Google's Cloud AI lead on the three frontiers of model capability | TechCrunch

Google's Cloud AI lead on the three frontiers of model capability | TechCrunch

TechCrunch - AI 7 min read Article

Summary

Michael Gerstenhaber, Google's VP of Cloud AI, discusses the three frontiers of AI model capabilities: raw intelligence, response time, and cost-effectiveness for large-scale deployment.

Why It Matters

Understanding these frontiers is crucial for businesses leveraging AI, as it highlights the balance between intelligence, speed, and affordability in deploying AI solutions. This insight can guide organizations in optimizing their AI strategies and investments.

Key Takeaways

  • AI models are advancing in three key areas: intelligence, response time, and cost-effectiveness.
  • Latency is critical in applications like customer support where quick responses are essential.
  • Google's vertical integration in AI infrastructure provides a competitive advantage.

As a product VP at Google Cloud, Michael Gerstenhaber works mostly on Vertex, 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. When I spoke with Michael, I was particularly struck by one idea I hadn’t heard before. As he put it, AI models are pushing against three frontiers at once: raw intelligence, response time, and a third quality that has less to do with raw capability than with cost — whether a model can be deployed cheaply enough to run at massive, unpredictable scale. It’s a new way of thinking about model capabilities, and a particularly valuable one for anyone trying to push frontier models in a new direction. This interview has been edited for length and clarity. 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, I’ve been at Google almost half a year now. I run Vertex, Google’s developer platform. Most of our customers are engineers building their own applications. They want access to agentic patterns. They want access to an agentic platform. They want access to the inference of the smartest models in the world. I provide them that, but I don’t provide the applications themselves. That’s for Shopify, Thomson Reuters, and our various customers to provide in their own domains....

Related Articles

Machine Learning

Ml project user give dataset and I give best model [D] [P]

Tl,dr : suggest me a solution to create a ai ml project where user will give his dataset as input and the project should give best model ...

Reddit - Machine Learning · 1 min ·
Machine Learning

[D] ICML Reviewer Acknowledgement

Hi, I'm a little confused about ICML discussion period Does the period for reviewer acknowledging responses have already ended? One of th...

Reddit - Machine Learning · 1 min ·
Llms

Claude Opus 4.6 API at 40% below Anthropic pricing – try free before you pay anything

Hey everyone I've set up a self-hosted API gateway using [New-API](QuantumNous/new-ap) to manage and distribute Claude Opus 4.6 access ac...

Reddit - Artificial Intelligence · 1 min ·
Machine Learning

[D] ICML reviewer making up false claim in acknowledgement, what to do?

In a rebuttal acknowledgement we received, the reviewer made up a claim that our method performs worse than baselines with some hyperpara...

Reddit - Machine Learning · 1 min ·
More in Machine Learning: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime