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OpenAI launched its new Images 2.6 model on November 4, 2026, rolling out alongside updates to ChatGPT for all users. The core feature allows users to generate GIF animations directly within ChatGPT using prompts or uploaded photos, with options to adjust animation speed and edit individual frames. Previously, ChatGPT could not natively generate GIFs in a single step, often requiring external tools for conversion or editing.

In addition to GIF support, OpenAI stated that Images 2.6 enables faster image generation. The company disclosed that users now generate over 4 billion images per week across ChatGPT and its API, a significant increase from the 3 billion reported in September. This metric highlights the growing reliance on AI image generation tools.

The update also includes new model variants for developers: GPT-Image-2.6 Flare and Sunburst, designed for faster and higher-quality image generation. Images 2.6 is available across web, desktop, and mobile apps for both ChatGPT and Codex.

According to TechRadar, Russian President Vladimir Putin has signed an emergency decree allowing domestic companies to temporarily use overseas data centers. This policy shift follows a series of Ukrainian drone attacks on Russia's own data center infrastructure, aimed at mitigating service disruptions caused by physical damage to key facilities.

The context for this adjustment is the tangible impact on critical digital assets. Reports indicate that three major Yandex facilities across Sasovo, Kaluga, and Vladimir were impacted, taking some Yandex services offline as well as other services operated by third parties through Yandex's infrastructure. Yandex is responsible for three major supercomputers, one of which was ranked 19th globally in a 2021 study, playing a crucial role in model training. While believed to be intact, their operational status has been severely challenged.

Crucially, the decree does not represent a complete opening of data borders. Companies cannot simply abandon homegrown data centers; any arrangements require government approval, likely assessing the availability of domestic compute before granting permission. Additionally, operators must reserve 10% of their capacity for emergency government use, available at just 24 hours' notice if needed.

To address future threats, Russia is also exploring extreme physical protection measures. State-owned telecoms company Rostelcom is reportedly in discussions with mining company Norilsk, while one governor has suggested building data centers inside open-pit coal mines to shield them from drones. These developments highlight how digital infrastructure has become a primary target in modern warfare and underscore the practical difficulties facing Russia's push for 'digital sovereignty.'

Brazilian artificial intelligence startup Dharma-AI released Dharma Field Extractor, a 2-billion-parameter small language model (SLM), on November 4, 2026. The model is designed to directly extract specific fields requested by users from various types of document images and output them as structured JSON. Unlike traditional Optical Character Recognition (OCR) technology, which only returns all text printed on a page, Field Extractor understands context and layout to map specific information—such as supplier names, total amounts, or patient names—from invoices, medical requisitions, or ID cards into designated key-value pairs.

Technically, the model is built upon DharmaOCR-LITE v1.1, trained through supervised fine-tuning and Direct Preference Optimization (DPO), with an additional LoRA adapter trained on a dataset of 43,384 examples. The training data covers English multi-domain documents, Portuguese Brazilian local documents, and a smaller share of other European and Asian languages. The model supports two request modes: an "open" mode where the model identifies and extracts all structured fields on its own, and a "schema-directed" mode where users specify exact field names and data types, with the model returning null for fields not present on the document.

According to internal benchmark results published by Dharma-AI, the model achieved a composite score of 0.834 across 546 test documents, outperforming several frontier general-purpose LLM configurations, including Claude Opus 5 and GPT-5.6 Luna. In terms of cost, because the model is designed for self-hosted deployment, its inference cost on a single L40S GPU is approximately 6 to 147 times lower than calling frontier APIs. This result highlights the potential economic advantage of domain-specialized small models in high-frequency enterprise applications. It should be noted that these performance comparisons and cost savings are based on benchmarks and hardware environments constructed by Dharma-AI itself, and have not yet been independently reproduced by third parties.

According to Tom's Hardware, Taiwan-based memory manufacturer Macronix is phasing out its ROM business to focus on producing NOR and NAND flash chips for AI data centers. This strategic shift was highlighted by analyst David Gibson on X and corroborated by the Taipei Times. Gibson noted that Macronix's ROM sales dropped to 5% of total revenue in Q3, down from 26% the previous year, and stated there will be no new ROM products.

