Navigating Tech Acquisitions and AI Futures

What’s next for AI agents, and how will they change the way we work? In this conversation, Stanislas Polu (CEO of Dust, formerly research at OpenAI) and Harrison Chase (CEO of LangChain, one of the most influential open-source AI frameworks) unpack the current state and future of AI agents. They reflect on their early conversations in the pre-ChatGPT days, how the landscape has evolved, and where it's headed next.

Stan and Harrison share lessons from building today’s agent infrastructure—from chat interfaces to the future of ambient, autonomous systems—and discuss the challenges of operating in the chaotic "fog of AI." We dig into the open questions, early insights, and messy realities of building in today’s fast-moving AI landscape.

In our conversation, we explore:

• What sparked Stan and Harrison’s early interest in LLMs

• The pre-ChatGPT era and how the AI landscape has evolved since late 2022

• High-leverage use cases for agents inside Dust and LangChain today

• The critical differences between AI workflows and true agents—and why agents may unlock more powerful, long-term solutions

• Why reliability is the main blocker to ambient agents

• Real-world enterprise use cases for AI agents across customer support, sales, and engineering

• How to build in the “fog of AI” and the challenge of maintaining product vision when foundations shift every six months

• Strategies for creating defensibility in a world where tech giants can quickly replicate features

• The future of multi-agent systems and how they could transform enterprise productivity

• The current state of the AI talent market

• And much more

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Transcript: https://www.generalist.com/p/the-evolution-of-ai-agents

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Timestamps

(00:00) Intro

(02:33) Brief overviews of Dust and LangChain

(03:30) The early days of LLM product development

(11:02) Harrison's journey to founding LangChain

(14:35) Dust's evolution and focus on enterprise productivity

(17:15) Tobi’s AI memo

(18:42) An overview of AI agents and how they differ from AI workflows

(26:43) High-leverage use cases for agents at Dust and LangChain

(30:41) How to interact with agents and an explanation of ambient agents

(36:21) What the future of agents may look like

(40:52) Current limitations of AI agents and reliability challenges

(45:40) Will we converge to one agent or many specialized ones?

(51:32) How to solve the sycophant problem

(56:04) The challenges of building AI companies in a rapidly changing landscape

(01:03:06) Recent AI talent acquisitions and market dynamics

(01:05:28) How Dust and LangChain attract talent

(01:09:12) How far off AGI may be

(01:12:52) Final meditations

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Follow Stanislas Polu

LinkedIn: https://www.linkedin.com/in/spolu/

X: https://x.com/spolu

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Follow Harrison Chase

LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/

X: https://x.com/hwchase17

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Resources and episode mentions:

https://www.generalist.com/p/the-evolution-of-ai-agents

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