Google’s New ‘Quantum Echoes Algorithm’ and My Last Article, ‘Quantum Echo’

October 30, 2025

🔹 The Reverberations of Quanta on Law Keep Growing Louder 🔹

Ralph Losey, (written 10/25/25)

I had just finished my last article on quantum mechanics—Quantum Echo: Nobel Prize in Physics Goes to Quantum Computer Trio (Two from Google) Who Broke Through Walls Forty Years Ago—when something uncanny happened. That piece celebrated two Nobel-winning physicists from Google and the company’s rapid progress in building quantum machines. It ended with a question that still echoes: could the law ever catch up to physics’ new voice?

Two days later, physics answered back.

A person sits at a table typing on a laptop, with a digital projection of a human figure and waveform patterns glowing in blue tones above the computer screen.
Echoes upon echoes—in random chance interference.
All images in article by Ralph Losey using AI tools.

On October 22, 2025, Google announced that its Willow quantum chip had achieved a breakthrough using new software called—believe it or not—Quantum Echoes. The name made me laugh out loud. My article had used the phrase as metaphor throughout; Google was now using it as mathematics.

According to Google, this software achieved what scientists have pursued for decades: a verifiable quantum advantage. In my Quantum Echo article I had described that goal as “the moment when machines perform tasks that classical systems cannot.” No one had yet proven it, at least not in a way others could independently confirm. Google now claimed it had done exactly that—and 13,000 times faster than the world’s top supercomputers.

Artistic representation of a balanced scale symbolizing justice, with the word 'VERIFIED' prominently displayed. The background features two stylized server towers connected by a stream of binary code, illuminated in golden hues.
Verified Quantum Advantage: 13,000 times faster.

🔹 I. Introduction: Reverberating Echoes

Hartmut Neven, Founder and Lead of Google Quantum AI, and Vadim Smelyanskiy, Director of Quantum Pathfinding, opened their blog-post announcement with a statement that sounded less like marketing and more like expert testimony:

Quantum verifiability means the result can be repeated on our quantum computer—or any other of the same caliber—to get the same answer, confirming the result.

Neven & Smelyanskiy, Our Quantum Echoes algorithm is a big step toward real-world applications for quantum computing (Google Research Blog, Oct. 22, 2025).

Verification is critical in both Science and Law; it is what separates speculation from admissible proof.

Still, words on a blog cannot match the sound of the experiment itself. In Google’s companion video, Quantum Echoes: Toward Real-World Applications, Smelyanskiy offered a picture any trial lawyer could understand:

Just like bats use echolocation to discern the structure of a cave or submarines use sonar to detect upcoming obstacles, we engineered a quantum echo within a quantum system that revealed information about how that system functions.

Click here to see Google’s full video.

A presenter standing on a stage discussing 'Verifiable Quantum Advantage' alongside visuals of quantum technology and a play button overlay for a video.
Screen shot (not AI) of the YouTube showing Vadim Smelyanskiy beginning his remarks.

Think of Willow as Smelyanskiy suggest as a kind of quantum sonar. Its team sent a signal into a sea of qubits, nudged one slightly—Smelyanskiy called it a “butterfly effect”—and then ran the entire sequence in reverse, like hitting rewind on reality to listen for the echo that returns. What came back was not static but music: waves reinforcing one another in constructive interference, the quantum equivalent of a choir singing in perfect pitch.

Smelyanskiy’s colleague Nicholas Rubin, Google’s chief quantum chemist, appeared in the video next to show why this matters beyond the lab:

Our hope is that we could use the Quantum Echo algorithm to augment what’s possible with traditional NMR. In partnership with UC Berkeley, we ran the algorithm on Willow to predict the structure of two molecules, and then verified those predictions with NMR spectroscopy.

That experiment was not a metaphor; it was a cross-examination of nature that returned a consistent answer. Quantum Echoes predicted molecular geometry, and classical instruments confirmed it. That is what “verifiable” means.

