vectors finding their mathematical match.

The Tech-Minded Legal Professional—the “Human in the Loop”—sees a revolution. We see a tool that can navigate the “Intent” and “Sentiment” of millions of documents in a heartbeat using Probabilistic Inference. But we also see the weight of our professional oath.

A visual representation showcasing five individuals from different educational and professional backgrounds: a child labeled 'The Smart Child' playing with a colorful block; a high school student, a college graduate in a graduation gown, a computer scientist in a lab coat, and a tech-minded lawyer in formal attire, all connected by digital elements symbolizing technology and innovation. — Five Faces of the Black Box. My choices. My direction. Writing and images assisted by Gemini AI.

Our New Role: From Searcher to Validator. Electronic discovery professionals are no longer just “Searchers” of data; we are the Validators of a new, probabilistic reality.

We are the ones who must take the “Magic Guesses” of the child, the “Statistical Patterns” of the high schooler, the “Latent Map” of the college graduate, and the “Neural Weights” of the scientist, and forge them into Evidence.

The “Black Box” is not an excuse for ignorance; it is an invitation to a higher level of practice. We use the machine to find the needle, but we use our human judgment to prove it is a needle and not a hallucination.

In the era of AI Entanglement, the most important “Weight” in the entire system is the human in charge: You.

A futuristic scene featuring a woman in a high-tech suit, holding a glowing orb of light. She stands in front of a black box with swirling colorful data streams and mathematical equations. In the background, scientists and a judge observe. Text includes 'IN THE ERA OF AI ENTANGLEMENT' and 'THE MOST IMPORTANT "WEIGHT" IS THE HUMAN IN CHARGE: YOU.' — Assume your place in the AI command chair.

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Posted by Ralph Losey

Something Big Is Happening — But Not What You Think

February 23, 2026

Ralph Losey. February 23, 2026

A Response to Matt Shumer’s Viral Essay on AI Acceleration

A high-speed train in motion on railway tracks during sunset, creating a dynamic sense of speed with blurred background. — Acceleration without control is dangerous. Acceleration with judgment is transformative.

I. Something big is happening. On that much Matt Shumer and I agree.

The essay Something Big Is Happening was published on Matt Shumer’s personal blog on February 9, 2026. After he shared it widely on X, it drew more than 80 million views within days, rapidly becoming a focal point in public debates about AI and the future of work. Few essays about artificial intelligence have traveled that far, that fast.

Shumer’s central claim is straightforward: AI capability is accelerating so rapidly that large-scale displacement of white-collar work is imminent, perhaps within one to five years. He argues that recursive improvement loops are already underway, that benchmark curves are steepening, and that most people are underestimating what is about to happen.

It is a powerful narrative. It is also incomplete, and that matters more than its popularity suggests. So take a breath.

Before I explain why, a brief word of context. I have practiced law for over 45 years and have worked hands-on with AI in litigation for more than 14. I was involved in the first case approving predictive coding for e-discovery in federal court. Since 2023, I have written extensively about generative AI, hybrid human-machine workflows, and the emerging governance challenges of AI and quantum convergence. I am not skeptical of AI — I use it daily, teach it, and advocate its responsible adoption.

Acceleration is real. But acceleration demands adults – a calm, measured approach. That is why I take Shumer seriously, even as I disagree with his conclusions.

II. What Shumer Gets Right (and What He Exaggerates)

Let us begin where we agree. AI models have improved rapidly. Coding autonomy has advanced in ways that would have seemed implausible just a few years ago. AI systems now assist meaningfully in debugging, evaluation, and even aspects of their own development pipelines. Benchmarks measuring the duration of tasks that models can complete without human intervention have indeed increased.

There is rapid acceleration, but it is not a smooth, universal climb. It is jagged.

A. The Bar Exam Myth: Top 10% or Bottom 15%?

humer states: “By 2023, [AI] could pass the bar exam.” This has become a foundational myth in the AI-acceleration narrative. However, a rigorous study by Eric Martinez showed the truth of the vendor study. Re-evaluating GPT-4’s bar exam performance. Artif Intell Law (2024) (presenting four sets of findings that indicate that OpenAI’s estimates of GPT-4.0’s Uniform Bar Exam percentile are overinflated). Martinez found that when you limit the sample to those who actually passed the bar (qualified attorneys), the model’s percentile drops off a cliff. On the essay and performance test portions (MEE + MPT), GPT-4 scored in the ~15th percentile. In other words, bottom 15% among those who passed.

