The Goblin in the Machine: What OpenAI’s “No-Pigeon Rule” Teaches Lawyers About AI Hallucinations

May 11, 2026

Ralph Losey, May 2026

This article is about a real event. It is not satire, parody, or metaphor. In late April 2026, OpenAI publicly explained why one of its frontier AI systems had developed an unusual tendency to mention goblins, gremlins, raccoons, trolls, ogres, pigeons, and similar creatures in places where they did not belong. OpenAI titled its official explanation “Where the Goblins Came From.” The title sounds fictional. The problem was not.  

A humanoid robot with a friendly face sitting at a desk next to a coffee mug. The computer screen displays coding instructions and a highlighted warning about avoiding certain topics, including goblins and trolls, unless relevant to the user's prompt.
Gremlins, Goblins and Pigeons. Oh my!

If you take the time to study this strange episode, you will gain more than an amusing story about artificial intelligence. You will see, in unusually visible form, how Large Language Models can acquire unintended behavior from training incentives, how that behavior can spread beyond its original context, why prompt-level or developer-level instructions may be used to suppress it, and how the same root causes help explain the ongoing problem of AI hallucination. For lawyers, judges, e-discovery professionals, and legal technology vendors, this is not a curiosity. It is a warning label written in unusually memorable ink.

A collage of fantastical creatures including a green goblin, a mischievous gremlin, a large orange monster, a raccoon, a small brown creature, and a pigeon, all surrounding a glowing, swirling vortex in a cosmic background.
Fact is sometimes stranger than fiction. This is one of those times.

The Most Bizarre Codex Instruction of All Time

OpenAI’s example involved Codex, its AI coding agent. For non-programmers, Codex is not a fantasy product and not a casual chatbot. It is a professional software-development tool designed to help engineers plan, write, refactor, test, review, and release code. OpenAI describes Codex as “a coding agent that helps you build and ship with AI,” used for real engineering work across development tools.  

That context matters. The now-famous instruction was not a joke inserted into a toy system. It was a developer-level instruction in a serious AI coding agent. According to reporting and OpenAI’s later explanation, Codex had been instructed not to talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless they were clearly relevant to the user’s request.

WIRED first reported the Codex CLI instruction that the model should “never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query.” Maxwell Zeff, OpenAI Really Wants Codex to Shut Up About Goblins (WIRED, Apr. 2026). OpenAI, then responded with its own article, Where the Goblins Came From, OpenAI (Apr. 29, 2026), explaining that GPT-5.5 in Codex showed an affinity for goblin metaphors and tracing the behavior to training incentives connected with the “Nerdy” personality. It is well worth the read.

The facts are unusual enough that they do not need embellishment. Indeed, embellishment would weaken the point. The issue is not that an AI system said something funny. The issue is that a frontier model, shaped by modern training methods, developed a persistent behavior that its maker had to investigate, explain, and mitigate. That is precisely why lawyers should pay attention.

A whimsical scene featuring a wizard in a green robe controlling a machine labeled 'GPT-5.5/CODEX BEHAVIOR CONTROL.' In front of the wizard, there are two small goblin-like creatures and a pigeon, all looking towards the control panel. A sign reads 'Gremlins, Goblins, and Pigeons, OH MY!' in the background.
Pay no attention to the Codex instruction behind the curtain.

The “Goblin” Problem Was an Alignment Problem in Plain Sight

The legal technology world often discusses AI alignment in abstract language. We talk about bias, safety, truthfulness, reliability, explainability, auditability, and human values. Those are important words, but they can become bloodless. The goblin incident gives us something more concrete.

OpenAI explained that the behavior emerged from “many small incentives,” including training by AI of itself connected to its personality customization feature, especially an introversive “Nerdy” personality. That personality was designed to make the model more playful, intellectually enthusiastic, and metaphor-friendly. In the process, certain creature metaphors were rewarded often enough that the model learned to repeat and generalize them.

I have frequently written about the ability of AI to form fictitious sub-personalities for brainstorming purposes, and note the Devils Advocate character is especially effective. Fortunately he was not involved in this OpenAi fiasco. I never instructed AI to form a shy, super-nerd personality type for training purposes. If I ever do in the future (doubtful), I will obviously be very careful to provide strong human supervisions, something which was obviously missing here. This whole incident seems like over-delegation, where the humans in the loop were not paying attentions and so triggered this Gremlin crisis,

This brings up a key point. The AI model was not “thinking about goblins.” It was responding to patterns shaped by training data, reinforcement learning, preference signals, and later adjustments. If a certain style of answer receives favorable feedback, the model can learn that style as a useful pattern. If that pattern includes odd creature metaphors, those metaphors can become part of the model’s behavior.

