Nick Khami of Morph breaks down the economics of AI compute, debunking the idea that there is infinite demand for raw inference. He explains how the Neocloud credit crunch will play out, why open-source models reaching frontier level creates a need for verifiable inference, and how inference clusters differ from training clusters. Check out our sponsor Lovable: https://lovable.dev. Turn ideas into software people love.
MTS chronicles the singularity, live. Monitoring the situation of tech and AI.
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Mongo DB - Anyone can prototype an agent. Learn how to get them into production on September 30th in New York - register at https://www.mongodb.com/events/mongodb-local/nyc
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Timestamps:
(00:00) Introduction
(00:49) What Morph Is And Morph Ultra
(01:23) Anthropic And Formally Verifying Inference
(03:47) How Zk-SNARKs Prove Model Authenticity
(04:49) The Neocloud Credit Crunch Narrative
(06:49) Why Compute Demand Isn’t Infinite
(09:30) Building Inference Clusters For Coding Agents
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Note: This podcast is not investment, legal, or tax advice, and is intended for informational and entertainment purposes only. Hosts and guests may hold positions in the companies and securities discussed; do your own research before acting on anything you hear.
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