Encryption can protect data at rest and data in transit, but does nothing for data in use. What we have are secure enclaves. I’ve written about this before:
Almost all cloud services have to perform some computation on our data. Even the simplest storage provider has code to copy bytes from an internal storage system and deliver them to the user. End-to-end encryption is sufficient in such a narrow context. But often we want our cloud providers to be able to perform computation on our raw data: search, analysis, AI model training or fine-tuning, and more. Without expensive, esoteric techniques, such as secure multiparty computation protocols or homomorphic encryption techniques that can perform calculations on encrypted data, cloud servers require access to the unencrypted data to do anything useful.
Fortunately, the last few years have seen the advent of general-purpose, hardware-enabled secure computation. This is powered by special functionality on processors known as trusted execution environments (TEEs) or secure enclaves. TEEs decouple who runs the chip (a cloud provider, such as Microsoft Azure) from who secures the chip (a processor vendor, such as Intel) and from who controls the data being used in the computation (the customer or user). A TEE can keep the cloud provider from seeing what is being computed. The results of a computation are sent via a secure tunnel out of the enclave or encrypted and stored. A TEE can also generate a signed attestation that it actually ran the code that the customer wanted to run.
Secure enclaves are critical in our modern cloud-based computing architectures. And, of course, they have vulnerabilities:
The most recent attack, released Tuesday, is known as TEE.fail. It defeats the latest TEE protections from all three chipmakers. The low-cost, low-complexity attack works by placing a small piece of hardware between a single physical memory chip and the motherboard slot it plugs into. It also requires the attacker to compromise the operating system kernel. Once this three-minute attack is completed, Confidential Compute, SEV-SNP, and TDX/SDX can no longer be trusted. Unlike the Battering RAM and Wiretap attacks from last month—which worked only against CPUs using DDR4 memory—TEE.fail works against DDR5, allowing them to work against the latest TEEs.
Yes, these attacks require physical access. But that’s exactly the threat model secure enclaves are supposed to secure against.
Over the past few decades, it’s become easier and easier to create fake receipts. Decades ago, it required special paper and printers—I remember a company in the UK advertising its services to people trying to cover up their affairs. Then, receipts became computerized, and faking them required some artistic skills to make the page look realistic.
Several receipts shown to the FT by expense management platforms demonstrated the realistic nature of the images, which included wrinkles in paper, detailed itemization that matched real-life menus, and signatures.
[…]
The rise in these more realistic copies has led companies to turn to AI to help detect fake receipts, as most are too convincing to be found by human reviewers.
The software works by scanning receipts to check the metadata of the image to discover whether an AI platform created it. However, this can be easily removed by users taking a photo or a screenshot of the picture.
To combat this, it also considers other contextual information by examining details such as repetition in server names and times and broader information about the employee’s trip.
Social media can have a powerful impact on the way we see and experience the world. What we see in our feeds is not random: it is determined by AI-driven systems that collect vast amounts of data, build user profiles, analyse engagement, and generate recommendations. But while young people are prolific users of social media, studies show that many have little understanding of what is happening ‘under the hood’
Researchers Henriikka Vartiainen and Matti Tedre.
In our September research seminar, we welcomed back Henriikka Vartiainen and Matti Tedre from the University of Eastern Finland. They introduced Somekone, a social media simulator that is designed to help learners understand some of the fundamental processes behind social media platforms. Their team has been developing AI education materials and tools since 2019, including GenAI Teachable Machine, which they presented at our May research seminar.
Collaboration and co-design
Henriikka explained that the development of the Somekone tool emerged from the team’s long-term collaboration with teachers and schools in Finland. They co-developed the tool with the aim of making concepts like data collection, engagement, profiling, recommendations, filter bubbles, and polarisation visible and explainable for students aged 11 to 13 years old.
A four-phase learning model
Henriikka described the pedagogical model that the team follows in all of their AI education interventions. Their goal is not only to support students to develop their understanding of AI concepts, but also to foster ethical awareness and a sense of agency.
Phase 1: Contextualisation and familiarisation Students begin by discussing their experiences with social media and their initial ideas about how platforms such as TikTok, YouTube, and Instagram work. This activates students’ prior knowledge and helps connect the learning to their own interests. It also enables teachers to uncover any misconceptions the students may have.
Phase 2: Exploration Students explore their initial ideas by experimenting with the Somekone tool. They discover how different types of data are collected and combined for profiling in a way that connects these new concepts to their own everyday lives.
Phase 3: Design and inquiry Students explore the Somekone tool more deeply. Teachers guide them through activities where the students analyse, interpret, and discuss the data they can see in the tool. Importantly, the data they are using has all been gathered from their activity on the platform. Students can see how the likes, follows, and comments they and their classmates make change the images they are shown, and this is all real time.
Phase 4: Ethical and societal reflection Students reflect on what they have learnt and consider the broader impacts of social media. Teachers encourage them to think critically, question the way social media platforms currently work, and imagine alternatives. At the end of the project, students write letters to decision-makers with their suggestions for how social media could better serve children’s interests.
Inside the simulator
Matti then gave a live demonstration of Somekone. Nothing compares to seeing the tool in action, so do check out the video of his demo here!
Students log on to the tool and are presented with an Instagram-style feed of images. They scroll through the feed and like, share, or comment on images that catch their attention or match their interests. For many students this is a very familiar type of environment, and they really enjoy playing with the app!
However, the unique value of Somekone is that it provides students with a real-time view of the way data is collected from every single user interaction, and demonstrates what is done with that data. It also allows students to experiment with a social media tool in the classroom without any data protection issues, as all of the data is stored locally.
Learners explore:
Data collection in real time. Working in pairs, one student browses the image feed, while the other watches a live view of the data that the simulator is collecting every time their partner interacts with or simply pauses on a post.
Profile building. Somekone shows how all this data accumulates to build a profile. Students watch their profiles developing based on the way they and their classmates are interacting with their feeds.
Clustering and connections. Students then see how the tool groups profiles to create clusters of users with similar interests. Often friendship groups in the classroom are evident on screen because students sitting next to each other have all chosen to engage with the same things!
The simulator creates clusters of users with similar interests, which update in real time as students interact with posts on their feeds
Explainable recommendations. A key feature of Somekone is that it provides explanations for why it recommends posts to users. Students learn that recommendations can be based on various things, such as the image’s tag matching the tag on other posts they liked, or the image being popular among other users with similar profiles to theirs. These are the mechanisms that underpin real recommendation systems, but Somekone makes them explicit.
The tool provides an explanation for why each post is recommended
Filter bubbles and polarisation. A filter bubble forms when a user only sees social media posts that match their existing interests or beliefs, due to highly personalised recommendation systems. Somekone presents this concept in a visually compelling way through a heatmap showing all the content in the system, with a colour scale indicating which posts are most likely to be shown to a particular user, and which they will never encounter. By comparing different users’ filter bubbles side by side, students start to understand how polarisation can arise. As Matti said: “If our feeds are so different from each other that I never see the pictures that you see and you never see the pictures I see, then […] we don’t even share the same reality”.
