Amit Rawal

Chief

Instructor for CHIEF’s AI-Powered Executive Toolkit, a 3-part leadership accelerator built to help senior women executives harness AI as a force multiplier for decision-making, storytelling, and change leadership.

Each session blends strategic coaching, real-time GPT tools, and live simulation labs, focused on amplifying executive clarity, communication, and influence through applied AI.

The 3-course arc:

⨠ Smart Decisions, Faster
How to build a personal “Decision OS” using mental models + Decision GPT, so they can act decisively under pressure without second-guessing.

⨠ Storytelling That Sells
How to become magnetic communicators using a proven 4-part narrative arc and a Storytelling GPT that sharpens tone, adapts messaging, and simulates audience reactions.

⨠ Lead AI Change with Empathy & Authority
How to champion AI transformation inside orgs with credibility and care — using Change GPT to map resistance, segment stakeholders, and draft high-trust rollout narratives.

Why It Matters:
This isn’t about prompt tricks. It’s about giving non-technical but highly strategic leaders the mindset, tools, and confidence to integrate AI into how they think, communicate, and lead change.

I built this course to scale a new breed of AI-native leadership: empathetic, data-savvy, and future-ready.

Instructor: AI Executive Toolkit

Maven

✨ Ever wish you could tell stories like Steve Jobs—stories that move, connect, and inspire? ✨

Now, it’s possible, with AI 🚀

I have spent 1000s of hours over the last decade collecting books, insights, articles, presentations and research papers on the world’s top storytelling techniques and data analysis. I’m synthesizing this treasure trove of knowledge into this 3-week course along with a custom GPT that can be your personal coach in making you a 10X storyteller.

👉 Reserve your spot for the next cohort: https://bit.ly/3APcJkA

AI Instructor: AI Supercharged Storytelling & Data Analysis

CAFU

Cafu exists so you can get your most precious and finite resource back - your time. Using advanced technologies in ML, Generative AI, and IoT, Cafu delivers next-gen mobility services, enabling a sustainable and delightful path for energy transition.

Chief Product and AI Officer

Section

Teaching how to use the latest and greatest in AI to become a Data and Analysis Ninja. Come join me at Section School, which is revolutionizing how we all learn new-age skills at a fraction of the cost of top universities. 

Here's what the students who have attended have attended before had to say about the course:

"The Prof Amit Rawal is extraordinary in his teaching style with absolute precision for learning in such a short time."

"He was amazing. One of the best classes. His examples were perfect. The story was great. I was testing out ideas while I was listening because I was so inspired!"

"This was the most helpful and interesting AI course I've taken on Section so far. I have already shared this course with my team."

Professor: AI for Data Analysis and AI for Research

Apple

Building the future of AI-driven insights, analytics, and decision science with a team of dreamers and doers.

Global Product Leader: AI/ML Decision Science & Analytics Platform, WW Channel Strategy & Ops

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.

Global Head of Product: Digital 360 Analytics & Insights

- Created a user-centric approach to identify, evaluate, and prioritize opportunities across the highly complex and diverse data and analytics landscape, simplifying and driving deep focus on what matters most 

- Led the design and architecture of new technology stack, including evaluation and selection of platforms such as Snowflake, Salesforce, Amazon Cloud, Alteryx, Dremio & ThoughtSpot

- Created a Playbook to drive greater data collaboration with partners and to streamline data pipelines required to scale

- Built a digital sandbox to rapidly test and iterate use-cases, reducing time to market by up to 80% for new partner data sets

- Built an advanced analytics strategy to generate automated ai-driven insights, including diagnostics, predictive, and prescriptive analytics, expected to improve insights-to-action cycle time

- Created global measurement standards to align geo teams on a “single version of truth”, enabling performance benchmarking and optimization as key levers for growth

- Built knowledge and change management capabilities to create a data-driven culture and scale analytics across all levels

Senior Product Manager: Global Digital Data and Analytics

Ounass

Led the retail digital transformation in the UAE region along with some of the brightest and the best in the business. Managed the P&L of the largest luxury e-commerce player in the Middle-East region. Key responsibilities included:

