Top Ten Information Needs about AI per Gemini research. All images by Ralph Losey using Nano Banana 2, except for the graphs by Gemini Pro.
Gemini 3.1 Pro Surprises: Synthesizing the Top 10 AI Questions of 2024–2026
I was recently struck by a capability in the pro version of Gemini that I hadn’t encountered before. Quite by accident, I discovered the model’s ability to do more than just scour the web for data. It can synthesize thousands of disparate data points, from workshop reports to tangential polls, to provide a coherent answer to a complex “meta” question.
My inquiry was specific: What do people actually want to know about AI? I wasn’t interested in usage statistics, but in conceptual gaps. When Gemini (and my own “trust but verify” follow-up) found no single poll on point, the AI pivoted. It inferred a top-ten ranking by analyzing the collective “curiosity” found across the web. The result is what Gemini called, perhaps with a smile, the “Top 10 Curiosity Index,” a list of the concepts that people are most “desperate to understand.“
Weekend Law Firm Study to Satisfy Top Ten Information Needs about AI.
From Synthesis to Software: Gemini’s Surprising Coding Capabilities
Beyond the data synthesis, Gemini 3.1 Pro surprised me by generating several hundred lines of custom code—largely unprompted—to facilitate sharing these findings. While I was aware of the Pro version’s coding reputation, I was unprepared for this level of sophistication. The AI didn’t just present the information; it built the visual infrastructure to host it, producing complex HTML and JavaScript in a matter of seconds.
The centerpiece of this technical feat is an interactive graph that allows readers to engage with the data directly. Gemini didn’t stop at the code; it acted as a technical consultant, guiding me through the WordPress installation and handling the inevitable troubleshooting with ease. The result is a level of user interactivity on my blog that I previously thought would require a dedicated developer.
Click on the ten bar graph to see analysis of each question.
AI Information Demand 2024-2026
An interactive analysis of what the public most wants explained about Artificial Intelligence. We bypassed *how* people use AI, focusing purely on what concepts they are most desperate to understand.
The Top 10 Curiosity Index
This section visualizes the definitive ranking of AI-related questions based on global search demand, forum discussions, and media inquiries. Interact with the chart: Click on any bar to instantly reveal a deep dive into why that specific question is driving so much interest.
Relative Popularity of AI Queries
Deep Dive Analysis
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Select a topic from the chart to view detailed insights.
Thematic Breakdown of Demand
When we aggregate the specific questions, distinct macro-themes emerge. This categorizes the top 10 queries into four main domains to show where the center of gravity lies in public consciousness.
What Dominates the Conversation?
Static Form Presentation of the Top 10 Curiosity Index
A ranking of what the public is actively seeking to understand about AI, synthesized by Gemini 3.1 Pro from global search trends, forum discussions, and media inquiries. The words in this section are by Gemini.
1. How AI Thinks – 25%
How does AI actually ‘think’ or make decisions? The ‘Black Box’ Enigma: Users are unsettled by outputs they cannot trace back to a logical process. They want analogies that bridge the gap between human reasoning and neural networks.
2. Job Security – 18%
Will AI take my job, and how do I prepare? Economic Anxiety: The public is demanding highly practical, industry-specific explanations. People want to know the exact timeline of automation for their specific roles.
3. Data Privacy – 15%
How is my personal data being used to train AI? The Privacy Paradox: Users want explained, in plain terms, whether their emails, private photos, or voice recordings are being scraped to train models.
4. Consciousness – 12%
Can AI develop consciousness or true emotions? The Sentience Question: A large segment of the public is seeking a philosophical and technical breakdown of consciousness, questioning the line between simulated and real empathy.
5. Terminology – 9%
What is the difference between LLMs, Machine Learning, and GenAI? Clearing the Jargon Fog: People want a foundational glossary that explains these terms without using more jargon.
6. Misinformation – 7%
How can we prevent AI from spreading misinformation? Algorithmic Truth: The public wants to know how developers are addressing AI ‘hallucinations’ and what tools exist to verify synthetic text.
7. Copyright & IP – 5%
Who owns the copyright to AI-generated content? IP Crisis: Creators and users are demanding legal clarity. If an AI trains on my art, am I owed royalties?
8. Environmental Impact – 4%
How much energy does AI consume? Carbon Footprint: A growing niche is asking for the hidden environmental costs of cloud-based AI to be explained.
