Google’s Cloud AI leads on the three frontiers of model capability

Source: TechCrunch

As a product VP at Google Cloud, Michael Gerstenhaber works mostly on Vertex AI, the company’s unified platform for deploying enterprise AI. It gives him a high‑level view of how companies are actually using AI models, and what still needs to be done to unleash the potential of agentic AI.

This interview has been edited for length and clarity.

Background and Role at Google

Why don’t you start by walking us through your experience in AI so far, and what you do at Google?

I’ve been in AI for about two years now. I was at Anthropic for a year and a half, and I’ve been at Google almost half a year. I run Vertex AI, Google’s developer platform. Most of our customers are engineers building their own applications. They want access to agentic patterns, an agentic platform, and the inference of the smartest models in the world. I provide them that, but I don’t provide the applications themselves—that’s up to Shopify, Thomson Reuters, and our various customers in their own domains.

Why Google?

What drew you to Google?

Google is, I think, unique in the world because we have everything from the interface to the infrastructure layer. We can build data centers, buy electricity, and even build power plants. We have our own chips, our own model, the inference layer we control, and the agentic layer we control. We offer APIs for memory and interleaved code writing, an agent engine that ensures compliance and governance, and chat interfaces with Gemini Enterprise and Gemini Chat for consumers. That vertical integration is a major strength.

Model Capability Frontiers

It’s odd because, even with all the differences between companies, it feels like all three of the big labs are really close in capabilities. Is it just a race for more intelligence, or is it more complicated than that?

I see three boundaries:

1. Raw intelligence – Models like Gemini Pro are tuned for maximum capability (e.g., writing the best possible code). Latency isn’t a concern; you just want the best result, even if it takes 45 minutes.

2. Latency – For use‑cases like customer support, you need the answer quickly. The model must fit within a latency budget; otherwise the user hangs up. Here you pick the most intelligent model that can respond within the required time.

3. Cost & scalability – Companies such as Reddit or Meta need to moderate massive volumes of content. They must balance intelligence with the ability to run at massive, unpredictable scale. Cost becomes the decisive factor.

Challenges for Agentic Systems

One of the things I’ve been puzzling about is why agentic systems are taking so long to catch on. It feels like the models are there and I’ve seen incredible demos, but we’re not seeing the kind of major changes I would have expected a year ago. What do you think is holding it back?

The technology is only about two years old, and critical infrastructure is still missing. We lack:

• Auditing patterns for what agents are doing.
• Authorization frameworks for data access by agents.

These gaps require work before production‑ready deployments are possible. Production adoption is always a trailing indicator of what the technology can achieve.

At Google, the adoption has been unusually fast because it fits neatly into the software development lifecycle. We have a dev environment where it’s safe to break things, then promote to test. Code changes require two people to audit and approve them before they carry Google’s brand. This human‑in‑the‑loop process makes implementation low‑risk, but similar patterns need to be built for other domains and professions.