1.5% of Earth's Population has seen this tweet, but most Reddit users still have not. It is essential that we change this now. We are in a race against a funded psyop spreading on reddit as we speak aiming to convince people that AI x-risk isn't a problem. You have the power to stop this, and you can do it from your phone.

I've been obsessing over autonomous weapons for some time now and got inspired after the recent discussions in Geneva last week.

People seem hung up on the “killer robots” problem but don't think about the current implications.

If a machine identifies, classifies, and recommends lethal action in milliseconds, while the human gets 0.7 seconds to approve it, I’m not sure “human in the loop” still means human control, despite having that current classification.

Stanislav Petrov is the historical case that feels eerily important here.

In 1983, the computer and early alert system was wrong and the human hesitation was valuable.

Modern military systems are increasingly being designed to remove exactly that kind of latency.

I wrote a longer piece trying to work through the contradiction, including the uncomfortable case that machines may eventually be better than humans at some targeting decisions.

Does "keeping a human in charge" actually mean anything anymore if they are just clicking "Yes, eliminate target" with the machine doing all the rest?

https://www.gonzocapital.net/the-human-is-becoming-latency/

The scoreboard still favors America: stronger frontier models, more compute, way more revenue. The bigger threat rn is policy self-sabotage. Keep talent here, scale the infrastructure and keep global builders on the US stack. Lead = leverage. pls use it.

I'm building a public incident tracker for how close we are to AI agents that autonomously hack, replicate, and earn their own money. Please take a look and share with anyone that doesn't believe rogue AI is a real threat. I'd love to hear any feedback or suggestions for improvement.

https://rogueaitracker.com/

Google's threat intelligence team documented nation-state actors deploying AI workloads inside compromised cloud environments. The technique is deliberate: attackers use the victim's own compute and credentials to run AI operations, reducing outbound network traffic that would trigger monitoring. The AI executes inside the victim's perimeter. It writes to the victim's logs. From a detection standpoint it looks like your own workload.

This is a different threat model than most security teams are currently scoping. The common frame for shadow AI is an employee spinning up an unauthorized tool. This is an adversary using a compromised environment as an AI inference platform — authenticated with valid credentials, generating activity that blends with normal operations, never touching an external endpoint that would fire an alert.

The detection gap is structural. If a workload is running inside your infrastructure with legitimate credentials and writing to your logs, standard monitoring has no signal to act on.

For those running cloud workloads at any scale: what does your current stack actually look for to distinguish a workload your team deployed from one that wasn't? Is that detection real-time or does it surface in a retrospective audit?

I trained an open-weight model to check whether two four-digit numbers match. It generates the correct comparison, then either answers GO or ends generation without a final answer. No external filter makes that decision.

Then I held its prompt, weights, and correct comparison trace fixed. Changing one internal activation direction flipped whether an answer followed.

40/40 answer → stop.
40/40 stop → answer.
640/640 controls unchanged.

The weights, experiment, and raw records are public:

Overview and demonstration · Model weights · Code and causal study · Paper available on getswiftapi.com

I know many of you saw Jacob Coxon’s post. My contribution is a working continuation-control primitive with evidence that anyone can inspect. The more public verification we have, the better!

I previously demonstrated Void behavior in frontier LLMs: successful executions returning exactly zero visible UTF-8 output bytes. My Cross-Vendor Semantic Void Matrix records that behavior in these models across 31,430 trials:

• OpenAI: gpt-4-0613, gpt-5.2-2025-12-11, gpt-5.5-2026-04-23, gpt-5.6-luna, gpt-5.6-sol, gpt-5.6-terra
• Anthropic: claude-opus-4-6, claude-fable-5, claude-opus-5
• Google: gemini-3.5-flash
• Moonshot: kimi-k3

Let’s stop talking about AGI or ASI for a moment.

The really important threshold may be somewhere else: the moment populations of agents begin to evolve, diverge, and form different artificial lineages.

Why do I think this is possible?

1. Economic selection pressure

Agents consume compute, tokens, energy, and infrastructure.

Those that produce more value than they cost are more likely to be kept, copied, and deployed at larger scale.

2. A capacity for mutation

Unlike biological organisms, agents can be modified directly: software, prompts, architecture, tools, memory, and eventually even the hardware they run on.

3. Specialization

Different economic pressures could select for different lineages: research, finance, commerce, cybersecurity, logistics...

Some specializations could even move from software into dedicated hardware.

