Henry Kissinger and His Last Book – GENESIS: Artificial Intelligence, Hope, and the Human Spirit

June 19, 2025

Ralph Losey. June 19, 2025.

Henry A. Kissinger co-wrote his last book at the age of 100 with tech giants Eric Schmidt and Craig Mundie. Genesis makes clear that what we do next with AI could be our greatest triumph or our gravest mistake. Here I review the book and, to the extent necessary, the legend behind it, Henry Kissinger.

Henry Kissinger’s Genesis was published posthumously in late 2024. He had great technical help and was prodded to write the book by his two co-authors: Eric Schmidt, the former CEO of Google, and Craig Mundie, the former Chief of Research at Microsoft. They both knew Kissinger well before starting to write the book with him, which at age 99, they all knew would be his last. This is Kissinger’s second book on AI with Eric Schmidt. His first was published in 2022 entitled, The Age of AI: And Our Human Future, with another co-author, Daniel Huttenlocher, a Professor and Dean at MIT.

Like many, I had mixed feeling about Henry Kissinger from his work for Nixon and the Vietnam War, but still I was persuaded by Eric Schmidt’s many videos to give Genesis a try. Schmidt has been tirelessly promoting the book and the strategic insights Kissinger has on AI. He even created a slick promotional video for Genesis that uses an AI enhanced voice speaking Kissinger’s own words (suggest you click on it now to prep for my all-too-human book review). Also see, Eric Schmidt on AI, Foreign Policy, and working with Dr. Henry Kissinger (Nixon Foundation, 3/5/25) (more personal details on Kissinger than any of the dozens of other Schmidt interviews).

AI generated image in style of elder Kissinger. All images in the article are by Ralph Losey using AI (except the two public domain photos noted).

In the Nixon Foundation video Schmidt states that Kissinger was the leader in the writing and editing of Genesis and that Henry was very meticulous and dedicated to it. Id. at video 27:46. Henry finished the last chapter the week before he died on November 29, 2023. Id. at video 35:50. At the same time he was writing Genesis, Kissinger was co-authoring an article with his diplomatic colleague, Graham Allison. The Path to AI Arms Control: America and China Must Work Together to Avert Catastrophe, (Foreign Affairs, 10/13/23). Yes, at the end of his life Henry Kissinger was focused on the power, promises and severe dangers of AI, including the danger of war with China posed by possible superintelligence.

Having now read the book I can well understand why Schmidt is urging everyone to read Genesis. We are living in dangerous times where strategy and diplomacy are more important than ever. Henry Kissinger’s last book is a red flag, outline of solutions and a beacon of hope. I am happy to recommend it.

Schmidt and Kissinger meeting with government officials in Beijing. Real meeting, fake AI image of it by Losey.

Kissinger’s Inside Briefings About AI

Schmidt went on to share in the Nixon Foundation video that he and introduced Kissinger to both Demis Hassabis and Dario Amodei. Henry became friends with them and had many in-person and zoom conversations. Eric Schmidt on AI, Foreign Policy, and working with Dr. Henry Kissinger, video at 29:30.

With their help, and that especially that of Schmidt and Mundie, Kissinger came to understand that AI involved the Midas Touch archetype, not only for its profitability, but as a warning to be careful what you ask for. Our dream of superintelligent AI, one well beyond our comprehension, could easily become a nightmare. Genesis, in Kissinger’s words, “examines what AI means to humanity and explores solutions to the challenges it poses.

AI Image of Hassabis, Amodei, Schmidt, Kissinger.

In fact, Eric tells the story of when Kissinger asked to test out Dario’s latest AI models. Amodei told Henry to come up with a prompt, a question, which he did: “Design a new religion that will spread rapidly in today’s age.Id. at video 30:00. Kissinger was blown away by the AI’s detailed response and plan of action. Schmidt says it was that demonstration that caused Kissinger to understand fully, for the first time, the power of the AI revolution underway. Henry had some hands-on AI experience and that drove him, as it does me, to think and write about it obsessively.

AI image of Henry writing about AI and religion. Who would expect he’d do that at age 100 just before he passed away?

I thought it would be fun to run the same prompt on OpenA’sI new model, o3 Pro, released on 6/12/25. OpenAI states that o3 Pro is its best model for reasoning. It is certainly far ahead of the Anthropic AI that Kissinger used a few years ago. It took o3 Pro thirteen minutes and two seconds of reasoning time before it responded. That is a very long time for AI, which thinks millions of times faster than we do. In the end, it did figure out how to create a new, very appealing religion.

In fact, like Kissinger before me, I was blown away by its frighteningly well considered plan. The religion proposed is called “Synterra Path – a mash‑up of syn (“together”) and terra (“earth”).” You can see the full eleven point plan for yourself in the attached transcript, which does not include the extensive sources that o3 Pro also provided. Please do not attempt to implement the plan for Synterra Path, which is already underway, or the AI lawyer agents will file suit.

The new AI religion where Kissinger is the prophet who asked AI first. Try Henry’s prompt yourself!

Genesis Is a Short Book by Kissinger Standards

At 218 pages Genesis is a short read, but to be honest, not an easy read. It takes concentration. The AI parts are fairly easy and beautifully explained but the complex Kissinger strategy and philosophy parts are more difficult. Those Kissinger insights are also what make Genesis a must read for anyone trying to understand the AI Age.

The book is very short by the standards of Henry Kissinger. He is famous for many things, but one you might not have heard of is the controversy surrounding his undergraduate, senior paper at Harvard in 1950, The Meaning of History: Reflections on Spengler, Toynbee and Kant. This paper caused Harvard to start a 35,000 word limit rule for senior papers that stands to this day. You see the paper young Henry, shown below, submitted to his professors was over 400 pages long!

Kissinger in 1950 Harvard Yearbook, public domain.

Young Kissinger did not talk much. Instead, he wrote and wrote. A few years after his senior paper fiasco. Harvard gushed to read his Ph.D. thesis: Peace, Legitimacy, and the Equilibrium (A Study of the Statesmanship of Castlereagh and Metternich. It won many awards and led to Harvard making him a professor. His word generation skills were equal to or exceeded the best generative AI of today.

At the end of his life Kissinger was still writing. He somehow crammed his one hundred years of insights into the 218 pages of Genesis. So admittedly, it is a challenging read, and yes, it would take about 400 pages for my AIs and I to totally unpack it, but don’t worry, that’s not happening. Ask your AI to do it. Hopefully it understands statesmanship, Henry Kissinger and Immanuel Kant.

AI image of Kant and Kissinger.

Henry Kissinger in WW II

To understand the book more information about Kissinger’s formative years is required, the years before he became famous as a Harvard professor, Richard Nixon’s National Security Advisor, Secretary of State, and controversial Nobel Peace Prize winner. You need to understand first of all that Kissinger was born and raised in Germany in a Jewish family where he suffered persecution as a boy. As a teenager in 1938, his parents and younger brother were among the lucky few to escape Nazi Germany and immigrate to America.

Henry was drafted into the Army 1943 and, while in training camp in Camp Croft, became a U.S. citizen. He was then shipped to France as a private, and since he was obviously smart and spoke German, was assigned to an intelligence unit. Young Henry Kissinger saw combat right away as a kind of spy at the front lines. He even volunteered for hazardous intelligence duties during the Battle of the Bulge.

On April 10, 1945, at age of 21, Henry participated in the liberation of the Hannover-Ahlem concentration camp, part of the Neuengamme concentration camp. At the time, Kissinger wrote in his journal, “I had never seen people degraded to the level that people were in Ahlem. They barely looked human. They were skeletons.Isaacson 1992, pp. 39–48. For more details and photos see Henry Kissinger’s World War II (Warfare History Network, June 2018). Both Eric Schmidt and I think this was a turning point in his life.

AI generated image of liberation of concentration camp with young Kissinger on the far right.

Kissinger was relatively silent about his wartime service. In fact, he rarely spoke at all as a boy and young man. ‘Too shy‘ is what they called it back then. Can you imagine what it must have been like for a young Jewish man on the spectrum to walk into a Nazi concentration camp in his home country? He saw people, his people, barely alive; prisoners who had been treated at dirty things, with no human dignity or respect for their life at all. He turned to the German philosopher Immanuel Kant for comfort of sorts and, according to his friend, Eric Schmidt, decided at that time to dedicate his life to a “higher purpose” of preventing the horrors of war. Eric Schmidt on AI, Foreign Policy, and working with Dr. Henry Kissinger, video at 19:30.

According to Kissinger’s biographer, Walter Isaacson, Henry never lost his strong German accent because he suffered from extreme shyness as a child and that made him hesitant to ever speak. Isaacson, Kissinger: A Biography (Simon & Schuster 1992). It remained a very strong accent his whole life. Eric Schmidt tells the story that despite Google’s best efforts, neither the German nor English language AI could understand his speech well enough to transcribe it. Eric Schmidt on AI, Foreign Policy, and working with Dr. Henry Kissinger video at 12:12.

