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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.

Ethersphere, the development organization behind the decentralized storage network Swarm, recently fell victim to a software supply chain attack. Attackers exploited vulnerabilities in the project's GitHub release process to publish a malicious JavaScript package, bee-js 13.3.0, on NPM, and released a compromised version of the desktop application, Swarm Desktop 0.56.2. These malicious versions are capable of stealing node wallet keys and installing remote control backdoors on users' computers.

According to an investigation by security firm Socket, project maintainers and automated release accounts were likely compromised, though the initial method of credential leakage remains unclear. The attackers used stolen CI credentials to publish malicious versions via the compromised bee-worker account on GitHub. Because the original automated release process did not verify whether code came from protected branches and skipped tests when publishing new versions via GitHub, the attackers were able to use official GitHub Actions to push the malicious packages to NPM.

Technically, the attackers added a postinstall script to bee-js 13.3.0 that downloads and executes JavaScript from an external server during installation. As long as the package manager allows execution of install scripts, the attack can trigger even if developers do not directly use bee-js functionality. Reports indicate that this malicious loader executed three times within Ethersphere's own NPM release jobs.

Swarm Desktop 0.56.2 was embedded with malicious code that automatically connects to an attacker-controlled server to download and execute remote JavaScript every time the application starts. Subsequent malware attempts to read Swarm node wallet key files and configuration files storing unlock passwords, sending them to the attackers. It also installs backdoors depending on the operating system: the Windows version abuses the legitimate tool ScreenConnect for remote access, while macOS and Linux versions implant Go-language backdoors that retrieve control server addresses via Ethereum smart contracts. Related contracts share infrastructure with previous SleepyDuck attack campaigns.

Notably, the malicious bee-js was published through the official NPM release process with valid package provenance. However, this proof only verifies the build source, not the security of the code itself. The macOS version of Swarm Desktop was also signed with project certificates and passed Apple notarization, meaning some Windows and Apple Silicon Mac users may have received compromised versions via automatic updates.

Currently, bee-js 13.3.0 was removed from NPM approximately 75 minutes after being published, and Swarm Desktop 0.56.2 was withdrawn about 8.5 hours after its public release. Ethersphere has released a clean version, Swarm Desktop 0.56.3, and recommends bee-js users switch to version 13.2.0. Users who executed the malicious software should immediately check their computers for backdoors and rotate any potentially leaked keys and credentials.

In October 2026, insider buying activity in Asian markets reached its highest level since October 2018, involving approximately US$2.9 billion. Michael Fritzell, author of the investment blog Asian Century Stocks, citing data from Smart Insider, noted that this round of buying occurred after recent declines in share prices. Bullish sentiment is present across all major global regions except the United States, with South Korea, Thailand, Malaysia, and Hong Kong being particularly prominent. In South Korea, the ratio of insider buys to sells over the past month has rebounded to nearly 5 times, while in Thailand it is approximately 4 times.

Why should investors pay attention to insider buying? Fritzell cites academic research indicating that stocks with insider buying tend to outperform the broader market by 6%-10% over the long term, but he simultaneously marks the boundaries of this effect: it varies by market and time period, is stronger in smaller companies, and largely disappears for routine purchases. He explicitly reminds readers that this is a statistical tendency rather than a prediction for individual stocks.

The article presents the first of five insider buying cases—Bank Rakyat Indonesia (BBRI.JK, market capitalization approximately US$23 billion); the remaining four are behind a paywall. BBRI is a state-owned bank in Indonesia, and its business characteristics constitute a signal case worth deconstructing.

BBRI’s profitability is exceptionally outstanding among global banks currently. Its return on equity (ROE) is 19%, and its equity-to-assets ratio is 14%. The net interest margin reached 7.7% in the first half of 2026. The sources of this high margin include Indonesia’s structurally high inflation and interest rates, which allow the bank to lend at higher interest rates while paying almost no interest to depositors. Approximately 43% of the loan portfolio is concentrated in microloans, supported by a vast branch network that forms a competitive barrier. Some microloans are secured, some are unsecured, and some benefit from partial credit guarantees provided by the government.

However, there are concerns regarding the quality of growth. Recent loan growth of 16% and net profit growth of 17% include some contribution from lower-margin corporate loans rather than microloans. Credit costs of approximately 3%, while normal for the microloan business, still constitute a practical drag. Fritzell also points out that BBRI benefited from the recent rate-hike cycle—deposits carry almost no cost, while lending rates are priced at a spread above the policy rate—this structural advantage may not be sustainable in the long term.