- Driving the development of the annual strategic plan, budgeting process, and key-initiatives roadmap 

- Leading Strategy and Operations functions for the entire value chain of the retail business

- Managing a team of 70+ members across business and technology functions, including activities of hiring, grooming, and growing team members 

- Providing thought leadership on emerging technologies and practices to either enhance CX or create operational efficiencies 

- Executive stakeholder management by communicating results and plans with the C-suite and the Board

- Setting key priorities for Product and Technology teams to ensure that tech investments are aligned with the strategic priorities for the year

- Representing the organization at key industry events and speaking engagements to create brand awareness and attract top talent for the business

- Leading the weekly business review meetings to ensure that execution activities are delivering the desired results, and to provide executive coverage in addressing risks and roadblocks

Managing Director: E-commerce | Growth, Product, and Technology 

Tata CLiQ

Tata has a strong heritage in building and growing marquee luxury businesses such as the Taj Hotels and Jaguar / Land Rover. I had the privilege to set-up and lead the world's first and only omnichannel marketplace for some of the most prestigious brands in the world.

As a part of the leadership team reporting to the CEO:

- Developed a roadmap and strategy for implementing leading AI and Machine learning technologies to improve operational efficiency, reduce risk and fraud, and enhance experience across the customer journey

- Created a go-to-market strategy for the brand with a goal of achieving leadership position within 24 months from launch

- Authored the overall brand and digital marketing strategy to achieve above-market performance at the most efficient acquisition costs

- Re-imagined and prototyped a new mobile platform delivering personalized and multi-channel experience with a scale to power millions of consumers 

- Designed omnichannel marketing capabilities as service for third-party sellers on the platform

- Created automated customer lifecycle management strategy aimed at improving customer retention rates by over 25% and CLTV by over 40%

- Responsible for the driving revenue growth and P&L management of the business unit

Head of E-commerce Strategy & Platform

ELITIFY.com

As the chief strategist and founder, I built India's first and leading e-commerce platform for luxury goods and services. Over a period of four years, achieved the following:

- Grew the business from 0 to $15M in annualized GMV
- Achieved industry's highest contribution margins of 25%+
- Raised over $1M in funding and got offers from some of the most prestigious VC firms in the world including Sequoia and IDG Ventures
- Built strategic partnerships with some of the best brands in the world
- Successfully sold and exited the business
- Developed deep expertise in digital marketing, analytics and customer experience design

Founder & CEO/CPO (Sold, Exited)

Thomson Reuters

Responsible for P&L management and guiding the strategic direction of global IT shared services with close to $2B in spend and operations in over 200 countries. Key activities included product and service strategy, pricing, cost allocations, and headcount management. In addition, evaluated the financial feasibility of new technology platforms and initiatives.

Head of Business Management, Global Technology Operations

Responsible for providing BI and Analytics services to all clients of Technology Services across all Thomson Reuters Businesses.

Head Of Business Intelligence & Analytics 

Deloitte Consulting

Advised CXOs of fortune 50 companies on managing billions of dollars of technology spend.  He has a successful track record of creating and implementing strategic solutions that drive sustainable value and create competitive advantages. He lead M&A integration of technology infrastructure for some of the largest mergers in history. Core skills include IT operations strategy and planning, financial management, performance management and analytics, technology investments-feasibility studies, enterprise cost reduction, and growth strategies.

Engagement Manager - Strategy & Operations Consulting

Senior Strategy Consultant

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

I build at the intersection of AI, storytelling, and human potential.

AI, the Agentic Future with Human Insight:

⨠ Building the agentic future at one of the Middle East’s leading mobility tech startups — designing autonomous, intelligent AI systems that drive real-world business value.

⨠ Built a global AI platform at Apple to scale its multi-billion-dollar sales channel — using advanced analytics, insights, and AI-powered decision-making to optimize performance at scale.