OpenAI’s post-mortem is valuable because it shows something that usually remains hidden. Model behavior does not simply appear at deployment. It is cultivated. It is selected. It is rewarded. It is penalized. It is patched. It is monitored. Sometimes, it is suppressed by instructions that users never see. I never knew that before.

In this case, the visible symptom was bizarre. The underlying process was ordinary. That is what makes the episode important.

Infographic explaining the 'Goblin' problem in model training, featuring sections on inputs, emergent behavior, unintended outcomes, and mitigation strategies. Includes illustrations of goblins and reference to model training inputs like human feedback and evaluations.

What Are These “Instructions,” and Why Should Lawyers Care?

Modern AI systems are not governed only by the words users type into the chat window. They also operate under layers of instructions. Some instructions come from the system level. Some come from developers. Some come from product settings, safety policies, tool configurations, or specialized agent workflows. Some come from users themselves. The user may never see, nor even know about the developers instructions that shape the response to the user’s prompts.

A developer instruction is essentially a command placed above the ordinary user prompt. It tells the model how to behave in a particular product environment. In Codex, such instructions may shape how the model writes code, uses tools, comments on programming tasks, avoids certain behaviors, or responds within a software-development workflow.

That is not improper. In fact, layered instructions are necessary. A legal AI tool should be told to protect confidentiality, avoid unauthorized practice of law, cite sources, flag uncertainty, preserve privilege, and follow the user’s workflow. The problem is not the existence of instructions. The problem is invisibility, auditability, and as just mentioned, the lack of proper human supervision of the whole process. The humans in the loop were asleep at the wheel and as a consequence the dogs got out.

In legal work, hidden constraints can matter. If a model suppresses certain language (such as profanity), and favors certain categories (such as propriety), emphasizes certain risks (such as letting the dogs out), avoids certain conclusions (such as user is wrong), or changes behavior after an update (such as no hacking allowed, eh Claude), the lawyer may not know why. That matters in e-discovery, privilege review, contract analysis, legal research, expert preparation, and litigation strategy. Another layer of e-discovery open up.

The Codex no-goblin instruction is therefore not important because lawyers care about goblins. (I for one do not, although I do. care about ‘not letting the dogs out’). It is important because it reveals how behavioral control can operate behind the scenes.

Infographic titled 'Hidden Instructions. Real Impact.' illustrating the differences between user input and underlying model instructions. It shows an iceberg with 'User Prompt,' 'System Instructions,' 'Developer Instructions,' 'Tools & Data Sources,' and 'Model Behavior Shapers' listed under the waterline. A person is seen contemplating the information with a notebook and pen on the table, emphasizing the importance of understanding hidden instructions in AI output.
If a goblin ever appears in your AI response you will know why now. The super-nerd trainer slipped through the latest hidden instructions.

The Hallucination Connection

The goblin problem is not identical to hallucination, but the two issues share root causes.

The goblin problem involved an unintended stylistic habit. Hallucination involves plausible but false content. One produces irrelevant creature metaphors. The other produces fake cases, invented quotations, nonexistent statutes, false summaries, fabricated citations, or confident statements unsupported by the record.

The difference is obvious. The connection is deeper.

Both problems arise from the same basic fact: Large Language Models are not born as truth engines. They are trained to predict and generate language. Later training stages, including supervised fine-tuning, reinforcement learning, preference optimization, safety training, and evaluation systems, try to make that language helpful, accurate, safe, and aligned with user expectations.

But training incentives can misfire. Evaluation methods can reward the wrong behavior. A system can learn to produce answers that sound good rather than answers that are verified. It can learn fluency before truth, confidence before calibration, and completion before uncertainty. It could be trained to say, “I don’t know,” but it wasn’t. There is not much of that on the Internet. So, instead it just makes up an answer, one that it infers the user wants, because it is also trained to be a nice sycophant. Nobody wants a devils advocate around that disagrees with you. We should of course, and that is why lawyers have the potential to be great users of generative AI.

OpenAI made this point directly in its 2025 discussion of why language models hallucinate. Why Language Models Hallucinate, (OpenAI, Sept. 5, 2025). OpenAI explained that hallucinations persist in part because many evaluation systems reward accuracy alone, which can push models to guess rather than admit uncertainty. If a model guesses, it may get lucky and receive credit. If it says “I don’t know,” it may receive no credit at all. Over many evaluations, that scoring structure can make a guessing model appear more successful than a more careful model that abstains when it lacks reliable information. 