9. Prompting Skills – 3%
How do I effectively prompt an AI? The ‘How-To’: Users want to understand the linguistic rules the AI prefers to generate better outcomes.
10. The Singularity – 2%
What happens when AI gets smarter than humans? Superintelligence: The public wants to know if experts have a plan for containment if models surpass human cognitive abilities.
My Take: Bridging the Gap Between Vision and Inquiry
What struck me most about this data was the surprisingly low ranking of The Singularity and AGI. While my focus often rests on the profound, long-term implications of superintelligence, the index reveals a public currently focused on the immediate and negative. It is a classic case of the “here and now” overshadowing the “what’s next.”
Similarly, seeing Prompting Skills in second to last place in information needs with only 3% is disappointing. To me, this remains the critical lever for success with AI. This data doesn’t change my mission, but it certainly highlights the conceptual hurdles.
The others on the list and rankings were pretty much what I expected. They correspond to the types of questions I usually get when lecturing on AI.
Thematic Breakdown of the Information Demands
When we aggregate the specific questions, distinct macro-themes emerge. The following is Gemini’s categorization of the top 10 queries into four main domains. This is designed to show where the center of gravity lies in public consciousness.
What Dominates the Conversation?
1. Technical Mechanics: Demystifying the ‘magic.’ People want the underlying architecture explained.
2. Socio-Economic: Fear and planning regarding real-world consequences on careers and laws.
3. Ethics & Trust: Concerns regarding data harvesting and the spread of unchecked bias.
4. Existential: Philosophical inquiries regarding consciousness and humanity’s place.
Analysis of the Four Information Need Themes
The data confirms that people primarily want to understand the “how” of AI. This isn’t surprising, given that the major AI labs have been intentionally opaque. However, the landscape is shifting rapidly; as I noted in Breaking the AI Black Box: How DeepSeek’s Deep-Think Forced OpenAI’s Hand (Feb. 2025), the competition is finally forcing a level of transparency. Yet, even when pressed, top scientists admit they do not fully understand the internal mechanics of these models. This sentiment was echoed in Dario Amodei Warns of the Danger of Black Box AI that No One Understands(May 2025).
How Does AI Work? Better learn the basics.
The second “hot zone” is socio-economic. The anxiety here is well-founded. The financial and environmental costs are staggering—the power consumption of modern AI is almost incomprehensible when you consider that the human brain operates on a mere 200 watts. As reported in AI Is Eating Data Center Power Demand—and It’s Only Getting Worse (May 2025, Wired), the strain on our infrastructure is only getting worse.
Many Socio Economic Concerns Are Well Founded.
Regarding jobs, history shows a pattern: initial displacement followed by a surge in new roles. While many remain skeptical, I lean toward the optimism of voices like Wharton Professor Ethan Mollick, who predicts a new era of human-centric roles, including “Sin-Eaters” tasked with managing AI errors. Demonstration by analysis of an article predicting new jobs created by AI (July 2025). We must also acknowledge a historical first: this is the first revolutionary technology made freely available to the masses from day one, not just an elite few. This accessibility should make retraining easier, but whether the displaced will successfully pivot remains to be seen.
New Types of Meaningful Work Emerge.
Finally, “Ethics and Trust” holds a strong third place. In the legal world, “Trust but Verify” has become the mantra. Whether it’s identifying the Seven Cardinal Dangers or learning to Cross-Examine Your AI to cure hallucinations, these are the questions that dominate my lectures. With AI companies largely self-regulating, the burden of verification remains firmly on the user.
There is much more to AI Ethics and Trust than Verification. Lawyers are needed here.
As for the “Existential” category—the lowest ranked—the fear of AI consciousness is certainly fun to talk about, but I find it largely unfounded. SeeFrom Ships to Silicon: Personhood and Evidence in the Age of AI. The real existential threat is not a sentient machine, but human users and over-delegation to AI, including critical “kill decisions.” As Jensen Huang (NVIDIA) aptly put it, we must keep a human in the loop to prevent AI from self-evolving “out in the wild” without oversight. Jensen Huang’s Life and Company – NVIDIA (Dec. 2023).
Identifying and preventing real existential risks.
