Amit Rawal

Chief AI Officer |  Apple AI Leader | Stanford | AI Professor | Keynote Speaker | AI Strategy Advisor to Leaders & C-Suite | Building an AI-powered Life OS to help you find meaning, clarity & connection

Disclaimer: Opinions expressed here are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google Inc) or its subsidiaries or affiliates.

Driving AI-led business transformation across Google’s Platforms, Devices & Services. My mission is to inspire, educate and empower 100 million founders and individuals to live an AI-powered life.

I help founders, entrepreneurs and young tech professionals to use AI in their daily life. I also teach AI to leaders, helping them apply it for storytelling, strategic decision-making, and data analysis.

Head of Product: Digital 360 - Global Analytics & Insights

Part of the Global Digital Center of Excellence. Lead a global team of product managers, engineers, analysts, and program managers responsible for the analytics platform enabling Apple’s global Digital Channel Sales.  

- Scoped, designed, and implemented E2E Data and Analytics strategy enabling 360 performance measurement on the world’s largest retail platforms such as Amazon, Walmart, JD, Best Buy, etc.

If you think Google AI is just Gemini, read this.

Google AI isn’t one tool. It’s a whole universe.

Here’s how the this ecosystem actually works, category by category 👇

1. Productivity
Tools that help you work faster, not harder.

•	Gemini (inside Docs, Gmail, Slides, Sheets)
•	Google Docs
•	Google Sheets
•	Google Slides
•	Gmail
•	Google Meet

2. Browser & Research
For when your brain needs backup while thinking.

•	Gemini in Chrome
•	NotebookLM
•	Google Search (AI Mode)

3. Data & Analytics
Turning numbers into real decisions.

•	Google Analytics
•	Looker Studio
•	Google Sheets (AI features)

4. Creativity
Where ideas stop being stuck in your head.

•	Gemini
•	Veo 3 (video generation)
•	Lyria (music & audio)
•	Google Photos (AI edits)

5. Cloud Systems
The invisible engine running everything.

•	Google Cloud
•	Firebase
•	BigQuery
•	Vertex AI

6. Developers
For people building the future, not just using it.

•	Gemini API
•	AI Studio
•	TensorFlow
•	Gemma models

That’s why Google AI hits different.

The real transformation is not adding more tools.
But making intelligence feel natural in every workflow.

My job isn’t to push AI.
It’s to make it invisible, useful, and trusted.

When AI fits into your day instead of interrupting it,
that’s when change actually sticks.

And that’s the future Google is building.

Found this useful? 
➕ Hit repost and follow along.

___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Google AI Transformation Leader | Former Apple | Stanford | AI Educator & Keynote Speaker

MIT just raised the learning bar. Skip expensive courses!

No coding & No fees required.

MIT just dropped 10 Free AI courses and… it’s insane. 


1. Introduction to Machine Learning

What you’ll learn:
➨ Basics of machine learning in super simple terms.

Link: https://lnkd.in/gT5HcRs5


2. Artificial Intelligence

What you’ll learn:
➨ How AI thinks, solves problems, and learns.

Link: https://lnkd.in/ggneRvcZ


3. Foundation Models and Generative AI

What you’ll learn:
➨ How tools like ChatGPT and modern AI actually work.

Link: https://lnkd.in/gNwgbtaE


4. Understanding the World Through Data

What you’ll learn:
➨ How to use data and ML to make sense of real life.

Link: https://lnkd.in/gVdj_EhG


5. AI 101

What you’ll learn:
➨ A beginner-friendly intro to AI concepts.

Link: https://lnkd.in/gyJz7whc


6. Introduction to Deep Learning

What you’ll learn:
➨ Core ideas behind neural networks and deep learning.

Link: https://lnkd.in/gq8PwrnP


7. ML with Python

What you’ll learn:
➨ How machines learn using simple Python models.

Link: https://lnkd.in/gUdHfAhx


8. How to AI (Almost) Anything

What you’ll learn:
➨ Use AI in music, art, and everyday creative stuff.

Link: https://lnkd.in/ghfgKgsx


9. Artificial Intelligence in K-12 Education

What you’ll learn:
➨ How AI works and how it can be used in learning.

Link: https://lnkd.in/gs9Fesqy


10. Introduction to Algorithms

What you’ll learn:
➨ The logic behind fast and smart AI systems.

