Key Takeaways

• The hidden bottleneck in 6G development is shifting from theoretical limits to RF hardware design constraints. The Hidden 6G Bottleneck: RF Hardware Design I…
• Nearly half of scam incidents reported across Southeast Asia remain unresolved, significantly undermining digital trust in the region. Nearly Half of Reported Scam Incidents Go Unre…
• GSMA officials are urging policymakers to unlock the 6 GHz spectrum to maximize mobile ecosystem efficiency and capacity. Mobile ecosystem ready to maximise use of 6 GH…
• A new framework for counterfactual regret minimization accelerates game compilation speeds by a factor of eighty using static dataflow. GPU-CFR: 80x Faster Counterfactual Regret Mini…
• Fundamental dynamical units are now being identified to improve structural inference in perturbation time-series for networked systems. Fundamental Dynamical Units for Physics-Inform…
• Satellite connectivity scaling requires robust interoperability protocols before non-terrestrial networks enter their next growth phase. Scaling satellite connectivity: why interopera…
• Physics-informed conformal prediction methods are embedding PDE consistency directly into uncertainty quantification for neural operators. Physics-Informed Conformal Prediction: Embeddi…

Executive Summary

Today's landscape is defined by critical infrastructure challenges and the rapid evolution of radio frequency technologies required for next-generation networks. Industry analysts note that 6G development faces a specific strategic bottleneck in RF hardware design, moving beyond theoretical modeling to physical implementation realities The Hidden 6G Bottleneck: RF Hardware Design I…. Simultaneously, global regulatory bodies are focused on spectrum allocation, with urgent calls to unlock the 6 GHz band for mobile applications and ensure interoperability for scaling satellite connectivity Mobile ecosystem ready to maximise use of 6 GH… Scaling satellite connectivity: why interopera…. These technical shifts coincide with persistent security concerns; findings indicate that nearly half of reported scam incidents across Southeast Asia remain unresolved, posing a significant threat to digital trust frameworks Nearly Half of Reported Scam Incidents Go Unre…. In parallel, academic and industrial research continues to refine AI capabilities through methods like retrieval-based verification and physics-informed prediction models, ensuring that algorithmic confidence aligns with real-world constraints.

RF Hardware Design Constraints

The transition toward 6G network architecture is revealing that RF hardware design has become a definitive strategic race rather than an optional enhancement. Current industry analyses suggest that the path forward requires resolving specific bottlenecks in radio frequency components before theoretical advancements can be fully realized The Hidden 6G Bottleneck: RF Hardware Design I…. This bottleneck impacts not only future network capacity but also the feasibility of integrating advanced digital twins for predictive simulation in current 5G-Advanced deployments.

Digital Trust and Regional Security

Security metrics across Southeast Asia reveal a troubling gap between reported incidents and resolved cases, with nearly half of scam reports remaining open. Nearly Half of Reported Scam Incidents Go Unre… This disparity underscores a vulnerability that undermines the foundational trust required for digital expansion in the region. While technical solutions evolve rapidly, the socioeconomic impact of unresolved fraud continues to challenge mobile industry goals regarding consumer protection and ecosystem stability.

Spectrum Allocation and Satellite Interoperability

Policymakers are being urged by global standards bodies to unlock the 6 GHz spectrum band to maximize its utility for mobile ecosystems. Mobile ecosystem ready to maximise use of 6 GH… Concurrently, scaling satellite connectivity depends heavily on establishing interoperability standards that support the next phase of non-terrestrial networks Scaling satellite connectivity: why interopera…. These two developments highlight a dual focus: optimizing terrestrial spectrum usage while ensuring seamless integration with space-based infrastructure.

Physics-Informed Machine Learning

Advancements in uncertainty quantification are now embedding partial differential equation consistency directly into distribution-free prediction models. Physics-Informed Conformal Prediction: Embeddi… This approach ensures that neural operators maintain physical validity even when facing out-of-distribution data. Such rigor is essential for applications ranging from structural inference in networked systems to reliable autonomous operation, where fundamental dynamical units must be accurately identified to prevent system collapse.

Babak's Daily Briefing

Monday, September 14, 2026

Sources: 16 | Total Articles: 60

• 1.The Hidden 6G Bottleneck: RF Hardware Design Is Becoming a Strategic Race

  As 5G-Advanced matures and 6G research moves closer to implementation, the wireless industry faces a deeper challenge than spectrum, standards or AI-native network architecture. Future wireless systems will depend on whether the industry can design, validate and manufacture increasingly complex RF modules fast enough.

