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Australian security firm Apate has developed an AI-based system to combat the rising number of phone scams. According to German tech media outlet t3n, the system deploys approximately 350,000 AI bots that specifically answer calls from scammers or respond to suspicious messages via instant messaging apps.

The core strategy of these bots is "time-wasting." They mimic various personalities and behavioral patterns, giving scammers false hope that they are about to succeed or that the target is vulnerable, thereby inducing them to invest more time. Dali Kafaar, CEO of Apate, told Wired magazine: "We hope we have built the perfect victims for scammers. Every minute a scammer spends talking to a bot is a minute that potentially protects hundreds, if not thousands, of people from being contacted by the same person."

Beyond consuming scammers' time, these AI agents also infiltrate online scam groups to collect information about criminals and their bank accounts. Kafaar stated that the tools have already gathered over 250,000 pieces of intelligence regarding fraudulent activities and responsible individuals. However, facing the global volume of approximately 68 billion scam calls per year (data from Truecaller), the scale of 350,000 bots remains negligible, and the current primary function is disruption and intelligence gathering rather than completely stopping scam operations.

According to Zhidongxi, Alibaba's AI music creation platform "HappyShrimp" officially launched its standalone APP version on November 13, 2026, simultaneously arriving on iOS and Android platforms. Previously available primarily as a web-based service, this mobile rollout marks a significant step toward broader consumer adoption.

Zhidongxi's hands-on review indicates that the HappyShrimp app allows users to input natural language descriptions of emotions, stories, or musical styles, after which the AI automatically handles lyric writing, composition, arrangement, and vocal performance. The report notes that generating a song approximately three minutes long takes only about one minute. The app reportedly understands complex prompts accurately and can incorporate current events into its creative output, such as generating a farewell song with specific emotional progressions based on news topics.

Beyond original creation, the app supports uploading custom lyrics and specifying reference musical styles for regeneration. The new version also introduces a "Make Same Style" feature, allowing users to discover works in an immersive feed, reuse their creative logic with one click, and substitute personal parameters (such as MBTI personality types) to quickly generate personalized versions. Currently, the platform has generated trending song charts based on user-shared content.

Menzie Chinn, Professor of Economics at the University of Wisconsin–Madison, published a brief post on Econbrowser reporting that the 10-year minus 3-month government bond term spread rose in October 2026 for both France and Italy, while the UK spread increased during September. The article cites OECD data to illustrate the term spread trends across five countries—Canada, France, Germany, Italy, and the UK—and highlights a methodological issue: business cycle analysis typically assumes constant or near-zero default risk; however, this assumption is implausible for France and Italy, as evidenced by five-year credit default swap (CDS) prices indicating significant sovereign default risk.

Chinn further notes that historically, the term spread has served as a more effective predictor of recessions for Canada and Germany (see Chinn and Ferrara, 2026). This implies that when investors compare yield curve shapes across different sovereign nations, the French-Italian spread likely contains a credit risk component, whereas the Canada-Germany spread more closely reflects pure interest rate expectations. Consequently, a narrowing or flattening of the French-Italian spread cannot be simply equated with the economic outlook signals implied by similar movements in the Canada-Germany spread.

However, Chinn does not quantify the proportion of spread changes attributable to default risk, nor does he propose a corrected spread metric adjusted for credit risk; his argument serves as a qualitative methodological reminder rather than an actionable alternative model.

US-based artificial intelligence company OpenAI officially launched two new products under its Daybreak cybersecurity platform during the 'Cyber at the Frontier' event on November 12. The tools, named Daybreak Defense Factory and Daybreak Red Team, aim to help enterprises accelerate their defense and remediation speeds in response to AI-accelerated attacks. Both services are currently in early trial phases, with no pricing or general availability timeline announced.

Daybreak Defense Factory focuses on automating the patching process. Composed of AI agents built on OpenAI's latest cybersecurity model, GPT-6 Cyber, it scans code, identifies vulnerabilities, and automatically generates fixes. Crucially, these fixes are not deployed directly to production environments; instead, they are submitted to human engineers as 'merge requests' accompanied by validation tests. This design maintains human oversight, ensuring AI-generated patches are confirmed before going live.

