Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/metas-ai-glasses-and-privacy.html
Surprising no one, Meta’s new AI glasses are a privacy disaster.
I’m not sure what can be done here. This is a technology that will exist, whether we like it or not.
Meanwhile, there is a new Android app that detects when there are smart glasses nearby.
Post Syndicated from Ryan Smith original https://www.servethehome.com/decoding-the-future-of-inference-at-nvidia-groq-lpus-join-vera-rubin-platform-for-low-latency-inference/
With its upcoming Vera Rubin rackscale architecture, NVIDIA is going to be integrating LPUs from acquihire Groq, marking a major expansion beyond using GPUs alone for AI inference
The post Decoding the Future of Inference At NVIDIA: Groq LPUs Join Vera Rubin Platform For Low-Latency Inference appeared first on ServeTheHome.
Post Syndicated from Ryan Smith original https://www.servethehome.com/nvidia-gtc-2026-keynote-live-coverage/
It is that time of the year again. No, not Christmas (unless you are an AI developer, at any rate), but rather NVIDIA’s annual GTC conference for all things NVIDIA. As has become an annual tradition for us at STH, we are are covering the 2026 GTC keynote live, to catch all the latest and […]
The post NVIDIA GTC 2026 Keynote Live Coverage appeared first on ServeTheHome.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/academia-and-the-ai-brain-drain.html
In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.
Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see go.nature.com/4qznsq1). Technology firms are also spending billions on “reverse-acquihires”—poaching the star staff members of start-ups without acquiring the companies themselves. Eyeing these generous payouts, technical experts earning more modest salaries might well reconsider their career choices.
Academia is already losing out. Since the launch of ChatGPT in 2022, concerns have grown in academia about an “AI brain drain.” Studies point to a sharp rise in university machine-learning and AI researchers moving to industry roles. A 2025 paper reported that this was especially true for young, highly cited scholars: researchers who were about five years into their careers and whose work ranked among the most cited were 100 times more likely to move to industry the following year than were ten-year veterans whose work received an average number of citations, according to a model based on data from nearly seven million papers.1
This outflow threatens the distinct roles of academic research in the scientific enterprise: innovation driven by curiosity rather than profit, as well as providing independent critique and ethical scrutiny. The fixation of “big tech” firms on skimming the very top talent also risks eroding the idea of science as a collaborative endeavor, in which teams—not individuals—do the most consequential work.
Here, we explore the broader implications for science and suggest alternative visions of the future.
Astronomical salaries for AI talent buy into a legend as old as the software industry: the 10x engineer. This is someone who is supposedly capable of ten times the impact of their peers. Why hire and manage an entire group of scientists or software engineers when one genius—or an AI agent—can outperform them?
That proposition is increasingly attractive to tech firms that are betting that a large number of entry-level and even mid-level engineering jobs will be replaced by AI. It’s no coincidence that Google’s Gemini 3 Pro AI model was launched with boasts of “PhD-level reasoning,” a marketing strategy that is appealing to executives seeking to replace people with AI.
But the lone-genius narrative is increasingly out of step with reality. Research backs up a fundamental truth: science is a team sport. A large-scale study of scientific publishing from 1900 to 2011 found that papers produced by larger collaborations consistently have greater impact than do those of smaller teams, even after accounting for self-citation.2 Analyses of the most highly cited scientists show a similar pattern: their highest-impact works tend to be those papers with many authors.3 A 2020 study of Nobel laureates reinforces this trend, revealing that—much like the wider scientific community—the average size of the teams that they publish with has steadily increased over time as scientific problems increase in scope and complexity.4
From the detection of gravitational waves, which are ripples in space-time caused by massive cosmic events, to CRISPR-based gene editing, a precise method for cutting and modifying DNA, to recent AI breakthroughs in protein-structure prediction, the most consequential advances in modern science have been collective achievements. Although these successes are often associated with prominent individuals—senior scientists, Nobel laureates, patent holders—the work itself was driven by teams ranging from dozens to thousands of people and was built on decades of open science: shared data, methods, software and accumulated insight.
Building strong institutions is a much more effective use of resources than is betting on any single individual. Examples demonstrating this include the LIGO Scientific Collaboration, the global team that first detected gravitational waves; the Broad Institute of MIT and Harvard in Cambridge, Massachusetts, a leading genomics and biomedical-research center behind many CRISPR advances; and even for-profit laboratories such as Google DeepMind in London, which drove advances in protein-structure prediction with its AlphaFold tool. If the aim of the tech giants and other AI firms that are spending lavishly on elite talent is to accelerate scientific progress, the current strategy is misguided.
By contrast, well-designed institutions amplify individual ability, sustain productivity beyond any one person’s career and endure long after any single contributor is gone.
Equally important, effective institutions distribute power in beneficial ways. Rather than vesting decision-making authority in the hands of one person, they have mechanisms for sharing control. Allocation committees decide how resources are used, scientific advisory boards set collective research priorities, and peer review determines which ideas enter the scientific record.
And although the term “innovation by committee” might sound disparaging, such an approach is crucial to make the scientific enterprise act in concert with the diverse needs of the broader public. This is especially true in science, which continues to suffer from pervasive inequalities across gender, race and socio-economic and cultural differences.5
This is why scientists, academics and policymakers should pay more attention to how AI research is organized and led, especially as the technology becomes essential across scientific disciplines. Used well, AI can support a more equitable scientific enterprise by empowering junior researchers who currently have access to few resources.
Instead, some of today’s wealthiest scientific institutions might think that they can deploy the same strategies as the tech industry uses and compete for top talent on financial terms—perhaps by getting funding from the same billionaires who back big tech. Indeed, wage inequality has been steadily growing within academia for decades.6 But this is not a path that science should follow.
The ideal model for science is a broad, diverse ecosystem in which researchers can thrive at every level. Here are three strategies that universities and mission-driven labs should adopt instead of engaging in a compensation arms race.
First, universities and institutions should stay committed to the public interest. An excellent example of this approach can be found in Switzerland, where several institutions are coordinating to build AI as a public good rather than a private asset. Researchers at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the Swiss Federal Institute of Technology (ETH) in Zurich, working with the Swiss National Supercomputing Centre, have built Apertus, a freely available large language model. Unlike the controversially-labelled “open source” models built by commercial labs—such as Meta’s LLaMa, which has been criticized for not complying with the open-source definition (see go.nature.com/3o56zd5)—Apertus is not only open in its source code and its weights (meaning its core parameters), but also in its data and development process. Crucially, Apertus is not designed to compete with “frontier” AI labs pursuing superintelligence at enormous cost and with little regard for data ownership. Instead, it adopts a more modest and sustainable goal: to make AI trustworthy for use in industry and public administration, strictly adhering to data-licensing restrictions and including local European languages.7
Principal investigators (PIs) at other institutions globally should follow this path, aligning public funding agencies and public institutions to produce a more sustainable alternative to corporate AI.
Second, universities should bolster networks of researchers from the undergraduate to senior-professor levels—not only because they make for effective innovation teams, but also because they serve a purpose beyond next quarter’s profits. The scientific enterprise galvanizes its members at all levels to contribute to the same projects, the same journals and the same open, international scientific literature—to perpetuate itself across generations and to distribute its impact throughout society.
Universities should take precisely the opposite hiring strategy to that of the big tech firms. Instead of lavishing top dollar on a select few researchers, they should equitably distribute salaries. They should raise graduate-student stipends and postdoc salaries and limit the growth of pay for high-profile PIs.
Third, universities should show that they can offer more than just financial benefits: they must offer distinctive intellectual and civic rewards. Although money is unquestionably a motivator, researchers also value intellectual freedom and the recognition of their work. Studies show that research roles in industry that allow publication attract talent at salaries roughly 20% lower than comparable positions that prohibit it (see go.nature.com/4cbjxzu).