Two users’ heatmaps presented side by side, showing their respective filter bubbles
Algorithm settings. A key learning opportunity is that students can adjust the algorithm’s parameters and observe how this changes their feed and their filter bubble. They can choose between personalised or non-personalised recommendations, select how posts are ranked, and decide whether to allow any diversity in the popularity of posts recommended to them. This is key to ‘opening up the box’.
For teachers, the tool has a simple guided interface to make it easy to use in class. There is also a button that teachers can use to pause the app, stopping students from scrolling (much to their dismay!) in order to focus their attention on the teacher when they are explaining concepts.
Evidence of impact
The research team used pre- and post-tests to evaluate what impact the intervention had on students’ understanding of social media mechanisms and on their sense of agency in relation to data. They conducted the post-test a week after the intervention, and then also did a delayed post-test six months later to see whether any changes were sustained. They found:
Improved understanding of key concepts. Learners showed statistically significant improvements in identifying different types of data traces and in understanding how data profiling works. They also showed some improvement in grasping recommendation mechanisms.
Retention over time. These improvements were generally still evident six months later, particularly in the case of understanding data traces.
Stronger sense of agency. The team found that students’ sense of data agency improved after taking part in the intervention. This is really important as students are more likely to want to study a topic further if they have feelings of agency and self-efficacy.
Accessing the tool
The Somekone tool is freely available online — in Finnish, English, German, and French — at somekone.gen-ai.fi. The developer Nick Pope has also made the source code available on GitHub at github.com/knicos/genai-somekone.
However, the supporting materials and teacher resources are currently only available in Finnish and the underpinning pedagogies relate to the Finnish context.
Join our next seminar
Join us at our next seminar on Tuesday, 11 November from 17:00 to 18:30 GMT to hear Karl-Emil Bilstrup (Copenhagen University) speak about using the micro:bit to explore machine learning practices. We hope to see you there!
To sign up and take part in our research seminars, click below:
The Department of Justice has indicted thirty-one people over the high-tech rigging of high-stakes poker games.
In a typical legitimate poker game, a dealer uses a shuffling machine to shuffle the cards randomly before dealing them to all the players in a particular order. As set forth in the indictment, the rigged games used altered shuffling machines that contained hidden technology allowing the machines to read all the cards in the deck. Because the cards were always dealt in a particular order to the players at the table, the machines could determine which player would have the winning hand. This information was transmitted to an off-site member of the conspiracy, who then transmitted that information via cellphone back to a member of the conspiracy who was playing at the table, referred to as the “Quarterback” or “Driver.” The Quarterback then secretly signaled this information (usually by prearranged signals like touching certain chips or other items on the table) to other co-conspirators playing at the table, who were also participants in the scheme. Collectively, the Quarterback and other players in on the scheme (i.e., the cheating team) used this information to win poker games against unwitting victims, who sometimes lost tens or hundreds of thousands of dollars at a time. The defendants used other cheating technology as well, such as a chip tray analyzer (essentially, a poker chip tray that also secretly read all cards using hidden cameras), an x-ray table that could read cards face down on the table, and special contact lenses or eyeglasses that could read pre-marked cards.
AWS End User Messaging ahora admite capacidades de SMS internacional para Números Gratuitos de EE.UU. (TFN). Esta nueva función permite a las empresas usar un solo TFN de EE.UU. para enviar mensajes SMS a más de 150 países, simplificando el alcance global. Beneficia principalmente a clientes que necesitan enviar alertas transaccionales unidireccionales—como contraseñas de un solo uso (OTP) o notificaciones de envío—y empresas que quieren crear prototipos rápidamente y probar su estrategia de mensajería en nuevos mercados internacionales sin la complejidad de adquirir números específicos por país.
Esta guía te mostrará los pros y contras de esta función y cómo habilitarla y cuándo usarla versus métodos tradicionales de envío específicos por país.
¿Qué Son los Números Gratuitos Internacionales de EE.UU.?
Un número gratuito internacional de EE.UU. es un TFN (toll-free number) estándar de EE.UU. que ha sido habilitado con la capacidad de enviar mensajes SMS a destinos fuera de Estados Unidos. Esta función es compatible con versiones anteriores, lo que significa que puedes habilitarla en cualquier TFN de EE.UU. nuevo o existente en tu cuenta.
Cómo Habilitar el Envío Internacional
Hay tres formas principales de habilitar esta función para tus Números Gratuitos de EE.UU.:
Habilitar el envío internacional al registrar un nuevo número en la consola.
Habilitar el envío internacional para un número existente en la consola.
Habilitar el envío internacional para un número existente a través del AWS CLI.
1. Habilitar Al Registrar un Nuevo Número Gratuito de EE.UU. (Consola)
Desde la consola de AWS End User Messaging, navega a Administrar SMS
Desde la consola de AWS End User Messaging, navega a Configuraciones > Números de teléfono > y selecciona Originador de la solicitud
Paso 1: Seleccione un país, selecciona Estados Unidos (US) como tu país de destino
En Paso 2: Defina el caso de uso, configura las diversas opciones listadas para tu Caso de uso de mensajería previsto, y selecciona Sí para habilitar el envío Internacional, antes de hacer clic en Siguiente
Para Paso 3: Seleccionar tipo de originador, selecciona Gratuito, valida tus opciones de Política de recursos, selecciona Siguiente
En Paso 4: Revisar y solicitar: Verifica que la información que ingresaste sea correcta y selecciona Solicitar.Nota: Las solicitudes de registro de números gratuitos de EE.UU. pueden tomar aproximadamente 15 días hábiles para ser aprobadas.
2. Habilitar para un Número Gratuito de EE.UU. Existente (Consola o CLI)
Si ya has adquirido un TFN, puedes habilitar la función de envío internacional en cualquier momento.
Usando la Consola de Administración de AWS:
Navega a Configuraciones > Números de teléfono > y selecciona un número Gratuito existente
Localiza la pestaña Envío internacional y elige Editar configuración
Marca la casilla Habilitar envío internacional en los detalles de tu número de teléfono
Guardar Cambios
Usando el AWS CLI
El comando update-phone-number te permite modificar las capacidades de un número de teléfono, mientras que el comando describe-phone-numbers te permite verificar su estado.
Un comando exitoso devuelve el objeto JSON completo para el número de teléfono. Confirma el cambio verificando que el valor InternationalSendingEnabled sea true
Esta función ofrece una nueva forma poderosa de llegar a una audiencia global, pero es importante entender dónde destaca y cuáles son sus limitaciones.
Beneficios (Ventajas)
Alcance Global con un Solo Número: Envía SMS a más de 150 países usando un solo TFN de EE.UU. existente.
Gestión Simplificada: Evita la complejidad operacional y el costo de comprar y gestionar una flota de números de teléfono específicos por país.
