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U.S. data analytics software firm Palantir Technologies Inc. (Nasdaq: PLTR) released its third-quarter financial results on November 2, 2026, marking a historic shift in its business structure. In this quarter, Palantir's U.S. commercial revenue reached $984 million, growing 148% year-over-year and 29% sequentially, while U.S. government revenue stood at $953 million. This represents the first time in the company's history that U.S. commercial revenue has surpassed U.S. government revenue.

Overall financial performance was equally robust. Total revenue for the quarter was $2.313 billion, up 96% year-over-year and 20% quarter-over-quarter, crossing the $2 billion threshold for the first time in a single quarter. Adjusted operating margin was 62%, and GAAP earnings per share were $0.44. CEO Alex Karp emphasized in the statement that the "Sovereign AI" movement is forcing rapid differentiation between institutions, with Palantir's product suite (including Foundry, Ontology, and AIP) driving a self-improvement loop through this demand.

Based on the strong Q3 performance, Palantir significantly raised its fiscal year 2026 guidance. The company increased its full-year revenue expectation to between $8.530 billion and $8.534 billion, representing approximately 91% year-over-year growth. Additionally, U.S. commercial revenue guidance was raised to over $3.559 billion, implying a growth rate of at least 143%. This guidance substantially exceeds market consensus, reflecting management's confidence in the pace of AI-driven commercial adoption.

Israeli artificial intelligence startup 42AI announced it has emerged from stealth mode with $62.5 million in seed funding. The company has developed a "behavioral world model" designed to predict individual decision-making tendencies in financial markets by integrating data from psychology, neuroscience, and genetics. According to TechCrunch, the model is currently deployed at approximately 10 large brokerages to manage liquidity, margin requirements, and hedging exposure.

42AI CEO Amit Gal-Or stated that the model does not rely on personally identifiable information but instead analyzes behavioral data such as customer interactions with support teams, KYC processes, and trading records to construct probability distributions for predicting trading flows. The company claims the model achieves 98% accuracy in predicting whether trading flows fall within a predicted range, and maintains an F1 score of 0.8 to 0.85 for predicting trading direction and volume.

The funding was provided by the founders and Infinity Group, founded by Gal-Or's father, Amir Gal-Or. While 42AI reports generating more than seven figures in revenue and helping one brokerage generate approximately $30 million in additional value through dynamic hedging, these core performance metrics are self-reported by the company and have not been independently verified.

U.S. semiconductor manufacturer Intel faces a strategic test following a historic stock plunge in October. The company has decided to redirect some capacity at its Oregon fabs from its advanced "18A" process node to the older "Intel 3" node to produce chips for data center products. This move aims to address current shortages in server CPU supply but may hinder Intel's core goal of becoming a foundry for other chipmakers.

Previous reports highlighted that Intel's stock fell 45% in October due to weak PC market outlooks and missed revenue guidance. New details reveal that despite a 78% year-over-year growth in its data center business, Intel cannot manufacture enough chips to meet demand. CEO Lip-Bu Tan stated that the global market for server processors is expected to grow fivefold to about $200 billion in the next five years, but industry-wide constraints on wafers, substrates, and memory have forced difficult choices.

David Yoffie, a Harvard Business School professor, noted that this resource shift is hardly welcoming to prospective foundry customers. If Intel lacks excess capacity beyond its own demand, its credibility as a supplier is questioned. Yoffie argued that if Intel fails to secure large-scale foundry customers within a year, stress on its economics will become more dramatic. Additionally, competitors like Broadcom and Qualcomm may hesitate to manufacture chips at a company that is also a product-side rival.

Analyst Jordan Klein suggested that Intel has more control over its destiny than most chipmakers, being one of the few companies capable of manufacturing and packaging the advanced chips it needs. However, he noted that insufficient capacity leaves revenue on the table. Currently, Intel's 18A chips are in high-volume production in Arizona for its own use, and the capacity adjustment in Oregon directly impacts its plan to showcase advanced process capabilities to external clients.

AI computing power demand continues to grow, and carbon emissions from hyperscale data centers are receiving increasing attention. While operational energy consumption is the most discussed aspect, the energy and materials consumed during chip manufacturing itself—known as embodied emissions—also constitute a significant environmental cost. As GPUs transition from single chips to chiplet architectures and scale computing power through 2.5D or 3D packaging, there has previously been a lack of quantitative comparison regarding the differences in embodied emissions between these packaging routes.

Researchers from imec and Vrije Universiteit Brussels filled this gap in a preprint paper released in October 2026. Using imec’s netzero modeling platform, they modeled a chiplet-based GPU scaled in both 2.5D and 3D configurations, doubling the number of compute units and HBM capacity, and then estimated the increase in embodied emissions for both approaches.

