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cover of episode Ep 62: CEO of Cohere Aidan Gomez on Scaling Limits Emerging, AI Use-cases with PMF & Life After Transformers

Ep 62: CEO of Cohere Aidan Gomez on Scaling Limits Emerging, AI Use-cases with PMF & Life After Transformers

2025/4/15
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Unsupervised Learning
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Jacob Efron
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Jacob Efron: 我很好奇在未来五年或十年内，哪种企业部署大型语言模型的模式将会胜出，例如咨询模式、Palantir模式和开箱即用产品模式。 Aidan Gomez: 长期来看，我认为一种折中的模式将会胜出。这项技术很复杂，企业需要一定程度的支持才能有效地采用它。AI 智能体只有在访问到与人类相同的信息时才能有效地驱动自动化，这需要大量的访问权限，这带来了隐私问题，因为很少有软件需要这种程度的访问权限。每个公司使用的软件堆栈都不同，这需要定制化设置才能将所有上下文整合到模型中。我们正在努力使我们的新智能体平台 North 更加易于定制和集成到每个公司的软件堆栈中。未来，我们将能够自动化部分问题，但不能完全自动化，因为访问敏感数据（如薪资数据）的错误代价很高。 Jacob Efron: 你如何对目前已经实现产品市场匹配的企业级生成式AI用例进行分类或列举？ Aidan Gomez: 我认为目前大型语言模型在客户支持和增强人类研究能力这两个领域已经实现了产品市场匹配。在医疗保健领域，我们希望使医生的笔记记录和表格填写工作更容易。在电信、医疗保健和金融服务等各个行业，客户支持的需求非常大，而且技术已经成熟。另一个用例是增强人类的研究能力，使财富经理能够更快更有效地进行研究。

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Aidan joined this week’s Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what’s breaking in the foundation model stack. 

If you’re building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere’s agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they’ve made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea)

 

(0:00) Intro(0:32) Enterprise AI(3:23) Custom Integrations and Future of AI Agents(4:33) Enterprise Use Cases for Gen AI(7:02) The Importance of Reasoning in AI Models(10:38) Custom Models and Synthetic Data(17:48) Cohere's Approach to AI Applications(23:24) Future Use Cases and Market Fit(27:11) Building a Unified Automation Platform(27:34) Strategic Decisions in the AI Journey(29:19) International Partnerships and Language Models(31:05) Future of Foundation Models(32:27) AI in Specialized Domains(34:40) Challenges in Data Integration(35:06) Emerging Foundation Model Companies(35:31) Technological Frontiers and Architectures(37:29) Scaling Hypothesis and Model Capabilities(42:26) AI Research Culture and Team Building(44:39) Future of AI and Societal Impact(48:31) Addressing AI Risks

 

With your co-hosts:  

@jacobeffron  

  • Partner at Redpoint, Former PM Flatiron Health

  

@patrickachase  

  • Partner at Redpoint, Former ML Engineer LinkedIn

  

@ericabrescia  

  • Former COO Github, Founder Bitnami (acq’d by VMWare)

  

@jordan_segall  

  • Partner at Redpoint