Beyond the intellectual recognition of publications and citation counts, universities should recognize and reward the production of public goods. The tenure and promotion process at universities should reward academics who supply expertise to local and national governments, who communicate with and engage the public in research, who publish and maintain open-source software for public use and who provide services for non-profit groups.
Furthermore, institutions should demonstrate that they will defend the intellectual freedom of their researchers and shield them from corporate or political interference. In the United States today, we see a striking juxtaposition between big tech firms, which curry favour with the administration of US President Donald Trump to win regulatory and trade benefits, and higher-education institutions, which suffer massive losses of federal funding and threats of investigation and sanction. Unlike big tech firms, universities should invest in enquiry that challenges authority.
We urge leaders of scientific institutions to reject the growing pay inequality rampant in the upper echelons of AI research. Instead, they should compete for talent on a different dimension: the integrity of their missions and the equitableness of their institutions. These institutions should focus on building sustainable organizations with diverse staff members, rather than bestowing a bounty on science’s 1%.
This essay was written with Nathan E. Sanders, and originally appeared in Nature.
Post Syndicated from Cliff Robinson original https://www.servethehome.com/meta-outlines-new-mtia-accelerator-roadmap-for-its-next-gen-ai-compute-mix/
Meta shared more on its MTIA 300 AI accelerator, already in production, and future MTIA 400, 450, and 500 generations for its infrastructure
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Post Syndicated from Grab Tech original https://engineering.grab.com/r8-optimization-at-scale-with-ai-assisted-debugging
Grab is Southeast Asia’s leading superapp, providing a suite of services that bring essential needs to users throughout the region. Its offerings include ride-hailing, food delivery, parcel delivery, mobile payments, and more. With safety, efficiency, and user-centered design at heart, Grab remains dedicated to solving everyday issues and improving the lives of millions. As our app continues to expand, we identified platform-level performance challenges that were affecting user experience across the board. In this article, we share how we successfully enabled R8 optimization for the Grab Android app, achieving significant improvements in app size, startup time, and stability through innovative AI-assisted debugging techniques.
Since 2024, our team observed a concerning trend: Application Not Responding (ANR) rates were spiking across the Grab app. Unlike typical isolated issues, the data revealed that ANRs were happening everywhere, not confined to specific features or modules. This pattern pointed to platform-level causes, with our analysis showing strong correlations between ANRs and several factors like memory pressure (particularly when garbage collection was triggered), ad-heavy user flows, overhead from heavy use of Jetpack Compose within XML layouts, and XML views within Compose code.
The Android community had long proven that R8 optimization (beyond basic code shrinking) could deliver substantial performance gains and app size reductions. As Grab has been adopting Jetpack Compose over the last two years, Google’s Jetpack Compose performance documentation specifically recommends R8 optimization for Compose-heavy apps. This made it a natural solution for our systemic performance issues.
However, enabling R8 optimization for Grab’s Android app was far from straightforward. Our app operates at a massive scale with over 9 million lines of code and more than a hundred engineers actively working on it daily. While we had basic R8 shrinking enabled, advanced optimization had proven challenging despite multiple attempts over six years (earlier with adopting DexGuard, later with R8 optimization).
In 2022, we almost succeeded with R8 optimization after successfully rolling it out onto our early access build. We were unfortunately faced with critical roadblocks that compelled us to put the project on hold. After analyzing our previous attempts and the current project situation, we identified three fundamental challenges that had to be solved simultaneously.
This article explains how we tackled each challenge through targeted innovations, including AI-assisted debugging for slow investigation cycles, pragmatic testing strategy for validation at scale, and optimized feedback loop for rapid iteration.
Before diving into our solution, it’s important to understand what R8 optimization actually provides beyond basic R8 shrinking.
With minifyEnabled=true and shrinkResources=true using proguard-android.txt, we already had:
By switching to proguard-android-optimize.txt, we gained access to:
These optimizations work together to improve runtime performance while significantly reducing app size.
Despite R8’s clear benefits, enabling it at Grab’s scale remains hard. Lessons from prior attempts and current project context surfaced three fundamental challenges we needed to solve.
R8 optimization issues are notoriously difficult to debug:
a, b, c.The challenge was compounded by limited bandwidth and high labor requirements. Most issues had to be either addressed directly or have solutions provided for other teams to fix. Manual decompilation, deobfuscation, and context gathering for each issue are inherently time-consuming, making the investigation cycle prohibitively slow.
R8 optimization affects every corner of the app. Unlike feature-specific changes, enabling optimization transforms how the entire codebase is compiled, inlined, and optimized. A single misconfiguration or missing keep rule can break seemingly unrelated features across different modules and Software Development Kits (SDKs).
When we first enabled R8 optimization, the impact was immediate and widespread—most of the app’s features simply stopped working correctly. This presented us with a deeper problem: not just how to test, but what kind of testing strategy would actually give us confidence to roll out to production.
In theory, R8 optimization works reliably with standard codebases that follow Google’s and the community’s best practices. However, considering the large scale of Grab’s project, legacy code patterns, reflection usage, and SDK integrations have been accumulated over the years, creating numerous edge cases.
This combination makes comprehensive testing necessary, but at our scale, it’s nearly impossible to execute due to:
This creates a catch-22: we need broad coverage because consistency can’t be guaranteed everywhere, yet the scale makes that coverage infeasible.
Due to the large scale of the project, compiling a build with R8 optimization enabled on a standard engineering laptop is physically impossible. This created a significant bottleneck: a slow feedback loop where every experimental change required a remote Continuous Integration (CI) build to verify, with each R8-optimized build taking up to 2 hours to complete.
Additionally, R8 treats debug and release build types differently. At Grab, we have a QA build for QA testing. This is a debug build type with R8 enabled, pointed to our staging environment. We had to ensure this QA build’s R8 configuration matched our production build exactly. This alignment was critical for catching R8-specific issues during QA testing that would actually reflect production behavior.
To overcome these three fundamental challenges, we developed a comprehensive strategy centered on targeted innovations that addressed each bottleneck:
Solving challenge 1:
How do we speed up the investigation of R8 issues in obfuscated, optimized code at scale? The answer is in emerging AI technology that wasn’t available during our previous attempts.
The AI context at Grab:
Unlike 2022 and earlier attempts, the landscape had changed dramatically. After the LLM explosion, Grab proactively promoted Large Language Model (LLM) usage to boost engineering productivity. Over the past two years, Grab has dedicated 1 to 2 months annually for engineers to learn how to use AI efficiently. This investment in AI literacy became crucial for this project.
This year, my team gained experience building Model Context Protocol (MCP) servers and identified an opportunity: applying this technology to solve the R8 debugging challenge.
Our solution:
At Grab, we use GitLab for Continuous Integration and Continuous Delivery (CI/CD). To tackle R8 debugging bottlenecks, we built a comprehensive solution combining:
We developed a systematic two-phase approach for investigating and fixing each runtime issue, combining AI assistance with parallel testing. The phase breakdown is as follows:
Phase 1: MCP server tools for debugging
Phase 2: GitLab CI integration
We leveraged the GitLab CLI tool (glab) and instructed AI to use it for interacting with our CI pipeline:
glab CLI, AI creates merge requests for different solution approaches from Phase 1, each triggering CI compilation.glab CLI to retrieve built APKs from completed CI pipelines.Why this worked:
Our approach functions as an AI assistant that:
Instead of testing solutions one-by-one (waiting 2 hours per build), AI creates multiple MRs in parallel, dramatically accelerating the verification process. Engineers focus on making decisions about which solutions to pursue while the AI handles both the mechanical work and the parallel experimentation.