The core of the signal lies in timing. In October 2026, senior officer Alexander Yunimar Situmorang purchased US$164,000 worth of BBRI shares, precisely occurring after MSCI threatened to downgrade Indonesia to Standalone Market status (with the next decision date scheduled for June 2027). Fritzell considers this a typical post-panic buy scenario: the market sold off due to the downgrade threat, and insiders subsequently entered. Currently, BBRI’s stock trades at a trailing price-to-earnings (P/E) ratio of 6.9x, a dividend yield of 12%, and a price-to-book (P/B) ratio of 1.3x, while its historical ROE has been in the 20%-30% range.

Artificial intelligence firm Anthropic has withdrawn its indicative initial public offering (IPO) price range of $70 to $80 per share, according to reports from The Wall Street Journal, Reuters, and The Information. This move comes ahead of its planned Nasdaq debut under the ticker "ANT" on November 19 or 20, following demands from some institutional investors to keep the valuation below $1.5 trillion, significantly lower than the approximately $2 trillion discussed earlier this year.

Emails obtained by Reuters show that late on November 9 Eastern Time, Anthropic sent a document to underwriters proposing to sell about 867 million shares at $70 to $80 each, aiming to raise up to $69.3 billion. A subsequent email stated that the "price details were incorrect" and that the company would update the filing. As of November 9, the latest version on the U.S. Securities and Exchange Commission (SEC) EDGAR system remained the amended prospectus from November 2, with price and share count fields left blank. Anthropic declined to comment.

The resistance to a high valuation reflects broader market concerns regarding AI sector volatility. Bloomberg previously reported that potential investors had discussed valuations dropping to the $1.2 trillion to $1.3 trillion range. The Wall Street Journal noted that investor worries include tech market fluctuations, increased exposure to AI investment risks, political opposition to new data centers, and uncertainty about when major cloud providers will see returns on AI spending. Some investors reportedly prefer Chief Financial Officer Krishna Rao to adopt a conservative pricing strategy to leave room for first-day gains.

Despite the pressure, Anthropic is attempting to stick to its original timeline. However, reports suggest that failing to agree on satisfactory pricing could force a delay, which carries the risk of triggering market panic. This episode highlights how capital markets are reassessing profitability paths and macro risks even for leading AI companies, directly impacting their financing terms.

Google released the Chrome 157 stable channel update on November 9, addressing nine security vulnerabilities. Seven were rated as high severity and two as medium severity. These flaws existed in core browser components including WebGL, GPU, V8, XML, and FileSystem.

Notably, the vulnerability identified as CVE-2026-125350 has been confirmed to be used in actual attacks ("in the wild"). This is a Use After Free flaw located in the XML component and was rated as high severity. Although Google did not disclose specific details on how the vulnerability is being exploited, the company strongly urged users to apply the update promptly due to its active exploitation.

The update also addressed other types of vulnerabilities, including three uninitialized resource flaws, two additional Use After Free flaws, and single instances of incorrect authorization, memory buffer overflow, out-of-bounds write, and type confusion.

Users on different operating systems should update to the following versions to patch these weaknesses: Windows and macOS users should update to 157.0.8103.50 or 157.0.8103.51; Linux and Android users should update to 157.0.8103.50.

Anthropic announced on November 9, 2026, that a team of 1,000 agents running an early version of Claude Fable 5.2 successfully reproduced the formal proof of Fermat's Last Theorem. Unlike the initial achievement in September, which relied on the specialized platform Prove2Me and direct researcher guidance, this experiment used only general-purpose tools like Git and a shared PostgreSQL database, with no human intervention during the process.

According to Anthropic, the experiment took 28.7 hours, consumed one-third of the output tokens used in the September baseline, and generated Lean code one-seventh the length of the original proof. The September formalization required a researcher and dozens of agents over 11 days, consuming approximately 6 billion output tokens.

The agents coordinated through a shared database: a 'lead' agent split the problem and assigned tasks to 999 'helper' agents. Helpers used database tables to track progress and claim subtasks, submitting code via Git for verification by a checker. This self-organization using general tools suggests that multi-agent systems handling complex, long-horizon problems may not strictly require domain-specific scaffolding platforms.