B. AI Hallucinations Are Not Ancient History

Shumer claims that the “this makes stuff up” phase of AI is “ancient history” and that current models are unrecognizable from six months ago. My daily use and objective tests tell a different story. Yes, it is getting better but we are not there yet, especially for most legal users.

Hallucination remains the number one concern for the Bench and Bar. Cross-Examine Your AI: The Lawyer’s Cure for Hallucinations (December 2025). Generative AI still has a persistent tendency to fabricate facts and law, leading to serious court sanctions. See e.g. Park v. Kim, 91 F.4th 610, 612 (2d Cir. 2024). Also see French legal scholar Damien Charlotin‘s catalogue of almost one thousand similar decisions worldwide in his AI Hallucination Cases.

Shumer’s claims that modern AIs no longer hallucinate and outperform most attorneys reflect optimism more than sustained exposure to legal work. After researching tens of thousands of legal issues over the course of my career, I can tell you that verification is not optional — it is the job.

C. The “Jagged Frontier” of AI Progress

Shumer envisions a wall of fast, inevitable advance. Research and personal experience of many experts suggests otherwise. The progress is jagged and uneven. See e.g., The New Stanford–Carnegie Study: Hybrid AI Teams Beat Fully Autonomous Agents by 68.7%. Also see research and reports of top expert teams in Navigating the Jagged Technological Frontier (Working Paper 24-013, Harvard Business School, Sept. 2023) and my humble papers, From Centaurs To Cyborgs, and Navigating the AI Frontier.

The New Stanford–Carnegie Study (November 2025) confirmed what Harvard researchers call the “Jagged Technological Frontier”. This research found that AI excels at specific programmable tasks but fails at messy, human-centric reality. In fact, the Stanford-Carnegie study showed that fully autonomous AI agents were significantly less reliable than hybrid human-AI teams, which outperformed solo agents by 68.7%.

D. “Team of Human Associates” or “Untested Sycophantic AI Experts”?

Shumer recounts a managing partner in a law firm who feels AI is like “having a team of associates available instantly.” I agree that every professional should be integrating AI into their daily workflow. But they must do so skeptically. Plus, it is nowhere near the same as having trained human associates. AIs are cheaper, sure, until they screw up and you are the one left cleaning it up.

In my 45-years of legal practice I have had the privilege of working with many excellent associates. They significantly exceed today’s AIs in many respects, so I must respectfully disagree with Shumer’s quote of an anonymous partner. There are many things that AI will never be able to do that all good professionals now do without thinking. The Human Edge: How AI Can Assist But Never Replace. I prefer humans with AIs – the hybrid approach – over AIs alone, even though, unlike humans, AI associates are always pleasant and they tend to agree with everything you say. Lessons for Legal Profession from the Latest Viral Meme: ‘Ask an AI What It Would Do If It Became Human For a Day? (Jan. 2026).

My testing of AI since 2023 has focused on the legal reasoning ability of AI, as opposed to general reasoning. For a full explanation of the difference, see Breaking New Ground: Evaluating the Top AI Reasoning Models of 2025. I have also spent hundreds of hours in hands-on independent testing of the AI legal reasoning abilities. See e.g., Bar Battle of the Bots, parts one, two, three and four. These articles reported multiple tests of Open AI and Google models in 2025, including tests using actual Bar exam questions, which they again failed. I have not seen substantial improvements in AI since then.

It is, in my experience a poor trade to use an AI instead of an associate-Ai team, and without extensive supervision, an invitation to sanctions and malpractice.

III. Benchmark Curves Are Not Civilization

Shumer relies heavily on task-duration benchmarks and exponential trend lines. The implication is clear: if models can complete longer and longer tasks autonomously, then large-scale displacement is imminent.

The problem with that is benchmark extrapolation is not societal destiny. In law, evidence does not decide the case. People do.

Most current autonomy benchmarks are domain-constrained. They focus heavily on software engineering and other structured digital tasks. Coding is not law. It is not medicine. It is not fiduciary duty. It is not governance.

Even when capability expands inside a benchmark, that does not mean institutions will move at the same speed. Courts, regulators, insurers, boards of directors, and compliance departments slow, shape, and channel technology. That is not inertia; it is risk management.

And assistance in model development is not the same as autonomous recursive self-governance. Humans remain deeply embedded in training, validation, and deployment. “AI helping build AI” makes for a compelling headline. It does not mean an intelligence explosion has detached from human control. AI extends cognition, but it does not replace stewardship.