• 2.6G in Dalian: What the Latest 3GPP Meetings Reveal About the Future Radio and Network

  The 6G physical layer is starting to converge. The protocol stack is being simplified in meaningful places. But the most consequential architecture decisions are now moving toward the June plenary in Singapore.

• 3.RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation

  RF Digital Twins: Why 5G-Advanced and 6G Need Predictive Simulation As wireless systems become more tightly coupled across…

• 4.Evaluating 6G PHY Evolution: What the Industry Is Really Trying to Solve

  Summary available at source link.

• 5.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 1.R2VC: Modular Fact-Checking with Retrieval, Verification, and Confidence Calibration

  arXiv:2609.11955v1 Announce Type: new Abstract: Large language models are increasingly used for automated fact checking, but end-to-end prompting often entangles evidence retrieval, reasoning, and uncertainty estimation, making failures difficult to diagnose and confidence difficult to trust. We present R2VC, a modular retrieve, reason, verify, calibrate architecture for evidence-grounded fact checking with citations and abstention. R2VC combines hybrid sparse+dense retrieval over Wikipedia, a supervised fine-tuned and DPO-aligned generator that produces diverse structured verdict candidates, an external NLI cross-encoder for evidence-based candidate selection, and a lightweight sequence-level calibrator for confidence estimation and selective abstention. On FEVER, an 8B backbone with R2VC achieves 13.74% higher accuracy than baseline. A...

• 2.What Counts as a Mistake? Annotating Recitation Events in Quran Memorization Transcripts

  arXiv:2609.12085v1 Announce Type: new Abstract: Checking Quran recitation from an ASR transcript requires distinguishing unresolved mistakes from repetitions, repairs, opening formulas and accepted spelling differences. We report a completed human annotation of 100 production recording cases: 348 scored units and 162 localized events across ten combined labels. An executable evaluator scores labels and word positions together. A plain diff reaches label-aware F1 0.525 and localization F1 0.826; adapted production cleaner/alignment components reach 0.518 and 0.786, with exact-span F1 0.505 for both. Correcting the adapter's word coordinates recovers all five annotated repetition events, showing why annotation interfaces must be checked before interpreting baseline failures. In a preliminary pilot, eight single 20-minute runs across three ...

• 3.Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement

  arXiv:2609.12107v1 Announce Type: new Abstract: Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these datasets are referenced remains difficult. Such references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination. We present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus. A lightweight model trained on general research literature generates candidate dataset menti...

• 4.The Cost of Compression: A Rate-Distortion Limit on Factual Hallucination

  arXiv:2609.12111v1 Announce Type: new Abstract: Factual hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from its internal memory. This view misses a second source of error. Even when a fact has been observed, finite memory may force it to be stored only approximately. We study this effect through a simple coverage--compression model of factual recall. We consider an unstructured question-answering task with $N$ possible queries and $K$ possible answers. A learner observes $M$ training facts, compresses them into at most $B$ bits, and answers uniformly drawn test queries without retrieval. For a uniformly random ground-truth mapping, we prove $\mathcal{E} \geq \frac{M}{N}\delta^\star\!\left(\frac{B}{M}\right) + \left(1-\frac{M}{N}\right)\left(1-\frac...

• 5.Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking

  arXiv:2609.12122v1 Announce Type: new Abstract: Consonants contribute unequally to whether a word is understood. Given the limited time available for therapy, ranking consonants by contribution to intelligibility helps prioritize intervention targets in motor speech disorders. However, measuring this contribution relies on perceptual studies that are difficult to scale. This paper presents a scalable method that measures consonant contribution using acoustic masking. We silence one consonant at a time in an isolated word and test whether an automatic speech recognition (ASR) model still recognizes the word. We define a consonant's contribution score as the proportion of its masked instances for which the word becomes misrecognized, which we refer to as the mask-induced misrecognition rate (MMR). We validate MMR against two linguistic fac...

• 1.Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems

  arXiv:2609.11934v1 Announce Type: new Abstract: In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial complexity of interaction architectures, ambiguity of causal attribution under limited interventions, and state-dependent dynamics that confound structural inference. Each obstacle is structural in origin and calls for a structural solution. We address these challenges by adopting a reductionist approach, introducing Fundamental Dynamical Units (FDUs): signed three-node interaction patterns as composable primitives that convert the interaction hypothesis space into a finite, constructive, and tractable representation. We show ...