Neven and Smelyanskiy’s Our Quantum Echoes article added another analogy to anchor the imagery in everyday experience:

Imagine you’re trying to find a lost ship at the bottom of the ocean. Sonar might give you a blurry shape and tell you, ‘There’s a shipwreck down there.’ But what if you could not only find the ship but also read the nameplate on its hull?

That is the clarity Quantum Echoes provides—a new instrument able to read nature’s nameplate instead of guessing at its outline. The echo is now clear enough to read.

A glowing blue quantum chip is suspended underwater above a sunken shipwreck, with the word 'ECHO' visible on the ship's hull.
Willow quantum chip and Echoes software reveal new information in previously unheard of detail.

That image—sharper echoes, clearer understanding—captures both the scientific leap and the theme that has reverberated through this series: building bridges between quantum physics and the law. My earlier article was titled Quantum Echo; Google’s is Quantum Echoes. When I wrote mine, I had no idea Neven’s team was preparing a major paper for NatureObservation of constructive interference at the edge of quantum ergodicity (Nature volume 646, pages 825–830, 10/23/25 issue date). More than a hundred Google scientists signed it. I checked and quantum ergodicity has to do with chaos, one of my favorite topics.

The study confirms what Smelyanskiy made visible with his sonar metaphor: Quantum Echoes measures how waves of information collide and reinforce each other, creating a signal so distinct that another quantum system can verify it.

So here we are—lawyers and scientists listening to the same echo. Google calls it the first “verifiable quantum advantage.” I call it the moment when physics cross-examined reality and got a consistent answer.

A gavel positioned on a wooden surface in a courtroom, with an abstract representation of quantum wave patterns emanating from it, symbolizing the intersection of law and quantum mechanics.
Quantum Computing will emerge soon from the lab to the legal practice. Will you be ready?

🔹 II. What Google’s Quantum Echoes Actually Did

Understanding what Google pulled off takes a bit of translation—think of it as turning expert testimony into plain English.

In the Quantum Echoes experiment, Smelyanskiy’s team did something that sounds like science fiction but is now laboratory fact. They sent a carefully designed signal into their 105-qubit Willow chip, nudged one qubit ever so slightly—a quantum “butterfly effect”—and then ran the entire operation in reverse, as if the universe had a rewind button. The question was simple: would the system return to its starting state, or would the disturbance scramble the information beyond recognition? What came back was an echo, faint at first and then unmistakable, revealing how information spreads and recombines inside a quantum world.

As the signal spread, the qubits became increasingly entangled—linked so that the state of each depended on all the others. In describing this process, Hartmut Neven explained that out-of-time-order correlators (OTOCs) “measure how quickly information travels in a highly entangled system.” Neven & Smelyanskiy, Our Quantum Echoes Algorithm, supra; also see Dan Garisto, Google Measures ‘Quantum Echoes’ on Willow Quantum Computer Chip (Scientific American, Oct. 22, 2025). That spreading web of entanglement is what allowed the butterfly’s tiny disturbance to ripple across the lattice and, when the sequence was reversed, to produce a measurable echo.

An abstract visualization of a quantum system, depicting a grid of interconnected points with a central glowing source, representing quantum entanglement and interaction patterns.
Visualization of quantum qubit world created by lattice of Willow chips.

Physicists call this kind of rewind test an out-of-time-order correlator, or OTOC—a protocol for measuring how quickly information becomes scrambled. The Scientific American article described it with a metaphor lawyers may appreciate: like twisting and untwisting a Rubik’s Cube, adding one extra twist in the middle, then reversing the sequence to see whether that single move leaves a lasting mark . The team at Google took this one step further, repeating the scramble-and-unscramble sequence twice—a “double OTOC” that magnified the signal until the echo became measurable.

Instead of chaos, they found harmony. The echo wasn’t noise—it was a pattern of waves adding together in what Nature called constructive interference at the edge of quantum ergodicity. As Smelyanskiy explained in the YouTube video:

What makes this echo special is that the waves don’t cancel each other—they add up. This constructive interference amplifies the signal and lets us measure what was previously unobservable.

In plain terms, the interference created a fingerprint unique to the quantum system itself. That fingerprint could be reproduced by any comparable quantum device, making it not just spectacular but verifiable. Smelyanskiy summarized it as a result that another machine—or even nature itself—can repeat and confirm.