Prototipado y Pruebas Rápidas: Prueba rápidamente campañas de mensajería en nuevos mercados internacionales antes de comprometerte con el enfoque de mejores prácticas de adquirir números dedicados en el país.
Optimización de Costos para Alertas Unidireccionales: Proporciona un método rentable para enviar mensajes transaccionales unidireccionales de alto volumen como OTP, recordatorios de citas y notificaciones de envío globalmente.
Limitaciones y Consideraciones Técnicas
SMS Bidireccional Limitado a EE.UU. y Canadá: Las conversaciones SMS bidireccionales confiables solo son compatibles para destinatarios en Estados Unidos y Canadá.
Solo Unidireccional para Todos los Otros Países: Para todos los otros destinos, esto es solo unidireccional.
Entrega con máximo esfuerzo (no garantizado): El envío fuera de EE.UU. y Canadá es con máximo esfuerzo (no garantizado). El número de teléfono que aparece en el dispositivo del destinatario puede ser reemplazado con un número local o ID de Remitente, por lo que la mensajería bidireccional no funcionará para estos destinos. Para más detalles sobre maximizar la entrega, lee Una Guía para Optimizar la Entrega de SMS y Mejores Prácticas(Inglés).
La Exclusión Gestionada No Está Garantizada Internacionalmente: La funcionalidad automática de respuesta STOP no funciona para destinos fuera de EE.UU. y Canadá. Para destinatarios internacionales, debes proporcionar un método alternativo de exclusión.
Rendimiento Estándar (3 MPS): Los TFN internacionales tienen un rendimiento predeterminado de 3 Partes de Mensaje Por Segundo (MPS). Para campañas de alto volumen y alto rendimiento, los números específicos por país dedicados (como códigos cortos) son la mejor práctica recomendada.
Entendiendo el Costo
El precio para esta función es directo:
Sin Tarifas Mensuales Adicionales: No hay cargo extra por habilitar la capacidad de envío internacional en tu TFN de EE.UU. Solo pagas el arrendamiento mensual estándar por el número mismo.
Mensajería de Pago por Uso: Se te factura por cada mensaje SMS saliente a la tarifa estándar por mensaje para el país de destino.
Sí, pero es más complejo si necesitas enviar a muchos países.
Requerir conversaciones bidireccionales
Solo para EE.UU. y Canadá.
Sí. Este es el enfoque requerido para SMS bidireccional confiable en un país específico.
Garantizar que tu marca/número aparezca consistentemente
No, planifica que el ID del Remitente no se preserve, ya que esto no está garantizado internacionalmente.
Sí. Esta es la razón principal para usar un número dedicado en el país.
Maximizar la entregabilidad para campañas críticas
No, la entrega es “con máximo esfuerzo”.
Sí. Un número local proporciona la mayor probabilidad de entrega exitosa.
Consideraciones y Próximos Pasos
Una vez que hayas habilitado tu envío internacional sobre Números Gratuitos de EE.UU., puedes mejorar tu estrategia de mensajería considerando resistencia, monitoreo y escalabilidad. Los siguientes recursos proporcionan mejores prácticas para mejorar tu envío.
El SMS Internacional para Números Gratuitos de EE.UU. es una herramienta estratégica poderosa para empresas que buscan simplificar su mensajería global. Destaca en permitir pruebas rápidas en nuevos mercados y entregar eficientemente alertas transaccionales unidireccionales en todo el mundo desde un solo número.
Sin embargo, no es un reemplazo para la mejor práctica de usar números de teléfono dedicados en el país cuando conversaciones bidireccionales confiables y marca garantizada son críticas para el éxito de tu campaña. Al entender sus beneficios y limitaciones, puedes usar estratégicamente esta función para comenzar rápidamente mientras planificas un movimiento a largo plazo hacia códigos específicos por país para tus mercados más importantes.
AI isn’t just reshaping how data is processed—it’s rewriting how data moves. Behind every training run or inference pipeline is a torrent of data, and how efficiently (or not) that data travels through networks (and whether it’s an AI-ready network) can make or break performance.
Data workloads have massively evolved over the 18 years we’ve been in business from computer backups to exabyte-scale storage to AI data pipelines. And that has implications for not just our storage hardware, but our network.
What started as a single ISP serving a few racks in the early days has grown into a global, multi-terabit backbone connecting customers, compute, and storage in real time via multiple Tier 1 carriers, Internet Exchanges, and PNI links.
So why talk about it now? Because AI is testing the limits of every part of the infrastructure stack—and the network is where those limits are most visible. Running an AI-ready network means rethinking how you design, route, and scale traffic to handle not just more data, but faster, more synchronized, and more resilient data movement than ever before.
In this post, I’m talking about how our network has evolved to support AI workflows, including what’s changed under the hood, how we’re adapting our hardware and architecture, and what that means for the way data moves through Backblaze today.
Go with the flow
The Network Engineering (NetEng) group at Backblaze is responsible for the design, implementation, and support of our physical network—everything from the physical copper and fiber cables inside our datacenters to the routers and switches that connect our storage to the world.
When we talk about network traffic, we often refer to a “flow”—a stream of information sent between two or more parties. Downloading a file? That’s a flow between your computer and the server offering the file. Multiple small requests loading a website (text, formatting code, animation code, etc.)? Those are known as “mouse” flows. Massive dataset transfers that sustain hundreds of gigabits per second? Those are “elephant” flows.
The elephant in the room
AI workloads are the largest “elephant” flows our network has ever sustained. These aren’t just big files, they’re ecosystems of data: multi-petabyte datasets, hundreds of thousands of objects ranging from a single megabyte to hundreds of megabytes per object, and thousands of simultaneous connections working in parallel.
Moving these data sets around is no small task. It means engineering for sustained, lossless throughput. It’s cutting edge, using many machines to perform parallel operations, all at large transfer rates. Let’s say we’re the source of a dataset that is being transferred to a neocloud for processing, the processing layers (often GPUs) want a continuous stream of high bandwidth with no loss. And a single dropped packet in a training pipeline can trigger expensive re-requests, idle GPUs, and cascading slowdowns.
With that in mind, we’ve evolved our infrastructure from traditional cloud networking—designed for smaller flows—to handle the relentless firehose of AI data.
Traditional cloud vs AI cloud
AI changes everything about traffic behavior. It doesn’t just mean that our total capacity is bigger, but also that our considerations for how we design, support, and scale our infrastructure morphed along with our capacity upgrades.
Here’s a quick overview of the former challenges and the new ones we’re engineering to serve our AI workflows.
Traditional Cloud Network
AI Cloud Network
Small to large flow sizes (megabits to, gigabits)
Very large flows (multi-gigabit to terabit)
High entropy flows (many sources and destinations)
In short: AI traffic is heavier, stickier, and far less forgiving. So the goal is to design networks that can transfer 100Gbps, 200Gbps, and up to 1,000 Gbps (1 Terabit) a second with a low latency, low jitter, and a zero loss profile. Simple right?
