Quantum Echo: Nobel Prize in Physics Goes to Quantum Computer Trio (Two from Google) Who Broke Through Walls Forty Years Ago

October 24, 2025

Meanwhile, Even Bigger Breakthroughs by Google Continue

By Ralph Losey, October 21, 2025.

The Nobel Prize in Physics was just awarded to quantum physics pioneers John Clarke, Michel H. Devoret, and John M. Martinis for discoveries they made at UC Berkeley in the 1980s. They proved that quantum tunneling, where subatomic particles can break through seemingly impenetrable barriers, can also occur in the macroscopic world of electrical circuits. So yes, Schrödinger’s cat really could die.

A digital illustration featuring three scientists with varying facial expressions, posed in a futuristic setting, symbolizing breakthroughs in quantum computing. In the foreground, there is an artistic depiction of a cat with a skull overlay, creating a surreal contrast.
Quantum Physics Pioneers take home the Nobel Prize: John Clarke, Michel H. Devoret, and John M. Martinis. All images in this article are by Ralph Losey using AI image generation tools.

Their experiments showed that entire circuits can behave as single quantum objects, bridging the gap between theory and engineering. That breakthrough insight paved the way for construction of quantum computers, including the latest by Google.

Both Devoret and Martinis were recruited years ago by Google to help design its quantum processors. Although John Martinis (right, in the image above) recently departed to start his own company, Qolab, Michel Devoret (center) remains at Google Quantum AI as the Chief Scientist of Quantum Hardware. Last year, two other Google scientists, John Jumper and Demis Hassabis, shared a Nobel prize in chemistry for their groundbreaking work in AI.

Google is clearly on a roll here. As Google CEO Sundar Pichai joked in his congratulatory post on LinkedIn: “Hope Demis Hassabis and John Jumper are teaching you the secret handshake.”

A human hand shakes a holographic robotic hand in front of a quantum computer, with a Google logo in the background.
The secret handshake to Google’s Nobel Prizes is the combination of AI and Quantum.

🔹 Willow Breaks Through Its Own Barriers

Less than a year ago, Google’s new quantum chip, Willow, tunneled through its own barriers, performing in five minutes a calculation that would have taken ten septillion years (10²⁴) on the fastest classical supercomputers. That’s far longer than anyone’s estimate for the age of our universe—a good definition of mind-boggling.

This result led Hartmut Neven, director of Google’s Quantum Artificial Intelligence Lab, to suggest it offers strong evidence for the many-worlds or multiverse interpretation of quantum mechanics—the idea that computation may occur across near-infinite parallel universes. Neven and a number of leading researchers subscribe to this view.

I explored that seemingly crazy hypothesis in Quantum Leap: Google Claims Its New Quantum Computer Provides Evidence That We Live In A Multiverse (Jan 9, 2025). Oddly enough, it became my most-read article of all time—thank you, readers.

Today’s piece updates that story. The Nobel Prize recognition is icing on the cake, but progress has not slowed. Quantum computers—and the law—remain one of the most exciting frontiers in legal-tech. So much so that I’m developing a short online course on quantum computing and law, with more courses on prompt engineering for legal professionals coming soon. Subscribe to e-DiscoveryTeam.com to be notified when they launch.

The work of this year’s Nobel laureates—Clarke, Devoret, and Martinis—was done forty years ago, so delay in recognition is hardly unusual in this field. Perhaps someday Neven and other many-worlds interpreters of quantum physics will receive their own Nobel Prize for demonstrating multiverse-scale applications. In my view, far more evidence than speed alone will be required.

After all, it defies common sense to imagine, as the multiverse hypothesis suggests, that every quantum event splits reality, spawning a near-infinite array of universes. For example, one where Schrödinger’s cat is alive and another slightly different unoiverse where it is dead. It makes Einstein’s “spooky action at a distance seem tame by comparison.

An illustrated depiction of Schrödinger's cat concept, featuring a cartoon cat and a skeleton inside a wooden box, symbolizing the quantum mechanics thought experiment.
Spooky questions: Why are ‘you’ conscious in this particular universe? Are you dead in another?

In the meantime—whatever the true mechanism—quantum computers and AI are already producing tangible social and legal consequences in cryptography, cybercrime, and evidentiary law. See, The Quantum Age and Its Impacts on the Civil Justice System (Rand, April 29, 2025); Quantum-Readiness: Migration to Post-Quantum Cryptography (NIST, NSA, August, 2023); Quantum Computing Explained (NIST 8/22/2025); but see, Keith Martin, Is a quantum-cryptography apocalypse imminent? (The Conversation , 6/2/25) (“Expert opinion is highly divided on when we can expect serious quantum computing to emerge,” with estimates ranging from imminent to 20 years or more.)

Whether you believe in the multiverse or not, the practical implications for law and technology are already arriving.

Abstract illustration representing the multiverse theory with multiple cosmic spheres and the text 'MULTIVERSE THEORY' and 'INFINITE PARALLEL UNIVERSES'.
Might this theory someday seem like common sense? Or will most Universes discard it as another ‘spooky’ idea of experimental scientists?

🔹 Atlantic Quantum Joins Google Quantum AI

On October 2, 2025, Hartmut Neven, Founder and Lead, Google Quantum AI, announced in a short post titled “We’re scaling quantum computing even faster with Atlantic Quantum” that Google had just acquired. Atlantic Quantum is an MIT-founded startup developing superconducting quantum hardware. The announcement, written in Neven’s signature understated style, framed the deal as a practical step on Google’s long road toward “a large error-corrected quantum computer and real-world applications.”

Neven explained that Atlantic Quantum’s modular chip stack, which integrates qubits and superconducting control electronics within the cryogenic stage, will allow Google to “more effectively scale our superconducting qubit hardware.” That phrase may sound routine to non-engineers, but it represents a significant leap in design philosophy: merging computation and control at the cold stage reduces signal loss, simplifies architecture, and makes modular scaling—the key to fault-tolerant machines—realistically achievable. This is another great acquisition by Google.

Independent reporting quickly confirmed the deal’s importance. In Atlantic Quantum Joins Google Quantum AI, The Quantum Insider’s Matt Swayne summarized the deal succinctly:

• Google Quantum AI has acquired Atlantic Quantum, an MIT-founded startup developing superconducting quantum hardware, to accelerate progress toward error-corrected quantum computers. . . .
• The deal underscores a broader industry trend of major technology companies absorbing research-intensive startups to advance quantum computing, a field still years from large-scale commercial deployment.

The article noted that the integration of Atlantic Quantum’s modular chip-stack technology into Google’s program was aimed at one of quantum computing’s toughest engineering hurdles: scaling systems to become practical and fault-tolerant.

The MIT-born startup, founded in 2021 by a group of physicists determined to push superconducting design beyond incremental improvements, focused on embedding control electronics directly within the quantum processor. That approach reduces noise, simplifies wiring, and makes modular expansion far more realistic. For another take on the Atlantic story, see Atlantic Quantum and Google Quantum AI are “Joining Up” (Quantum Computing Report, 10/02/25).

These articles place the transaction within a broader wave of global investment in quantum technologies. Large-scale commercial deployment may still be years away but the industry has already entered a phase of consolidation. Research-heavy startups are increasingly being absorbed by major technology companies, a predictable evolution in a field defined by extraordinary capital demands and complex technical challenges.

For Google, the acquisition is less about headlines and more about infrastructure control, owning every layer of the superconducting stack from design to fabrication. For the industry, it signals that the next phase of quantum development will likely follow the same arc as classical computing: early-stage innovation absorbed by large, well-capitalized firms that can bear the cost of scaling.

For lawyers and regulators, that pattern has familiar consequences: intellectual-property concentration, antitrust scrutiny, export-control compliance, and the evidentiary standards that will eventually govern how outputs from such corporate-owned quantum systems are regulated and presented in court.

An illustration depicting the concept of innovation in the technology industry, contrasting 'Early-Stage Innovation' represented by small fish and a light bulb, with 'Large, Well-Capitalized Firms' represented by a shark featuring the Google logo. The background includes circuit patterns, symbolizing the tech ecosystem.
Familiar pattern and legal issues continue in our Universe.

🔹 Willow and the Many-Worlds Question

Before the Nobel bell rang in Stockholm, Google’s Quantum AI group had already changed the conversation with its Willow processor.

In my earlier piece on Willow’s mind-bending computations, I quoted Hartmut Neven’s ‘parallel universes’ framing to describe its behavior. Some heard music; others heard marketing. Others, like me, saw trouble ahead.

