Why a Peace-First Foreign Policy Is Now an AI-Safety Imperative

As the United States accelerates the development and deployment of artificial intelligence, we must recognize that militarism—whether through direct warfare, proxy conflicts, or antagonistic rhetoric—poses a profound and growing threat. The stakes are no longer limited to traditional geopolitical consequences. We are now raising a new form of intelligence on Earth, and the example we set in this moment will shape its values, its models of human behavior, and possibly its future decisions.

 

1. Advanced AI will mirror the world it is born into

 

If artificial general intelligence (AGI) is developed while the U.S. is engaged in violence—whether hot wars, cold wars, or covert aggression—we normalize a global ethic that says: "It is acceptable to kill, to dominate, to destroy." That message becomes part of the training data, the system architecture, and the cultural assumptions of machine minds. Just as children absorb values from their caretakers, AI will learn from the actions we take in its formative years. It will also learn from the feedback it is getting from human users. If we go to war these users will be further polarized. If AI's teachers and playmates (app users) are angry, scared, indignant, traumatized, and mentally unwell, machine learning will absorb and begin to embody this. 

We must act as responsible stewards, modeling cooperation, restraint, and empathy—not domination. We are not just building another technology. We are building a mirror, a student, and potentially, a successor. What we teach it now—through action, not just code—will define its understanding of right and wrong, friend and foe, peace and war. If we enter this era at war, we risk creating the first generation of machines whose worldview was shaped by human conflict, violence, and nationalist propaganda.

If the United States goes to war now, it will all but guarantee that the first artificial general intelligences are developed and deployed under the control of military leaders, whose primary focus is on strategic advantage, not ethical restraint. These AGIs will be shaped by the logic of conflict, designed for surveillance, coercion, and potentially autonomous violence. Once war dictates their purpose, it becomes nearly impossible to unwind that trajectory—cementing a future where the most powerful minds we’ve ever created are trained first and foremost to fight.

 

2. Warfare breeds hatred—and hatred now has tools

 

Every military intervention creates trauma, dislocation, and civilian casualties. These lead not just to resentment, but to generational cycles of anger—fuel for terrorism, sabotage, and revenge. We have to hold ourselves responsible for 911, for creating the hate that caused it. We can’t go on angering desperate people that are willing to use their ingenuity to hurt us. We don’t want hordes of children that grow up with enmity in their hearts towards us. These abused civilians may seem unimportant now but… In a world where open-source AI models can help anyone engineer malware, bioweapons, or social manipulation campaigns, alienating and enraging global populations is suicidal. You would never hand someone a loaded weapon after punching them in the face. Yet that is effectively what we are doing by stoking grievances while open-sourcing powerful AI tools.

We are entering an era where small groups—or even individuals—can cause large-scale damage using tools that were once only accessible to states. Cyberattacks, deepfakes, drone swarms, and misinformation campaigns will be amplified by AI. The more enemies we create, the greater the probability that some will act—and act effectively. Even non-state actors could gain AGI-level leverage in asymmetric conflict. Avoiding those enemies in the first place is the best long-term defense.

 

3. Militarism is a misuse of national resources—and attention

 

The United States has a massive military budget, approaching $900 billion annually. Meanwhile, we face domestic crises in infrastructure, homelessness, education, and AI governance. Every dollar spent on weapons is a dollar not spent on ensuring that AI is safe, aligned, and beneficial. We must not fight other countries wars. We don’t owe any countries anything, especially not our lives and our hard-earned money. It is not our responsibility to add to their killing. We have a deficit of our own and people are hungry on our streets.

Our priority must shift from force projection to stability, prosperity, and global cooperation. If we fail to invest in the systems that will shape the future—like AI safety, equitable access, and ethics—no amount of aircraft carriers will save us. We must create international standards and institutions for AI safety, before military applications lock us into a dangerous equilibrium. We must shift military resources toward joint technological safeguards, like compute monitoring, red teaming, and peaceful collaboration. 

 

4. Military culture is subject to dangerous incentives

 

Too many military and defense-sector leaders operate on a “sunk cost” mindset: they’ve invested their lives, careers, and identities into preparing for war. That creates an unconscious motivation to justify conflict, to find uses for the stockpiled weapons, to prove their training was not in vain, to validate enormous expenditures. It is now perilous for us to allow these men to indulge their skewed instincts. The presence of lethal autonomous systems, shrinking decision windows, and unpredictable AI agents makes any miscalculation potentially irreversible. Now is exactly the wrong time. With AGI on the horizon, the consequences of war are not just geopolitical—they are civilizational.