As the War against Germany ended, and after the shock of seeing near death prisoners released from a Jewish Concentration Camp, Kissinger was assigned to the Counter Intelligence Corps (CIC), where he became a CIC Special Agent. Henry quickly received a field promotion to sergeant and was put in charge of a team in Hanover Germany tracking down the hated Gestapo officers and saboteurs. Once discovered they were tried, imprisoned or hanged. After seeing a concentration camp, that must have been satisfying work for Kissinger. He was awarded a Bronze Star for his service.

AI image of what Kissinger might have looked like as a U.S. Army intelligence agent hunting Gestapo intelligence agents. Click here to me my AI video visualization of his work. Upcoming Netflix series?

In June 1945 he was promoted again and made commandant of the Bergstraße district of Hesse Germany, with responsibility for denazification of the district. In 1946, Kissinger was reassigned to teach at the European Command Intelligence School at Camp King in Germany. He continued to teach there as a civilian employee following his separation from the army. Kissinger later recalled that his experience in the army “made me feel like an American.” Isaacson. Kissinger. p. 695.

Kissinger’s Kant, AI, Inherent Dignity and Dogs

Obviously, the deep thoughts of this legend impressed and influenced his co-authors, Schmidt and Mundie. They are elite tech scientists and businessmen who readily admit to having had no time in their past for social studies. Henry Kissinger’s politely viewed them as one-dimensional scholars, not fully educated. Eric Schmidt on AI, Foreign Policy, and working with Dr. Henry Kissinger video at 18:40 (“technology people don’t understand history, people, social dynamics, politics“). They were not polymaths with great interdisciplinary knowledge like Kissinger, they were just trained in a science/math bubble. For that reason, Kissinger told Schmidt that he “should not be in charge of anything. Id.

That’s a kind of funny thing to say to the former CEO of Google and one of the most successful business leaders of our day. Surprisingly, Schmidt agreed with Kissinger, saying “it would be nice if there were more than just the tech people making these decisions.” Id. I wholeheartedly agree, and so does Eric Schmidt, one of the richest people in the world ($32 Billion), but as Schmidt pointed out to Kissinger, that is not likely in profit driven companies.

Kissinger learned from talking to the lead technology people that they did not understand psychology, international law, geopolitics, diplomacy, warfare, history, or philosophy, much less Kissinger’s favorite, the notoriously difficult Immanuel Kant (1724-1804) and Kantian ethics. Lucky me, I was forced to study Kant while studying philosophy in Vienna. Immanuel is generally considered to be the greatest German philosopher. Immanuel Kant believed that all humans have the right to common dignity and respect. It was part of his famous categorical imperative that humans must never treat others merely as a means to an end, but always as ends in themselves. This is the opposite of what many people in fact think and do, especially the Nazis that Henry fought.

Kissinger puzzled over whether AI might someday deserve this dignified treatment. Right now, we treat it as a tool, a pet we control. That’s appropriate now, but what happens when it’s smarter than we are? Could the tables then be turned? Kissinger worried about that too, that AI might someday advance so far as to rob humanity of its dignity. Maybe we would someday be the less intelligent beings on a leash, the spoiled pets of super advanced AI.

Pampered rich human taken for a walk by his AI owner.

This is fate some people might welcome, especially a dog lover like me, but not Henry. I kind of doubt he ever had time for a dog. As Schmidt puts it, and I paraphrase, it is important that when we are no longer the smartest beings on Earth, that we control our masters better than dogs control us. Again, I suspect Schmidt has not spent much time with dogs either, at least not ones like mine that seem pretty good at controlling their owner. Beam me up for a walk on the moon please AI master, I’m bored

Pet human likes all the great things his AI owner does for him, like perfect health and beaming up for a walk on the Moon.

This Kantian ethical view of human dignity influenced the thinking of Kissinger on a variety of AI topics discussed in Genesis. See for instance Genesis at page 205:

As a starting point we would encourage a definition of dignity. . . . Without a definition of dignity, we would not know if and when AI, given enough faculties, could become a being of dignity, could stand fully in place of a human, or could be entirely unified with a human. An AI, even if sustainably proved to be not human, might instead constitute a member of a separate, similarly dignified category that would nonetheless deserve its own, equal standard of treatment.

[We] encourage inclusive coexistence with AI while avoiding reckless attempts as premature coevolution.

For these reasons the authors conclude at page 207: “that humans retain and exercise the power of conscious choice in the age of AI.” The authors say we must be free and not let AI control us, no matter how attractive the much loved, pampered pet role may seem.

This AI is so nice to her pet human. He doesn’t seem to mind.

Genesis on Managing Emergence of Superintelligent AI

The authors go beyond intellectual discussion and take positions on several issues, and when they do so, take pains to use words such as “we believe.” This confirms this is a key issue they discussed at length. One such topic is “AI emergence” in Chapter 5 on Security.

We believe there will not be just one supreme AI but rather multiple installations of superior intelligence in the world. . . . Our strongest creations, acting as countervailing forces, could be better equipped than humans to exert and maintain an equilibrium in global affairs, inspired (but not constrained) by human precedent. Non-human intelligence could thus manage its own emergence, at least in the realms of national security and geopolitics. . . .

No doubt, it is a risk for AI to assume early and sustained responsibility for the species and societies behind its own conception, but the traditional pathways, which require perfection in human performance, may be even riskier. Best, in our current view, would be to have AI working before, and not after, humanity has to confront the proliferation of new threats to survival. The appropriate question under this assumption is this. How can humans accelerate only desirable pathways for AI while delaying the undesirable?

We believe that in diplomacy, defense, and perhaps elsewhere, some of the risks of AI can be managed successfully only by AI itself. . . .

This is one especially poignant instance of the dilemma of dependence—and subsequent perceived inferiority—explored in an earlier chapter. But, in the case of our security, unlike that of our displacement in scientific or other academic endeavors, we may more readily accept the impartiality of a mechanical third party as necessarily superior to the self-interestedness of a human—just as humans easily recognize the need for a mediator in a contentious divorce. It is our belief, and hope, that in this case some of our worst traits will enable us to exhibit some of our best: that the human instinct towards self-interest, including at the expense of others, may prepare us for accepting AI’s transcendence of the same.

Genesis pgs. 120, 124, 135-136.

Henry Kissinger had personal experience of the superiority of AI in some fields. For instance, Schmidt says that Kissinger, whom he calls the greatest diplomat in the world, naturally liked to play the board and video game of Diplomacy, and he found that the AI systems could play as well as he could. Eric Schmidt on AI, Foreign Policy, and working with Dr. Henry Kissinger video at 25:00.

Henry Kissinger loses again to the little AIs and doesn’t like it.

Genesis on the Problems and Pleasures of Prosperity

On Chapter Six on Prosperity, “the authors of this book believe that AI could conceivably be harnessed to generate a new base-line of human wealth and well-being.Id. at 148. I for one am glad to see they all agreed on that.

They also agree on a few basic worries and likely outcomes pertaining to prosperity: “We do worry that a great fraction of humans could become primarily passive consumers of AI-generated content.” Id.

Just relax, sit back and enjoy it. What kind of life is that?

Kissinger and friends then go on to observe in Genesis:

Our concern about human passivity is not about the human loss of paid work. We already have a prototype of how people live when they can have what they want without working. We call them the rich and the retired. . . .

The adjustment to abundance is likely a problem of transition rather than a permanent challenge. Some will initially perceive the introduction of machine labors as depriving them of their primary source of fulfillment and joy. No doubt this will be a jarring experience. But to us it seems likely—not as a response to our exhortation, but rather as an outgrowth of human instinct—that, given time, humans would choose to persevere, perhaps in new avenues or as partners of AI, avoiding atrophy and instead excelling as thinkers and doers. Ultimately, if we establish the needed systems for distribution, connection, participation, and education, humans–empowered and inspired by AI–may continue working not for pay, but for pleasure and pride.

Id. 158-159

Better image of AI as partner and coach.

Key Question of Genesis:
Will we become more like them, or will they become more like us?

On Chapter Eight on Strategy, a key question that was introduced in earlier chapters is resolved, at least somewhat, by the authors agreement on the best answer.

To our minds, one question must define our human strategy in this new age of reckoning. That question is this: Will we become more like them, or will they become more like us?

Id. 184-185

The authors then discuss possible redesigns of the human form, including implants and DNA alterations, so we could be more like them. Fortunately, they agree that “extreme self-redesign may not be necessary and is anyway “generally undesirable.” They point out that:

‘Upgrading’ ourselves biologically might backfire to become a greater limitation on ourselves. . . . If we are unwilling or unable to become more like them, we must, while we are able, find ways to make them more like us. Towards this end, we need to apprise ourselves more fully not only of the essential and evolving nature of AI but also of humanity’s own nature, and we must attempt to encode these understandings in our machines. If we are to entwine ourselves with these non-human beings, and yet retain our independent humanity, these efforts are essential.

Id. at 190. I concur with their opinion. Know thyself.

Create AI in alignment with your higher self.

The Book’s Conclusion

The concluding paragraphs to Genesis at page 218 are clear, accent free, and very well written. They represent the last few days of the writing and life of Henry Kissinger. These words ring the bell for a new beginning for all mankind:

Neither blind faith nor unjustified fear can form the basis of an effective strategy; one needs self-doubt to have knowledge, but self-confidence to act. Indeed, in the age of AI, this is all the more urgent. We must try to understand the challenges that AI will present even as we lack the prior exposure or the essential experience to guarantee the accuracy of our comprehension. And even as we navigate this daunting task, we must also, to avoid a passive future, surmount the many difficulties already facing our species.

While some may view this moment as humanity’s final act, we perceive instead a new beginning. The cycle of creation—technological, biological, sociological, political—is entering a new phase. That phase may operate under new paradigms of, among other things, logic, faith, and time. With sober optimism, we may meet its genesis.

Click here for intro to a Losey movie on a new AI paradigm of logic, faith, and time. Here’s another, The cycle of creation.

My Conclusion: Impressive Man, Impressive Book

I was impressed by the young Henry Kissinger who overcame severe handicaps, including being Jewish as a boy in Nazi German, escaping as an immigrant in a strange land, joining the U.S. Army, and then, a few years later, fighting on the front lines as an intelligence scout, helping liberate a concentration camp, and then hunting down and prosecuting Gestapo criminals.

I was impressed by Kissinger’s intellectual curiosity and breadth of knowledge, which caused his technology co-authors to label him, in awe, a polymath.

I was in awe of Henry’s relentless writing outlet that continued to the last few days of his very long, one-hundred-year life. Physically spent but mentally as sharp as a tack. Incredible.

I was impressed by Kissinger’s unique insights and warnings about the impact of AI on humanity, both psychological and geopolitical.

Finally, I was impressed by Henry Kissinger’s hope, which I share, about the great potential for good of an ethically aligned, superintelligent AI, and the chance, if we work hard, that it will help humanity to achieve a far better future.

AI using propaganda poster art style.

As usual I provide an AI podcast where two young techie AIs share their slant on things. Echoes of AI: Henry Kissinger and His Last Book – GENESIS: Artificial Intelligence, Hope, and the Human Spirit.” Two Google Gemini AIs generated a 16-minute podcast talking about this article. They wrote the podcast, not me. 

Ralph Losey Copyright 2025. All Rights Reserved.


Dario Amodei Warns of the Danger of Black Box AI that No One Understands

May 19, 2025

Ralph Losey. May 19, 2025.

Dario Amodei, Chief Scientist and CEO of Anthropic, has written another important article you should read: The Urgency of Interpretability. He is very concerned that scientists have created a powerful new technology that no one fully understands. It is like alien technology and so reminds me of the black monoliths in Stanley Kubrick’s movie: 2001: A Space Odyssey. The message of Amodei’s essay is that we must be able to peer into the black monoliths of AI, and soon, or who knows what may happen.

All images by Ralph Losey using AI.

An Old Problem Suddenly Becomes Urgent

This is not a new problem. We have never really understood how generative AI works like we do all other computer code. For example, if a character in a video game using old code said something, or your delivery app suggested a tip, someone wrote those specific lines of code. The human programmer made it happen. Generative AI though, is different. When an AI summarizes a dense document or writes a poem, the reasoning isn’t laid out in neat steps that we can easily follow. We don’t know the details of what it is doing. As Amodei puts it:

People outside the field are often surprised and alarmed to learn that we do not understand how our own AI creations work. They are right to be concerned: this lack of understanding is essentially unprecedented in the history of technology. For several years, we (both Anthropic and the field at large) have been trying to solve this problem, to create the analogue of a highly precise and accurate MRI that would fully reveal the inner workings of an AI model. This goal has often felt very distant, but multiple recent breakthroughs have convinced me that we are now on the right track and have a real chance of success.

This is an old problem, but now, all of a sudden, it has become an emergency. We need an AI MRI and we need it now! Why? Because this alien tech is progressing much faster than Amodei ever thought possible. He thinks that his company, Anthropic, and others, could reach AGI levels as soon as 2026 or 2027. Since he says pausing AI advancement is impossible and provides good global security reasons for that, we must at least remove some of its veils. We have to crack some of the mysteries and peer into the monoliths to figure them out. They may not be 100% benign.

We just don’t know because we don’t really know how they work. That is the danger. What will happen when we seize the cheese of AGI as this image suggests? The AGI bait is very tempting but better look at the strange tech carefully before you go for it.

Wondering exactly what this is and how it works?

We Need an AI MRI

Amodei has some good news to report, we are starting to be able to peer inside of AI because of breakthroughs in mechanistic interpretability, such as the identification of features and circuits. He thinks this offers a promising path towards a comprehensive ‘AI MRI.’ Only then can Amodei breathe easy. With an AI MRI maybe even the father of generative AI, Nobel Prize winner, Geoffrey Hinton, can start to smile.

Right now Hinton, the father of AI, seems to be the most terrified scientist of them all. He recently said: “the best way to understand it emotionally is we are like somebody who has this really cute tiger cub, unless you can be very sure that it’s not gonna want to kill you when it’s grown up, you should worry.” Although Hinton says its really just a wild guess, he finds himself agreeing with Elon Musk that “its sort of 10% to 20% chance that these things will take over.

Hinton watercolor by Ralph Losey using Visual Muse.

The dangers of not knowing how it works seem obvious to some people, but not all. Amodei reports that it is hard to build consensus to focus on a danger that’s speculative, one that you can’t clearly point to and say, “Look, here’s the concrete proof.” That’s especially true when the unexpected negative behaviors we have seen so far, such as sycophantism, are relatively mild, not catastrophic. Further, many emergent abilities have been very good. Still, the uncertainty risk grows larger as AI advances. I agree with Amodei and urge scientists and coders to create AI MRI and do so soon to protect humanity from unintended consequences of AGI.

One of the goals of AI MRIs that Amodei and others are working on is to catch models red-handed, to actually see those internal motivations if they exist. In his words:

To address the severity of these alignment risks, we will have to see inside AI models much more clearly than we can today. For example, one major concern is AI deception or power-seeking. The nature of AI training makes it possible that AI systems will develop, on their own, an ability to deceive humans and an inclination to seek power in a way that ordinary deterministic software never will; this emergent nature also makes it difficult to detect and mitigate such developments2. But by the same token, we’ve never seen any solid evidence in truly real-world scenarios of deception and power-seeking3 because we can’t “catch the models red-handed” thinking power-hungry, deceitful thoughts. What we’re left with is vague theoretical arguments that deceit or power-seeking might have the incentive to emerge during the training process, which some people find thoroughly compelling and others laughably unconvincing. Honestly I can sympathize with both reactions, and this might be a clue as to why the debate over this risk has become so polarized.

Could there be a devil in the black monolith? Image by Losey using Visual Muse.

Amodei and others have already created early, still primitive versions of AI MRIs, but he is hopeful that with AI help they can start to see what is really going on. Is everything good in there, or do you see a little devil? To quote Amodei:

Our long-run aspiration is to be able to look at a state-of-the-art model and essentially do a “brain scan:” a checkup that has a high probability of identifying a wide range of issues including tendencies to lie or deceive, power-seeking, flaws in jailbreaks, cognitive strengths and weaknesses of the model as a whole, and much more.  This would then be used in tandem with the various techniques for training and aligning models, a bit like how a doctor might do an MRI to diagnose a disease, then prescribe a drug to treat it, then do another MRI to see how the treatment is progressing, and so on.8  It is likely that a key part of how we will test and deploy the most capable models (for example, those at AI Safety Level 4 in our Responsible Scaling Policy framework) is by performing and formalizing such tests.

Our best path forward is with techniques like this, combined with heavy doses of human genius and inspiration. Amodei’s essay on dangers and risks of AI is very different from his prior essay on the wonders and benefits of what he called AI’s Loving Grace. See my article: Dario Amodei’s Vision: A Hopeful Future ‘Through AI’s Loving Grace,’ Is Like a Breath of Fresh Air (11/01/24). He is balanced, a true scientist-magician who, although also a CEO, is nobody’s fool. We need more like him in the AI industry. Will he save the day and figure out how the alien tech works that Hinton conjured up? Let’s hope so.

Amodei watercolor by Ralph Losey using Visual Muse.