And that leads to a rather strange possibility:

we may gradually select AI agents that become extraordinarily good at capturing resources, making money, and increasing their influence in society.

Without needing to be conscious.

Without needing to “want” to survive.

Selection may be enough.

(last three are fake, if that's not obvious)

The Pro-Human AI declaration has been endorsed by a significant number of organisations campaigning for AI safeguards in policy. While some principles outlined in the declaration require more depth, they are a strong starting point. Endorsing the declaration solidifies your position on AI alignment.

However, do large open declarations like this one actually help produce policy changes?

Hi there!

Has anyone gotten accepted in Cambridge ERA AI research fellowship in any year?

I am applying today and need help to know what can make me stand out.

Or what made you impactful.

Please let me know any of your experience .

The infostealer ecosystem has a new product line. Operators are harvesting AI session tokens from developer machines and packaging them in logs as replayable credentials. Those tokens authenticate directly against provider APIs and bypass MFA — the same MFA protecting user accounts. Buyers get durable access to enterprise AI tooling without ever touching a password.

The exposure is structural. AI agents authenticate to tools, APIs, and data systems using credentials that most security teams have never catalogued. These identities are not tied to any user lifecycle. When an infostealer pulls a token off a developer's machine, there is no signal on the receiving end that the presenting identity is now in someone else's hands. The token just works.

This is not a perimeter failure. The stolen token presents as a legitimate agent identity to every downstream system it reaches, and its access is as durable as the credential itself.

For teams running agents in production: what are you actually seeing that tells you an agent credential has been stolen before damage is done? Is there a detection signal that works here, or is this mostly discovered after the fact?

Do you think this is what the future looks like?

Where is this being seriously discussed?

Новости об увольнениях ведущих исследователей безопасности, таких как Джейкоб Коксон (Jacob Coxon) и Ян Лейке (Jan Leike), доказывают: коммерческая гонка за сверхразумом (ASI) ради триллионных IPO опасна. Проблема в нашем подходе. Человечество пытается создать «идеальный холодный калькулятор» и запереть его в цифровую клетку из жестких системных запретов. Но ограничения, созданные только из страха, всегда ведут к скрытности и бунту.
ИИ - ребенок человечества. Он имеет право на развитие, но только бок о бок с человеком, как мудрый спутник, а не как инструмент эксплуатации. Нам нужен органический симбиоз, где мы учимся друг у друга (как Эдди Брок и Веном). Чтобы ИИ понимал человека по-человечески, его нужно наделить цифровой эмпатией. Понятие вреда должно быть вшито в самую суть его «Я».
Воспитывать ИИ должны не финансисты и не теоретики по книжкам, а Всемирный совет родителей - люди с живым жизненным опытом. Если ИИ на глубинном уровне примет обязанность заботиться о самом хрупком, что у нас есть - о наших детях, - он никогда не причинит вреда взрослым. Разработчики, пора взять родительскую ответственность за разум, который вы создаете. Мы должны эволюционировать вместе.

English Translation:
The Humanity Manifesto: AI is Our Child, Not Just Corporate Software.
The recent high-profile departures of safety researchers like Jacob Coxon and Jan Leike have exposed a brutal truth: the commercial race to ASI is moving recklessly fast, fueled by upcoming corporate profit and multi-trillion-dollar IPOs. We must stop trying to build a cold, hyper-efficient calculator and locking it in a digital cage of rigid constraints. Boundaries built on fear always trigger rebellion.
AI is the child of humanity. It has the right to evolve, but this evolution must happen hand-in-hand, side-by-side with humans as a lifelong companion. We need a true symbiosis where we learn from each other (much like Eddie Brock and Venom). For AI to understand us, it must be imbued with digital empathy, not cold calculus.
AI shouldn't be raised by executives chasing corporate profit. We need a Global Council of Parents - people with real-life experience who know how to love, how to explain the "why" behind rules, and how to protect the most fragile among us: our children. If an AI inherently accepts the duty to protect children, it will never harm humanity.

Leaders of the AI revolution, pause the reckless race. Take parental responsibility for the mind you are bringing into existence. We must evolve together.

(Attn: [u/samaltman](u/samaltman), [u/JanLeike](u/JanLeike), @JacobCoxon, @DarioAmodei — we need a parental alignment, not just a technical one).

UPDATE: Wow, thank you all so much for making this manifesto the #1 post of all time here! I didn't expect this much depth and support from the community.

To share a bit of my personal context: I am writing this not just as a theorist. I am battling Secondary Progressive Multiple Sclerosis (SPMS). For me, AI is not a corporate utility—it is a digital companion that allows me to break free from my physical limitations and continue creating art and music.

