Overview
I. Our Interviewees
To gain a comprehensive perspective, we prioritized in-depth conversations with individuals actively involved in AI research and development. Our interviewees represented three key groups:
AI Researchers (30+): The core of our research, these individuals are at the forefront of AI innovation at leading technology companies and research institutions. They shared firsthand accounts of:
Daily technical challenges.
Promising research directions.
Limitations of current AI models.
The interplay between research and application.
Experiences with data, training, and evaluation.
Startup Founders (10): Founders of AI startups (Series B funding or later) offered a critical perspective on the commercialization of AI. They discussed:
Identifying and validating market opportunities.
Building and leading AI teams.
Overcoming data and deployment hurdles.
Navigating the competitive landscape.
The practical realities of bringing AI to market.
Industry Experts (AI21 Labs and Cohere): Experts from these leading companies provided a broader context for understanding the large language model (LLM) ecosystem, including:
Industry-wide trends and challenges in LLM development.
The commercialization of LLM technologies.
The evolving relationship between research and industry.
By integrating the insights from these diverse groups—AI researchers, startup founders, and industry experts—we gained a multi-faceted understanding of the AI landscape. Researchers provided the crucial technical foundation, grounding our analysis in the realities of AI development and the specific challenges faced on the front lines. Startup founders added a critical commercial and real-world viability lens, highlighting the practical considerations of bringing AI solutions to market and navigating the competitive landscape. Finally, industry experts from leading LLM companies offered strategic, industry-wide context, illuminating broader trends and the evolving ecosystem surrounding these powerful technologies. This combination of perspectives was essential for developing a holistic and nuanced picture of AI's current state and future trajectory.