The development of Claude Cowork demonstrates a massive acceleration in product velocity. The entire application was written by its underlying AI agent, Claude Code, in just a week and a half. This showcases how AI-driven coding is collapsing development cycles for new software products.

Showcasing a massive leap in productivity, the Sora Android app went from concept to public launch in 28 days with just 2-3 engineers. They used Codex to port functionality from the existing iOS app, demonstrating how AI teammates can drastically compress development timelines for complex projects.

GTM leaders no longer need to delegate strategy implementation. With tools like ChatGPT, their spoken words can become code, allowing them to rapidly prototype and test complex, data-driven prospecting campaigns themselves, directly connecting high-level strategy to on-the-ground execution.

Building a functional AI agent demo is now straightforward. However, the true challenge lies in the final stage: making it secure, reliable, and scalable for enterprise use. This is the 'last mile' where the majority of projects falter due to unforeseen complexity in security, observability, and reliability.

The core technology behind ChatGPT was available to developers for two years via the GPT-3 API. Its explosive adoption wasn't due to a sudden technical leap but to a simple, accessible UI, proving that distribution and user experience can be as disruptive as the underlying invention.

Tech companies often use government and military contracts as a proving ground to refine complex technologies. This gives military personnel early access to tools, like Palantir a decade ago, long before they become mainstream in the corporate world.

The Department of War's top AI priority is "applied AI." It consciously avoids building its own foundation models, recognizing it cannot compete with private sector investment. Instead, its strategy is to adapt commercial AI for specific defense use cases.

For companies given a broad "AI mandate," the most tactical and immediate starting point is to create a private, internalized version of a large language model like ChatGPT. This provides a quick win by enabling employees to leverage generative AI for productivity without exposing sensitive intellectual property or code to public models.

While AI models improved 40-60% and consumer use is high, only 5% of enterprise GenAI deployments are working. The bottleneck isn't the model's capability but the surrounding challenges of data infrastructure, workflow integration, and establishing trust and validation, a process that could take a decade.