⨠ Teach “AI-Supercharged Data Analysis & Storytelling” and other courses on Section and Maven — empowering leaders to think clearly, tell better stories, and drive impact with AI.

⨠ Founder of Superchargelife.ai — creating an AI-powered Life OS to help one billion people work smarter, live longer, and grow richer with clarity, meaning, and connection.

Startups & Scale:

⨠ Founded and scaled 3 ventures, including one that reached 200M+ in 18 months.

⨠ Full-stack operator across product, GTM, AI integration, and P&L.

⨠ Known for uniting tech, strategy, and soul into systems that scale.

Advisory & Consulting:

⨠ Advised Fortune 100 leaders on AI strategy, M&A, and digital innovation.

⨠ Led $1B+ post-merger tech integrations across global TMT firms.

⨠ Combine systems thinking with human insight to unlock breakthrough decisions.

The future of AI isn’t just about intelligence.
It’s about building systems that serve and elevate what makes us most human.



Head of Product: Digital 360 - Global Analytics & Insights

Google just released a game-changing 54-page blueprint on AI agents, and it’s not what you think.

This isn’t about building smarter chatbots. 
It’s about creating complete autonomous systems that think, act, and evolve, what Google calls “LMs in a loop with tools to accomplish objectives.”

———
The document breaks down agents into three core components:

• The Model (the “Brain”) - for reasoning and decision-making
• Tools (the “Hands”) - for taking real-world actions
• Orchestration Layer (the “Nervous System”) - managing the Think → Act → Observe loop

Google maps out 5 levels of agentic evolution:

Level 0: Core reasoning (the model alone)
Level 1: Connected problem-solver (model + tools)
Level 2: Strategic planner (context engineering + multi-step plans)
Level 3: Multi-agent collaboration (teams of specialists)
Level 4: Self-evolving systems (agents that create new agents and tools)

The Game-Changer: Agent Ops
This is where it gets real. Traditional prompt engineering evolves into a full discipline:

• Metrics-driven development (business KPIs, not just pass/fail)
• LM-as-Judge for quality evaluation
• OpenTelemetry traces for debugging agent “thought processes”
• Human feedback loops that convert bug reports into permanent test cases

Enterprise-Grade Security:

Agents introduce a new security principal, not human users, not service accounts, but autonomous actors with their own identity (SPIFFE), policies, and least-privilege permissions.

Google’s already thinking ahead: 

Agent2Agent (A2A) protocols for inter-agent communication, Agent Payment Protocol (AP2) for autonomous transactions, and central governance layers to prevent “agent sprawl.”
———

As an AI leader, I think we’re not just adding AI features anymore. 

We’re building systems where context window curation becomes an art form, 
where agents manage other agents, 
and where the developer shifts from “bricklayer” to “director.”

What do you think?

🔃 Hit REPOST and get the full PDF straight to your dms. 
___________________________________________

👋 I am 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

Google quietly dropped 12 free AI tools. Nobody’s talking about them.

I tested them all. Here’s what impressed me:

1. NotebookLM - Uploaded a 50-page report, got a podcast summary I could listen to

2. Veo - I typed “sunset over mountains” and got a usable video in 30 seconds

3. Gemini Live - Ran a team meeting with AI taking notes in real-time

4. Gems - Created a custom writing assistant trained on my style

5. App Builder - Launched a simple app using only text prompts

6. Imagen - Generated professional-looking images for my presentations

7. Gemini in Sheets - Asked it to analyze sales data. Done in 10 seconds

8. Cloud Vision API - Uploaded a photo and it identified objects, text, and faces instantly

9. Nano Banana - Edited AI images until they looked exactly right

10. Ask on YouTube - Chatted with a 2-hour tutorial to find exactly what I needed

11. Firebase Studio - Built a landing page without touching code

12. AI Studio - Compared 3 different AI models to see which worked best

Perfect for anyone creating content, building projects, or running a business more efficiently.

Share your best experience with any of these in the comments. 
___________________________________________

👋 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.