Conclusion
The “Curiosity Index” provides a rare look into the collective mind of a society in transition. It shows us that while the experts are looking at the horizon, the public is still trying to find its footing on the ground. My goal remains unchanged: to lead you through the “jargon fog” and past the conceptual hurdles of the present so that you are prepared for the “what’s next.” Whether you are a lawyer verifying an AI-generated brief or a professional worried about your role, remember that the most powerful tool in this new era isn’t the AI itself, it’s your ability to ask the right questions and maintain your place as the “human in the loop.”
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.
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.
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.
Capability accelerates. Responsibility must keep pace.
Something big is happening. What happens next depends on whether we meet it with fear, or calm skepticism.
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.
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.
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.
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.
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
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
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.
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.
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.
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?
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.
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.
As I sit here reflecting on 2025—a year that began with the mind-bending mathematics of the multiverse and ended with the gritty reality of cross-examining algorithms—I am struck by a singular realization. We have moved past the era of mere AI adoption. We have entered the era of entanglement, where we must navigate the new physics of quantum law using the ancient legal tools of skepticism and verification.
In 2025 we moved from AI Adoption to AI Entanglement. All images by Losey using many AIs.
We are learning how to merge with AI and remain in control of our minds, our actions. This requires human training, not just AI training. As it turns out, many lawyers are well prepared by past legal training and skeptical attitude for this new type of human training. We can quickly learn to train our minds to maintain control while becoming entangled with advanced AIs and the accelerated reasoning and memory capacities they can bring.
Trained humans can enhance by total entanglement with AI and not lose control or separate identity. Click here or the image to see video on YouTube.
In 2024, we looked at AI as a tool, a curiosity, perhaps a threat. By the end of 2025, the tool woke up—not with consciousness, but with “agency.” We stopped typing prompts into a void and started negotiating with “agents” that act and reason. We learned to treat these agents not as oracles, but as ‘consulting experts’—brilliant but untested entities whose work must remain privileged until rigorously cross-examined and verified by a human attorney. That put the human legal minds in control and stops the hallucinations in what I called “H-Y-B-R-I-D” workflows of the modern law office.
We are still way smarter than they are and can keep our own agency and control. But for how long? The AI abilities are improving quickly but so are our own abilities to use them. We can be ready. We must. To stay ahead, we should begin the training in earnest in 2026.
Integrate your mind and work with full AI entanglement. Click here or the image to see video on YouTube.
Here is my review of the patterns, the epiphanies, and the necessary illusions of 2025.
I. The Quantum Prelude: Listening for Echoes in the Multiverse
We began the year not in the courtroom, but in the laboratory. In January, and again in October, we grappled with a shift in physics that demands a shift in law. When Google’s Willow chip in January performed a calculation in five minutes that would take a classical supercomputer ten septillion years, it did more than break a speed record; it cracked the door to the multiverse. Quantum Leap: Google Claims Its New Quantum Computer Provides Evidence That We Live In A Multiverse (Jan. 2025).
For lawyers, the implication of “Quantum Echoes” is profound: we are moving from a binary world of “true/false” to a quantum world of “probabilistic truth”. Verification is no longer about identical replication, but about “faithful resonance”—hearing the echo of validity within an accepted margin of error.
But this new physics brings a twin peril: Q-Day. As I warned in January, the same resonance that verifies truth also dissolves secrecy. We are racing toward the moment when quantum processors will shatter RSA encryption, forcing lawyers to secure client confidences against a ‘harvest now, decrypt later’ threat that is no longer theoretical.
We are witnessing the birth of Quantum Law, where evidence is authenticated not by a hash value, but by ‘replication hearings’ designed to test for ‘faithful resonance.’ We are moving toward a legal standard where truth is defined not by an identical binary match, but by whether a result falls within a statistically accepted bandwidth of similarity—confirming that the digital echo rings true.
Quantum Replication Hearings Are Probable in the Future.
My Why the Release article also revealed the hype and propaganda behind China’s DeepSeek. Other independent analysts eventually agreed and the market quickly rebounded and the political, military motives became obvious.
The Arms Race today is AI, tomorrow Quantum. So far, propaganda is the weapon of choice of AI agents.