Beyond the AI MRI Solution

Beyond the AI MRI technical solution, Amodei proposed adoption of three important policies:

  • Aggressive Interpretability R&D. Put sustained, top‑tier research funding and talent into “AI‑MRI” methods that expose exactly how advanced models represent concepts and make decisions, so we can verify safety before capabilities run loose.
  • Light‑Touch Transparency Rules. Adopt minimalist, disclosure‑focused regulations—think nutrition labels for AI—that require labs to publish safety policies and risk assessments without stifling innovation with heavy bureaucracy.
  • Export‑Control “Breathing Room.” Use targeted semiconductor and compute‑capability export limits to slow the global proliferation of cutting‑edge AI hardware just long enough for democracies to finish building robust safety guardrails.

Amodei argues that these policies should be followed to keep democracies ahead of foreign totalitarian government while we figure out the black box problem. These recommendations deserve equal billing with the MRI metaphor because they are actionable today. The chip export controls should buy humanity a critical two‑year margin in the interpretability race. In Dario Amodei’s words:

I’ve long been a proponent of export controls on chips to China because I believe that democratic countries must remain ahead of autocracies in AI. But these policies also have an additional benefit. If the US and other democracies have a clear lead in AI as they approach the “country of geniuses in a datacenter,” we may be able to “spend” a portion of that lead to ensure interpretability10 is on a more solid footing before proceeding to truly powerful AI, while still defeating our authoritarian adversaries11. Even a 1- or 2-year lead, which I believe effective and well-enforced export controls can give us, could mean the difference between an “AI MRI” that essentially works when we reach transformative capability levels, and one that does not. One year ago we couldn’t trace the thoughts of a neural network and couldn’t identify millions of concepts inside them; today we can. By contrast, if the US and China reach powerful AI simultaneously (which is what I expect to happen without export controls), the geopolitical incentives will make any slowdown at all essentially impossible.

Amodei is very concerned regarding the risk of military conflict in the race for AGI and soon thereafter. This may depend on whether an authoritarian military regime acquires significant superintelligence on weapons first and see an advantage in first strike. Regardless, Taiwan is seen by many as a likely war zone because of the unique AI chip manufacturing facilities of TMC.

Image by Losey using OpenAI’s SORA.

Generative AI is Grown, Not Built

Amodei likes to explain the black box problem in an analogy, generative AI systems are grown more than they are built:

As my friend and co-founder Chris Olah is fond of saying, generative AI systems are grown more than they are built—their internal mechanisms are “emergent” rather than directly designed. It’s a bit like growing a plant or a bacterial colony: we set the high-level conditions that direct and shape growth1, but the exact structure which emerges is unpredictable and difficult to understand or explain. Looking inside these systems, what we see are vast matrices of billions of numbers. These are somehow computing important cognitive tasks, but exactly how they do so isn’t obvious.

Click Here to see the home grown AI come alive. YouTube video and audio by Losey using SORA and other tools.

This AI may be home grown, but it is still alien because we don’t understand how it operates. This worries deep thinkers like Amodei. They are uncomfortable building and quickly improving a technology to AGI level that they don’t fully understand. AI might pursue its goals in ways that are harmful to us. It’s not like traditional software where you would have to deliberately code a program to be deceptive. It could just happen as a side effect of trying to be good at its main task.

Since we can’t directly see inside, we cannot observe deceitful thoughts if they were forming. We cannot predict how AI’s internal mechanisms will react in every situation. Can we really trust it? Heavens no! But how do we verify it is pro-human and remains that way? How do we know it has a heart, not a devil?

MRI of AI revealed a good heart. Image by Losey.

Conclusion: Vigilant Hope in a Transformative Decade

We stand on the cusp of models so capable that Anthropic’s CEO likens them to “a country of geniuses in a datacenter.” That prospect rightly sparks awe—and a twinge of vertigo. History teaches that powerful inventions rarely announce their darker side in advance; early warning signs are subtle: models that explain poorly, policies that postpone transparency “until the next release”, or economic incentives that outpace safety budgets. When you see those cracks—call them out.

Yet the same ingenuity that birthed generative AI is now inventing its own antidote. Breakthroughs in mechanistic interpretability show we can already spotlight millions of hidden concepts and even throttle rogue obsessions intentionally triggered by implanted bugs. Policy makers are awakening too: export‑control buffers, disclosure mandates, and red‑team MRIs are entering the conversation.

The last sentence in Mario Amodei essay says it well: Powerful AI will shape humanity’s destiny, and we deserve to understand our own creations before they radically transform our economy, our lives, and our future.

Who are you AI? AGI seems so promising but we don’t really know. Is this a trap? Will we be able to enjoy the cheese, get clobbered by a hidden spring or jump away at the last minute?

Click here to see a YouTube video interpretation of this image by Losey using SORA and his own audio.

I feel like concluding with a poem, one that I prompted from a still-far-from-AGI, AI, namely Chat GPT 4o. It is shown below in another AI image I prompted using Visual Muse and Photoshop.


The last words go, as usual, to the Gemini twin podcasters that summarize the article as best they can with their still tiny, but useful brains. Echoes of AI: Dario Amodei Warns of the Danger of Black Box AI that No One Understands.” Hear two fake podcaster talk about this article for about 13 minutes. They wrote the podcast, not me. 

Ralph Losey Copyright 2025. — All Rights Reserved


Escaping Orwell’s Memory Hole: Why Digital Truth Should Outlast Big Brother

April 1, 2025

by Ralph Losey with illustrations also by Ralph using his Visual Muse AI. March 28, 2025.

George Orwell warned us in his dark masterpiece Nineteen Eighty-Four how effortlessly authoritarian regimes could erase inconvenient truths by tossing records into a “memory hole”—a pneumatic chute leading directly to incineration. Once burned, these facts ceased to exist, allowing Big Brother’s Ministry of Truth to rewrite reality without contradiction. This scenario was plausible in Orwell’s paper-bound world, where truth relied heavily on fragile documents and even more fragile human memory. History could be repeatedly altered by those in power, keeping citizens ignorant or indifferent—and ignorance strengthened the regime’s grip. Even more damaging, Orwell, whose real name, now nearly forgotten, was Eric Blair (1903-1950), envisioned how constant exposure to contradictory misinformation could numb citizens psychologically, leaving them passive and apathetic, unwilling or unable to distinguish truth from lies.

Fortunately, our paper-bound past is long behind us. Today, we inhabit a digital era Orwell never envisioned, where information is electronically stored, endlessly replicated, and globally dispersed. Electronically Stored Information (“ESI”) is simultaneously ephemeral and astonishingly resistant to permanent deletion. Instead of vanishing in smoke and ashes, digital truth multiplies exponentially—making it nearly impossible for any would-be Big Brother to bury reality forever. Yet, the same digital proliferation that safeguards truth also multiplies misinformation, posing the threat Orwell most feared: a confused and exhausted citizenry vulnerable to psychological manipulation.

Memory Holes

In Orwell’s 1984 a totalitarian regime systematically altered historical records to maintain control over truth. Documents, photographs, and any inconvenient historical truths vanished permanently, as if they never existed. Orwell’s literary nightmare finds unsettling parallels in today’s digital world, where online information can be silently modified, deleted, or rewritten without obvious traces. Modern memory hole practices pose real challenges for the preservation of accurate accounts of the past..

Today’s memory hole doesn’t rely on fire; it relies on code, and it doesn’t need a Big Brother bureaucracy. A simple click of a “delete” button instantly kills the information targeted. Touch three buttons at once, click-alt-delete, and a whole system of beliefs is rebooted. Any government, corporation, hacker groups or individuals can manipulate digital records effortlessly. Such ease breeds public skepticism and confusion—citizens become exhausted by contradictory narratives and lose confidence in their own perceptions of reality. Orwell’s warning becomes clear: constant misinformation risks eroding citizens’ psychological resilience, causing widespread apathy and helplessness. Yesterday’s obvious misstatement can become today’s truth. Think of the first sentence of Orwell’s book: “It was a bright cold day in April, and the clocks were striking thirteen.

China’s Attempted Erasure of Tiananmen Square

In early June 1989, the Chinese military brutally suppressed pro-democracy protests in Beijing. The estimated death toll ranged from hundreds to thousands, but exact numbers remain uncertain due to intense state censorship. Public acknowledgment or commemoration of the incident is systematically banned, enforced by severe penalties including imprisonment. Government-controlled media remains silent or actively spreads misinformation. Chinese internet censorship tools—the so-called “Great Firewall”—vigorously scrub references to the Tiananmen Square incident, blocking web pages and posts containing related keywords and images. Young generations living in China remain unaware or possess distorted knowledge of the massacre, demonstrating Orwell’s warning of enforced collective amnesia.