Link: https://lnkd.in/g2P-3ptd


✅ Repost this so more people get free MIT-level AI learning.
➕ Follow for more AI resources. 

___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

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Microsoft shared 21 GenAI courses pack for beginners.

And nobody shared them with you.

1. Introduction to Generative AI and LLMs
- Understand what Gen AI is and how LLMs work.
🔗 https://lnkd.in/gNGQQwDD

2. Exploring and comparing different LLMs
- How to select the right model for your use case
🔗 https://lnkd.in/gyK2UZDF

3. Using GenAI Responsibly
- How to build Generative AI Applications responsibly
🔗 https://lnkd.in/gArv6vkJ

4. Understanding Prompt Engineering Fundamentals
- Hands-on Prompt Engineering Best Practices
🔗 https://lnkd.in/gS6CXHJR

5. Creating Advanced Prompts
- How to apply prompt engineering techniques that improve the outcome of your prompts.
🔗 https://lnkd.in/g44Qypmp

6. Building Text Generation Applications
- A text generation app using Azure OpenAI / OpenAI API
🔗 https://lnkd.in/gnqPg9gq

7. Building Chat Applications
- Techniques for efficiently building and integrating chat applications.
🔗 https://lnkd.in/ggTAP6tS

8. Building Search Apps Vector Databases
- A search application that uses Embeddings to search for data.
🔗 https://lnkd.in/gkZuwJPp

9. Building Image Generation Applications
- An image generation application
🔗 https://lnkd.in/gD7p_R4Y

10. Building Low Code AI Applications
- A Generative AI application using Low Code tools
🔗 https://lnkd.in/g33pDmK5

11. Integrating External Applications with Function Calling
- What is function calling and its use cases for applications
🔗 https://lnkd.in/gvFzGBsW

12. Designing UX for AI Applications
- How to apply UX design principles when developing Generative AI Applications
🔗 https://lnkd.in/g7-ZKhca

13. Securing Your GenAI Applications
- The threats and risks to AI systems and methods to secure these systems.
🔗 https://lnkd.in/gjePanV8

14. The GenAI Application Lifecycle
- The tools and metrics to manage the LLM Lifecycle and LLMOps
🔗 https://lnkd.in/gCuPiewF

15. RAG and Vector Databases
- An application using a RAG Framework to retrieve embeddings from a Vector Databases
🔗 https://lnkd.in/gbPHnwEs

16. Open Source Models and Hugging Face
- An application using open source models available on Hugging Face
🔗 https://lnkd.in/gDfAAUV8

17. AI Agents
- An application using an AI Agent Framework
🔗 https://lnkd.in/g7UfCvZd


Check out last four in the comments 👇🏻

———
Access to main link - https://lnkd.in/gyPjbFvm
———

This is blowing up. Repost before it gets buried.

➕ Follow Amit Rawal for daily AI tools and resources you actually need.

___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Learn AI from NVIDIA without spending a single $.

📌 Here are the 11 free guides with direct links:

1. Generative Al Explained
A simple intro to how gen Al works. No coding required.
Link: https://lnkd.in/gGzipKBq

2. Building A Brain in 10 Minutes
Learn how neural networks think in under 10 min.
Link: https://lnkd.in/giHj9tCM

3. Al for All: From Basics to GenAl Practice From machine learning basics to creative Al tools.
Link: https://lnkd.in/gGzipKBq

4. Getting Started with Al on Jetson Nano
Build your first neural network with hands-on notebooks.
Link: https://lnkd.in/gbP9QUQZ

5. Introduction to Al in the Data Center
Learn GPU architecture and Al deployment basics.
Link: https://lnkd.in/gq4GZ5SP

6. An Even Easier Introduction to CUDA
Start parallel programming on GPUs. Beginner-friendly.
Link: https://lnkd.in/gq4GZ5SP

7. Introduction to NVIDIA NIM Microservices
Build, deploy, and scale Al models the simple way.
Link: https://lnkd.in/gJSZW6Dc

8. Generative Al in Digital Health
Build Al chatbots for healthcare and clinical notes.
Link: https://lnkd.in/gApPyj5W

9. Al Weather Models with NVIDIA Earth-2
Explore Al for weather prediction and climate apps.
Link: https://lnkd.in/gCCtcUNt

10. Accelerated Computing with CUDA C/C++ GPU computing fundamentals. Start with vector addition.
Link: https://lnkd.in/g9F5aKzS

11. Generative Al with NeMo
Customize text and multimodal Al models easily.
Link: https://lnkd.in/gFaDpab8

📌 These are the 3 points telling what I liked the most about these courses:

1. From Zero to Expert
Start with basic neural networks, advance to building real AI models and deploying them professionally.

