The AI community is developing better tools that allow end users to easily customize AI systems without needing extensive technical knowledge.

As companies scale their AI applications, it's crucial to find the optimal balance across quality, cost, and speed to achieve sustainable growth.

As we deploy AI technologies, we face massive systems and engineering challenges to make them efficient and accessible to users without deep knowledge of machine learning.

To optimize AI applications, we need to predict multiple tokens at once, align hardware selection with application needs, and implement various tuning mechanisms for quality.

As we gather more data and build a larger platform, we can create more granular and personalized evaluations of AI models.

A software development company expanded its AI feature rollout from 100,000 developers to 25 million in three months, highlighting the fascinating speed of scaling in AI.

Exciting low-code and no-code tools are emerging, enabling users to customize AI systems based on their specific needs.

In contrast, using prompt-based AI can significantly reduce development time, allowing for quicker deployment of AI solutions, with applications that used to take six months to a year to build now being developed in about a week, opening up a flood of new AI applications.

We realized that there needed to be an infrastructure platform, like AWS or Stripe, specifically focused on data to power the AI industry.