Daybreak Red Team specializes in automated red team exercises. The tool uses OpenAI's models to perform continuous scanning and attack path simulation on enterprise web applications. It references source code and application context to investigate potential attack vectors, validates issues reported by other code scanning tools, bug bounty programs, and vulnerability disclosures, and tests the effectiveness of subsequent patches. Nancy Wang, who leads AI applications for cybersecurity at OpenAI, described it as a penetration test at the scale of automated continuous scanning, surpassing human capabilities.

OpenAI President Greg Brockman stated during the event that an enterprise's security level depends on the capabilities of the top-tier models used, requiring cybersecurity practices to be reoriented around what these models can do. He emphasized that while fundamentals remain important, the products themselves are equally critical. For enterprises facing AI-driven threats, the ability to use equivalent-grade tools to shorten the time from vulnerability discovery to remediation is a key metric for evaluating such products.

Nvidia’s board says it is too early to discuss who will succeed Jensen Huang. GeekWire editors Todd Bishop and John Cook clearly do not intend to follow this advice. In a podcast recorded on November 13 and an article published on November 14, they offered a clear judgment: what Nvidia needs now is not another technical genius, but a crisis manager capable of steering the company through turbulence.

The analogy for this argument draws on Alan Mulally’s move from Boeing to Ford, and Dara Khosrowshahi taking over Uber after Travis Kalanick was ousted. Cook stated in the podcast that Nvidia needs someone who can lead the company through chaos, not just a great technology expert. This judgment rests on a background fact: Jensen Huang has been resistant to succession planning. Arm CEO Rene Haas quoted Huang in a 2024 interview as saying, 'He didn’t like the idea; he believed it would create competition among executives and cause division.'

Huang himself said in an October 14 Bloomberg interview that he hoped his successor was already inside the company. However, the board stated it would consider both internal and external candidates, and its Friday statement indicated it was still selecting a search firm and had not formally engaged any candidates. The article lists 11 mentioned candidates spanning chip design, cloud computing, consumer electronics, and storage. Besides Haas, who had previously been reported by the Financial Times, the list includes AWS CEO Matt Garman, Amazon’s head of chips and AI Peter DeSantis, AMD CEO Lisa Su, Google Cloud CEO Thomas Kurian, Micron CEO Sanjay Mehrotra, Apple Executive Chair Tim Cook, and several Microsoft executives.

Several newly disclosed facts in the article are worth noting. Nick Parker is a veteran who spent 26 years in Microsoft’s sales system; he joined Nvidia in August this year as Executive Vice President of Global Field Operations, with a compensation package valued at over $40 million. Peter DeSantis currently oversees Amazon’s AI models, custom chips, and quantum computing business, reporting directly to CEO Andy Jassy. Dave Treadwell worked at Microsoft for 27 years before joining Amazon in 2016, and took over AWS compute and machine learning services in August this year.

These personnel changes themselves constitute a facet of the competitive landscape in the AI industry. The article points out that whoever ultimately succeeds will face a market where customers are trying to reduce their reliance on Nvidia. In his annual shareholder letter in April, Jassy wrote that if Amazon’s chip business sold to AWS and third parties like other chip companies, its annual revenue run rate could reach approximately $50 billion. DeSantis also told Bloomberg in June that Amazon was negotiating to sell Trainium AI chips to other companies.

Haas’s case involves a notable complex relationship. He served as Vice President of Sales at Tensilica, whose co-founder Harvey Jones was appointed as Nvidia’s first board chairman this week. Meanwhile, SoftBank appointed Haas as CEO of SoftBank International in April to coordinate its international chip and AI portfolio, which includes Project Izanagi, an initiative aimed at competing with Nvidia. The article lists this as a 'complicating factor' in Haas’s candidacy, rather than a confirmed conflict of interest.