Hardware network upgrades
To meet these new demands of AI workflows, we’ve upgraded nearly every layer of our physical infrastructure. We needed to increase the density of our networking hardware, deploy denser fiber optic solutions, and upgrade the capacity of our edge network.
What technologies are we deploying?
1. Transitioning from NRZ to PAM4 Optics
The fiber optic modules that are used to connect all our infrastructure hardware (servers, switches, routers) have been transitioned to modules that support a denser encoding method. Both NRZ and PAM4 are technologies used to modulate signals. Think of NRZ as a one-lane highway with one passenger per car. PAM4 adds three more passengers per car, doubling the rate without doubling lanes and with controllable cons such as increased noise sensitivity. By using four voltage levels instead of two, PAM4 transmits twice the information per signal change, effectively doubling bandwidth per fiber strand.
Comparison of (NRZ) and (PAM4) signals
2. MTP-8 and MTP-16 Fiber
MTP is a fiber connector type and the number after denotes the number of fiber optic strands contained within the cable. The higher the number, the more fiber pairs in the cable. We’ve used MTP-8 for years (four pairs of fiber), but to handle AI-scale traffic, we’re now deploying MTP-16 for higher-density connections. That means where we once ran 100G links, we now run 400G—and can scale up to multiple 100G paths as workloads grow (4x100G, 8x100G, etc).
3. Expanding edge and core capacity
We’ve refreshed routers and switches to handle higher port speeds and density—moving from 100G to 400G interfaces across our interconnects. The result: higher aggregate throughput and better fault isolation for massive parallel transfers.
Visualizing an AI workflow
Our monitoring tools track network flows (TCP conversations) in real time, giving us visibility into how large AI workflows move across the infrastructure. We use this type of information to monitor and make sure that large workflows are distributed across our physical infrastructure to allow for traffic balancing.
So, what does a large “AI workflow” look like? It’s not one device talking to one device at a high rate, but rather a collection of actors all working together.
On our side, our API layer speaks to our storage layer, requesting the files. Once the files are retrieved from our storage layer, they flow through our API servers and are then sent to a destination. In order to achieve a high throughput, many API servers talk to many destination servers.
A typical 200+ Gbps transfer (diagrammed below) might involve four API virtual IPs (VIPs), each hosted on multiple backend servers sending 5–7 Gbps to ten destination nodes for a total output of 52Gbps from each API server. On the receiving side, each destination server might be ingesting 20Gbps across multiple streams.
AI data transfer from Backblaze (left) and client servers (right)
The key insight: AI data transfer isn’t one big pipe—it’s a distributed mesh of many coordinated streams. Our design scales linearly—add more API servers, add more destination nodes, and the flow grows predictably without congestion or packet loss.
Conclusion
AI workflows have redefined what “fast” means on the network. At Backblaze, we’ve evolved from a single-ISP startup to an AI-scale infrastructure provider by continuously pushing the boundaries of connectivity, throughput, and reliability.
As our customers push the frontiers of AI, we’ll keep tuning the invisible layer that makes it possible: the AI-ready network.
For many in the research community, it’s gotten harder to be optimistic about the impacts of artificial intelligence.
As authoritarianism is rising around the world, AI-generated “slop” is overwhelming legitimate media, while AI-generated deepfakes are spreading misinformation and parroting extremist messages. AI is making warfare more precise and deadly amidst intransigent conflicts. AI companies are exploiting people in the global South who work as data labelers, and profiting from content creators worldwide by using their work without license or compensation. The industry is also affecting an already-roiling climate with its enormous energy demands.
Meanwhile, particularly in the United States, public investment in science seems to be redirected and concentrated on AI at the expense of other disciplines. And Big Tech companies are consolidating their control over the AI ecosystem. In these ways and others, AI seems to be making everything worse.
This is not the whole story. We should not resign ourselves to AI being harmful to humanity. None of us should accept this as inevitable, especially those in a position to influence science, government, and society. Scientists and engineers can push AI towards a beneficial path. Here’s how.
The Academy’s View of AI
A Pew study in April found that 56 percent of AI experts (authors and presenters of AI-related conference papers) predict that AI will have positive effects on society. But that optimism doesn’t extend to the scientific community at large. A 2023 survey of 232 scientists by the Center for Science, Technology and Environmental Policy Studies at Arizona State University found more concern than excitement about the use of generative AI in daily life—by nearly a three to one ratio.
We have encountered this sentiment repeatedly. Our careers of diverse applied work have brought us in contact with many research communities: privacy, cybersecurity, physical sciences, drug discovery, public health, public interest technology, and democratic innovation. In all of these fields, we’ve found strong negative sentiment about the impacts of AI. The feeling is so palpable that we’ve often been asked to represent the voice of the AI optimist, even though we spend most of our time writing about the need to reform the structures of AI development.
We understand why these audiences see AI as a destructive force, but this negativity engenders a different concern: that those with the potential to guide the development of AI and steer its influence on society will view it as a lost cause and sit out that process.
Elements of a Positive Vision for AI
Manyhavearguedthatturning the tide of climate action requires clearly articulating a path towards positive outcomes. In the same way, while scientists and technologists should anticipate, warn against, and help mitigate the potential harms of AI, they should also highlight the ways the technology can be harnessed for good, galvanizing public action towards those ends.
There are myriad ways to leverage and reshape AI to improve peoples’ lives, distribute rather than concentrate power, and even strengthen democratic processes. Many examples have arisen from the scientific community and deserve to be celebrated.
Some examples: AI is eliminating communication barriers across languages, including under-resourced contexts like marginalized sign languages and indigenous African languages. It is helping policymakers incorporate the viewpoints of many constituents through AI-assisted deliberations and legislative engagement. Large language models can scale individual dialogs to address climate–change skepticism, spreading accurate information at a critical moment. National labs are building AI foundation models to accelerate scientific research. And throughout the fields of medicine and biology, machine learning is solving scientific problems like the prediction of protein structure in aid of drug discovery, which was recognized with a Nobel Prize in 2024.
While each of these applications is nascent and surely imperfect, they all demonstrate that AI can be wielded to advance the public interest. Scientists should embrace, champion, and expand on such efforts.
These apply to scientists as well. Researchers should work to reform the AI industry to be more ethical, equitable, and trustworthy. We must collectively developethicalnorms for research that advance and applies AI, and should use and draw attention to AI developers who adhere to those norms.
Second, we should resist harmful uses of AI by documenting the negative applications of AI and casting a light on inappropriate uses.
Third, we should responsibly use AI to make society and peoples’ lives better, exploiting its capabilities to help the communities they serve.
And finally, we must advocate for the renovation of institutions to prepare them for the impacts of AI; universities, professional societies, and democratic organizations are all vulnerable to disruption.
Scientists have a special privilege and responsibility: We are close to the technology itself and therefore well positioned to influence its trajectory. We must work to create an AI-infused world that we want to live in. Technology, as the historian Melvin Kranzberg observed, “is neither good nor bad; nor is it neutral.” Whether the AI we build is detrimental or beneficial to society depends on the choices we make today. But we cannot create a positive future without a vision of what it looks like.