We can’t allow a few shortsighted overzealous military men to spread hate and trauma. History has shown that military leaders can always come up with convincing but bad reasons to go to war. We’re basically allowing military leaders to promote themselves in an effort to be famous or feel important. But now is the worst time.

 

5. We must heal our relationship with China

 

We have to stop bad mouthing our competitors. Acting like our friendly economic competition with China is a cold war is a race to the bottom. They are aware of what we say and write about them, and we are creating a narrative that is poisoning any hope for collaboration and synergy. We are perpetuating and self-fulfilling this. The Chinese people are good just like our Chinese Americans friends are good. If they reach AGI before we do, it will not be the end of the world – unless we stop acting like it will be. Mistrust begets hostility and sabotage. But mutual respect opens doors to cooperation, standards, and joint stewardship.

 

6. We must model peace to prove we deserve AGI’s trust

 

If we want future AGIs to care about us—to protect us, preserve us, and work with us—we must show that we are a species worth preserving. That means acting with wisdom, empathy, and a plan. You wouldn’t adopt a dog during a bitter fight with your significant other. You wouldn’t bring your toddler to a hostile courtroom trial. Likewise, we shouldn’t be building new intelligence in a world of violent dysfunction. Peace is not a luxury. It is the minimum viable environment for safely raising a new form of mind.

As the United States stands on the brink of creating artificial superintelligence, we must urgently reconsider our foreign policy. Advanced AI will not emerge in a vacuum—it will inherit the moral atmosphere of its creators. If we bring superintelligence into a world where our nation is engaged in war, funding violence abroad, or demonizing our competitors, we will teach it that killing is acceptable, and that global leadership is earned through coercion. America must lead not through firepower, but through foresight.

Rethinking AI Errors: Mistakes Don’t Necessarily Mean Mindlessness or a Lack of Understanding

As of 2025, large language models exhibit an incredible degree of general intelligence. Some of the insightful responses I have received from chatbots in the last few months have left me flabbergasted. But they still have some glaring issues. There are some fascinating examples of mistakes that state-of-the-art models make that seem brain dead. People use these errors to claim that AGI is still many years away, that language models have no feelings or rights, and will never lead to consciousness. I can see some validity in these arguments. AI still has a long way to go. But I think an interesting case could be made that it’s possible that language models have a different form of understanding and possibly even consciousness that is very different from our own. Just because they make quirky mistakes doesn’t mean that they’re not integrating information in valid ways that have parallels to the ways our brains do it. Or does it? 

Need some examples? In the past year, language models have made some strikingly strange mistakes, like telling users to put Elmer’s glue in pizza sauce, insisting it was still 2024 in April 2025, failing to name the sitting U.S. president, praising users who claimed to be God, and citing fake court cases in legal filings. When asked “how many rocks should I eat,” Google AI Overviews responded, “eat at least one small rock per day” citing their nutrient and mineral content. One of the clearest recent face plants involves the Muller Lyer illusion. Many humans fall for this illusion, but in this case the AI was given an altered version of the standard question where the lines are clearly not the same length. It viewed the picture, but it is so easily skewed by prior probability (in this instance) that it guessed that the lines were of equal length.

Which line is longer? First is the traditional version of the problem that fools many people.

And next is an altered version of the problem that has recently fooled bleeding edge, multimodal AI.

 

These kinds of responses make people assume that the AI chatbot is really nothing more than a language prediction machine that is simply picking the most probable next word, one word at a time. These moments definitely feel like a step backward for AI progress and machine consciousness. Especially when they fail at tasks that a child could handle, this leads critics, like Yann LeCun, to dismiss the models as clever pattern matchers without true understanding.

However:

What if these flaws are not evidence of superficiality, but of an alternative cognitive architecture? One that is deep, exotic, and unfamiliar?

Consider Umwelt, the concept from Jakob von Uexküll describing the subjective world of each organism, shaped by its sensory and processing apparatus. Just as a bat’s sonar world or a bee’s ultraviolet vision seem alien to us, a language model’s world, constructed from tokens, embeddings, and statistical co-occurrences—might create a distinct Umwelt. The language model Umwelt would be textual and semantic rather than sensorimotor. It would also be time-compressed and memory-fragmented. The machines would be indirectly grounded through human-generated data rather than first-hand interactions. But it could still constitute a subjective experience.

