India AI Impact Summit 2026

engineering-the-future-full-stack-ai-with-google-hosted-by-google

8 min read

TRACK:Economic Growth and Social Good
SPEAKERS
  • Sh. Anand Rangarajan, VP, Engineering in Google DeepMind
  • Smt. Paige Bailey, Engineering Lead for GenAI Developer Experience at Google
  • Sh. Amrit Sanjeev, Staff Developer Relations Engineer for Android at Google
  • Sh. Prashanth Subrahmanyam, Lead Developer Adoption for Google Cloud in APAC
  • Ms. Aneesha Dhar, Head-India ecosystem for Google play
SESSION OVERVIEW

Harness the power of Gemini 3.0 and the Antigravity IDE to build sophisticated, agentic AI logic natively on Google Cloud. This 1.2-hour intensive deep-dive with Google experts guides you through architecting high-performance, intelligent use cases tailored for the Android ecosystem. Learn to transform raw code into a global success story by leveraging Google Play's advanced growth and quality tools to scale your innovation to billions.

VIDEO RECORDING

Engineering the Future: Full-Stack AI with Google (Hosted by Google)

Abstract

The 1.2‑hour intensive deep‑dive showed how Google’s latest generative‑AI stack – Gemini 3.0, the Antigravity IDE, AI Studio, Vertex AI, and Android on‑device tools – can be leveraged to create “full‑stack” AI products that scale from a single developer to billions of users. Anand opened with a look at Google’s startup support ecosystem (Google for Startups Accelerator, Build with AI events, and a showcase of the education‑startup Dendry that used Gemini to boost retention by 20 %). Paige walked through hands‑on resources (AI Studio, Gemini CLI, Nano‑Banana) and challenged the audience to start prototyping. Prashant laid out the “AI OS” – the end‑to‑end developer workflow, from product strategy to delivery, highlighting Notebook LM, Antigravity, Cloud Run with GPUs, and the Agent Development Kit. Amrit turned the focus to Android, comparing on‑device versus cloud inference, demonstrating ML Kit Gen‑AI APIs, Gemini Nano/Pro models, and the new image‑generation models (Nano‑Banana, Imagine) plus low‑latency voice APIs. Finally, Aneesha explained how Google Play’s growth, quality, and monetisation tools (Android Vitals, store‑listing experiments, custom listings, price experiments) help developers turn great AI‑enabled apps into sustainable businesses. The session closed with announcements for upcoming accelerator cohorts, the Gen AI Academy, and a quick audience Q&A.

Detailed Summary

  • Community & events: Google Developer Groups, “Build with AI” workshops, city‑wide meet‑ups.
  • Google for Startups Accelerator: 10‑year program, ≈ 20 cohorts, ≈ 1 700 alumni, ≈ $30 B total funding raised by alumni, ≈ 110 k jobs created.
  • 2025 Accelerator Impact Report – published, showcases the quantitative success.
  • Annual programs: Startup School, AI Day for Startups, partnership with Intelligent Agents (‑200 startups equipped with AI agents).

Call‑to‑Action

  • Applications for the 2024‑25 accelerator open until 19 April.

Startup Showcase – Dendry (Education‑AI)

  • Founder story: “struggled with education, wanted affordable local‑language learning.”
  • Problem: Scaling teacher content across Indian languages.
  • Solution: Leveraged Gemini (multilingual capabilities) to build AI Teaching Assistant and Interview Coach – chat‑based Q&A, content summarisation.
  • Impact: ~20 % increase in user retention after AI features launch.
  • Lesson: GPU‑accelerated Gemini + Google Play distribution = rapid growth, similar to Android’s historic scaling.

Transition: “Thank you – let’s give a shout‑out to the next speaker, Paige.”

2. Hands‑On with Gemini & AI Studio (Paige Bailey)

Audience Pulse

  • Roughly half the room had already used AI Studio; others were urged to try it now.

Core Resources Introduced

ResourceWhat It DoesHow to Access
AI Studio (AI.dev)Browser‑based IDE for Gemini, Nano‑Banana, model building, API‑key generation, one‑click deployment to Google Cloud.Visit ai.studio or ai.dev.
Gemini CLICommand‑line tool for model interaction, dataset upload, fine‑tuning, inference.Via gcloud or standalone installer.
Nano‑BananaLightweight image‑generation/small‑scale visual model (chat‑style).Accessible through AI Studio “Build” tab.
Gemini 3.0State‑of‑the‑art multimodal model (reasoning, tool use, planning).Integrated in AI Studio and Gemini CLI.