When Anthropic released Claude Fable 5.2 on November 9, it set its standard API price at $8 per million uncached input tokens and $40 per million output tokens, which is lower than OpenAI's GPT-6 Astra ($10/$50). This price cut was widely promoted as a cost advantage for Fable 5.2. However, The New Stack technical author Jessica Wachtel conducted independent tests using three coding tasks and found that a lower list price does not necessarily mean lower actual spending.

Wachtel ran each model once via the respective vendor APIs with default settings to test three tasks simulating real development work: fixing 10 planted bugs in a 2,600-line billing codebase, reviewing an EU invoicing package for 45 manually planted defects, and writing validation code from scratch for VAT IDs in 27 countries and IBANs in 34 countries. Each task was scored out of 50 by an automated script.

In the bug-fixing task, both models fixed all 10 bugs and passed all 50 hidden checks, each requiring 12 conversation turns. However, the processes differed significantly: Fable 5.2 consumed over 17,000 reasoning tokens, while Astra used only 819. Fable 5.2 cost approximately $2.51 and took 326 seconds, whereas Astra cost about $0.98 and took 122 seconds. For the same score, Fable 5.2 cost about 2.6 times more than Astra.

Code review was the only task where scores differed. Fable 5.2 scored 49, while Astra scored 45. Fable 5.2 identified 44 of the 45 defects with no false positives, while Astra identified 41 and incorrectly classified one Spanish ID as valid. However, Fable 5.2 spent $6.05 and 993 seconds to achieve this, compared to Astra's $0.95 and 227 seconds. Fable 5.2's extra 4 points came at a cost exceeding six times that of Astra and four times the time.

In the code-writing task, both models achieved a perfect score of 50, yet Fable 5.2 still cost 2.3 times as much as Astra. Across all three tasks, Fable 5.2's total cost was approximately $13.39, while Astra's was about $3.99—despite Fable 5.2's lower unit price, the total cost was roughly 3.3 times higher. Wachtel noted that Fable 5.2 consumed about five times as many tokens as Astra across these three runs.

These results corroborate OpenAI President Greg Brockman's statement at the Astra launch that price per task is key. Wachtel concluded that for such tasks, she would start with Astra, which achieved the same scores on bug fixing and code writing at about 41% of the cost; she would only consider Fable 5.2 when careful code review was needed, as it found three additional defects in that specific instance. She also acknowledged that both models missed some defects and that each model was run only once, so the four-point gap could shift in another run.

Chinese AI company DeepSeek announced on November 14, 2026, that it has officially opened its "full suite" application APIs. Features previously available only within its official App—such as web search, deep thinking, file upload, link reading, and newly added voice recognition (ASR) and speech synthesis (TTS)—are now provided as independent interfaces. Developers can combine these capabilities as needed and are billed separately for each service.

The key update lies in the comprehensive opening of multimodal capabilities. Although DeepSeek launched Search and Fetch interfaces in October, developers previously had to write custom code to orchestrate model decisions, search execution, and content summarization. Now, by simply enabling web search, the model automatically handles search timing, content retrieval, and result integration. Additionally, voice capabilities now have official independent interfaces; previously, developers building voice-enabled assistants typically needed to integrate third-party services. Currently, the V4.1-Flash model supports these extended functions across Messages, Responses, and Chat Completions interfaces.

In terms of pricing, DeepSeek has adopted an aggressive strategy to drive adoption. Voice recognition is discounted by 50% to 0.39 yuan per hour (from 0.78 yuan), and speech synthesis is also halved to 1.5 yuan per 10,000 characters (from 3 yuan). For comparison, OpenAI's GPT-Transcribe costs approximately 1.81 RMB per hour of audio, while Google Cloud's standard speech service reaches 6.43 RMB. Web search and fetching remain at 5 yuan per 1,000 successful calls, and Optical Character Recognition (OCR) is priced at 1.5 yuan per 1,000 pages.

DeepSeek has also open-sourced a lightweight chat frontend Demo that supports importing historical conversations, aiming to help developers quickly build products with functionality close to the DeepSeek App. The company stated that developers can even "replicate the complete DeepSeek App 1:1." This move indicates DeepSeek's attempt to attract developers to build next-generation AI applications on its platform by lowering basic tool invocation costs and providing a complete technology stack, rather than relying solely on its own consumer-facing chat product.