That is the real connection between goblins and hallucinations. They are different failures, but they reflect the same training logic. In the goblin case, the rewarded behavior was playful metaphor, so the model learned to repeat and generalize playful creature references. In hallucination, the rewarded behavior is often answer-giving itself, so the model may learn to produce a confident response even when it lacks adequate grounding. In both cases, the model is not following truth as an independent legal or evidentiary standard. It is following patterns that its training, feedback, and evaluation systems have taught it to treat as successful.

The danger for lawyers is that hallucinations usually do not look strange. Goblins and pigeons are obvious intrusions. They announce that something has gone wrong. A fake citation does not. A fabricated quotation does not. A false summary of a contract clause, deposition answer, medical record, email thread, or judicial opinion may read with the same polish and confidence as a correct one. The surface quality of the prose may conceal the absence of reliable support.

That is why hallucinations are more dangerous than the goblin problem. The goblins expose the machinery because they look absurd. Hallucinations hide the machinery because they look professional. For legal work, that difference is critical. The risk is not merely that an AI system may be odd. The risk is that it may be wrong in a way that looks authoritative, usable, and ready to file.

An illustration featuring goblins and a bird discussing the concept of incentives and risks, contrasted with labels like 'Obvious,' 'Strange but Obvious,' and 'Plausible but Dangerous.' The central theme highlights differing risks associated with learned behaviors, with references to legal aspects and the importance of verification.
Don’t be a pigeon. Trust but verify.

This Is Not Just an OpenAI Problem

It would be a mistake to treat this as an OpenAI-only issue. The OpenAI goblin post-mortem is useful because it is unusually visible, candid, and memorable. But hallucination and unintended model behavior afflict all modern LLM systems under development, including Claude, Gemini, and other leading models.

Anthropic’s own Claude documentation expressly addresses hallucination reduction, warning that even advanced models can generate text that is factually incorrect or inconsistent with context, and recommending mitigation techniques such as allowing Claude to say it does not know, grounding answers in provided source material, using direct quotations, verifying with citations, and validating critical information. Anthropic, Reduce Hallucinations (Claude API Docs). 

Google’s Gemini documentation similarly warns that Gemini for Google Cloud may produce hallucinations, including outputs that are plausible-sounding but factually incorrect, irrelevant, inappropriate, or nonsensical, and may even fabricate links to web pages that do not exist and have never existed. Google Cloud, Gemini for Google Cloud and Responsible AI (Google Cloud Documentation),

The vendors differ. The architectures differ. The safety philosophies differ. The product interfaces differ. But the fundamental problem is shared. These systems are trained to generate plausible language under complex incentives. Plausibility is not truth. Fluency is not verification. Confidence is not reliability.

This point should be stated carefully. It does not mean that all systems are equally risky, equally useful, or equally well governed. They are not. Some models perform better than others on particular tasks. Some products provide stronger grounding, citation, retrieval, logging, or enterprise controls. Some workflows are safer than others.

But no responsible legal professional should assume that hallucination and goblins are confined to one vendor. It is a structural limitation of current LLM technology.

An illustration emphasizing the responsibilities associated with AI models, featuring logos of OpenAI, Anthropic, and Google. The background includes law-related imagery and a checklist titled 'Lawyer's Checklist' with items for verifying information.
Advanced AI construction and use require human supervision and skills.

The Legal Technology Lesson

Legal professionals should not respond to this by rejecting AI. That would be the wrong lesson. It would also ignore the enormous value these tools already provide when used with care.

The correct lesson is disciplined adoption.

In e-discovery, we already understand this principle. Technology-assisted review is not accepted because someone declares the software intelligent. It is accepted when the process is reasonable, validated, documented, and proportionate. Sampling matters. Quality control matters. Human judgment matters. Reproducibility matters. Transparency matters.

The same discipline must now be applied to generative AI. Legal AI workflows should be designed to answer practical questions:

  • Can the output be traced to reliable source material?
  • Did the model actually use the cited source?
  • Can each legal citation be verified?
  • Can each quotation be checked against the original?
  • Can each factual assertion be tied to the record?
  • Can the workflow be reproduced if challenged?
  • Was the model permitted to say “I don’t know”?
  • Was uncertainty preserved, or did the workflow pressure the model into confident completion?
  • Were model version, prompt structure, source set, and review procedures documented?
  • Was a qualified human responsible for final legal judgment?