That $200,000 Stanford AI degree just lost most of its value. (UPDATED LIST OF COURSES)

Not because the education got worse.

Because Stanford University just made their entire flagship AI and Machine Learning curriculum free on YouTube.

This is a game-changer for AI education.

Here’s what just became accessible to everyone:

These aren’t simplified versions. These are the exact courses Stanford students pay thousands for:

↳ CS230: Deep Learning | Autumn 2025
Link: https://lnkd.in/dQ-DHdsJ

↳ CS329H: Machine Learning from Human Preferences 
Link: https://lnkd.in/d_6GzDAr

↳ CS25: Transformers 
Link: https://lnkd.in/dbMtpim5

↳ CS231N: Deep Learning for Computer Vision | 2025
Link: https://lnkd.in/djXeGyse

↳ CME295: LLM Evaluation | Autumn 2025
Link: https://lnkd.in/dTPTwh_M

↳ CS336: Language Modeling from scratch | 2025
Link: https://lnkd.in/dthjnD7E

Let’s be honest about what just happened:
Stanford didn’t do this out of kindness.

They did it because the AI industry moved so fast that traditional degrees can’t keep up.

By the time you finish a 4-year program:
→ The tools have changed
→ The frameworks have evolved
→ The job requirements are different

The new reality:

AI careers aren’t built in classrooms anymore. They’re built in public.

→ Learn by building
→ Share what you create
→ Get hired for what you can do

So here’s my challenge:
• Pick ONE course from this list before Friday.
• Watch the first lecture this weekend.
• Build something small by next week.

Comment which course you chose. Make it public. Make yourself accountable.

🧠 Follow Amit Rawal for more AI educational content.

___________________________________________

👋 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.

No alternative text description for this image

You don’t need $100k to learn AI.
MIT just dropped their entire Deep Learning course for free.

81 lessons. 20 hours. Zero cost.

While others are paying tuition, you could be building neural networks.

This is the AI course everyone wishes they had in college.

Completely free. Taught by Alexander Amini  

The course covers:
• Deep learning foundations
• Neural nets from scratch
• NLP, computer vision, and more
• LLMs and generative AI
• Real projects you can showcase

All you need? Basic calculus and linear algebra.

No degree. No gatekeeping. Just learn.

The best AI education is now free. What’s your excuse?

🫱🏻‍🫲🏼 Repost and share with those who needs to know this.

___________________________________________

👋 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.

I have read 20+ AI/ML books in last 5 years.
But these 9 books are the must-read ones in the list.

📌 The books:

1. AI Agents in Practice by Valentina Alto  
- Production-ready agent systems

2. AI Engineering by Chip Huyen  
- RAG, prompting, and LLM infrastructure

3. Mathematics of Machine Learning by Tivadar Danka  
- Great intuitions for the underlying math

4. Practical Statistics for Data Scientists by Bruce & Bruce
- A/B testing and data drift detection

5. Designing Data-Intensive Applications by Martin Kleppmann  
- Your model needs solid infrastructure

6. Python NLP Cookbook by Antic & Chakravarty
- Classical NLP techniques

7. NLP with Transformers by Tunstall, von Werra, Wolf
- What’s actually happening inside LLMs

8. Designing Machine Learning Systems by Chip Huyen  
- Getting models to production

9. ML System Design Interview by Aminian & Xu
- Scaling patterns from 1K to 100M users

📌 Why this list matters:

Most AI/ML content focuses on tutorials and toy examples. 

These books taught me how to actually ship systems that 
- handle real traffic, 
- real data problems, and 
- real business constraints.

📌 How to use this list:

Don’t read all 9 at once. Pick one based on your biggest pain point right now:

• Struggling with production? → Start with #8 (Designing ML Systems)
• Building LLM apps? → Go for #2 (AI Engineering)
• Math feels shaky? → Begin with #3 (Mathematics of ML)
• Agents confuse you? → Read #1 (AI Agents in Practice)

Build something after each book. That’s when concepts actually stick.