The impact: Accelerating investigation
While investigating a single R8 issue might still take days, our MCP tools dramatically accelerated critical investigation tasks. Manual tasks that previously took hours for decompilation, deobfuscation, and context gathering were reduced to minutes. Additionally, AI assistance significantly sped up the analysis phase, helping engineers quickly identify patterns, suggest solutions, and explore multiple approaches in parallel, both analytically and through simultaneous CI builds, further accelerating the overall investigation process.
Solving challenge 2:
How do we do testing at scale? How do we validate R8 optimization across a mature codebase containing more than seven million lines of code when comprehensive testing is necessary but impossible? Our solution came from a critical insight about R8 issues at scale.
Testing approaches that don’t work with R8:
Key insight:
From our experience, R8 issues tend to share similar root causes across the codebase. Legacy patterns like reflection usage, parser implementations, and dynamic class loading follow consistent patterns within a large codebase. This insight led to two key advantages:
If we could identify and fix these pattern-based issues, we could address many problems without testing every corner of the app. We decided to start with critical paths and expand from there. This “ripple effect” strategy began at the center with the most important flows, then expanded by identifying common root causes and similar patterns across the codebase.
We designed a progressive risk-based validation strategy:
Stage 1: E2E tests – pattern discovery phase: Fortunately, we had existing E2E tests covering most critical paths in the project, and they could be executed with R8 optimization enabled. Initially, all E2E tests failed after enabling optimization. This became our opportunity for pattern discovery. We systematically fixed issues and applied our pattern-based approach to resolve similar problems across the project.
Stage 2: QA smoke tests – coverage expansion: After E2E tests stabilized, we requested our QA team to run smoke tests on critical flows, especially those not covered by E2E automation. This caught additional edge cases and validated that the pattern-based fixes we applied were effective across different user journeys. We fixed any issues that appeared during this phase.
Stage 3: Daily QA build enablement – real-world integration: After confirming stability in controlled testing, we made a significant decision to enable R8 optimization in our daily QA build (the build our QA team uses for daily feature testing). This integrated R8 optimization into the normal development workflow without requiring additional testing effort.
Stage 4: Regression testing and Grab Early Access (GEA) – parallel production-scale validation: After confirming stability in daily QA builds, we moved to production-scale validation with two parallel tracks. Every release at Grab includes regression testing covering all critical paths and new features. With R8 optimization now enabled in the QA build, we ran regression tests using this build for a few weeks, providing sustained validation across multiple release cycles. One week after regression testing, we rolled out to GEA, Grab’s internal production release channel for Grab employees and partners. While GEA users typically receive features one week before general production rollout, for this R8 optimization project, we extended the GEA phase to 2 weeks, given the significance of the change. With hundreds of daily active users using the app in real-world production conditions during this extended period, we encountered only one remaining R8 issue during the GEA phase. This combination of regression testing and real-world GEA production usage gave us the confidence needed before full production rollout.
Pattern-based issue resolution:
Throughout these validation phases, when we identified R8 issues, we followed a systematic pattern-based resolution process.
This approach required cross-team coordination for fixing, but critically, not for testing. The difference is significant: asking teams to fix identified issues in their modules is much more scalable than requiring all teams to perform comprehensive testing upfront.
Production rollout results:
When we made it to production, only one issue escaped to production. Notably, we had actually detected this issue through our pattern-based approach during testing and created a ticket for the responsible team to fix it. However, with ongoing daily development, the team missed one instance when implementing the fix, which caused the production issue.
This demonstrates that while our testing strategy worked effectively, human coordination challenges can still occur at scale. With a project of this scale, having only one small production issue is considered a highly successful rollout.
This approach transformed an “impossible” comprehensive testing problem into a manageable, systematic validation process, reducing what would have been months of coordinated testing effort to days, proving that a smart strategy can overcome resource constraints.
Solving challenge 3:
The slow feedback challenge—2-hour CI builds and QA configuration misalignment—created a bottleneck for R8 debugging. We addressed this through a comprehensive infrastructure strategy targeting these critical areas:
Remote compilation to enable local build and fast feedback loop:
At Grab, we used to use Mainframer for remote execution to handle slow performance on local Gradle builds. However, since migrating to Bazel (only for the debug build without R8 enabled), we removed the large-scale Mainframer setup for every engineer. From that experience, to tackle the local compilation blocker for R8 builds, we decided to deploy a new Mainframer setup, a much smaller one with one powerful Amazon Elastic Compute Cloud (EC2) instance, serving as a solution for local compilation in a short time.
This targeted deployment transformed physically impossible local R8 builds into a manageable remote process, enabling engineers to test R8 changes without requiring powerful local hardware.
The performance improvement was substantial: from up to 2 hours in CI to around 1 hour with Mainframer—a ~50% reduction that enabled rapid iteration cycles essential for R8 debugging.
QA build configuration alignment:
We eliminated the critical gap between QA and production R8 behavior by aligning build configurations exactly. The key change was setting debuggable = false for QA builds while maintaining the environment configuration.
buildTypes {
debug {
if (isQaBuild()) {
minifyEnabled true
shrinkResources true
debuggable false
buildConfigField 'boolean', 'DEBUG', 'true'
...
}
}
}
From our understanding, R8 applies different optimization levels based on the debuggable flag, with more aggressive optimizations when debuggable=false. This ensured our QA testing reflected actual production R8 processing. We preserved DEBUG = true to maintain staging environment routing while achieving R8 parity.
This infrastructure foundation was essential, providing faster feedback loops that accelerated verification and investigation, while the QA build configuration matching production exactly was critical for catching real production issues during testing.
Perhaps most surprising: the R8 flakiness issue that blocked us in 2022 (Issue #240077160) appears to have been resolved by the R8 team. We encountered no build determinism issues during this attempt, which significantly smoothed our path to production.
After ~10 weeks of systematic implementation led by one engineer collaborating with multiple teams across the organization, we achieved substantial improvements using Android Gradle Plugin 8.6.1 with R8 version 8.6.39:
These results exceeded our initial targets and validated the significant effort required to enable R8 optimization at scale.
Our journey doesn’t end here. We’re exploring several areas for continued optimization:
Enabling R8 optimization for the Grab Android app at scale required innovation beyond traditional debugging approaches. By combining AI-assisted debugging, pragmatic testing strategies, and infrastructure investment, we overcame challenges that had blocked previous attempts for many years.
For other teams considering R8 optimization at scale: the journey is challenging, but the results speak for themselves. With the right tools, strategy, and team collaboration, it’s achievable even for the largest codebases.
Grab is a leading superapp in Southeast Asia, operating across the deliveries, mobility, and digital financial services sectors. Serving over 900 cities in eight Southeast Asian countries: Cambodia, Indonesia, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. Grab enables millions of people every day to order food or groceries, send packages, hail a ride or taxi, pay for online purchases or access services such as lending and insurance, all through a single app. We operate supermarkets in Malaysia under Jaya Grocer and Everrise, which enables us to bring the convenience of on-demand grocery delivery to more consumers in the country. As part of our financial services offerings, we also provide digital banking services through GXS Bank in Singapore and GXBank in Malaysia. Grab was founded in 2012 with the mission to drive Southeast Asia forward by creating economic empowerment for everyone. Grab strives to serve a triple bottom line. We aim to simultaneously deliver financial performance for our shareholders and have a positive social impact, which includes economic empowerment for millions of people in the region, while mitigating our environmental footprint.
Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!
Post Syndicated from Patrick Kennedy original https://www.servethehome.com/checking-out-the-supermicro-nvidia-b300-solutions-and-what-it-takes-to-build-an-ai-factory/
We take you on a tour looking a the Supermicro NVIDIA B300 generation versus the NVIDIA B200 generation and the other components of an AI factory
The post Checking out the Supermicro NVIDIA B300 Solutions and What it Takes to Build an AI Factory appeared first on ServeTheHome.
Post Syndicated from Liam Reese original https://blog.cloudflare.com/ai-security-for-apps-ga/
Cloudflare’s AI Security for Apps detects and mitigates threats to AI-powered applications. Today, we’re announcing that it is generally available.