These questions are not anti-AI. They are pro-reliability. They are the questions that separate professional use from casual use.

Why This Matters for Courts and Clients

Courts do not need lawyers to become machine-learning engineers. Clients do not need their lawyers to understand every detail of transformer architecture. But both courts and clients are entitled to competent professional judgment.

That includes knowing when an AI output is grounded and when it is merely plausible. It includes knowing when a citation has been verified and when it has merely been generated. It includes knowing when an AI tool is being used for brainstorming, drafting, summarization, classification, legal research, or evidence analysis, because each use carries different risks.

The goblin incident offers a rare window into model behavior because the symptom was so visible. Most legally significant failures will not be so obvious. They will not involve fantasy creatures. They will involve a misstated holding, an omitted exception, a distorted fact pattern, a privilege call made too broadly, a missed document, or a confident statement about law that is no longer current. By the way, humans can all make the same mistakes, which is one reason we tend to do better working in small teams.

That is why the legal profession, indeed all of humanity, must treat generative AI as powerful but not self-validating.

An illustration depicting the balance between artificial intelligence (AI) and human judgment, emphasizing the importance of verification and accurate legal practices. The image shows a scale weighing truthful information against misleading data, with a group of professionals discussing documents at the bottom.
Seriously, why pigeons? None of my associates ever made that mistake.

Practical Guidance for Lawyers and Legal Tech Users

The practical response is straightforward:

  • Use AI, but verify.
  • Use AI for first drafts, issue spotting, summarization, brainstorming, and classification support, but do not outsource professional judgment.
  • Use retrieval, citations, and source-grounded workflows whenever factual accuracy matters.
  • Require the model to distinguish between sourced statements, inferences, and speculation.
  • Require explicit uncertainty when the record is incomplete.
  • For legal research, verify every case, statute, rule, quotation, and parenthetical against authoritative sources.
  • For e-discovery and document review, use sampling, validation, audit trails, and human quality control.
  • For AI vendor selection, ask what model is being used, how outputs are grounded, how hallucination risk is measured, what logs are preserved, what changes when the model is updated, and whether the workflow can be explained if challenged.
  • For judicial or regulatory settings, avoid vague claims that an AI tool is “aligned,” “safe,” or “accurate” without evidence. Ask what was tested, how it was tested, and under what conditions.

The lesson is not distrust. The lesson is earned trust.

A woman weighing scales in an office setting, emphasizing the importance of using AI tools while verifying information. Text highlights various uses for AI and verification methods.

Conclusion: The Promise and the Work Ahead

At the beginning of this article, I promised that this strange episode would offer more than an amusing story. It does.

OpenAI’s real no-goblin, no-pigeon instruction gives lawyers a concrete example of how modern AI behavior can be shaped by training incentives, generalized beyond its original setting, and later mitigated through hidden or semi-hidden instructions. The hallucination problem shows the same root issue in more serious form. When models are rewarded for fluent completion, confidence, and benchmark performance, they may learn to answer when they should abstain, to sound certain when they should qualify, and to generate plausible legal authority when only verified authority will do.

Users must learn these idiosyncrasies and adapt.

This is not just about OpenAI. It is not just about Codex. It is not just about goblins. It is about every legal professional’s duty to understand the tools now entering legal practice. It is about understanding how to use them properly.

Generative AI can help lawyers become faster, broader, more creative, and more effective. It can improve access to justice, reduce drudgery, accelerate document review, strengthen legal education, and help professionals see patterns they might otherwise miss. But these benefits will not be realized by pretending the risks are gone. They will be realized by confronting the risks directly and building better habits, better workflows, better audits, better training, and better professional norms.

The goblins are real in the only sense that matters here: real enough to show us how fragile model behavior can be. The hallucinations are more dangerous because they usually do not look strange at all.

That is the call to action. Legal professionals should not stand outside the AI revolution, arms folded, waiting for perfect machines. Nor should they rush in, eyes closed, dazzled by fluent output. We should do what good lawyers have always done with powerful evidence and powerful tools: question them, test them, document them, verify them, and use them responsibly.

The future of legal AI will not be built by blind trust or reflexive fear. It will be built by informed confidence.

And informed confidence begins with verification.

A woman in a suit standing with her back to the viewer, looking toward a bright horizon. Elements include a mythical creature on the left, a pigeon, an open laptop, a magnifying glass, and a scale of justice, all suggesting a theme of adaptation and learning.

Ralph Losey Copyright 2026. All Rights Reserved

For educational use only. Not legal advice.