Macronix is a key supplier of ROM chips used in Nintendo Switch and Switch 2 game cartridges. As manufacturing capacity shifts toward high-margin AI infrastructure, Nintendo faces potential pressure to increase prices on physical games or rely more heavily on Game-Key cards (which hold download keys rather than full game data), accelerating the transition to digital sales. While ROHM Group companies still supply ROM chips to Nintendo, making an immediate full digital shift unlikely, the exit of a major supplier tightens the supply chain for physical media.

The move poses significant risks for fabless chip design company MegaChips. MegaChips designs ROM chips for Nintendo but lacks manufacturing capacity, outsourcing production to Macronix. According to MegaChips' 2022 annual report, Nintendo accounted for 87.8% of its sales, while Macronix represented 67.8% of its outsourced manufacturing purchases. The report previously warned that if Macronix ceased ROM manufacturing, "MegaChips’ operating results may be impacted." It remains unclear what contingency plans MegaChips has in place for this emerging risk.

Macronix's decision reflects the massive demand pull from AI data centers. The company allocated its entire NT$37.8 billion capital expenditure budget to NOR and NAND flash, leaving none for ROM. This reallocation not only alters investment logic in the semiconductor industry but also ripples through to consumer electronics, demonstrating how AI infrastructure buildout is impacting the cost and availability of everyday gaming hardware.

On November 4, 2026, Barry Ritholtz, founder of Ritholtz Wealth Management, published a short essay on the United States midterm elections. He recorded how three tools he previously used to track political trends all failed simultaneously that night: stock index futures had already sold off deeply by 6 p.m.; prediction markets such as Polymarket severely underestimated the win probability of Democratic candidates; and while opinion polls got the general direction roughly right, they exhibited significant magnitude errors, completely failing to predict the election of Democratic senators in Kansas and South Carolina, or the sharp reversal in Texas. By noon on November 4, the Dow Jones Industrial Average had rebounded approximately 3% from its previous day's low, while the S&P 500 and Nasdaq Composite each rose about 2%.

Ritholtz attributes these failures to three factors he considers inherent human cognitive defects: tribalism, a bad information diet, and the difficulty of accurately assessing one’s own abilities. He describes these three elements as fundamental obstacles when analyzing social events and politics, stating bluntly that when this combination is applied to capital allocation decisions, the consequences are fatal. He cites screenwriter William Goldman’s famous quote, "Nobody knows anything," to encapsulate this unknowability, but immediately admits that using this quote feels like cheating—it exploits cognitive limitations formed in an evolutionary environment to explain prediction failures in modern financial markets, limitations that most people either do not know about or do not care about.

However, Ritholtz also explained why this particular election was especially difficult to predict. He noted that the President won in 2024 by broadening traditional Republican demographics, adding young males, less-educated groups, and Hispanic voters to the coalition, which made the Republican alliance vulnerable in a normal midterm cycle. He listed three specific policy factors—the aggressive immigration enforcement by Immigration and Customs Enforcement (ICE), a muscular foreign policy supporting multiple wars, and chaotic tariff policies—as catalysts for the coalition's disintegration. Exit polls showed that 43% of voters cast their ballots to oppose Trump, compared to only 16% who voted to support him; turnout models completely underestimated the mobilization intensity among voters leaning toward the Democratic Party.

For investors, the value of this short essay lies not in providing a new predictive model, but in exposing an operational issue through a concrete case: when futures, prediction markets, and opinion polls all point in the same direction, does this consistency truly represent triple independent verification? Ritholtz does not directly answer this question, nor does he offer an alternative solution. However, his approach of juxtaposing the failures of the three tools and then attributing them to the same set of human cognitive defects forces readers to re-examine the evidentiary logic behind relying on multi-signal cross-confirmation for political risk.

What is the real question when a systematic investor faces N trading strategies or N leverage levels? Rob Carver provides a clear reframing in his blog: whether bootstrapping historical returns to obtain a distribution of final Compound Annual Growth Rate (CAGR), or bootstrapping different leverage levels to generate a series of discrete possible outcomes, both are fundamentally about choosing which of N outcome distributions you prefer. You are not selecting the average performance of a specific strategy, but rather which complete outcome distribution you favor.