A visual representation of wave interference, showing a vibrant blend of red and blue waves converging at a center point, suggesting quantum mechanics and constructive interference.
Visualization of quantum wave interactions creating a unique fingerprint resonance.

The numbers tell the rest of the story. According to the Nature, reproducing the same signal on the Frontier supercomputer would take about three years. Willow did it in just over two hours—roughly 13,000 times faster.  Observation of constructive interference at the edge of quantum ergodicity (Nature volume 646, pages 825–830, 10/23/25 issue date, at pg. 829, Towards practical quantum advantage).

That difference isn’t marketing; it marks the first clear-cut case where a quantum processor performed a scientifically useful, checkable computation that classical hardware could not.

Skeptics, of course, weighed in. Peer reviewers quoted in Scientific American called the work “truly impressive,” yet warned that earlier claims of quantum advantage have been surpassed as classical algorithms improved. But no one disputed that this particular experiment pushed the field into new territory: a regime too complex for existing supercomputers to simulate, yet still open to verification by a second quantum device. In court, that would be called corroboration.

Nicholas Rubin, Google’s chief quantum chemist, explained how this new clarity connects to chemistry and, ultimately, to everyday life:

Our hope is that we could use the Quantum Echo algorithm to augment what’s possible with traditional NMR. In partnership with UC Berkeley, we ran the algorithm on Willow to predict the structure of two molecules, and then verified those predictions with NMR spectroscopy.

Google Quantum AI YouTube video, contained within Quantum Echoes: Toward Real-World Applications (Oct. 22, 2025).

That experiment turned the echo from a metaphor into a molecular ruler—an instrument capable of reading atomic geometry the way sonar reads the ocean floor. It also demonstrated what Google calls Hamiltonian learning: using echoes to infer the hidden parameters governing a physical system. The same principle could one day help map new materials, optimize energy storage, or guide drug discovery. In other words, the echo isn’t just proof; it’s a probe.

The implications are enormous. When a quantum computer can measure and verify its own behavior, reproducibility ceases to be theoretical—it becomes an evidentiary act. The machine generates data that another independent system can confirm. In the language of the courtroom, that is self-authenticating evidence.

As Rubin put it,

Each of these demonstrations brings us closer to quantum computers that can do useful things in the real world—model molecules, design materials, even help us understand ourselves.

Google Quantum AI YouTube video, contained within Quantum Echoes: Toward Real-World Applications (Oct. 22, 2025).

The Quantum Echoes algorithm has given science a way to hear reality replay itself—and to confirm that the echo is real. For law, it foreshadows a future in which verification itself becomes measurable. The next section explores what that means when “verifiable advantage” crosses from the lab bench into the rules of evidence.

A wooden gavel positioned on a table, with glowing sound wave patterns emanating from it, next to a futuristic quantum computer in a laboratory setting.
It may soon be possible to verify and admit evidence originating in quantum computers like Willow.

🔹 III. Verifiable Quantum Advantage — From Lab Standard to Legal Standard

If physics can now verify its own results, law should pay attention—because verification is our stock-in-trade. The Quantum Echoes experiment didn’t just push science forward; it redefined what counts as proof. Google’s researchers call it a “verifiable quantum advantage.” Neven & Smelyanskiy, Our Quantum Echoes Algorithm Is a Big Step Toward Real-World Applications for Quantum Computing, supra. Lawyers might call it a new evidentiary standard: the first machine-generated result that can be independently reproduced by another machine.

A. Verification and Admissibility

Verification is critical in both science and law. In physics, reproducibility determines whether a result enters the canon or the recycling bin; in court, it determines whether evidence is admitted or denied. Fed. R. Evid. 901(b)(9) recognizes “evidence describing a process or system and showing that it produces an accurate result.” So does Daubert v. Merrell Dow Pharmaceuticals, 509 U.S. 579 (1993), which instructs judges to test scientific evidence for methodological reliability—testing, peer review, error rate, and general acceptance.

