Michael J. Stattelman is a visionary technology executive at Falcons.AI and is known for his forward-thinking approach within the spheres of artificial intelligence, machine learning, and digital transformation. Anchored in over 15 years of experience building and leading global technical teams, he stands at the forefront of emerging technological advancements and AI ethics. His experience includes advising and leading initiatives within the gaming, pharmaceutical, and precision agriculture sectors. He is committed to delivering scalable technical solutions that balance innovation with practical application, bridging the gap between complex code and strategic business objectives.
 

As Co-Founder and Chief Executive Officer of Falcons.AI, Michael spearheads the development of advanced AI solutions with a specialised focus on content moderation. He is the architect behind the world’s leading NSFW image classification model, a pioneering achievement that has garnered over 1 billion downloads on HuggingFace and exceeded 64 million runs on the Replicate platform. His expertise focuses on contextual awareness in AI models, creating robust frameworks for digital safety and automated content governance.
 

In addition to his corporate leadership, Michael possesses a strong academic and pedagogical foundation, having served as a Professor of Computer Science at Gwinnett Technical College. Drawing upon his Master of Science in Information Systems and specialised certifications in AI development from Google and NVIDIA, Michael provides strategic and innovative technical insights to organisations ranging from startups to global enterprises. His influence extends internationally, particularly within the Middle East, where he has featured as a key speaker at prominent events such as the Global AI Show in Dubai.
 

Michael is widely recognised as a thought leader in the critical areas of AI safety and ethics. He actively shapes the industry narrative around responsible AI development, advocating for transparency and high-level accuracy in machine learning models to solve complex real-world challenges in safety and compliance.