AGI is a Damaging Distraction: Current Transformer-based LLMs are fundamentally incapable of achieving AGI or making independent scientific breakthroughs; the industry hype around it is harmful misinformation.
Enterprise Utility Over Benchmarks: Cohere is singularly focused on the enterprise market, which requires a different approach to model training (synthetic data), efficiency (GPU usage), and evaluation (real-world utility over gamed public benchmarks).
Capital Efficiency as a Moat: Cohere has deliberately built its foundational models with 'orders of magnitude' less capital than competitors, signaling a more focused and sustainable business strategy.
Data is the Primary Bottleneck: The main constraint on improving LLMs is the availability of high-quality data, not fundamental algorithmic changes, as the core Transformer architecture has remained largely static since 2017.
Sovereign AI is a Geopolitical Asset: Language models are a form of national infrastructure, and Cohere's Canadian identity is a business advantage for international partners seeking non-US technology.
▶Pragmatic Skepticism of AGIMar 2026
Frosst is a vocal critic of what he calls the 'damaging and confusing' hype around Artificial General Intelligence (AGI). He argues that the current Transformer architecture is fundamentally limited and will not lead to AGI or independent scientific breakthroughs, viewing these claims as a distraction from the technology's practical applications.
This stance positions Cohere as a stable, predictable partner for enterprises that are wary of existential risk narratives and prefer to focus on tangible ROI, making Cohere's technology seem less like a speculative research project and more like a reliable business tool.
▶The Enterprise-First DoctrineMar 2026
Cohere's strategy, as articulated by Frosst, is singularly focused on the enterprise market. This is reflected in their use of synthetic business data for training, their model design for efficient deployment on common enterprise hardware (e.g., two GPUs), and their dismissal of public benchmarks in favor of real-world business utility.
By deliberately avoiding the consumer-facing AI race, Cohere carves out a defensible niche, allowing it to compete on utility, cost-efficiency, and data privacy rather than on generalized, public-facing model performance, which is a capital-intensive battle.
▶Capital Efficiency as a Competitive AdvantageMar 2026
A core tenet of Frosst's narrative is Cohere's capital efficiency, claiming the company has spent 'orders of magnitude less' than competitors to develop its models. This is linked to a focused strategy and a belief that data quality and clever application are more important than simply scaling compute power.
For investors, this signals a potentially more sustainable business model with a clearer path to profitability, contrasting with competitors engaged in an expensive and potentially unending 'arms race' for computational dominance.
▶AI as Sovereign Infrastructure
Frosst advocates for viewing large language models as a form of national infrastructure, emphasizing the benefits for countries to develop sovereign models with local cultural fluency. He presents Cohere's Canadian nationality as a strategic asset, appealing to international clients seeking non-American technology partners.
This geopolitical positioning allows Cohere to tap into a global market of nations and corporations concerned about over-reliance on US-based tech giants, turning its nationality into a key differentiator for international sales and partnerships.