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Texas Governor-elect Gina Hinojosa has warned artificial intelligence companies that the era of unchecked data center expansion in the state is over. In an interview with CNN, Hinojosa stated that data centers have moved into rural communities without rules or laws to protect Texans, asserting that "Texans are being taken advantage of."

She pledged to introduce legislation to reverse this trend, calling for a pause on new data center developments until protections are in place. "We need to stop what we are doing with data centers until we have those protections," Hinojosa said. She emphasized that communities must have a seat at the table to decide if and how these facilities enter their areas. "It is not that I hate data centers. I hate the way they are taking advantage of Texas, and that stops when I'm Governor."

Texas has long been one of the most data center-friendly locations in the US, but recent polls show 75% of respondents oppose building such facilities near their homes. With Democratic candidates winning key races in the mid-term elections, including Hinojosa's victory for governor, there is growing political momentum against rapid AI infrastructure growth.

This shift could impact plans for major AI firms like Anthropic, whose recent filings noted that delays or failures in their complex data center supply chains could significantly affect the company.

According to MIT Technology Review, former California State Senator Scott Wiener has won the congressional seat vacated by former House Speaker Nancy Pelosi and intends to make federal artificial intelligence regulation a cornerstone of his political career. Wiener previously authored SB 1047 in California (vetoed by Governor Newsom) and sponsored the lighter-touch SB 53, both of which have inspired proposed federal legislation.

Wiener expressed concern following a recent METR report widely interpreted as evidence that Anthropic may have achieved 'recursive self-improvement,' stating that time to regulate AI is running out. He advocates for decisive Congressional action, including establishing a strong, empowered federal regulator, implementing whistleblower protections, requiring testing and auditing, and mandating 'kill switches' for advanced AI models.

Although Wiener's campaign received over $3 million from Public First Action, a nonprofit funded substantially by Anthropic, leading to criticism about being too close to the industry, Wiener rejected these claims. He stated he would be eager to serve on a potential Congressional select committee on AI and opposes broad federal preemption of state AI laws, arguing states should remain 'laboratories' capable of nimble regulation.

US-based certificate management company DigiCert confirmed on November 5 that a data breach occurred due to a configuration error during a server migration in February 2025. This error inadvertently exposed an internal business system, originally accessible only via VPN, to the internet. Attackers exploited an authorization control vulnerability to create unauthorized accounts and gain read-only access to archived customer data.

According to notifications sent to affected customers, the leaked information includes names, corporate email addresses, phone numbers, and company addresses. Because some customers had previously provided identity verification documents to complete certificate validation procedures, the breach may also have exposed driver's licenses, passports, utility bills, and other documents containing names and addresses.

In an incident report submitted to Mozilla Bugzilla, DigiCert stated that it first discovered unauthorized accounts in the system in June 2026 but initially believed they could not access customer information because they lacked application assignments. It was only after receiving reports through its security research program that the company confirmed external actors could create new accounts. Further investigation on October 19 confirmed that unauthorized accounts had accessed customer data between June 27 and July 2, 2026.

Once written, files are essentially immutable, whereas data naturally requires modification. Rows need correction, new observations must be recorded, and old records must be deleted. This contradiction is the sole reason for the existence of table formats: since files cannot be modified, changes are expressed by creating new files. Appending a row writes a new file; deleting a row writes a marker file; compacting data writes a batch of new files; even acquiring and releasing table locks result in new files. The cost is that at any given moment, a single table corresponds to multiple files, some of which are not data files but deletion markers or indexes. Consequently, someone must define a set of rules specifying which files are currently valid and what role each plays.

Writing the rules themselves is not difficult. The true difficulty lies in this: when two writers simultaneously wish to submit their respective sets of new files, how does one become the 'current version' while the other fails? Weston Pace refers to this problem as the 'atomic kernel' of table formats and notes that all table formats—regardless of whether they are called timelines, catalogs, or other names—ultimately rely on some mechanism to synchronize concurrent writers. The choice of this mechanism has profound impacts on the performance and complexity of the table format, and it clearly divides the design space into two categories.

