The Interlude Show

Daniel Björkegren is a development economist and a professor at Columbia studying how AI is being used in low-income countries and what it will take for its benefits to reach the people who need them most.

We get into why teachers in Sierra Leone took up an AI chatbot on WhatsApp, how bandwidth costs change the economics of accessing information, and why speaking the right language is only part of making a model useful locally. We also discuss whether AI could make knowledge services more accessible without following the same development path as wealthier countries, what experiments can tell us about its economic impact, and the opportunities and risks that robotics creates for low-income economies.

Hope you enjoy the sixth episode of The Interlude Show!

Chapters:

00:00 - AI beyond traditional knowledge work

01:00 - Introducing Daniel Björkegren

02:15 - How AI is being used in low-income countries

03:38 - Teachers, WhatsApp, and the cost of information

10:14 - Local languages and model usefulness

17:09 - AI, services, and economic development

20:57 - Measuring AI's economic impact

27:05 - Should AI be a public utility?

33:01 - Robotics and low-income economies

37:58 - What experiments can and cannot tell us

44:05 - What bond markets might tell us about AI

47:44 - Who benefits

World Models are slated to be the next frontier after LLMs.

We had the pleasure of chatting with Avik Sethia, Founding Engineer at Reactor, who recently raised a $59M Seed and Series A to be the Developer Platform for World Models. Tune in to find out what world models are, the unique challenges behind serving them compared to LLMs and exciting future applications!

Chapters

00:00 - Introduction

01:09 - What is Reactor?

01:57 - What is a world model?

05:10 - Interactivity differences from Fei Fei Li and Yann LeCun's world models

12:47 - How world models are used today

15:03 - Challenges of serving world models

19:10 - Differences between serving World Models and LLMs

21:17 - KV Cache Challenges

28:06 - How LLM serving experience translate to world models

30:00 - Architecture differences

34:56 - Open Source World Models

39:35 - Robotics Models

40:51 - Future Applications

Jasper Gilley is a member at South Park Commons researching new AI architectures inspired by how the brain works - specifically the cerebellum, and what it would take for a model to introspect on its own computation the way humans do for free. Before that he was at Yutori, building vision-based web agents that emulate human users.

We get into why models need expensive post-hoc interpretability while humans can dissect their own thinking automatically, his forward self-modeling work (training a deliberately undersized model to predict a transformer's own activations, then reading the residual as a novelty signal), why weighting grokking data by Zipf's law turns training into an infinite oscillator that discovers the solution and forgets it forever, why vision beats the DOM for browser agents, why software is an "exoskeleton of intelligence" and demand for it is still elastic, and what humans were actually evolutionarily designed for.

Chapters:

0:00 Cold open

1:02 Intro

1:43 Researching brain-inspired architectures at South Park Commons

4:05 The cerebellum as a forward model

6:39 The Forward Self-Modeling paper

14:03 Grokking, explained

18:37 Zipfian Grokking: finding the solution and forgetting it

22:26 Yutori: why vision beats the DOM

31:23 Why Jevons' Paradox won't save your software engineering job

41:51 The window where creativity is the whole job

45:39 What humans were evolutionarily designed for

51:09 The future of post-training

Ryan Tsang is building Harmonize Health - an AI-native operating system for medical clinics. Ryan spent four years at Microsoft working on Azure OpenAI's fine-tuning product, then trained models for open-source AI safety benchmarks at MLCommons.

We get deep into how Harmonize Health builds and grades evals for voice agents (including a user-simulator agent that role-plays the patient calling in), why you should get to production before you build the perfect eval set, how Harmonize scrubs patient data out of transcripts, and the tension between forward-deployed engineering and scaling across multiple clinics.

Hope you enjoy the third episode of The Interlude Show!

Chapters:

00:00 - Move fast and break things doesn't work in healthcare

00:41 - Meet Ryan Tsang and what Harmonize is building

02:40 - Why healthcare, and the path from pre-med to CS

05:45 - Inside a primary care clinic: the work nobody sees

10:50 - Safety is the hardest technical problem

13:55 - Data plumbing vs. AI: EHRs and tribal knowledge

20:36 - Encoding a clinic's operating procedures into agents

23:35 - Inside their eval harness for voice agents

27:07 - What can we really not screw up?

30:16 - Healthcare moves at the speed of trust

38:57 - TempShot: pitching his high school app to us as VCs

49:53 - The YC all-nighter, quitting our jobs, and why compounding wins

Vatsal built ML Serving and Inference architectures at Netflix and Microsoft for 4-5 years. In this episode, we get into personalization, generative recommendations architecture, inference / serving optimizations and China vs. US on ML research.

Hope you enjoy the second episode of the Interlude Podcast!

Chapters:

00:00 - Intro

02:18 - How Netflix Personalizes Everything

04:23 - Model Serving vs. Inference

06:19 - Generative & Conversational Recommendations

08:13 - Measuring Success: Metrics & A/B Testing

11:29 - China vs. the US: Open Source & the Road Ahead

17:00 - Risks, Incentives & Regulation

20:17 - Serving LLMs vs. Classical ML & Observability

Jake was the first engineer at a YC startup shipping AI in production. We get into how AI actually works, harness engineering vs. post training, the importance of evaluations and war stories from deploying browser agents in the wild.Hear from him directly on the first episode of the Interlude Podcast.

Chapters

00:00 - Intro

00:52 - CloudCruise (YC W24)

02:41 - Building a Self-Recovering Browser Agent

04:18 - What Are AI Evaluations?

05:01 - Building Eval Datasets: 90% of the Work

09:46 - Offline vs Online Evals & Distribution Shift

12:06 - Harness Engineering vs Fine-Tuning

15:18 - Owning Your AI in Regulated Industries

17:35 - Hot Take: The OpenAI/Anthropic Threat

20:52 - The Future of Browser Automation