Homework for the Audience

  • Sign‑up on AI.dev, generate an API key, and run a quick Gemini prompt.

Community Events Reminder

  • Build with AI workshops (hands‑on labs on model integration with Android, Cloud, Play).
  • Accelerator cohort deadline (19 Apr) reiterated.

Transition: “Now let’s hear from Prashant on the full‑stack developer side.”

3. The AI “Operating System” – Cloud Stack (Prashant Subrahmanyam)

3.1. The Need for an AI‑Centric Development OS

  • Speed of change: New AI releases weekly; developers must adopt quickly.
  • Four constant pillars:
    1. Planning (good product strategy) – still vital.
    2. Security – never goes away.
    3. Reliability / Scalability – essential for billions‑scale apps.
    4. Quality – always matters.

3.2. Product Lifecycle Powered by Gemini 3

  • Strategy phase: Use Gemini Deep Research (Enterprise) to analyse codebases, market papers, and generate concise opportunity briefs.
  • Discovery / Prototyping:
    • Notebook LM – upload research, ask questions, surface insights.
    • AI Studio + Gemini CLI + Antigravity – “wipe‑code” rapid prototyping (no IDE setup).
  • Delivery:
    • Conductor extension (Gemini CLI) – generate deployment plans (security, observability, cost).
    • Run‑time options: Google Kubernetes Engine, Cloud Run, Agent Engine (serverless GPU‑enabled containers).

3.3. Antigravity IDE – “IDE + Browser + CLI”

  • Agentic workflow: Spin up a chain of agents; each agent tackles a sub‑task.
  • Demo location: Physical pavilion (Hall 5) – live demos available.

3.4. Cloud Run with GPUs (on‑demand, pay‑per‑second)

  • Use‑cases: Real‑time LLM inference, image generation, video/3D rendering.
  • Cost model: Zero charge when idle, per‑second billing.

3.5. Agent Development Kit (ADK)

  • Open‑source, model‑agnostic: Works with Gemini, Gemini‑Nano, any containerised model.
  • Built‑in tools: Google Search, Maps grounding; code execution; can import LangGraph tools.
  • Enterprise‑ready: Managed MCP servers, observability, evaluations.

3.6. Gen AI Academy (2026 version)

  • Quarterly cohort, gamified challenges, instructor‑led workshops, self‑learning modules, assessments, project submissions, hackathons.
  • Free enrolment – QR code shown on slides.

Takeaway: A single, integrated stack (Gemini 3 → Antigravity → Cloud Run → ADK) lets developers move from idea to production in weeks, not months.

Transition: “Now let’s dive into Android – the mobile side of the stack.”

4. Building Intelligent Android Apps (Amrit Sanjeev)

4.1. Landscape: On‑Device vs. Cloud

AspectOn‑DeviceCloud
Offline availability✅ (no network needed)❌
Privacy✅ (data never leaves device)⚠️ (data transmitted)
Inference cost✅ (no server spend)❌ (pay‑per‑use)
Model capabilitySmaller, faster, limited contextLarger, richer, long context
Device reachOnly supported devices (high‑end)All connected devices
ModalitiesText + Image (current)Text, Image, Audio, Video, …

4.2. Model Suite on Android

  • Gemini Nano (on‑device, same architecture as open‑source Gemma 3N).
  • Gemini Pro / Flash (cloud, high‑performance).
  • Nano‑Banana (chat‑based image generation).
  • Imagine (fast, controllable image editing).

4.3. Practical APIs

  • ML Kit Gen‑AI APIs – ready‑made functions (summarisation, proofreading, rewriting, image generation).
    • Example: MLKit.generateSummary(text) → returns concise text.
  • Open‑Prompt API – free‑form text input, supports text and image modalities, returns text.
  • Firebase AI Logic SDK – tiny snippet (~15 lines) to connect mobile app to Gemini API (via Firebase).

4.4. Decision‑Tree for Choosing Edge vs. Cloud

  1. Modality (text, image, video, audio) – start here.
  2. Complexity – simple (summarisation) → on‑device; complex (financial modeling) → cloud.
  3. Context window – short prompts → on‑device; long‑form (hours of video) → cloud.

4.5. Demo Highlights

  • Chat‑based image generation with Nano‑Banana – “dog as a chef → change veggies to meat.”
  • Image‑editing with Imagine – mask‑based re‑generation (pancakes → omelet).
  • Low‑latency voice interaction – bidirectional WebSocket, real‑time interruption handling for conversational UI.

