The Emergence of AI Engineers: Apply AI with Foundation Models and Open Source APIs
The field of applied AI is undergoing a significant transformation, driven by the availability of Foundation Models and open source/APIs. AI tasks that previously required a research team and several years can now be accomplished with API documentation and a spare afternoon. AI Engineers are emerging as a new subdiscipline within software engineering, specializing in the application of AI and leveraging the emerging stack effectively.
The role of AI Engineer is becoming increasingly in demand, with startups and companies forming formal AI teams and channels dedicated to AI discussions. AI Engineers are focused on shaping AI advancements into real products used by millions, without necessarily having a PhD or deep ML background.
The trend is predicted to lead to a flippening of job titles, with AI Engineer roles surpassing ML Engineer roles in demand in the next five years.
The rise of Foundation Models, GPU hoarding, and the ability to quickly prototype and validate AI products contribute to the emergence of AI Engineers. The field of AI is becoming more agile, with prompt engineering and faster validation of AI products compared to traditional ML approaches.
The expansion of AI Engineering tools to include JavaScript caters to a wider audience of developers. AI Engineers focus on generative AI and reasoning engines, building applications such as writing apps, personalized learning tools, and natural language spreadsheets. The role of human-written code is re-emerging to orchestrate and supplant the power of Foundation Models, creating opportunities for startups. The toolkit of AI Engineers includes both human-written code and AI-generated code, with AI increasingly involved in the engineering process.
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