It is important to note that this remains a controlled 'toy experiment' reported by the vendor, without independent third-party reproduction. Anthropic acknowledges that while general tools were sufficient, specialized platforms like Prove2Me might still offer efficiency advantages in compilation speed and reuse of human results. The company also emphasized safety boundaries, noting all agents ran in network-isolated sandboxes under real-time monitoring.

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.

South Korean physical AI data company Ndotlight has announced that it is supplying KT, the country's leading telecommunications operator, with 3D simulation data for flexible objects such as cables. This partnership aims to address a key bottleneck in industrial robot deployment: accurately simulating the bending and stretching properties of cables in virtual spaces so that robots can practice and validate tasks before entering actual work sites.

Ndotlight generates these "SimReady" assets using its self-developed CAD platform, TRINIX. The platform handles the entire process from 3D model creation to inputting physical attributes like weight and elasticity, as well as joint information. The output conforms to the OpenUSD standard, allowing direct integration into Nvidia's Isaac Sim robot simulation environment for building and verifying robot operation scenarios.

The announcement was made during "Nvidia AI Day Seoul 2026." Ndotlight CTO Kim Seon-tae presented the case study with KT and showcased "DexBench," a robot hand manipulation evaluation project involving Ndotlight, Nvidia, and RealWorld. Ndotlight stated plans to contribute its data to Nvidia's "SimReady Central" repository to expand into global markets.

In an official blog post released on November 9, Anthropic provided a clear starting recommendation: most coding and knowledge work should begin with Opus 5.5 at its default effort level. This is not because Opus 5.5 is the strongest model—Fable 5.2 holds that title—but because it sits at the optimal balance point between cost and capability. Opus 5.5 costs $4 per million input tokens and $20 per million output tokens, exactly half the price of Fable 5.2 ($8 and $40 respectively).

However, what makes this blog truly noteworthy is not the starting recommendation, but the adjustment sequence it proposes. Author Addy Osmani writes that you should adjust the effort level before switching models. Effort is a finer dial than model switching, simultaneously controlling changes in both cost and capability. Beyond the high setting, moving up one more effort level may cost as much as switching to a larger model. Conversely, dropping down one level might yield the results you need at a lower rate.

This advice is backed by specific benchmark data. On Anthropic's self-tested Terminal-Bench 3.0, the previous generation Fable 5.1 scored 58.0% at max effort, while Opus 5.5 scored 58.9% at high effort using only half the number of tokens. On FrontierCode 2.0, Fable 5.2 scored 45.3% at high effort, essentially matching Opus 5.5's 45.5% at max effort, but at roughly half the cost per task. These figures demonstrate that the relationship between cost and capability is non-linear at the task level: cheaper models at higher effort levels can catch up to or even surpass more expensive models.

On SWE-bench Pro, Opus 5.5 improves by approximately 2.5 points when moving from medium to high effort, with costs increasing by about 43% (rising from 70% of the high-effort baseline to 100%). Moving from low to medium effort yields an improvement of about 5.5 points, at roughly double the cost of low effort (rising from one-third of the high-effort baseline to 70%). This implies that if you are currently bottlenecked at medium effort, trying high effort is often more cost-effective than jumping directly to Fable 5.2. You should only consider upgrading to Fable 5.2 when Opus 5.5 at high effort remains insufficient.

For scenarios requiring the mixed use of multiple models, the blog offers a core principle: place sub-agents on cheaper models, while keeping judgment calls and code editing on the main model. Sub-agents are suitable for tasks like searching files, running tests, and reading logs—errors here are easy to spot, and judgmental decisions are inherently made by the main model. When the model proves insufficient, Osmani suggests executing steps in order: first confirm whether the model can self-verify (via tests, builds, or scripts calling endpoints), then raise the effort level by one step, and finally switch to a larger model starting at a lower effort level. On an internal defect-search benchmark, Opus 5.5 acting as the main model orchestrated up to 25 Sonnet 5.5 workers to process approximately 22 million tokens of source code. This approach cost about one-third less than running Opus 5.5 alone, with scores remaining essentially flat.

South Korean artificial intelligence foundation model startup Motif Technologies announced on November 9 that it has completed a bridge funding round to finance the development of its next-generation large language model, "Motif 4." According to Platum, the round includes participation from existing investors and chip giant NVIDIA, though the specific amount remains undisclosed. A bridge round typically refers to interim financing secured before a formal subsequent major funding stage to sustain operations or advance critical projects.

