Google I/O 2026: What Every AI Builder Should Build Next

The Part That Actually Matters

Google I/O has a habit of delivering spectacular demos that quietly disappear six months later. So the right question going into any I/O recap isn't "what did they announce?" — it's "what is still running in a year and which of it changes how I build?"

This year, the answer to that second question is unusually clear. Google shipped four things at I/O 2026 that directly affect the AI builder stack: Jules, ADK 1.0, Veo 3 in the Gemini API, and a signals-only preview of a next-generation Gemini model with a 2 million token context window. Here is what each one means and, more importantly, what you should do with it.

Jules: The Async Coding Agent Is Real Now

Jules is not a chat assistant or an IDE plugin. It is an asynchronous coding agent that spins up a cloud virtual machine, clones your repository, writes a plan using Gemini, runs your tests, and opens a pull request — while you do something else entirely. That distinction is critical.

Synchronous agents like GitHub Copilot and Cursor's Agent mode keep you in the loop at every step. Useful, but it means you're still the bottleneck. Jules is designed for parallel execution: you can submit ten tasks simultaneously and return to ten pull requests. That's a fundamentally different relationship with an AI coding assistant.

The Gemini CLI integration brings Jules directly into your terminal. Install the extension and you can delegate tasks without switching context:

# Install Jules extension for Gemini CLI
gemini extensions install https://github.com/gemini-cli-extensions/jules --auto-update

# Submit an async task
/jules Fix the race condition in the payment processor and add regression tests

The VM runtime includes Node.js, Python, Go, Rust, and Java. Jules isn't limited to TypeScript shops, which matters if your backend is anything other than a standard web stack.

The honest take on Jules

Google's track record on developer tools is genuinely mixed. Stadia, Duplex for the web, and half of Google's Cloud services have walked the same hype-to-discontinuation path. Jules joins a category — async AI coding agents — where Anthropic's Claude Code and OpenAI Codex are already operating. Those are strong competitors with more established developer trust.

The things that could make Jules win anyway: deep GCP integration, pricing (if Google subsidizes compute to drive adoption), and the multi-language runtime that Codex doesn't offer natively. Whether those advantages materialize depends on execution, not announcement. Get on the Jules waitlist if you want to form your own opinion quickly. The developer keynote at this year's I/O dedicated three separate sessions to Jules — that's not a one-slide mention. Google is making it the story of 2026.

ADK 1.0: The Multi-Language Agent Framework Gap Is Closed

Google's Agent Development Kit went to 1.0 GA at I/O, and the release deserves more attention than it's getting. ADK now supports Python, TypeScript, Java, and Go with first-class stability guarantees. LangGraph, CrewAI, and the OpenAI Agents SDK are Python-first — often Python-only. If your production backend is Java microservices or Go services, ADK is currently the only mainstream agent framework that doesn't require adding a Python dependency to ship agentic workflows.

The headline features in ADK 1.0:

• AgentTeam API (Python 2.0 beta) — Coordinate multiple specialized agents as a team. One orchestrator, multiple specialists, clean separation of concerns.
• A2A protocol (Agent2Agent) — Treats tasks as first-class objects with Server-Sent Events for progress streaming. No more polling long-running operations.
• Event Compaction — Summarizes older context instead of truncating it. In benchmarks Google shared, this cut token usage by 38% and improved latency by 18%. That's not cosmetic — on a production agent handling thousands of tasks daily, 38% token reduction is real money.

Here's a basic ADK Python agent setup to get oriented:

from google.adk.agents import Agent, AgentTeam
from google.adk.tools import tool

@tool
def search_docs(query: str) -> str:
    """Search internal documentation."""
    # Your search logic here
    return results

@tool
def create_ticket(title: str, description: str, priority: str) -> dict:
    """Create a support ticket."""
    # Your ticketing system integration
    return ticket

# Single agent
support_agent = Agent(
    model="gemini-3.1-pro",
    name="support_agent",
    tools=[search_docs, create_ticket],
    system_prompt="You are a support agent. Search docs before creating tickets."
)

# Multi-agent team
team = AgentTeam(
    orchestrator=support_agent,
    specialists=[research_agent, escalation_agent]
)

response = await team.run("Customer is getting 403 on the billing API after upgrading their plan")

Compared to alternatives:

Framework	Languages	Best For
Google ADK	Python, TS, Java, Go	GCP-native teams, multi-language backends
OpenAI Agents SDK	Python	Quick prototypes, Python shops
LangGraph	Python, JS	Complex graph-based orchestration
Claude Code SDK	Python, TS	Complex reasoning chains

If you're already on GCP and running a multi-language stack, ADK 1.0 GA is the clearest signal to evaluate it seriously. The 1.0 stability guarantee removes the "might break everything next quarter" risk that kept teams on the sidelines.

Veo 3 in the Gemini API: Video as a Development Primitive

Veo 3 generated serious attention at I/O 2025 for its video quality. What's new at I/O 2026 is that it's in the Gemini API at pricing that independent developers can actually use.

