# Eiso Kant (CTO poolside) - Superhuman Coding Is Coming!

Page: https://stenobird.com/podcast/machine-learning-street-talk/eiso-kant-cto-poolside-superhuman-coding-is-coming
Text version: https://stenobird.com/podcast/machine-learning-street-talk/eiso-kant-cto-poolside-superhuman-coding-is-coming.md
Podcast: [Machine Learning Street Talk (MLST)](https://stenobird.com/podcast/machine-learning-street-talk)
Published: 2025-04-02T19:58:20+00:00
Episode link: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/Eiso-Kant-CTO-poolside---Superhuman-Coding-Is-Coming-e3117gu
Audio file: https://anchor.fm/s/1e4a0eac/podcast/play/100752350/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2025-3-2%2F9a64e682-db0b-c9ec-bed7-1469af13f755.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/machine-learning-street-talk/episodes/eiso-kant-cto-poolside-superhuman-coding-is-coming
Duration seconds: 5788

## Resource

Eiso Kant, CTO of poolside AI, discusses the company's approach to building frontier AI foundation models, particularly focused on software development. Their unique strategy is reinforcement learning from code execution feedback which is an important axis for scaling AI capabilities beyond just increasing model size or data volume. Kant predicts human-level AI in knowledge work could be achieved within 18-36 months, outlining poolside's vision to dramatically increase software development productivity and accessibility. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** Eiso Kant: https://x.com/eisokant https://poolside.ai/ TRANSCRIPT: https://www.dropbox.com/scl/fi/szepl6taqziyqie9wgmk9/poolside.pdf?rlkey=iqar7dcwshyrpeoz0xa76k422&dl=0 TOC: 1. Foundation Models and AI Strategy [00:00:00] 1.1 Foundation Models and Timeline Predictions for AI Development [00:02:55] 1.2 Poolside AI's Corporate History and Strategic Vision [00:06:48] 1.3 Foundation Models vs Enterprise Customization Trade-offs 2. Reinforcement Learning and Model Economics [00:15:42] 2.1 Reinforcement Learning and Code Execution Feedback Approaches [00:22:06] 2.2 Model Economics and Experimental Optimization 3. Enterprise AI Implementation [00:25:20] 3.1 Poolside's Enterprise Deployment Strategy and Infrastructure [00:26:00] 3.2 Enterprise-First Business Model and Market Focus [00:27:05] 3.3 Foundation Models and AGI Development Approach [00:29:24] 3.4 DeepSeek Case Study and Infrastructure Requirements 4. LLM Architecture and Performance [00:30:15] 4.1 Distributed Training and Hardware Architecture Optimizat…

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