The first category is zero-infrastructure atomics. Almost every cloud storage provider offers put-if-not-exists semantics, while traditional file systems generally support rename-if-not-exists. By leveraging these primitives, a version sequence can be built without deploying any additional services: writers attempt to atomically create the metadata file for the next version number; the winner succeeds, and the loser retries. The benefits are obvious—fifty EC2 instances can simultaneously publish data to the same table on S3 without any coordination service. However, the author provides a specific performance ceiling: as of his writing, cloud storage put-if-not-exists operations are limited to approximately ten commits per second. For scenarios involving high-frequency small writes, this becomes a bottleneck.

The second category is centralized coordination. DuckLake uses a Postgres instance as a commit coordinator, synchronizing writers internally via mutexes or CAS (Compare-And-Swap) primitives. Iceberg's REST catalog also falls into this category. This approach potentially allows for higher transaction rates (though the author admits uncertainty on this point) and works across all file systems, including non-mainstream ones that do not support put-if-not-exists or POSIX rename operations. The cost is the need to maintain a long-running service, which increases operational burden and the attack surface for failures.

These two approaches are not a matter of superiority versus inferiority, but rather different applicability scenarios. Tables with read-heavy workloads suffice with zero infrastructure; tables with high-frequency small writes may require centralized coordination; tables with large-batch writes can use either. Lance defaults to zero infrastructure but allows extension to centralized coordination; the standard path for Iceberg is REST-based coordination, but it also provides a zero-infrastructure 'boring catalog'. The author concludes that table formats face numerous subtle technical decisions, each favoring a certain type of workload. The best practice is often to implement multiple paths and use heuristics to automatically select the appropriate one, rather than forcing users to navigate a configuration nightmare.

A recent report by the Electronic Frontier Foundation (EFF) highlights a dramatic collapse in the number of Inter Partes Review (IPR) petitions filed with the Patent Trial and Appeal Board (PTAB), a unit of the U.S. Patent and Trademark Office (USPTO). Established by Congress in 2012, IPR allows the public to challenge improperly granted patents within the Patent Office at a fraction of the cost of federal court litigation. It has been a crucial line of defense against low-quality software patents used by patent trolls. However, USPTO leadership is increasingly refusing to consider well-founded challenges for reasons unrelated to their merits, rendering the process so unreliable that many potential challengers have stopped filing altogether.

According to USPTO trial statistics, only 97 IPR petitions were filed in the third quarter of 2026 (July–September), down from 327 during the same period in 2025—a drop of over 70%. Furthermore, the institution rate (the percentage of petitions accepted for full review) plummeted to 41% in fiscal year 2026, compared to a historical range of 66–68% between fiscal years 2022 and 2024. This means fewer people are attempting challenges, and those who do face significantly lower odds of having their case heard.

Beyond higher barriers to entry, USPTO Director John Squires has engaged in unprecedented retroactive intervention by vacating cases that had already been instituted. The report notes that Squires has vacated at least 29 institution decisions, terminating proceedings before a final written decision on the merits. In one notable example, Revvo Technologies v. Cerebrum Sensor Technologies, the Director reversed two separate institution decisions based on inconsistencies in claim construction arguments between different courts. Such actions destroy the predictability that previously encouraged companies to invest in IPRs as a viable defense strategy.

For the technology sector, the erosion of the IPR system signifies a sharp increase in defensive costs. Startups and software developers previously relied on this administrative process to quickly and cheaply clear invalid patents blocking product development. With this avenue closing, victims are forced into expensive and protracted federal court litigation. The EFF warns that if this trend continues, bad patents will regain their power as weapons for extracting settlements, stifling innovation and freedom to build new technology.

In March 2025, the United States imposed a comprehensive 25% tariff on imports from Canada and Mexico. Subsequently, the aggregate export trends of the two countries to the U.S. moved in opposite directions: over the 12 months ending in April 2026, Canadian goods exports to the United States decreased by approximately 10% compared to the full year of 2024, while Mexican exports increased by about 16%—despite Mexico facing a higher average effective tariff rate. In FEDS Notes released in November 2026, Chris Avalos, Musa Orak, and Avani Pradhan, researchers at the Board of Governors of the Federal Reserve System, posed a core question: Does this aggregate divergence imply that tariffs were effective for Canada but had little impact on Mexico?

