From Demo to Deployment: Lessons from Anthropic, Gamma, and Clay on Real‑World AI Use

From Demo to Deployment: Lessons from Anthropic, Gamma, and Clay on Real‑World AI Use

AI demos often dazzle in a controlled setting, but the real test begins when customers embed the technology into their daily operations. At TechCrunch Disrupt 2026, executives from Anthropic, Gamma, and Clay discussed the gap between impressive prototypes and sustained, production‑grade adoption.

Cat de Jong, Head of Applied AI at Anthropic, explained that successful deployments share three traits: clear problem‑fit, measurable performance benchmarks, and a feedback loop that lets teams tune the model as usage patterns evolve. Organizations that skip any of these steps tend to remain stuck in pilot mode for months or even years.

Grant Lee, CEO of Gamma, described how his team moved from an AI‑enhanced slide maker to a broader visual communication platform. He noted that unexpected use cases—such as generating marketing copy or designing social assets—emerged only after users started experimenting, forcing the product team to iterate quickly on reliability and UI.

Kareem Amin, CEO of Clay, highlighted the importance of infrastructure that can handle agentic workflows at scale. He pointed out that when AI becomes part of go‑to‑market motions, latency and data freshness become critical, and that GPU‑backed compute is often the deciding factor between a smooth experience and a frustrating bottleneck.

Why it matters for GPU / AI infrastructure

For GPU cloud providers like AiGpu, these insights translate into demand for flexible, high‑performance instances that can support both bursty experimentation and steady‑state production workloads. Ensuring low‑latency access to accelerated compute helps enterprises move from AI pilots to deployments that deliver real business value.