Don't just sprinkle AI features onto your existing product ('AI at the edge'). Transformative companies rethink workflows and shrink their old codebase, making the LLM a core part of the solution. This is about re-architecting the solution from the ground up, not just enhancing it.

Ring’s founder clarifies his vision for AI in safety is not for AI to autonomously identify threats but to act as a co-pilot for residents. It sifts through immense data from cameras to alert humans only to meaningful anomalies, enabling better community-led responses and decision-making.

Apple's seemingly slow AI progress is likely a strategic bet that today's powerful cloud-based models will become efficient enough to run locally on devices within 12 months. This would allow them to offer powerful AI with superior privacy, potentially leapfrogging competitors.

Unlike traditional SaaS, AI applications have a unique vulnerability: a step-function improvement in an underlying model could render an app's entire workflow obsolete. What seems defensible today could become a native model feature tomorrow (the 'Jasper' risk).

When developing AI-powered tools, don't be constrained by current model limitations. Given the exponential improvement curve, design your product for the capabilities you anticipate models will have in six months. This ensures your product is perfectly timed to shine when the underlying tech catches up.

According to Ring's founder, the technology for ambitious AI features like "Dog Search Party" already exists. The real bottleneck is the cost of computation. Products that are technically possible today are often not launched because the processing expense makes them commercially unviable.

Ring's founder argues that seemingly permanent hardware choices, like communication protocols, are not truly "one-way doors." By offloading intelligence to the cloud, even legacy hardware can be continuously upgraded with new features like AI, mitigating the risk of being stuck on an outdated standard.

A cost-effective AI architecture involves using a small, local model on the user's device to pre-process requests. This local AI can condense large inputs into an efficient, smaller prompt before sending it to the expensive, powerful cloud model, optimizing resource usage.

Instead of streaming all data, Samsara runs inference on low-power cameras. They train large models in the cloud and then "distill" them into smaller, specialized models that can run efficiently at the edge, focusing only on relevant tasks like risk detection.