Carver distinguishes three selection methods. The first is the strict expectation method: selecting the distribution with the best median value, i.e., the best 50th percentile. As he has previously argued, this is mathematically equivalent to Kelly optimal, which maximizes long-term compound growth. The second is the gambler's method: selecting the distribution with the best right tail, such as the option with the most favorable 1% right-tail point. This corresponds to high-leverage, cryptocurrency, or short-gamma strategies—paths that do not blow up in the best possible alternative histories generated by the bootstrap. Carver's assessment is that nobody should be a gambler, except occasionally. The third is the conservative method he favors: selecting the distribution with the best left tail, i.e., the option with the most favorable 1st, 10th, or 25th percentile point, meaning the option that yields the best outcome if bad things happen.

The extreme version of the conservative method is MinMax from game theory: selecting the option with the best worst-case outcome. Carver points out that this is exactly what all chess-playing software does—treating the market as an adversary and striving for the least bad result in the worst possible state. However, he immediately acknowledges that if the number of bootstrap iterations is sufficiently large, the lower bound of all distributions will converge toward zero (since compounded returns and absolute returns greater than -100% cannot mathematically fall below zero), making them hard to distinguish. Therefore, the most realistic extreme version in practice is selecting the option with the most optimal 1st-percentile left-tail point. Given that this is likely to be negative, it would be the option with the least worst 1st-percentile tail.

Carver does not suggest directly optimizing for the 1st-percentile point. He cites an old saying in the trading community: there are no old, bold traders. Good traders know how to quantify risk and when to take risks, but they also understand the importance of keeping their capital mostly intact for the future. His conclusion is that optimizing around the 10th or 25th percentile point is a reasonable compromise in practice. The 1st-percentile point is hard to distinguish in bootstrapping, and more extreme results likely exceed the range predictable from historical samples; while the 50th-percentile point (Kelly optimal) maximizes long-term growth, the corresponding path volatility may exceed the psychological tolerance of most people. The 10% to 25% interval implies that in every four to ten possible outcomes, you are optimizing for the worse-performing instance rather than the worst-case scenario.

Systematic trading practitioner Rob Carver has proposed a new method for handling trading costs in the 11th article of his 2026 portfolio optimization series. His starting point is a widely accepted observation in quantitative investment: trading costs are easier to predict than expected returns. Costs are relatively deterministic figures, whereas return estimates are filled with noise. So, when optimizing predicted weights, how can this asymmetry be naturally reflected in the results?

Carver lists six existing solutions, ranging from completely ignoring costs and using net returns, to multiplying costs by a factor greater than 1 to artificially amplify their weight. He argues that while the multiplier method seems intuitively correct—since costs are more certain, they should carry greater weight—it introduces an additional parameter, 'factor,' which requires further optimization and increases complexity. His proposed new solution is to first perform a shrinkage estimate on the gross-return Sharpe ratio, and then directly deduct each transaction's cost from the shrunk value.

The logic behind this method is that shrinkage itself already handles the uncertainty of return estimates. When you shrink a noisy Sharpe ratio estimate, you are essentially saying, 'I am not very confident about how good this return prediction is.' Costs do not require shrinkage because they are known. Therefore, shrinking first and then deducting naturally achieves the effect of giving more weight to certain costs and less weight to uncertain returns, without needing to manually set a multiplier. The specific parameters Carver provides are: a Sharpe ratio shrinkage of 0.5, a correlation coefficient shrinkage of 0.75, and higher shrinkage when data spans less than five years.

However, Carver admits that this method suffers from a cliff effect: he sets a threshold of 0.01 Sharpe ratio points as the cost cap; tools exceeding this threshold are dynamically excluded by the optimizer. An instrument with a cost of 0.012 and one with a cost of 0.008 receive drastically different treatment in the optimization. His mitigation strategy is to cap costs at 0.01 at the optimization level, making high-cost instruments behave as if their cost is exactly 0.01 during weight calculations, but still excluding them at the actual execution level. He acknowledges this remains a double standard and somewhat messy, but it is better than the previous approach of completely ignoring costs.

The article concludes by presenting Carver's current 'gold standard' architecture for forecast weight optimization: from outside to inside, it consists of rolling out-of-sample optimization, cross-instrument average pooling (averaging optimizations based on three datasets for gross returns: instrument-specific, asset-class-level, and full-sample), reverse weight processing (allowing for the discovery of effective strategies the author did not anticipate), clustering (targeting six clusters), and finally, innermost shrinkage plus cost deduction. Instrument weights use the same structure but omit the pooling step. Carver explicitly states that the next article will test this architecture on a smaller, tractable portfolio.