Save this list for when you’re ready to level up your ML skills.

And if you found this helpful, ♻️ share it with someone stuck in tutorial hell.​​​​​​​​​​​​​​​​

___________________________________________

👋 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.

10 steps. That’s all it takes to build an AI agent from scratch.

Here’s the roadmap I use every single time:

Step 1: Define the Agent’s Role and Goal
- Decide what your agent will do.
- Identify who it will help.
- Determine the kind of output it should create.
- Example: An assistant that reads medical images and explains findings.

Step 2: Design Structured Input & Output
- Plan exactly what data your agent will receive and return.
- Use clear, structured formats like forms or tables, avoid messy text.

Step 3: Prompt, Tune, and Define Agent Protocol
- Write clear instructions that describe the agent’s role.
- Adjust these prompts for consistent behavior.
- Define how inputs and outputs should flow for all tasks.

Step 4: Add Reasoning and Tool Use
- Allow your agent to think through steps logically.
- Let it use other tools like web search or calculators if needed.

Step 5: Structure Multi-Agent Logic (if needed)
- Break complex tasks into smaller ones handled by separate agents.
- For example, have one agent plan, another research, and another report.

Step 6: Add Memory and Long-Term Context
- Equip your agent to remember previous interactions and context.
- This helps it provide better, more personalized responses.

Step 7: Add Voice or Vision Capabilities (Optional)
- Let your agent talk or understand images if needed.

Step 8: Deliver the Output (in Human or Machine Format)
- Format the output so humans or other systems can understand it easily.
- Use markdown, PDFs, or structured data formats.

Step 9: Wrap in a UI or API (Optional)
- Create a simple interface (app or website) for people to use your agent.
- Or allow other software to interact with your agent via API.

Step 10: Evaluate and Monitor Your Agent’s Performance
- Test your agent regularly with different inputs.
- Track how well it performs and make improvements.

———

This roadmap guides you from the initial idea through development to deployment and refinement. 
It’s meant to be easy to follow, even if you’re new to building AI agents.

Steps 1-4 will get you 80% of the way there. 
The rest? That’s for making it production-ready.

Write ‘I will make AI Agents’ in comments if you’re going to start today with these steps.

And repost ♻️ so others can learn with you.

___________________________________________

👋 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 build AI Agents from scratch

I tested 100+ AI prompts over 6 months. These 7 got flagged as “too effective.”

When I first started using AI, I kept wondering why the output felt generic, until I realised one simple truth:
AI performs at the level of the prompt you give it.

This carousel has 7 “banned” prompts, not because they’re harmful, but because they work too well.

They turn any AI tool into:
• a $10K/hour business consultant
• a world-class copywriter
• a skill coach compressing 10 years into 10 minutes
• a systems architect designing your workflow
• a transformation coach
• a reverse-engineering analyst
• a brilliant teacher who adapts to your level

These are the prompts I wish someone had shown me earlier.
They save time, remove guesswork, and help you get expert-quality output without hiring an expert.

Use them for strategy, writing, learning, productivity, or deep thinking.
Use them with any AI tool. Any goal. Any industry.

📌 Which “banned” prompt will you try first?

Save this carousel OR share with others. 

PS: Slide 9 is the nuclear option. Use it to make ANY prompt 10x better.

___________________________________________

👋 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 Banned Prompts 

You know what’s funny about this ‘Do nothing’ ad? 
It’s actually onto something big.
As an AI leader, I see how much these technologies are taking over everyday tasks. 
And honestly? That’s exactly the point. 
We’re building AI to handle the boring, repetitive stuff so humans can focus on what really matters.

This “Nothing University” campaign is hilarious, but it’s also smart. 
It’s asking a real question: if AI does all the work, what do we do? 
The answer isn’t actually “nothing”, it’s about doing things that make us human. 

Creativity, relationships, enjoying a good meal, spending time with loved ones. 
These are things AI can’t replace.

Maybe we’ve been so busy trying to be productive that we forgot how to just live. 
This ad reminds us that progress isn’t just about doing more, it’s about having the freedom to choose what matters to us. 

