Conor Bronsdon
78 episodes listed below
AI systems, examined where they break.
Chain of Thought follows long-form conversations with AI builders, founders, researchers, and engineering leaders. The show treats agents, evals, memory, hiring, and data architecture as practical systems problems, not abstract hype cycles.
PRESS PLAY
Chain of Thought covers AI from the builder’s side of the table. Its center of gravity is not prediction, punditry, or glossy strategy. It is the messy middle where agents need memory, evals need sharper targets, data systems need better context, and engineering teams need workflows that survive production. The conversations are interview-led and technically fluent. A typical episode begins with a concrete tension: job applications rising while hires fall, agent demos stalling before deployment, hallucinations being blamed on models, or APIs flooding an agent’s context window. Then the show slows down. Guests explain the system beneath the headline, often through examples from hiring platforms, benchmark programs, graph databases, agent memory libraries, open-source connectors, or AI-first software workflows. The pacing is measured. The language can get technical, with terms like post-training, autonomy horizon, context poisoning, knowledge graphs, and agentic benchmarks appearing alongside direct questions about what engineers should do next. That combination is the show’s useful lane. It is comfortable with theory, but it keeps returning to implementation. Episodes with Daniel Chait examine how AI changes job applications, interviews, and recruiter overload. Conversations with Alex Ratner and Dan Klein focus on evaluation, data, benchmarks, and the gap between plausible model output and reliable action. Discussions with Anoush Elangovan, Richmond Alake, Sudhir Hasbe, and Michelle Tricot move through agent-first development, memory, graph-backed context, and data integration. The show also leaves room for ecosystem stories, such as Eugene Tang’s work around AI communities and events. Chain of Thought suits listeners who already follow AI closely and want the operational details behind the slogans. It is not an intro-level explainer. It is a working notebook for people trying to understand what actually changes when AI systems move from demo to infrastructure.
Made for: Built for engineers, founders, technical operators, and AI-curious leaders who want production-level detail. It especially suits listeners tracking agents, evals, infrastructure, and the business consequences of AI adoption.
What sets it apart: The show gives guests enough space to build careful technical arguments, then ties those arguments back to deployment, workflow, and organizational stakes. It favors concrete failure modes over broad AI takes.
AI is reshaping infrastructure, strategy, and entire industries. Host Conor Bronsdon talks to the engineers, founders, and researchers building breakthrough AI systems about what it actually takes to ship AI in production, where the opportunities lie, and how leaders should think about the strategic bets ahead. Chain of Thought translates technical depth into actionable insights for builders and decision-makers. New episodes bi-weekly. Conor Bronsdon is an angel investor in AI and dev tools, Head of Technical Ecosystem at Modular, and previously led growth at AI startups Galileo and LinearB.
Based on 5 episodes we listened to · September 2026
Chain of Thought makes production AI concrete by pressing company builders on evaluation, infrastructure, model choices, and agentic coding workflows.
Chain of Thought follows AI from model choice to the awkward systems around it: hiring funnels overwhelmed by automated applications, evaluation gaps, agent context, and changing software-development workflows.
A million resolved support chats a week sounds great, until you ask what 'resolved' means.
Press play if you want
Short reviews from the PlayNext desk, based on the episodes we processed.
A hard look at AI hiring pressure and the case for AI-led interviews.
Chain of Thought frames the AI hiring doom loop as an operational problem rather than a vague future shock. The episode makes the case plainly: applications are up 239 percent since ChatGPT launched, resume hacks are up 500 percent, and recruiters are staring at 500-to-one…
A sharp look at why agent progress has outrun the tools used to measure it.
Conor Bronson starts from a familiar AI problem: when a model needs to act differently, the answer is usually better data rather than a new architecture.
AMD's AI software chief makes the agentic coding shift feel immediate and operational.
Anoush Elangovan and Connor Bronson shape this episode into a clear look at how software work is changing inside AMD. The focus stays on the move from normal development toward an agentic workflow, with AMD's Corporate VP of AI Software weighing compilers, hardware, and agents…
Airbyte's agent bet turns context efficiency into a production problem.
Chain of Thought stays focused on why agents can slip when context gets messy, linking that problem to Airbyte's public beta of its agent engine.
A practical account of Intercom's move from prompt craft to fine-tuned open-weight models.
Connor Bronson opens on a question that sits near the center of current AI product work: when fine tuning is the better choice than a frontier model, especially as model releases keep shifting.
Rob Walling on bootstrapped SaaS growth, plateaus, and exits — with founders who lived them.
Abbey Ashley's playbook for turning your job skills into a virtual assistant business.
Naval Ravikant on epistemology, startups, and crypto, from David Deutsch to Vitalik Buterin.
Alex Theuma interviews SaaS founders and investors on scaling to $10M-plus ARR.
Guy Raz traces founders' origin stories and fields live advice-line calls from entrepreneurs.
Alex Blumberg interviews guests like Megan Phelps-Roper and Shane McAnally about failure and reinvention.
The host opens with a blunt question about job applications and resumes in the AI era.
Conor Bronson opens with a plain idea: if you want a model to act differently, you need different data.
Connor Bronson brings back Anoush Elangovan, AMD's Corporate VP of AI Software, for a conversation about how agentic coding is reshaping the pace of software work.
This episode explores Sudhir Hasbe's argument that AI hallucinations stem from data architecture issues rather than model flaws, emphasizing knowledge graphs as a solution.
This episode features an interview with Dan Klein, co-founder and CTO of Scaled Cognition, discussing his background in AI research and entrepreneurship, and exploring why LLMs function as plausibility engines rather than truth engines.
This episode features an interview with Richmond Alake, an expert on agent memory in AI systems, discussing its importance as a key battleground in the AI stack and his work on open-source tools and educational content.
Connor Bronson starts by rejecting the claim that MCP is dead.
The host interviews Eugene Tang about his journey from AI research and software engineering to building the r/AI_Agents community and launching a VC fund, while discussing his background in events and developer outreach.
Host Connor Bronson interviews Sterling Chin about Marvin, a personal AI assistant he built to automate 90% of his workday.
Connor Bronson opens on the moving target every AI product team is facing.
This episode features a conversation with AI research scientist Melissa Russack, exploring how her naive curiosity as a math teacher in China led to accidental algorithmic breakthroughs in machine learning.
This episode features an interview with Aurimas Griciūnas, CEO of Swirl AI, discussing how to build sustainable AI businesses for enterprises, covering topics like infrastructure, engineering, and his AI engineering bootcamp.
This episode discusses the pitfalls of relying on generic metrics in AI engineering and introduces the concept of a 'tools trap' that companies often fall into when building AI products.
This episode features an interview with Poolside's co-founders discussing their unique approach to building AGI by focusing on AI for software development and code.