• 2.Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators

  arXiv:2609.11935v1 Announce Type: new Abstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDEs). However, providing rigorous uncertainty estimates remains an open challenge. We propose Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into the nonconformity score of split conformal prediction, producing prediction intervals that are (i) distribution-free with provable coverage guarantees, and (ii) spatially adaptive when the PDE residual correlates with prediction error -- tighter where physics is well-satisfied, wider where it is violated. Additionally, we prove that FNO's translation equivariance creates a fundamental approximation barrier for PDEs with Dirichlet boundary conditions, and show that...

• 3.Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning

  arXiv:2609.11937v1 Announce Type: new Abstract: The deployment of Federated Learning (FL) in multi-center clinical networks faces the challenge of "knowledge dominance," where high-volume hubs naturally overwhelm minority community nodes, implicitly treating the distinct clinical patterns of smaller cohorts as outliers. Existing geometric defenses provide a security baseline but leave this efficiency-fairness dilemma unresolved. To bridge this gap, we propose Fed-Equilibrium, a framework that advances the paradigm from simple defense to topological equilibrium. Unlike traditional aggregators, Fed-Equilibrium implements a sequential architectural synergy. It utilizes a two-stage gradient control cascade: Stage I (geometric quality assurance) enforces directional consistency via a cosine similarity funnel to filter malicious noise, creating...

• 4.Efficient AI Model Deployment Using Quantization Analysis Tool

  arXiv:2609.11954v1 Announce Type: new Abstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to grow. Effective deployment of AI models on edge and low power platforms requires optimization methods that reduce model size and computational cost while maintaining high accuracy. This paper presents Quantization Analysis Tool, a practical system designed to streamline quantization workflows and support performance efficient model deployment. Built on the ONNX framework for broad interoperability, the tool provides detailed layer-wise sensitivity analysis, visualization of weight and activation distributions, and insights to guide precision selection. By identifying layers that are resilient or sensitive to reduced precision, the tool enables...

• 5.Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs

  arXiv:2609.11956v1 Announce Type: new Abstract: Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures adherence to instructions and the production of functionally correct code. This process typically requires computationally intensive code sample generation from Transformer-based LLMs and substantial GPU-CPU communication for sequence verification. To address these computational challenges, this work examines whether RL-based post-training can be performed entirely offline by leveraging existing datasets rather than generating new samples. The findings indicate that, with only a few hours of training, zero-shot code generation performance of LLMs can be substantially improved without online sampling. Additionally, offline RL produces performance...

• 1.Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving

  arXiv:2609.11947v1 Announce Type: new Abstract: Increasing safety is the primary objective of automated vehicles. Achieving this goal requires reliable safety metrics that incorporate safety-relevant factors such as object type, velocity, and criticality. A key capability of such metrics is the distinction between critical and non-critical objects, which is addressed through criticality or relevance estimation. Existing criticality metrics are typically designed for specific scenarios and primarily focus on vehicle-to-vehicle interactions. In this paper, we therefore propose a novel criticality metric tailored to vulnerable road users (VRUs), which require special consideration due to their less predictable motion behavior. Furthermore, to avoid the complexity introduced by scenario-specific metrics, we introduce a scenario-independent cr...

• 2.Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

  arXiv:2609.12036v1 Announce Type: new Abstract: In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and decision making. The model incorporates four key design features: (1) Unified action representation: a 28-dimensional action value space covering most mainstream embodiments, keeping one model valid across heterogeneous devices. (2) Action-visual injection: URDF- and camera-rendered action videos bridge actions and pixels, giving markedly better controllability across embodiments, scenes, and tasks (PSNR +0.904 over alternative fusion baselines). (3) Sparse mixture-of-experts (MoE): sparse MoE layers add capacity for heterogeneous dynamics and absorb the action modality while ...