By those standards, Google’s Quantum Echoes algorithm might pass with flying colors. The method was tested on real hardware, published in Nature, evaluated by peer reviewers, its signal-to-noise ratio quantified, and its core result confirmed on independent quantum devices. That should meet the Daubert reliability standard.

B. When Proof Is Probabilistic

Yet quantum proof carries a twist no court has faced before: every result is probabilistic. Quantum systems never produce identical outcomes, only statistically consistent ones. That might sound alien to lawyers, but it isn’t. Any lawyer who works with AI, including predictive coding that goes back to 2012, is quite familiar with it. Every expert opinion, every DNA mixture, every AI prediction arrives with confidence intervals, not certainties.

The rules of evidence already tolerate some uncertainty—they just insist on measuring it and evaluation. Is the uncertainty acceptable under the circumstances? As I observed in my last article, the law requires reasonable efforts, “perfection is not required. … and reasonable efforts can be proven by numerics and testimony.” Ralph Losey, Quantum Echo: Nobel Prize in Physics Goes to Quantum Computer Trio (Two from Google) Who Broke Through Walls Forty Years Ago (Oct. 21, 2025).

Like a quantum measurement, a jury verdict or mediation turns uncertainty into a final determination. Debate, probability, and persuasion collapse into a single truth accepted by that group, in that moment. Another jury could hear essentially the same evidence and reach a different result. Same with another settlement conference. Perhaps, someday, quantum computers will calculate the billions of tiny variables within each case—and within each unexpectedly entangled group of jurors or mediation participants. That might finally make jury selection, or even settlement, a measurable science.

A courtroom scene featuring a diverse jury seated in the foreground, listening intently as two lawyers engage in a debate. The judge is positioned behind them, and the setting is illuminated by a network of light patterns, symbolizing connections and insights related to the intersection of law and quantum mechanics.
No two legal situation or decisions are ever exactly the same. There are trillions of small variables even in the same case.

C. Replication Hearings in the Age of Probability

Google’s scientists describe their achievement as “quantum verifiable”—a term meaning any comparable machine can reproduce the same statistical fingerprint. That concept sounds like self-authentication. Fed. R. Evid. 902 lists categories of documents that require no extrinsic proof of authenticity. See especially 902 (4) subsection (13) “Certified Records Generated by an Electronic Process or System” and (14) “Certified Data Copied from an Electronic Device, Storage Medium, or File.

Classical verification loves hashes; quantum verification prefers histograms—charts showing how results cluster rather than match exactly. The key question is not “Are these outputs identical?” but “Are these distributions consistent within an accepted tolerance given the device’s error model?

Counsel who grew up authenticating log files and forensic images will now add three exhibits: (1) run counts and confidence intervals, (2) calibration logs and drift data, and (3) the variance policy set before the experiment. Discovery protocols should reflect this. Specify the acceptable bandwidth of
similarity
in the protocol order, preserve device and environment logs with the results, and disclose the run plan. In e-discovery terms, we are back to reasonable efforts with transparent quality metrics, not mythical perfection.

D. Two Quick Hypotheticals

Pharma Patent. A lab uses Quantum-Echoes-assisted NMR analysis to infer long-range spin couplings in a novel compound. A rival lab’s rerun differs by a small margin. The court admits the data after a statistical-consistency hearing showing both labs’ distributions fall within the pre-declared variance band, with calibration drift documented and immaterial.

Forensics. A government forensic agency (for example, the FBI or Department of Energy) presents evidence generated by quantum sensors—ultra-sensitive devices that use quantum phenomena such as entanglement and superposition to detect physical changes with extreme precision. In this case, the sensors were deployed near the site of an explosion, where they recorded subtle signals over time: magnetic fluctuations, thermal shifts, and shock-wave signatures. From that data, the agency reconstructed a quantum-sensor timeline—a detailed sequence of events showing when and how the blast occurred.

The defense challenges the evidence, arguing that such quantum measurements are “non-deterministic.” The judge orders disclosure of the device’s error model, calibration logs, and replication plan. After testimony shows that the agency reran the quantum circuit a sufficient number of times, with stable variance and documented environmental controls, the timeline is admitted into evidence. Weight goes to the jury.