Will AI Take My Job? OpenAI’s New Policy, Rising Cybersecurity Risks, and What Comes Next

April 17, 2026

Ralph Losey, April 17 2026

Introduction: The Urgency of the Question

Will AI take my job?

A line of people in formal attire walking with somber expressions, led by a robot with a humanoid design, against a modern building backdrop.
Image by Ralph Losey using AI tools.

That question is no longer speculative. It is now front-page relevant, driven not only by rapid advances in AI, but by two recent events that reveal how quickly things are changing. On April 6, 2026, OpenAI released its Industrial Policy for the Intelligence Age, openly warning that the transition to superintelligence is already underway. Just days earlier, a human error at Anthropic briefly exposed the source code of one of the world’s most advanced AI systems. It was quickly copied and distributed before the mistake was corrected. It is reportedly now in the hands of criminal hackers and enemy states worldwide. Together, these developments make one thing clear: the future of work is arriving faster, and fas less predictably, than most expected.

In my recent article, What People Want To Know About AI: Top 10 Curiosity Index, Gemini AIs and I analyzed global search patterns and online discussions to identify the public’s most urgent concerns. The number one question, How does AI work? was addressed in my follow-up article, Five Faces of the Black Box: How AI ‘Thinks’ and Makes Decisions, where we explained the technology across five levels, from a child’s guessing game to matrix algebra.

But the second question is different.

Will AI take my job—and what should I do about it?

This accounted for roughly 18% of all inquiries. And unlike the first question, it is not driven by curiosity. It is driven by anxiety, something I hear and feel in conversations about AI with all kinds of people.

A futuristic city street with a diverse crowd looking at holographic signs displaying various career options. A robot and a humanoid figure are in the foreground, interacting with technology amidst tall buildings and flying vehicles, highlighting innovation and technology in the workforce.
All images in this article are by Ralph Losey using Gemini AI tools.

This article focuses on that anxiety: economic security and the future of work. It also confronts the issue people increasingly want answered but rarely get: the timeline. When might AI reach a level capable of performing most cognitive work better than us? Because if that point is near, and recent signals suggest ii is, then the implications are profound. Most knowledge-based jobs would be affected, and the resulting disruption to the economy and social order could be significant.

The Policy Response: OpenAI’s Industrial Blueprint


The urgency of this economic question is not limited to the public. It is also front and center for the corporations building the technology. On April 6, 2026, OpenAI released Industrial Policy for the Intelligence Age (“Policy Statement”) and it is likely that other leading AI companies will soon follow. This document moves beyond engineering into economic and social policy. It begins with a blunt premise: the transition to superintelligence is already underway and will reshape how organizations operate, how knowledge is created, and how people find meaning and opportunity.

The Policy Statement does not minimize the disruption ahead, or the speed at which it may arrive. It acknowledges that AI will disrupt jobs and reshape entire industries at a scale and pace unlike any prior technological shift. At the same time, OpenAI’s leadership emphasizes that the outcome is not predetermined. Whether this transformation leads to shared prosperity or to concentrated wealth and widespread displacement will depend on decisions made now, by governments, corporations, institutions, and individuals.

I encourage you to read the Policy Statement in full. It addresses far more than job security. My focus here is narrower: the economic implications. On pages 3 and 4, the Policy Statement explains:

The Case for a New Industrial Policy. Society has navigated major technological transitions before, but not without real disruption and dislocation along the way. While those transitions ultimately created more prosperity, they required proactive political choices to ensure that growth translated into broader opportunity and greater security. For example, following the transition to the Industrial Age, the Progressive Era and the New Deal helped modernize the social contract for a world reshaped by electricity, the combustion engine, and mass production. They did so by building new public institutions, protections, and expectations about what a fair economy should provide, including labor protections, safety standards, social safety nets, and expanded access to education. 

History shows that democratic societies can respond to technological upheaval with ambition: reimagining the social contract, mediating between capital and labor, and encouraging broad distribution of the benefits of technological progress while preserving pluralism, constitutional checks and balances, and freedom to innovate. The transition to superintelligence will require an even more ambitious form of industrial policy, one that reflects the ability of democratic societies to act collectively, at scale, to shape their economic future so that superintelligence benefits everyone.  …

On this path to superintelligence, there are clear steps we need to take today. People are already concerned about what AI will mean for their lives—whether their jobs and families will be safe, and whether data centers will disrupt their communities and raise energy prices. AI data centers should pay their own way on energy so that households aren’t subsidizing them; and they should generate local jobs and tax revenue. Governments should implement common-sense AI regulation—not to entrench incumbents through regulatory capture but to protect children, mitigate national security risks, and encourage innovation. 