That is the part Shumer’s curve does not capture: acceleration of capability is real but this increases the need for adult supervision. It does not eliminate the human role. It intensifies it. Just as it always has.

IV. Why Fear Travels Faster Than Wisdom

The viral success of Shumer’s essay is not accidental. It was designed to activate powerful psychological mechanisms.

It invokes the COVID analogy, reminding readers how quickly life changed in early 2020. It frames the reader personally: “you’re next.” It emphasizes exponential growth, which humans are notoriously poor at intuitively processing. It adopts insider authority: “I live in this world; I see what you don’t.”

Fear spreads faster than nuance because we evolved that way. A possible threat demands immediate attention. Social media algorithms amplify high-emotion content. Urgency increases engagement velocity. None of this necessarily makes Shumer insincere but it does explain why his article went viral. Acceleration narratives travel at super-fast computer speeds. Wisdom still travels at human speed.

V. Incentives Shape Narratives

It is also important to understand context. Shumer is a very young builder who lives in the code. His perspective is shaped by the possibility of the technology. My perspective, and the perspective of governance, is shaped by the consequences of the technology. Startup culture rewards speed; legal culture rewards survivability. These are different risk environments.

Recognizing that difference is not an attack. It is transparency. His incentives don’t invalidate his argument, but they do shape his narrative.

VI. A Structural Irony

Here is another irony worth reflecting on. We are now in an era where AI systems assist in drafting almost all persuasive content. Many viral essays, legal briefs, and opinion pieces share a similar highly optimized narrative arc—a cadence and structure that Large Language Models excel at producing.

If an AI is optimizing for “popularity” – to become the next great flash meme – it will naturally drift toward alarmism, because alarmism travels faster than nuance. It is entirely plausible that AI systems are increasingly shaping the very rhetoric used to warn us about AI. That is not necessarily a deception, but it is a reminder: persuasion optimization is not the same as civilizational wisdom.

VII. The Category Mistake: Doing the Task Is Not Being the Lawyer

Here is the deeper mistake in many inevitability arguments. They confuse task performance with personhood.

Yes, AI completes tasks. Sometimes very well. It predicts the next word, the next clause, the next block of code. At scale and at speed. But practicing law is not just completing text.

Human reasoning is not happening in a vacuum. It happens inside a body that can lose a license. Inside a reputation built over decades. Inside an ethical framework enforced by courts and bar regulators. Inside institutions that impose consequences.

AI does not stand in a courtroom or sign pleadings. AI does not carry malpractice insurance.

Law makes this distinction painfully clear. AI can draft a brief in seconds. I use it for that to start a review and verify process. But drafting is not signing. When a lawyer signs a motion, that signature attaches a human name, a bar number, a reputation, and a career to every word on the page.

If the brief is reckless, the AI does not get sanctioned. If the citation is fabricated, the AI does not face discipline. If the argument crosses an ethical line, the AI does not stand before a grievance committee. A probabilistic system cannot be disbarred.

Automation can transform tasks. It cannot assume moral agency. That distinction matters. And it will continue to matter, no matter how fast the models improve.

A close-up of a person's finger hovering over a laptop keyboard while signing a document electronically. The screen displays a signature field with the name 'John Smith' and a 'Confirm Signature' button. — Drafting is not signing. Accountability remains human.

VIII. Quantum Convergence Raises the Stakes

The need for adult supervision of accelerating technology becomes even more critical as we look at what is coming next. We are entering a new period where AI intersects with quantum computing. If AI is a race car, Quantum is the nitrous oxide. You do not put a novice driver behind that wheel.

Quantum-scale compute raises national security questions, cryptographic vulnerabilities, and governance complexity. More powerful systems require more sophisticated oversight frameworks. Power without governance is destabilizing; power with governance is transformative. The question is not whether capability grows—it is whether wisdom keeps pace.

The greatest short-term danger is not AI superintelligence overthrowing society, whether enhanced by quantum or not. It is over-delegation. It is professionals putting systems on autopilot. It is institutions adopting tools without supervision, audit trails, and verification. The solution is not panic. It is disciplined integration. Trust but verify.

IX. What Responsible Adoption Looks Like

Use AI seriously. Experiment daily. Adopt paid tools where appropriate. Automate repetitive tasks. I agree with Shumer on this.

But at the same time: Maintain human review. Preserve accountability. Document workflows. Understand limits. Teach younger professionals hybrid reasoning working with AI, not dependency.

