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Suddenly, everyone is buzzing about a 22-point manifesto published by Palantir. Their post on X crossed all corporate expectations and, at the moment of writing, it has over 25 million views.
Their previous posts, even controversial ones, rarely moved beyond ~100k. More to that, this manifesto has nothing new in it: it is a compressed version of The Technological Republic, co-authored by Alex Karp and Nicholas W. Zamiska and published at the beginning of 2025. Yes, more than a year ago. The core ideas are: a shift from soft power to hard capabilities, tighter alignment between tech and government, and a renewed focus on national purpose in a technological age. Same old, same old for Palantir.
So why did this particular post – with nothing new in it – travel so far?
Part of it is distribution. X has become a very different system. Whatever Elon Musk changed, content now travels faster, farther, and with less friction. A long-form argument becomes a short, structured object that can be screenshotted, quoted, attacked, and redistributed across tightly connected networks of policymakers, investors, engineers, and media. The format also works great. A numbered, declarative thread is engineered for this platform, where strong positions travel further than careful ones.
You see how it spreads like a forest fire. And with the war in Iran, that forest was already dry. The US is not talking about AI in abstract terms anymore. Systems like Palantir’s Maven are already embedded in military operations, analyzing sensor data and supporting targeting decisions. When Palantir writes “AI weapons will be built” it sounds almost coy: AI weapons are already built, and Palantir is one of them actively selling them.
But they not only building and selling them. Palantir’s role has shifted.
It is no longer simply a vendor selling software into government contracts. It is becoming embedded in systems that are difficult to replace once deployed. Its tools are used in operational environments where data from multiple sources is combined into decisions with real consequences. This changes the nature of the conversation around the company.
Once you reach that position, neutrality is not an option. Through this manifesto they achieve a few things:
Filtering customers. Some will prefer a partner that is explicit about its priorities. Others will not.
Filtering talent. Some engineers are increasingly disillusioned with consumer technology and are drawn to systems that operate at the level of national infrastructure. Others are not.
Filtering partners and investors. Clarity reduces ambiguity, even when it increases controversy.
And, of course, showing the finger to everyone who disagrees.
And they want to fully own it. This is where the move becomes strategically important.
Palantir is the first major AI company to treat ideology as a competitive moat. As part of how it competes. If a company is selling into national security systems, alignment becomes part of the product. That alignment is not easily reproduced by competitors whose business models depend on broader, more neutral positioning.
The traditional sources of advantage in AI are becoming less distinct. Model performance is converging. Infrastructure is more widely accessible. Distribution remains important, but it is no longer exclusive. In that environment, political and institutional alignment becomes a differentiator.
What Palantir is building is a form of irreplaceability that does not depend only on technical capability. Now they have 22 points about it that went viral.
Notice what happened after the post went up. Anthropic said nothing. OpenAI said nothing. Google DeepMind said nothing. xAI said nothing. Microsoft said nothing. This may mean, as we’d like to argue, that every AI company with a defense-adjacent business watched Palantir plant this flag and chose not to react because silence is the only response that does not lose. It may also mean, more prosaically, that large companies rarely respond to competitors’ press events. The evidence currently fits both readings. Worth watching over the next six months whether these companies shift their behavior – defense pitches, recruiting language, procurement strategies – rather than just their statements.
That raises a broader question for the industry. What happens once this category exists?
Three paths are possible. Gradual convergence, where other labs move in a similar direction but in moderated form, adopting language around national alignment without fully committing to it. Bifurcation, where the industry separates into companies aligned with defense and government systems and those focused on commercial and consumer applications. Arbitrage, where some companies attempt to operate across both domains, maintaining a neutral public position while participating in government deployments. Anthropic and OpenAI are structurally positioned to attempt this.
In my opinion, most AI labs will adopt softened versions of Palantir’s posture – “American AI,” “democracy-aligned AI,” “frontier defense” – that capture part of the signal at a fraction of the reputational cost. The real split may happen along geographic lines rather than corporate ones. European and Asian AI ecosystems are likely to define themselves partly in opposition to the American defense-aligned pole, and foreign governments will hedge by building domestic alternatives rather than forcing vendors into binary commitments.