In the AI era, that might be the most valuable skill of all.

✍🏻 What do you think?

___________________________________________

👋 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.

Every boardroom’s new question: Where’s the ROI in AI?
Here’s what the latest 2025 data says.

1. Google: The ROI of AI in 2025
Google’s latest insights on how AI agents are shaping the next generation of generative AI and enterprise ROI.
🔗 https://lnkd.in/grMaEPaE

2. McKinsey & Company: Seizing the Agentic AI Advantage
See how AI agents transform simple productivity workflows into ROI-driven business systems.
🔗 https://lnkd.in/g-NTiNan

3. OpenAI: Identifying & Scaling AI Use Cases
Learn how to spot high-impact use cases early and scale them effectively to achieve real ROI.
🔗 https://lnkd.in/gA6Sbu8J

4. KPMG: Agentic AI Advantage: Unlocking Next-Level Value
Explore how agentic AI delivers measurable value from an executive and stakeholder lens.
🔗 https://lnkd.in/gNWQasNg

5. McKinsey & Company: One Year of Agentic AI: Six Lessons from the Field
After a year of building and deploying AI agents, McKinsey reveals key lessons learned — and how these initiatives have directly influenced ROI.
🔗 https://lnkd.in/gUaD9r9n

6. PwC: One Agent to Rule Them All
Move from strong data foundations to an agent-powered enterprise — balancing innovation and governance for ROI.
🔗 https://lnkd.in/gSgdg4YT

7. Deloitte: Unlocking the Right Agentic AI Use Cases
Start by identifying which agentic AI use cases actually drive value for leading enterprises.
🔗 https://lnkd.in/gfggEA8w

8. Boston Consulting Group (BCG): Rethinking Software Pricing in Agentic AI
Discover how agentic AI is redefining software pricing models and customer value creation.
🔗 https://lnkd.in/gGdexMSX

Every founder and exec should bookmark these, they’re the playbooks for the next decade.
Before your next AI meeting, read these. They’ll change how you think about ROI.

___________________________________________

👋 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.

Top Reports on ROI of AI Agents 2025 

“When you think you’re done, you’re only at 40%.”
I read this line from David Goggins three years ago.

It rewired how I approach every hard thing.
Most people quit the moment it gets uncomfortable.
That’s exactly where growth starts.

Goggins calls it the 40% Rule: 
When your brain screams “I’m done,” you’ve barely scratched the surface of what you’re capable of.

Here’s what changed for me after applying his framework:
SUCCESS = Discipline × Pain × Consistency

Not motivation. Not inspiration. Not perfect conditions.

10 Key Takeaways That Hit Different:
1.    Callous Your Mind – Challenges are sandpaper. Let them toughen you.
2.   Mirror Test – Write down who you are vs. who you must become.
3.   The 40% Rule – When you feel done, you’ve got 60% left in the tank.
4.	Cookie Jar – Store memories of past wins. Reach for them when life punches hard.
5.	Embrace the Suck – Pain is a teacher. Discomfort builds dominance.
6.	No Talent Required – Let challenges toughen you, not break you.
7.	Stand Apart – Don’t aim to be better than others. Aim to be exceptional among the elite.
8.	Armor Your Mind – Use past trauma as training, not torment.
9.	Failure = Feedback – Each failure is a data point. Study it. Adjust. Attack again.
10.	Ask “What If?” – What if you went all in? What if you stopped quitting?

5 Mental Traps to Avoid:
❌ Chasing motivation 
✅ Build discipline instead

❌ Waiting for perfect conditions 
✅ Start ugly, refine later

❌ Seeking comfort 
✅ Choose hard on purpose

❌ Comparing your journey 
✅ Compete only with your last version

❌ Fearing failure 
✅ Treat it as a rep in the gym of life

The Goggins Mindset Loop:
→ Face It (tell yourself the truth)
→ Embrace It (lean into discomfort)
→ Push It (go past your limit)
→ Own It (reflect and repeat)

This book isn’t motivational. It’s operational.