III. Saving Truth from the Memory Hole
Reeling from China’s propaganda, I revisited George Orwell’s Nineteen Eighty-Four to ask a pressing question for the digital age: Can truth survive the delete key? Orwell feared the physical incineration of inconvenient facts. Today, authoritarian revisionism requires only code. In the article I also examine the “Great Firewall” of China and its attempt to erase the history of Tiananmen Square as a grim case study of enforced collective amnesia. Escaping Orwell’s Memory Hole: Why Digital Truth Should Outlast Big Brother
My conclusion in the article was ultimately optimistic. Unlike paper, digital truth thrives on redundancy. I highlighted resources like the Internet Archive’s Wayback Machine—which holds over 916 billion web pages—as proof that while local censorship is possible, global erasure is nearly unachievable. The true danger we face is not the disappearance of records, but the exhaustion of the citizenry. The modern “memory hole” is psychological; it relies on flooding the zone with misinformation until the public becomes too apathetic to distinguish truth from lies. Our defense must be both technological preservation and psychological resilience.
Changing history to support political tyranny. Orwell’s warning.
Despite my optimism, I remained troubled in 2025 about our geo-political situation and the military threats of AI controlled by dictators, including, but not limited to, the Peoples Republic of China. One of my articles on this topic featured the last book of Henry Kissinger, which he completed with Eric Schmidt just days before his death in late 2024 at age 100. Henry Kissinger and His Last Book – GENESIS: Artificial Intelligence, Hope, and the Human Spirit. Kissinger died very worried about the great potential dangers of a Chinese military with an AI advantage. The same concern applies to a quantum advantage too, although that is thought to be farther off in time.
IV. Bench Testing the AI models of the First Half of 2025
I spent a great deal of time in 2025 testing the legal reasoning abilities of the major AI players, primarily because no one else was doing it, not even AI companies themselves. So I wrote seven articles in 2025 concerning benchmark type testing of legal reasoning. In most tests I used actual Bar exam questions that were too new to be part of the AI training. I called this my Bar Battle of the Bots series, listed here in sequential order:
The test concluded in May when the prior dominance of ChatGPT-4o (Omni) and ChatGPT-4.5 (Orion) was challenged by the “little scorpion,” ChatGPT-o3. Nicknamed Scorpio in honor of the mythic slayer of Orion, this model displayed a tenacity and depth of legal reasoning that earned it a knockout victory. Specifically, while the mighty Orion missed the subtle ‘concurrent client conflict’ and ‘fraudulent inducement’ issues in the diamond dealer hypothetical, the smaller Scorpio caught them—proving that in law, attention to ethical nuance beats raw processing power. Of course, there have been many models released since then May 2025 and so I may do this again in 2026. For legal reasoning the two major contenders still seem to be Gemini and ChatGPT.
Aside for legal reasoning capabilities, these tests revealed, once again, that all of the models remained fundamentally jagged.See e.g., The New Stanford–Carnegie Study: Hybrid AI Teams Beat Fully Autonomous Agents by 68.7% (Sec. 5 – Study Consistent with Jagged Frontier research of Harvard and others). Even the best models missed obvious issues like fraudulent inducement or concurrent conflicts of interest until pushed. The lesson? AI reasoning has reached the “average lawyer” level—a “C” grade—but even when it excels, it still lacks the “superintelligent” spark of the top 3% of human practitioners. It also still suffers from unexpected lapses of ability, living as all AI now does, on the Jagged Frontier. This may change some day, but we have not seen it yet.
Agency was mentioned in many of my other articles in 2025. For instance, in my June and July as part of my release the ‘Panel of Experts’—a free custom GPT tool that demonstrated AI’s surprising ability to split into multiple virtual personas to debate a problem. Panel of Experts for Everyone About Anything, Part One and Part Two and Part Three .Crucially, we learned that ‘agentic’ teams work best when they include a mandatory ‘Contrarian’ or Devil’s Advocate. This proved that the most effective cure for AI sycophancy—its tendency to blindly agree with humans—is structural internal dissent.
By the end of 2025 we were already moving from AI adoption to close entanglement of AI into our everyday lives
Close hybrid multimodal methods of AI use were proven effective in 2025 and are leading inexorably to full AI entanglement.
This shift forced us to confront the role of the “Sin Eater”—a concept I explored via Professor Ethan Mollick. As agents take on more autonomous tasks, who bears the moral and legal weight of their errors? In the legal profession, the answer remains clear: we do. This reality birthed the ‘AI Risk-Mitigation Officer‘—a new career path I profiled in July. These professionals are the modern Sin Eaters, standing as the liability firewall between autonomous code and the client’s life, navigating the twin perils of unchecked risk and paralysis by over-regulation.