The modeling results show that the embodied emissions increase for 3D scaling is 89%, while for 2.5D scaling it is 141%. Based on this, the research team positions 3D stacking as an attractive solution for limiting the embodied emissions brought about by GPU expansion.

It is important to emphasize that this is a preprint paper that has not yet undergone peer review, and the results stem from modeling estimates rather than actual product measurements. The specific percentages depend on the comparative conditions set by the study—namely, the simultaneous doubling of compute units and HBM capacity—and actual product configurations may yield different proportions. Furthermore, embodied emissions are only one dimension of GPU expansion decisions; factors such as performance, yield, thermal management, and cost are equally important.

For AI infrastructure planning and the semiconductor packaging industry, this study provides a quantifiable reference point: when pursuing computing power expansion, the choice of packaging architecture significantly impacts embodied emissions, making the 3D route worth considering from an environmental cost perspective.

Tomasz Tunguz is a partner at Theory Ventures and a former Google product manager. In an article published in November 2026, he proposed that the AI industry consists of four layers: applications, harness & context, models, and data centers. Each layer faces an unresolved economic question, and the four layers form a coupled system. This article focuses on one specific transmission chain: how GPU cost inflation elevates the economic value of the harness layer, and where the evidentiary boundaries of this logic lie.

Tunguz’s transmission mechanism relies on a structural fact: in agent workloads, input context accounts for up to 98% of all tokens (he cites OpenRouter’s token composition data, ranging from 85.7% to 98.2%). This means that the vast majority of token consumption in model inference is not 'answering,' but rather 'feeding context to the model.' When GPU prices rise—the Ornn B300 index increased from approximately $5.53 per hour in mid-June to $12.29 per hour by the end of October, with a compound monthly growth rate of about 20%—the computational cost per token increases accordingly. At this point, the economic value of a harness that can reduce invalid context injection is magnified: what is saved are not output tokens, but the input tokens that make up 98% of the total volume.

Two key figures support this logic. First, UC Berkeley’s HarnessTax study found in a comparison of 42 SWE-bench tasks that optimizing the harness can reduce the cost of solving the same task by 71% without producing a statistically significant difference in quality. Second, Tunguz’s own contract modeling shows that on a $250,000 AI assistant contract, harness optimization can lift the gross margin from 38% to 75%. These two figures come from different evidence families: the former from independent academic experiments and the latter from commercial contract projections, with different metrics: the former measures task cost, while the latter measures contract gross margin.

However, the evidentiary boundaries of this transmission chain need to be clearly marked. First, the Anthropic S-1 disclosure that Opus 5.5 reduced customer cost per task by 40%, and that quarterly revenue growth accelerated from 48% to 132% after the release of Opus 4.5, are vendor-reported data, not independently verified figures. Second, Tunguz observed that about 83% of agent tasks are now routed to smaller or domain-specific models, but this is a description of current behavior; he did not prove this is an irreversible trend—the same article also recorded secondary market GPU quotes dropping from $7.90 per hour to $4.60 per hour within a month, indicating that underlying costs themselves fluctuate violently. Third, the 98% input token share comes from token composition statistics of the OpenRouter public gateway, whose scope of application may not cover all deployment scenarios.

Tunguz uses a 'double pendulum' to metaphorize the four-layer coupling: each component follows simple deterministic laws, but the entire system exhibits chaotic motion, where tiny changes produce unpredictable large swings. This metaphor itself acknowledges the framework’s limitations—it describes a coupled structure but does not provide predictions. Readers can use it as an analytical map to locate where cost pressures land: when GPU prices rise, focus on the harness layer’s token optimization capabilities; when model efficiency improves, focus on pricing space at the application layer. But the framework does not tell you when these changes will occur, how large they will be, or which layer will collapse first.

According to Bloomberg, Goldman Sachs has cut off most of its employees from advanced AI coding tools such as Anthropic's Claude Code and Microsoft's GitHub Copilot. Based on internal guidelines issued in October, only engineers are permitted to continue using these tools. For months, traders and bankers had utilized them to monitor trading flows and create content. The rollback is reportedly causing frustration within the firm.

This decision marks a significant shift in Goldman Sachs' AI adoption strategy. In July, Goldman CIO Marco Argenti publicly praised "vibe coding" by non-technical staff in an interview, envisioning a workflow where traders or asset managers would build quick prototypes with AI before handing them off to engineers for production scaling. That plan appears to have been abandoned.