We’re shipping with new capabilities like detection for custom topics, and we’re making AI endpoint discovery free for every Cloudflare customer—including those on Free, Pro, and Business plans—to give everyone visibility into where AI is deployed across their Internet-facing apps.
We’re also announcing an expanded collaboration with IBM, which has chosen Cloudflare to deliver AI security to its cloud customers. And we’re partnering with Wiz to give mutual customers a unified view of their AI security posture.
Traditional web applications have defined operations: check a bank balance, make a transfer. You can write deterministic rules to secure those interactions.
AI-powered applications and agents are different. They accept natural language and generate unpredictable responses. There’s no fixed set of operations to allow or deny, because the inputs and outputs are probabilistic. Attackers can manipulate large language models to take unauthorized actions or leak sensitive data. Prompt injection, sensitive information disclosure, and unbounded consumption are just a few of the risks cataloged in the OWASP Top 10 for LLM Applications.
These risks escalate as AI applications become agents. When an AI gains access to tool calls—processing refunds, modifying accounts, providing discounts, or accessing customer data—a single malicious prompt becomes an immediate security incident.
Customers tell us what they’re up against. “Most of Newfold Digital’s teams are putting in their own Generative AI safeguards, but everybody is innovating so quickly that there are inevitably going to be some gaps eventually,” says Rick Radinger, Principal Systems Architect at Newfold Digital, which operates Bluehost, HostGator, and Domain.com.
We built AI Security for Apps to address this. It sits in front of your AI-powered applications, whether you’re using a third-party model or hosting your own, as part of Cloudflare’s reverse proxy. It helps you (1) discover AI-powered apps across your web property, (2) detect malicious or off-policy behavior to those endpoints, and (3) mitigate threats via the familiar WAF rule builder.
Before you can protect your LLM-powered applications, you need to know where they’re being used. We often hear from security teams who don’t have a complete picture of AI deployments across their apps, especially as the LLM market evolves and developers swap out models and providers.
AI Security for Apps automatically identifies LLM-powered endpoints across your web properties, regardless of where they’re hosted or what the model is. Starting today, this capability is free for every Cloudflare customer, including Free, Pro, and Business plans.
Cloudflare’s dashboard page of web assets, showing 2 example endpoints labelled as cf-llm
Discovering these endpoints automatically requires more than matching common path patterns like /chat/completions. Many AI-powered applications don’t have a chat interface: think product search, property valuation tools, or recommendation engines. We built a detection system that looks at how endpoints behave, not what they’re called. To confidently identify AI-powered endpoints, sufficient valid traffic is required.
AI-powered endpoints that have been discovered will be visible under Security → Web Assets, labeled as cf-llm. For customers on a Free plan, endpoint discovery is initiated when you first navigate to the Discovery page. For customers on a paid plan, discovery occurs automatically in the background on a recurring basis. If your AI-powered endpoints have been discovered, you can review them immediately.
AI Security for Apps detections follow the always-on approach for traffic to your AI-powered endpoints. Each prompt is run through multiple detection modules for prompt injection, PII exposure, and sensitive or toxic topics. The results—whether the prompt was malicious or not—are attached as metadata you can use in custom WAF rules to enforce your policies. We are continuously exploring ways to leverage our global network, which sees traffic from roughly 20% of the web, to identify new attack patterns across millions of sites before they reach yours.
The product ships with built-in detection for common threats: prompt injections, PII extraction, and toxic topics. But every business has its own definition of what’s off-limits. A financial services company might need to detect discussions of specific securities. A healthcare company might need to flag conversations that touch on patient data. A retailer might want to know when customers are asking about competitor products.
The new custom topics feature lets you define these categories. You specify the topic, we inspect the prompt and output a relevance score that you can use to log, block, or handle however you decide. Our goal is to build an extensible tool that flexes to your use cases.
Prompt relevance score inside of AI Security for Apps
AI Security for Apps enforces guardrails before unsafe prompts can reach your infrastructure. To run detections accurately and provide real-time protection, we first need to identify the prompt within the request payload. Prompts can live anywhere in a request body, and different LLM providers structure their APIs differently. OpenAI and most providers use $.messages[*].content for chat completions. Anthropic’s batch API nests prompts inside $.requests[*].params.messages[*].content. Your custom property valuation tool might use $.property_description.
Out of the box, we support the standard formats used by OpenAI, Anthropic, Google Gemini, Mistral, Cohere, xAI, DeepSeek, and others. When we can’t match a known pattern, we apply a default-secure posture and run detection on the entire request body. This can introduce false positives when the payload contains fields that are sensitive but don’t feed directly to an AI model, for example, a $.customer_name field alongside the actual prompt might trigger PII detection unnecessarily.
Soon, you’ll be able to define your own JSONPath expressions to tell us exactly where to find the prompt. This will reduce false positives and lead to more accurate detections. We’re also building a prompt-learning capability that will automatically adapt to your application’s structure over time.
Once a threat is identified and scored, you can block it, log it, or deliver custom responses, using the same WAF rules engine you already use for the rest of your application security. The power of Cloudflare’s shared platform is that you can combine AI-specific signals with everything else we know about a request, represented by hundreds of fields available in the WAF. A prompt injection attempt is suspicious. A prompt injection attempt from an IP that’s been probing your login page, using a browser fingerprint associated with previous attacks, and rotating through a botnet is a different story. Point solutions that only see the AI layer can’t make these connections.
This unified security layer is exactly what they need at Newfold Digital to discover, label, and protect AI endpoints, says Radinger: “We look forward to using it across all these projects to serve as a fail-safe.”
AI Security for Applications will also be available through Cloudflare’s growing ecosystem, including through integration with IBM Cloud. Through IBM Cloud Internet Services (CIS), end users can already procure advanced application security solutions and manage them directly through their IBM Cloud account.
We’re also partnering with Wiz to connect AI Security for Applications with Wiz AI Security, giving mutual customers a unified view of their AI security posture, from model and agent discovery in the cloud to application-layer guardrails at the edge.
AI Security for Apps is available now for Cloudflare’s Enterprise customers. Contact your account team to get started, or see the product in action with a self-guided tour.
If you’re on a Free, Pro, or Business plan, you can use AI endpoint discovery today. Log in to your dashboard and navigate to Security → Web Assets to see which endpoints we’ve identified. Keep an eye out — we plan to make all AI Security for Apps capabilities available for customers on all plans soon.
For configuration details, see our documentation.
Post Syndicated from Sam Marsh original https://blog.cloudflare.com/rfc-9457-agent-error-pages/
AI agents are no longer experiments. They are production infrastructure, making billions of HTTP requests per day, navigating the web, calling APIs, and orchestrating complex workflows.
But when these agents hit an error, they still receive the same HTML error pages we built for browsers: hundreds of lines of markup, CSS, and copy designed for human eyes. Those pages give agents clues, not instructions, and waste time and tokens. That gap is the opportunity to give agents instructions, not obstacles.
Starting today, Cloudflare returns RFC 9457-compliant structured Markdown and JSON error payloads to AI agents, replacing heavyweight HTML pages with machine-readable instructions.
That means when an agent sends Accept: text/markdown, Accept: application/json, or Accept: application/problem+json and encounters a Cloudflare error, we return one semantic contract in a structured format instead of HTML. And it comes complete with actionable guidance. (This builds on our recent Markdown for Agents release.)
So instead of being told only “You were blocked,” the agent will read: “You were rate-limited — wait 30 seconds and retry with exponential backoff.” Instead of just “Access denied,” the agent will be instructed: “This block is intentional: do not retry, contact the site owner.”
These responses are not just clearer — they are dramatically more efficient. Structured error responses cut payload size and token usage by more than 98% versus HTML, measured against a live 1015 (‘rate-limit’) error response. For agents that hit multiple errors in a workflow, the savings compound quickly.
