The performance gains from Nvidia's Hopper to Blackwell GPUs come from increased size and power, not efficiency. This signals a potential scaling limit, creating an opportunity for radically new hardware primitives and neural network architectures beyond today's matrix-multiplication-centric models.

While AI can attempt complex, hour-long tasks with 50% success, its reliability plummets for longer operations. For mission-critical enterprise use requiring 99.9% success, current AI can only reliably complete tasks taking about three seconds. This necessitates breaking large problems into many small, reliable micro-tasks.

The plateauing performance-per-watt of GPUs suggests that simply scaling current matrix multiplication-heavy architectures is unsustainable. This hardware limitation may necessitate research into new computational primitives and neural network designs built for large-scale distributed systems, not single devices.

AI product quality is highly dependent on infrastructure reliability, which is less stable than traditional cloud services. Jared Palmer's team at Vercel monitored key metrics like 'error-free sessions' in near real-time. This intense, data-driven approach is crucial for building a reliable agentic product, as inference providers frequently drop requests.

While AI inference can be decentralized, training the most powerful models demands extreme centralization of compute. The necessity for high-bandwidth, low-latency communication between GPUs means the best models are trained by concentrating hardware in the smallest possible physical space, a direct contradiction to decentralized ideals.

Unlike deterministic SaaS software that works consistently, AI is probabilistic and doesn't work perfectly out of the box. Achieving 'human-grade' performance (e.g., 99.9% reliability) requires continuous tuning and expert guidance, countering the hype that AI is an immediate, hands-off solution.

To operate thousands of GPUs across multiple clouds and data centers, Fal found Kubernetes insufficient. They had to build their own proprietary stack, including a custom orchestration layer, distributed file system, and container runtimes to achieve the necessary performance and scale.

When splitting jobs across thousands of GPUs, inconsistent communication times (jitter) create bottlenecks, forcing the use of fewer GPUs. A network with predictable, uniform latency enables far greater parallelization and overall cluster efficiency, making it more important than raw 'hero number' bandwidth.

Responding to the AI bubble concern, IBM's CEO notes high GPU failure rates are a design choice for performance. Unlike sunken costs from past bubbles, these "stranded" hardware assets can be detuned to run at lower power, increasing their resilience and extending their useful life for other tasks.