4.6. Advanced Topics (mentioned briefly)

  • Function calling, RAG (retrieval‑augmented generation), model fine‑tuning, AI Core – encouraged for “homework”.

Transition: “Now let’s hear from our Play ecosystem lead about turning all this into a business.”

5. Monetising AI‑Enabled Apps (Aneesha Dhar)

5.1. Google Play Scale & Impact

  • ≈ 600 million Indian users on Play Store (≈ 2.5 billion globally).
  • 2024 India Play revenue: 4 lakh € (≈ ₹ 3.3 cr) – a tiny slice of total ecosystem.
  • Over‑seas contribution: 16.7 % of downloads for Indian‑origin apps (≈ 120 M downloads).

5.2. Four Pillars of App Success

  1. Useful features – solve a real problem.
  2. Beautiful design & UX – store‑listing & in‑app aesthetics matter (first impression).
  3. Reliability & performance – Android Vitals (crash, ANR, battery) must be within thresholds.
  4. Adoption of latest Android features – keep target SDK ≥ Android 15 for best compatibility.

5.3. Quality & Policy Compliance

  • Policy‑compliance programs: Play on Air webinars, Trust & Safety guidelines.
  • Metric: A single ANR or high crash rate can trigger a store removal warning; developers have 21 days to resolve.

5.4. Store‑Presence Optimisation

  • Store‑listing experiments – A/B test icons, screenshots, videos; dashboards show conversion lift.
  • Custom store listings – up to 50 variations (localised per country, language, or user segment).
  • 6 MB rule: Every 6 MB increase in APK size ≈ 1 % drop in install‑conversion.

5.5. Monetisation Toolkit

GoalFeatureHow It Helps
AcquisitionFree trials, intro pricing, localized price settingsLower friction, adapt to market price sensitivity
RetentionGrace periods, subscription pauses, account holdReduce churn, keep users engaged
Churn ManagementCancel‑survey prompts, in‑app messaging, price experimentsIdentify why users leave, test new price points
Revenue OptimisationDynamic price experiments, ad‑network integration, custom‑store‑listingsIncrease ARPU, discover optimal price tier per region

5.6. Call‑to‑Action

  • Visit the Play Console to enable Android Vitals alerts, run store‑listing experiments, and explore price‑experiment UI.
  • Leverage the Gen AI Academy (mentioned earlier) for continued learning.

Closing Transition: “Break for 10 minutes… we’ll reconvene for the next session.”

Key Takeaways

  • Google for Startups Accelerator has produced ≈ $30 B in follow‑on funding and ≈ 110 k jobs, showing the power of structured AI‑focused mentorship.
  • Gemini 3.0 is the central model across cloud and mobile, offering multimodal reasoning, tool use, and planning capabilities that accelerate product development.
  • Antigravity IDE and AI Studio enable “wipe‑code” prototyping: developers can spin up functional AI prototypes in hours, not weeks.
  • Agent Development Kit (ADK) provides an open‑source, model‑agnostic framework to build, orchestrate, and manage fleets of AI agents with built‑in Google services (Search, Maps, Code Execution).
  • Cloud Run with GPUs offers serverless, per‑second billing for on‑demand LLM inference, image/video generation, and 3‑D rendering, eliminating idle‑cost overhead.
  • On‑device vs. Cloud trade‑offs: offline availability & privacy vs. richer models & broader device reach; the decision tree (modality → complexity → context window) should guide architecture choices.
  • ML Kit Gen‑AI APIs let Android developers add summarisation, rewriting, image generation, and custom prompts with just a few lines of code.
  • Nano‑Banana & Imagine empower developers to add conversational image generation and interactive editing directly into mobile experiences.
  • Google Play’s scale (≈ 600 M Indian users, > 2.5 B globally) and quality‑driven policies mean that Android Vitals compliance and store‑listing optimisation are mandatory for discoverability and revenue.
  • Store‑listing experiments and custom listings (up to 50 variants) let you A/B test UI assets per market, while price experiments reveal optimal pricing for different user segments.
  • Gen AI Academy and quarterly hackathons provide a gamified path for developers to master full‑stack AI building, from prototype to production‑grade app.

The session delivered a comprehensive roadmap—from startup‑stage support, through end‑to‑end AI development tooling, to mobile‑focused deployment and monetisation—showcasing how Google’s ecosystem enables developers to turn AI ideas into globally‑scaled products.

See Also:

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