It’s a manual for building mental armor when everything around you is trying to break you.

What’s one thing you’re quitting at 40% right now?
Drop it below. Let’s push past it together. 👇

♻️ Repost if this hit you.

And follow me for more frameworks that build mental strength.
___________________________________________

👋 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.

I analyzed hiring trends in IT sector from 6 major platforms. The results will shock you. 

Here's what's HOT and what's NOT in demand in the tech market in 2025.

Top 5 In-Demand Skills:

✓ Al/ML Engineering - Building the brains of tomorrow
✓ LLM Development - Mastering ChatGPT-style tech
✓ Tech R&D Scientists - Pushing innovation boundaries
✓ Big Data Specialists - Taming the data tsunami
✓ Prompt Engineering - The new "coding" for GenAl

Declining Roles (Negative job growth):

✗ Traditional IT Support (-700 jobs)
✗ Manual Software Testing (-600 jobs)
✗ Web Development (-350 jobs)
✗ Data Entry Roles (-650 jobs)

Key Takeaways:
• Al skills dominate the future job market
• Even "safe" tech roles are being automated
• Specialization in emerging tech = career insurance

Which side of this list are you on? Comment below.

___________________________________________

👋 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.

In-Demand vs Declining Tech Skills 2025

Stop Googling “best AI resources” and getting the same 10 results.

I just organized 60+ hidden gems for AI agents in one place.

Here’s what you get:

📜 Papers that matter:

• Chain-of-Thought, ReAct, Reflexion frameworks
• Generative agents & RAG surveys

🧑‍🏫 Courses that teach:

• Building agents with HuggingFace
• MCP with Anthropic
• Vector databases & LLMOps

💻 Repos with code:

• Microsoft’s AI Agents guide
• GenAI implementation examples
• Prompt engineering techniques

📹 Videos that explain:

• Stanford’s agentic AI overview
• Building agents from scratch
• Real evaluation strategies

📚 Books:

• Understanding Deep Learning (free online)
• Building LLMs from Scratch
• The LLM Engineering Handbook
• Practical implementation of AI Agents

And many more… 

Access the main doc here: https://lnkd.in/d7ufgENQ

These aren’t beginner resources. 
They’re for builders who want to go beyond tutorials.

From vector databases to multi-agent systems. 
From LLMOps to computer use with Anthropic.

Simple next step:
• Save this post
• Pick 1 resource
• Block 2 hours to learn it
• Build something small

🫡  Repost OR Share it with your engineering team.

___________________________________________

👋 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.

CVS Health

Observability and monitoring, forecasting, anomaly detection

Principal Data Scientist

Amazon Web Services (AWS)

Recommender systems and personalization, embeddings

Applied Scientist III

eMed

Staff Applied Scientist

Visa

Transaction fraud detection under low latency

Senior Staff Machine Learning Scientist

Habana Labs

Staff Machine Learning Engineer

Expedia Group

Senior Machine Learning Engineer

Advisory Software Engineer

The University of Texas at Austin

Lecturer of Computer Science

College of William and Mary

Instructor for Data Structures and Algorithms

Visiting Assistant Professor

University of Virginia

Graduate Research Assistant

Machine Learning Scientist at Babylon

Data scientist and former computer science professor based in Austin, TX. With over a decade of experience in machine learning and AI, I've led innovative projects at industry giants like AWS and pioneering health tech companies. My work spans recommendation systems, personalization algorithms, and predictive modeling, always with a focus on driving real-world impact.
When I'm not diving into data or coding, you'll find me paddleboarding on Lady Bird Lake or exploring the beautiful green spaces Austin has to offer. My transition from academia to industry has given me a unique perspective on bridging theoretical concepts with practical applications.
Passionate about mentoring the next generation of data scientists and solving complex problems that make a difference. Always eager to connect with fellow tech enthusiasts, outdoor lovers, and anyone interested in the transformative power of data.

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The Battle

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