Generative AI: The Ultimate Co-Founder You Didn’t Know You Needed
The rise of generative AI is one of the biggest shifts in business today. Let’s explore why generative AI is a must-know for every founder and how it can fuel your startup’s growth.
In a world that’s evolving undeniably faster, every founder is looking for an edge- something that helps them move faster, work smarter, and stretch limited resources. You’re juggling product development, customer acquisition, fundraising, and a dozen other things at once. What if AI could take some of that weight off your shoulders? What if it could help you test ideas faster, refine your messaging, and free up time to focus on what truly matters- building a great business?
Whether it’s crafting compelling marketing copy in seconds, generating lines of code with a simple prompt, designing stunning visuals, or even putting together an investor-ready pitch deck- AI seems to be doing it all.
A staggering 73% of the Indian population surveyed is using Generative AI, highlighting its rapid adoption across industries and everyday tasks.
But with all the hype surrounding Generative AI, the real question for founders isn’t just about what AI can do, but rather: How can you use Generative AI for your startup?
This article is inspired by an insightful session with Satvik Parakusham, founder of Build Fast with AI and an IIT Delhi alumnus. With a deep passion for AI and a strong background in data science, Satvik is on a mission to make Generative AI more accessible—helping businesses integrate AI seamlessly into their products and empowering individuals to upskill in this fast-evolving space.
What is Generative AI?
Generative AI refers to AI models capable of generating new content, be it text, images, videos, music, or even software code. Unlike traditional AI, which focuses on analysing and predicting based on existing data, Generative AI creates original content by learning patterns from vast datasets.
At its core, Generative AI is powered by advanced deep learning models, particularly Large Language Models (LLMs) and Generative Adversarial Networks (GANs).
Traditional AI vs. Generative AI
Before diving in, let’s break down the difference between Traditional AI and Generative AI in a way that actually makes sense for founders:
Traditional AI is like a super-smart assistant that helps you analyse data and make better decisions. Think of recommendation engines on Netflix suggesting what to watch next, fraud detection systems flagging suspicious transactions, or chatbots answering basic customer queries with pre-written responses. It works within set rules and patterns, helping businesses optimise processes and improve efficiency.
Generative AI takes things a step further by creating new content based on the data it has been trained on. Instead of just classifying or predicting, it generates entirely new text, images, code, and even videos. It doesn’t just recognise patterns- it creates them.
In short, Traditional AI helps you make sense of the world as it is, while Generative AI gives you the power to create something new faster than ever before.
Core Concepts of Generative AI
To truly understand how Generative AI works, we first need to break down some core concepts that power its capabilities.
Large Language Models (LLMs)
If you’ve ever used ChatGPT, Google Gemini, or any AI writing assistant, you’ve already interacted with a Large Language Model (LLM)- the powerhouse behind text-based Generative AI.
They are trained on massive text datasets to predict and generate human-like language. These models power applications such as chatbots, automated content creation, and code generation.
LLMs can be broadly categorised into open-source and closed-source models, each offering distinct advantages and trade-offs.
Open-source LLMs (like LLaMA 2 or Mistral) give you full control, letting you tweak, fine-tune, and self-host them for privacy. But they require tech expertise and infrastructure. Closed-source LLMs (like GPT-4 or Claude) work out of the box with an API, fast and easy, but you’re relying on a third party and paying per use.
Use Cases of LLMs
Draft emails, blogs, and reports in minutes instead of hours.
Help with coding by suggesting fixes and generating snippets.
Automate customer support with smart chatbots.
Summarise research and generate insights without spending hours reading.
The cost of using a Large Language Model depends on several factors, including API usage, model size, hosting, and fine-tuning.
Most AI providers, such as OpenAI, Google, and Anthropic, charge based on tokens (chunks of words) used for input and output.
Services like OpenAI’s ChatGPT Plus charge $20/month for unlimited access to GPT-4-turbo.
For startups with technical expertise, self-hosting open-source LLMs (like Meta’s LLaMA, Mistral, or Falcon) can reduce long-term costs.
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) are AI models designed to create new, realistic data from scratch, whether it's images, videos, music, or text. They're widely used in fields like image generation, data augmentation, and deepfake creation.
GANs consist of two neural networks working against each other in a game-like setup:
Generator (Creator) – Tries to create fake data that looks real.
Discriminator (Critic) – Tries to distinguish between real and fake data.