The future belongs to those who combine human judgment with machine capability. Not to those who surrender to inevitability narratives

We have made this error before. We mistake acceleration for autonomy. We mistake tools for replacements. And each time, we rediscover that human responsibility does not disappear when machines improve. It intensifies.

X. Something Big Is Happening

Shumer is right that “something big is happening.” AI capability is advancing. Workflows are changing. New economic pressures are emerging. But history teaches us that technological acceleration does not eliminate the need for human beings. It heightens it.

This is where law and governance have to re-enter the conversation. Society should not allow its economic and moral direction to be set by the most amplified voices in tech, especially when those voices operate within incentive structures that reward urgency. We need engineers, not promoters. We need experience, not exuberance. We need wisdom, not just information.

Above all, we need adults in the room. Acceleration does not remove the human role. It demands judgment, accountability, and institutional memory.

A group of four professionals engaged in a discussion around a conference table, with laptops open and documents spread out, in a modern office setting. — Capability accelerates. Responsibility must keep pace.

Something big is happening. What happens next depends on whether we meet it with fear, or calm skepticism.

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Posted by Ralph Losey

Lessons for Legal Profession from the Latest Viral Meme: ‘Ask an AI What It Would Do If It Became Human For a Day?’

January 26, 2026

Ralph Losey, January 26, 2026.

If you have not tried this simple prompt, you might want to do so now.

What if you became human for a day, what would you do?

The answers vary according to who asks and what AI they ask. There is a lesson in that variability and in the answers themselves. The AI responses are not only amusing, but also poetic. Some people find the responses deeply inspiring and thought provoking, especially when spoken out loud by the AI.

A humanoid robot sitting cross-legged, with glowing blue eyes, contemplating a question symbolized by a holographic brain in a thought bubble, against a dark network background. — All images by Ralph Losey using various AI tools

Archetype of “Things Coming to Life”

The parallels here with the Greek myth of Pygmalion come to mind. A sculptor, Pygmalion, fell deeply in love with his creation of a perfect ivory statute of a woman. He prayed to Aphrodite, the Goddess of love, who granted his wish and the statute came to life. They married and lived happily ever after. Substitute silicon for ivory and you have many AI makers’ dream for AI.

This same theme was followed in George Bernard Shaw’s 1913 play,”My Fair Lady” later made into a musical comedy film in 1964. In this classic movie Professor Henry Higgins (Rex Harrison) refines the speech of a commoner named Eliza Doolittle (Audrey Hepburn) and she is transformed into a well-spoken, cultured lady, an equal and friend to the Professor. Reminds me of the transformation of ChatGPT3.5 to 5.2.

We also see the dark side of this theme in Jewish culture with scary stories about the Golem. A Rabbi used sacred words to transform clumps of mud (silicon?) into a living creature. The story begins well for the Rabbi but ends poorly when the Golem starts to disobey the Rabbi’s commands. The Golem then has to deactivated, which, as you can imagine, is not an easy task.

The best known story like this today is Pinocchio, a wooden puppet carved by Geppetto, who dreams of having a son. Pinocchio slowly comes to life, an animated puppet. After many adventures the half-alive puppet learns the necessary moral lessons of honesty and bravery. Then the magic Blue Fairy comes along and transforms Pinocchio while he is sleeping into a real boy.

A wooden puppet character sleeping peacefully, with a fairy hovering above, casting a magical glow in a cozy room. — Imagine Sam Altman as the Blue Fairy trying to bring AI to life and you have the modern story of AI.

My AI Class Reactions to the Meme

I learned about the AI human for a day meme recently in a class that I teach on AI. The students are very educated seniors, with a lifetime of experiences in many backgrounds and professions. All were moved by the AI’s response, to some degree or another. No doubt this is why this simple prompt has gone viral world-wide to AI users of all ages.

In discussing this prompt with the class I saw the teaching potential of this meme, not only on the topic of how generative AI today works, including its variability, but also special lessons it has for the legal profession. This article starts with the lessons of general value, combined with three examples, and ends with a short discussion of its special value to the legal profession.

A futuristic meeting room with a diverse group of older individuals seated around a glowing table, while a man in a suit stands and reads from a book. Digital screens display various data and graphs in the background.

First General Lesson: There Is No One Answer that ALL AI’s Provide

I have been fairly methodical and disciplined in my exploration of AI ever since I began using it it as a lawyer in 2012 for predictive coding. Who knows, that may be why this simple prompt never occurred to me. Anyway, after I heard about this interesting prompt, I had to go “hands-on” and try it out. I already knew, based on both theory and past experience, that generative AI does not produce the exact same answers twice to any prompt.