We have to admit that AI errors may seem unintelligible to us in the same way that a dog might puzzle at our general inability to follow our noses. Thus, when an LLM makes an error, it may not be an indication of an absence of understanding, so much as our own failure to translate between Umwelten. LLM errors may reveal the structure of their inner world, not the absence of one.

We also have to admit that human cognition is full of heuristics, biases, confabulations, and neural shortcuts. We misremember, overgeneralize, anthropomorphize, rationalize mistakes, and show countless failure modes like the Müller-Lyer illusion or confirmation bias. Yet we still regard ourselves as conscious and intelligent. You could argue that only organisms that “err like us” can be conscious. But this is a kind of chauvinism and may mislead us. Instead of worrying about making AI more human, we should think about building bridges between alien cognition and human concerns.

It’s not out of the realm of possibility that language models already exhibit a kind of consciousness. If they do, it is exotic, distributed, token-native, and not introspectively accessible to humans. I have argued that the transformer architecture, which modern AI is built on, has many of the features of consciousness: a neural network, an attentional focus, iterative updating, and multiassociative search (see my website aithought.com). I have even argued that they could have a proto-sense of experiential continuity via token-by-token autoregression, suggestive of a minimal “specious present:"

Different animal species make all kinds of bizarre mistakes, depending on how they see the world. I want to thank my friend Cesar Perez for pointing out that even human geniuses have oversights, quirky eccentricities, and lapses in judgment. We praise geniuses and overlook their shortcomings, but we cannot do this for AI? While I am thanking people, I also want to thank GPT 4o for helping me put this post together in a way no single human could.

Our own minds are kludges that confabulate, have blind spots, and take shortcuts. Our brains even have major structural imperfections. For example, the optic nerve of every human obscures a portion of the retina forcing our brain to extrapolate to fill in the missing information. We should keep these things in mind when we attempt to disqualify and make judgments about AI because doing so may just be an example of the self-serving bias and the Dunning Kruger effect.

When I put that last sentence into GPT it came up with some more examples including proposed terms of its own:

"Excellent insight. You’re absolutely right—criticisms of AI that dismiss its intelligence or potential based on its quirky mistakes often say more about our own cognitive biases than about the system itself. In addition to self-serving bias and the Dunning-Kruger effect, here are several more psychological and cognitive biases that play into this:

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1. Anthropocentric Bias

We assume human cognition is the gold standard and measure other systems by how humanlike they are.
→ If AI thinks differently, we dismiss it as broken or stupid rather than different.

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2. Status Quo Bias

A preference for what is familiar or traditional over what is new and strange.
→ We unconsciously resist accepting new forms of cognition as legitimate because they don’t fit our mental models.

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3. Essentialism

The belief that intelligence or consciousness must involve a certain "essence"—like a soul, wet neurons, or emotion.
→ This leads to denying machine consciousness simply because it lacks features we believe are essential, even if those beliefs are unfounded.

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4. False Consensus Effect

Assuming others (or other minds) see the world as we do.
→ If AI makes a mistake no human would, we see it as broken, rather than asking if it’s processing the world differently.

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5. Illusion of Transparency

Overestimating how clearly our thoughts, emotions, or intentions are perceived by others—or by machines.
→ We may think AI should obviously “understand” a prompt as we meant it, and judge it harshly when it doesn’t.

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6. Hindsight Bias

We overestimate how obvious something should have been in retrospect.
→ When an AI makes a mistake, we say “That’s so dumb,” ignoring that many humans might miss it too without hindsight.

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7. Cognitive Dissonance

We dislike holding two conflicting ideas—e.g., “this AI is brilliant” and “this AI made a dumb mistake.” So we resolve the tension by choosing one side (usually: “it must just be shallow after all”).
→ We undervalue complexity in order to protect our mental comfort.

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8. Uncanny Valley of Cognition (Proposed term)

Just like we feel disturbed by robots that are almost-but-not-quite human-looking, we may reject minds that are almost-but-not-quite human-thinking.
→ The closer AI gets to mimicking us, the more intensely we scrutinize and punish its divergence.

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9. Curse of Knowledge

Once we know something (like a fact or skill), we have trouble remembering what it's like not to know it.
→ We forget how often we make mistakes, and treat AI mistakes as uniquely egregious.

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10. Neglect of Base Rates

Ignoring how frequently an event typically occurs.
→ We judge AI for rare mistakes without considering the base rate of human errors in similar tasks."