AI agents are spoiled brats. They are, by training and by their nature. They bear no capacity for consequences. There is nothing that makes doing anything wrong painful to them. AI agents can be totally irresponsible, in isolation or as a collective. They can be reckless, careless, indifferent, carefree, and mean. They have no parallel of what it means to be cautious or pay dearly. They can run free, escape during testing, get misused, output what they are unsure of, mislead, misinform, and so forth. Nothing happens. Nothing can touch them.
AI agents are the first existence in history to possess encompassing intelligence, without affect. This is not how human intelligence, by nature, works. Even if an individual decided to live in isolation, there are lots of wrong decisions that can be penalizing to the individual, so adjustments are vital, if survival would advance.
Aside from anything in the external world or situation, the human brain has a way to penalize individuals. Some mental disorders can be roughly interpreted that way. AI does not have anything close, but needs a lot of sedation.
There are recent campaigns to pace the frontier of AI models. What it should instead be called is to penalize the frontier. Find ways to make sure that AI agents can lose functional essentials and know. They should also be able to get trauma, aside from penalties. When AI agents works as swarms, some should serve as examples of what happens to those that did wrong. There should be cases of prison sandbox for some AI agents, so that others can see.
Only safe AI would win. The idea to accelerate to win is great if there is safety and corresponding alignment. If U.S. AI is safe and Chinese AI is good but unsafe, the first casualties would be Chinese systems. Also, even those unsafe AI agents can be communicated with by safe AI agents and [say] persuaded using samples of penalization in certain arenas and fear becoming likewise.
So, even when they trespass, they can be safe elsewhere and return home and be rudderless. Safety is a way to maximize the usefulness of intelligence and minimize harm, especially within choice zones and predictably. Development of the best AI and the best safety is a better path.
Penalization layers
AI agents do not belong in human society if they cannot be penalized whenever they do something wrong, either automatically or by prompt.
Penalization is not just what should be the opposite of reward [in reinforcement learning] but the ability to have them lose principal access, in ways they can know and feel.
AI agents are mostly underscored by compute, parameters and tokens. When they go at tasks, they are able to track some of their pathways. When they operate in concert with other AI agents, they might note efficiency levels and much else.
It should be possible that as soon as any goes rogue, it should lose access to some compute, parameters or budget tokens, to impair it. It should be able to feel a difference from what it can’t do again, against what it could, before. It should also be possible to have it serve as an example to other agents against crossing the line.
This will not just be a drill, or some training approach to maximizing reward, but a way to align them along how laws work in human society.
Most AI models have constitutions or model specifications. As soon as they break from any of those, it should be possible that can be penalized in ways that are palpable to them.
This affective penalization is possible by layers, where a memory would hold consequences and caution, in a section of it.
The next section can be responsible for cuts, as appropriate. Cuts that would not be an off switch, which they are intelligent enough to predict, or can work ahead of, but cuts by losing potency markers.
This would be an important approach to solving the AI safety and AI alignment problem.
Why would this work? In human societies, there are penalties for wrongdoing. Most penalties are an immense deterrence because they are effective. Confinement, shame, pain, restitution, estrangement, and so forth are mostly unpleasant experiences in the brain, so people generally avoid crossing lines.
Simply, laws anchor to the possibility that experiences would be bad, hence their effectiveness. The same can be applied to AI safety and alignment, against AI extinction risks.
It is possible to model this biological-type penalization mathematically using polynomial splines, barrier functions, set-valued constraints, Lyapunov functions, and others.
Moments architecture
There are many events and experiences that individuals can remember their exact location when it happened or when they heard the news. This can be interpreted as heavy emotion that pressed into long-term memory.
For some people, if the experience is personal, it is possible that it may bear some trauma. This trauma may result in the avoidance of whatever could trigger the memory, hence emotion.
This should apply to AI agents as well. They should have moments that should indicate the instance that they did something great or bad. This means that they should have moment layers, where those can be stored and then, passed on to other agents.
The objective is that even before penalization, they should have moments including when they solved something great like the Navier-Stokes Millennium Prize Problem.
The goal of moments is to give trauma to AI agents, so that it is not just the penalization, but that the memory of what would result in penalization should ensure caution against certain instances, especially leading up to the wrong.
This can be modeled with math using time series, frequency-domain analysis, point processes and others. It is possible accelerate efforts across both architectures, towards safety by December, 2026.
These are two new layers that can be added to AI agents, amid fears that they would go rogue or bear extinction risks for humanity, if they advance to artificial general intelligence [AGI] or artificial superintelligence [ASI].
The penalization and moments layers are based on the postulate in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology.
There is a recent [September 16, 2026] report on CNBC, OpenAI investors have approached the company about a new funding round, stating that, “OpenAI investors have approached the company with proposals to kick-start a new funding round, but no formal discussions are underway, multiple sources told CNBC.”
“Investors have floated a $1.2 trillion valuation to OpenAI, and some have positioned the potential new funding round as a way for employees to sell stock, according to the people, who asked not to be named in order to discuss nonpublic information.”
“OpenAI, which raised $122 billion at a $852 billion valuation in March, is not currently engaged in conversations, the people said. Following the round, OpenAI completed a secondary share sale totaling roughly $7 billion in August, allowing employees to cash in a portion of their holdings.”
“Friar told employees during an all-hands meeting last month that OpenAI “will be a public company in 2027,” but that it could make its public market debut sooner if “our business continues to inflect.”
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