The results of the United States midterm elections have created a unique political landscape in Washington, D.C.: a Republican president alongside a Democratic-controlled Congress. James Picerno, author of The Capital Spectator, argues that this divided government is not a negative factor for the market; rather, it provides new support logic for asset pricing through two distinct channels.

The first channel is policy predictability. A divided government means that tax code revisions, large-scale deregulation, or expansive fiscal stimulus face significant hurdles in Congress. Picerno points out that reducing policy surprises itself provides businesses and investors with a more stable operating environment, a level of certainty that markets have historically preferred.

The second channel is fiscal discipline. Picerno contends that the combination of a Democratic-controlled Congress and a Republican White House makes it harder to pass legislation that significantly expands the budget deficit. While this gridlock is unlikely to reverse the long-term upward trend of federal debt, it lowers the probability of new, large-scale fiscal stimulus. A verifiable anchor for this judgment is the yield on the 10-year U.S. Treasury note: if yields stabilize or decline post-election, it may indicate that bond market investors recognize divided government as an effective constraint on deficit expansion; if yields continue to rise, it suggests investors remain concerned that the trajectory of federal debt will not change regardless of which party controls Congress. Picerno notes that the 10-year yield has recently fallen back below 5%, implying that the market is temporarily less worried about inflation and fiscal risk.

However, Picerno simultaneously identifies a sectoral exception: AI data center infrastructure. Citing a poll released by Politico last week, he notes that approximately half of Americans oppose building data centers within a few miles of their homes, a figure that has risen sharply from 28% in January. As public sentiment shifts toward supporting restrictions, Democrats may seek stricter regulation of data center development. The market appears to be pricing in this possibility: two ETFs serving as proxies for data center and digital infrastructure stocks have fallen sharply in recent weeks, while the S&P 500 ETF (SPY) has remained largely flat. This implies that the transmission mechanism of divided government to the AI infrastructure sector is opposite to its effect on the broader stock market—policy predictability benefits the broad market, but expectations of tighter regulation weigh on specific sectors.

Picerno’s analysis also leaves open two risk channels still under the President’s unilateral control. First is tariffs: even with a divided Congress, the President can impose or raise tariffs through executive action and existing trade laws, limiting Congress’s ability to restrain such moves. Second is geopolitics: some analysts argue that a lame-duck president may feel fewer constraints in pursuing a more aggressive foreign policy agenda, including a tougher stance toward Iran. Any escalation in tensions could disrupt oil supplies, push up energy prices, and further complicate the inflation outlook. Currently, the U.S. benchmark crude oil, West Texas Intermediate (WTI), is trading below $100 per barrel. Picerno poses the question: does this reflect a durable easing of geopolitical concerns, or merely a temporary pause before markets reassess the risks of renewed tensions in the Middle East?

Why do two borrowers who appear nearly identical in credit score and income level receive significantly different annual percentage rates (APRs) when applying for a credit card at the same bank? In a study published on November 4, 2026, on Liberty Street Economics, the blog of the Federal Reserve Bank of New York, Kyle Herkenhoff, a consultant at the Federal Reserve Bank of Minneapolis, and Juan M. Morelli, a research economist in the Research and Statistics Group of the Federal Reserve Bank of New York, provide a mechanistic explanation: credit card issuers pass through changes in funding costs to a lesser extent in segmented markets where they hold a larger market share.

Understanding this conclusion requires an understanding of the structure of the credit card market. Issuers do not compete in a single, undifferentiated national market; rather, they design products and pricing strategies targeting specific types of borrowers. Industry reports, regulatory guidance, and academic research all document market segmentation based on credit risk, income levels, card characteristics, and acquisition channels. A prime borrower applying online for a standard non-co-branded card belongs to a completely different market than a subprime borrower accepting a pre-approved offer for a co-branded card. The same issuer can dominate one such segment while holding a very small share in another.

Guided by this framework, Herkenhoff and Morelli divided the national market into 960 distinct segments, combining four credit-score groups, card and product types, acquisition channels, and income groups based on the borrower’s ZIP code. In their dataset, an issuer operates in approximately 200 of these markets on average per week. Within each market, the issuer’s share is defined as its revenue relative to total revenue in that market.