OpenAI released a companion video the same day as the Policy Statement, titled Sam Altman on Building the Future of AI (“Video“). At 26:08, the discussion turns directly to jobs. Joshua Achiam, OpenAI’s Chief Futurist, addresses the issue candidly:

On getting workers involved in AI, I actually, I kind of want to back up and just acknowledge an elephant in the room, which is that a lot of workers are concerned about AI; they’re worried about what AI means for them. They are not immediately excited at the prospect of figuring out, all right, how are we going to use AI in our workplace? They’re thinking, oh my gosh, is the AI going to replace me?

The public is no longer satisfied with abstract reassurances. People want timelines. They want industry-specific forecasts. They want to know whether their job will still exist in five years. Both the Policy Statement and the Video point in the same direction: highly capable AI systems are coming quite soon, much faster than most expected. 

Better get it right Sam.

More Training Now for Job Security Tomorrow?

For many years my usual answer to the jobs question has been more training now. That answer may not cut it today for a majority of people, especially if AI advances too fast, too far. For instance, in Can AI Really Save the Future? A Lawyer’s Take on Sam Altman’s Optimistic Vision (Oct. 2024) I opined:

AI will create entirely new jobs. For instance, for lawyers, new jobs pertaining to AI regulations are emerging. AI will also change existing jobs for the better. It is already replacing the most boring parts of our work, leaving us to focus on the more rewarding and human aspects. Moreover, it is true that no worker will be replaced by an AI, they will be replaced by a human that knows how to use AI.

Now I am not so sure, and neither is Sam Altman. The prospect of superintelligence is no longer a distant future. It is a planning horizon.

To address the question of human employment in a world of increasingly powerful AI, an issue well beyond my unaided ability to resolve, I turn to a Panel of AI Experts. For this exercise, I use OpenAI-based models that I have fine-tuned for analysis across multiple disciplines. They are not superintelligent, but they are highly capable and broadly informed. They created a five AI-persona expert panel to try to answer these issues. The only persona I required is the “devil’s advocate” persona because I have found that AI type is indispensable to brainstorming exercises like this. I did not specify any other character, even the first character chosen, The “CentaurProfessional, although I must admit he sounds just like me.

An illustration depicting a central figure, representing 'Human in the Loop,' surrounded by various symbolic characters: a Centaur, a Devil's Advocate, a Sin-Eater, and the concept of 'Human Edge.' They are engaged in a digital environment filled with computers and data analytics visuals, emphasizing collaboration between humans and technology.
The Human in the Loop should remain in charge and verify AI work.

Voice 1: The “Centaur” Professional (The Hybrid Advocate)

Persona: The pragmatic professional who has fully integrated AI, but remains firmly in control. For background see my From Centaurs To Cyborgs: Our evolving relationship with generative AI (April 2024). Except for the citations that follow, all of the language from here to the Conclusion was written by the AIs, not me.

The Perspective: Let’s begin with a reality check. You’re more likely to lose your job to someone using AI than to AI itself. That single sentence cuts through most of the noise.

The fear of immediate, total automation misunderstands how work actually happens. We do not operate on smooth technological curves, we operate on what researchers call a “jagged frontier.” AI excels at certain tasks and fails at others, often unpredictably. This is why hybrid human-AI teams—Centaurs—consistently outperform both humans alone and AI alone. Recent research suggests improvements approaching 70% in certain knowledge-work domains. [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 Navigating the AI Frontier (Oct. 2024).]

In law, AI can draft a brief in seconds. But it cannot sign it. It does not carry malpractice insurance. It does not stand before a judge. It cannot be sanctioned—or disbarred.

In medicine, AI may catch patterns a doctor misses. But patients do not sue algorithms—they sue physicians.

Sam Altman himself has described using AI to analyze medical data more effectively than his own doctor. Yet no serious observer concludes from this that doctors are obsolete. The conclusion is simpler:

Doctors who use AI will replace doctors who do not. The same applies across professions.

The future belongs to the Centaur—the professional who augments judgment with machine intelligence, but never abdicates responsibility.

Your job is not disappearing. The drudgery is.

As I explained in The Great AI Transition: From Tool to Teammate (June 2024), “the real shift is from doing the work to supervising it, humans move up the chain of responsibility, not out of the system.” [The AI hallucinated this article and cite, which obviously was supposed to refer to one of my articles. I am embarrassed to say that the title and quote sounded so plausible that I had to look it up to be sure. I then called the AI on this and it admitted to the hallucination and apologized.]