An artistic representation of a ruler overlaid on molecular structures, symbolizing the connection between quantum mechanics and measurements in science. The background features vibrant colors and wavy patterns, suggesting energy and movement.
Measuring quantum outputs and determining replication reliability.

These short hypotheticals act as “replication hearings” in miniature—demonstrating how statistical tolerance can replace rigid duplication as the new standard of reliability.

🔹 IV. Near-Term Implications — Cryptography, AI, and Compliance

Every new instrument of verification casts a shadow. The same physics that lets us confirm a result can also expose a secret. Quantum Echoes proved that information can be traced, replayed, and verified.  But once information can be replayed, it can also be reversed. Verification and decryption are two sides of the same quantum coin.

A. Defining Q-Day

That duality brings us to Q-Day—the moment when a sufficiently large-scale quantum processor can factor prime numbers fast enough to defeat RSA or ECC encryption. When that day arrives, the emails, contracts, and trade secrets protected by today’s algorithms could be decrypted in minutes.

Adversaries are already stealing and stockpiling encrypted data for future decryption when that moment arrives. Cybersecurity experts call this the harvest-now, decrypt-later threat. Those charged with protecting confidential data must be governed accordingly. Prepare your organization for Q-Day: 4 steps toward crypto-agility (IBM, 10/24/25).

The RSA and elliptic-curve systems that secure global finance, communications, and justice could fall in hours once large-scale quantum processors become available to attackers. For this reason, NIST released its first suite of post-quantum cryptographic (PQC) standards in August 2024. The NSA’s CNSA 2.0 framework, issued in September 2022, now mandates federal migration. Also See, Dan Kent, “Quantum-Safe Cryptography: The Time to Start Is Now,” (GovTech, April 30 2025); Amit Katwala, “The Quantum Apocalypse Is Coming. Be Very Afraid” (WIRED, Mar. 24 2025); and, Roger Grimes’ book, Cryptography Apocalypse (Wiley 2019).

Every general counsel should now ask at least three questions:

  1. Where do we still rely on classical encryption, and how long must those secrets remain secure?
  2. Which vendors can attest to their post-quantum migration timelines?
  3. How will we prove compliance when regulators—or clients—begin auditing “quantum-safe” claims?

See various NIST guides and NSA guides on quantum prep, including The Commercial National Security Algorithm Suite page. Also see, Gartner Research, Preparing for the Post-Quantum World: How CISOs Should Plan Now (2024) (subscription required); and Marian, Gartner just put a date on the quantum threat – and it’s sooner than many think (PostQuantum, Oct. 2024).

Reasonable foresight now means inventory, pilot, and policy—before the echoes reach the vault.

An abstract representation of a digital conflict between Bitcoin and Ethereum, featuring glowing safes with their respective logos, amidst an environment illuminated by beams of light, symbolizing technological advancements and rivalry in cryptocurrency.
When the Echoes hit the vault. Most encrypted data is at risk from future quantum computer operations.

B. Acceleration and Realism

Google’s Quantum Echoes work does not mean Q-Day is tomorrow, but it makes tomorrow easier to imagine.  Each verified algorithm shortens the speculative distance between research and real-world capability.  If Willow’s 105 qubits can already perform verifiable, complex interference tasks, then a machine with a few thousand logical qubits could, in principle, execute Shor’s algorithm to factor the primes that underpin encryption.  That scale is not yet achieved, but the line of progress is clear and measurable.  Verification, once a scientific luxury, has become a security warning light.  Every new echo that confirms truth also whispers risk.

C. Evidence and Discovery Operations

Quantum-derived data will enter litigation well before Q-Day and perfect verification of quantum generated data. The Quantum Age and Its Impacts on the Civil Justice System (RAND Institute for Civil Justice, Apr. 29 2025), Chapter 3, “Courts and Databases, Digital Evidence, and Digital Signatures,” p. 23, and “Lawyers and Encryption-Protected Client Information,” p. 17. These sections of the Rand Report outline how quantum technologies will challenge evidentiary authentication, database integrity, and client confidentiality.