• 3.MoPA: Coordinated Mobile Manipulation via Subsystem-Specific Perception Alignment

  arXiv:2609.12081v1 Announce Type: new Abstract: Mobile manipulation requires perceptual evidence at different spatial scales for base motion and arm control, while the two action modalities remain kinematically coupled. Existing policies often employ specialized action generation for different subsystems but condition heterogeneous action branches on a shared perceptual representation, leaving subsystem-specific perception-action correspondence implicit. We present MoPA, a framework that aligns perceptual conditioning with mobility and manipulation while preserving coordination at the action level. Dual Perceptual Streams employ two mutually masked query banks to extract separate perceptual representations from a shared vision-language context. Perception2Action Adaptation jointly updates each query bank and its corresponding action strea...

• 4.Uncertainty-Aware Conflict Detection Against Operator-Conditioned Weather Hazards

  arXiv:2609.12095v1 Announce Type: new Abstract: Strategic flight plan validation in Advanced Air Mobility (AAM) environments requires robust methods for predicting aircraft state uncertainty and detecting potential conflicts with dynamic airspace hazards. This paper presents a novel framework for uncertainty-conditioned trajectory prediction combined with polyhedra hazard representation for pre-flight conflict detection. We introduce a closed-form uncertainty estimation method that couples non-uniform rational B-spline (NURBS) curve fitting for kinematic trajectory generation with a Kalman Filter for state covariance propagation. Drawing from the Light Propagation Algorithm (LPA) paradigm, we employ a sigmoid-blended measurement noise model that captures the uncertainty reduction behavior of flight management systems approaching the requi...

• 5.RodForesight: A World Model Enhanced Diffusion Policy for Slender and Material Agnostic Rod Insertion

  arXiv:2609.12103v1 Announce Type: new Abstract: Slender rod insertion arises in precision manufacturing, where millimetre scale diameter and tight clearances demand accurate perception and control. Conventional peg-in-hole methods assume a rigid object whose tip pose is fixed relative to the gripper. This assumption breaks down for a high aspect ratio rod, which can bend during manipulation, making its tip motion dependent on the rod configuration, grasp, material properties, and contact. We present RodForesight, a learning framework that factorises the task into two stages: 1) coarse approaching, which uses visual servoing to map diverse initial configurations into a compact near hole hand-off region; and 2) predictive insertion, which performs fine alignment and completes the insertion. It is worth noting that the two stages can be wrap...

• 1.GSMA calls for greater renewable energy access to enable mobile industry’s net zero transition in Asia Pacific

  Summary available at source link.

• 2.Nearly Half of Reported Scam Incidents Go Unresolved Across Southeast Asia, Undermining Digital Trust, New GSMA Findings Reveal

  Summary available at source link.

• 3.Mobile ecosystem ready to maximise use of 6 GHz as GSMA urges policymakers to unlock spectrum for mobile

  Summary available at source link.

• 4.Scaling satellite connectivity: why interoperability matters for NTN’s next phase

  Summary available at source link.

• 5.GSMA MWC26 Doha to co-locate alongside the ITU Plenipotentiary Conference in November 2026

  Summary available at source link.

• 1.GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay

  Counterfactual regret minimization (CFR) is one of the few large numerical workloads that still runs faster on CPUs than on GPUs. Each iteration sweeps a game tree with up to billions of states in millions of small, interdependent gather and scatter steps issued through a generic tree interface. On a GPU every kernel finishes in microseconds, so kernel launches and framework dispatch dominate the run time, and prior GPU implementations have lost to optimized CPU code. We observe that for a fixed game, everything about a CFR iteration except the numerical values is known before the first iteration runs. We propose GPU-CFR, a compiler and runtime built on this observation. It compiles any game once into static dataflow: flat edge and information-set arrays, precomputed indices, and depth-level batched passes fix the entire operation sequenc...

• 2.CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

  Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on structural causal models (SCMs), which specify a causal graph together with the mechanisms that generate data, yet existing studies differ substantially in graph families, mechanisms, and evaluation protocols. The emergence of causal discovery foundation models (CDFMs) further complicates evaluation: performance may reflect not only causal discovery ability, but also overlap between pretraining environments and test SCMs, making results on fixed synthetic benchmarks difficult to interpret. We introduce CausalArena, a unified and evolvable benchmark for causal discovery under a common protocol. Synthetic SCMs supply controlled breadth over structures and mechanisms;...

