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Promotions and funding in China depend on the volume of published papers. Rewards for hitting certain targets are often more explicit than in the West. Now questions are being asked how much fraud lurks in Chinese scientific research

Jensen Huang just told you you’re the slowest part of your own computer. And that he fixed it. For forty years, the entire architecture of personal computing depended on a single biological bottleneck. You had to click. You had to type. You had to translate every thought into the rigid language of the machine just to make it do anything. The computer was a passive terminal. It did nothing without explicit human permission. And buried inside that dependency was a word we never questioned. Personal. Your files. Your commands. Your keystrokes. That word meant total, uncontested human authority over a machine. Every interaction was permission-based. Every output was authored by you. That was the contract. Huang: “40 years later, Microsoft and NVIDIA are going to reinvent the PC. It took this long to completely reinvent how the PC is going to work.” He and Satya Nadella spent three years quietly dismantling that contract from the silicon up. No leaks. No breadcrumbs. Three years of silence before retiring the most important human-machine agreement in computing history. They didn’t build a faster processor. They assassinated the interface. The old PC was application-driven. You opened programs. Navigated file systems. Clicked through menus. We spent decades learning how to input. The machine finally learned how to listen. The new PC is agentic. It reasons. It anticipates. It generates. You don’t operate it. You deploy intent. And the moment that gap collapses, the biological intermediary isn’t the operator anymore. It’s the bottleneck. When a machine understands human context natively, the concept of a “user” ceases to exist. The PC was the last workspace where a human had complete control over a machine. No algorithm curating your attention. No feed ranking your reality. Just a blinking cursor and total authority. That space is being surrendered. Willingly. Enthusiastically. And marketed as progress. Because “personal” is about to mean its opposite. We spent forty years defining ourselves by how well we could operate the machine. Only to realize the machine was just waiting to operate itself.

Introducing PAI 1.0: The First Version of Personal Super Intelligence PAI is the world's first context-aware, widget based AI agent that floats over your screen to read your context in real time and proactively suggest next steps, ensuring safety by executing actions only with your explicit approval. The floating AI interface designed to eliminate the prompt inequality and barrier. Structurally eliminating the "navigate to a chat window, write a prompt, explain your context" workflow that existing AI services have treated as a given for years.

Excited to introduce StereoPolicy, led by @EvansXuHan. 📷📷🤖StereoPolicy is an effective way to add geometric cues to modern robot policy models while keeping the strengths of pretrained 2D encoders. ⁉️Why stereo for robot manipulation? Monocular RGB often lacks the depth cues needed for precise manipulation, while RGB-D and point clouds can be noisy or brittle, especially on reflective and transparent objects in real-world deployment. Instead of explicitly reconstructing disparity, depth, or point clouds, StereoPolicy directly fuses synchronized left/right RGB views to learn implicit stereo cues, avoiding extra reconstruction latency that can make real-time manipulation difficult. Project Page:

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

The CIA’s Post-War Vassalization of Japan Until 1941, Japan carried out these crimes against humanity with the assistance of the United States and Britain. And the pro-Kuomintang China Lobby inside the United States and the Wisemen (John J. McCloy, Dean Acheson, Averell Harriman, etc.) prevented the Roosevelt Administration from even engaging with the Communist Party of China led by Mao Zedong. This was despite the fact that General Joseph Stilwell, Colonel David Barrett, and John S. Service described the People’s Liberation Army as the most effective fighting force against the Japanese Imperial Army, as opposed to Chiang Kai Shek who was more concerned with repressing internal opponents and had zero interest in uniting with the Communist Party to fight against the Japanese occupation, despite the support for a united front from Mao and Stilwell. That year though, the US, Britain and the Netherlands finally decided to take action against Japan for its imperial aggression in China and the Asian continent, imposing crushing oil embargo. It was on that basis that Japan attacked Pearl Harbor, bringing the United States into World War II. However, after World War II, John J. McCloy became the President of the World Bank and decided that the new strategy was to once again build up Japan as an opponent to China and the Soviet Union. According to Washington and the US Treasury, the policy was supposed to be that all Asian countries are subservient to a new Japanese empire that was really just a colony of the US. And the role of this new US-led Asian order is that all countries on the continent were to export raw materials to Japan so that they can be turned into manufactured goods. This strongly differed from the approach of US President Ulysses S. Grant who toured Asia after leaving office and advocated for China to lead the way on the continent’s industrial and commercial development. He further emphasized a US-China relationship that is based on mutual respect and explicitly denounced European colonialism. And now newly declassified JFK Files released by the Trump Administration reveal how Japan, through the ruling Liberal Democratic Party, became a permanent US vassal during the Cold War with CIA funding. One document in particular from March 1996 reveals that Washington and Tokyo were still working overtime to hide the existence of the CIA’s Tokyo Station. And their reasoning was the protect this notion that Washington created that Japan was a sovereign state. The US State Department memo was titled “Official Acknowledgement of Tokyo Station” and it shows former US Vice President turned Ambassador to Japan Walter Mondale, along with Japanese officials in Tokyo, in full damage-control mode. Two years before, the New York Times wrote a report exposing secret CIA funding for Japan’s ruling right-wing Liberal Democratic Party during the 1950s and 1960s. Their fear was that if they confirmed the CIA’s presence in Tokyo, it would re-ignite the scandal by confirming the allegations in the NYT’s report. Then-Japanese Foreign Affairs Minister Yohei Kono warned Mondale to keep it secret because the official confirmation would hurt the LDP far more than the NYT’s allegations and threaten the entire post-war security framework between the Washington and Tokyo because it would expose Japan as nothing more than an imperial colony dependent on the United States. Kono had previously claimed, in response to NYT’s allegations, that Japan had “no knowledge” of any organized CIA presence inside the country.

Still thinking about the dirtiest, most explicit dream I had... I can still feel the heat between my legs. 🥵 Drop a 🔞 if you think you could handle making that dream a reality today... let's talk in the comments. 😏👇

$XFAB (photonics + power semis) is an interesting long idea at $1.28B MC, that I took positions in. Given EU CHIPS act 2 is today as the catalyst for European photonics players. > 800 VDC power semi exposure to $NVDA push through $NVTS + $POWI > Silicon Photonics / CPO exposure with $NVDA as evaluation stage for high volume manufacturing (optical transceivers/switches) > The only high-volume SiC foundry in the US. > One of the critical MEMS foundries > ~1.29 P/B, which was around what $SOI was sitting at when I went long. Depressed valuations due to legacy drag > ~6.5-8.5 fwd p/e 2028 personal est. > backstopped by Government: - EU CHIPS act, $128M Euros - US CHIPS act $50M PMT (department of commerce). With likely more coming (just signals critical importance to Western supply chains). So at a certain point with all the grants, they’re just getting the capex funded by the Governments. EU CHIPS act 2 is coming out this week, and I’m gonna go ahead and guess $XFAB might get included given they were before, and this package is specifically targeting photonics. ~$1.3B MC seems compelling to me if it can pull a Soitec reversal (low p/b, very high growth segments, auto legacy drag). As for the $NVDA silicon photonics relationships it’s under “photonixFAB”. Markets probably missed this silicon photonics relationship (like $TSEM when I went long) with Nvidia since XFab leads this… Just under a different name. For power semis, XFAB is named for SiC + $NVTS. In PCN-22181, $POWI explicitly names XFAB as its foundry.  Given its exposure to power semis and photonics as growth, low P/B, gov backstop (of course dyor, just sharing my personal thoughts) Thought it personally seemed compelling.