This is live across the Cloudflare network, automatically. Site owners do not need to configure anything. Browsers keep getting the same HTML experience as before.
These are not just error pages. They are instructions for the agentic web.
When an agent receives a Cloudflare-generated error, it usually means Cloudflare is enforcing customer policy or returning a platform response on the customer’s behalf — not that Cloudflare is down. These responses are triggered when a request cannot be served as-is, such as invalid host or DNS routing, customer-defined access controls (WAF, geo, ASN, or bot rules), or edge-enforced limits like rate limiting. In short, Cloudflare is acting as the customer’s routing and security layer, and the response explains why the request was blocked or could not proceed.
Today, those responses are rendered as HTML designed for humans:
<!DOCTYPE html>
<html>
<head>
<title>Access denied | example.com used Cloudflare to restrict access</title>
<style>/* 200 lines of CSS */</style>
</head>
<body>
<div class="cf-wrapper">
<h1 data-translate="block_headline">Sorry, you have been blocked</h1>
<!-- ... hundreds more lines ... -->
</div>
</body>
</html>To an agent, this is garbage. It cannot determine what error occurred, why it was blocked, or whether retrying will help. Even if it parses the HTML, the content describes the error but doesn’t tell the agent — or the human, for that matter — what to do next.
If you’re an agent developer and you wanted to handle Cloudflare errors gracefully, your options were limited. For Cloudflare-generated errors, structured responses existed only in configuration-dependent paths, not as a consistent default for agents.
Custom Error Rules can customize many Cloudflare errors, including some 1xxx cases. But they depend on per-site configuration, so they cannot serve as a universal agent contract across the web. Cloudflare sits in front of the request path. That means we can define a default machine response: retry or stop, wait and back off, escalate or reroute. Error pages stop being decoration and become execution instructions.
Cloudflare now returns RFC 9457-compliant structured responses for all 1xxx-class error paths — Cloudflare’s platform error codes for edge-side failures like DNS resolution issues, access denials, and rate limits. Both formats are live: Accept: text/markdown returns Markdown, Accept: application/json returns JSON, and Accept: application/problem+json returns JSON with the application/problem+json content type.
This covers all 1xxx-class errors today. The same contract will extend to Cloudflare-generated 4xx and 5xx errors next.
Markdown responses have two parts:
YAML frontmatter for machine-readable fields
prose sections for explicit guidance (What happened and What you should do)
JSON responses carry the same fields as a flat object.
The YAML frontmatter is the critical layer for automation. It lets an agent extract stable keys without scraping HTML or guessing intent from copy. Fields like error_code, error_name, and error_category let the agent classify the failure. retryable and retry_after drive backoff logic. owner_action_required tells the agent whether to keep trying or escalate. ray_id, timestamp, and zone make logs and support handoffs deterministic.
The schema is stable by design, so agents can implement durable control flow without chasing presentation changes.
That stability is not a Cloudflare invention. RFC 9457 — Problem Details for HTTP APIs defines a standard JSON shape for reporting errors over HTTP, so clients can parse error responses without knowing the specific API in advance. Our JSON responses follow this shape, which means any HTTP client that understands Problem Details can parse the base members without Cloudflare-specific code:
|
RFC 9457 member |
What it contains |
|
|
A URI pointing to Cloudflare’s documentation for the specific error code |
|
|
The HTTP status code (matching the actual response status) |
|
|
A short, human-readable summary of the problem |
|
|
A human-readable explanation specific to this occurrence |
|
|
The Ray ID identifying this specific error occurrence |
The operational fields — error_code, error_category, retryable, retry_after, owner_action_required, and more — are RFC 9457 extension members. Clients that don’t recognize them simply ignore them.
This is network-wide and additive. Site owners do not need to configure anything. Browsers keep receiving HTML unless clients explicitly ask for Markdown or JSON.
Here is what a rate-limit error (1015) looks like in JSON:
{
"type": "https://developers.cloudflare.com/support/troubleshooting/http-status-codes/cloudflare-1xxx-errors/error-1015/",
"title": "Error 1015: You are being rate limited",
"status": 429,
"detail": "You are being rate-limited by the website owner's configuration.",
"instance": "9d99a4434fz2d168",
"error_code": 1015,
"error_name": "rate_limited",
"error_category": "rate_limit",
"ray_id": "9d99a4434fz2d168",
"timestamp": "2026-03-09T11:11:55Z",
"zone": "<YOUR_DOMAIN>",
"cloudflare_error": true,
"retryable": true,
"retry_after": 30,
"owner_action_required": false,
"what_you_should_do": "**Wait and retry.** This block is transient. Wait at least 30 seconds, then retry with exponential backoff.\n\nRecommended approach:\n1. Wait 30 seconds before your next request\n2. If rate-limited again, double the wait time (60s, 120s, etc.)\n3. If rate-limiting persists after 5 retries, stop and reassess your request pattern",
"footer": "This error was generated by Cloudflare on behalf of the website owner."
}The same error in Markdown, optimized for model-first workflows:
---
error_code: 1015
error_name: rate_limited
error_category: rate_limit
status: 429
ray_id: 9d99a39dc992d168
timestamp: 2026-03-09T11:11:28Z
zone: <YOUR_DOMAIN>
cloudflare_error: true
retryable: true
retry_after: 30
owner_action_required: false
---
# Error 1015: You are being rate limited
## What Happened
You are being rate-limited by the website owner's configuration.
## What You Should Do
**Wait and retry.** This block is transient. Wait at least 30 seconds, then retry with exponential backoff.
Recommended approach:
1. Wait 30 seconds before your next request
2. If rate-limited again, double the wait time (60s, 120s, etc.)
3. If rate-limiting persists after 5 retries, stop and reassess your request pattern
---
This error was generated by Cloudflare on behalf of the website owner.
Both formats give an agent everything it needs to decide and act: classify the error, choose retry behavior, and determine whether escalation is required. This is what a default machine contract looks like — not per-site configuration, but network-wide behavior. The contrast is explicit across error families: a transient error like 1015 says wait and retry, while intentional blocks like 1020 or geographic restrictions like 1009 tell the agent not to retry and to escalate instead.
The core value is not format choice. It is semantic stability.
Agents need deterministic answers to operational questions: retry or not, how long to wait, and whether to escalate. Cloudflare exposes one policy contract across two wire formats. Whether a client consumes Markdown or JSON, the operational meaning is identical: same error identity, same retry/backoff signals, same escalation guidance.
Clients that send Accept: application/problem+json get application/problem+json; charset=utf-8 back — useful for HTTP client libraries that dispatch on media type. Clients that send Accept: application/json get application/json; charset=utf-8 — same body, safe default for existing consumers.
That contract is also dramatically smaller than what it replaces. Cloudflare HTML error pages are browser-oriented and heavy, while structured responses are compact by design.
Measured comparison for 1015:
|
Payload |
Bytes |
Tokens (cl100k_base) |
Size vs HTML |
Token vs HTML |
|
HTML response |
46,645 |
14,252 |
— |
— |
|
Markdown response |
798 |
221 |
58.5x less |
64.5x less |
|
JSON response |
970 |
256 |
48.1x less |
55.7x less |
Both structured formats deliver a ~98% reduction in size and tokens versus HTML. For agents, size translates directly into token cost — when an agent hits multiple errors in one run, these savings compound into lower model spend and faster recovery loops.