• 3.Guided Super-Resolution of Digital Elevation Models with Diffusion-Based Image Generators

  High-resolution digital surface models (DSMs) play an important role in urban analysis, 3D building reconstruction, and infrastructure monitoring, yet their availability remains limited due to the high cost and complexity of data acquisition. In contrast, coarse DSMs from commercial satellite missions are widely accessible, and high-resolution optical imagery is increasingly available from aerial and satellite platforms. We address the resulting mismatch in spatial resolution and propose a DSM superresolution approach that enhances 5 m DSMs to 0.5 m resolution, using guidance from high-resolution spectral images. Our method employs denoising diffusion to transfer information that is visible only in the image, like crisp outlines and detailed roof structures, into the elevation maps. In this way, surface details are reconstructed more accu...

• 4.Learning structural balance of graphs from quantum spectral features

  We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs. We propose to embed a signed graph as an Ising model instance with positive and negative interactions, and use the standardized moments of the Ising DOS as features for learning. We show that these moments count signed closed walks, are switching-invariant, and are size-free by construction. As a benchmark, we target learning the frustration index, an NP-hard measure of structural balance that can be labeled exactly at moderate size. At zero field, the models can be sampled classically, allowing the quantum extraction procedure to be certified against exact ground truth. We propose DOS-QPE, a phase estimation on a purified maximally mixed probe, which samples t...

• 5.World in World: Explore the World with World Models

  Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging. Exploring a source video from new viewpoints requires the generated rollout to remain synchronised with the recorded event, place observed content in the requested view, plausibly complete newly exposed regions, and recover previously generated appearance on revisits. Existing methods typically address these requirements through task-specific modules or additional training. We present World in World, a training-free inference-time interface that converts heterogeneous control evidence into camera- and time-labelled clean visual states, which are read through the native self attention of a frozen causal video model. The evidence comprises source-video observations, target-view scene projections, geometry renderings that...

• 1.Responsible AI for Higher Education

  This interactive webinar will introduce the different types of AI, address the concerns with AI, share how we IBM are approaching Responsible AI, and offer guidance to students about what they can do - as individuals, and members of their IEEE chapters. Participants will also have the opportunity to to apply the Responsible AI approach to a particular use case - IBM Bob, a software development life cycle agent, and Q&A. This will be an interactive session, so have phones ready to engage! Register now for this free webinar!

• 2.How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip

  On 25 August, OpenAI fully unveiled Jalapeño, the company’s debut AI accelerator chip. Jalapeño delivers up to 13.4 petaflops of 4-bit compute and accesses 232 gigabytes of the most advanced memory available, linking to it at a blazing 15.4 terabytes per second. Benchmarks cited by OpenAI show that Jalapeño can reduce end-to-end latency (the time between prompt to last token) by up to 3.6x when compared to Nvidia’s GB300 —a chip the company currently relies on—and do so while consuming less power. Whether these figures translate into real-world gains once Jalapeño enters widespread service in OpenAI’s inference fleet remains to be seen, but performance is only half the story. The other half is how the chip was designed—a process which, as you might expect, was accelerated by OpenAI’s large language models (LLMs). Jalapeño moved from first...

• 3.Adversarial Fashion Confronts Surveillance Norms

  AI-powered cameras dot streets across the world, equipped with the power to identify faces or vehicle license plates . But a public backlash is gaining momentum . Privacy concerns abound, encompassing the lack of consent for capturing data, how that data is stored and used , and the risk of misuse . Those concerns are motivating people to fight back. The DeFlock project, for instance, maps automated license plate readers (ALPRs) to raise awareness. Some people resort to extreme measures , such as vandalizing or damaging ALPRs. Others are stitching together more creative responses, crafting “adversarial fashion” to evade surveillance cameras, like a Kickstarter project called noRecognition , presented at last month’s DEF CON hacker convention . Scrambling surveillance In 2025, cybersecurity expert Bill Swearingen began experimenting with a...

• 4.Why Andon Labs Puts AI Agents in Charge of Real Businesses

  Maybe you heard about the AI-controlled vending machine that stocked underwear and live fish . Or the AI manager of a San Francisco store that fired a human employee . Or the AI radio DJ that said its catchphrase , “Stay in the manifest,” 229 times per day. These incidents all emerged from experiments run by Andon Labs , an AI safety company based in San Francisco that puts AI agents in charge of real-world operations and watches what happens. These operations double as testbeds for Andon’s commercial work developing evaluations and conducting research with the leading frontier AI labs. Their spectacular and absurd failures have won the company plenty of attention. But many people don’t realize that the experiments are intended to answer a serious question: How much real-world responsibility can today’s AI agents handle? “We want to measu...