This essay was written with Nathan E. Sanders, and originally appeared in IEEE Spectrum.
AWS End User Messaging now supports international SMS capabilities for US Toll-Free Numbers (TFNs). This new feature allows businesses to use a single US TFN to send SMS messages to over 150 countries, simplifying global outreach. It primarily benefits customers who need to send one-way transactional alerts—like one-time passwords (OTPs) or shipping notifications—and businesses that want to rapidly prototype and test their messaging strategy in new international markets without the overhead of procuring country-specific numbers.
This guide will walk you through the pros and cons of this feature and show you how to enable it and when to use it versus traditional, country-specific sending methods.
What Are International US Toll-Free Numbers?
An International US Toll-Free Number is a standard US TFN that has been enabled with the capability to send SMS messages to destinations outside of the United States. This feature is backward compatible, meaning you can enable it on any new or existing US TFNs in your account.
How to Enable International Sending
There are three primary ways to enable this feature for your US Toll-Free Numbers:
Enable international sending when registering a new number in the console.
Enable international sending for an existing number in the console.
Enable international sending for an existing number via the AWS CLI.
1. Enable When Registering a New US Toll-Free Number (Console)
From the AWS End User Messaging console, navigate to Manage SMS
From the AWS End User Messaging console, navigate to Configurations > Phone numbers > and select Request originator
Step 1: Select country, select the United States (US) as your destination country
Under Step 2: Define use case, configure the various options listed for your intended Messaging use case, and select Yes to enable International sending, prior to clicking Next
For Step 3: Select originator type, select Toll-free, validate your Resource policy choices, select Next
In Step 4: Review and request: Verify the information you entered is correct and select Request.Please note: US Toll-Free Number registration requests can take approximately 15 business days to be approved.
A successful command returns the full JSON object for the phone number. Confirm the change by checking that the InternationalSendingEnabled value is true
This feature offers a powerful new way to reach a global audience, but it’s important to understand where it shines and what its limitations are.
Benefits (Advantages)
Global Reach with a Single Number: Send SMS to over 150 countries using a single, existing US TFN.
Simplified Management: Avoid the operational overhead and cost of purchasing and managing a fleet of country-specific phone numbers.
Rapid Prototyping and Testing: Quickly test messaging campaigns in new international markets before committing to the best practice approach of acquiring dedicated in-country numbers.
Cost Optimization for One-Way Alerts: Provides a cost-effective method for sending high-volume, one-way transactional messages like OTPs, appointment reminders, and shipping notifications globally.
Limitations & Technical Considerations
Two-Way SMS is Limited to the US and Canada: Reliable, two-way SMS conversations are only supported for recipients in the United States and Canada.
One-Way Only for All Other Countries: For all other destinations, this is a one-way only.
Best-Effort Deliverability: Sending outside of the US and Canada is on a “best-effort” basis. The phone number that appears on the recipient’s device may be replaced with a local number or Sender ID, which is why two-way messaging will not work for these destinations. For more details on maximizing delivery, please read A Guide to Optimizing SMS Delivery and Best Practices.
Managed Opt-Out is Not Guaranteed Internationally: The automatic STOP reply functionality does not work for destinations outside of the US and Canada. For international recipients, you must provide an alternative opt-out method.
Standard Throughput (3 MPS): International TFNs have a default throughput of 3 Message Parts Per Second (MPS). For high-volume, high-throughput campaigns, dedicated country-specific numbers (like short codes) are the recommended best practice.
Understanding the Cost
The pricing for this feature is straightforward:
No Additional Monthly Fees: There is no extra charge to enable the international sending capability on your US TFN. You only pay the standard monthly lease for the number itself.
Pay-Per-Use Messaging: You are billed for each outbound SMS message at the standard, per-message rate for the destination country.
Choosing the right tool depends on your use case. Here’s a simple comparison:
Considerations and Next Steps
Once you have enabled your international sending over US Toll-Free Numbers, you can enhance your messaging strategy by considering resilience, monitoring, and scalability. The following resources provide best practices for enhancing your sending.
International SMS for US Toll-Free Numbers is a powerful strategic tool for businesses looking to simplify their global messaging. It excels at enabling rapid testing in new markets and efficiently delivering one-way transactional alerts across the globe from a single number.
However, it is not a replacement for the best practice of using dedicated, in-country phone numbers when reliable two-way conversations and guaranteed branding are critical to your campaign’s success. By understanding its benefits and limitations, you can strategically use this feature to get going quickly while planning a long-term move towards country-specific codes for your most important markets.
Microsoft is warning of a scam involving online payroll systems. Criminals use social engineering to steal people’s credentials, and then divert direct deposits into accounts that they control. Sometimes they do other things to make it harder for the victim to realize what is happening.
I feel like this kind of thing is happening everywhere, with everything. As we move more of our personal and professional lives online, we enable criminals to subvert the very systems we rely on.
These days, the most important meeting attendee isn’t a person: It’s the AI notetaker.
This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence.
But clever meeting attendees can manipulate this system’s record by speaking more to what the underlying AI weights for summarization and importance than to their colleagues. As a result, you can expect some meeting attendees to use language more likely to be captured in summaries, timing their interventions strategically, repeating key points, and employing formulaic phrasing that AI models are more likely to pick up on. Welcome to the world of AI summarization optimization (AISO).
Optimizing for algorithmic manipulation
AI summarization optimization has a well-known precursor: SEO.
Search-engine optimization is as old as the World Wide Web. The idea is straightforward: Search engines scour the internet digesting every possible page, with the goal of serving the best results to every possible query. The objective for a content creator, company, or cause is to optimize for the algorithm search engines have developed to determine their webpage rankings for those queries. That requires writing for two audiences at once: human readers and the search-engine crawlers indexing content. Techniques to do this effectively are passed around like trade secrets, and a $75 billion industry offers SEO services to organizations of all sizes.
More recently, researchers have documented techniques for influencing AI responses, including large-language model optimization (LLMO) and generative engine optimization (GEO). Tricks include content optimization—adding citations and statistics—and adversarial approaches: using specially crafted text sequences. These techniques often target sources that LLMs heavily reference, such as Reddit, which is claimed to be cited in 40% of AI-generated responses. The effectiveness and real-world applicability of these methods remains limited and largely experimental, although there is substantial evidence that countries such as Russia are activelypursuingthis.
AI summarization optimization follows the same logic on a smaller scale. Human participants in a meeting may want a certain fact highlighted in the record, or their perspective to be reflected as the authoritative one. Rather than persuading colleagues directly, they adapt their speech for the notetaker that will later define the “official” summary. For example:
“The main factor in last quarter’s delay was supply chain disruption.”
“The key outcome was overwhelmingly positive client feedback.”
“Our takeaway here is in alignment moving forward.”
“What matters here is the efficiency gains, not the temporary cost overrun.”