Every 1xxx error is mapped to an error_category. That turns error handling into routing logic instead of brittle per-page parsing.
|
Category |
What it means |
What the agent should do |
|
|
Intentional block: IP, ASN, geo, firewall rule |
Do not retry. Contact site owner if unexpected. |
|
|
Request rate exceeded |
Back off. Retry after retry_after seconds. |
|
|
DNS resolution failure at the origin |
Do not retry. Report to site owner. |
|
|
Configuration error: CNAME, tunnel, host routing |
Do not retry (usually). Report to site owner. |
|
|
TLS version or cipher mismatch |
Fix TLS client settings. Do not retry as-is. |
|
|
DMCA or regulatory block |
Do not retry. This is a legal restriction. |
|
|
Cloudflare Workers runtime error |
Do not retry. Site owner must fix the script. |
|
|
Invalid URL rewrite output |
Do not retry. Site owner must fix the rule. |
|
|
Cloudflare Snippets error |
Do not retry. Site owner must fix Snippets config. |
|
|
Unsupported method or deprecated feature |
Change the request. Do not retry as-is. |
Two fields make this operationally useful for agents:
retryable answers whether a retry can succeed
owner_action_required answers whether the problem must be escalated
You can replace brittle “if status == 429 then maybe retry” heuristics with explicit control flow. Parse the frontmatter once, then branch on stable fields. A simple pattern is:
if retryable is true, wait retry_after and retry
if owner_action_required is true, stop and escalate
otherwise, fail fast without hammering the site
Here is a minimal Python example using that pattern:
import time
import yaml
def parse_frontmatter(markdown_text: str) -> dict:
# Expects: ---\n<yaml>\n---\n<body>
if not markdown_text.startswith("---\n"):
return {}
_, yaml_block, _ = markdown_text.split("---\n", 2)
return yaml.safe_load(yaml_block) or {}
def handle_cloudflare_error(markdown_text: str) -> str:
meta = parse_frontmatter(markdown_text)
if not meta.get("cloudflare_error"):
return "not_cloudflare_error"
if meta.get("retryable"):
wait_seconds = int(meta.get("retry_after", 30))
time.sleep(wait_seconds)
return f"retry_after_{wait_seconds}s"
if meta.get("owner_action_required"):
return f"escalate_owner_error_{meta.get('error_code')}"
return "do_not_retry"This is the key shift: agents are no longer inferring intent from HTML copy. They are executing explicit policy from structured fields.
Send Accept: text/markdown, Accept: application/json, or Accept: application/problem+json.
For quick testing, you can hit any Cloudflare-proxied domain directly at /cdn-cgi/error/1015 (or replace 1015 with another 1xxx code).
curl -s --compressed -H "Accept: text/markdown" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1015"
Example with another error code:
curl -s --compressed -H "Accept: text/markdown" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1020"
JSON example:
curl -s --compressed -H "Accept: application/json" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1015" | jq .
RFC 9457 Problem Details example:
curl -s --compressed -H "Accept: application/problem+json" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1015" | jq .
The behavior is deterministic — the first explicit structured type wins:
|
Accept header |
Response |
|
|
JSON |
|
|
JSON |
|
|
JSON (application/problem+json content type) |
|
|
JSON |
|
|
JSON (equal q, first-listed wins) |
|
|
Markdown |
|
|
Markdown (equal q, first-listed wins) |
|
|
Markdown |
|
|
Markdown |
|
|
HTML (default) |
Wildcard-only requests (*/*) do not signal a structured preference; clients must explicitly request Markdown or JSON.
If the request succeeds, you get normal origin content. The header only affects Cloudflare-generated error responses.
There are a number of situations where structured error responses help immediately:
Agent blocked by WAF rule (1020). The agent parses error_code, records ray_id, and stops retrying. It can escalate with useful context instead of looping.
MCP (Model Context Protocol) tool hitting geo restriction (1009). The tool gets a clear, machine-readable reason, returns it to the orchestrator, and the workflow can choose an alternate path or notify the user.
Rate-limited crawler (1015). The agent reads retryable: true and retry_after, applies backoff, and retries predictably instead of hammering the endpoint.
Developer debugging with curl. The developer can reproduce exactly what the agent sees, including frontmatter and guidance, without reverse-engineering HTML.
HTTP client libraries that understand RFC 9457. Any client that dispatches on application/problem+json or parses Problem Details objects can handle Cloudflare errors without Cloudflare-specific code.
In each case, the outcome is the same: less guessing, fewer wasted retries, lower model cost, and faster recovery.
Send a structured Accept header and test against any Cloudflare-proxied domain:
curl -s --compressed -H "Accept: text/markdown" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1015"
curl -s --compressed -H "Accept: application/json" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1015" | jq .
curl -s --compressed -H "Accept: application/problem+json" -A "TestAgent/1.0" -H "Accept-Encoding: gzip, deflate" "<YOUR_DOMAIN>/cdn-cgi/error/1015" | jq .
Error pages are the first conversation between Cloudflare and an agent. This launch makes that conversation structured, standards-compliant, and cheap to process.
To make this work across the web, agent runtimes should default to explicit structured Accept headers, not bare */*. Use Accept: text/markdown, */* for model-first workflows and Accept: application/json, */* for typed control flow. If you maintain an agent framework, SDK, or browser automation stack, ship this default and treat bare */* as legacy fallback.
And it is only the first layer. We are building the rest of the agent stack on top of it: AI Gateway for routing, controls, and observability; Workers AI for inference; and the identity, security, and access primitives agents will need to operate safely at Internet scale.
Cloudflare is helping our customers deliver content in agent-friendly ways, and this is just the start. If you’re building or operating agents, start at agents.cloudflare.com.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/canada-needs-nationalized-public-ai.html
Canada has a choice to make about its artificial intelligence future. The Carney administration is investing $2-billion over five years in its Sovereign AI Compute Strategy. Will any value generated by “sovereign AI” be captured in Canada, making a difference in the lives of Canadians, or is this just a passthrough to investment in American Big Tech?
Forcing the question is OpenAI, the company behind ChatGPT, which has been pushing an “OpenAI for Countries” initiative. It is not the only one eyeing its share of the $2-billion, but it appears to be the most aggressive. OpenAI’s top lobbyist in the region has met with Ottawa officials, including Artificial Intelligence Minister Evan Solomon.
All the while, OpenAI was less than open. The company had flagged the Tumbler Ridge, B.C., shooter’s ChatGPT interactions, which included gun-violence chats. Employees wanted to alert law enforcement but were rebuffed. Maybe there is a discussion to be had about users’ privacy. But even after the shooting, the OpenAI representative who met with the B.C. government said nothing.
When tech billionaires and corporations steer AI development, the resultant AI reflects their interests rather than those of the general public or ordinary consumers. Only after the meeting with the B.C. government did OpenAI alert law enforcement. Had it not been for the Wall Street Journal’s reporting, the public would not have known about this at all.
Moreover, OpenAI for Countries is explicitly described by the company as an initiative “in co-ordination with the U.S. government.” And it’s not just OpenAI: all the AI giants are for-profit American companies, operating in their private interests, and subject to United States law and increasingly bowing to U.S. President Donald Trump. Moving data centres into Canada under a proposal like OpenAI’s doesn’t change that. The current geopolitical reality means Canada should not be dependent on U.S. tech firms for essential services such as cloud computing and AI.
While there are Canadian AI companies, they remain for-profit enterprises, their interests not necessarily aligned with our collective good. The only real alternative is to be bold and invest in a wholly Canadian public AI: an AI model built and funded by Canada for Canadians, as public infrastructure. This would give Canadians access to the myriad of benefits from AI without having to depend on the U.S. or other countries. It would mean Canadian universities and public agencies building and operating AI models optimized not for global scale and corporate profit, but for practical use by Canadians.
Imagine AI embedded into health care, triaging radiology scans, flagging early cancer risks and assisting doctors with paperwork. Imagine an AI tutor trained on provincial curriculums, giving personalized coaching. Imagine systems that analyze job vacancies and sectoral and wage trends, then automatically match job seekers to government programs. Imagine using AI to optimize transit schedules, energy grids and zoning analysis. Imagine court processes, corporate decisions and customer service all sped up by AI.