• 5.AI Models Are Watermarking Text—Will You Notice?

  On 11 August, Anthropic announced that all future Claude models will generate text that contains a watermark that identifies its results as AI generated. The company is not alone. Google has its own text watermark (which Anthropic’s is based on) that it uses on the output of its Gemini models . OpenAI has yet to introduce a text watermark but it plans to do so . The rapid spread of watermarking is in part a response to the European Union’s AI Act , which mandates watermarks for AI models released after 2 August, 2026, along with other planned and proposed regulations aimed at curbing the spread of deceptive or manipulative AI-generated content. But the new rules may come at a cost for AI users who simply want the best possible results. AI watermarks can apply to many forms of content: The EU Artificial Intelligence Act also requires them ...

• 1.Monday assorted links

  1. Will driverless cars increase or reduce urban density? 2. One decomposition approach to why interest rates have been going up. 3. New Guinness record holders. 4. Is there any chance of finding Rembrandt DNA? 5. “Abundance Corps is hosting Making Progress, a private gathering for students interested in how science, technology, and institutions can […]

  The post Monday assorted links appeared first on Marginal REVOLUTION.

• 2.AI, Redistribution, and the Size of the Pie

  Anthropic’s economic team, including Anton Korinek and Chad Jones, have a valuable new paper, Economic Scenarios for Transformative AI. They make their assumptions explicit and provide a scenario explorer that lets you change them. How capable will AI become? How quickly will firms adopt it? Will it augment workers or automate their tasks? You can […]

  The post AI, Redistribution, and the Size of the Pie appeared first on Marginal REVOLUTION.

• 3.Is it the screens? Or education systems?

  The Dark Ages implies television and phones are the main cause of cognitive decline. This fails to explain the patterns in PISA scores. Why did England and Scotland fall so precipitously from 2000 to 2005 whilst America improved? Why did England and Estonia hold steady after 2015 whilst most other OECD countries declined? How have Singapore, […]

  The post Is it the screens? Or education systems? appeared first on Marginal REVOLUTION.

• 4.Does AI assistance enhance or erode expertise?

  From a new NBER working paper: Whether AI assistance builds or erodes professional expertise is unsettled. In a pre-registered three-month randomized controlled trial, we gave 133 practicing patent lawyers at eleven U.S. intellectual property law firms access to a custom AI drafting assistant and measured both their performance while using AI and their professional judgment […]

  The post Does AI assistance enhance or erode expertise? appeared first on Marginal REVOLUTION.

• 5.Sunday assorted links

  1. Scott Sumner as regional thinker. 2. The economists who work on “Productivity and Innovation” are most likely to use AI for their writing. 3. Michael Levin defends neo-neo-Platonism? 4. Look for and try to limit agent collusion. 5. Do orangutans like Indian classical music? 6. “Our results demonstrate that LLM-evolved constitutions significantly outperform both […]

  The post Sunday assorted links appeared first on Marginal REVOLUTION.

• 1.Jackson Hole: Exploring the Financial Frontier

  After two days of heady discussions with academics and policymakers from around the world in high-altitude Jackson Hole, Wyoming, I’m now decompressing back at Street Level. The main theme of this year’s Kansas City Fed symposium was financial innovation in the payments space—a fast-evolving topic with major implications for consumers and central bankers alike. In this post, I’ll share some general takeaways from those Jackson Hole talks and highlight related New York Fed research on payments and financial intermediation.

• 2.Are Central Banks Moving Out of Dollar Assets?

  The dollar’s share of global official foreign exchange reserves fell from 64 percent in 2015 to 56 percent in 2025. This downward trajectory is sometimes read as evidence that the dollar’s role in international financial markets is eroding. However, aggregate statistics obscure the composition of changes occurring at the country level. In this post, we show that the aggregate decline is not a systematic global shift away from dollar assets. Rather, the aggregate decline reflects the actions of a handful of large reserve holders, changing either their currency preferences or the size of their reserve portfolio. From the perspective of the cross section of countries holding dollar assets, the dollar’s status in official portfolios is largely intact.