The Nobel Prize did not validate the many-worlds interpretation of quantum mechanics, nor did it disprove it. Neven has not backed away from the theory, nor have others, and Neven has just gotten the best talent from MIT to join his group. What the Nobel Prize did confirm—beyond any reasonable doubt—is that macroscopic superconducting circuits, at a size you can see, can exhibit genuine quantum behavior under controlled laboratory conditions. That is the solid foundation a judge or regulator can stand on: devices now exist in our world that generate outputs with quantum fingerprints reproducible enough to test and verify.

Meanwhile, the frontier continues to move. In September 2025, researchers at UNSW Sydney demonstrated entanglement between two atomic nuclei separated by roughly twenty nanometers, See, “New entanglement breakthrough links cores of atoms, brings quantum computers closer” (The Conversation, Sept. 2025). Twenty nanometers is not big, but it is large enough to measure.

Moreover, even though the electrical circuits themselves are large enough to photograph, the quantum energy was not. That could only be measured indirectly. The researchers used coupled electrons as what lead scientist Professor Andrea Morello called “telephones” to pass quantum correlations and make those measurements.

An artistic representation of quantum entanglement, featuring glowing atomic particles connected by luminous paths, illustrating the complex interactions in quantum mechanics.
Electrons acting like telephones passing quantum correlations on measurable scales.

The telephone metaphor is apt. It captures the engineering ambition behind the result—connecting quantum rooms with wires, not whispers. Whispers don’t echo. Entanglement is not a philosophical idea; it is a measurable resource that can be distributed, controlled, and eventually commercialized. It can even call home.

For the legal system, this is where things become concrete. When entanglement leaves the lab and enters communications or sensing devices, courts will be asked to evaluate evidence that can be measured and described but cannot be seen directly. The question will no longer be “Is this real?” but “How do we authenticate what can be measured but not observed?”

That’s the moment when the physics of quantum control becomes the jurisprudence of evidence—and it’s coming faster than most practitioners realize.

A surreal painting depicting several figures whispering to each other in an arched, dimly lit setting, with wave-like patterns of light radiating from a central source.
Whispers Don’t Echo.

🔹 Defining the Echo: When Evidence Repeats With a Slight Accent

The many-worlds interpretation of quantum mechanics has always sat on the thin line between physics and philosophy. First proposed in 1957 by Hugh Everett, it replaces the familiar ‘collapse‘ of the wave-function with a more radical notion: every quantum event splits reality into separate branches, each continuing independently. Some brilliant physicists take it seriously; others reject it; many remain agnostic. Courts need not resolve that debate. For law, the relevant question is simpler: can a party show a method that reliably connects a claimed quantum mechanism to a particular output? If yes, the court’s job is to hear the evidence. If not, the court’s job is to exclude it.

In its early decades, the idea was mostly dismissed as metaphysical excess. Then  Bryce DeWittDavid DeutschMax Tegmark and Sean Carroll each found ways to refine and defend it. David Deutsch, known as the Father of Quantum Comnputing, first argued that quantum computers might actually use this multiplicity to perform computations—each universe branch carrying part of the load. See e.g., Deutsch, The Fabric of Reality: The Science of Parallel Universes–and Its Implications (Penguin, 1997) (Chapter 9, Quantum Computers). Deutsch even speculates in his next (2011) book The Beginning of Infinity (pg. 294) that some fiction, such as alternate history, could occur somewhere in the multiverse, as long as it is consistent with the laws of physics.

The many-world’s argument, once purely theoretical, gained traction after Google’s Willow experiments. Hartmut Neven’s reference to “parallel universes” was not an assertion of proof but a shorthand for describing interference effects that defy classical intuition. It is what he believes was happening—and that opinion carries weight because he works with quantum computers every day.

When quantum behavior became experimentally measurable in superconducting circuits that were large enough to photograph, the Everett question—’Are we branching universes or sampling probabilities?‘—stopped being rhetorical. The debate moved from thought experiment to instrument design. Engineers now face what philosophers only imagined: how to measure, stabilize, and interpret outcomes that occur across many possible worlds and never converge on a single, deterministic path.

For the law, the relevance lies not in metaphysics but in method. Whether the universe splits or probabilities collapse, the data these machines produce are inherently probabilistic—repeatable only within margins, each time with a slight accent. The courtroom analog to wave-function collapse is the evidentiary demand for reproducibility. If the physics no longer promises identical outputs, the law must decide what counts as reliable sameness—echoes with an accent.

That shift from metaphysics to methodology is the lawyer’s version of a measurement problem. It’s not about believing in the multiverse. It’s about learning how to authenticate evidence that depends on it.

A vibrant abstract representation of quantum physics, featuring concentric circles and spheres radiating in a spectrum of colors, symbolizing subatomic particles and quantum behavior.
Repeatable measurements through parallel universes to explain quantum computer calculations. Crazy but true?

🔹 The Law Listens: Authenticating Echoes in Practice

If each quantum record is an echo, the law’s task is to decide which echoes can be trusted. That requires method, not metaphysics. The legal system already has the tools—authentication, replication, expert testimony—but they need recalibration for an age when precision itself is probabilistic.

1. Authentication in context.
Under Rule 901(b)(9), evidence generated by a process or system must be shown to produce accurate results. In a quantum context, that showing might include the type of qubit, its error-correction protocol, calibration logs, environmental controls, and the precise code path that produced the output. The burden of proof doesn’t change; only the evidentiary ingredients do.

2. Replication hearings.
In classical computing, replication is binary—either a hash matches, or it doesn’t. In quantum systems, replication becomes statistical. The question is no longer “Can this be bit-for-bit identical?” but “Does this fall within the accepted variance?” Probabilistic systems demand statistical fidelity, not sameness. A replication hearing becomes a comparison of distributions, not exact strings of bits.

Similar logic already guides quantum sensing and metrology, where entanglement and superposition improve precision in measuring magnetic fields, time, and gravitational effects. See Quantum sensing and metrology for fundamental physics (NSF, 2024); Review of qubit-based quantum sensing (Springer, 2025); Advances in multiparameter quantum sensing and metrology (arXiv, 2/24/25); Collective quantum enhancement in critical quantum sensing (Nature, 2/22/25). Those readings vary from one run to the next, yet the variance itself confirms the physics—each measurement is a statistically faithful echo of the same underlying reality. The variances are within a statistically acceptable range of error.

An abstract illustration showing a silhouette of a person standing next to a swirling vortex surrounded by circular shapes and geometric lines, representing concepts of quantum mechanics and the multiverse.
Each measurements is slightly different but similar enough to be statistically faithful echoes of the same underlying reality.

🔹 Two Examples from the Quantum Frontier

1. Quantum Chemistry In Practice.

One of the most mature quantum applications today is the Variational Quantum Eigensolver (VQE), a hybrid quantum-classical algorithm used to estimate the ground-state energy of molecules. See, The Variational Quantum Eigensolver: A review of methods and best practices (Phys. Rep., 2023); Greedy gradient-free adaptive variational quantum algorithms on a noisy intermediate scale quantum computer (Nature, 5/28/25). Also see, Distributed Implementation of Variational Quantum Eigensolver to Solve QUBO Problems (arXiv, 8/27/25); How Does Variational Quantum Eigensolver Simulate Molecules? (Quantum Tech Explained, YouTube video, Sept. 2025).

VQE researchers routinely run the same circuit hundreds of times; each iteration yields slightly different energy readings because of noise, calibration drift, and quantum fluctuations. Yet the outputs consistently cluster around a stable baseline, confirming both the accuracy of the physical model and the reliability of the machine itself.

Now picture a pharmaceutical patent dispute where one party submits quantum-derived binding data for a new molecule. The opposing side demands replication. A court applying Rule 702 may not expect identical numbers—but it could require expert testimony showing that results consistently fall within a scientifically accepted margin of error. If they do, that should become a legally sufficient echo.

This is reminiscent of prior disputes e-discovery concerning the use of AI to find relevant documents. It has been accepted by all courts that perfection, such as 100% recall, is never required, but reasonable efforts are required. Judge Andrew Peck, Hyles vNew York City, No. 10 Civ. 3119 (AT)(AJP), 2016 WL 4077114 (S.D.N.Y. Aug. 1, 2016). This also follows the official commentary of Rule 702, on expert testimony, where “perfection is not required.” Fed. R. Evid. 702, Advisory Committee Note to 2023 Amendment.

The reasonable efforts can be proven by numerics and testimony. See for instance my writings in the TAR Course: Fifteenth Class- Step Seven – ZEN Quality Assurance Tests (e-Discovery Team, 2015) (Zero Error Numerics); ei-Recall (e-Discovery Team, 2015); Some Legal Ethics Quandaries on Use of AI, the Duty of Competence, and AI Practice as a Legal Specialty (May, 2024).