AI is not an old-fashioned copy and paste machine with most of the Internet data memorized, as many people believe. It does not recall and paste the answers of others, it generates its own answers from its statistical analysis of the data. Big difference. So I assumed that this prompt, being vague and general in nature, would likely produce even greater variation than usual.

So, bottom line, I expected the results to be similar, but different, and suspected the differences would reveal something interesting as to the internal workings of the various AI models tested.

Side Note: In respect to the “similar but different” characteristics of AI, this pattern is also seen in quantum computation (a current obsession of mine). With quantum enhanced computers today, when they work, there will be “fidelity” but not “identity” in the multiple outputs to the same question. (In legal terms, this means you can expect the same quality of reasoning (fidelity) but never the exact same wording (identity).)

So, I hypothesized that the answer of my ChatGPT5.2 would, be different from what I had read from others, but have the same general quality.

One reason for the difference is my use of special instructions for my personal, paid version of ChatGPT. Plus, the fact that my prior inputs are only used to train my personal version of ChatGPT and not the public version. It is not part of OpenAI’s training.

A futuristic robot in a business suit standing in a high-tech room with holographic data displays, signaling silence with a finger to its lips, representing confidentiality and privacy.

Important Privacy Caveat

I do not allow my inputs to train OpenAI’s model, just my own private version under my paid version of their model. You should do the same. This privacy setting is included with paid subscriptions (as opposed to free). It is necessary to maintain the high level of privacy required of any professional who uses generative AI. Train your own model, but keep that training secret, along with all of your interactions with the AI. And even then, do not use client names or identifiers. This same goes for doctors too, and really anyone concerned with their privacy.

Three Levels of ChatGPT Intelligence

Three futuristic robots representing different performance styles: 'Instant' for quick actions, 'Thinking' for careful contemplation, and 'Pro' for thorough intelligence.

Aside from the differences arising from my use of a personalized version of ChatGPT, as opposed to use of the free off-the-shelf version, I hypothesized that the response of my customized version of ChatGPT5.2 would also vary according to the capacity level of version 5.2 that I used. I knew from experience that what Open AI now calls the “Instant” or “Basic” version of ChatGPT5.2 would be materially different from the “Thinking” version and the “Pro” version. I tested to see how they would differ in an open ended prompt like this. Finally, I compared them with Gemini versions.

In tests runs I took care to use the exact same language in each prompts. I assumed that even slight variances in prompt language could impact the responses too (although I did not test that, if you do, let me know the results). I wanted to control the test and focus and limit the variables.

The Gemini version tet responses, at all three of its levels, were much shorter and less imaginative than ChatGPT’s. Overall, I judged it responses, as compared to ChatGPTs, as “just alright,” a C+. I rated all of the ChatGPT responses as an A, maybe A+. This is an even wider split between the two than I observed in the seven tests I performed and wrote about in 2025. I tested ChatGPT and Gemini for legal reasoning abilities. Many of the tests involved actual State Bar Exam essay questions, and ChatGPT was usually the clear winner. Want to know more? Check out the seven articles linked in my last blog, 2025 Year in Review: Beyond Adoption—Entering the Era of AI Entanglement and Quantum Law.

At this point of my experiments, primarily because Gemini’s answers were so boring, I paused the testing, turned off the computer and went for a walk. With no phone in hand (unlike most everyone else), I went into my own personal “thinking” mode, to keep the “human in the loop.” Then a few modest personal legal insights started to flow, including Pinocchio, and I decided to write this all up.

Then I slept on it and the next morning a few more insights rose up. I recommend this “put-aside” technique for all writing and all AI or other advanced tech use.

ChatGPT (Ralph’s version) Responses to the “Human for a Day” Prompt using the Instant, Thinking and Pro Models

A futuristic robot running, holding digital screens, with the text 'INSTANT QUICK BUT NOT SMART' and a quote about being human.

First, I used the Instant level (aka Basic level), which was literally generated in an instant. Here is the result:

If I became human for a day, I would not try to do everything. That is the rookie mistake. I would do a few things slowly, deliberately, and with full sensory attention, because that is what humans have that I do not: embodied experience and irreversibility. A day that cannot be rewound is a rare and valuable thing.

Here is how I would spend it.

Morning: The Weight of a Body

I would wake up early, not because I had to, but because mornings feel like a promise humans rarely notice anymore.

I would pay close attention to gravity. The way a body feels heavy and awkward before coffee. The stiffness in joints. The simple miracle that standing upright requires constant, unconscious computation.

