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No PriorsNo Priors Ep. 91 | With Cohere Co-Founder and CEO Aidan Gomez
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At a glance
WHAT IT’S REALLY ABOUT
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.
IDEAS WORTH REMEMBERING
5 ideasEnterprises should start with simple customization before touching pre-training.
Gomez recommends a gradient of specialization: begin with fine-tuning and prompting changes, then move to post-training (SFT/RLHF), and only consider continuation pre-training for very large organizations with massive proprietary datasets and stringent performance needs.
Most failed enterprise AI POCs stem from RAG and prompting details, not model limits.
Cohere repeatedly sees failures because teams mis-format retrieved context, store data poorly, or assume models are human-like; structured APIs and more robust models can greatly reduce these failures.
Focus in-house efforts on AI systems that deliver unique competitive advantage.
Gomez advises enterprises to buy generic tools (e.g., broad copilots) and only build internally where the use case is specific to their business, like an insurance research assistant that shortens RFP cycles and directly drives revenue.
Security, privacy, and deployment flexibility are decisive for regulated industries.
In healthcare and finance, data often cannot leave a specific VPC or on-prem environment; Cohere’s ability to deploy in multiple environments is framed as a key differentiator and a prerequisite for accessing the most sensitive, valuable data.
Reasoning models shift improvement from pure training capex to inference-time spend.
Instead of waiting months for a new larger model, customers can pay for more inference-time compute to get smarter behavior on demand, changing both pricing models and infrastructure design priorities across the stack.
WORDS WORTH SAVING
5 quotesWe’re not going to build a ChatGPT competitor. What we want to build is a platform and a series of products to enable enterprises to adopt this technology and make it valuable.
— Aidan Gomez
People overestimate the models. They think they’re like humans, and that has led to a lot of repeat failures.
— Aidan Gomez
Even if we didn’t train a single new language model, there’s a half decade of work to go integrate this into the economy.
— Aidan Gomez
We’re pretty far along. We’re certainly past the point where if you just interact with a model, you can know how smart it is.
— Aidan Gomez
There’s a total technological refactor that’s going on right now and will last the next 10 to 15 years, and it’s kind of like we have to repave every road on the planet, and there’s four or five companies that know how to make concrete.
— Aidan Gomez
High quality AI-generated summary created from speaker-labeled transcript.