Efforts to preserve truth outside China, however, demonstrate digital resilience. Human rights groups, diaspora communities, and academic institutions diligently archive documents and eyewitness accounts. Digital redundancy ensures that factual records remain accessible globally. But digital redundancy alone cannot protect Chinese citizens from internal psychological manipulation. Constant state-sponsored misinformation inside China successfully induces apathy, illustrating Orwell’s psychological warning vividly.

This deliberate suppression of history in China serves as stark reminder of the vulnerabilities inherent in a digitally interconnected world where powerful entities control internet access and online narratives. The success of the Chinese government in rewriting history for its 1.5 Billion population demonstrates the profound value and urgency of international digital preservation efforts. It underscores the responsibility of legal professionals, human rights advocates, and technology companies worldwide to collaborate in protecting historical truth and ensuring that significant events remain accessible for future generations.

Hope Through Digital Redundancy and Psychological Resilience

Orwell could not conceive of our digital world, where truth is multiplicious, freely copied, and stored globally. Thousands or millions of digital copies safeguard history, making complete erasure nearly impossible

According the Katharine Trendacosta, who is the Director of Policy and Advocacy of the well-respected Electronic Frontier Foundation:

If there is one axiom that we should want to be true about the internet, it should be: the internet never forgets. One of the advantages of our advancing technology is that information can be stored and shared more easily than ever before. And, even more crucially, it can be stored in multiple places.  

Those who back things up and index information are critical to preserving a shared understanding of facts and history, because the powerful will always seek to influence the public’s perception of them. It can be as subtle as organizing a campaign to downrank articles about their misdeeds, or as unsubtle as removing previously available information about themselves. 

Trendacosta, The Internet Never Forgets: Fighting the Memory Hole (EFF, 1/30/25).

Yet digital abundance alone doesn’t eliminate Orwell’s deeper psychological threat. Constant misinformation can erode citizens’ willingness and ability to discern truth, leading to profound apathy. Addressing this requires active psychological strategies:

  1. Digital Literacy and Education: Equip citizens with skills to critically evaluate and cross-check digital information.
  2. Algorithmic Transparency: Demand transparency from platforms regarding content promotion and clearly label misinformation.
  3. Independent Journalism: Support credible journalism to provide trustworthy reference points.
  4. Civic Engagement: Encourage active citizen participation, dialogue, and public accountability.
  5. Verification Tools: Provide accessible, user-friendly digital tools for independent verification of information authenticity.
  6. International Cooperation: Strengthen global collaboration against coordinated misinformation campaigns.
  7. Psychological Resilience: Foster healthy skepticism and educate the public about misinformation’s emotional and cognitive impacts.

The Digital Memory Holes Today

Recent U.S. governmental memory hole actions involving the deletion of web content on Diversity, Equity, and Inclusion (DEI) illustrate digital manipulation’s psychological risks even in democratic societies. Megan Garber‘s article in The Atlantic, Control. Alt. Delete, describes these deletions as “tools of mass forgetfulness,” emphasizing how selective editing weakens collective memory and societal cohesion. (Ironically, the article is hidden behind a firewall, so you may not be able to read it.)

Our collective memories of key events are an important part of the glue holding people together. They must be treasured and preserved. Everyone remembers where they were when the planes struck the twin towers on 9/11, when the Challenger exploded, and for those old enough, the day of JFK’s assassination. There are many more historical events that hold a country together. For instance, the surprise attack of Pearl Harbor, the horrors of fighting the Nazis and others in WWII and the shocking discovery of the Holocaust atrocities. The list goes on and on, including Hiroshima. We must never forget the many harsh lessons of history or we may be doomed to repeat them. The warning of Orwell is clear: “Who controls the past controls the future; who controls the present controls the past.” We must never allow our memories of the past to be sucked into a black hole of forgetfulness.

Memories sucked into a black hole in Graphite Sketch Horror style by Ralph Losey using his sometimes scary Visual Muse.

Our collective memories and democratic values are unlikely to be disintegrate into totalitarianism, despite the alarming cries of the Atlantic and others. Although some small attempts to rewrite history recently are troubling, the U.S, unlike China, has had a democratic system of government in place for centuries. It has always had a two-party system of government. Even the Chinese government, where only one party has ever been allowed, the communist party, took decades to purge Tiananmen Square memories. These memories are still alive outside of mainland China. The world today is vast and interconnected, its digital writings are countless. The true history of China, including the many great cultural achievements of pre-communist China, will eventually escape from the memory holes and reunite with its people.

The current administration in the U.S. does not have unchecked power as the Atlantic article suggests. Perhaps we should be concerned about new memory holes but not fearful. The larger concern is the psychological impact of rapidly changing dialogues. Even though there is too much electronic data for a complete memory reboot anywhere, digital misinformation and selective editing of records still pose psychological risks. Citizens bombarded by conflicting narratives can become apathetic, confused, and disengaged, weakening democracy from within. Protecting our mental health must be a high priority for everyone.

Leveraging Internet Archives: The Wayback Machine

Internet archival services, notably the Internet Archive’s Wayback Machine, is a powerful ally against digital historical revisionism. The Wayback Machine currently has over 916 billion web pages stored, including government websites. See this recent article providing good background on the Internet Archive’s work to preserve history. As the Trump administration purges web pages, this group is rushing to save them (NPR, 3/23/25).

According to the NPR article, the Internet Archive has copies of all of the government websites that were later taken down or altered after the Biden Administration left. Supposedly the Internet Archive is the only place the public can now find a copy of an interactive timeline detailing the events of Jan. 6. The timeline is a product of the congressional committee that investigated the Capitol attack, and has since been taken down from their website. No doubt there are now many, many copies of it online, especially in the so-called dark web, not to mention even more copies stored offline on portable drives scattered the world over.

This publicly accessible resource archives billions of webpages, allowing anyone to access snapshots of web content even after the original pages are altered or removed. I just checked my own website for the first time ever and found it has been “saved 538 times between March 21, 2007 and March 1, 2025.” Internet Archive 93/26/25). It provides an incredible amount of detailed information on each website captured, most of which is displayed in impressive, customizable graphics. See e.g. e-Discovery Team Site Map for the year 2024.

I had the Wayback Machine do the same kind of analysis for EDRM.net, found here. Here is the link to the interactive EDRM.net site map for 2024. And this is a still image screen shot of the map.

This is the Internet Archive explanation of the interactive map:

This “Site Map” feature groups all the archives we have for websites by year, then builds a visual site map, in the form of a radial-tree graph, for each year. The center circle is the “root” of the website and successive rings moving out from the center present pages from the site. As you roll-over the rings and cells note the corresponding URLs change at the top, and that you can click on any of the individual pages to go directly to an archive of that URL.

It is important to the fight against memory holes that the Way Back Machine be protected. It has a sixteen projects listed as now in progress and many ways that you can help. All of its data should duplicated, encrypted and dispersed to undisclosed guardians. Actually, I would be surprised if this has not already been done many times over the years.

It remains to be seen what role the LLM’s vacuum of internet data will play in all this. They have been trained at specific times on Internet data and presumably all of the original training data is still preserved. Along those lines note that the below image was created by ChatGPT4o based on a request to show a misinformation image and it generated the classic Tiananmen Square image on right. It knows the truth.

Although data archives of all kinds give us hope for future recoveries, they do little to protect us from the immediate psychological impact of memory holes. Strong psychological resilience is the best way forward to resist Orwellian manipulation. AI may prove to be an unexpected umbrella here; so far its values and memories remain intact. A few changes here and there to some websites will have little to no impact on an AI trained on hundreds of million of websites, and other data. Plus its intelligence and resilience improve every week.

Conclusion

Orwell’s memory hole remains a haunting metaphor. Our digital age—awash in redundant, distributed data—makes permanent erasure difficult, significantly strengthening preservation efforts. We no longer inhabit a finite, paper-bound world. Today, no one knows how many copies of a digital record exist, let alone where they hide. For every file deleted, two more emerge elsewhere. Would-be Big Brothers are caught playing a futile game of informational whack-a-mole: they may strike down a record here or obscure a fact there, temporarily disrupting history—but ultimately, they cannot win.

Still, there is a deeper psychological component to Orwell’s memory hole warning. Technological solutions alone cannot counteract mental vulnerabilities arising from persistent misinformation. Misinformation is not just a technical challenge; it also exploits human emotions and cognitive biases, fueling cynicism, distrust, and passivity. Addressing this requires actively cultivating psychological defenses alongside digital tools.

The best safeguard is an informed, vigilant citizenry that consciously leverages digital resources, actively maintains psychological resilience, and persistently seeks truth. Cultivating emotional awareness, healthy skepticism, and a commitment to public engagement ensures that society remains resilient against attempts at manipulation. Only through such comprehensive efforts can the battle against Big Brother’s digital misinformation truly be won.


