Google's approach to artificial intelligence development is shifting away from a single measure of success. Instead of focusing solely on making AI smarter, the company's cloud division is now advancing on three separate frontiers that each demand different engineering solutions.
The three frontiers are straightforward: raw intelligence, response time, and what engineers call extensibility—essentially whether a model can run affordably at massive, unpredictable scale. This framework represents a fundamental change in how the industry measures AI progress.
Key Takeaways
- Google is advancing AI models across three distinct frontiers: raw intelligence, response time, and cost-effective scalability
- Different use cases require different priorities—code writing needs maximum intelligence, while customer service needs speed
- The company sees itself as uniquely positioned through vertical integration, controlling everything from foundation models to user interfaces
- Missing infrastructure and patterns for auditing AI agents remain major obstacles to widespread business deployment
Background
For years, the AI race looked simple. Companies competed to build the smartest models. Bigger datasets. More parameters. Better benchmarks. The winner was whoever could claim the highest performance on standardized tests.
But real-world business problems don't always reward pure intelligence. A software engineer writing code can wait 45 minutes for the best possible answer—they'll use it for months and maintain it in production anyway. A customer service representative applying company policy has maybe 30 seconds before the caller loses patience. A retailer trying to recommend products to millions of shoppers simultaneously can't afford to run expensive computations on every interaction.
Google's realization is that these different problems require different model characteristics. And pursuing all three simultaneously creates new technical challenges that go beyond simply training larger models.
Key Details
The Three Frontiers Explained
The first frontier is raw intelligence. Models like Gemini Pro are tuned to be as capable as possible, regardless of how long they take to produce an answer. This matters for complex tasks where accuracy outweighs speed.
The second frontier is latency. For customer support scenarios, a model needs to be intelligent enough to apply company policies correctly, but it also needs to respond within seconds. Intelligence becomes worthless if the answer arrives too late.
The third frontier is extensibility—the ability to deploy models cheaply enough to run at massive scale with unpredictable demand. This isn't about raw capability. It's about economics. A model that requires expensive hardware for every inference can't power millions of simultaneous interactions.
"If I'm doing customer support and I need to know how to apply a policy, you need intelligence to apply that policy. Are you allowed to transact a return? Can I upgrade my seat on an airplane? But it doesn't matter how right you are if it took 45 minutes to get the answer."
Google sees this three-frontier approach as a competitive advantage. The company is vertically integrated across the AI stack—from foundation models like Gemini to APIs for memory and code writing, to agent engines that handle compliance, all the way to consumer-facing chat interfaces. This integration means decisions made at one level can optimize for the other levels.
The Infrastructure Problem
But there's a significant catch. The technology underlying AI agents is barely two years old, and important infrastructure patterns haven't been built yet. Companies don't have standard ways to audit what agents are doing. They lack patterns for authorizing data access to agents. They're still figuring out how to put these systems into production safely.
This matters because production deployment is always behind what's technically possible. Two years isn't enough time to see what the intelligence truly supports in real business environments. Companies are struggling because they're trying to deploy technology that's advancing faster than the operational patterns to manage it.
What This Means
Google's three-frontier framework is already shaping real business partnerships. The company recently announced a major collaboration with Unilever, the consumer goods giant behind brands like Dove and Hellmann's. The partnership will build what Unilever calls an "AI-first digital backbone" using Google Cloud's technologies, including agentic workflows that can execute complex tasks across the company's business processes.
Unilever's deal focuses on three pillars: agentic commerce and marketing intelligence, an integrated data and cloud foundation, and advanced AI capabilities. This is exactly the kind of enterprise transformation that requires balancing all three frontiers. Marketing campaigns need to discover and convert customers quickly. Data platforms need to generate insights fast enough to respond to market shifts. But the whole system needs to run at a cost that makes business sense.
For the broader AI industry, this framework suggests the competition isn't really about who builds the smartest model anymore. It's about who can optimize across multiple dimensions simultaneously. That's harder than it sounds. It requires different expertise. It demands different infrastructure choices. And it means companies need to think about AI not as a single capability, but as a system with competing demands.
Google sees vertical integration as a strength is telling. When you control the hardware, the models, the APIs, and the user interfaces, you can make trade-offs that a company relying on third-party components can't make. You can accept slightly lower intelligence in some cases to get better speed. You can sacrifice raw capability to reduce costs. You can optimize the entire chain rather than just one piece.
Meanwhile, companies trying to deploy AI agents in production are hitting real walls. They can build prototypes. They can demonstrate impressive capabilities. But they're still figuring out how to audit, govern, and secure these systems at scale. That gap between what's technically possible and what's operationally safe is where real progress happens next.
Frequently Asked Questions
What does "extensibility" mean in AI models?
Extensibility refers to whether an AI model can be deployed affordably at massive scale with unpredictable demand. A highly capable model that requires expensive hardware for every inference can't serve millions of users simultaneously. Extensibility is about cost efficiency and scalability, not raw intelligence.
Why can't Google just build one model that's great at everything?
Different use cases have conflicting requirements. Code writing can tolerate 45-minute response times but needs maximum intelligence. Customer service needs answers in seconds but can accept slightly lower intelligence. Building one model that excels at both is technically difficult and economically wasteful. It's often better to optimize different models for different purposes.
What's holding back AI agents from widespread business use?
While AI agents are technically capable, the operational infrastructure to deploy them safely is still being built. Companies lack standard patterns for auditing agent actions, authorizing data access, and managing governance at scale. Production deployment always lags behind technical capability, and that gap is where the real work happens next.
Frequently Asked Questions
What does ‘extensibility’ mean in AI models?
Extensibility refers to whether an AI model can be deployed affordably at massive scale with unpredictable demand. A highly capable model that requires expensive hardware for every inference can’t serve millions of users simultaneously. Extensibility is about cost efficiency and scalability, not raw intelligence.
Why can’t Google just build one model that’s great at everything?
Different use cases have conflicting requirements. Code writing can tolerate 45-minute response times but needs maximum intelligence. Customer service needs answers in seconds but can accept slightly lower intelligence. Building one model that excels at both is technically difficult and economically wasteful.
What’s holding back AI agents from widespread business use?
While AI agents are technically capable, the operational infrastructure to deploy them safely is still being built. Companies lack standard patterns for auditing agent actions, authorizing data access, and managing governance at scale. Production deployment always lags behind technical capability.