An illustration emphasizing the phrase 'Reasonable efforts required, not perfection,' featuring a checklist with a checkmark, scales of justice, and a prohibition symbol.
There is no perfect case, evidence or efforts. In reality, ‘perfect is the enemy of the good.’

2. Quantum-Secure Archives.

As quantum computing and quantum cryptography advance, most (but not all) of today’s encryption will become obsolete. This means the vast amount of encrypted data stored in corporate and governmental archives—maintained for regulatory, evidentiary, and operational purposes—may soon be an open book to attackers. Yes, you should be concerned.

Rich DuBose and Mohan Rao, Harvest now, decrypt later: Why today’s encrypted data isn’t safe forever (Hashi Corp., May 21, 2025) explain:

Most of today’s encryption relies on mathematical problems that classical computers can’t solve efficiently — like factoring large numbers, which is the foundation of the Rivest–Shamir–Adleman (RSA) algorithm, or solving discrete logarithms, which are used in Elliptic Curve Cryptography (ECC) and the Digital Signature Algorithm (DSA). Quantum computers, however, could solve these problems rapidly using specialized techniques such as Shor’s Algorithm, making these widely used encryption methods vulnerable in a post-quantum world.

Also see, Dan Kent, Quantum-Safe Cryptography: The Time to Start Is Now (Gov.Tech., 4/30/25) and Amit Katwala, The Quantum Apocalypse Is Coming. Be Very Afraid (Wired, Mar. 24, 2025), warning that cybersecurity analysts already call this future inflection point Q-Day—the day a  quantum computer can crack the most widely used encryption. As Katwala writes:

On Q-Day, everything could become vulnerable, for everyone: emails, text messages, anonymous posts, location histories, bitcoin wallets, police reports, hospital records, power stations, the entire global financial system.

Most responsible organizations with large archives of sensitive data have been preparing for Q-Day for years. So too have those on the other side—nation-states, intelligence services, and organized criminal groups—who are already harvesting encrypted troves today to decrypt later. See, Roger Grimes, Cryptography Apocalypse: Preparing for the Day When Quantum Computing Breaks Today’s Crypto (Wiley, 2019). The race for quantum supremacy is on.

Now imagine a company that migrates its document-management system to post-quantum cryptography in 2026. A year later, a breach investigation surfaces files whose verification depends on hybrid key-exchange algorithms and certificate chains. The plaintiff calls them anomalies; the defense calls them echoes. The court won’t choose sides by theory—it will follow the evidence, the logs, and the math.

An artistic representation of an hourglass with celestial spheres and swirling galaxies, symbolizing the concept of time and the multiverse in quantum physics.
The metrics are what should matter, not the many theories

🔹 Building the Quantum Record

Judicial findings and transparency. Courts can adapt existing frameworks rather than invent new ones. A short findings order could document:
(a) authentication steps taken;
(b) observed variance;
(c) expert consensus on reliability; and
(d) scope limits of admissibility.
Such transparency builds a common-law record—the first body of quantum-forensic precedent. I predict it will be coming soon to a universe near you!

Chain of custody for the probabilistic age. Future evidence protocols may pair traditional logs with variance ranges, confidence intervals, and error budgets. Discovery rules could require disclosure of device calibration history, firmware versions, and known noise parameters. The data once confined to labs will become essential for authentication.

The law doesn’t need new virtues for quantum evidence; it needs old ones refined. Transparency, documentation, and replication remain the gold standard. What changes is the expectation of sameness. The goal is no longer perfect duplication, but faithful resonance: the trusted echo that still carries truth through uncertainty.

An artistic depiction of a swirling vortex, featuring an hourglass shape with vibrant colors, symbolizing the concept of multiverses and quantum physics. Small planets are depicted within the flow, representing various realities branching out from a central point of light.
Metrics carry the truth through uncertainty.

🔹 Conclusion: The Sound of Evidence

The Nobel Committee rang the bell. Google’s engineers adding instruments. Labs in Sydney and elsewhere wired new rooms together. The rest of us—lawyers, paralegals, judges, legal-techs, investigators—must learn how to listen for echoes without hearing ghosts. That means resisting hype, insisting on method, and updating our checklists to match what the devices actually do.

Eight months ago in Quantum Leap, I described a canyon where a single strike of an impossible calculation set the walls humming. This time, the sound came from Stockholm. If the next echo is from quantum evidence in your courtroom—perhaps as a motion in limine over non-identical logs—don’t panic. Listen for the rhythm beneath the noise. The law’s task is to hear the pattern, not silence the world.

Science, like law, advances by listening closely to what reality whispers back. The Nobel Committee just honored three physicists for demonstrating that quantum behavior can be engineered, measured, and replicated—its fingerprints recorded even when the phenomenon itself remains invisible. Their achievement marks a shift from theory to tested evidence, a shift the courts will soon confront as well.

When engineers speak of quantum advantage, they mean a moment when machines perform tasks that classical systems cannot. The legal system will have its own version: a time when quantum-derived outputs begin to appear in contracts, forensic analysis, and evidentiary records. The challenge will not be cosmic. It will be procedural. How do you test, authenticate, and trust results that vary within the bounds of physics itself?

The answer, as always, lies in method. Law does not require perfection; it requires transparency and proof of process. When the next Daubert hearing concerns a quantum model rather than a mass spectrometer, the same questions will apply: Was the procedure sound? Were the results reproducible within accepted error? Were the foundations laid? The physics may evolve, but the evidentiary logic remains timeless.

In the end, what matters is not whether the universe splits or probabilities collapse. What matters is whether we can recognize an honest echo when we hear one—and admit it into evidence.

An artistic representation of a cosmic hourglass surrounded by swirling galaxies and planets, symbolizing time, the universe, and the concept of the multiverse.
It is only a matter of time before quantum generated evidence seeks admission to your world.

🔹 Postscript.

Minutes before this article was published Google announced an important new discovery called “Quantum ECHO.” Yes, same name as this article, written by Ralph Losey with no advance notice from Google of the discovery or name. A spooky entanglement, perhaps? Ralph will publish a sequel soon that spells out what Google has done now. In the meantime, here is Google’s announcement by Hartmut Neven\ and Vadim Smelyanskiy, Our Quantum Echoes algorithm is a big step toward real-world applications for quantum computing (Google, 10/22/25).

🔹 Subscribe and Learn More

If this exploration of Quantum Echoes and evidentiary method has sparked your curiosity, you can find much more at e-DiscoveryTeam.com — where I continue to write about artificial intelligence, quantum computing, evidence, e-discovery, and the future of law. Go there to subscribe and receive email notices of new blogs and upcoming courses, and special events — including an online course, with a working title Quantum Law: From Entanglement to Evidence,‘ that will expand on the ideas introduced here. It will discuss how quantum physics and AI converge in the practice of law, from authentication and reliability to discovery and expert testimony.

That program will be followed by two other, longer online courses that are also near completion:

  • Beginner “GPT-4 Level” Prompt Engineering for Legal Professionals,’ a practical foundation in AI literacy and applied reasoning.
  • Advanced “GPT-5 Level” Prompt Engineering for Legal Professionals,’ an in-depth study of prompt design, model evaluation, and AI ethics.

All courses are part of my continuing effort to help the legal profession adapt responsibly to the next wave of technology — with integrity, experience and whatever wisdom I may have accidentally gathered from a long life on Earth.

A contemplative figure stands in a futuristic hallway lined with framed portals, each leading to different cosmic landscapes, while a bright light emanates from above.
Ralph looking back on the many worlds of technology he has been in. What a long, strange trip its been.

Subscribe at e-DiscoveryTeam.com for notices of new articles, course announcements, and research updates.

Because the future of law won’t be written by those who fear new tools, but by those who understand the evidence they produce.


Ralph C. Losey is an attorney, educator, and author of e-DiscoveryTeam.com, where he writes about artificial intelligence, quantum computing, evidence, e-discovery, and emerging technology in law.

© 2025 Ralph C. Losey. All rights reserved.



Navigating AI’s Twin Perils: The Rise of the Risk-Mitigation Officer

July 28, 2025

Ralph Losey, July 28, 2025

Generative AI is not just disrupting industries—it is redefining what it means to trust, govern, and be accountable in the digital age. At the forefront of this evolution stands a new, critical line of employment: AI Risk-Mitigation Officers. This position demands a sophisticated blend of technical expertise, regulatory acumen, ethical judgment, and organizational leadership. Driven by the EU’s stringent AI Act and a rapidly expanding landscape of U.S. state and federal compliance frameworks, businesses now face an urgent imperative: manage AI risks proactively or confront severe legal, reputational, and operational consequences.

Click to see photo come to life. Image and movie by Losey with AI.

This aligns with a growing consensus: AI, like earlier waves of innovation, will create more jobs than it eliminates. The AI Risk-Mitigation Officer stands as Exhibit A in this next wave of tech-era employment. See e.g. my last series of blogs, Part Two: Demonstration by analysis of an article predicting new jobs created by AI (27 new job predictions); Part Three: Demo of 4o as Panel Driver on New Jobs (more experts discuss the 27 new jobs). See Also: Robert Capps, NYT magazine article: A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You.  (NYT Magazine, June 17, 2025). In a few key areas, humans will be more essential than ever.

Risk Mitigation Officer team image by Losey and AI.

Defining the Role of the AI Risk-Mitigation Officer

The AI Risk-Mitigation Officer is a strategic, cross-functional leader tasked with identifying, assessing, and mitigating risks inherent in AI deployment.

While Chief AI Officers drive innovation and adoption, Risk-Mitigation Officers focus on safety, accountability, and compliance. Their mandate is not to slow progress, but to ensure it proceeds responsibly. In this respect, they are akin to data protection officers or aviation safety engineers, guardians of trust in high-stakes systems.

This role requires a sober analysis of what can go wrong—balanced against what might go wonderfully right. It is a job of risk mitigation, not elimination. Not every error can or should be prevented; some mistakes are tolerable and even expected in pursuit of meaningful progress.

The key is to reduce high-severity risks to acceptable levels—especially those that could lead to catastrophic harm or irreparable public distrust. If unmanaged, such failures can derail entire programs, damage lives, and trigger heavy regulatory backlash.

Both Chief AI Officers and Risk-Mitigation Officers ultimately share the same goal: the responsible acceleration of AI, including emerging domains like AI-powered robotics.

The Risk-Mitigation Office should lead internal education efforts to instill this shared vision—demonstrating that smart governance isn’t an obstacle to innovation, but its most reliable engine.

Team leader tries to lighten the mood of their serious work. Click for video by Losey.

Why The Role is Growing

The acceleration of this role is not theoretical. It is propelled by real-world failures, regulatory heat, and reputational landmines.

The 2025 World Economic Forum’s Future of Jobs Report underscores that 86% of surveyed businesses anticipate AI will fundamentally transform their operations by 2030. While AI promises substantial efficiency and innovation, it also introduces profound risks, including algorithmic discrimination, misinformation, automation failures, and significant data breaches.

A notable illustration of these risks is the now infamous Mata v. Avianca case, where lawyers relied on AI to fabricate case law, underscoring why human verification is non-negotiable. Mata v. Avianca, Inc., No. 1:2022cv01461 – Document 54 (S.D.N.Y. June 22, 2023). Courts responded with sanctions. Regulators took notice. The public laughed, then worried.

The legal profession worldwide has been slow to learn from Mata the need to verify and take other steps to control AI hallucinations. See Damien Charlotin, AI Hallucination Cases (as of July 2025 over 200 cases and counting have been identified by the legal scholar in Paris with an ironic name for this work, Charlotin). The need for risk mitigation is growing fast. You cannot simply pass the buck to AI.

Charlatan lawyers blame others for their mistakes. Image by Losey.

Risk Mitigation employees and other new AI related hires will balance out the AI generated layoffs. The 2025 World Economic Forum’s Future of Jobs Report at page 25 predicted that by 2030 there will be 11 million new jobs created and 9 million old jobs phased out. We think the numbers will be higher in both columns, but the 11/9 ratio may be right overall all, meaning a 22% net increase in new jobs.

We think the ratio the WEF predicts is right for all industries overall, but the numbers are too low, meaning greater disruption but positive in the end with even more new jobs. Image by AI & Losey.

Core Responsibilities

AI Risk-Mitigation Officers are part legal scholar, part engineer, part diplomat. They know the AI Act, understand neural nets, and can hold a room full of regulators or engineers without flinching. Key responsibilities encompass:

  • AI Risk Audits: Spot trouble before it starts. Bias, black boxes, security flaws—find them early, fix them fast. This involves detailed pre-deployment evaluations to detect biases, security vulnerabilities, explainability concerns, and data protection deficiencies. Good practice to follow-up with periodic surprise audits.
  • Incident Response Management: Don’t wait for a headline to draft a playbook. Develop and lead response protocols for AI malfunctions or ethical violations, coordinating closely with legal, PR, and compliance teams.
  • Legal Partnership: Speak both code and contract. Collaborate with in-house counsel to interpret AI regulations, draft protective contractual clauses, and anticipate potential liability.
  • Ethics Training: Culture is the best control layer. Educating employees on responsible AI use and cultivating an ethical culture that aligns with both corporate values and regulatory standards.
  • Stakeholder Engagement: Transparency builds trust. Silence breeds suspicion. Bridging communication between technical teams, executive leadership, regulators, and the public to maintain transparency and foster trust.
One Core Responsibility of the Rick Mitigation Team. Image by Losey.

Skills and Pathways

Professionals in this role must possess:

  • Regulatory Expertise: Detailed knowledge of EU AI Act, GDPR, EEOC guidelines, and evolving state laws in the U.S.
  • Technical Proficiency: Deep understanding of machine learning, neural networks, and explainable AI methodologies.
  • Sector-Specific Knowledge: Familiarity with compliance standards across sectors such as healthcare (HIPAA, FDA), finance (SEC, MiFID II), and education (FERPA).
  • Strategic Communication: Ability to effectively mediate between AI engineers, executives, regulators, and stakeholders.
  • Ethical Judgment: Skills to navigate nuanced ethical challenges, such as balancing privacy with personalization or fairness with automation efficiency.

Career pathways for AI Risk-Mitigation Officers typically involve dual qualifications in fields like law and data science, certifications from professional organizations and practical experience in areas like cybersecurity, legal practice, politics or IT. Strong legal and human relationship skills are a high priority.

Image in modified isometric style by Ralph Losey using modified AIs.

U.S. and EU Regulatory Landscapes

The EU codifies risk tiers. The U.S. litigates after the fact. Navigating both requires fluency in law, logic, and diplomacy.

The EU’s AI Act classifies AI systems into four risk categories:

  • Unacceptable. AI banned altogether due to the high-risk of violating fundamental rights. Examples include social scoring systems and real-time biometric identification in public spaces, including emotion recognition in workplaces and schools. Also bans AI-enabled subliminal or manipulative techniques that can be used to persuade individuals to engage in unwanted behaviors.
  • High. AI that could negatively impact the rights or safety of individuals. Examples include AI systems used in critical infrastructures (e.g. transport), legal (including policing and border patrol), medical, educational, financial services, workplace management, and influencing elections and voter behavior.
  • Limited. AI with lower levels of risk than high-risk systems but are still subject to transparency requirements. Examples include typical chatbots where providers must make humans aware Ai is used.
  • Minimal. AI systems that present little risk of harm to the rights of individuals. Examples include AI-powered video games and spam filters. No rules.
Transparent AI in office setting. Click for Losey movie.

Regulation is on a sliding scale with Unacceptable risk AIs banned entirely and Minimal to No risk category with few if any restrictions. Limited and High risk classes require varying levels of mandatory documentation, human oversight, and external audits.

Meanwhile, U.S. regulatory bodies like the FTC and EEOC, along with state legislatures and state enforcement agencies, are starting to sharpen oversight tools. So far the focus has been on controlling deception, data misuse, bias and consumer harm. This has become a hot political issue in the U.S. See e.g. Scott Kohler, State AI Regulation Survived a Federal Ban. What Comes Next? (Carnegie’s Emissary, 7/3/25); Brownstein, et al, States Can Continue Regulating AI—For Now (JD Supra, 7/7/25).

AI Risk-Mitigation Officers must navigate these disparate regulatory landscapes, harmonizing proactive European requirements with the reactive, litigation-centric U.S. environment.

Legal Precedents and Ethical Challenges

Emerging legal precedents emphasize human accountability in AI-driven decisions, as evidenced by litigation involving biased hiring algorithms, discriminatory credit scoring, and flawed facial recognition technologies. Ethical dilemmas also abound. Decisions like prioritizing efficiency over empathy in healthcare, or algorithmic opacity in university admissions, require human-centric governance frameworks.

In ancient times, the Sin Eater bore others’ wrongs. Part Two: Demonstration by analysis of an article predicting new jobs created by AI (discusses new sin eater job in detail as a person who assumes moral and legal responsibility for AI outcomes). Today’s Risk-Mitigation Officer is charged with an even more difficult task; try to prevent the sins from happening at all or at least reduce them enough to avoid hades.