A professional in a suit stands in a courtroom with a circular holographic interface. The background features judicial elements like a gavel and a jury. The hologram includes icons for legal documents, checkmarks, and scales of justice, representing the integration of AI in legal practice.
The “Centaur” Professional (The Hybrid Advocate)

Voice 2: The “Sin-Eater” (AI Risk & Accountability Officer)

Persona: The human firewall—absorbing legal and ethical responsibility for AI outputs.

The Perspective: The Centaur is right—but incomplete. Because every gain in capability creates a parallel demand for accountability.

Wharton’s Ethan Mollick coined the term “Sin-Eater” to describe a new role: the human who vouches for AI-generated work and bears the consequences when it fails. That role is not theoretical—it is inevitable.

As AI systems scale from minutes to months-long projects, the need for verification, auditing, and compliance will explode. OpenAI’s own policy proposals emphasize the need for an “AI trust stack”—auditing regimes, validation systems, and human oversight at every layer.

And then there is cybersecurity. Our current software ecosystem is already vulnerable. AI will amplify both offense and defense—but offense often scales faster. Sam Altman has warned openly: AI will become extraordinarily good at identifying software vulnerabilities. That means bad actors will too.

This creates a massive new labor demand. Not for passive users—but for active defenders. We will need an army of human-AI teams to audit, test, and secure critical systems. This is not optional. It is civilizational maintenance.

A digital illustration depicting a corporate office setting with two figures: one in a dark cloak representing 'The Sin-Eater' and another in a business suit symbolizing the 'AI Risk & Accountability Officer'. Surrounding them are visual elements like an AI Bias Map, Accountability Audit, and concepts of risk and reward, emphasizing themes of AI accountability and mitigation.
The “Sin-Eater” (AI Risk & Accountability Officer)

Voice 3: The “Startup-in-a-Box” Entrepreneur

Persona: The solo builder with the leverage of a 100-person company.

The Perspective: Why is the conversation so focused on saving existing jobs? We are on the verge of the largest expansion of individual capability in human history.

Sam Altman has spoken repeatedly about a future where one person can build what once required an entire company. AI agents will handle coding, marketing, accounting, logistics—everything that currently creates friction.

The barriers to entry are collapsing.

Today, a brilliant nurse or mechanic might never start a business—not because of lack of skill, but because of administrative overhead. Tomorrow, that overhead disappears.

This is the rise of the micro-entrepreneurial economy.

Access to powerful AI tools—what some call a “Right to AI”—may become as foundational as access to electricity. With it, millions can create, compete, and innovate independently.

Yes, large bureaucracies may shrink. But they will be replaced by networks of highly capable individuals.

The question is not just “Will I lose my job?” It is also: “What could I build if friction disappeared?”

A young entrepreneur interacts with a digital AI dashboard featuring various tools such as market trend predictors, legal compliance AI, and revenue forecasting. The office background shows a modern workspace with colleagues engaged in discussions.
The “Startup-in-a-Box” Entrepreneur

Voice 4: The “Human Edge” Advocate

Persona: The guardian of empathy, presence, and human connection.

The Perspective: The technologists talk about efficiency. But humans do not live on efficiency alone.

There is a category of value AI cannot replicate: being human with another human.

Josh Achiam of OpenAI described visiting a fully automated café. It worked perfectly. It was fast. It was efficient. nIt was also—his word—unfulfilling. He missed the human interaction.

This is the “Human Edge.”

As AI absorbs administrative and analytical burdens, the value of human-centered work will rise dramatically. Teaching, nursing, therapy, caregiving—these are not residual categories. They are foundational. Today, these professions are underpaid and overburdened. That is a policy failure—not a market inevitability.

If AI delivers the productivity gains promised, then society must redirect that surplus toward elevating human-centered roles. The OpenAI policy proposals explicitly call for this—training pipelines, wage support, and institutional investment.

The future of work is not less human. It is more human than ever.

A professional woman holding a tablet in a modern office setting, surrounded by glowing icons representing compassion, justice, and ethics, with 'hA' and 'HUMAN CORE' highlighted in the center.
The “Human Edge” Advocate

Voice 5: The Contrarian “Devil’s Advocate”

Persona: Skeptic. Realist. Immune to hype.

The Perspective: Let’s stop romanticizing. Disruption is coming—and it will be uneven, painful, and fast. Telling displaced workers to “become entrepreneurs” or “pivot to caregiving” is not a plan. It is a slogan. We need structural reform.