Their finding is that beneath the divergent aggregates, there exists a common sectoral pattern in both countries. Within both Canada and Mexico, sectors with higher tariff exposure indeed experienced weaker export performance to the United States following the shock in March 2025. USMCA compliance exemptions and product-specific tariffs on metals and automobiles created significant differences in exposure across various sectors, and these inter-sectoral disparities translated into observable export divergence in both countries.

Specific estimates indicate that a 10 percentage point difference in tariff exposure between sectors corresponds to roughly a 5% reduction in exports in Canada and about a 12% reduction in Mexico. The research team notes that Mexico's larger estimated response may be related to its higher average tariffs and exposure being dispersed across more sectors. However, they simultaneously emphasize that the difference between the estimates for the two countries is not statistically significant; therefore, it cannot be determined that exposed sectors in Mexico are indeed more sensitive to tariffs than those in Canada.

Regarding sectoral distribution, Canada's tariff response is concentrated in steel, aluminum, and copper—the sectors covered by the specific metal tariffs; Mexico's exposure is more dispersed. The original text truncates here, without fully presenting the specific description of Mexico's sectoral distribution.

The study uses a fixed 2024 U.S. import basket to calculate effective tariff rates, reflecting announced tariffs and exemptions as of June 2026, rather than dynamic adjustments in actual trade structures after 2025.

Evaluating whether monetary policy has genuinely improved faces a fundamental difficulty: policymakers cannot choose the economic environment they face. A central banker may confront a severe banking panic, while another presides over a long period of stability. Even if the former makes higher-quality decisions, she may still experience higher inflation and unemployment simply because the shocks were larger and harder to stabilize. This confounding between policy quality and economic environment has long plagued cross-historical comparisons—was the Great Moderation the result of better policy or smaller, milder shocks? Should the early Federal Reserve be judged by the severity of the Great Depression, or by its effectiveness in handling unconventional challenges?

In a paper forthcoming in Econometrica, Regis Barnichon (Senior Research Advisor at the Federal Reserve Bank of San Francisco) and Geert Mesters (Economic Research Advisor at the Federal Reserve Bank of New York) propose a framework to disentangle these two factors. Their method does not require a fully specified structural model but instead uses empirically estimated impulse responses to compare the gap between actual policy paths and the optimal feasible response given specific shocks. This yields two complementary metrics: policy-path correction (the percentage adjustment required in the interest rate path to reach the optimum) and distance to minimum loss (the amount of macroeconomic loss—measured by expected squared deviations in inflation and unemployment—that could have been avoided under optimal policy).

Applying this framework to four periods—the Gold Standard era (1879–1912), the early Federal Reserve (1913–1941), the postwar period (1951–1984), and the post-Volcker era (1990–2019)—yields a core conclusion: meaningful policy improvements occurred only in the last thirty years. The first three periods exhibited large, statistically significant policy-path corrections, indicating systematic reaction functions far from optimal. Only in the post-Volcker era did these corrections shrink substantially and become statistically insignificant, with an average absolute correction of around 30 percent.

Improvements concentrated in two specific channels. Regarding responses to financial shocks: during the early Federal Reserve era, the Fed raised policy rates during banking panics. Because panics simultaneously suppressed inflation, real policy rates rose sharply, making policy contractionary precisely when the economy needed support; optimal policy would have lowered rates. In the post-Volcker era, the Fed already lowers rates following financial shocks, and estimated corrections are small and insignificant, resulting in negligible additional macroeconomic losses compared to the early Fed. Regarding responses to supply shocks: the postwar Fed reacted too weakly to energy price shocks, productivity shocks, and inflation-expectation shocks. Following an inflation-expectation shock, inflation rose steadily while policy rates responded only weakly, causing real interest rates to fall and reinforcing inflationary pressures; the Great Inflation was a reflection of this weak response.