Use Cases of GANs
AI-Generated Art & Creativity; Example: Deepart AI, Runway Ml
Create hyper-realistic human faces; Example: DeepFaceLab
Enhance low-resolution images into high-definition (HD) quality; Example: Gigapixel AI
Virtual Try-On & Fashion Design; Example: Stitch Fix AI
Retrieval-Augmented Generation (RAG)
LLMs are powerful, but they have a limitation—they rely on pre-trained knowledge and may not always have the most up-to-date or domain-specific information. This is where Retrieval-Augmented Generation (RAG) comes in.
RAG is an AI architecture that combines LLMs with real-time data retrieval from external sources like databases, documents, APIs, or the web. Instead of relying solely on the AI's training, RAG allows it to "fetch" the most relevant, updated, and context-specific information before generating a response.
Use Cases of RAG
AI-powered search engines (e.g., retrieving knowledge base articles for customer support). Example: Perplexity AI
Technical Documentation; Example: GitHub Copilot
Legal and financial AI assistants (fetching real-time regulations and market data). Example: Harvey AI
E-commerce AI (pulling in live product details, inventory status, and reviews). Example: Shopify AI
Healthcare AI (accessing the latest research papers or patient records securely). Example: Google Med-PaLM
Finetuning
Finetuning is the process of training a pre-existing AI model (like GPT-4, LLaMA, or Falcon) on a custom dataset to improve its performance for a specific task, domain, or style. Instead of training a model from scratch, finetuning helps adapt an AI model to your business, industry, or product.
As a founder, your time is limited, and you need AI that truly understands your business, customers, and industry. Fine-tuning an AI model allows you to create a highly specialised assistant that aligns with your startup’s needs.
Use Cases of Finetuning
Customer support & chatbots: Finetuned AI can handle complex customer queries with more accuracy and personalised responses.
Personalised content & marketing: Instead of generic AI-generated content, finetuning ensures AI writes in your brand’s voice and style.
Legal & compliance document automation: Finetuned AI can assist in drafting contracts, compliance documents, and legal policies based on industry-specific regulations.
AI Agents
AI agents are autonomous systems that not only generate responses like an LLM, but also take actions, make decisions, and interact with tools or environments. Unlike basic chatbots, they go beyond answering questions. They plan tasks, execute workflows, and continuously learn from interactions.
AI agents function using a loop of perception, reasoning, and action.
Use Cases of AI Agents
AI Customer Support & Sales Agents; Example: Intercom AI, Freshdesk AI
AI Market Research & Competitive Intelligence; Example: Feedly AI, Auto GPT
AI-Powered Software Development Agents; Example: GitHub Copilot, Devin AI
How Founders Can Leverage Generative AI?
Automate Repetitive Tasks
Example: Instead of manually handling customer inquiries, an AI-powered chatbot like Intercom or Drift can respond to FAQs, qualify leads, and even schedule meetings- freeing up your team for high-value interactions.
Boost Product Development
Example: AI-assisted coding tools like GitHub Copilot or Trae AI help developers auto-complete code, debug faster, and generate boilerplate code, accelerating software development cycles and reducing engineering bottlenecks.
Enhance Personalisation
Example: E-commerce brands use Klaviyo & Bloomreach to send hyper-personalised email and SMS marketing campaigns that increase conversions.
Manage Marketing & Content Creation
Startups use Jasper AI & Copy.ai to generate marketing copy for websites, ads, and social media, saving time and resources.
These are just a few ways you can use Generative AI in your startup but honestly, we’re only scratching the surface. As AI keeps evolving, the startups that experiment, adapt, and integrate it wisely will be the ones staying ahead of the curve.
Final Thoughts
The rapid evolution of Generative AI suggests a future where AI agents don’t just assist us- they think, predict, and act autonomously. From AI agents that draft personalised marketing campaigns to systems that analyse data and suggest business strategies, the technology is no longer a futuristic concept- it’s happening now.
And this isn’t just a trend, this is the very future we will live in. But this might bring up a big question- What about AI replacing humans? Is AI here to take over jobs?
Well, the answer is simple. It’s not AI vs. humans! It’s humans who know how to use AI vs. those who don’t. The key is figuring out where AI can make the biggest impact in your startup- whether it's in marketing, product development, customer engagement, or business intelligence.
At Razorpay Rize, we get it—building a startup is tough. That’s why we’re more than just a space for connecting with other founders. We’ve got programs, tools, and services designed to take some of the weight off the shoulders and make the journey just a little bit easier.
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