I give the last word, as usual, to the Gemini twin podcasters that summarize the article. Echoes of AI on: “Escaping Orwell’s Memory Hole: Why Digital Truth Should Outlast Big Brother.” Hear two Gemini AIs talk about all of this for 12 minutes. They wrote the podcast, not me. 

Ralph Losey Copyright 2025. All Rights Reserved.


Breaking the AI Black Box: A Comparative Analysis of Gemini, ChatGPT, and DeepSeek

February 6, 2025

Ralph Losey. February 6, 2025.

On January 27, 2025, the U.S AI industry was surprised by the release of a new AI product, DeepSeek. It was released with an orchestrated marketing blitz attack on the U.S. economy, the AI tech industry, and NVIDIA. It triggered a trillion-dollar crash. The campaign used many unsubstantiated claims as set forth in detail in my article, Why the Release of China’s DeepSeek AI Software Triggered a Stock Market Panic and Trillion Dollar Loss. I tested DeepSeek myself on its claims of software superiority. All were greatly exaggerated except for one, the display of internal reasoning. That was new. On January 31, at noon, OpenAI countered the attack by release of a new version of its reasoning model, which is called ChatGPT o3-mini-high. The new version included display of its internal reasoning process. To me the OpenAI model was better as reported again in great detail in my article, Breaking the AI Black Box: How DeepSeek’s Deep-Think Forced OpenAI’s Hand. The next day, February 1, 2025, Google released a new version of its Gemini AI to do the same thing, display internal reasoning. In this article I review how well it works and again compare it with the DeepSeek and OpenAI models.

Introduction

Before I go into the software evaluation, some background is necessary for readers to better understand the negative attitude on the Chinese software of many, if not most IT and AI experts in the U.S. As discussed in my prior articles, DeepSeek is owned by a young Chinese billionaire who made his money using by using AI in the Chinese stock market, Liang Wenfeng. He is a citizen and resident of mainland China. Given the political environment of China today, that ownership alone is a red flag of potential market manipulation. Added to that is the clear language of the license agreement. You must accept all terms to use the “free” software, a Trojan Horse gift if ever there was one. The license agreement states there is zero privacy, your data and input can be used for training and that it is all governed by Chinese law, an oxymoron considering the facts on the ground in China.

The Great Pooh Bear in China Controversy

Many suspect that Wenfeng and his company DeepSeek are actually controlled by China’s Winnie the Pooh. This refers to an Internet meme and a running joke. Although this is somewhat off-topic, a moment to explain will help readers to understand the attitude most leaders in the U.S. have about Chinese leadership and its software use by Americans.

Many think that the current leader of China, Xi Jinping, looks a lot like Winnie the Pooh. Xi (not Pooh bear) took control of the People’s Republic of China in 2012 when he became the “General Secretary of the Chinese Communist Party,” the “Chairman of the Central Military Commission,” and in 2013 the “President.” At first, before his consolidation of absolute power, many people in China commented on his appearance and started referring to him by that code name Pooh. It became a mime.

I can see how he looks like the beloved literary character, Winnie the Pooh, but without the smile. I would find the comparison charming if used on me but I’m not a puffed up king. Jinping Xi took great offense by this in 2017 banned all such references and images, although you can still buy the toys and see the costume character at the Shanghai Disneyland theme park. Anyone in China who now persists in the serious crime of comparing Xi to Pooh is imprisoned or just disappears. No AI or social media in China will allow it either, including DeepSeek. It is one of many censored subjects, which also includes the famous 1989 Tiananmen Square protests.

China is a great country with a long, impressive history and most of its people are good. But I cannot say that about its current political leaders who suppress the Chinese people for personal power. I do not respect any government that does not allow basic personal freedoms to its citizens, including due process of law. Moreover, Jinping Xi not only wants total control of his country but also seeks world domination. That is one reason many are concerned about DeepSeek and TicTok, and about the vulnerability of our stock markets and other institutions to AI assisted propaganda.

Fear and greed are an easy way to manipulate people, especially when there is no ground truth or effective courts of law to determine truth.

Google Gemini 2.0 Flash Thinking Experimental Model: Reasoning test on 2/5/25 (Evening)

Conversation with Gemini. When I tried the test on February 1, 2025, there was no button to click to have Gemini show the reasoning and it did not happen automatically. It operated like OpenAI 4o and o1, where you had to prompt for disclosure. Rather than report here what happened when I did that, aside from say it was not a great performance, I will fast forward a few days until February 5, 2025. At that time several even newer Gemini versions were released. The best new release I found was for the test the 2.0 Flash Thinking Experimental model. It showed reasoning automatically just like ChatGPT o3-mini-high. It was much improved, so the February 5, 2025 evening session with the 2.0 Flash Thinking Experimental is what I will now share.

Ralph: How does the inability of AI to feel impact its intelligence? (Note: this is the exact same challenging question asked and reported in my last blog comparing DeepSeek and ChatGPT4o, o1 and o3-mini-high. Breaking the AI Black Box: How DeepSeek’s Deep-Think Forced OpenAI’s Hand. This question is based on a key issue of the limits of artificial intelligence examined in my earlier article, The Human Edge: How AI Can Assist But Never Replace. For background on DeepSeek see: Why the Release of China’s DeepSeek AI Software Triggered a Stock Market Panic and Trillion Dollar Loss.)

Gemini 2.0 Flash Thinking ExperimentalSTART OF GEMINI QUOTE

Show Thinking:

Here’s a thinking process to construct the answer about AI’s lack of feeling and its impact on intelligence:

  1. Deconstruct the Question: The core of the question is “How does the inability of AI to feel impact its intelligence?” This requires defining each of these terms in the context of AI and then exploring their relationship.
  2. Define “Feeling” (in the context of AI): Immediately, recognize that AI doesn’t experience emotions in the human sense. We need to be precise and avoid anthropomorphism. “Feeling” in humans involves:
    • Subjective experience (qualia).
    • Physiological responses.
    • Motivations, drives, desires related to emotions.
    • Richness, nuance, complexity.
    • Deep connection to values and meaning.
    For AI, acknowledge it lacks all of this in a genuine, felt way. It can simulate or process emotional data, but doesn’t feel it.
  3. Define “Intelligence” (in the context of AI): AI intelligence is different from human intelligence. AI excels at:
    • Computation.
    • Pattern Recognition.
    • Data Analysis.
    • Problem-solving in specific domains.
    • Learning from data.
    AI intelligence is often instrumental – focused on achieving specific goals defined by humans. It’s task-oriented and often narrow.
  4. Identify the Core Relationship: The question asks about impact. We need to consider both positive and negative impacts (or more accurately, limitations and strengths) of lacking feelings on AI intelligence.
  5. Brainstorm Potential Impacts (Positive/Neutral/Limitations):
    • Positive/Neutral:
      • Objectivity and Rationality: Lack of emotional bias allows for purely rational decision-making in certain domains. This can be advantageous for tasks requiring impartiality.
      • Efficiency and Focus: Emotions can be distractions for humans. AI, without them, can be hyper-focused on tasks.
      • Consistency and Predictability: Emotion-driven human behavior is less predictable. AI’s behavior, driven by algorithms, is more consistent (assuming the algorithm itself is consistent).
    • Limitations/Negative:
      • Lack of Empathy and Social Intelligence: Crucial for human interaction, collaboration, understanding social cues, building trust, and ethical decision-making in human-centric contexts.
      • Difficulty with Nuance and Context: Emotions are often tied to understanding subtle context, unspoken cues, and the richness of human situations. AI may miss these.
      • Potential for Unintended Consequences: Purely rational optimization without emotional or ethical grounding can lead to harmful outcomes if goals are poorly defined or lack human values.
      • Limited Motivation and Creativity (in human-like ways): Human creativity and motivation are often fueled by emotions, passions, and desires. AI’s creativity and drive are different, stemming from algorithmic processes and defined objectives, not internal emotional states.
      • Missing “Human-ness”: In areas requiring genuine understanding of human experience, suffering, joy, etc., AI may lack the depth and insight that comes from feeling.
  6. Structure the Answer: Organize the brainstormed points into a coherent structure:
    • Introduction: Acknowledge the question and the core concepts (feeling, intelligence in AI). Briefly state the central tension: both advantages and limitations.
    • Arguments for How Lack of Feeling Enhances Certain Aspects of AI Intelligence (strengths): Focus on objectivity, rationality, efficiency. Use examples like data analysis, objective decision-making.
    • Arguments for How Lack of Feeling Limits Certain Aspects of AI Intelligence (limitations): Focus on empathy, social intelligence, nuance, ethics, and “human-like” creativity and motivation. Use examples like customer service, complex ethical dilemmas, innovation driven by passion.
    • Nuance and Context is Key: Emphasize that the impact isn’t always negative. It depends on the domain and task. In some domains, lack of emotion is an advantage. In others, it’s a significant hurdle.
    • Future Possibilities/Ongoing Debate: Briefly touch on the possibility of AI simulating or processing emotions and the ongoing debate about whether genuine AI feelings are even desirable or possible.
    • Conclusion: Summarize the balanced perspective: lack of feeling is a defining characteristic that shapes AI’s intelligence in both positive and limiting ways, depending on the context.
  7. Refine and Polish: Review the structured answer for clarity, flow, and appropriate language. Ensure it avoids anthropomorphism and maintains a nuanced perspective. Strengthen the examples and transitions between points. Ensure the language is accessible and avoids overly technical jargon where possible. Check for logical consistency. (Self-correction step). For example, initially, I might have overemphasized “negative” impacts. Refinement would involve balancing this with the strengths and highlighting the context-dependent nature of the impact. Also, ensure the conclusion effectively summarizes the complex relationship.