In the field of computer vision, as the number of neural network layers increases, models often face the challenge of being difficult to train. The paper "Deep Residual Learning for Image Recognition" proposed by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun offers an innovative solution to this core pain point. The study explicitly states that "Deeper neural networks are more difficult to train," so the authors introduced a residual learning framework aimed at making it easier to train networks significantly deeper than those previously used.

The core of this method lies in redefining the role of network layers. While traditional networks attempt to directly learn unreferenced functions, residual networks explicitly reconstruct layers to learn residual functions with reference to the input. This mechanism allows the network to be optimized more easily and achieve accuracy gains from significantly increased depth. Empirical evidence shows that these residual networks are not only easy to optimize but also maintain or improve performance even when depth is substantially increased.

Evaluations on the ImageNet dataset show that the authors tested residual networks up to 152 layers deep, which is 8 times the depth of VGG networks but with lower complexity. An ensemble model composed of these residual networks achieved a 3.57% error rate on the ImageNet test set and won first place in the ILSVRC 2015 classification task. Additionally, due to the advantages brought by extremely deep representations, the authors obtained a 28% relative improvement on the COCO object detection dataset and won multiple competitions. For researchers and engineers seeking to understand the foundations of modern deep learning architectures, this paper, submitted in 2015 and formally published in 2016, provides key insights into how structural innovations can break through depth limitations. It is recommended to start with the abstract and methodology sections, focusing on the definition of residual functions and their impact on the optimization process.

In 1984, Warren Buffett delivered his renowned essay "The Superinvestors of Graham-and-Doddsville," aimed at challenging the efficient market hypothesis. He used a thought experiment involving a "national coin-flipping contest" to illustrate that if 225 million people participated in flipping coins, after 20 rounds, 215 individuals would inevitably remain who had guessed correctly every time. These individuals might boast of exceptional skill, but if one discovered that 40 of them came from the same zoo or the same "intellectual village," such success could not be explained by luck alone—it must instead be attributed to a shared intellectual origin. Buffett argued that these long-term outperformers of the market all originated from the value investing framework established by Benjamin Graham and David Dodd, known as "Graham-and-Doddsville."

The core principle shared by these investors is identifying the discrepancy between a company's intrinsic value and its market price. They disregard academic concepts such as beta coefficients, capital asset pricing models, or covariance of security returns, focusing instead on just two variables: price and value. For example, Walter Schloss never attended college, yet he adhered to Graham’s principles by reviewing handbooks and annual reports to find securities trading significantly below the valuation a private owner would assign. He did not worry about whether he bought on a Monday or Thursday, nor did he consider seasonal factors—only whether a business was worth one dollar while he could buy it for 40 cents. This exploitation of the gap between price and value forms the foundational mechanism of value investing.

Another key principle is the "margin of safety." Buffett cited the Washington Post Company, noting that in 1973 its market capitalization was only $80 million, yet its assets could be sold to any of ten potential buyers for no less than $400 million. If the stock price fell further to a $40 million valuation, traditional risk measures (such as beta) would suggest increased risk—but in reality, the lower the purchase price, the higher the potential return, and the lower the actual risk. This is akin to building a bridge designed to carry 30,000 pounds, but only allowing trucks weighing 10,000 pounds to cross. That substantial buffer is the margin of safety, ensuring that even if estimates are off, investors will not suffer permanent loss.

For today’s readers, overwhelmed by information overload and market noise, maintaining rationality remains a universal challenge. Suppose you are evaluating a new technology company whose market sentiment is extremely optimistic, driving its price-to-earnings ratio to 100 times, while your fundamental analysis suggests a fair P/E ratio of only 20 times. Although the stock may continue rising in the short term, the lack of a margin of safety implies massive downside risk if expectations fail. Conversely, if a mature company’s stock price drops to 50% of its liquidation value, and management is trustworthy, even if the market temporarily ignores its worth, this significant divergence between price and value provides a protective margin of safety similar to that described in the text. This scenario is hypothetical, but it reflects the core logic in the original article regarding the relationship between price and value.