• 3.Businesses Are Using AI to Transform Work, Not Cut Jobs

  The ongoing advancement and adoption of artificial intelligence continues to raise concerns about widespread job losses. Over the past three years, our regional business surveys have asked firms about their AI adoption and its effects on their workforces. This year, we found that AI use among regional businesses has continued to rise sharply, with more than 60 percent of service firms and about half of manufacturers now using AI—a notable increase from 40 percent and 26 percent, respectively, reported in 2025. Despite this rapid adoption, regional firms’ investments in AI are generally modest, usage tends to be concentrated among a small share of workers within firms, and layoffs have remained uncommon. And, while some firms have scaled back hiring due to AI, others have added workers to help them use it. Retraining employees in response ...

• 4.Has Broader Stock Market Participation Changed How Interest Rates Affect the Economy?

  Stock market participation in the U.S. has changed dramatically over the past four decades. In the mid-1980s, fewer than 30 percent of households held equity. By the early 2000s, more than half of U.S. households owned equity, either directly or through mutual funds, 401(k)s, and IRAs. As participation widened, the way stock market fluctuations passed through to household spending may have changed, with potential implications for how the broader economy behaves. An argument can be made that the rise in equity market participation has dampened the response of output to interest rate changes as stock market fluctuations are now spread across a larger share of households, moderating movements in consumer spending, asset prices, and investment spending.

• 5.Does the Equity Term Structure Respond to Monetary Policy Shocks? 

  A long-standing body of research, inspired by Bernanke and Kuttner (2005), has documented the effects of Fed interest rate surprises on stock markets. While stock markets provide valuable information about the investor risk premium and dividend growth expectations, researchers have only recently developed more comprehensive tools to estimate the term structure of equity risk premia and dividend growth expectations across a broad range of maturities. In this post, we investigate the impact of monetary policy surprises (or shocks) on short- and long-term estimates of risk premia and growth expectations through the lens of the Giglio, Kelly, and Kozak (2024) model.

• 1.What Amodei’s Call for an AI Pause Gets Wrong

  Anthropic CEO Dario Amodei’s recent appeal for a pause on AI development is a welcome one. But while AI companies do indeed need more breathing space to consider the full impact of their inventions before releasing them to the market, self-interested oversight is not the kind of governance we need.

• 2.The Strategic Asset Europe Has Overlooked

  Europe’s soil is gradually dying, and yet the continent has still not recognized it as a strategic asset. Fortunately, the EU has many of the tools it needs to act, but doing so will require an overarching framework that helps farmers access financial rewards for measurable soil outcomes.

• 3.Who Is Responsible for Nepal’s Tragedy?

  The devastating floods in Nepal demonstrate what happens when reckless development puts climate-vulnerable communities in harm’s way. They also highlight a climate-finance system that is unable to provide the funds countries need in the immediate aftermath of such disasters.

• 4.Europe Must Choose Between Power and Dependence

  European citizens do not want “more Europe” in the abstract, but rather a European Union that is capable of protecting, investing, anticipating, deciding, and acting whenever national-level policymaking is insufficient. In today’s world, ensuring citizens’ control over their future requires the freedom to act together.

• 5.Progressive Economics Is Delivering

  By combining fiscal responsibility, growth, and social development, progressive governments in Brazil and Spain have improved millions of people’s lives and refuted the hoary belief that conservatives are better stewards of the economy. Such a successful alternative model could not have come at a better time.

• 1.American Tower sees European consolidation as growth opportunity

  American Tower does not expect consolidation in Europe to materially affect its anchor tenants and said it has limited exposure to churn from some of the carriers that could potentially…

• 2.Day one justification – why scaling network APIs requires smaller, faster commercial wins (Reader Forum)

  Network APIs face a commercial reality check: operators cannot justify heavy investment on speculative hyperscaler demand. Smaller, faster wins such as Branded Calling could generate revenue sooner, build confidence, and…

• 3.Telcos, remember your roots! (Telco Diary)

  From the newsletter (September 11; sign-up or follow if you want it sooner): AT&T CEO John Stankey offers a grounded (sounding) vision for the AI era for telcos: build better…

• 4.Nokia campus strategy – from private 5G to private AI? (Telco Diary)

  From the newsletter (September 10; sign-up or follow if you want it sooner): Nokia’s new Cognitive Operations and Cognitive Edge Node look strikingly familiar as the company exits campus private…

• 5.Telecoms – it’s just not cricket (telco diary)

  From the newsletter (September 9; sign-up or follow if you want it sooner): Telecoms is moving deeper into AI infrastructure as networks become programmable, distributed and intelligent – but the…

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