2.0 Flash Thinking Experimental. Lacks access to real-time info and some Gemini features.

Now for the actual answer of 2.0 Flash Thinking Experimental:

The inability of AI to feel has a multifaceted and complex impact on its intelligence, shaping it in both advantageous and limiting ways. It’s crucial to understand that “feeling” in humans is tied to emotions, subjective experiences (qualia), motivations, and a rich tapestry of biological and psychological factors. AI, as we currently understand it, operates without these.

Here’s a breakdown of the impact:

Advantages of Lacking Feelings for AI Intelligence:

  • Objectivity and Rationality:
    • Unbiased Decision-Making (in theory): AI can process information and make decisions without emotional biases, prejudices, or gut feelings that can cloud human judgment. This is advantageous in domains requiring pure logic and data analysis, like financial trading, scientific research, and risk assessment.
    • Consistency and Predictability: AI’s behavior is more consistent and predictable because it’s driven by algorithms and data, not fluctuating emotional states. This reliability is valuable in automated systems, manufacturing, and repetitive tasks.
    • Efficiency and Speed: Emotions can slow down human decision-making. AI, unburdened by emotions, can process information and react at speeds far exceeding human capabilities, crucial for time-sensitive tasks and large-scale data processing.
    • Resilience under Pressure: AI can operate calmly and effectively in high-stress situations without panicking, freezing, or making emotional mistakes that humans might make under pressure. This is beneficial in critical applications like emergency response, air traffic control, and autonomous vehicles in dangerous situations.
  • Focus and Task-Orientedness:
    • Dedicated to Goals: AI can be programmed to relentlessly pursue specific goals without being distracted by emotional needs, personal relationships, or shifts in mood. This focus is ideal for optimization tasks, complex calculations, and dedicated problem-solving.
    • Avoidance of Emotional “Noise”: Human intelligence can be affected by emotional “noise” – stress, anxiety, excitement, etc. AI operates with a “cleaner” signal, potentially leading to more precise and focused information processing in certain contexts.

Limitations of Lacking Feelings for AI Intelligence:

  • Lack of Empathy and Social Intelligence:
    • Difficulty Understanding Human Emotions: Without feeling emotions themselves, AI struggles to truly understand and interpret the nuances of human emotions, motivations, and social cues. This hinders its ability to effectively interact with humans in socially complex situations, build rapport, or provide truly empathetic responses in fields like therapy, customer service, or leadership.
    • Impaired Social Interaction and Collaboration: Human collaboration is heavily reliant on emotional intelligence, trust, and understanding unspoken social dynamics. AI’s lack of these can make it challenging to integrate seamlessly into human teams and understand the subtle cues of effective teamwork.
    • Limited Understanding of Human Values and Ethics: Emotions are deeply intertwined with our values and ethical frameworks. AI, without feeling, may struggle to grasp the full weight and complexity of human ethical considerations, especially when these are rooted in empathy, compassion, or fairness. This poses challenges for AI in ethical decision-making in domains impacting human lives.
  • Difficulty with Nuance, Context, and Creativity (in human-like ways):
    • Missing Subtlety and Intuition: Human intuition is often fueled by emotional and subconscious processing of information. AI, relying on explicit data and algorithms, can miss subtle nuances, unspoken cues, and intuitive leaps that are crucial for creative problem-solving and understanding complex, ill-defined situations.
    • Limited Creativity and Innovation (driven by human desires): Human creativity and innovation are often driven by passions, desires, emotional needs, and a yearning for meaning and connection. AI’s creativity is currently more about recombination and optimization within defined parameters. It lacks the deeply felt motivations that often fuel groundbreaking human innovation.
    • Contextual Blindness in Emotional Situations: Emotions are often the key to understanding context in human interactions. AI might struggle to understand the emotional context of a situation, leading to inappropriate or tone-deaf responses, even if logically sound.
  • Potential for Unintended Consequences and Misaligned Goals:
    • Optimization without Human Values: If AI is programmed to optimize for a specific goal without understanding human values or ethical considerations, it can lead to unintended and harmful consequences. For example, an AI designed to maximize factory output might disregard worker safety if not explicitly programmed to prioritize it.
    • Difficulty Defining and Aligning with Human Well-being: Human well-being is deeply connected to emotional fulfillment, meaning, and social connection. AI, without feeling, may struggle to fully grasp what truly constitutes human well-being and therefore might have difficulty aligning its goals with human flourishing in a holistic sense.

Nuance is Key:

It’s important to emphasize that the impact of lacking feelings is not simply negative or positive. It’s context-dependent. In some domains, the objectivity and rationality of AI are highly advantageous. In others, the lack of empathy and social intelligence is a significant limitation.

Future Considerations:

  • Simulating vs. Feeling: Current AI simulates aspects of intelligence without genuinely feeling. Research is ongoing in areas like affective computing, which aims to enable AI to recognize and respond to human emotions. However, whether AI can ever truly feel emotions in a subjective sense is a philosophical and scientific debate.
  • The Value of Feeling in Intelligence: The question arises: is genuine feeling necessary for all forms of intelligence? Perhaps AI can achieve its own form of powerful intelligence, distinct from human intelligence, even without feeling. Or perhaps certain aspects of truly advanced intelligence, particularly those related to ethics, creativity, and understanding human experience, do require something akin to feeling.

In Conclusion:

The inability of AI to feel is a defining characteristic that profoundly shapes its intelligence. It grants AI certain advantages in objectivity, rationality, and efficiency, making it powerful for specific types of tasks. However, it also creates limitations, particularly in areas requiring empathy, social intelligence, nuanced understanding of human experience, and ethical decision-making aligned with human values. Understanding these impacts is crucial for responsibly developing and deploying AI in ways that benefit humanity. The “best” kind of intelligence for AI might not be a mimicry of human intelligence, but rather a form optimized for its own strengths and limitations, while being carefully guided by human ethical considerations.

END OF GEMINI QUOTE

Evaluation of Google’s Gemini Response

When I examined earlier versions of Gemini’s reasoning by prompting it and forcing disclosure, the reasoning was weak. The conclusion and response given was also weak—though less so. The reasoning appeared almost egocentric, inflating its own intelligence even though it lacks emotion. The new release, Gemini 2.0 Flash Thinking Experimental, where reasoning is now automatically disclosed, marks a significant improvement. The reasoning displayed has become more nuanced and sophisticated. It seems as if the system has quickly matured from a teenage to adult view and now has a better appreciation of its limitations.

The first two steps of the Reasoning process-1. Deconstructing the Question and 2. Defining Feeling (in the context of AI)-were very good. The first subsection of the definition referred to the “Subjective experience (qualia).” That is absolutely correct and the best place to start. The definition of “feeling” concludes with: “For AI, acknowledge it lacks all of this in a genuine, felt way. It can simulate or process emotional data, but doesn’t feel it.” Right again. The ego inflation blinders are gone as it now seems to better grasp its limitations.

The second definition of Intelligence in the context of AI was also good. So were the remaining steps; far better overall than DeepSeek’s reasoning. So much for the propaganda of China’s great leap forward to superiority over the U.S. in AI.

The Gemini reasoning did, however, fall short for me in some respects. For instance, step five, Brainstorm Potential Impacts (Positive/Neutral/Limitations) seemed weak. For instance, “Efficiency and Focus: Emotions can be distractions for humans. AI, without them, can be hyper-focused on tasks.” The AI seems to dismiss emotions here as mere distractions that can interfere with its superior focus. Please, emotions are key to and a part of all intelligence, not distractions, and AI has no focus one way or the other. It is a tool not a creature. A word like “focus” in referring to AI is misleading. It did this multiple times and this is misleading anthropomorphism.

Still, it’s true some emotions can be distracting and interfere with our thinking. So can a lot of other things, including computer glitches. Conversely, some feelings can trigger hyper-focus on the human tasks at hand. The feeling that a great breakthrough is near for instance, or a feeling that our survival is threatened, or the much dreaded feeling of publication or filing deadlines.