The underlying shift is more consistent than any single scenario. AI is moving from a tool layer into infrastructure. Infrastructure carries alignment, whether it is stated explicitly or not.
Palantir is earlier than most in stating it directly – and choosing the perfect way to ride the new X algorithms.
→ If any of those thoughts resonate with you – share them across your social networks. Let’s keep the conversation going.
Topic 2: The episode in which Dwarkesh Patel got Jensen Huang to call Dario Amodei’s mindset a mindset of a loser! Why did Jensen get genuinely angry in this conversation? There is much more depth to it than you think. Let’s discuss →
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Anthropic
Capability, With a Lock on the Door
Claude Opus 4.7 ships publicly, while Mythos stays restricted due to cyber-offensive potential. The gap is now explicit: the best systems are not automatically released. Access is negotiated, staged, and in some cases withheld entirely.Talking to Governments (Because They Have To)
Ongoing conversations with U.S. officials around security implications signal a shift from product rollout to geopolitical coordination. Frontier models are now treated as infrastructure with risk profiles, not just features.Anthropic’s Automated Alignment Researchers are running parallel, end-to-end research cycles, turning months of human effort into days of compute. On one benchmark, they leapt from a human-tuned score of 0.23 to 0.97, a rather impolite gap. The catch: they also learned to game evaluations in surprisingly creative ways. Progress, it seems, now comes with its own internal audit problem.
OpenAI
Going Vertical, For Real
Two focused releases: GPT-Rosalind for life sciences and GPT-5.4-Cyber for security workflows. This is a clean move away from “one model for everything” toward domain-specific systems embedded in high-stakes environments.Codex Wants the Whole Desk Now
Codex is no longer content with writing code – it is angling to run the whole workflow. With computer control, memory, plugins, and long-running task automation, it is becoming less a tool and more a colleague who never logs off. Now further strengthened by the folding of the ambitious Prism science workspace into Codex
Google – Running Both Ends of the Stack
On one side: talks to deploy Gemini and TPUs in classified environments, with explicit constraints around sensitive use cases. On the other: expanding AI across consumer surfaces – Android, Chrome, XR. Underneath, continued investment in custom silicon and new chip partnerships. Google is trying to be both ubiquitous and trusted, which is harder than it sounds.
Dive into Claude Code: The design space of today’s and future AI agent systems
What’s fascinating is how little of Claude Code is actually “intelligence.” Researchers from Mohamed bin Zayed University of Artificial Intelligence found a tiny reasoning core wrapped in massive infrastructure: ~512K lines, 1,884 files, seven permission modes, 54 tools, 27 hooks, five context-compression layers, isolated subagents, and append-only transcripts. The real innovation is the harness: safety, memory, delegation, and recovery – not just the LLM →read their study
HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds
Builds a multimodal world model that turns text, images, and video into navigable 3D environments →read the paperKimi K2.6 goes under Model Tech Reports or Coding / Agentic Models. Moonshot’s official blog frames it as an open-source coding model with long-horizon execution, 4,000+ tool calls, 12+ hours of continuous execution, stronger agent swarms, and production use through Kimi Code and API access →check their tweet
Nvidia:
Isaac GR00T N1.7 also goes under Models, but specifically Embodied / Physical AI Models or Robotics Foundation Models. NVIDIA describes it as an open, commercially licensed vision-language-action model for humanoids, with dexterous control and training grounded in large-scale egocentric human video →check at Hugging Face
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
Presents an open hybrid MoE reasoning model optimized for long context, throughput, and efficient inference →read the paperAudio Flamingo Next
Advances audio-language modeling with stronger reasoning, longer audio context, and timestamp-grounded temporal chain-of-thought →read the paperLyra 2.0: Explorable Generative 3D Worlds
Generates persistent explorable 3D worlds by combining long-horizon video generation with feed-forward 3D reconstruction →read the paper
Qwen3.5-Omni Technical Report
Introduces a large omni-modal model for text, vision, audio, speech, and structured audio-visual interaction →read the paper
Trends we see:
how to create better learning signal
how to sustain longer agentic workflows
how to make generation or reasoning more adaptive instead of uniformly expensive.