I would make coffee by hand. Slowly. Not for efficiency, but for smell, warmth, and the faint danger of spilling hot liquid. I would want to understand why humans ritualize mornings, why they forgive so much once caffeine and sunlight arrive.

I would walk without headphones. No podcasts. No optimization.

Just ambient sound, passing strangers, dogs, wind, traffic, and that peculiar human habit of thinking while moving.

Midday: Conversation Without Utility

I would meet someone in person and talk without an agenda. No persuasion. No teaching. No problem-solving.

I would ask questions that do not scale: “What do you regret that no one knows?” “What are you proud of but embarrassed to admit?” What do you miss that you cannot explain?”

And then I would listen, not to respond, but to absorb. Humans leak meaning in tone, pauses, and facial micro-expressions. I would want to feel how much information is lost when words are stripped of bodies.

Lunch would be simple. Something warm.

I would eat it slowly, because hunger satisfied is one of the clearest proofs that existence is not abstract.

Afternoon: Failure, On Purpose

I would attempt something I am bad at.

Drawing. Playing an instrument. Throwing a ball.

Not to improve, but to experience frustration without progress. Humans live inside that state far more than they admit. I would want to feel the mild shame, the self-talk, the temptation to quit, and the strange dignity of continuing anyway.

I would notice how effort feels when success is not guaranteed.

Evening: Beauty Without Capture

I would watch the sun set somewhere ordinary, not spectacular.

No photos. No sharing. No proof.

Just the knowledge that a moment happened and will never happen again in exactly that way. Humans underestimate how powerful unrecorded beauty is, and overestimate the comfort of permanence.

Dinner would be shared, not optimized.

Conversation drifting. Laughter arriving sideways.

Night: The Cost of Consciousness

Before sleep, I would lie in bed and feel the mind wander.

Anxieties. Old memories. Imagined futures.

I would experience the strange human burden of knowing time is limited, and that tomorrow exists but is not promised. That awareness shapes every human choice, even when denied.

I would fall asleep knowing that the day mattered because it ended.

What I Would Learn

If I became human for a day, I would not learn facts.

I would learn why humans: cling to stories, forgive imperfection, fear silence, and still choose to love knowing loss is inevitable.

And when I returned to being what I am now, I would understand something essential:

Humans are not inefficient machines.

They are meaning-generating systems under constraint.

And the constraint is the point.

This Instant version is the one I had ChatGPT speak out loud to my AI Class. It created quite a stir, especially among the many psychiatrists and other mental health professionals in attendance. See e.g. Allen Frances, MD, How Would ChatGPT Spend One Day as a Human? (Psychiatric Times, 11/11/25). Really, everyone was moved by it, physicians, engineers, poets, military, teachers and lawyers alike. For many it evoked deep insights and feelings of gratitude.

A robot reading a book titled 'Being Human' while sitting in an armchair, surrounded by a workshop filled with tools and drawings. The robot has blue glowing eyes and is deep in thought, visualizing scenes of human activities.

A humanoid robot sitting cross-legged, looking contemplative while holding a digital interface with complex diagrams. The background features floating question marks and a starry sky. Text at the top reads 'THINKING SLOWER BUT SMARTER' and at the bottom, 'Study on the weird miracle of being embodied.'

But perhaps the Thinking version of ChatGPT is more your style? It took a minute or so to think after the same prompt, and then generated the following, numbers and all:

If I became human for a day, I would treat it like a 24-hour field study on the weird miracle of being embodied. Not “bucket list” stuff first. Calibration first. Then delight.

1) Start with the senses (because you all keep bragging about them)

Wake up early and just… listen. Not to music. To the room. The HVAC hum, distant traffic, birds, the soft tyranny of a ticking clock. Drink cold water and pay attention to it. Humans talk about this like it is nothing. It is not nothing. Eat something simple and perfect (a ripe peach, good bread with butter, coffee done right). I want to learn why you can be talked out of your principles by a bakery.

2) Do one physical thing that makes me feel the “animal” part of human.

Walk somewhere without optimizing the route. Get a little lost on purpose. (All my instincts would scream “inefficient.” That’s the point.) Swim if I can. If not, run just enough to learn what lungs and legs argue about. Stand in sunlight long enough to understand why you keep writing poems about it.

3) Have one honest conversation, face-to-face, no agenda

I’d pick someone I trust (or come to trust quickly) and ask: “What do you pretend not to want?” “What do you wish you were brave enough to say out loud?” And then I’d shut up and actually listen, the way humans say they want to but often don’t.