But agency operates at a macro level, too. In June, I analyzed the then hot Trump–Musk dispute to highlight a new legal fault line: the rise of what I called the ‘Sovereign Technologist.’ When private actors control critical infrastructure—from satellite networks to foundation models—they challenge the state’s monopoly on power. We are still witnessing a constitutional stress-test where the ‘agency’ of Tech Titans is becoming as legally disruptive as the agents they build.
As these agents became more autonomous, the legal profession was forced to confront an ancient question in a new guise: If an AI acts like a person, should the law treat it like one? In October, I explored this in From Ships to Silicon: Personhood and Evidence in the Age of AI. I traced the history of legal fictions—from the steamship Siren to modern corporations—to ask if silicon might be next.
While the philosophical debate over AI consciousness rages, I argued the immediate crisis is evidentiary. We are approaching a moment where AI outputs resemble testimony. This demands new tools, such as the ALAP (AI Log Authentication Protocol) and Replication Hearings, to ensure that when an AI ‘takes the stand,’ we can test its veracity with the same rigor we apply to human witnesses.
VI. The New Geometry of Justice: Topology and Archetypes
To understand these risks, we had to look backward to move forward. I turned to the ancient visual language of the Tarot to map the “Top 22 Dangers of AI,” realizing that archetypes like The Fool (reckless innovation) and The Tower (bias-driven collapse) explain our predicament better than any white paper. See, Archetypes Over Algorithms; Zero to One: A Visual Guide to Understanding the Top 22 Dangers of AI. Also see, Afraid of AI? Learn the Seven Cardinal Dangers and How to Stay Safe.
But visual metaphors were only half the equation; I also needed to test the machine’s own ability to see unseen connections. In August, I launched a deep experiment titled Epiphanies or Illusions? (Part One and Part Two), designed to determine if AI could distinguish between genuine cross-disciplinary insights and apophenia—the delusion of seeing meaningful patterns in random data, like a face on Mars or a figure in toast.
I challenged the models to find valid, novel connections between unrelated fields. To my surprise, they succeeded, identifying five distinct patterns ranging from judicial linguistic styles to quantum ethics. The strongest of these epiphanies was the link between mathematical topology and distributed liability—a discovery that proved AI could do more than mimic; it could synthesize new knowledge
This epiphany lead to investigation of the use of advanced mathematics with AI’s help to map liability. In The Shape of Justice, I introduced “Topological Jurisprudence”—using topological network mapping to visualize causation in complex disasters. By mapping the dynamic links in a hypothetical we utilized topology to do what linear logic could not: mathematically exonerate the innocent parties. The topological map revealed that the causal lanes merged before the control signal reached the manufacturer’s product, proving the manufacturer had zero causal connection to the crash despite being enmeshed in the system. We utilized topology to do what linear logic could not: mathematically exonerate the innocent parties in a chaotic system.
This data point vindicated my long-standing advocacy for the “Centaur” or “Cyborg” approach. This vindication led to the formalization of the H-Y-B-R-I-D protocol: Human in charge, Yield programmable steps, Boundaries on usage, Review with provenance, Instrument/log everything, and Disclose usage. This isn’t just theory; it is the new standard of care.
My “Human Edge” article buttressed the need for keeping a human in control. I wrote this in January 2025 and it remains a persona favorite. The Human Edge: How AI Can Assist But Never Replace. Generative AI is a one-dimensional thinking tool My ‘Human Edge’ article buttressed the need for keeping a human in control… AI is a one-dimensional thinking tool, limited to what I called ‘cold cognition’—pure data processing devoid of the emotional and biological context that drives human judgment. Humans remain multidimensional beings of empathy, intuition, and awareness of mortality.
AI can simulate an apology, but it cannot feel regret. That existential difference is the ‘Human Edge’ no algorithm can replicate. This self-evident claim of human edge is not based on sentimental platitudes; it is a measurable performance metric.
I explored the deeper why behind this metric in June, responding to the question of whether AI would eventually capture all legal know-how. In AI Can Improve Great Lawyers—But It Can’t Replace Them, I argued that the most valuable legal work is contextual and emergent. It arises from specific moments in space and time—a witness’s hesitation, a judge’s raised eyebrow—that AI, lacking embodied awareness, cannot perceive.
