Everything Google just revealed about Gemini and AI at I/O 2026

Google I/O 2026 made one thing obvious: Google is betting everything on Gemini. Instead of just organizing the web with blue links, the company is trying to turn Gemini into the default interface for how we search, work, and interact with the digital world.

The agentic Gemini era: AI inside everything

Google’s big narrative this year is what it calls the “agentic Gemini era.” In plain terms, that means Google wants every major product to act less like a static tool and more like an active AI agent that can understand goals, take actions, and handle tasks on your behalf.

Search, Gmail, Android, and even smart glasses are being reframed as Gemini-powered agents. Instead of just returning results or showing you emails, these products are being rebuilt so Gemini can interpret context, make decisions, and help you get things done with minimal manual input.

This shift also signals something bigger: Google is moving beyond the classic idea of a search engine. Rather than just pointing you to information, it wants Gemini to become a layer between you and reality—able to simulate, summarize, and act across apps and devices.

Scaling AI to quadrillions of tokens

Behind all the flashy demos is a huge infrastructure story. In just two years, Google has gone from serving 9.7 trillion tokens per month to an enormous 3.2 quadrillion tokens per month. That’s a sign of how quickly AI usage is exploding across Google’s products.

To keep up, Alphabet has massively increased its capital spending, building out data centers and hardware to support everything from chatbots to image generation. If you’ve ever spammed an image model with bizarre prompts, you’re part of the reason this infrastructure exists.

New TPUs: one chip to learn, one chip to respond

To power this scale, Google is evolving its custom AI hardware: TPUs (Tensor Processing Units). At I/O 2026, Google introduced a split design:

TPU-T is optimized for training models. This is the heavy-duty chip that teaches large models how to think, reason, and understand the world.

TPU-I is optimized for inference. This chip handles the real-time work of responding to user queries and generating outputs at scale.

By specializing chips for training versus inference, Google can push both performance and efficiency—training massive models on TPU-T, then serving them globally with TPU-I.

Gemini Omni: a multimodal world model

The headline announcement was Gemini Omni, a multimodal model that can take in almost any kind of input—text, audio, video—and produce any type of output.

What makes Omni notable is that it’s not just “drawing pixels” or “predicting words.” It’s built as a kind of world model: it learns about language, physics, motion, and everyday interactions well enough to simulate realistic scenarios on demand. That’s what enables things like understanding video context, reacting to live audio, or reasoning about real-world tasks instead of just answering static questions.

Neural Expressive: a UI that builds itself

Alongside Omni, Google introduced a new design system for the Gemini app called Neural Expressive. At first glance, it looks like a visual refresh: new icons, gradients, and a more modern aesthetic.

The interesting part is under the surface: Neural Expressive is designed so Gemini can generate UI elements on the fly. That includes:

• Diagrams and charts tailored to your prompt
• Timelines that visualize events or plans
• Mini apps and interactive components that didn’t exist before you asked for them

Instead of just giving you text, Gemini can spin up custom interfaces that match the task—like a small dashboard, a workflow editor, or a visual explanation—without a human designer or developer manually building it.

Gemini Flash 3.5: fast, smart, and more expensive

On the core LLM side, Google announced Gemini Flash 3.5, the latest “fast” model in the lineup. It’s not the biggest or most powerful model Google has, but it’s designed to hit a sweet spot of speed and intelligence.

According to Google’s own benchmarks, Flash 3.5 performs close to top-tier models like GPT-5.5 and Claude Opus 4.7 on many tasks, while responding much faster. In internal charts, it sits alone in a high-speed, high-capability quadrant.

However, this performance jump comes with a trade-off: cost. Gemini 3.5 Flash is now:

• About 3× more expensive than the previous Flash version
• Around 30× more expensive than Gemini 1.5 Flash

It’s still cheaper than some competitors like Claude for many workloads, but it’s no longer the ultra-budget option it once was. Developers and companies will have to think more carefully about when to use Flash versus smaller or cheaper models.

The true flagship model, Gemini 3.5 Pro, remains unreleased and is expected later in the summer, which left many people wanting a bit more from this I/O on the “maximum intelligence” front.

Anti-gravity IDE: from coding assistant to agent orchestrator

Google also showed off a major update to its AI-powered coding environment, now called the anti-gravity IDE (previously known as Windserve). It started life as a straightforward AI coding assistant, similar to tools like Cursor, but its latest version leans heavily into agent orchestration.

Instead of just suggesting code completions, anti-gravity is becoming a control center for multiple AI agents that can:

• Plan and scaffold large codebases
• Implement features across files and services
• Debug and patch issues end-to-end

Some traditional developers may dislike the shift away from hands-on coding, but the live demo was still impressive. Using anti-gravity, Google’s team generated an entire operating system from scratch in about 12 hours and billions of tokens. Initially, the OS couldn’t run Doom due to missing drivers, but on stage they had Gemini generate the necessary drivers in seconds—after which Doom ran successfully.

The demo highlighted two things: how fast these models can generate code at scale, and how close we’re getting to AI systems that can build complex software stacks with relatively minimal human intervention.

HTML on Canvas: a powerful new API for web developers

Amid all the AI news, there was a gem for web developers: the new HTML on Canvas API in Chrome.

As the name suggests, this API lets you render native HTML elements directly inside a canvas. That means you can:

• Use WebGL or WebGPU to control every pixel of a highly interactive experience
• Still rely on regular HTML elements for UI pieces like buttons, inputs, and overlays
• Combine game-like visuals or data visualizations with accessible, standard HTML controls

This opens the door to richer web apps where you don’t have to choose between a pure-canvas experience and traditional HTML layouts—you can blend both. For developers experimenting with AI-powered UIs, simulations, or dashboards, this could be a very useful tool.

If you’re exploring how AI tools fit into productivity workflows, you might also like our breakdown of the best AI tools inside Excel and what actually works, which looks at similar ideas from a more practical, day-to-day perspective.

AI agents for app development

The broader theme across I/O and the wider ecosystem is clear: we’re moving from single-model prompts to coordinated AI agents that can handle entire workflows. Google’s own tools are heading that way, and third-party platforms are doing the same.

One example highlighted alongside these trends is Emergent, a platform that uses multiple specialized agents to build full-stack applications. Instead of one model guessing how to build everything, Emergent spins up agents for the frontend, backend, database, tests, and deployment, all working in parallel from a single prompt.

The idea is similar to what we’re seeing with systems like Perplexity’s new computer-like environments and other agentic platforms. If you’re curious about that broader shift, our guide to Perplexity Computer for beginners is a good intro to how these AI-first environments are being designed.

What Google I/O 2026 really tells us

Putting it all together, Google I/O 2026 wasn’t just about new models or shiny demos. It showed a clear strategy:

• Turn every major Google product into an AI agent powered by Gemini
• Scale infrastructure to handle quadrillions of tokens per month
• Use specialized TPUs to train and serve ever-larger models efficiently
• Push multimodal, world-model-style systems like Gemini Omni
• Let AI generate not just answers, but entire interfaces and applications

For users, this means more AI woven into everyday tools. For developers, it means new APIs, faster models, and a growing expectation that apps will be AI-native from day one. Whether you’re excited or cautious, one thing is clear: Google’s AI endgame is no longer theoretical—it’s shipping.