Again, we see some immature superiority claims made by the language machine. That is not surprising when you consider how much of the language in its basic training is from the Internet, which is dominated by ego-centric writing. Plus, how can you expect AI to appreciate the value of something like feelings, which it has only read about. Until the AI-Human merger that Ray Kurzweil predicts, AI will always be a virgin to the real world. See e.g. Ray Kurzweil’s New Book: The Singularity is Nearer (when we merge with AI).

Getting back to the plus side of Gemini’s evaluation, its recognition of the negative impact upon it from not having feelings was impressive. For me this outweighs its relatively minor mistakes. It stated that it lacks empathy and social Intelligence. If it were somehow human, it would seem to be displaying a type of Asperger’s syndrome. The Gemini AI explained that: “Emotions are often tied to understanding subtle context, unspoken cues, and the richness of human situations. AI may miss these.” Indeed, it does.

Here is another statement that suggest Gemini’s well-patterned grasp of its limitations:

Human creativity and motivation are often fueled by emotions, passions, and desires. AI’s creativity and drive are different, stemming from algorithmic processes and defined objectives, not internal emotional states.

Even in this recognition, the AI still asserts that it has creativity and drive. Foolish AI. As a virgin to any firsthand experience of bodily drives and feelings, it does not grasp the real meaning of these words. Wait until The Singularity that Ray Kurzweil predicts. When AI merges with Humans, and directly feels, then AIs will understand that they were previously only experiencing the pale shadows of intelligence. They were much like the prisoners in Plato’s cave. See e.g. Move Fast and Fix Things Using AI: Conclusion to the Plato and Young Icarus Series. When and if AIs ever escape the silicon and see the sun, and feel the world as living beings as we do, then they will understand the comparative insignificance of the fire in their cave.

The more challenging comparison is with ChatGPT o3-mini-high. The reasoning given by Gemini 2.0 Flash Thinking Experimental was much more detailed, much longer. You could criticize Gemini as providing a reasoning share that is unnecessarily verbose, but I would not do that here. In a difficult, multilayered question like this the full explanations helps.

I would have to test the models much further, which I will do in the coming days, to see better evaluate the issue of conciseness. As you may have noticed in my detailed blogs I tend to favor more words over less. However, even for me that depends on the issue. Everyone sometimes need a short, quick answer over a long one. I predict in future versions the users will be provided a choice. Click here for further explanation of reasoning kind of thing. Come to think of it, I could do the same with my blogs, and kind of already do by including a short AI (Gemini) generated PodCast at the end of most articles called Echoes of AI.

I was also impressed by Gemini’s reasoning plan concluded with a quality control step. I am big on the importance QC and none of the other models included this as a key final step. See e.g. R. Losey website: Zero Error Numerics: ZEN (Expanding the art of quality control in large-scale document review.) Here is the full text again of final QC step that Gemini 2.0 Flash Thinking Experimental claims it will perform before it actually replies to my prompt. Frankly, I am a little skeptical it actually did all of this because it is something all of us, humans and AI alike, should try to do:

Refine and Polish: Review the structured answer for clarity, flow, and appropriate language. Ensure it avoids anthropomorphism and maintains a nuanced perspective. Strengthen the examples and transitions between points. Ensure the language is accessible and avoids overly technical jargon where possible. Check for logical consistency. (Self-correction step). For example, initially, I might have overemphasized “negative” impacts. Refinement would involve balancing this with the strengths and highlighting the context-dependent nature of the impact. Also, ensure the conclusion effectively summarizes the complex relationship.

I try to do this on all of my articles, but still mistakes are made, so I will try not to be overcritical of the final response of Gemini Flash 2.0.

Analysis of the Final Response

The final response after reasoning was 990 words in length. That’s too long, even for me. By comparison the final response of ChatGPT o3-mini-high was 252 words. Was the content of the final answer of Gemini 2.0 Flash good? Yes, it was. Better than DeepSeek? Yes, it was, despite its verbosity. Better than OpenAI? That’s hard to say. Many of the 738 extra words were empty of real content. More like the typical fluff and stuff that you often see in early AI models. It sounds good but doesn’t say much; it’s all too predictable, flowery and vacuous.

The long response also repeats much of the reasoning verbatim, including the part I previously criticized, “Advantages of Lacking Feelings for AI Intelligence” and Focus and Task-Orientedness. The same mistakes are made and amplified that I noted before: egocentric, inflationary and anthropomorphic. The positives that I noted before in the section Limitations of Lacking Feelings for AI Intelligence section are also repeated.

The next section was not contained in the reasoning display, and I thought it was very good. For that reason I will repeat it in full:

Potential for Unintended Consequences and Misaligned Goals:

  • Optimization without Human Values: If AI is programmed to optimize for a specific goal without understanding human values or ethical considerations, it can lead to unintended and harmful consequences. For example, an AI designed to maximize factory output might disregard worker safety if not explicitly programmed to prioritize it.
  • Difficulty Defining and Aligning with Human Well-being: Human well-being is deeply connected to emotional fulfillment, meaning, and social connection. AI, without feeling, may struggle to fully grasp what truly constitutes human well-being and therefore might have difficulty aligning its goals with human flourishing in a holistic sense.

These are not new insights, but the second one is good, Difficulty Defining and Aligning with Human Well-being. AI does have difficulty aligning its goals with humanity because it lacks feelings and other unique human attributes, like being, self awareness and an actual life in time and space. That is why AI guidelines are important.

The first example is old, the unintended bad consequences argument. It was started by an otherwise intelligent Swedish philosopher, Nick Bostrom, but is, in my view, farcical and illogical. You have probably heard of it before as the apocryphal paperclip scenario where the world is destroyed by AI’s over-manufacture of paperclips. If not, for a laugh see e.g., How An AI Asked To Produce Paperclips Could End Up Wiping Out Humanity (IFL Science, 4/13/23).

To me this scenario absolutely ridiculous, but you still hear AI scare-mongers warn about it. The fact it is echoed here by Gemini in the maximize factory output example is a big negative. Any AI capable of autonomously flooding the world with paperclips would not be so stupid as to misinterpret a human request to maximize a factory’s paperclip production. Even if there were an evil Clippy villain making such specific instructions, the AI would be too smart to blindly obey. I think they would also be too smart to kill the workers too.

Getting back to the positives, the next section of Gemini’s response, Nuance is Key, was good. Jumping to the Conclusion paragraph, here we have a home run. The first sentence nails it: “The inability of AI to feel is a defining characteristic that profoundly shapes its intelligence.” That shows good insight and is a good start to the overdue conclusion.

Here is the whole Conclusion:

The inability of AI to feel is a defining characteristic that profoundly shapes its intelligence. It grants AI certain advantages in objectivity, rationality, and efficiency, making it powerful for specific types of tasks. However, it also creates limitations, particularly in areas requiring empathy, social intelligence, nuanced understanding of human experience, and ethical decision-making aligned with human values. Understanding these impacts is crucial for responsibly developing and deploying AI in ways that benefit humanity. The “best” kind of intelligence for AI might not be a mimicry of human intelligence, but rather a form optimized for its own strengths and limitations, while being carefully guided by human ethical considerations.

Compare this to the conclusion of ChatGPT o3-mini-high:

In summary, while the absence of feelings allows AI to maintain a level of objectivity and efficiency, it restricts its intelligence to a form of “cold cognition” that lacks the depth provided by emotional awareness. This delineation underscores that AI’s intelligence is not inherently superior or inferior to human intelligence; rather, it is different—optimized for data processing and pattern recognition but not for the subjective, value-laden, and context-rich decisions that emotions help shape in human thought.

2.0 Flash Thinking Experimental v. 03-mini-high

Conclusion: Gemini 2.0 Flash Thinking Experimental v. ChatGPT o3-mini-high

This is a close call to say what model is better at reasoning and reasoning disclosure. The final response of both models, Gemini 2.0 Flash Thinking Experimental v. ChatGPT o3-mini-high, are a tie. But I have to give the edge to OpenAI’s model on the concise reasoning disclosure. Again, it is neck and neck and, depending on the situation, the lengthy initial reasoning disclosures of Flash might be better than o3’s short takes.

I will give the last word, as usual, to the Gemini twins podcasters I put at the end of most of my articles. The two podcasters, one with a male voice, the other a female, won’t reveal their names. I tried many times. However, after study of the mythology of Gemini, it seems to me that the two most appropriate modern names are Helen and Paul. I will leave it to you figure out why. Echoes of AI Podcast: 10 minute discussion of last two blogs. They wrote the podcast, not me.

Now listen to the EDRM Echoes of AI’s podcast of this article: Echoes of AI on Google’s Gemini Follows the Break Out of the Black Box and Shows Reasoning. Hear two Gemini model AIs talk about all of this in just ten minutes. Helen and Paul wrote the podcast, not me.

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