First Time Founders: Is Cohere the Next AI Powerhouse?

What It Covers

Nick Frost, cofounder of Cohere — a $7 billion enterprise AI company founded in 2019 by three former Google engineers — explains why only 10 companies globally can build foundational models, how Cohere differs from OpenAI and Anthropic, and why AGI remains a distraction from AI's real economic impact.

Key Questions Answered

• •Enterprise-only positioning: Cohere deliberately excludes consumer products, targeting only medium-to-large enterprises with deployment models that keep customer data private and inaccessible to Cohere itself. This produces SaaS-like margins rather than the losses consumer AI companies absorb per user, creating a fundamentally different and more sustainable financial structure for eventual public markets.
• •Foundational model barriers to entry: Roughly 10 companies worldwide can build large language models because the process resembles rocket engineering — requiring massive compute clusters, enormous curated datasets, human annotation teams, and hundreds of specialized engineers working in tight coordination. This concentration exists across only four countries: the US, Canada, China, and France.
• •Three-stage training pipeline: Modern LLMs are built through sequential data layers — first training on the entire open web, then fine-tuning on human-generated chat dialogues with rated responses, then running reinforcement learning on synthetic data the model generates itself. Chat fine-tuning specifically was the underestimated breakthrough that made models accessible to non-technical users after 2022.
• •AI's realistic productivity ceiling: Current transformer-based AI can automate roughly 20–30% of desk-based knowledge work across all organizational levels, not just entry-level roles. It cannot replace strategic thinking, cultural interpretation, or interpersonal coordination. Framing AI as a full job replacement rather than a productivity multiplier misrepresents what the technology actually does at this stage.
• •Career advice under technological uncertainty: Rather than chasing predicted high-demand roles — which forecasters consistently get wrong — young people should optimize for personal curiosity and genuine interest. Intrinsic motivation produces higher performance and adaptability than strategically chosen career paths, particularly in chaotic technological transitions where the landscape shifts faster than any prediction model can track.