They first utilized confidential account-level data collected by the Office of the Comptroller of the Currency (OCC), a U.S. banking regulator, to observe the relationship between interest rates and market share. After finely grouping accounts by characteristics such as income, credit score, and card type, they found a positive correlation of 54% between the average APR within each group and the average market share of the issuing lender within that group. This means that among borrowers with similar characteristics, cards issued by lenders with higher market shares carry higher interest rates.

However, correlation does not imply causation. High-share markets may differ in unobserved dimensions that also affect rates. To obtain more direct evidence, the authors turned to a natural experiment: when an issuer’s funding costs undergo an exogenous change, do its pricing adjustments differ between high-share and low-share markets?

The analysis focused on January 2008, which saw one of the largest unexpected drops in the federal funds rate (25 basis points). For new accounts, issuers set the initial APR and its spread over the benchmark rate at origination. The authors compared cards issued shortly before and after this unexpected rate change by the same issuer, stripping out market-wide common rate movements and controlling for observable differences in borrower credit scores, income, and card types. What remained was the difference in pass-through between high-share and low-share markets for the same issuer under the same exogenous shock.

UK AI Minister Kanishka Narayan recently admitted in a parliamentary answer that the £400 billion economic boost figure widely cited by the government over the past two years primarily originated from a 2023 report commissioned by Google and produced by consultancy firm Public First (now PF Global). Narayan stated that the government's own latest assessment projects an AI and data investment benefit of between £55 billion and £140 billion by 2030, with a central estimate of £120 billion—significantly lower than the previously promoted £400 billion.

This disclosure highlights issues regarding public policy reliance on industry-funded research. While government departments and agencies used the £400 billion figure to calm fears about job losses and economic crashes caused by AI, officials had access to the lower internal estimate since at least November 2025. Skills England, a government agency responsible for apprenticeships, continued publishing the old figure four months later.

Jonathan Dupont, head of economics at PF Global, explained that the disparity partially stems from changed views on AI adoption speed. Their 2023 model assumed faster technology uptake, whereas current government estimates are based on slower real-world adoption rates. Dupont emphasized that their research was independent and all judgments were their own.

Critics from opposition parties and regulatory bodies have raised concerns. Conservative peer Lord Holmes noted that the government should have clarified the data source earlier, as billions in public funding depend on robust data. Labour MP Dame Chi Onwurah, chair of the Commons Science, Innovation and Technology Committee, argued that overstating technology benefits undermines the case for digital transformation and called for greater transparency.

Sumedha Deshmukh of the Ada Lovelace Institute warned that many precise-looking AI economic figures contain embedded assumptions and industry incentives. She urged the government to test the numbers before building policy on them, fund independent research, and verify whether promised gains are actually delivered.

U.S.-based semiconductor and infrastructure software company Broadcom Inc. has announced an expansion of its VMware portfolio, focusing on simplifying artificial intelligence workload deployment, strengthening infrastructure security, and offering lower-cost virtualization options. The updates span the flagship private cloud platform, VMware Cloud Foundation (VCF), and a new product aimed at smaller customers, vSphere Express.

Regarding AI deployment, Broadcom introduced "AI Infrastructure Quickstart," an automated capability that consolidates vCenter cluster deployment, vSphere Supervisor activation, and VMware Kubernetes Service cluster provisioning into a single workflow. According to Prashanth Shenoy, Broadcom’s vice president of marketing for cloud platform, infrastructure, and solutions, this change allows users to complete initial configurations in minutes, replacing previously manual processes. Additionally, multitenant model sharing, already available in VCF 9.1.1, enables different business units to use centrally deployed AI models while maintaining separate security boundaries, thereby reducing GPU requirements and total cost of ownership (TCO).

To broaden its market reach, Broadcom also released vSphere Express, a low-cost virtualization platform based on vSphere 9 designed for environments with up to 128 processor cores. The product includes essential features like live virtual machine migration, high availability, and distributed networking but omits the complex components of larger private cloud platforms. Shenoy stated that many customers need only basic virtualization without purchasing expensive comprehensive platforms, and vSphere Express provides an upgrade path as their requirements grow. While specific pricing was not disclosed, officials indicated it would be substantially less expensive than higher-end offerings.

Other updates include native S3-compatible object storage through VMware vSAN to support AI applications dependent on unstructured data, as well as integrated disaster recovery and confidential computing support within the core VCF. These moves reflect Broadcom’s strategic intent to position VCF as a unified infrastructure platform for traditional virtual machines, containers, and increasingly demanding AI workloads.