Sin Eater in combined quasi digital styles by Losey using sin-free AIs.

Balancing Innovation and Regulation

Cases such as Cambridge Analytica (personal Facebook user data used for propaganda to influence elections) and Boeing’s MCAS software (use of AI system undisclosed to pilots led to crash of a 737) demonstrate that innovation without reasonable governance can be an invitations to disaster. The obvious abuses and errors in these cases could have been prevented had there been an objective officer in the room, really any responsible adult considering risks and moral grounds. For a more recent example, consider XAI’s recent fiasco with its chatbot. Grok Is Spewing Antisemitic Garbage on X (Wired, 7/8/25).

Cases like this should put us on guard but not cause over-reaction. Disasters like this easily trigger too much caution and too little courage. That too would be a disaster of a different kind as it would rob us of much needed innovation and change.

Reasonable, practical regulation can foster innovation by mitigating uncertainty and promoting stakeholder confidence. The trick is to find a proper balance between risk and reward. Many think regulators today tend to go too far in risk avoidance. They rely on Luddite fears of job loss and end-of-the world fantasies to justify extreme regulations. Many think that such extreme risk avoidance helps those in power to maintain the status quo. The pro-tech people instead favor a return to the fast pace of change that we have seen in the past.

Polarized protestors for and against technology by Losey using AI job thieves.

We went to the Moon in 1969. Yet we’re still grounded in 2025. Fear has replaced vision. Overregulation has become the new gravity.

That is why anti-Luddite technology advocates yearn for the good old days, the sixties, where fast paced advances and adventure were welcome, even expected. If you had told anyone back in 1969 that no Man would return to the Moon again, even as far out as 2025, you would have been considered crazy. Why was John F. Kennedy the last bold and courageous leader? Everyone since seems to have been paralyzed with fear. None have pushed great scientific advances. Instead, politicians on both sides of the aisle strangle NASA with never-ending budget cuts and timid machine-only missions. It seems to many that society has been overly risk adverse since JFK and this has stifled innovation. This has robbed the world of groundbreaking innovations and the great rewards that only bold innovations like AI can bring.

Take a moment to remember the 1989 movie Back to the Future Part II. In the movie “Doc” Brown, an eccentric, genius scientist, went from the past – 1985 – the the future of 2015. There we saw flying cars powered by Mr. Fusion Home Energy Reactors, which were trash fueled fusion engines. That is the kind of change we all still expected back in 1989 for the far future of 2015; but now, in 2025, ten years past the projected future, it is still just a crazy dream. The movie did, however, get many minor predictions right. Think of flat-screen tvs, video-conferences, hoverboards and self-tying shoes (Nike HyperAdapt 1.0.) Fairly trivial stuff that tech go-slow Luddites approved.

Back to the Future movie made in 1989 envisioning flying cars in 2015. Now in 2025, what have we got? Photo and VIDEO by Losey using AI image generation tools.

Conclusion

The best AI Risk-Mitigation Officers will steer between the twin monsters of under-regulation and overreach. Like Odysseus, they will survive by knowing the risks and keeping a steady course between them.

They will play critical roles across society—in law firms, courts, hospitals, companies (for-profit, non-profit, and hybrid), universities, and government agencies. Their core responsibilities will include:

  • Standardization Initiatives: Collaborating with global standards organizations such as ISO, NIST, and IEEE to craft reasonable, adaptable regulations.
  • Development of AI Governance Tools: Encouraging the use of model cards, bias detection systems, and transparency dashboards to track and manage algorithmic behavior.
  • Policy Engagement and Lobbying: Actively engaging with legislative and regulatory bodies across jurisdictions—federal, state, and international—to advocate for frameworks that balance innovation with public protection.
  • Continuous Learning: Staying ahead of rapid developments through ongoing education, credentialing, and immersion in evolving legal and technological landscapes.
AI Risk Management specialists will end up with sub-specialties, maybe dept. sections, such as lobbying and research. Image by Losey daring to use AI.

As this role evolves, AI Risk Management specialists will likely develop sub-specialties—possibly forming distinct departments in areas such as regulatory lobbying, algorithm auditing, compliance architecture, and ethical AI research, both hands-one and legal study.

This is a fast-moving field. After writing this article we noticed the EU passed new rules for the largest AI companies only, all US companies of course, except one Chinese. It is non-binding at this point and involves highly controversial disclosure and copyright restriction. EU’s AI code of practice for companies to focus on copyright, safety (Reuters, July 10, 2025). It could stifle chatbot development and lead the EU in a stagnant deadline as these companies withdraw from the EU rather than kill their own models to comply.

Slowdown or reduction is not a viable option at this point because of national security concerns. There is a military race now between the US and China based on competing technology. AGI level of AI will give the first government to attain it a tremendous military advantage. See e.g., Buchanan, Imbrie, The New Fire: War, Peace and Democracy in the Age of AI (MIT Press, 2022); Henry Kissinger, Allison. The Path to AI Arms Control: America and China Must Work Together to Avert Catastrophe, (Foreign Affairs, 10/13/23); Also See, Dario Amodei’s Vision (e-Discovery team, Nov. 2024) (CEO of Anthropic, Darion Amodei, warns of danger of China winning the race for AI supremacy).

Race to super-intelligent AGI image by Ralph Losey.

As Eric Schmidt explains it, this is now an existential threat and should be a bipartisan issue for survival of democracy. Kissinger, Schmidt, Mundie, Genesis: Artificial Intelligence, Hope, and the Human Spirit, (Little Brown, Nov. 2024). Also See Former Google CEO Eric Schmidt says America could lose the AI race to China (AI Ascension, May 2025).

Organizations that embrace this new professional archetype will be best positioned to shape emerging regulatory frameworks and deploy powerful, trusted AI systems—including future AI-powered robots. The AI Risk-Mitigation Officer will safeguard against catastrophic failure without throttling progress. In this way, they will help us avoid both dystopia and stagnation.

Yes, this is a demanding job. It will require new hires, team coordination, cross-functional fluency, and seamless collaboration with AI assistants. But failure to establish this critical function risks danger on both fronts: unchecked harm on one side, and paralyzing caution on the other. The best Risk-Mitigation Officers will navigate between these extremes—like Odysseus threading his ship through Scylla and Charybdis.

Odysseus successfully steering his ship between monsters on either side. Image and Video by Losey using various AIs.

We humans are a resilient species. We’ve always adapted, endured, and risen above grave dangers. We are adventurers and inventors—not cowering sheep afraid of the wolves among us.

The recent overregulation of science and technology is an aberration. It must end. We must reclaim the human spirit where bold exploration prevails, and Odysseus—not  Phobos—remains our model.

We can handle the little thinking machines we’ve built, even if the phobic establishment wasn’t looking. Our destiny is fusion, flying cars, miracle cures, and voyages to the Moon, Mars, and beyond.

Innovation is not the enemy of safety—it is its partner. With the right stewards, AI can carry us forward, not backward.

Let’s chart the course.

Manifest Destiny of Mankind. Image and VIDEO by Losey using AI.

PODCAST

As usual, we give the last words to the Gemini AI podcasters who chat between themselves about the article. It is part of our hybrid multimodal approach. They can be pretty funny at times and have some good insights, so you should find it worth your time to listen. Echoes of AI: The Rise of the AI Risk-Mitigation Officer: Trust, Law, and Liability in the Age of AI. Hear two fake podcasters talk about this article for a little over 16 minutes. They wrote the podcast, not me. For the second time we also offer a Spanish version here. (Now accepting requests for languages other than English.)

Click here to listen to the English version of the podcast.

Ralph Losey Copyright 2025 – All Rights Reserved.


Panel of Experts for Everyone About Anything – Part Three: Demo of 4o as Panel Driver on New Jobs

July 18, 2025

by Ralph Losey, July 18, 2025

This is the conclusion to the article, Panel of Experts for Everyone About Anything – Part One and Part Two. Here we give another demonstration of the software described in Part One. Part Two provided a demonstration where the software used the advanced reasoning model of OpenAI, ChatGPT 4.5. The demonstration included expert discussion of the hot issue of future employment. Here we use the free version, ChatGPT 4o, to see how it does and get another perspective about new jobs that will be needed with AI. The results are surprising.

Expert panels debating. All images in article are in Steampunk style this time and by Ralph Losey using AI.

Introduction

In Panel of Experts for Everyone About Anything – Part Two, we demonstrated how the Panel software works to analyze an article. We picked a NYT Magazine article that predicted 24 new jobs that would be created by AI. The demonstration used ChatGPT 4.5 as the engine. OpenAI now allows a user to select any model to which they have access, to drive a custom GPT. (For reasons unknown, this feature currently does not work on the Mac desktop version of ChatGPT.) The 4.5 model used in Part Two now requires an upgraded subscription, whereas model 4o is still free.