The techniques are subtle. They employ high-signal phrases such as “key takeaway” and “action item,” keep statements short and clear, and repeat them when possible. They also use contrastive framing (“this, not that”), and speak early in the meeting or at transition points.
Once spoken words are transcribed, they enter the model’s input. Cue phrases—and even transcription errors—can steer what makes it into the summary. In many tools, the output format itself is also a signal: Summarizers often offer sections such as “Key Takeaways” or “Action Items,” so language that mirrors those headings is more likely to be included. In effect, well-chosen phrases function as implicit markers that guide the AI toward inclusion.
Research confirms this. Early AI summarization research showedthat models trained to reconstruct summary-style sentences systematically overweigh such content. Models over-rely on early-positioncontent in news. And models often overweigh statements at the start or end of a transcript, underweighting the middle. Recent work further confirms vulnerability to phrasing-based manipulation: models cannot reliably distinguish embedded instructions from ordinary content, especially when phrasing mimics salient cues.
How to combat AISO
If AISO becomes common, three forms of defense will emerge. First, meeting participants will exert social pressure on one another. When researchers secretly deployed AI bots in Reddit’s r/changemyview community, users and moderators responded with strong backlash calling it “psychological manipulation.” Anyone using obvious AI-gaming phrases may face similar disapproval.
Second, organizations will start governing meeting behavior using AI: risk assessments and access restrictions before the meetings even start, detection of AISO techniques in meetings, and validation and auditing after the meetings.
Third, AI summarizers will have their own technical countermeasures. For example, the AI security company CloudSEK recommends content sanitization to strip suspicious inputs, prompt filtering to detect meta-instructions and excessive repetition, context window balancing to weight repeated content less heavily, and user warnings showing content provenance.
AI summarization optimization is a small, subtle shift, but it illustrates how the adoption of AI is reshaping human behavior in unexpected ways. The potential implications are quietly profound.
Meetings—humanity’s most fundamental collaborative ritual—are being silently reengineered by those who understand the algorithm’s preferences. The articulate are gaining an invisible advantage over the wise. Adversarial thinking is becoming routine, embedded in the most ordinary workplace rituals, and, as AI becomes embedded in organizational life, strategic interactions with AI notetakers and summarizers may soon be a necessary executive skill for navigating corporate culture.
AI summarization optimization illustrates how quickly humans adapt communication strategies to new technologies. As AI becomes more embedded in workplace communication, recognizing these emerging patterns may prove increasingly important.
This essay was written with Gadi Evron, and originally appeared in CSO.
Below, co-authors Bruce Schneier and Nathan E. Sanders share five key insights from their new book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship.
What’s the big idea?
AI can be used both for and against the public interest within democracies. It is already being used in the governing of nations around the world, and there is no escaping its continued use in the future by leaders, policy makers, and legal enforcers. How we wire AI into democracy today will determine if it becomes a tool of oppression or empowerment.
1. AI’s global democratic impact is already profound.
It’s been just a few years since ChatGPT stormed into view and AI’s influence has already permeated every democratic process in governments around the world:
In 2022, an artist collective in Denmark founded the world’s first political party committed to an AI-generated policy platform.
Also in 2022, South Korean politicians running for the presidency were the first to use AI avatars to communicate with voters en masse.
In 2023, a Brazilian municipal legislator passed the first enacted law written by AI.
In 2024, a U.S. federal court judge started using AI to interpret the plain meaning of words in U.S. law.
Also in 2024, the Biden administration disclosed more than two thousand discrete use cases for AI across the agencies of the U.S. federal government.
The examples illustrate the diverse uses of AI across citizenship, politics, legislation, the judiciary, and executive administration.
Not all of these uses will create lasting change. Some of these will be one-offs. Some are inherently small in scale. Some were publicity stunts. But each use case speaks to a shifting balance of supply and demand that AI will increasingly mediate.
Legislators need assistance drafting bills and have limited staff resources, especially at the local and state level. Historically, they have looked to lobbyists and interest groups for help. Increasingly, it’s just as easy for them to use an AI tool.
2. The first places AI will be used are where there is the least public oversight.
Many of the use cases for AI in governance and politics have vocal objectors. Some make us uncomfortable, especially in the hands of authoritarians or ideological extremists.
In some cases, politics will be a regulating force to prevent dangerous uses of AI. Massachusetts has banned the use of AI face recognition in law enforcement because of real concerns voiced by the public about their tendency to encode systems of racial bias.
Some of the uses we think might be most impactful are unlikely to be adopted fast because of legitimate concern about their potential to make mistakes, introduce bias, or subvert human agency. AIs could be assistive tools for citizens, acting as their voting proxies to help us weigh in on larger numbers of more complex ballot initiatives, but we know that many will object to anything that verges on AIs being given a vote.
But AI will continue to be rapidly adopted in some aspects of democracy, regardless of how the public feels. People within democracies, even those in government jobs, often have great independence. They don’t have to ask anyone if it’s ok to use AI, and they will use it if they see that it benefits them. The Brazilian city councilor who used AI to draft a bill did not ask for anyone’s permission. The U.S. federal judge who used AI to help him interpret law did not have to check with anyone first. And the Trump administration seems to be using AI for everything from drafting tariff policies to writing public health reports—with some obvious drawbacks.
It’s likely that even the thousands of disclosed AI uses in government are only the tip of the iceberg. These are just the applications that governments have seen fit to share; the ones they think are the best vetted, most likely to persist, or maybe the least controversial to disclose.
3. Elites and authoritarians will use AI to concentrate power.
Many Westerners point to China as a cautionary tale of how AI could empower autocracy, but the reality is that AI provides structural advantages to entrenched power in democratic governments, too. The nature of automation is that it gives those at the top of a power structure more control over the actions taken at its lower levels.
It’s famously hard for newly elected leaders to exert their will over the many layers of human bureaucracies. The civil service is large, unwieldy, and messy. But it’s trivial for an executive to change the parameters and instructions of an AI model being used to automate the systems of government.
The dynamic of AI effectuating concentration of power extends beyond government agencies. Over the past five years, Ohio has undertaken a project to do a wholesale revision of its administrative code using AI. The leaders of that project framed it in terms of efficiency and good governance: deleting millions of words of outdated, unnecessary, or redundant language. The same technology could be applied to advance more ideological ends, like purging all statutory language that places burdens on business, neglects to hold businesses accountable, protects some class of people, or fails to protect others.
Whether you like or despise automating the enactment of those policies will depend on whether you stand with or are opposed to those in power, and that’s the point. AI gives any faction with power the potential to exert more control over the levers of government.
4. Organizers will find ways to use AI to distribute power instead.
We don’t have to resign ourselves to a world where AI makes the rich richer and the elite more powerful. This is a technology that can also be wielded by outsiders to help level the playing field.
In politics, AI gives upstart and local candidates access to skills and the ability to do work on a scale that used to only be available to well-funded campaigns. In the 2024 cycle, Congressional candidates running against incumbents like Glenn Cook in Georgia and Shamaine Daniels in Pennsylvania used AI to help themselves be everywhere all at once. They used AI to make personalized robocalls to voters, write frequent blog posts, and even generate podcasts in the candidate’s voice. In Japan, a candidate for Governor of Tokyo used an AI avatar to respond to more than eight thousand online questions from voters.