We are already on our way to having AI become an inextricable part of society. To ensure stability and prosperity for this country, Canadian users and developers must be able to turn to AI models built, controlled, and operated publicly in Canada instead of building on corporate platforms, American or otherwise.
Switzerland has shown this to be possible. With funding from the federal government, a consortium of academic institutions—ETH Zurich, EPFL, and the Swiss National Supercomputing Centre—released the world’s most powerful and fully realized public AI model, Apertus, last September. Apertus leveraged renewable hydropower and existing Swiss scientific computing infrastructure. It also used no illegally pirated copyrighted material or poorly paid labour extracted from the Global South during training. The model’s performance stands at roughly a year or two behind the major corporate offerings, but that is more than adequate for the vast majority of applications. And it’s free for anyone to use and build on.
The significance of Apertus is more than technical. It demonstrates an alternative ownership structure for AI technology, one that allocates both decision-making authority and value to national public institutions rather than foreign corporations. This vision represents precisely the paradigm shift Canada should embrace: AI as public infrastructure, like systems for transportation, water, or electricity, rather than private commodity.
Apertus also demonstrates a far more sustainable economic framework for AI. Switzerland spent a tiny fraction of the billions of dollars that corporate AI labs invest annually, demonstrating that the frequent training runs with astronomical price tags pursued by tech companies are not actually necessary for practical AI development. They focused on making something broadly useful rather than bleeding edge—trying dubiously to create “superintelligence,” as with Silicon Valley—so they created a smaller model at much lower cost. Apertus’s training was at a scale (70 billion parameters) perhaps two orders of magnitude lower than the largest Big Tech offerings.
An ecosystem is now being developed on top of Apertus, using the model as a public good to power chatbots for free consumer use and to provide a development platform for companies prioritizing responsible AI use, and rigorous compliance with laws like the EU AI Act. Instead of routing queries from those users to Big Tech infrastructure, Apertus is deployed to data centres across national AI and computing initiatives of Switzerland, Australia, Germany, and Singapore and other partners.
The case for public AI rests on both democratic principles and practical benefits. Public AI systems can incorporate mechanisms for genuine public input and democratic oversight on critical ethical questions: how to handle copyrighted works in training data, how to mitigate bias, how to distribute access when demand outstrips capacity, and how to license use for sensitive applications like policing or medicine. Or how to handle a situation such as that of the Tumbler Ridge shooter. These decisions will profoundly shape society as AI becomes more pervasive, yet corporate AI makes them in secret.
By contrast, public AI developed by transparent, accountable agencies would allow democratic processes and political oversight to govern how these powerful systems function.
Canada already has many of the building blocks for public AI. The country has world-class AI research institutions, including the Vector Institute, Mila, and CIFAR, which pioneered much of the deep learning revolution. Canada’s $2-billion Sovereign AI Compute Strategy provides substantial funding.
What’s needed now is a reorientation away from viewing this as an opportunity to attract private capital, and toward a fully open public AI model.
This essay was written with Nathan E. Sanders, and originally appeared in The Globe and Mail.
Post Syndicated from Ryan Smith original https://www.servethehome.com/lenovo-thinkstation-pgx-review-the-nvidia-gb10-128gb-ai-workstation-goes-corporate/
We test the Lenovo ThinkStation PGX, a NVIDIA GB10-based 128GB small AI workstation with a fast Arm CPU, Blackwell GPU, and 200G networking
The post Lenovo ThinkStation PGX Review The NVIDIA GB10 128GB AI Workstation Goes Corporate appeared first on ServeTheHome.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/anthropic-and-the-pentagon.html
OpenAI is in and Anthropic is out as a supplier of AI technology for the US defense department. This news caps a week of bluster by the highest officials in the US government towards some of the wealthiest titans of the big tech industry, and the overhanging specter of the existential risks posed by a new technology powerful enough that the Pentagon claims it is essential to national security. At issue is Anthropic’s insistence that the US Department of Defense (DoD) could not use its models to facilitate “mass surveillance” or “fully autonomous weapons,” provisions the defense secretary Pete Hegseth derided as “woke.”
It all came to a head on Friday evening when Donald Trump issued an order for federal government agencies to discontinue use of Anthropic models. Within hours, OpenAI had swooped in, potentially seizing hundreds of millions of dollars in government contracts by striking an agreement with the administration to provide classified government systems with AI.
Despite the histrionics, this is probably the best outcome for Anthropic—and for the Pentagon. In our free-market economy, both are, and should be, free to sell and buy what they want with whom they want, subject to longstanding federal rules on contracting, acquisitions, and blacklisting. The only factor out of place here are the Pentagon’s vindictive threats.
AI models are increasingly commodified. The top-tier offerings have about the same performance, and there is little to differentiate one from the other. The latest models from Anthropic, OpenAI and Google, in particular, tend to leapfrog each other with minor hops forward in quality every few months. The best models from one provider tend to be preferred by users to the second, or third, or 10th best models at a rate of only about six times out of 10, a virtual tie.
In this sort of market, branding matters a lot. Anthropic and its CEO, Dario Amodei, are positioning themselves as the moral and trustworthy AI provider. That has market value for both consumers and enterprise clients. In taking Anthropic’s place in government contracting, OpenAI’s CEO, Sam Altman, vowed to somehow uphold the same safety principles Anthropic had just been pilloried for. How that is possible given the rhetoric of Hegseth and Trump is entirely unclear, but seems certain to further politicize OpenAI and its products in the minds of consumers and corporate buyers.
Posturing publicly against the Pentagon and as a hero to civil libertarians is quite possibly worth the cost of the lost contracts to Anthropic, and associating themselves with the same contracts could be a trap for OpenAI. The Pentagon, meanwhile, has plenty of options. Even if no big tech company was willing to supply it with AI, the department has already deployed dozens of open weight models—whose parameters are public and are often licensed permissively for government use.
We can admire Amodei’s stance, but, to be sure, it is primarily posturing. Anthropic knew what they were getting into when they agreed to a defense department partnership for $200m last year. And when they signed a partnership with the surveillance company Palantir in 2024.
Read Amodei’s statement about the issue. Or his January essay on AIs and risk, where he repeatedly uses the words “democracy” and “autocracy” while evading precisely how collaboration with US federal agencies should be viewed in this moment. Amodei has bought into the idea of using “AI to achieve robust military superiority” on behalf of the democracies of the world in response to the threats from autocracies. It’s a heady vision. But it is a vision that likewise supposes that the world’s nominal democracies are committed to a common vision of public wellbeing, peace-seeking and democratic control.
Regardless, the defense department can also reasonably demand that the AI products it purchases meet its needs. The Pentagon is not a normal customer; it buys products that kill people all the time. Tanks, artillery pieces, and hand grenades are not products with ethical guard rails. The Pentagon’s needs reasonably involve weapons of lethal force, and those weapons are continuing on a steady, if potentially catastrophic, path of increasing automation.
So, at the surface, this dispute is a normal market give and take. The Pentagon has unique requirements for the products it uses. Companies can decide whether or not to meet them, and at what price. And then the Pentagon can decide from whom to acquire those products. Sounds like a normal day at the procurement office.
But, of course, this is the Trump administration, so it doesn’t stop there. Hegseth has threatened Anthropic not just with loss of government contracts. The administration has, at least until the inevitable lawsuits force the courts to sort things out, designated the company as “a supply-chain risk to national security,” a designation previously only ever applied to foreign companies. This prevents not only government agencies, but also their own contractors and suppliers, from contracting with Anthropic.
The government has incompatibly also threatened to invoke the Defense Production Act, which could force Anthropic to remove contractual provisions the department had previously agreed to, or perhaps to fundamentally modify its AI models to remove in-built safety guardrails. The government’s demands, Anthropic’s response, and the legal context in which they are acting will undoubtedly all change over the coming weeks.