The cost of 4.5 may be beyond the reach of legal aid groups and some in the public using it for non-law-related questions. Thus, to be consistent with the public service intent behind the free software mentioned in Part One, we here demonstrate the software using ChatGPT 4o. For ease of comparison, and because the question of new jobs created by AI is so important, we repeat the issue again but throw in a few variations to keep it interesting.

Panels of Experts do battle to see who is the best. Image by Ralph Losey using AI.

How well will the ChatGPT 4o expert team do against the allegedly better 4.5 model? Will we learn anything new about jobs created by AI in Part Three that we did not already know from Part Two? Which panel is the winner? Which gave the best demonstration?

Read on and discover using classic steampunk style images, because, after all, once Einstein discovered Relativity, and Science quickly jumped into Quantum, shouldn’t we have continued to advance at that accelerated rate and discovered AI long ago? Think where we’d be today if we did, eh Mr. Babbage?

Charles Babbage (1791-1871), ‘father of computers’ shown in steampunk style by Losey. What took us so long?

First Demo of 4o Model Version

In some respects, I like the 4o version of the session better than the Model 4.5 version shown in Part Two. Here are the key excerpts from the response of the Panel of Experts for Everyone About Anything when we repeated the same initial User Prompt, namely to analyze the NYT’s article by Robert Capps, A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You.

4o PANEL: Let’s curate a panel to explore the implications of the NYT article “A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You”. Before we begin the panel discussion, I’ll propose eight candidate experts based on the article’s focus on AI disruption, new job creation, and socio-technical dynamics. You’ll choose four, and I’ll automatically add the mandatory Contrarian.

🎯 Article Focus:

  • Trust-based roles (e.g., AI auditors, ethicists)
  • Integration roles (e.g., AI plumbers, integrators)
  • Taste and creative decision-making (e.g., world designers)
  • Socioeconomic shifts due to AI (entrepreneurial entry-level roles)

👥 Suggested Panelists:

  1. Ethical AI Lawyer – interprets legal responsibility and liability frameworks for trust-based roles.
  2. AI Engineer – evaluates technical feasibility and skill demands of new AI-centric jobs.
  3. Creative Storyteller – articulates the evolution of taste-driven roles like world designers and content curators.
  4. Risk Officer – identifies governance and regulatory gaps in emerging hybrid AI-human workplaces.
  5. Data Visionary – maps future employment and systemic transformation under AI’s rise.
  6. Litigation Strategist – examines legal risks of roles like “legal guarantor” or “AI auditor” under current jurisprudence.
  7. Public Health Advocate – reflects on AI’s impact on human dignity, trust, and socio-emotional labor.
  8. Quantum Analyst – cross-reads how AI role disruption mirrors past technology shifts (like CAD/CAM or simulation in science).

Please select four experts from this list. I’ll then add the Contrarian and begin the panel.

USER: I choose 1, 2, 3 and 5.

4o PANEL: . . .

Panel on new jobs created by AI in steampunk style by Losey.

🎓 Ethical AI Lawyer — Legal and Ethical Responsibility Architect

The article’s treatment of trust-based roles like “AI auditors,” “legal guarantors,” and “trust authenticators” directly engages legal norms surrounding delegation of accountability. These roles resemble constructs already debated in U.S. case law like Mata v. Avianca Inc., where counsel submitted AI-generated briefs with hallucinated citations, prompting sanctions and affirming that humans remain the accountable party despite automation.

The concept of the “sin eater” — someone who absorbs A.I.’s mistakes — raises sharp questions about the limits of vicarious liability and professional standards. No matter how advanced the AI, current jurisprudence requires traceability to a human agent. This aligns with the Fed. R. Evid. 901(b)(9) authentication requirements for complex systems, demanding explainability and provenance.

Further, the emergence of “AI translators” and “ethics boards” intersects with legal debates over due process in algorithmic decision-making, especially under frameworks like Article 22 of the GDPR and proposals for AI-specific tort law in the EU AI Liability Directive. These developments could create novel duties of disclosure and internal governance.

Bottom line: AI may generate drafts, workflows, or evaluations, but legally enforceable trust must still be human-certified.

Engineer dissecting AI in steampunk style by Ralph Losey.

🧠 AI Engineer — Dissecting Feasibility and Tech-Skill Transitions

The article outlines plausible near-future roles such as AI plumbers, virtual twin managers, and integration specialists — all of which track well with emerging enterprise needs. From a systems engineering standpoint, each represents a layering of human interface over increasingly agentic AI.

A role like “AI plumber” is especially apt: as we move to multi-agent, multi-modal systems, debugging becomes less about code and more about tracing decision nodes through dynamic graphs. Tools like LangSmith or OpenTelemetry for AI will support this, but human oversight must close the loop.

Equally pragmatic is the “AI trainer” role, particularly in retrieval-augmented generation (RAG) settings where corporate data must be contextualized with extreme care. Hallucination risk escalates when source curation is poor — an engineer versed in prompt architectures and data labeling becomes indispensable.

However, I’d caution that some roles (e.g., “AI personality director”) may prove transient or cosmetic. Unless this impacts UX or regulatory tone (e.g., health chatbots), it will likely be a rebranded comms function.

Still, the job market is aligning. “AI consultant” and “integration lead” roles are skyrocketing per LinkedIn data. These aren’t speculative—they’re already hiring.

Human taste guiding AI image by Losey guiding AI.

✨ Creative Storyteller — Reframing Jobs Around Taste, Not Tasks

This article is really about the return of judgment as labor. The shift from doing to curating, from generating to refining, is the beating heart of the “taste economy” the author envisions.

Take the imagined roles of “story designer,” “differentiation designer,” or “world builder.” These all recast creativity as orchestration — selecting from near-infinite AI outputs to assemble emotionally resonant or brand-coherent narratives. In storytelling terms, we’re moving from penmanship to direction.

The Rick Rubin anecdote nails it: craft may fade, but curation ascends. Rubin’s “confidence in his taste” mirrors what’s being asked of tomorrow’s knowledge workers. We may not sculpt every sentence, but we’ll decide which sentences shape the message.

This resonates with media trends already: TikTok creators “direct” AI-generated visuals, musicians remix AI stems, and even journalists act more like narrative editors than sole authors. The job isn’t dying—it’s mutating.

And taste isn’t just aesthetic. It’s ethical, strategic, and human. As the article wisely closes: “We are the designers of our AI future. Let’s hope we have great taste.”

Robot Puppet Theatre in Steampunk style by Losey’s AI puppets.

🛰 Data Visionary — Mapping the Labor Realignment

The piece offers more than speculative roles—it maps a nascent transformation in the labor-capital interface. It posits three durable human moats: trust, integration, and taste. This triptych aligns with observed market inflections.

Trust anchors around accountability and transparency: humans ensure AI outputs meet institutional thresholds. Integration speaks to workflow orchestration—systems-thinking roles that bridge software and organizational context. Taste, the most abstract, becomes a differentiator in saturated generative landscapes.

This trifocal framework is not utopian. It mirrors what Erik Brynjolfsson calls the “complementarity imperative”: value will accrue where humans can amplify machine leverage, not where they replicate it. The World Economic Forum’s projection of 11 million new jobs by 2030 versus 9 million displaced jobs captures the macro realignment. [EDITOR: Future of Jobs Report 2025 (WEF, 01/7/25) at pg. 25 (“trends in AI and information processing technology are expected to create 11 million jobs, while simultaneously displacing 9 million others, more than any other technology trend.“)]

We must recognize, however, the uneven geography of adoption. As the article’s robotics example shows, access to “AI integrators” could determine regional economic divergence. Without policy scaffolding (training pipelines, local incentives), AI may exacerbate inequality.

Still, this is no mere displacement story. It is a diversification story, seeded with entrepreneurial possibility.

Fictitious vision of where new jobs may appear and old be replaced. Image by Ralph Losey using AI.

⚔️ The Contrarian

The panel indulges in optimism but skirts real constraints. “AI trainer”? Perhaps. But many orgs will under-invest in proper RAG or hallucination testing. “AI plumber”? Realistically, that’s a role for the already-employed IT person, not a new hire.

The legal take? Idealistic. In practice, AI auditors will lack statutory teeth unless regulation mandates it—and current efforts like the EU AI Act remain unenforced in key domains.

And “taste” as job defense? That’s privileged territory. It assumes stability in jobs where creative discretion is rewarded. But what of the displaced clerk, paralegal, or content writer who once did real intellectual work?

