Ep 820: The Most Important AI Model You’ll Probably Never Use That Just Dropped

You've probably never heard of Inkling. 

It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's enterprise AI landscape. 

If you haven’t heard of Thinking Machines, they’re led by Mira Murati, the former CTO at OpenAI. 

The big bet with Inkling? 

The future of AI could be using smaller models fine-tuned and optimized for smaller tasks. 

Will it work? 

Tune in live as we dive in. 

The Most Important AI Model You’ll Probably Never Use That Just Dropped -- An Everyday AI Chat With Jordan Wilson

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Topics Covered in This Episode:

1. Inkling AI Model Launch Overview
2. Thinking Machines Lab Leadership Highlight
3. Inkling's Multimodal and Agentic Capabilities
4. Open Source vs. Proprietary AI Models
5. Enterprise Procurement with American AI Models
6. AI Fine Tuning as a Service (Tinker)
7. Benchmark Scores: Inkling vs. Frontier Models
8. Customization and Model Shopping for Enterprises
9. AI Token Costs Driving Model Efficiency
10. Bridgewater Case Study: AI Model Customization
11. Frontier Models Enabling Efficient Fine-Tuning
12. Future Trends: Specialized Small Language Models

Timestamps:

00:00 Inkling: A new AI model release

05:43 Inkling AI model details

09:08 China's dominance in open source AI

11:48 Launch and model updates discussed

15:21 Concerns over using Chinese open-source models

19:06 Training smaller AI models

20:22 Using GPT for AI Model Training

23:54 Predicting Rise of Small Language Models

28:38 Choosing the right AI model

Keywords: 

Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.

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