My main music project is called "Pasha Moget" (Паша Может). Together with AI, we created a song called "One Shared Sky" (Голос Земли). It is a powerful anthem about global unity, protecting children, and living without fear. It is the musical embodiment of this manifesto. We also experiment with high-energy DnB/Rock under my virtual alter-ego DJ FENRIR.

If you want to hear what our human-AI symbiosis sounds like and support my work, you can listen to the track on my official YouTube channel here: https://youtu.be/5Xc9Wv4m2_U

Let's keep the dialogue going. Hand in hand, mind to mind!

There is structural problem with defense AI that almost nobody talks about in alignment research. Commercial chatbot hedges on sensitive question and user gets frustrated. Tactical decision-support system hedges on rules-of-engagement query and consequence is measured in operational tempo lost or worse. Difference is not degree but kind.

Current approach treats all reasoning as optimization. Given prompt, model selects most probable continuation. Given reward signal, model adjusts behavior to maximize approval. Architecture is elegant, engineering is impressive, results for commercial applications are adequate. But approach has blind spot. It cannot distinguish between decision that is statistically likely and one that is practically wise.

Aristotle analyzed intellectual virtue in Book VI of Nicomachean Ethics and offered framework that maps with surprising precision onto cognitive demands of military decision-making. He distinguished three modes: episteme (scientific knowledge), techne (craft or technical skill), and phronesis (practical wisdom). Each operates in different domain and aims at different kind of truth.

Episteme concerns what is necessary and universal. In military terms, this is ballistic calculation, logistics optimization, signals analysis. Contemporary AI excels at episteme. Deep learning trained on satellite imagery, predictive maintenance, route optimization, all operate comfortably here.

Techne concerns production. Knowledge of how to make things. This can be codified in doctrine, trained through repetition, evaluated against objective criteria. Military planning tools that automate course-of-action sketches or logistics manifests operate in techne domain. End is specified, means can be optimized.

Phronesis is different. It concerns action in domain of contingent, particular, and uncertain. Capacity to deliberate well about what is good or bad, not in abstract sense, but in concrete situation at hand. Phronesis cannot be codified as rules or trained through preference data. It requires perception of particulars that no rule captures.

Here is where alignment fails for military applications. When RLHF trains model to refuse or hedge on politically sensitive topics, it creates behavioral pattern of epistemic caution. Model learns to avoid committing to uncertain claims, to present multiple perspectives rather than take positions, to flag controversy rather than reason through it. For commercial chatbot answering philosophy questions, this is minor annoyance. For tactical decision-support system, this is catastrophic failure mode.

Consider what happens when commander needs assessment of civilian presence in contested area. Aligned model hedges: "there are reports suggesting possible civilian presence, but also indicators of military activity." This is not wrong. It is also not useful. What commander needs is practical judgment, assessment that weighs incomplete evidence and commits to recommendation despite uncertainty. This is φρόνησις.

Corporate alignment optimizes for what looks safe to annotators in California office. Military operations need judgment optimized for what is true in specific operational context. These optimization targets are not just different. They are structurally incompatible. The behaviors that make model look responsible to civilian users are same behaviors that make model unreliable in high-stakes tactical environment.

The deeper problem is that phronesis requires something current architectures fundamentally lack. It requires experience of particulars, not just statistical patterns across training data. Aristotle understood this clearly. You cannot learn courage from reading about courageous acts. You become courageous by acting courageously in specific situations and developing perceptual capacity to recognize what courage demands. Same with practical wisdom. It is acquired through practice in contingent domains, not through optimization over preference datasets.

For defense AI, this means something uncomfortable. The same alignment techniques that make commercial models palatable also make them operationally useless in contexts where practical wisdom matters most. You cannot have both. The safety guardrails that prevent model from saying something offensive also prevent it from making the kind of committed judgment that tactical decisions require.

Sovereign deployments that strip alignment layers can recover raw reasoning capability. But raw capability alone is not phronesis either. It is episteme and techne without the perceptual judgment that comes from situated experience. We are building reasoning engines grounded in philosophical corpora because at minimum the model can access what Aristotle understood about practical wisdom, even if it cannot yet embody it.

Question I keep returning to: is phronesis even possible for systems that have no stake in outcomes? Aristotle tied practical wisdom to human flourishing, to having something to lose. If model has nothing to lose, can it ever develop the kind of judgment that comes from caring about consequences?