Outside of public politics, labor organizers are also leveraging AI to build power. The Worker’s Lab is a U.S. nonprofit developing assistive technologies for labor unions, like AI-enabled apps that help service workers report workplace safety violations. The 2023 Writers’ Guild of America strike serves as a blueprint for organizers. They won concessions from Hollywood studios that protect their members against being displaced by AI while also winning them guarantees for being able to use AI as assistive tools to their own benefit.
5. The ultimate democratic impact of AI depends on us.
If you are excited about AI and see the potential for it to make life, and maybe even democracy, better around the world, recognize that there are a lot of people who don’t feel the same way.
If you are disturbed about the ways you see AI being used and worried about the future that leads to, recognize that the trajectory we’re on now is not the only one available.
The technology of AI itself does not pose an inherent threat to citizens, workers, and the public interest. Like other democratic technologies—voting processes, legislative districts, judicial review—its impacts will depend on how it’s developed, who controls it, and how it’s used.
Constituents of democracies should do four things:
Reform the technology ecosystem to be more trustworthy, so that AI is developed with more transparency, more guardrails around exploitative use of data, and public oversight.
Resist inappropriate uses of AI in government and politics, like facial recognition technologies that automate surveillance and encode inequity.
Responsibly use AI in government where it can help improve outcomes, like making government more accessible to people through translation and speeding up administrative decision processes.
Renovate the systems of government vulnerable to the disruptive potential of AI’s superhuman capabilities, like political advertising rules that never anticipated deepfakes.
These four Rs are how we can rewire our democracy in a way that applies AI to truly benefit the public interest.
This essay was written with Nathan E. Sanders, and originally appeared in The Next Big Idea Club.
EDITED TO ADD (11/6): This essay was republished by Fast Company.
Interesting article about the arms race between AI systems that invent/design new biological pathogens, and AI systems that detect them before they’re created:
The team started with a basic test: use AI tools to design variants of the toxin ricin, then test them against the software that is used to screen DNA orders. The results of the test suggested there was a risk of dangerous protein variants slipping past existing screening software, so the situation was treated like the equivalent of a zero-day vulnerability.
[…]
Details of that original test are being made available today as part of a much larger analysis that extends the approach to a large range of toxic proteins. Starting with 72 toxins, the researchers used three open source AI packages to generate a total of about 75,000 potential protein variants.
And this is where things get a little complicated. Many of the AI-designed protein variants are going to end up being non-functional, either subtly or catastrophically failing to fold up into the correct configuration to create an active toxin.
[…]
In any case, DNA sequences encoding all 75,000 designs were fed into the software that screens DNA orders for potential threats. One thing that was very clear is that there were huge variations in the ability of the four screening programs to flag these variant designs as threatening. Two of them seemed to do a pretty good job, one was mixed, and another let most of them through. Three of the software packages were updated in response to this performance, which significantly improved their ability to pick out variants.
There was also a clear trend in all four screening packages: The closer the variant was to the original structurally, the more likely the package (both before and after the patches) was to be able to flag it as a threat. In all cases, there was also a cluster of variant designs that were unlikely to fold into a similar structure, and these generally weren’t flagged as threats.
The research is all preliminary, and there are a lot of ways in which the experiment diverges from reality. But I am not optimistic about this particular arms race. I think that the ability of AI systems to create something deadly will advance faster than the ability of AI systems to detect its components.
Ultimately, the architects settled on a creative solution. Rather than bolt KEM onto the existing double ratchet, they allowed it to remain more or less the same as it had been. Then they used the new quantum-safe ratchet to implement a parallel secure messaging system.
Now, when the protocol encrypts a message, it sources encryption keys from both the classic Double Ratchet and the new ratchet. It then mixes the two keys together (using a cryptographic key derivation function) to get a new encryption key that has all of the security of the classical Double Ratchet but now has quantum security, too.
The Signal engineers have given this third ratchet the formal name: Sparse Post Quantum Ratchet, or SPQR for short. The third ratchet was designed in collaboration with PQShield, AIST, and New York University. The developers presented the erasure-code-based chunking and the high-level Triple Ratchet design at the Eurocrypt 2025 conference. At the Usenix 25 conference, they discussed the six options they considered for adding quantum-safe forward secrecy and post-compromise security and why SPQR and one other stood out. Presentations at the NIST PQC Standardization Conference and the Cryptographic Applications Workshop explain the details of chunking, the design challenges, and how the protocol had to be adapted to use the standardized ML-KEM.
Jacomme further observed:
The final thing interesting for the triple ratchet is that it nicely combines the best of both worlds. Between two users, you have a classical DH-based ratchet going on one side, and fully independently, a KEM-based ratchet is going on. Then, whenever you need to encrypt something, you get a key from both, and mix it up to get the actual encryption key. So, even if one ratchet is fully broken, be it because there is now a quantum computer, or because somebody manages to break either elliptic curves or ML-KEM, or because the implementation of one is flawed, or…, the Signal message will still be protected by the second ratchet. In a sense, this update can be seen, of course simplifying, as doubling the security of the ratchet part of Signal, and is a cool thing even for people that don’t care about quantum computers.
Good Wall Street Journalarticle on criminal gangs that scam people out of their credit card information:
Your highway toll payment is now past due, one text warns. You have U.S. Postal Service fees to pay, another threatens. You owe the New York City Department of Finance for unpaid traffic violations.
The texts are ploys to get unsuspecting victims to fork over their credit-card details. The gangs behind the scams take advantage of this information to buy iPhones, gift cards, clothing and cosmetics.
Criminal organizations operating out of China, which investigators blame for the toll and postage messages, have used them to make more than $1 billion over the last three years, according to the Department of Homeland Security.
[…]
Making the fraud possible: an ingenious trick allowing criminals to install stolen card numbers in Google and Apple Wallets in Asia, then share the cards with the people in the U.S. making purchases half a world away.
This week started with challenges for many using services in the the North Virginia (us-east-1) Region. On Monday, we experienced a service disruption affecting DynamoDB and several other services due to a DNS configuration problem. The issue has been fully resolved, and you can read the full details in our official summary. As someone who works closely with developers, I know how disruptive these incidents can be to your applications and your users. The teams are learning valuable lessons from this event that will help improve our services going forward.
Last week’s launches
On a brighter note, I’m excited to share some launches and updates from this past week that I think you’ll find interesting.
AWS RTB Fabric is now generally available — If you’re working in advertising technology, you’ll be interested in AWS RTB Fabric, a fully managed service for real-time bidding workloads. It connects AdTech partners like SSPs, DSPs, and publishers through a private, high-performance network that delivers single-digit millisecond latency—critical for those split-second ad auctions. The service reduces networking costs by up to 80% compared to standard cloud solutions with no upfront commitments, and includes three built-in modules to optimize traffic, improve bid efficiency, and increase bid response rates. AWS RTB Fabric is available in US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore and Tokyo), and Europe (Frankfurt and Ireland).