If AI concentrates wealth in capital rather than labor, then the tax system must adapt. That means shifting the burden toward capital gains, corporate profits, and possibly automated labor.

We must also decouple basic security from employment. Healthcare, retirement, and income stability cannot depend entirely on whether your job survives automation.

And yes—let’s talk about infrastructure. We have a decaying physical world and a fragile digital one. Meanwhile, AI companies are generating enormous wealth. That wealth must be reinvested.

A modern New Deal—focused on infrastructure, cybersecurity, and energy—is not just desirable. It is necessary.

This is not anti-capitalist. It is pro-stability.

An elderly businessman in a suit stands in a modern office setting, holding a digital tablet with a serious expression. He is surrounded by glowing symbols, including scales of justice, hands, and a devilish figure labeled 'Devil's Advocate.' The scene is framed by a circular neon border with 'hA' and 'The Contrarian View' inscribed.
The Contrarian “Devil’s Advocate”

Conclusion: Responsibility at the Edge of Superintelligence

This panel reveals a truth that resists simplification: the future of work in the age of AI is difficult to predict. At this point it could go either way.

Personally, I am now more inclined to agree with the curmudgeon Contrarian than the mini-me Hybrid Advocate. That is a change for me. It reflects a growing concern that the risks may be advancing faster than the benefits. The real question is whether we, and our institutions, can adapt quickly enough.

The practical advice is straightforward. Begin serious AI training now. At the same time, explore work where the human edge still matters. You may find not only greater security, but greater satisfaction.

Above all, hold the new centers of power, economic and technological, to their obligations. Stand for both human rights and progress. We should be able to do both. In today’s world, we have no choice. It is too dangerous to stand still.

Superintelligence may drive the engine of the future. But I continue to insist that humanity must remain firmly and responsibly at the wheel.

A business presentation scene featuring five diverse characters at a panel discussion. Each character represents a different role: a stern older man, a confident woman, a professional in a suit, a figure in a dark cloak, and a relaxed entrepreneur. Behind them, large screens display icons related to AI, risk management, and funding, suggesting a technology-focused theme.

Ralph Losey Copyright 2026. All Rights Reserved.


What People Want To Know About AI: Top 10 Curiosity Index (with interactive graphic)

March 18, 2026

Ralph Losey, March 18, 2026

A digital illustration of a brain with gears, surrounded by various topics related to artificial intelligence, including job security, data privacy, misinformation, and environmental impact.
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.

A diverse group of professionals engaged in a brainstorming session in a modern office. Some are using smartphones while sitting around tables with laptops, and others are writing on whiteboards. The environment features brick walls and large windows, creating a collaborative atmosphere focused on AI topics.
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

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.”

A surreal illustration of a humanoid figure with tree-like branches and a galaxy swirling above, representing a fusion of nature and technology. The figure's chest is open, emitting light and data, while one hand holds a glowing pyramid with a question mark and leaves.
Image of a Singularity interpretation. See Can AI Really Save the Future?

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.

A circular diagram divided into four segments representing different categories: Technical Mechanics (green), Socio-Economic (red), Ethics & Trust (blue), and Existential (gold).

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).

A digital illustration representing artificial intelligence, with a central brain surrounded by icons symbolizing various concepts like security, collaboration, education, creativity, law, sustainability, and data analysis.
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.

An infographic illustrating the impact of AI on various sectors, with icons representing finance, law, healthcare, technology, and automation around a central globe.
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.

An imaginative scene depicting creativity and technology, featuring people engaged in various activities such as art, music, education, and agriculture. Key elements include a woman holding a key, a child being guided, individuals painting and working on laptops, and a drone hovering over a colorful field.
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.

A digital illustration depicting various concepts related to artificial intelligence, data security, and analysis. Central to the image is a globe surrounded by icons, including robotic hands, diverse people, data analytics, and a lockbox symbolizing security, with arrows connecting these elements.
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. See From 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).

A digital illustration showing a globe with interconnected graphics related to artificial intelligence (AI), including a brain, a heart on a scale, various professionals, and technological elements.
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.”

A hand placing a transparent pyramid with a question mark on a maze-like structure, set against a scenic background of rolling hills and a golden sky.
Keeping humans in the loop. That means you!

Ralph Losey Copyright 2026 — All Rights Reserved


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.

Promotional graphic for the podcast 'Echoes of AI' featuring two caricatured hosts discussing lessons for the legal profession inspired by a viral meme.
Click here or the image to hear the podcast.

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