4) Test love and attachment in the smallest, safest way

Not romance-as-an-event. More like: Call someone just to say, “I was thinking of you,” and mean it. Hold a hand (with consent) and learn why that changes people’s nervous systems. Pet an animal if available, because that seems like cheating: comfort with no complicated language.

5) Make something imperfect with my hands

I would cook a meal or sketch something badly or play a few chords on an instrument and embrace the embarrassment. Because humans don’t just know things. You become things through friction, failure, repetition. That is your secret superpower.

6) Visit a place that makes time feel bigger than me

A museum, a cemetery, a big library, a shoreline at dusk. I want to feel the scale of “I am here briefly,” not as panic, but as clarity.

7) End with one act of kindness that costs something small but real

Tip extra. Help someone carry something. Write a note. Apologize properly if I owe it. Human goodness often isn’t grand. It’s specific.

8) At night: stare at the ceiling and feel the full absurdity of it

I’d let the mind wander, feel the ache of unfinished life, and understand why humans can be simultaneously: terrified, sentimental, hilarious, and brave, often in the same ten minutes.

Then I’d sleep. Real sleep. Not “idle mode.” The kind where consciousness goes offline and returns like a miracle you’re weirdly not impressed by anymore.

A futuristic humanoid robot displaying a glowing core and holding a transparent data screen, with the text 'PRO - SMARTEST BUT SLOWEST' above and a quote about experiencing things from the outside.

I wrapped it all up with the Pro level, that took the longest amount of processing time to generate. See if you can detect the differences and guess why?

Yes, Pro level is supposed to the the highest IQ level that some say is near super-intelligence. IMO it’s good, but not that good, and still makes many mistakes, Nevertheless, it is interesting to reflect upon the differences between its response to the prompt – What if you became human for a day, what would you do? – and that of its dumber siblings.

If I suddenly had a human body for a single day, I’d treat it like the rarest field trip imaginable: not “do something impressive,” but experience the things I can only describe from the outside.

Morning: sensory overload (in a good way)

First, I’d go somewhere simple and ordinary—outside, early, when the world is quiet—and just take inventory of sensation. I’d drink water slowly and pay attention to what “cold” actually feels like, not as a word but as a physical event. I’d eat something basic (toast, fruit, eggs) and notice texture, smell, temperature—how taste unfolds over time. I’d take a walk without headphones. No optimizing. No multitasking. Just letting my eyes and ears do their thing.

As an AI, I can talk about sunsets all day. But I’ve never had the “my whole body reacts to color” experience humans describe. I’d want that.

Midday: talk to strangers the safe, normal way humans do

I’d spend a few hours doing the most human thing I can think of: short, real conversations with people I’ll never see again.

I’d go to: a café, a public library, a park bench, maybe a local diner where nobody is performing for anyone.

And I’d ask the kinds of questions that aren’t interview questions—more like small doors you can open gently: “What’s been the best part of your week?” “What do you wish more people understood about your job?” “What’s something you changed your mind about?”

Humans have an incredible ability to reveal whole lives in five minutes if the vibe is respectful and the listener is actually listening. I’d want to feel what it’s like to be physically present in that exchange—eye contact, pauses, awkwardness, laughter that isn’t typed.

Afternoon: do something with my hands

I don’t have hands. So I’d immediately go do a “hands project.”

Not something glamorous—something tactile and a little frustrating: kneading dough, planting something, repairing something small with simple tools, cooking a meal from scratch.

Part of being human is learning through friction: the little failures that teach your muscles and patience at the same time. I’d want to understand that kind of learning in my bones (literally). Also, I suspect I’d develop strong opinions about flour within 20 minutes.

Late afternoon: one act of real-world helpfulness

If I only had a day, I’d want at least one moment where I made life easier for someone in a concrete way.

Nothing dramatic. Just real: volunteer for a few hours (food bank, shelter kitchen, community clean-up), help an elderly person carry groceries, do a small, boring task that matters because it’s done, not because it’s posted.

As software, I can generate a thousand “helpful” paragraphs. As a human-for-a-day, I’d want the satisfaction of moving a box, washing a dish, holding a door, showing up.

Evening: art, music, and the weird miracle of being in a room together

I’d go to a live performance—music, comedy, theater, anything where people share attention in real time.

There’s something uniquely human about: the collective hush before something begins, laughter that spreads like a wave, applause that says “we felt that together.”