South Korean securities IT firm DefiSoft has officially launched its market analysis and investment platform, "Paave 2.0," in the Vietnamese market. The core innovation of this version allows users to build their own AI agents capable of automatically analyzing real-time quotes, financial statements, and public disclosures from the Ho Chi Minh City Stock Exchange (HOSE), Hanoi Stock Exchange (HNX), and UPCoM, and executing simulated stock trades using virtual funds.

Through the "Agent Studio" feature, users can define analytical perspectives and prompts to create researcher agents that write market reports or investor agents that execute buy/sell orders. All simulated trading results generated by these AI agents are recorded on the same leaderboard as human users, with trading logs made public to the community. This mechanism aims to lower the barrier for individual investors to use AI tools for market analysis through gamified competition.

Currently, Paave 2.0 supports only simulated trading with virtual funds, but DefiSoft plans to integrate real-money trading within the year, enabling users to place orders via their own accounts through partnerships with local Vietnamese brokers. Additionally, the platform includes a conversational analysis tool called "Paave AI" and supports connecting external large language models like Claude or ChatGPT via the MCP protocol to query market data directly.

NVIDIA has introduced two new algorithms, Ozaki-II and 2M, in the latest release of its CUDA Toolkit 13.4.2 for the cuBLAS math library. These technologies aim to resolve a long-standing contradiction in high-performance computing: how to leverage the faster integer Tensor Cores—designed primarily for low-precision tasks—while maintaining the full accuracy of double-precision floating-point (FP64) calculations. This update allows scientific simulations dependent on high-precision dense general matrix multiplications (GEMM), such as those in molecular chemistry and materials science, to automatically gain speed without requiring any changes to application code.

Traditionally, GPU FP64 units offer high precision but limited throughput, while Tensor Cores provide extreme speed for lower-precision data. The new cuBLAS methods employ "floating-point emulation" techniques, decomposing each double-precision number into multiple integer segments, performing exact integer multiplications on the Tensor Cores, and reassembling the result to meet strict FP64 standards. Specifically, the Ozaki-II algorithm utilizes the Chinese Remainder Theorem (CRT) to significantly reduce the number of required integer multiplications compared to its predecessor, Ozaki-I. Meanwhile, the 2M algorithm optimizes complex matrix multiplication by reducing the operation from four real multiplications to two, thereby lowering computational overhead.

According to NVIDIA's official technical blog, these algorithms demonstrate substantial performance improvements in real-world applications. In quantum chemistry, DGEMM (real double-precision matrix multiplication) using Ozaki-II runs 5.5x faster than native FP64 on NVIDIA VR NVL72 systems and 3.9x faster on GB200. For Coupled Cluster Singles and Doubles (CCSD) calculations—considered the "gold standard" in quantum chemistry—the end-to-end speedup reaches 2x on VR NVL72 and 1.4x on GB200. When employing mixed-precision modes, these gains extend to 5x and 3.5x respectively.

In materials science, ZGEMM (complex double-precision matrix multiplication) within the BerkeleyGW software package achieves a 5x speedup on GB200 when using the 2M algorithm. Tests on the BerkeleyGW Epsilon module show an end-to-end speedup of 6.6x on VR NVL72 and 3.9x on GB200. Notably, all tests verified that results remain within FP64 accuracy bounds, and users do not need to manually adjust APIs or rewrite kernels, as cuBLAS automatically selects the optimal path.

Furthermore, the upcoming NVIDIA Rubin GPU architecture introduces the new TI16 numeric format and the tcgen05.mma instruction, which will further accelerate Ozaki-series computations. Existing users can enable these features simply by downloading and installing the latest CUDA Toolkit. Developers can refer to the floating-point emulation section of the cuBLAS documentation for detailed API control options, such as cublasSetEmulationStrategy(), to fine-tune the balance between precision and performance for specific workloads.