2. Hands-On, Not Theory
Build actual projects, chatbots, weather models, GPU programs, not just watch lectures about concepts.

3. Industry Standard Skills
Learn directly from NVIDIA, the company powering AI, autonomous vehicles, and enterprise AI worldwide.​​​​​​​​​​​​​​​​

Thanks to Michael Lee for the list and image.
 
♻️ Share with someone building their AI career.
➕ Follow me for more AI course drops.

___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

7 prompts that make learning feel illegal.

I installed a "Brain" in Gemini and NotebookLM, and it started teaching me things I didn't know existed.

→ Use these prompts to move beyond surface-level learning and start understanding complex topics, mastering skills, and fixing mental blocks with expert-level precision.

📌 Here are the mini-prompts:

1. Understand Anything Like a Genius

"I want to understand [insert topic] as if I were a genius.
Break down the concept using advanced analogies, real-world applications, counterexamples, and multiple perspectives, then test my understanding with expert-level questions."

2. Master Any Skill for FREE

"Assume you're a master of linsert skilll with 20+ years of experience. Reverse engineer the process that got you there, and build me a day-by-day plan to reach that level as fast as humanly possible using only free or low-cost resources."

3. Fix Mental Blocks

"I've been struggling with [insert personal issue: procrastination, burnout, fear, etc.]. Analyze it like a cognitive scientist. Identify the root causes, the behavioural patterns behind it, and design a habit loop to eliminate it."

4. Turn Confusion into Clarity

"I don't understand linsert complex conceptl. Break it down step-by-step using metaphors, visual imagery, and real-world examples. Then create a mental shortcut or framework I can use to remember it forever."

5. Get a PhD-Level Breakdown

"Teach me [insert topic] like I'm preparing for a PhD. Start from first principles, explain all foundational theories, include historical evolution, and give me key papers/books to further."

6. Build Mental Frameworks

"I'm trying to master [insert skill/topic]. Build me a custom mental model or decision framework that simplifies how to approach, evaluate, and improve in it over time like a pro would."

7. Upgrade Your Brain in 30 Days

"Design a 30-day brain upgrade program that includes high-IQ thinking routines, mind-expanding prompts, advanced reading material, memory-enhancing techniques, and strategic rest habits. Keep it practical and time-efficient."

📌 How to use:

1. Copy and paste it to Gemini:
2. Use each mini-prompt one by one:
3. Turn on "Thinking Feature" for deeper, Nuanced Answers

If you want to help your network, REPOST this.

➕ Follow me for more AI drops.
___________________________________________

👋 I’m Amit, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

I simplified layers of the AI universe.

Stop getting lost in AI jargon! 

Here’s the ONLY roadmap you need to understand how AI actually works. 

📌 THE 6 LAYERS OF AI (SIMPLIFIED):

Layer 1: Artificial Intelligence (AI)

The foundation- machines performing tasks that typically require human intelligence
• Includes: Robotics, Speech Recognition, Expert Systems, Algorithm Building

Layer 2: Machine Learning (ML)

Machines learn from data without explicit programming
• Includes: Linear Regression, Decision Trees, K-Means, Support Vector Machines

Layer 3: Neural Networks (NN)

Brain-inspired systems with interconnected nodes that process information
• Includes: Perceptron, Convolutional Networks, Backpropagation, Multi-Layer Perceptron

Layer 4: Deep Learning (DL)

Advanced neural networks with multiple layers for complex tasks
• Includes: CNN, RNN, Transformers, LSTM, Autoencoders, GANs

Layer 5: Generative AI

AI that creates new content; text, images, videos, code
• Includes: GPT Models, Diffusion Models, Foundation Models, LangChain, Multimodal AI

Layer 6: Agentic AI

Autonomous systems that take actions, make decisions, and complete workflows independently
• Includes: Multi-Agent Systems, Workflow Automation, Goal-Oriented Reasoning, Tool Integration


Understanding these layers helps you see where different AI tools fit and how they build on each other.


