The 'scale is all you need' hypothesis is breaking down due to diminishing returns; future AI progress will come from new capabilities like learning from experience, not just bigger models [48, 56, 16, 55].
A vertically integrated strategy, where a company builds both the foundation model and the end-user application, is crucial for superior product quality in the enterprise space [3, 20].
The AI market is bifurcating, with Cohere focused on the enterprise, while competitors like OpenAI, Google, and Meta target the consumer market [38, 42, 65].
The most significant near-term AI risk is geopolitical, stemming from state-level bad actors gaining advanced capabilities, making it imperative for liberal democracies to maintain a lead [47].
For enterprise adoption, model efficiency and deployment flexibility (cloud-agnostic, on-premise) are more important than raw model size, leading Cohere to strategically cap its models to run on a maximum of two GPUs [11, 35, 52].
2017
Co-authored the 'Attention is All You Need' paper at Google over a 12-16 week period, introducing the Transformer architecture [7]. Gomez believes Google did not fully grasp the paper's significance at the time [68].
Post-2017
Founded Cohere in Toronto, Canada, at a time when he estimates only 15-30 people in the world had experience training large language models [1]. He considers the Toronto location a key strategic advantage for talent acquisition [41].
c. 2023-2024
Articulates that the AI industry is hitting diminishing returns on scaling [48, 56] and that the top models have converged in capability [59]. During this period, he claims synthetic data became the dominant source for training new models across all major labs [50].
Present Day
Leads Cohere as it experiences high demand, necessitating a rapid expansion of its sales and engineering teams [2]. The company's focus is on deploying a narrow set of use cases at massive scale within enterprises, often to tens of thousands of employees [51].
Near Future
Predicts the next generation of models (e.g., GPT-6) will feature the ability to learn from experience [55] and that the hardware scale for training will reach millions of GPUs within a couple of years [49].
▶The Enterprise AI PlaybookApr 2026
Gomez outlines a clear strategy for winning the enterprise market, emphasizing cloud-agnostic deployment, on-premise capabilities, and model efficiency over raw scale. This approach, exemplified by Cohere's 'two GPU constraint' [52], contrasts with the consumer-focused strategies of competitors like OpenAI [42].
This focus on practical enterprise needs (cost, security, integration) over chasing the largest possible model could give Cohere a durable advantage in the B2B space, even if its models do not top consumer-facing leaderboards.
▶Beyond Scale: The Next AI FrontierApr 2026
Gomez consistently argues that the era of easy gains from scaling models is ending, citing diminishing returns [48, 56]. He posits that the next breakthroughs will involve new capabilities like learning from experience [16, 63] and the strategic use of synthetic data, which he claims now constitutes the majority of training data at all major labs [50].
Investors should watch for R&D that moves beyond simple scaling laws and focuses on novel architectures and learning paradigms, as this is where Gomez believes future value and differentiation will be created.
▶A Pragmatic View of AI Risk and Geopolitics
Gomez dismisses existential AI risk narratives as overblown and potentially strategic posturing [57, 64]. His focus is on tangible, near-term risks, particularly the geopolitical implications of state actors gaining access to advanced AI and the need for liberal democracies to maintain a strategic advantage [47].
Gomez's geopolitical framing of AI competition suggests that national security and government contracts will be a critical battleground, potentially favoring companies like Cohere that can offer secure, on-premise deployments [11].
▶The Consolidation of the AI Stack
Gomez predicts a market evolution from a fragmented landscape of best-of-breed point solutions to consolidated, integrated platforms [33]. He believes enterprises will ultimately prefer a handful of powerful, versatile models over managing hundreds of specialized ones for each department [25].
This prediction suggests a future with a few dominant platform players. Companies offering niche AI solutions may face acquisition or obsolescence, while those building integrated platforms, like Cohere with its North application layer [20], are positioned for long-term market leadership.