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AI is moving in two very different directions at once: powerful models are becoming easier to run outside the cloud, while governments are preparing for the risks created when AI is used offensively. Today’s developments show that the next AI race will be shaped not just by model capability, but also by where AI runs and how safely it can be deployed.
Meta Just Put a Frontier-Scale Model on Your Desktop
Meta has released Muse Spark Open, an 816-billion-parameter open-weight model designed for coding, long-horizon agentic work and complex reasoning. It is natively multimodal, accepting text, images, audio and video, and has a 1-million-token context window.
The unusual part is that Meta has optimized the model to run locally on two Apple M5 Ultra Mac Studios using Apple's MLX framework. Meta says the two machines can effectively operate as one system with around 1TB of unified memory, allowing developers to run advanced AI without sending every request to a cloud provider.
Meta is also releasing the model weights to developers through platforms including Hugging Face and Amazon SageMaker, alongside hosted API options. The model can be customized and deployed using popular inference frameworks such as vLLM, SGLang, Transformers and MLX.
Why it matters
Frontier AI is moving closer to the device: Powerful models don't necessarily have to live entirely in hyperscale data centers.
Local AI can improve privacy: Organizations can keep sensitive prompts and data within their own infrastructure.
Open weights give developers more control: Models can be customized, fine-tuned and deployed independently.
Hardware and AI models are becoming tightly connected: Apple's high-memory architecture is becoming useful for running much larger local models.
The G7 Is Preparing for AI-Powered Cyberattacks
Financial leaders from the G7 have agreed to conduct tabletop exercises designed to test how governments and financial institutions would respond to cyberattacks involving AI. The decision was made by G7 finance ministers and central bank governors meeting in Bangkok.
The exercises will simulate scenarios in which AI is used as part of a cyberattack against financial systems. Authorities want to understand whether existing response procedures are sufficient when attackers can potentially use AI to make attacks faster, more adaptive or easier to scale.
Private financial institutions may also participate. The results are expected to inform updates to existing security manuals and response protocols, reflecting growing concern that traditional cybersecurity defenses may not be enough against increasingly sophisticated AI-enabled attacks.
Why it matters
AI is changing the threat model: Attackers can potentially automate reconnaissance, phishing and other parts of an attack.
Financial systems are a high-priority target: A successful AI-enabled attack could affect institutions far beyond a single company.
Governments are moving from warnings to preparation: Simulation exercises show that AI cyber risk is being treated as an operational issue.
Businesses need AI-specific security controls: Agent permissions, credentials and system access need to be tightly managed.
4 AI Workflows to Try
Run sensitive AI tasks locally: For confidential documents or proprietary code, evaluate whether a local model can handle the workload instead of sending data to a cloud API.
Build a model-routing workflow: Use a smaller model for routine tasks and reserve frontier models for complex reasoning. This can reduce cost and latency.
Audit what your AI agents can access: Review API keys, cloud services, databases and applications connected to your agents. Remove permissions they don't actually need.
Create an AI incident playbook: Define what happens if an AI system sends incorrect information, accesses something it shouldn't or behaves unexpectedly.
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