What Is AI? And How It’s Changing Our Lives
Issue #21 A super simple explainer on what AI is, how it works, and what the future holds—not just for techies, but for analysts, professionals, and everyday users.
You’ve probably heard all the buzz around AI—ChatGPT, Google Gemini, driverless cars, even stock-picking bots. But what exactly is AI? Where did it come from? And how is it actually helping people today—in real jobs, real decisions, and real workflows?
Let’s break it down—clearly and simply. No jargon. No hype. Just what you need to know.
AI: Where It All Began—and Where It’s Headed
Believe it or not, the idea of intelligent machines goes back over 2000 years. Greek philosopher Aristotle once imagined tools doing repetitive tasks so humans could focus on thinking.
Fast-forward to the 1940s, and British mathematician Alan Turing asked the defining question: “Can machines think like humans?” He also designed the Turing Test—if you can’t tell whether you’re chatting with a person or a machine, the machine “passes.”
AI had a rocky path—plenty of excitement but slow progress in the early years. These quiet periods are known as “AI winters.”
When AI Got Real
Things started heating up in the 1980s with breakthroughs in machine learning—a way for computers to learn from experience. A key figure was Geoffrey Hinton, who helped lay the groundwork for how AI learns today.
The turning point came in 2012 with AlexNet, a model trained to recognize images using large datasets and GPUs (graphics chips). Its performance shocked the tech world—and kicked off the modern AI boom.
In 2016, DeepMind’s AlphaGo defeated a world champion in the game of Go. One surprising move—Move 37—felt like creative intuition. It showed us that AI wasn’t just copying; it was “thinking.”
In 2017, a research paper called “Attention Is All You Need” introduced the Transformer model—the foundation for today’s most advanced AI tools like ChatGPT, Gemini, Grok, Claude, DeepSeek, and Copilot.
How AI Works (Simplified for Analysts)
Think of AI like a new research associate:
It reads a ton of data (say, 1000 investor calls)
Finds patterns (e.g., how management hints at margin compression)
Gets corrected when wrong (via user feedback or fine-tuning)
Improves over time with more data and feedback
AI Learns in 3 Ways:
Supervised: Labeled data (e.g., "This is a positive earnings surprise")
Unsupervised: Finds its own structure (e.g., clustering peers)
Reinforcement: Learns by trial and error (like improving forecasting models)
Why AI Is So Fast Now
AI uses GPUs—high-performance chips that can do thousands of calculations at once. Originally made for gaming, these chips are now central to AI research.
NVIDIA leads this field, and its ecosystem (called CUDA) is the backbone of most AI models today.
What Is Generative AI?
Generative AI doesn’t just analyze—it creates:
Equity reports
Investment memos
Excel models
Text, images, videos, charts, and even slide decks
It works by understanding patterns and then generating something new—like a fresh summary from a 60-page annual report.
Note: Generative AI can still “hallucinate”—i.e., confidently output wrong data. Always verify key financial or regulatory information.How Equity Analysts Are Using AI Today
AI is already saving analysts hours every week by handling repetitive tasks and improving decision-making. Here’s how different tools fit into the research workflow:
ChatGPT / Claude / Gemini – Summarize earnings calls, draft commentary, clean up technical language, and extract insights from reports
DeepSeek / Grok – Handle multi-step reasoning like thesis validation, scenario analysis, or peer comparisons
Perplexity – Quickly fetch sourced market views, macro context, and competitive intelligence
NotebookLM – Upload documents (transcripts, annual reports, rating notes) and ask questions directly from the content
GitHub Copilot – Automate Excel formulas, create charts, and reduce manual effort in PowerPoint or modeling
Choose the Right Tool Based on Your Need
Watch and Learn: Real-World AI for Research
Click here – X thread by Anil Tulsiram (Post 1)
Click here to view the original post on X (Twitter) - Anil Tulsiram shared a practical application of GenAI in the investing workflow. (Post 2)
Click here – X thread by Manan
Click here – Colossus Podcast: AI or Die – an in-depth conversation on the future of AI and its real-world impact.
Click here to read the AI conference‑call analysis by Dr. Vijay Malik.
Using AI for Long-Term Investing
@SuccessProject_ has released 9 videos with 7.5 hours of learning content on applying GenAI to long-term investing. It includes hands-on prompting for financial statement analysis and financial forensics, with real case studies.The creator has spent ~100 hours curating this.
Click here to access the video learning hub and downloadable prompts
View the original post on X (Twitter)
Prompting for Technical Analysis (Using AI)
Something many have been asking for—how to use GenAI for technical analysis. This video walks through the process step-by-step.
The prompts shown are for demonstration purposes only—company names used are not stock recommendations.A downloadable prompt will be uploaded soon.
Here are a few YouTube videos I recently came across:
Click here – YouTube: How I Use ChatGPT in Equity Research
Click here – YouTube: AI for Stock Market Research
Click here – YouTube Live: ChatGPT in Analyst Workflow
Click here – YouTube: Best AI Tools for Research Analysts
Click here – YouTube: Summarizing Earnings Calls with AI
Click here – YouTube: AI + Excel for Financial Modeling
Click here – YouTube: Prompt Engineering for Analysts
Click here – YouTube: Building Your AI Research Stack
Click here – YouTube: Automating Analyst Workflows with AI
What’s Next for AI + Equity Research
Smarter models – AI agents that track company performance autonomously
AGI (Artificial General Intelligence) – Future AI that understands nuance like a senior analyst
Fully custom research copilots – Trained on your house style, research database, and Excel templates
AI in India-specific research – Agriculture, infra, lending, healthcare, PSU tracking
AlphaFold-type breakthroughs – Deeper pharma and biotech analysis via AI prediction tools
One Final Thought
AI will not replace equity analysts. But analysts who use AI will leap ahead.
In a world where information is infinite, your edge isn’t having all the answers—it’s knowing what questions to ask and how to find the insights that matter.
Stay curious. Keep building your edge.
— Priyank
It really is super simple explaination of AI