Everyone wants a piece of general purpose models. Instacart has deployed ChatGPT for recipes and meal planning. The Mayo Clinic is using it to summarize patient records. Schneider Electric is using an OpenAI LLM to generate sustainability reports. 

With such powerful models, what’s the need for specialized models built for specific industries, especially in climate tech?

In this episode, Shayle talks to Sam Smith-Eppsteiner, partner at Innovation Endeavors. She recently wrote a blog post arguing that there may be a market that general purpose models struggle to meet: physical industries where training data is siloed, unstructured, and private. She talks through climate-relevant examples like Cadstrom’s copilot for electrical engineers, Hubflow’s automated trucker scheduling, WeaveBio’s AI-powered platform for regulatory approvals. Shayle and Sam also cover topics like:

• Applicable cases, like cross referencing complicated technical manuals, repetitive manual work that employees dislike, and technical compliance
• The technical knowledge lost when workers retire and how specialized AI could help
• What it takes to build specialized models, including data access, vector embedding, prompt engineering, and fine tuning 
• What budget categories businesses might use to pay for specialized models
• Selling the technology (i.e. the traditional SAAS model) vs selling the work (i.e. answers informed by models)

Recommended resources

• Innovation Endeavors: Specialized brains for industry: the immense potential for domain-specific AI
• Innovation Endeavors: The next industrial unicorn: Where is AI rapidly transforming the physical economy
• Catalyst: The coming robotics wave
• Latitude: How utilities are designing and embedding AI operating models

Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is executive editor.

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