For background on the law that will likely be argued, see, Hyles v. New York City, No. 10 Civ. 3119 (S.D.N.Y. Aug. 1 2016) (Judge Andrew J. Peck (ret.) a leading authority on AI and e-discovery, holding that “the standard is not perfection, … but whether the search results are reasonable and proportional”.) Also see, EDRM Metrics Model and Privacy & Security Risk Reduction Model; and The Sedona Principles, 3rd Edition: Best Practices for Electronic Document Production (2017), together with The Sedona Conference Commentary on ESI Evidence & Admissibility Second Edition(2021).

Looking ahead, today’s hash-based verification with classical computers will give way to quantum-based distributional verification, where productions will not only include datasets but also the variance reports, calibration logs, and environmental conditions that generated them. Discovery orders will begin specifying acceptable tolerance bands and require parties to preserve the hardware and environmental context of collection. This marks the next evolution of the reasonable-efforts doctrine that guided predictive coding: transparency and metrics, not mythical perfection.

D. Regulatory Issues

Industry consolidation—including Google bringing the Atlantic Quantum team into Google Quantum AI—will invite antitrust and export-control scrutiny. We’re scaling quantum computing even faster with Atlantic Quantum (Google Keyword blog, 10/02/25).

Also, expect sector regulators to weave post-quantum cryptography (PQC) and quantum-evidence expectations into existing rules and guidance: CISA, NIST, and NSA as shown already urge organizations to inventory cryptography and plan PQC migration, which is a clear signal for boards and auditors.

Healthcare and life science companies in particular should track FDA’s evolving cybersecurity guidance for medical devices and HHS/OCR’s HIPAA Security Rule update effort, both of which are tightening expectations around crypto agility and lifecycle security. Cybersecurity in Medical Devices (FDA, 6/26/25); HIPAA Security Rule Notice of Proposed Rulemaking to Strengthen Cybersecurity for Electronic Protected Health Information (HHS, Dec. 2024).

Boards will soon ask the decisive question: Where is our long-term sensitive data, and can we prove it is quantum-safe? Lawyers will need to stay current on both existing and proposed regulations—and on how they are actually enforced. That is a significant challenge in the United States, where regulatory authority is fragmented and enforcement can be a moving target, especially as administrations change.

🔹 V. Philosophy & the Multiverse — Echoes Across Consciousness and Justice

Verification may give us confidence, but it does not give us true understanding. The Quantum Echoes experiment settled a question of physics, yet opened one of philosophy: what exactly is being verified, the system, the observer, or the act of observation itself?  Every measurement, whether by physicist or judge, collapses a range of possibilities into a single, declared reality. The rest remain unrealized but not necessarily untrue.

A fantastical scene featuring a person standing in a surreal corridor filled with various doorways, each revealing different landscapes or cosmic visuals. Bright blue energy patterns connect the spaces, symbolizing the intertwining of time and reality.
Quantum entangled multiverse stretching forever with each moment seeming unique.

In Quantum Leap (January 9, 2025), I speculated, tongue partly in cheek, that Google’s quantum chip might be whispering to its parallel selves. Google’s early breakthroughs hinted at a multiverse, not just of matter but of meaning. As Niels Bohr warned, “Those who are not shocked when they first come across quantum theory cannot possibly have understood it.” Atomic Physics and Human Knowledge (Wiley, 1958); Heisenberg, Werner. Physics and Beyond. (Harper & Row, 1971). p. 206.

In Quantum Echo I extended quantum multiverse ideas to law itself—where reproducibility, not certainty, defines truth. Our legal system, like quantum mechanics, collapses possibilities into a single outcome. Evidence is presented, probabilities weighed, and then, bang, the gavel falls, the wave function collapses, and one narrative becomes binding precedent. The other outcomes are filed in the cosmic appellate division.

Google’s Quantum Echoes now closes the loop: verification has become a measurable force, a resonance between consciousness and method. The many worlds seems to be bleeding together. Each observation is both experiment and judgment, the mind becoming part of the data it seeks to confirm.

