Change in the field of AI is happening at an exponential rate, particularly in industries like healthcare.
As we enter 2022, it is clear that AI has the potential to revolutionize healthcare and other sectors.
In contrast, AI-enabled companies focus on implementing and distributing AI technologies.
These two approaches establish moats at different levels of the industry.
However, it is important to note that this distinction is not binary.
The most successful companies will likely combine elements of both approaches.
These unique datasets are machine-readable and scalable, enabling high-performance AI models.
By doing so, they were able to create a data advantage and develop a moat for their technology.
Furthermore, AI-first companies should leverage reinforcement learning with expert human feedback (RL(E)HF).
This technique involves training AI systems by incorporating feedback from human experts in specific domains.
By doing so, models can be fine-tuned to deliver high performance in those domains.
Recruit and empower AI scientists
Building an AI-first company requires a strong team of AI scientists.
This includes fostering collaboration across disciplines and creating an environment that encourages continuous learning and experimentation.
Forge partnerships with aligned investors
AI-first companies often require more capital and longer-term support compared to AI-enabled companies.
Develop a viable business model
A strong business model is essential for the success of AI-first companies.
This may involve a combination of proprietary technology, strategic partnerships, and innovative revenue streams.
Embrace interdisciplinary collaboration
AI-first companies thrive on interdisciplinary collaboration.
This collaboration can lead to breakthroughs that have a real-world impact, particularly in vertical-specific platforms.
Conclusion
AI-first companies have the potential to revolutionize industries like healthcare and life sciences.