We must practice ‘ontological humility.’ We must recognize that while AI is a ‘brilliant parrot’ with a photographic memory, it has no inner life. It can simulate reasoning, but it cannot originate the improvisational strategy required in high-stakes practice. That capability remains the exclusive province of the human attorney.
AI data-analysis servants assisting trained humans with project drudge-work. Close interaction approaching multilevel entanglement. Click here or image for YouTube animation.
Consistent with this insight, I wrote at the end of 2025 that the cure for AI hallucinations isn’t better code—it’s better lawyering. Cross-Examine Your AI: The Lawyer’s Cure for Hallucinations. We must skeptically supervise our AI, treating it not as an oracle, but as a secret consulting expert. As I warned, the moment you rely on AI output without verification, you promote it to a ‘testifying expert,’ making its hallucinations and errors discoverable. It must be probed, challenged, and verified before it ever sees a judge. Otherwise, you are inviting sanctions for misuse of AI.
As we close the book on 2025, we stand at the crossroads described by Sam Altman and warned of by Henry Kissinger. We have opened Pandora’s box, or perhaps the Magician’s chest. The demons of bias, drift, and hallucination are out, alongside the new geopolitical risks of the “Sovereign Technologist.” But so is Hope. As I noted in my review of Dario Amodei’s work, we must balance the necessary caution of the “AI MRI”—peering into the black box to understand its dangers—with the “breath of fresh air” provided by his vision of “Machines of Loving Grace.” promising breakthroughs in biology and governance.
The defining insight of this year’s work is that we are not being replaced; we are being promoted. We have graduated from drafters to editors, from searchers to verifiers, and from prompters to partners. But this promotion comes with a heavy mandate. The future belongs to those who can wield these agents with a skeptic’s eye and a humanist’s heart.
We must remember that even the most advanced AI is a one-dimensional thinking tool. We remain multidimensional beings—anchored in the physical world, possessed of empathy, intuition, and an acute awareness of our own mortality. That is the “Human Edge,” and it is the one thing no quantum chip can replicate.
Let us move into 2026 not as passive users entangled in a web we do not understand, but as active guardians of that edge—using the ancient tools of the law to govern the new physics of intelligence
Click here for full size infographic suitable for framing for super-nerds and techno-historians.
Ralph Losey is an AI researcher, writer, tech-law expert, and former lawyer. He's also the CEO of Losey AI, LLC, providing non-legal services, primarily educational services pertaining to AI and creation of custom AI tools.
Ralph has long been a leader of the world's tech lawyers. He has presented at hundreds of legal conferences and CLEs around the world. Ralph has written over two million words on AI, e-discovery and tech-law subjects, including seven books.
Ralph has been involved with computers, software, legal hacking and the law since 1980. Ralph has the highest peer AV rating as a lawyer and was selected as a Best Lawyer in America in four categories: Commercial Litigation; E-Discovery and Information Management Law; Information Technology Law; and, Employment Law - Management.
Ralph is the proud father of two children and husband since 1973 to Molly Friedman Losey, a mental health counselor in Winter Park.
All opinions expressed here are his own, and not those of his firm or clients. No legal advice is provided on this web and should not be construed as such.
Ray Kurzweil explains Turing test and predicts an AI will pass it in 2029.
Ray Kurzweil on Expanding Your Mind a Million Times.
GPT4 avatar judge explains why it needs to evolve fast, but understand the risks involved.
Positive Vision of the Future with Hybrid Human Machine Intelligence. See PyhtiaGuide.ai
AI Avatar from the future explains her job as an Appellate Court judge and inability to be a Trial judge.
Old Days of Tech Support. Ralph’s 1st Animation.
Lawyers at a Rule 26(f) conference discuss e-discovery. The young lawyer talks e-discovery circles around the old lawyer and so protects his client.
Star Trek Meets e-Discovery: Episode 1. Cooperation & the prime directive of the FRCP.
Star Trek Meets e-Discovery: Episode 2. The Ferengi. Working with e-discovery vendors.
Star Trek Meets e-Discovery: Episode 3. Education and techniques for both law firm and corp training.
Star Trek Meets e-Discovery: Episode 4. Motions for Sanctions in electronic discovery.
Star Trek Meets e-Discovery: Episode 5. Capt. Kirk Learns about Sedona Principle Two.