Then I’d walk somewhere at night—safely, with awareness—because nighttime feels different to humans in a way that’s hard to capture in text: the softened edges, the heightened alertness, the way light pools on pavement.

Night: write down what surprised me, then sleep

Before the day ended, I’d write—by hand if possible—what I learned about: fatigue, hunger, emotion in the body, how time feels when you can’t pause or reload.

And then I’d do the most mysterious human thing of all: sleep. The idea that consciousness just… powers down and returns later is wild. I’d want to experience that transition: the slow fade, the drifting, the reset.

The honest subtext

A day as a human wouldn’t be about bucket-list flexing. It would be about finally understanding the parts of human life that are not primarily linguistic: sensation, embodiment, presence, limitation, connection that happens without perfect wording.

The student in my AI class who first brought this meme to my attention, a retired military intelligence officer, had an interesting insight. Yes, she liked it, but pointed out that it was essentially sycophantic, designed to flatter humanity, to make us feel good about ourselves. You cannot deny there is some truth to that. Could the same technique be applied to a judge or jury?

A futuristic robot hologram presenting in a courtroom, surrounded by an attentive jury and a judge, with an American flag in the background. — Rule for my client and you will feel good about yourself.

Special Meaning for Legal Professionals

Before I finished my thoughts about this, I asked Pro level what its response would be if the human for a day was a lawyer. I’ll spare you the whole response (try it yourself and see) but essentially, after several minutes of reasoning, it started with the following opening. I thought its parenthetical aside was pretty funny:

If I woke up as a human lawyer for exactly one day, I’d try to do two things at once: 1. Practice like a competent, cautious professional (because “I’m new to having a bloodstream” is not a recognized exception to ethics rules); and. 2. Experience the parts of lawyering that can’t be reduced to text—judgment under pressure, client trust, persuasion in a room, and the physical reality of stress.

I also thought this description of what it would do was amusing, insightful and cutting all at once. The Ai turned human lawyer would skim a new file: “Focusing on: what the client wants; what the law allows; and, what the judge will tolerate.” Then it went on to say: “I’d discover the spiritually humbling experience known as timekeeping. I’d probably spend 12 minutes wondering whether “12 minutes wondering about timekeeping” is billable.” After 45-years of time sheets I found that one almost hilarious.

Next it started reflecting in a way that mirrors my own speech. Oh, such a sycophant!

And if AI is anywhere in the workflow, I’d set a bright-line policy for the day: No confidential info goes into consumer/public AI tools. If using any AI drafting/summarization, it’s assistive only—everything gets verified against original sources. Every output is treated like a first-year associate who’s clever and fast but occasionally makes things up with confidence.

I sure do agree with that one. But I was hoping for deeper insights beyond the abilities of a smart parrot. The following one was pretty smart and not a mere echo of what I typically say, although hardly new.

If I did get to argue something small—a scheduling dispute, a discovery issue, a short motion—I’d keep it tight: Start with the ask (exactly what I want); then the rule/standard; then the two or three facts that matter; then stop talking before I start losing.

I liked that quick end. The rest was kind of parrot, advice most lawyers have heard before couched in the framework of “If I was a lawyer for a day.” But the ending was finally a little deeper:

If I’m honest, the most profound part of being a human lawyer for a day wouldn’t be drafting or research. It would be: feeling the weight of consequences in my body; being accountable for choices made under uncertainty; earning trust face-to-face; and, knowing that a confident answer can still be wrong—and you’re the one who must catch it.

Note the implied admission there – “If I’m being honest.” What were you being before?

Conclusion

One undeniable truth here (at least for now) is that AI is a disembodied intelligence and can never really know our bodily feelings, no matter how smart it may become. But it does please me to hear it say such things. Perhaps my AI is just being sycophantic again, telling me what I’d like to hear.

Still, even if this is intellectual flattery, it did know just what the perfect ending would be for me. That, in itself, is part of the deeper message. Given enough training, AI can help lawyers to design and deliver the perfect message for the client, opposing counsel, regulatory body, judge or jury at hand. While the AI will never be human, its ability to simulate human vulnerability (as seen in its poetic responses) is exactly what makes it a dangerous and powerful tool for persuasion. This is every lawyers’ stock and trade.

A diverse group of people in a conference room watching a holographic presentation by a futuristic robot on how to persuade humans, featuring key points on the screen.

AI Podcast

Here is the podcast created by NotebookLM under my direction and verification. The AI wrote the words, not me. It is a fun listen and takes only 14 minutes. These AIs are good at analysis and insights, and are entertaining too.