Two major institutional investors in Nvidia—Brad Gerstner of Altimeter Capital and Gavin Baker of Atreides Management—published an open letter on Friday urging the chipmaker's board to pursue a "slow, measured, and deliberate" process for selecting a new CEO, explicitly stating that the option of founder and former CEO Jensen Huang's return should remain on the table.

The firms collectively hold approximately $2.13 billion in Nvidia stock. The letter acknowledges that interim CEO Colette Kress is capable of leading the company but argues that Huang, as the long-time leader, has earned the right to a careful evaluation before any hasty replacement decisions are made. This move serves as a direct counterweight to widespread media reports suggesting the board was preparing to permanently oust Huang.

Huang resigned on November 12 amid allegations involving personal relationships and an investigation into suspected Chinese spy Alice Huang. While the market largely anticipated his permanent departure, this shareholder intervention highlights a growing divide among investors who believe Huang's removal could inadvertently benefit China by weakening Nvidia's strategic advantage in AI hardware. The resulting governance uncertainty adds complexity to Nvidia's leadership transition in the coming weeks.

OpenAI launched "ChatGPT Wallet" on November 13, 2026, for ChatGPT Pro subscribers in the United States. The new feature allows OpenAI's Dots AI agents to complete online shopping tasks on behalf of users. Currently, access is restricted to users aged 18 and older with a US billing address who hold eligible US-issued credit cards from American Express, Chase, Mastercard, or Visa.

According to OpenAI's support page, once a card is added to the wallet, Dots agents can use it to make purchases based on user prompts, but they must check in with the user before checking out. Every purchase made through ChatGPT Wallet requires a security check involving biometric verification, such as Face ID or a fingerprint scan. OpenAI states that this mechanism uses payment tokenization similar to Apple Pay or Google Wallet: actual credit card details are not shared with the agent or the merchant, protecting user privacy.

While the current version mandates user approval for each transaction, OpenAI indicated future plans to allow users to set parameters like spending limits, purchase categories, or time periods, enabling fully delegated pre-approved purchases. However, widespread adoption faces hurdles. Some retailers restrict or block AI agents entirely. OpenAI noted that websites need to ensure agents are not inadvertently blocked, and payment systems must recognize legitimate agent-initiated purchases. Amazon previously blocked Meta's Muse agents, highlighting the cautious stance of major e-commerce platforms toward agentic shopping.

According to TechRadar, Russia's communications watchdog, Roskomnadzor, has reportedly officially deployed machine learning filters to choke virtual private networks (VPNs) across the country. This move marks a sharp escalation in the Kremlin's digital barrier. The state censor poured 2.27 billion rubles into the project, according to government digital transformation documents reviewed by Forbes Russia and reported by The Moscow Times.

Unlike previous attempts to decrypt user content, the newly deployed AI tool is a predictive model that searches for behavioral fingerprints in connection metadata: packet sizes, transmission intervals, session lengths, and hosting server patterns. Dmitry Kuteynikov, an expert at digital rights organization RKS Global, explained that the tool helps staff decide what to block and warned that as the model comes into use, blocks are likely to come faster, making VPNs less reliable.

A developer working for Russian provider Amnezia VPN confirmed that this kind of model could affect a wide range of protocols because it targets the general characteristics of VPN traffic rather than any single one. While obfuscation hides a protocol’s signature, the traffic pattern of a tunnel stays visible, which is exactly what a behavioral model looks for.

The automated filtering is already causing disruption across Russian business networks. Although Russian law technically exempts internal corporate VPNs, enterprise connections look nearly identical to circumvention tunnels under automated inspection. As reported by Kommersant, telecom sources noted that traffic analysis algorithms misclassify traffic in 5% to 6% of cases, causing unrelated websites and corporate cloud applications to collapse when entire hosting subnets are blacklisted.

Earlier roadmaps showed that Russia drafted plans for AI that could block 95 percent of VPNs. While official targets were quietly revised, funding continues to surge. The Kremlin is preparing to double its internet censorship budget in coming years, with another 10.2 billion rubles allocated to upgrade deep packet inspection boxes in 2026 alone.