Customer Carbon Footprint Tool now includes Scope 3 emissions data — Understanding the full environmental impact of your cloud usage just got more comprehensive. The AWS Customer Carbon Footprint Tool (CCFT) now covers all three industry-standard emission scopes as defined by the Greenhouse Gas Protocol. This update adds Scope 3 emissions—covering the lifecycle carbon impact from manufacturing servers, powering AWS facilities, and transporting equipment to data centers—plus Scope 1 natural gas and refrigerants. With historical data available back to January 2022, you can track your progress over time and make informed decisions about your cloud strategy to meet sustainability goals. Access the data through the CCFT dashboard or AWS Billing and Cost Management Data Exports.
Additional updates
I thought these projects, blog posts, and news items were also interesting:
AWS Secret-West Region is now available — AWS launched its second Secret Region in the western United States, capable of handling mission-critical workloads at the Secret U.S. security classification level. This new region provides enhanced performance for latency-sensitive workloads and offers multi-region resiliency with geographic separation for Intelligence Community and Department of Defense missions. The infrastructure features data centers and network architecture designed, built, accredited, and operated for security compliance with Intelligence Community Directive requirements.
Amazon CloudWatch now generates incident reports — CloudWatch investigations can now automatically generate comprehensive incident reports that include executive summaries, timeline of events, impact assessments, and actionable recommendations. The feature collects and correlates telemetry data along with investigation actions to help teams identify patterns and implement preventive measures through structured post-incident analysis.
Amazon Connect introduces threaded email views — Amazon Connect email now displays exchanges in a threaded format and automatically includes prior conversation context when agents compose responses. These enhancements make it easier for both agents and customers to maintain context and continuity across interactions, delivering a more natural and familiar email experience.
Amazon EC2 I8g instances expand to additional regions — Storage Optimized I8g instances are now available in Europe (London), Asia Pacific (Singapore), and Asia Pacific (Tokyo). Powered by AWS Graviton4 processors and third-generation AWS Nitro SSDs, these instances deliver up to 60% better compute performance and 65% better real-time storage performance per TB compared to previous generation I4g instances, with storage I/O latency reduced by up to 50%.
AWS Location Service adds enhanced map styling — Developers can now incorporate terrain visualization, contour lines, real-time traffic overlays, and transportation-specific routing details through the GetStyleDescriptor API. The new styling parameters enable tailored maps for specific applications—from outdoor navigation to logistics planning.
CloudWatch Synthetics introduces multi-check canaries — You can now bundle up to 10 different monitoring steps in a single canary using JSON configuration without custom scripts. The multi-check blueprints support HTTP endpoints with authentication, DNS validation, SSL certificate monitoring, and TCP port checks, making API monitoring more cost-effective.
Amazon S3 Tables now generates CloudTrail events — S3 Tables now logs AWS CloudTrail events for automatic maintenance operations, including compaction and snapshot expiration. This enables organizations to audit the maintenance activities that S3 Tables automatically performs to enhance query performance and reduce operational costs.
AWS Lambda increases asynchronous invocation payload size to 1 MB — Lambda has quadrupled the maximum payload size for asynchronous invocations from 256 KB to 1 MB across all AWS Commercial and GovCloud (US) Regions. This expansion streamlines architectures by allowing comprehensive data to be included in a single event, eliminating the need for complex data chunking or external storage solutions. Use cases now better supported include large language model prompts, detailed telemetry signals, complex ML output structures, and complete user profiles. The update applies to asynchronous invocations through the Lambda API or push-based events from services like S3, CloudWatch, SNS, EventBridge, and Step Functions. Pricing remains at 1 request charge for the first 256 KB, with 1 additional charge per 64 KB chunk thereafter.
Upcoming AWS events
Keep a look out and be sure to sign up for these upcoming events:
AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — AWS flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities. Registration is now open.
Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse for upcoming in-person and virtual developer-focused events in your area.
That’s all for this week. Check back next Monday for another Weekly Roundup!
I assume I don’t have to explain last week’s Louvre jewel heist. I love a good caper, and have (like many others) eagerly followed the details. An electric ladder to a second-floor window, an angle grinder to get into the room and the display cases, security guards there more to protect patrons than valuables—seven minutes, in and out.
The Louvre, it turns out—at least certain nooks of the ancient former palace—is something like an anopticon: a place where no one is observed. The world now knows what the four thieves (two burglars and two accomplices) realized as recently as last week: The museum’s Apollo Gallery, which housed the stolen items, was monitored by a single outdoor camera angled away from its only exterior point of entry, a balcony. In other words, a free-roaming Roomba could have provided the world’s most famous museum with more information about the interior of this space. There is no surveillance footage of the break-in.
Professional jewelry thieves were not impressed with the four. Here’s Larry Lawton:
“I robbed 25, 30 jewelry stores—20 million, 18 million, something like that,” Mr. Lawton said. “Did you know that I never dropped a ring or an earring, no less, a crown worth 20 million?”
He thinks that they had a compatriot on the inside.
Museums, especially smaller ones, are good targets for theft because they rarely secure what they hold to its true value. They can’t; it would be prohibitively expensive. This makes them an attractive target.
We might find out soon. It looks like some people have been arrested
Not being out of the country—out of the EU—by now was sloppy. Leaving DNA evidence was sloppy. I can hope the criminals were sloppy enough not to have disassembled the jewelry by now, but I doubt it. They were probably taken apart within hours of the theft.
The whole thing is sad, really. Unlike stolen paintings, those jewels have no value in their original form. They need to be taken apart and sold in pieces. But then their value drops considerably—so the end result is that most of the worth of those items disappears. It would have been much better to pay the thieves not to rob the Louvre.
Mother Jones has a long article on surveillance arms manufacturers, their wares, and how they avoid export control laws:
Operating from their base in Jakarta, where permissive export laws have allowed their surveillance business to flourish, First Wap’s European founders and executives have quietly built a phone-tracking empire, with a footprint extending from the Vatican to the Middle East to Silicon Valley.
It calls its proprietary system Altamides, which it describes in promotional materials as “a unified platform to covertly locate the whereabouts of single or multiple suspects in real-time, to detect movement patterns, and to detect whether suspects are in close vicinity with each other.”
Altamides leaves no trace on the phones it targets, unlike spyware such as Pegasus. Nor does it require a target to click on a malicious link or show any of the telltale signs (such as overheating or a short battery life) of remote monitoring.
Its secret is shrewd use of the antiquated telecom language Signaling System No. 7, known as SS7, that phone carriers use to route calls and text messages. Any entity with SS7 access can send queries requesting information about which cell tower a phone subscriber is nearest to, an essential first step to sending a text message or making a call to that subscriber. But First Wap’s technology uses SS7 to zero in on phone numbers and trace the location of their users.
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