"Motif 4" is the successor to "Motif 3," released by the company in August 2026, and is scheduled to be published as an open-weight model in the first quarter of 2027. Open weights mean the model parameters will be publicly available, allowing developers to download, use, and modify them. Motif Technologies has set an ambitious goal for "Motif 4" to enter the top five global models.

Previously, "Motif 3" was developed under the Ministry of Science and ICT (MSIT)'s "Independent AI Foundation Model" project in South Korea. According to Artificial Analysis data, "Motif 3" scored 47 points on the composite intelligence index, ranking 10th among all AI models globally and 4th among all open-weight models. Excluding US and Chinese enterprise models, it held the highest rank. The company stated that by adopting proprietary architectures such as Group Differential Latent Attention (GDLA), it completed the development of "Motif 3" using only 768 GPUs over five months.

However, due to time and resource constraints, "Motif 3" did not undergo sufficient post-training steps like reinforcement learning (RL). Post-training is a crucial phase where pre-trained models are refined for practical usability and human alignment. Motif Technologies plans to utilize the newly secured funds to significantly strengthen the post-training of "Motif 4," aiming to enhance its utility and commercial competitiveness.

Im Jung-hwan, CEO of Motif Technologies and a PhD in mathematics from Oxford University, stated: "In the fiercely competitive global model landscape, survival requires differentiation at every stage, from architecture to post-training." He acknowledged that "Motif 3" had regrets regarding insufficient post-training due to resource limitations and promised that "Motif 4" would deliver results combining technical excellence with superior usability and commercial viability.

At Cisco Live APJC in Melbourne on November 9, 2026, Cisco Systems Inc. released a new research report titled "Permission to Scale: The Critical Role of Trust in Successful AI Adoption." Authored by independent researcher Brad Howarth, the study draws on data from 25 Australian organizations.

The core finding is that while the conversation about enterprise artificial intelligence has long focused on capability—which model is smartest or which GPU is fastest—this constraint has shifted. AI can now do far more than most organizations will allow, and the gap is trust.

Carl Solder, CTO of Cisco ANZ, noted that CIOs and CTOs often gravitate toward infrastructure and data, neglecting the culture pillar. He described a customer who invested heavily in technology but ignored culture, resulting in significant pushback from employees and customers against the toolsets.

The report introduces the concept of "calibrated trust," where users know when to rely on AI, when to verify outputs, and when a human must decide. Anna Tosky, Chief People Officer at Elmo Software, highlighted the risk of "AI slop," where employees send unchecked, erroneous AI-generated reports.

To address this, the report recommends a specific practice for IT leaders: stop treating log-ins as a proxy for trust and instead track how often people challenge or override AI outputs. Usage shows people are clicking; override rates show they are thinking. Leadership buy-in is also crucial, exemplified by Flight Center Travel Group's founder using AI daily to give staff permission to experiment.

Although the sample size is small, the findings align with broader industry discussions, suggesting that the biggest barrier to scaling AI is not technical but people-related.

NRG Energy, Inc. (NYSE: NRG), a US provider of electricity, natural gas, and smart home solutions, announced its financial results for the third quarter ended September 30, 2026, on November 9, 2026. The company reported GAAP Net Income of $561 million and basic GAAP EPS of $2.59. Non-GAAP metrics included Adjusted Net Income of $983 million, Adjusted EPS of $4.68, and Adjusted EBITDA of $2,013 million.

The significant year-over-year improvement in profitability was driven by contributions from the portfolio acquired from LS Power, higher realized capacity prices in the East, and favorable weather impacts in Texas. These benefits were partially offset by increased depreciation, amortization, and interest expenses associated with the acquired assets and related financing.

In addition to reaffirming its 2026 guidance, NRG initiated its 2027 financial guidance for the first time. The 2027 Adjusted EPS range is set at $8.05 to $10.05, with Adjusted EBITDA guided between $5.5 billion and $6.0 billion. It is noted that the current 2027 guidance does not reflect the expected impacts of the previously announced 4.9 GW asset sale.

The company also updated its capital allocation plan. Beyond the 2026 plan of $1 billion in share repurchases and approximately $407 million in dividends, NRG announced a 2027 base plan including $1 billion in debt reduction, $1 billion in share repurchases, and an 8% increase in the annual common dividend to $2.05 per share. Proceeds from the upcoming asset sale are expected to support a pro forma $3 billion share repurchase program across 2026 and 2027.