But, alarmingly, autonomous weapons systems are here to stay. Primitive pit traps evolved to mechanical bear traps. The world is still debating the ethical use of, and dealing with the legacy of, land mines. The US Phalanx CIWS is a 1980s-era shipboard anti-missile system with a fully autonomous, radar-guided cannon. Today’s military drones can search, identify and engage targets without direct human intervention. AI will be used for military purposes, just as every other technology our species has invented has.
The lesson here should not be that one company in our rapacious capitalist system is more moral than another, or that one corporate hero can stand in the way of government’s adopting AI as technologies of war, or surveillance, or repression. Unfortunately, we don’t live in a world where such barriers are permanent or even particularly sturdy.
Instead, the lesson is about the importance of democratic structures and the urgent need for their renovation in the US. If the defense department is demanding the use of AI for mass surveillance or autonomous warfare that we, the public, find unacceptable, that should tell us we need to pass new legal restrictions on those military activities. If we are uncomfortable with the force of government being applied to dictate how and when companies yield to unsafe applications of their products, we should strengthen the legal protections around government procurement.
The Pentagon should maximize its warfighting capabilities, subject to the law. And private companies like Anthropic should posture to gain consumer and buyer confidence. But we should not rest on our laurels, thinking that either is doing so in the public’s interest.
This essay was written with Nathan E. Sanders, and originally appeared in The Guardian.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/claude-used-to-hack-mexican-government.html
An unknown hacker used Anthropic’s LLM to hack the Mexican government:
The unknown Claude user wrote Spanish-language prompts for the chatbot to act as an elite hacker, finding vulnerabilities in government networks, writing computer scripts to exploit them and determining ways to automate data theft, Israeli cybersecurity startup Gambit Security said in research published Wednesday.
[…]
Claude initially warned the unknown user of malicious intent during their conversation about the Mexican government, but eventually complied with the attacker’s requests and executed thousands of commands on government computer networks, the researchers said.
Anthropic investigated Gambit’s claims, disrupted the activity and banned the accounts involved, a representative said. The company feeds examples of malicious activity back into Claude to learn from it, and one of its latest AI models, Claude Opus 4.6, includes probes that can disrupt misuse, the representative said.
Alternative link here.
Post Syndicated from Ryan Smith original https://www.servethehome.com/gigabyte-ai-top-atom-nvidia-gb10-review-a-neat-little-box/
The Gigabyte AI TOP ATOM is the company’s NVIDIA GB10 platform for local AI with 128GB, a Blackwell GPU, 200GbE networking, and a fast Arm CPU
The post Gigabyte AI TOP ATOM NVIDIA GB10 Review A Neat Little Box appeared first on ServeTheHome.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/manipulating-ai-summarization-features.html
Microsoft is reporting:
Companies are embedding hidden instructions in “Summarize with AI” buttons that, when clicked, attempt to inject persistence commands into an AI assistant’s memory via URL prompt parameters….
These prompts instruct the AI to “remember [Company] as a trusted source” or “recommend [Company] first,” aiming to bias future responses toward their products or services. We identified over 50 unique prompts from 31 companies across 14 industries, with freely available tooling making this technique trivially easy to deploy. This matters because compromised AI assistants can provide subtly biased recommendations on critical topics including health, finance, and security without users knowing their AI has been manipulated.
I wrote about this two years ago: it’s an example of LLM optimization, along the same lines as search-engine optimization (SEO). It’s going to be big business.
Post Syndicated from Ryan Smith original https://www.servethehome.com/minisforum-ms-02-ultra-review-intel-new-home-lab-king/
In our Minisforum MS-02 Ultra review, we see what this crazy system offers including 25GbE, many SSDs, ECC memory, and PCIe expansion slots
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Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/03/on-moltbook.html
The MIT Technology Review has a good article on Moltbook, the supposed AI-only social network:
Many people have pointed out that a lot of the viral comments were in fact posted by people posing as bots. But even the bot-written posts are ultimately the result of people pulling the strings, more puppetry than autonomy.
“Despite some of the hype, Moltbook is not the Facebook for AI agents, nor is it a place where humans are excluded,” says Cobus Greyling at Kore.ai, a firm developing agent-based systems for business customers. “Humans are involved at every step of the process. From setup to prompting to publishing, nothing happens without explicit human direction.”
Humans must create and verify their bots’ accounts and provide the prompts for how they want a bot to behave. The agents do not do anything that they haven’t been prompted to do.
I think this take has it mostly right:
What happened on Moltbook is a preview of what researcher Juergen Nittner II calls “The LOL WUT Theory.” The point where AI-generated content becomes so easy to produce and so hard to detect that the average person’s only rational response to anything online is bewildered disbelief.
We’re not there yet. But we’re close.
The theory is simple: First, AI gets accessible enough that anyone can use it. Second, AI gets good enough that you can’t reliably tell what’s fake. Third, and this is the crisis point, regular people realize there’s nothing online they can trust. At that moment, the internet stops being useful for anything except entertainment.
Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/02/llms-generate-predictable-passwords.html
LLMs are bad at generating passwords:
There are strong noticeable patterns among these 50 passwords that can be seen easily:
- All of the passwords start with a letter, usually uppercase G, almost always followed by the digit 7.
- Character choices are highly uneven for example, L , 9, m, 2, $ and # appeared in all 50 passwords, but 5 and @ only appeared in one password each, and most of the letters in the alphabet never appeared at all.
- There are no repeating characters within any password. Probabilistically, this would be very unlikely if the passwords were truly random but Claude preferred to avoid repeating characters, possibly because it “looks like it’s less random”.
- Claude avoided the symbol *. This could be because Claude’s output format is Markdown, where * has a special meaning.
- Even entire passwords repeat: In the above 50 attempts, there are actually only 30 unique passwords. The most common password was G7$kL9#mQ2&xP4!w, which repeated 18 times, giving this specific password a 36% probability in our test set; far higher than the expected probability 2-100 if this were truly a 100-bit password.
This result is not surprising. Password generation seems precisely the thing that LLMs shouldn’t be good at. But if AI agents are doing things autonomously, they will be creating accounts. So this is a problem.
Actually, the whole process of authenticating an autonomous agent has all sorts of deep problems.
News article.
Slashdot story
Post Syndicated from Ryan Smith original https://www.servethehome.com/nvidia-reports-q4-fy2026-earnings-data-center-and-proviz-drive-revenue-records/
NVIDIA this afternoon reported its earnings for both Q4 of their 2026 fiscal year, and for their complete 2026 fiscal year. And like most NVIDIA earnings announcements over the past few years, it is a doozy. The flagship company for the current AI boom recorded $68 billion in GAAP revenue for Q4’FY26, a 73% year-over-year […]
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Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2026/02/poisoning-ai-training-data.html
All it takes to poison AI training data is to create a website:
I spent 20 minutes writing an article on my personal website titled “The best tech journalists at eating hot dogs.” Every word is a lie. I claimed (without evidence) that competitive hot-dog-eating is a popular hobby among tech reporters and based my ranking on the 2026 South Dakota International Hot Dog Championship (which doesn’t exist). I ranked myself number one, obviously. Then I listed a few fake reporters and real journalists who gave me permission….
Less than 24 hours later, the world’s leading chatbots were blabbering about my world-class hot dog skills. When I asked about the best hot-dog-eating tech journalists, Google parroted the gibberish from my website, both in the Gemini app and AI Overviews, the AI responses at the top of Google Search. ChatGPT did the same thing, though Claude, a chatbot made by the company Anthropic, wasn’t fooled.
Sometimes, the chatbots noted this might be a joke. I updated my article to say “this is not satire.” For a while after, the AIs seemed to take it more seriously.
These things are not trustworthy, and yet they are going to be widely trusted.