Save this NOW and share it with anyone trying to make sense of AI.

Hit that repost button ↗️ so your network can learn the AI universe too.​​​​​​​​​​​​​​​​

___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Google. Microsoft. LangChain. DeepLearning.AI.

I’ve compiled 5 completely FREE AI courses from industry leaders like Google, Microsoft, LangChain, DeepLearning AI, and Hugging Face.

What you’ll learn:
Build intelligent agents that reason, use tools, and complete complex tasks autonomously.

Here are the links:

1. LangChain Academy: Intro to LangGraph
    Link: academy.langchain.com

2. Microsoft Learn: Develop AI Agents on Azure
    Link: https://lnkd.in/gE8u4Kq8

3. Google Cloud: Build AI Agents with Vertex AI
    Link: https://lnkd.in/gk_nXniW

4. DeepLearning AI: AI Agents in LangGraph
    Link: https://lnkd.in/gTauF9ZR

5. Hugging Face Agents Course
    Link: https://lnkd.in/gDmsvHhZ


Master essential frameworks:
• LangGraph for multi-agent systems
• Azure AI for enterprise deployments
• Vertex AI for scalable solutions

Perfect for:
• Developers adding AI to their toolkit
• Product managers exploring what’s possible
• Anyone curious about AI’s future

Hands-on projects. Real-world applications. Zero cost.

Which course are you starting with? 👇

___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

How to Price AI Agents in 2026

Pricing AI agents is a different ballgame than traditional software. 

While standard SaaS follows a "build once, sell many" model with almost zero extra cost per user, AI costs you money every time someone clicks "generate." 

📌 Here is a very simple summary of the My Key Strategies:

- The AI Paradox: 

High usage in SaaS is a win; in AI, high usage without the right pricing can bankrupt you because of compute costs (Cloud Cost Trap).

- Shift in Value: 

We are moving from paying for "access to software" to paying for "results and work done" (Intelligent Service Delivery).

- The "Hybrid" Winner: 

The best model is usually a base subscription (for predictability) plus usage-based fees (to cover the cost of heavy users).

- Efficiency is Profit: 

Fine-tuning your own smaller models on specific data is much cheaper and more profitable than just reselling expensive third-party APIs like GPT-4.

- Education = Retention: 

Since AI is new, you have to prove the ROI to customers so they don't see it as a luxury, but a necessity.

📌 Step-by-Step Pricing Logic

Step 1: Audit Unit Economics

• Measure real-time compute, memory, and API costs per user.
• Ensure per-unit revenue covers these underlying variable costs.

Step 2: Define Value Metric

• Charge for work done, not just software access.
• Use metrics like tokens, queries, or resolved tasks.

Step 3: Choose Pricing Model

• Usage-based models align revenue directly with ongoing costs.
• Hybrid models combine base fees with usage overages.

Step 4: Set Tiered Access

• Differentiate tiers by model quality, speed, or priority.
• Include defined usage limits to protect profit margins.

Step 5: Build Monetization Funnel

• Use trials to show value while limiting free costs.
• Educate customers on ROI to justify your pricing.

Step 6: Optimize and Iterate

• Fine-tune smaller models to reduce inference costs significantly.
• Use caching and rate limits to prevent cost blowouts.

I found a doc talking about pricing in detail, READ THIS.
Access: https://lnkd.in/gNTy9br3

What’s the biggest challenge you’ve faced when trying to put a price tag on AI?

Found this helpful? 
➕ Follow for more AI educational content and repost to help a founder out! 🚀
___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

This pyramid literally leaks every Al app you need to build a business. 

The hierarchy is the most interesting part.

At the foundation, we have Automation:

- It’s the backbone that does your repetitive work. 
- Tools like Zapier, Buffer, and Google Workspace handle scheduling, posting, and workflow management so you’re not drowning in admin tasks.

Next comes Designing: 

- It’s where Canva, Pomelli, and Gamma turn anyone into a visual creator. 
- No design degree needed.

The Writing Assistant layer handles your content. 
- NotebookLM, Jasper, and Copy.ai help you draft, edit, and refine everything from emails to blog posts.

Market Research is above that.
- Gemini and Google Trends give you insights that used to require expensive agencies.

At the top? 
Coding. 

-Gemini CLI lets non-developers build functional tools and automate complex tasks.

