In a TechCrunch interview, Google’s product vice president of AI, Michael Gerstenhaber, outlined how the world’s biggest AI platforms are pushing against three simultaneous frontiers of model capability, not just raw intelligence. This framing helps explain where the industry is heading and why companies like Google Cloud are thinking beyond traditional performance benchmarks to guide their strategy and offerings.
At the heart of Google’s thinking is a recognition that the AI arms race isn’t just about building the smartest model in terms of reasoning power and benchmark scores. Instead, Gerstenhaber described three critical dimensions that enterprises now care about: intelligence, response time and extensibility. Each of these reflects a real business need that goes beyond academic or leaderboard performance.
The first frontier, raw intelligence, is the familiar battleground where models compete on tasks like complex reasoning, code generation, long-form understanding and problem solving. These capabilities tend to grab headlines when labs release new flagship models because they represent the cutting edge of what large language models can do. Google’s own recent advances with models such as Gemini 3.1 Pro, which aims to offer strong reasoning, accuracy and reliability across a range of applications, illustrate this ongoing push to improve core performance.
However, raw intelligence does not always equate to real-world usefulness. That’s where the second frontier, response time, or latency, becomes essential. A model that produces highly accurate answers but takes tens of seconds or minutes to respond isn’t practical for many applications. Imagine a customer service system that keeps callers waiting or a real-time operational dashboard that lags while processing queries. For many enterprise applications, speed matters at least as much as intelligence. Models must deliver answers quickly enough to fit into real workflows, user expectations and service level agreements.
The third frontier, which Gerstenhaber and his team describe as extensibility, focuses on how adaptable and cost-efficient a model is in real deployment settings. This dimension speaks directly to the economics of AI at massive scale. It isn’t enough for a model to be smart and fast; it also has to be cheap enough to run across unpredictable, widely varying workloads without bankrupting the customer. Enterprises like Reddit, Meta and large service providers may face millions of inference requests that vary day to day, and the ability to balance cost with performance is central to any sustainable deployment.
Google’s framework shows how the AI market is maturing from a one-dimensional race to build the most capable model into a more nuanced competition where infrastructure, integration and adaptability are equally important. Moving from theory to practice requires solving engineering challenges, not just designing better neural networks but building platforms that enable developers and enterprises to integrate, scale, govern and audit them reliably.
This broader perspective aligns with trends seen across the industry. As AI models grow in capability, the challenge of deploying them cost-effectively and at enterprise scale becomes critical. Observers note that many organisations are now focused on fine-tuning and adapting models to their specific data and workflows as a way to derive measurable business value, rather than chasing ever higher raw capability benchmarks alone. The idea of extensibility reflects this shift toward practical utility in live systems — from vertical-specific applications to real-time operational settings where speed and adaptability are essential.
Google’s position also underlines why cloud platforms that combine infrastructure, model access and tooling, such as Vertex AI, are increasingly important in the enterprise AI landscape. Instead of simply offering access to generic models, platforms are being designed to help customers manage the full lifecycle of AI applications, including governance, integration with internal data and optimisation for performance and cost.
In short, the three-frontier framework that Google Cloud’s AI team outlined points to a more competitive and integrated future for artificial intelligence, one where intelligence alone isn’t the only marker of success. Models must respond quickly, adapt to specific tasks and do so in a cost-effective manner if they are to power real industries and create sustainable business value. This evolution reflects how enterprises are thinking about AI in 2026 and beyond, as they shift from experimentation to real-world deployment amid a crowded landscape of capable competitors.