Lastly, from an ethical risk lens:

  • Bias: Are these new roles equally accessible? Or do they reinforce existing inequities?
  • Privacy: Will AI integrators mishandle sensitive data pipelines?
  • Regulatory: Could roles like “AI translator” mask liability via plausible deniability?

We risk over-indexing on shiny titles while ignoring structural frictions.

Contrarians tend to see the glass as half empty. Image by Losey.

Would you like to follow up, clarify, or pivot?


USER: Review each of the new jobs that the article mentions may be created by AI.

4o PANEL:

Here’s a comprehensive job-by-job breakdown of the new roles suggested in “A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You”. The article groups these under three themes: Trust, Integration, and Taste. I’ve organized the roles accordingly and added brief descriptions for each.

🛡 TRUST-Based Roles

Roles focused on ensuring transparency, accountability, and human oversight:

  1. AI Auditor
    Verifies what AI systems are doing, why, and whether outcomes meet legal or operational standards.
  2. AI Translator
    Bridges technical and business domains by explaining AI behavior and outcomes to non-expert stakeholders.
  3. Fact Checker (AI-Aided)
    Verifies AI-generated content for errors, hallucinations, and compliance issues.
  4. Compliance Officer (AI-Aware)
    Reviews AI-generated outputs in sensitive domains like law, finance, and health to ensure regulatory alignment.
  5. Trust Authenticator / Trust Director
    Ensures users can rely on AI-generated outputs, enforcing policies for fairness, accuracy, and traceability.
  6. AI Ethicist / Ethics Board Member
    Constructs defensible logic for decisions made by AI, ensuring ethical oversight and transparency.
  7. Legal Guarantor
    A licensed or certified person who legally vouches for AI-assisted work, assuming liability (akin to a “sin eater”).
  8. Consistency Coordinator
    Validates uniformity of AI output across systems (e.g., ensuring consistent product visuals or messaging).
  9. Escalation Officer
    Handles cases where AI fails emotionally or functionally—especially in customer service and education.
Buyer and seller must establish trust in robots to make sale. Combined styles in image by Losey.

🛠 INTEGRATION-Oriented Roles

Roles aimed at connecting AI capabilities to organizational workflows and technical systems:

  1. AI Integrator
    Designs and implements AI adoption strategies within specific business functions or verticals.
  2. AI Plumber
    Diagnoses and fixes errors in layered AI systems, often dealing with agentic or multi-step workflows.
  3. AI Assessor
    Continuously evaluates different AI models for specific uses (e.g., determining hallucination rates or performance drifts).
  4. AI Trainer (Domain-Specific)
    Feeds proprietary data into AI systems and fine-tunes model outputs for organizational accuracy.
  5. AI Personality Director
    Curates the “voice” and tone of an organization’s AI agents to match brand identity or customer expectations.
  6. Drug-Compliance Optimizer
    Uses AI to monitor and improve medication adherence—potentially in hospitals, insurance, or elderly care.
  7. AI-Human Evaluation Specialist
    Identifies tasks best suited for humans, AI, or hybrid approaches, especially in safety-critical industries.

🎨 TASTE-Driven Roles

Creative and strategic roles where human judgment and vision are irreplaceable:

  1. Product Designer (AI-Enabled)
    Expands beyond visuals to include brand strategy, user interaction, and narrative consistency, directing AI tools creatively.
  2. Article Designer
    Orchestrates content generated by AI—selecting structure, tone, and messaging across platforms.
  3. Story Designer
    In film, TV, or gaming, crafts the underlying plot or world that AI then fleshes out.
  4. World Designer
    Creates fictional or themed environments for marketing, gaming, or entertainment—used by AI to generate media.
  5. Human Resources Designer
    Shapes company culture, training, and employee experiences using AI-curated content and interfaces.
  6. Differentiation Designer
    Strategizes how a company distinguishes itself when competitors use the same powerful AI tools—aligning product, brand, and communication.
Steampunk taste team selected! Click to see celebration. Images, video by Losey.

Would you like me to visualize these 22 roles in a chart or cluster them by skillsets or industries?


USER: I would like the whole panel to discuss the “Sin Eater” job proposed by Wharton Business School Professor and noted AI expert, Ethan Mollick.

Ethan Mollick in Steampunk style by Losey.

USER: I would like the whole panel to discuss the “Sin Eater” job proposed by Wharton Business School Professor and noted AI expert, Ethan Mollick.

4o PANEL:

🎓 Ethical AI Lawyer — Legal and Normative Accountability

The “Sin Eater” role, as Mollick frames it, carries significant legal implications. It reflects the enduring legal doctrine that accountability cannot be delegated to a machine. In tort law, liability flows to the agent of record—currently, only humans or legal entities can occupy that role.

This aligns with precedents such as Mata v. Avianca Inc., where AI misuse led to sanctions because no human properly vetted its output. The sin eater, therefore, functions not just as a compliance actor but as a liability firewall, absorbing both reputational and legal risk.

But this framing raises concerns. Can one be a guarantor without actual authorship? If AI makes the decision and the sin eater merely signs off, we risk creating paper accountability. Regulatory models like the EU AI Act are moving toward traceability mandates, but as yet, U.S. law lacks statutory recognition of such roles.

In short, the sin eater concept is both legally plausible and ethically fraught—it acknowledges human limits while asking them to bear machine risk.

Sin Eaters will employ many lawyers. Image by Ralph Losey AI.

🧠 AI Engineer — Technical Risk ≠ Human Blame

From a systems view, the “Sin Eater” is a human checksum: the last human in the loop before AI action becomes irreversible. It’s a reactive model of risk management—and one that’s arguably too brittle.

We need better forward defenses, not just cleanup personnel. Sin eaters are useful when explainability fails or outputs are inherently opaque. But the real problem is epistemic opacity: even experts can’t always trace LLM decisions.

Instead, we should engineer auditable AI stacks, use differential logging, and isolate decision modules for forensic review. Relying on a human “approver” as a fix-all invites rubber-stamping, not real scrutiny.

And technically, sin eaters will need tooling—model trace maps, hallucination flags, metadata dashboards. Without this infrastructure, we’re just asking someone to trust what they can’t parse.

✨ Creative Storyteller — A Mythic Role, Made Literal

“Sin Eater” is such an evocative term because it draws from ritual and folklore. Historically, sin eaters consumed bread over a corpse, symbolically absorbing the sins of the dead. In this context, the sins are hallucinated citations, algorithmic bias, or wrongful denials.

That metaphor frames the sin eater as both guardian and scapegoat. They bear blame to protect the system’s perceived legitimacy. That’s powerful—but also dangerous. Mythic roles lack clear scope. Is the sin eater a validator? A fall guy? A moral compass?

We risk theatrical accountability: jobs that feel weighty but do little to prevent harm. The storytelling matters, because it shapes how we see responsibility. Are they heroes, martyrs, or cover-ups?

The role’s narrative weight is real, but it must be grounded in operational clarity.

Combined steampunk and medieval manuscript styles of historical Sin Eaters by Losey.

🛰 Data Visionary — Institutionalizing the “Last Human Standing”

Mollick’s metaphor reveals a deeper systemic truth: as AI scales, we need roles that enforce last-mile responsibility. The sin eater becomes the institutional locus of that burden.

In distributed systems, there’s a similar construct: the circuit breaker. When cascading failures occur, this mechanism stops the chain. The sin eater is a social circuit breaker. But who designs the thresholds? Who funds the liability insurance?

Scaling this requires:

  • Certification bodies to credential sin eaters
  • Policy frameworks (e.g., “AI final-signoff registries”)
  • Liability shields akin to whistleblower or auditor protections

Done well, this role could professionalize AI accountability, akin to CPAs or actuaries. But without those structures, it’s a fig leaf—blaming humans for systemic opacity.

Last Man Standing role shown in steampunk style by Losey. Only brave humans need apply for this job.

⚔️ The Contrarian

This entire discussion sanitizes an ugly reality: the sin eater is a liability sink, not a solution. It externalizes blame from the system designer to a human end-point. It masks architectural unaccountability.

And let’s check the ethical risk boxes:

  • Bias: Who becomes the sin eater? Likely junior, marginalized, or contractual workers—sacrificed for optics.
  • Regulatory: There’s no statute affirming this role. It’s an HR workaround, not a legal safeguard.
  • Hallucination: Will they detect model lies in high-stakes contexts (e.g., medical or legal reasoning)? Doubtful, unless they are domain experts.

In sum, the sin eater is not a visionary role—it’s institutional cowardice disguised as ethics.

Would you like to follow up, clarify, or pivot?

