No Priors Ep. 91 | With Cohere Co-Founder and CEO Aidan Gomez

In this episode of No Priors, Sarah is joined by Aidan Gomez, cofounder and CEO of Cohere. Aidan reflects on his journey to co-authoring the groundbreaking 2017 paper, “Attention is All You Need,” during his internship, and shares his motivations for building Cohere, which delivers AI-powered language models and solutions for businesses. The discussion explores the current state of enterprise AI adoption and Aidan’s advice for companies navigating the build vs. buy decision for AI tools. They also examine the drivers behind the flattening of model improvements and discuss where large language models (LLMs) fall short for predictive tasks. The conversation explores what the market has yet to account for in the rapidly evolving AI ecosystem, as well as Aidan’s personal perspectives on AGI—what it might look like and when it could arrive. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AidanGomez Show Notes: 0:00 Introduction 0:36 Co-authoring “Attention is all you need” 2:27 Leaving Google and founding Cohere 4:04 Cohere’s mission and models 6:15 Pitfalls of current AI 8:14 How enterprises are deploying AI today 10:58 Build vs. buy strategy for AI tools 14:37 Barriers to enterprise adoption 20:04 Which types of companies should pretrain models? 24:25 Addressing flaws in open-source models 25:12 Current and expected progress in scaling laws 29:54 Advances in multi-step problem solving and reasoning 32:29 Key drivers behind the flattening curve of model improvements 36:25 Exploring AGI 39:59 Limitations of LLMs 42:10 What the market has mispriced

Sarah GuohostAidan GomezguestElad Gilhost

Nov 21, 202444mWatch on YouTube ↗

EPISODE INFO

Released: November 21, 2024
Duration: 44m
Channel: No Priors
Watch on YouTube: ▶ Open ↗

EPISODE DESCRIPTION

In this episode of No Priors, Sarah is joined by Aidan Gomez, cofounder and CEO of Cohere. Aidan reflects on his journey to co-authoring the groundbreaking 2017 paper, “Attention is All You Need,” during his internship, and shares his motivations for building Cohere, which delivers AI-powered language models and solutions for businesses. The discussion explores the current state of enterprise AI adoption and Aidan’s advice for companies navigating the build vs. buy decision for AI tools. They also examine the drivers behind the flattening of model improvements and discuss where large language models (LLMs) fall short for predictive tasks. The conversation explores what the market has yet to account for in the rapidly evolving AI ecosystem, as well as Aidan’s personal perspectives on AGI—what it might look like and when it could arrive. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AidanGomez Show Notes: 0:00 Introduction 0:36 Co-authoring “Attention is all you need” 2:27 Leaving Google and founding Cohere 4:04 Cohere’s mission and models 6:15 Pitfalls of current AI 8:14 How enterprises are deploying AI today 10:58 Build vs. buy strategy for AI tools 14:37 Barriers to enterprise adoption 20:04 Which types of companies should pretrain models? 24:25 Addressing flaws in open-source models 25:12 Current and expected progress in scaling laws 29:54 Advances in multi-step problem solving and reasoning 32:29 Key drivers behind the flattening curve of model improvements 36:25 Exploring AGI 39:59 Limitations of LLMs 42:10 What the market has mispriced

SPEAKERS

Sarah Guo
host
Aidan Gomez
guest
Elad Gil
host

EPISODE SUMMARY

In this episode of No Priors, featuring Sarah Guo and Aidan Gomez, No Priors Ep. 91 | With Cohere Co-Founder and CEO Aidan Gomez explores cohere CEO Aidan Gomez on Enterprise AI, Reasoning, and Non-AGI Futures Aidan Gomez, co-founder and CEO of Cohere and co-author of the Transformer paper, discusses how Cohere focuses on serving enterprises rather than competing for consumer chatbots. He explains the importance of robust foundation models, but emphasizes that enterprise success also depends on security, deployment flexibility, product structure, and helping customers avoid common implementation mistakes. Gomez outlines key use cases like RAG-based Q&A, summarization, and domain-specific assistants, and argues that reasoning-focused models and inference-time scaling will structurally change how AI capability is delivered and priced. He is skeptical of imminent AGI takeoff narratives, instead seeing a long, practical refactor of the economy using already-powerful but imperfect models, with model commoditization overstated and specialized model builders retaining leverage.

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