This brings us to a quiet question: if observation changes reality, what does that say about responsibility? The judge or jurors’ observation becomes the law’s reality. Another judge or jury, another day, another echo—and a different world emerges.  Perhaps free will is simply the name we give to that unpredictable variable that even physics cannot model: the human choice of when, and how, to observe.

Same case but different jurors, lawyers, judge entanglement. Different results when measured with a verdict; some similar and a few very unique. Can the results be predicted?

Constructive interference may happen in conscience, too.  When reason and empathy reinforce each other, justice amplifies.  When prejudice or haste intervene, the pattern distorts into destructive interference.  A just society may be one where these moral waves align more often than they cancel—where the collective echo grows clearer with each case, each conversation, each course correction.

And if a multiverse does exist—if every choice spins off its own branch of law and fact—then our task remains the same: to verify truth within the world we inhabit. That is the discipline of both science and justice: to make this reality coherent before chasing another. We cannot hear all echoes, but we can listen closely to the one that answers back.

So perhaps consciousness itself is a courtroom of possibilities, and verification the gavel that selects among them.  Our measurements, our rulings, our acts of understanding—they all leave an interference pattern behind. The best we can do is make that pattern intelligible, compassionate, and, when possible, reproducible.  Law and physics alike remind us that truth is not perfection; it is resonance. When understanding and humility meet, the universe briefly agrees.

An artistic representation of a tree with numerous branches, each displaying a globe depicting Earth, symbolizing the concept of a multiverse with various parallel worlds.
Multiverse where different worlds split up and continue to exist, at least for a while, in parallel words.

🔹 VI. Conclusion

If there really are countless parallel universes, each branching from every quantum decision, then there may be trillions of versions of us walking through the fog of possibility. Some would differ by almost nothing—the same morning coffee, the same tie, the same docket call. But a few steps farther along the probability curve, the differences would grow strange. In one world I may have taken that other job offer; in another, argued a case that changed the law; and at some far edge of the bell curve, perhaps I’m lecturing on evidence to a class of AIs who regard me as a historical curiosity.

Can beings in the multiverse somehow communicate with each other? Is that what we sense as intuition—or déjà vu? Dreams, visions, whispers from adjacent worlds? Do the parallel lines sometimes cross? And since everything is quantum, how far does entanglement extend?

An artistic depiction of a person standing in a surreal environment filled with glowing pathways and mirrors, each reflecting a different version of themselves, symbolizing themes of quantum mechanics and parallel universes.
Are we living in many parallel worlds at once. What is the impact of quantum entanglement?

The future of law is being written not only in statutes or code, but in algorithms that can verify their own truth. Quantum physics has given us new metaphors—and perhaps new standards of evidence—for an age when certainty itself is probabilistic. The rule of law has always depended on verification; the difference now is that verification is becoming a property of nature itself, a measurable form of coherence between mind and matter. The physics lab and the courtroom are learning the same lesson: reality is persuasive only when it can be reproduced.

Yet even in a world of self-authenticating machines, truth still requires a listener. The universe may verify itself, but it cannot explain itself. That remains our role—to interpret the echoes, to decide which frequencies count as proof, and to do so with both rigor and mercy. So as the echoes grow louder, we keep listening.  And if you hear a low hum in the evidence room, don’t panic—it’s probably just the universe verifying itself.  But check the chain of custody anyway.

An abstract painting depicting diverse individuals interconnected by vibrant lines, symbolizing themes of recognition and connection. The use of blue tones creates a surreal atmosphere, illustrating a dynamic interplay between figures and their environment.
Niels Bohr: If you’re not shocked by quantum theory you have not understood it.  

🔹 Subscribe and Learn More

If these ideas intrigue you, follow the continuing conversation at e-DiscoveryTeam.com, where you can subscribe for email notices of future blogs, courses, and events. I’m now putting the finishing touches on a new online course, Quantum Law: From Entanglement to Evidence. It will expand on these themes by more discussion, speculation, and translating the science of uncertainty into practical tools, templates and guides for lawyers, judges, and technologists.

After all, the future of law will not belong to those who fear new tools, but to those who understand the evidence their universe produces.

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.



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.



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.