Two tiers:

• Veo 3.1 Lite at $0.05 per second of generated video — this is the first AI video API where you can prototype without burning a monthly budget in one afternoon
• Veo 3.1 Standard at $0.40/second — adds native synchronized audio and higher fidelity output

Both support text-to-video and image-to-video, with native 9:16 output for Shorts and Reels. The image-to-video path is the more interesting developer primitive: provide a reference image alongside a text prompt to maintain consistent characters and scenes across generated clips.

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

# Text to video
response = genai.generate_video(
    model="veo-3.1-lite",
    prompt="A product demo showing the app's onboarding flow, clean UI, 15 seconds",
    aspect_ratio="9:16",
    duration_seconds=15
)

# Image to video (consistent characters)
response = genai.generate_video(
    model="veo-3.1-standard",
    prompt="The character walks through a modern office and sits at a desk",
    reference_image="character_reference.jpg",
    aspect_ratio="16:9",
    duration_seconds=10
)

The use cases that become economically viable at these prices: product explainer videos generated from feature specs, app onboarding animations without a design team, educational content at scale, and personalized video outreach. None of these were worth engineering time at previous AI video pricing. At $0.05/second for Lite, they are.

The 2M Token Context Signal

Google hasn't officially announced "Gemini 4" by name, but session signals and pre-I/O leaks point toward a major model update with a 2 million token context window — double the current 1M ceiling on Gemini 3.1 Pro.

Why this matters specifically: at 2M tokens, a large codebase fits in context without retrieval augmentation. Jules, specifically, benefits from this. An agent that reads your entire codebase produces fewer nonsensical diffs than one sampling random chunks of it. The difference between a 100K context agent and a 2M context agent working on a large monorepo isn't just quantitative — it crosses a qualitative threshold where the agent actually understands the relationships between files and modules.

The calibrated question to ask before getting excited: what does it cost per token at 2M? Gemini 3.1 Pro at 1M is already expensive at production scale. The spec is interesting. The pricing will determine whether it's actually useful or just a benchmark number.

Android 17: The Forced Adaptive Layout Migration

If you build Android apps, this one has a hard deadline. Android 17 (API 37) removes the orientation and resizability opt-out for large screens. Target API 37 and your app must handle adaptive layouts. There is no escape hatch.

The implications:

• Apps that force portrait-only or fixed-window mode will break on tablets and foldables
• The ACCESS_LOCAL_NETWORK permission is now required for local network access (previously implicit)
• A new lock-free MessageQueue implementation reduces UI jank — performance wins if you migrate

Final Android 17 release is expected around July 2026. If you're maintaining Android apps, start the layout migration before the summer crunch. Cursor and Claude Code handle boilerplate layout XML well — use them to generate WindowSizeClass-aware layouts for each of your screens.

Firebase: The Agent-Native Backend

Less flashy than Jules but potentially more impactful for builders: Google is positioning Firebase as the agent-native backend. Firebase AI Logic now handles authentication, data persistence, and function triggers in a way that's designed for agentic workflows, not just traditional client-server patterns.

The architecture it enables: an agent runs in Jules, reads/writes state to Firestore, triggers Cloud Functions on external events, and authenticates users through Firebase Auth — all without building custom infrastructure. If you're starting an AI-native app today and you're not on GCP, this is worth evaluating before you build your own agent persistence layer.

What to Actually Build Next

After filtering the announcements for real developer utility, here is what I'd prioritize:

1. Get on the Jules waitlist now. Whether it lives up to the demo or not, async coding agents are the 2026 development paradigm. Get hands-on time early and form your own opinion before your competitors do.
2. Evaluate ADK if you have a multi-language backend. If you're running Go or Java services and you want to add agentic capabilities, ADK 1.0 GA is the only production-ready option that doesn't require introducing Python. The A2A streaming protocol and Event Compaction make it competitive on cost and latency.
3. Prototype one Veo 3 use case. Pick the lowest-effort, highest-value video use case in your product — product explainer, onboarding animation, personalized demo — and build a proof of concept with Veo 3.1 Lite. At $0.05/second, the risk of a two-day prototype is trivial.
4. Audit your agent's context usage. If you're building with Gemini 3.1 Pro today, benchmark your token usage per agent run. When the 2M context window ships, you'll want to know whether upgrading changes the quality of your agent's output. The delta will be more visible if you've already measured the baseline.
5. Start Android 17 layout migration. If you ship Android apps, this is the one with the hardest deadline. July 2026 is not far away.

Google I/O 2026 is genuinely different from the last few years. The announcements are not new models in a benchmark race — they are new primitives for building actual products. Jules, ADK, and Veo 3 in the API each unlock something that wasn't economically or technically viable before. That's the signal worth watching.

If you want to work through any of these with other builders who are shipping real things — not just watching demos — join AI Builder Club. We're building with these tools as they ship.