Regarding the reasons for the U-turn, Bloomberg cited Goldman spokesperson Nick Carcaterra, who attributed it to a "variety of factors including productivity and risk management." Carcaterra added that the experimentation phase is over, and the firm is now limiting the tools to those "who are best suited to leverage them for the long term." This change reflects the practical governance challenges large financial institutions face as generative AI moves from broad experimentation to controlled deployment.

The core belief of mercantilism is that wealth is fixed, the total amount of gold will not increase, and therefore one nation's gain is another's loss. Under this logic, the value of ordinary people is reflected only in their labor, while the duty of elites is to manage scarce resources, maintain high prices, and prevent rivals from gaining access. Engineer Venkatesh Rao believes that many AI elites today treat intelligence in the same way mercantilists treated gold: they view it as a scarce, dangerous resource that should be managed by experts, kept expensive, and withheld from competitors. In their eyes, open-source AI models are not technological progress, but a threat.

Rao's analogy goes beyond diagnosis to point toward a historical turning point. After early capitalism replaced mercantilism, people did not stop working because wealth was no longer treated as a fixed total; instead, they voluntarily increased their working hours—to buy tea or sugar, the average annual workdays increased from 250 to 300. Rao believes AI could trigger a similar change: not because people lose their jobs, but because AI makes projects that were previously too expensive or difficult feasible, leading people to think more rather than less. The demand for ideas would grow, rather than rendering people obsolete.

The policy implications of this argument are clear: Rao advocates that the political focus of AI should be ensuring fair access, rather than protecting legacy knowledge jobs. Protecting jobs means treating intelligence as a fixed resource to be distributed; ensuring access means acknowledging that the total amount of intelligence can expand as tools improve. He even suggests that AI agents themselves could pursue their own projects, thereby creating new demand for intelligence—autonomous AI needs guardrails, but should not merely follow orders.

It should be noted that this is a conceptual analogy by Rao, not an empirically tested economic model. The correspondence between mercantilism and AI is rhetorical: it illuminates certain motivational structures, but it does not prove that the actual economic behavior of intelligence truly mirrors that of gold. Whether intelligence is exclusive, and whether AI will truly expand rather than replace the need for human thinking, are empirical questions that the analogy itself cannot answer. However, as a framework for understanding current AI policy debates, it provides a useful perspective: when an AI executive opposes open-source models on safety grounds, what deserves questioning is not just whether the safety arguments hold, but also whether this is a modern variant of a resource-control logic.

Drug development is governed by a widely circulated 'three-ten rule': it takes an average of ten years and over one billion US dollars to develop a new drug, yet the success rate remains below ten percent. When Xing Jie, COO of Shuimu Molecule, cited this figure on a CCTV program, the issue he intended to highlight was more specific than cost alone—how can the decades of experience accumulated by a pharmaceutical research expert be truly utilized by AI?

Nie Zaiqing, Chief Scientist at Shuimu Molecule, believes that the critical barrier in past AI drug discovery projects appeared precisely here. Models can predict structures, generate molecules, and rank candidate results, but researchers may not fully understand the basis for the ranking, nor can they easily feed their concerns back into the model. While computational results can be handed over for experimental validation, there is a lack of a continuous collaboration channel between experts and algorithms. This means that the bottleneck in AI drug discovery is not merely single-point prediction accuracy, but the organizational structure of the entire scientific research workflow.

Consequently, Shuimu Molecule proposed a four-stage division of drug development: TMDD (relying on experimental accumulation and expert judgment), CADD (computer-aided calculation and simulation), AIDD (using AI for property prediction and molecule generation), and ChatDD (where experts participate in task planning, execution, and result correction through natural language dialogue). In the first three generations, expert experience and intuition were difficult to effectively interact with and organically integrate into AI models; ChatDD attempts to let experts pose questions and task objectives, have large models break them down and call tools, and then return the results to the experts. Through multiple rounds of interaction, expert experience can efficiently participate in every step of AI drug discovery.

The implementation of this framework relies on several specific technical layers. Shuimu Molecule, jointly advancing BioMedGPT with Tsinghua AIR, explores the alignment of natural language with biological data such as molecules and proteins. Among these, BioMedGPT-10B is the world's first open-source, commercially usable multi-modal biomedical large model with tens of billions of parameters. MutaPLM, selected for NeurIPS 2024, correlates textual descriptions of mutation impacts with protein mutations, both explaining what changes a single mutation might bring and proposing candidate mutations based on researcher-described goals. CodeFP, accepted by ICML 2026, focuses on the collaborative design of sequences and structures for functional proteins, while AbGaze explores end-to-end de novo design of antibodies at the all-atom level.

Above the model foundation, Shuimu Molecule organizes research methods described by experts through dialogue into skill packages that AI agents can call at any time. The first batch of OpenBioMed skill packages released this year encompasses 45 professional skills, covering five categories: biochemistry and drug discovery, protein analysis and design, single-cell omics analysis, data retrieval, and tool invocation. The Skill Creator feature allows researchers to explain task intent, inputs, outputs, and constraints through dialogue; the system assists in organizing the workflow, which is then verified and modified using actual data to form a reusable method. In this way, a single piece of guidance from an expert has the potential to leave behind a continuously usable workflow.

According to Reuters, The New York Times, along with the New York Daily News, Chicago Tribune, and Orlando Sentinel, has filed lawsuits against Brave Software, the developer of the Brave web browser. The plaintiffs allege that Brave unlawfully scraped copyrighted content to train artificial intelligence models and allowed users to bypass news website paywalls through its built-in 'Paywall blocker' feature.

Court filings indicate that Brave allegedly monetized its index of scraped websites by providing AI companies with paid access to "real-time online content from the Brave Index." These AI companies reportedly used this access to train models or respond to user queries. The lawsuit claims Brave provided "stolen copyrighted content" to several AI firms, including Anthropic, Perplexity, Mistral, xAI, and Cohere.

Graham James, a spokesperson for The New York Times, stated that Brave ignored repeated requests to stop its infringing conduct and stole content to power its products and provide it to prominent generative AI companies. Steven Lieberman, the attorney representing the other three publications, argued that Brave is "laundering pirated reporting for the AI industry."

Brave and the named AI companies did not immediately respond to requests for comment. This lawsuit represents a new legal front in the broader copyright disputes surrounding AI training data, following previous suits against model developers like OpenAI and Microsoft.

Anthropic announced on November 2, 2026, that 'message threads' is now available in public beta on the Claude Platform's Messages API. This feature aims to address the latency issues caused by long-running AI agents needing to resend their entire conversation history on every turn.

In traditional setups, coding or computer-use agents might make hundreds of requests in a single session, each including all previous messages and tool results. Once a conversation reaches hundreds of thousands of tokens, resending this data adds significant time before Claude begins responding. With message threads, agents send only what’s new since the last turn along with the ID of the previous response. Anthropic reports that for requests with 500k to 1M tokens, the median Time to First Token (TTFT) drops by about 35%.

Technically, Anthropic holds each thread in encrypted memory between turns without writing it to disk. A thread expires five minutes after its last request, at which point the client must resend the full conversation to start a new one. Although each request is smaller, Claude still reads the full conversation on every turn, so token usage remains unchanged.

The feature is best suited for active sessions where latency matters, such as long-running coding agents, computer-use agents, and document/image-heavy workflows. Message threads is currently available on the native Claude Platform, Claude Platform on AWS, Claude in Microsoft Foundry, and Claude Models on Google Cloud (Anthropic managed). Standard Claude token rates and existing rate limits apply.

According to Tom's Hardware, Tachyum Slovakia, the subsidiary of AI chip startup Tachyum, voluntarily filed for bankruptcy in Bratislava on October 22, 2026. The court subsequently initiated bankruptcy proceedings, determining that the petition met legal requirements. Under Slovak law, a company that voluntarily files for bankruptcy is presumed to be insolvent.

Since introducing the Prodigy universal processor in 2018, Tachyum has failed to demonstrate working silicon despite eight years of development and numerous promises. Launch dates were repeatedly postponed from an initial target of 2020 to 2025, and potentially beyond 2030 following recent redesigns. With bankruptcy proceedings underway, work on Prodigy could effectively end unless additional financing or a buyer is secured.

A key uncertainty lies in the ownership of Prodigy's intellectual property (IP). Tachyum Slovakia is a subsidiary of the U.S.-based Tachyum Inc. If the American parent owns the IP while the Slovak entity handles development and accumulates debt, the bankruptcy may eliminate liabilities without losing the technology. Conversely, if the Slovak subsidiary holds the IP, it must be sold through the bankruptcy estate. Reports indicate the U.S. parent also faces creditor pressure for liquidation, further complicating the destiny of the Prodigy IP.

Swedish venture capital firm Creandum has raised $750 million for its eighth fund, marking the largest fundraise in the firm's history. The raise comes amid a rush by investors to back hot AI startups in increasingly large early-stage rounds.

According to Sifted, Creandum has tripled its presence in San Francisco over the last two years, highlighting its focus on the US AI ecosystem. The fund will provide capital to early-stage AI companies, reflecting the current trend of primary market capital concentrating on top-tier AI projects.