Nobody likes the contrarian lawyer. Steampunk image by Losey.

New Request to the Panel

USER: Each panelist should imagine and describe in detail an entirely new line of work for humans that will likely be created by AI. It should not come within the scope of the job types mentioned in the article.

4o PANEL: Let’s break new ground. Each panelist below proposes a novel, AI-enabled human job that was not covered in the NYT article. These roles are speculative yet grounded in emerging technological and social trajectories.

🎓 Ethical AI Lawyer — Probabilistic Liability Architect

As AI systems increasingly operate on statistical inference (rather than deterministic logic), courts and regulators will need new doctrines for “probabilistic harm.” Enter the Probabilistic Liability Architect.

This legal role designs frameworks to allocate risk and assign partial liability when AI systems cause harm but no single actor is clearly negligent. For example:

  • A predictive policing system misidentifies a suspect with 70% confidence.
  • An autonomous delivery drone injures a bystander during path optimization.

The architect would:

  • Quantify and model AI system confidence thresholds.
  • Define shared responsibility regimes across vendors, deployers, and auditors.
  • Draft model legislation and policy for AI tort reform.

This job combines legal theory, policy design, and statistical reasoning—making it a bridge between law and machine logic.

Steampunk image by Losey.

🧠 AI Engineer — Synthetic Interaction Architect

As LLMs evolve into multi-agent swarms, orchestrating their interactions will become a full-time role. The Synthetic Interaction Architect manages the interplay of dozens or hundreds of autonomous AI agents assigned to a complex task (e.g., planning an international supply chain or modeling a political campaign).

Key functions:

  • Configuring inter-agent protocols (who talks to whom, when, and how).
  • Monitoring emergent behaviors and pruning dysfunctional collaboration patterns.
  • Designing reward structures that simulate human incentive alignment (e.g., competition, consensus, escalation).

Think of this as managing a hive mind—an ensemble of AI intelligences operating like a virtual team. The architect ensures synergy, not chaos.

AI Interaction Architect conducting a swarm of interactive AIs. Shown on steampunk style by Losey.

✨ Creative Storyteller — Emotion Wrangler

As AI becomes increasingly conversational and emotionally fluent, it will sometimes simulate emotions better than it understands them. This creates the need for an Emotion Wrangler—a specialist who trains, tests, and fine-tunes the emotional accuracy and appropriateness of AI systems.

They would:

  • Script nuanced emotional arcs for AI in education, therapy, or customer service.
  • Conduct “empathy audits” to ensure AIs express concern, humility, or apology convincingly—but not manipulatively.
  • Design cultural calibration modules (e.g., how grief or politeness differs across geographies).

This job safeguards the emotional realism of AI while guarding against artificial affect that deceives or coerces.

Humans can feel emotions, AI can’t. Emotion Wrangler in steampunk style by Losey.

🛰 Data Visionary — Synthetic Ecology Steward

Generative AI is already creating simulated environments for science, entertainment, and climate modeling. The Synthetic Ecology Steward curates and manages these ecosystems as long-running simulations with economic, environmental, and ethical stakes.

Examples:

  • A virtual planet used for testing urban planning at planetary scale.
  • A simulated biosphere for biotech AI to evolve new proteins.
  • An economic microcosm for experimenting with new market policies or governance rules.

This steward oversees:

  • The ethical boundaries of simulation (e.g., are emergent sentient entities possible?).
  • Cross-disciplinary calibration (from physics to sociology).
  • Integration with real-world policy levers or educational systems.

The role marries design, governance, and philosophy at the frontier of synthetic reality.

Syn Ecology Stewart image by Visual Muse & Ralph Losey.

⚔️ The Contrarian — Moral Exit Facilitator

In the coming wave of AI-induced identity dislocation, some people will choose to opt out—not from society, but from systems that make them feel obsolete, surveilled, or coerced. The Moral Exit Facilitator helps these individuals ethically detach.

It’s not just a career counselor. It’s a combination of:

  • Data minimization advocate (helping users erase or obscure their digital footprint).
  • Self-sovereignty designer (tools for autonomy outside AI ecosystems).
  • Philosophical guide (navigating moral disengagement from pervasive automation).

The role critiques the system while enabling graceful withdrawal—offering humans a structured way to say: No, thank you.

Helping people get tech-free, escape AI addictions. Steampunk style by Losey.

Would you like these roles visualized in a chart or used to seed a scenario?


USER: “Seed a scenario?” Not sure what that means, so sure, go ahead and do that, seed a scenario.

4o PANEL: Great — “seeding a scenario” means I’ll take those new job ideas and embed them in a realistic future situation, crafting a brief scenario that explores how these roles might interact, conflict, or evolve. Think of it as a mini-future vignette for testing policy, tech, or narrative ideas.

🌍 Scenario Seed: “The Eden Loop Disruption”

📅 Year: 2031

🧭 Setting: A high-fidelity AI simulation called Eden Loop, used globally for climate-risk modeling, educational immersion, and synthetic biodiversity experimentation.

Steampunk image by Losey using Visual Muse.

Something goes wrong in the Eden Loop and each of the new human roles worked together to deal with the problems. Dealing with the hypothetical would be interesting in a role-play type workshop, but not for this essay.

Conclusion

History tells us that all technology breakthroughs cause disruptions to existing society, and all put some people out of work, at first, but overall, they always create more new jobs than they destroy. There is no reason to think it will be any different this time with AI. And there are many reasons to think the new jobs created will be far more interesting, creative and enjoyable than the jobs lost. It may even change the negative meaning that comes with the word work for most people. Work will have far fewer boring drudgery tasks because that will all be done by the robots. Work and play may become synonymous.

Image of AI doing the hard dirty work while humans do the fun jobs. By Ralph Losey with his AI doing the boring drawing parts.

In the article that ChatGPT 4o version of the Panel of Experts found, Future of Jobs Report 2025 (WEF, 01/7/25), the World Economic Forum predicted a significant shift in how both humans and machine work will change. In 2025 employers reported 47% of work tasks are performed by humans alone, 22% by machines and algorithms alone, and 30% by a hybrid combination. By 2030, employers expect these task-delivery proportions to be nearly evenly split across all categories: one-third human only, one-third machine only and one third hybrid.

The World Economic Forum report explains that this is only a task-delivery proportionality measurement, it does not take into account the amount of work getting done in each of the three task categories. The Future of Jobs Report 2025 at page 27 goes on to state:

The relevance of the third category approach, human-machine collaboration (or “augmentation”) should be highlighted: technology could be designed and developed in a way that complements and enhances, rather than displaces, human work; and, as discussed further in the next chapter (Box 3.1), talent development, reskilling and upskilling strategies may be designed and delivered in a way to enable and optimize human-machine collaboration.34 It is the investment decisions and policy choices made today that will shape these outcomes in the coming years.35

Hybrid Man and Machine New Jobs Report is looking good. Image by Losey in Steampunk style

The global employers polled favored a hybrid approach, as did the World Economic Forum expert analysis. Generative AI requires human supervision now and certainly for the next five years. To again quote the WEF Report, at the mentioned Box 3.1 at page 44:

Skills rooted in human interaction – including empathy and active listening, and sensory processing abilities – and manual dexterity, endurance and precision, currently show no substitution potential due to their physical and deeply human components. These findings underscore the practical limitations of current GenAI models, which lack the physicality to perform tasks that require hands-on interaction – although advances in robotics and the integration of GenAI into robotic systems could impact this in the future.

After that, as the AI skills improve, there will in our opinion and that of the WTF, still be a need for human collaboration. The need and human skills required will change but will always remain. That is primarily because we are corporal, living beings, with emotion, intuitions and other unique human powers. Machines are not. See my essay, The Human Edge: How AI Can Assist But Never Replace (1/30/25). Even if Ai were to someday become conscious, and have some silicon based corporality, perhaps even a kind of chemical based feelings, it would need to work with us to gain our unique human feel, our vibes and our evolutionary connection with life on Earth. Real life in time and space, not just a simulation.

Factory floor with cool AI features in steampunk style. Click here to see video by Losey.

PODCAST

As usual, we give the last words to the Gemini AI podcasters who chat between themselves about the article. It is part of our hybrid multimodal approach. They can be pretty funny at times and have some good insights, so you should find it worth your time to listen. Echoes of AI: Panel of Experts for Everyone About Anything – Part Three: Demo of 4o as Panel Driver. Hear two fake podcaster talk about this article for 18 minutes. They wrote the podcast, not me. For the first time we also offer a Spanish version here.

Click here to listen to the English version of the podcast.

Ralph Losey Copyright 2025 – All Rights Reserved.