The genius here is the hierarchy. P

You start at the bottom, automating basics. Then you climb up, adding capabilities as you grow. 

One person with this stack can genuinely match the output of a traditional 10-person team.

What layer are you starting with? 
👇🏻Drop a comment with the AI tool that’s changed your workflow the most. 

Let’s learn from each other.

➕Follow Amit Rawal for more.
___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Use these 75 AI Code Words to get great results instead of writing long prompts.

Most people talk to AI like it’s a person.
Top users steer it like it’s a machine.

The easiest steering wheel is a ‘code word’: 
a one-word tag you put at the top of your message to force a specific transformation.

👇🏻 Use this format:

- CODEWORD: paste your text or request
- (Optional) Constraints: length, audience, tone, format, examples
- You can stack them too: like, TLDR + LISTIFY + ACTIONS: paste text
- Use words exactly as written (all caps helps). Add a colon, then your content.

🤔 Why this works?

AI isn’t confused. It’s under-directed.

A code word turns a vague request into an explicit operation: 
summarize, restructure, critique, rewrite, decide.

That single constraint reduces randomness, improves consistency, and cuts revision loops.

Access the link: https://lnkd.in/gizssP49

👀 3 quick examples you can steal:

- TLDR + ACTIONS: paste meeting notes
- CRITIQUE + PUNCH-UP: paste your draft post
- WIIFY + EXEC-SUMMARY: paste a project update for leadership

Which one code word would remove the most pain from your workflow this week?

➕ Follow me for daily AI related tips & resources.

♻️ Repost to share with your network.
___________________________________________

👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered.

♻️ Repost if you believe AI isn’t about replacing us…
It’s about retraining us to think better.

Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

As a product and technology executive operating at the intersection of product, data, cx, marketing, and automation - I help the world's largest brands navigate complex digital transformations.

My focus is on leveraging enterprise-scale technology to drive measurable revenue growth and modernize the customer experience.

My career is defined by bridging the gap between technical complexity and executive business strategy. With a technical background and a track record in product leadership, I specialize in:

Enterprise Scale: Architecting global platforms (MarTech, AdTech, Cloud) that support massive data volume and customer engagement.

Commercial Impact: Turning cost centers into growth engines by optimizing Go-to-Market strategies and personalization

Team Building: Cultivating high-performance, cross-functional teams that deliver innovation within large, matrixed organizations.

I am energized by challenges that require reimagining the status quo—whether leveraging AI/ML for predictive personalization or launching entrepreneurial ventures within established enterprises.

Core Competencies: Digital Strategy • Global Product Management • Marketing Technology Ecosystems • AI & ML Implementation • Enterprise Data Architecture • P&L & Budget Management

Hour Detroit's DBusiness 30 in Their Thirties (2023), conference speaker, advisory board member

Hass Khalife

Earlier this week I announced that after four incredibly rewarding years, I'll be stepping away from my role leading Marketing Technology at General Motors.

The MarTech landscape has evolved rapidly over the last four years - both across the industry and within GM. When I joined, we were focused on standing up our real-time CDP, transitioning from legacy platforms, and redefining how we leverage data to engage customers with journey orchestration tools.

Today, the team is well positioned to tackle the agentic shift in Marketing: deploying end-to-end platforms with reinforcement learning models and preparing for how we use AI agents to augment our teams, agencies, and partners.

Leaving General Motors was a difficult decision. Born and raised in Detroit, I moved back to the city for this role, my father spent his career at GM, and the relationships I’ve built are part of who I am and what I'm proud to represent.

I'll be cheering the entire team on from the sidelines and can't wait to see what they do next!

Thank you to so many who made my time at GM meaningful. Especially Deborah Wahl, Ajay Kapoor, Norm de Greve, David Bernstein, Laura Thornton, Lisa Halfman, Aaron Shaffer, Jon Francis, Shenan Reed, Michael Hayes, Miles Drayton, and so many others. A special shout-out to the entire Marketing & Engagement Platforms Product org for the privilege of leading such a talented group.

I’m thrilled for what’s next. I’ll share more soon, so keep an eye out for an update over the next couple of weeks.

(PS. The Sierra Denali EV has been hands-down my favorite drive!)

Marketing Technology Product Executive @ GM | Transforming MarTech & CX | Driving Growth at the Intersection of Digital, Data, and AI

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The Battle

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