100 CEOs Secret Meeting at Sequoia Capital: Unveiling the Future of Search Beyond ChatGPT and Bard

Explore the future of search beyond SEO with AI, vector embeddings, and large language models (LLMs). Uncover how these innovations revolutionize customer experiences and reshape the search industry.

If someone forwarded you this email, subscribe and join 5,000+ founders getting actionable golden nuggets that are tailored to make your business more profitable.

In today’s Caveminds Deep Dive…

  • 🤫 Get to know the most popular conversation at the 100 CEOs secret meeting in Sequoia Capitals HQ

  • 💥 Sal Khan dives into AI with Khanmigos — and how this will impact businesses

Listen to today's edition:

👋 Hello, my name is Andrew Amann and am one of the founders for Caveminds.com. I run a Product Engineering and Design Studio called NineTwoThree where we build web, mobile and AI apps.

Our team builds ML / AI models for enterprise companies and through osmosis and conversations, understands the applications that are going to make a larger difference than ChatGPT.

You might be familiar with the first wave of AI - you use it daily when you search the internet.

But what if I told you the sequel is even better? 

Think of it like a movie franchise: the first one's a classic, but the second one's got more action, drama, and maybe even some comic relief (just like this blog 😉).

So buckle up and get ready to explore this new frontier, as we dive into the world of Generative AI and discover how it's reshaping search in ways that'll make you say, "Wow, I didn't see that coming!" 🤯🚀

Google Search, Bing Search, Yahoo and even AskJeeves, were the first wave of AI which resulted in the ranking of information. — “Best Hair Dryers”, “Top Restaurants in Chicago”, “How to remove a nickel from my ear” and “top project management tools” are simply fed into the search machine by a human and returned with thousands of pages, ranked in order from best to worst.

But who (or what 🤖) chooses what appears 1st, or 2nd or 32nd? You see, that was the trillion-dollar question that Google locks down in a vault the like Coca-Cola locks its sweet-sparkling-nectar formula in a vault. 🥤

Google restructured the incentives of Politicians, journalists, entertainers and every other industry trying to compete for eyeballs. 👁️👁️.

But as goes with everything on the internet: Marketers. Ruin. Everything.

Every business has hired marketers to discover ways to reverse engineer Google and AskJeeves (I guess) creating the infamous SEO category that places their beloved content at the top of the list.

But before we dive into the second wave 🌊of AI let’s get a grasp on something a bit more scientific so that we can understand a very important concept that is being tossed around.

What is intent? Well, it's the semantic relevance of something typed according to what was expected. 🎯 It is the action, goal, or response that the searcher wants to see when they ask a question or assign a task.

Google is very good at intent because it uses vector embeddings –we'll see what they are in a few lines – just like every other rank-based system has been used since the 00’s.

Facebook’s Newsfeed, Spotify, Netflix, Google Search, and all other systems intending to show relevant content, will eventually have enough money and data to create a vector database to enhance the relevancy which will keep users on their platform longer. 

No wonder why Netflix's only true competition is sleep, because after you watched your 5th episode of “The O.C” then suddenly… – “But wait, here are 13 other shows from the 90’s that you might also like to watch at 3 a.m.”

Hold your groans and eye rolls for a sec, and consider why this might actually be worth your attention — what you need to understand, is this thing called vector embeddings.

So let's nerd out a bit more, shall we? 🤓 

Imagine you have a bunch of words like "dog", "cat", and "bird".

These words are just text, but if you want to teach a computer to understand what they mean, you need to turn them into something the computer can work with, like numbers. — That's where vector embeddings come in.

A vector is just a list of numbers, and the embedding is a way of taking a word and turning it into a vector. 💡

🪄 Where the magic happens

Continue reading this Deep Dive and get full access to our AI Intelligence platform, getting access to:

✔️ The full AI Deep Dives library

✔️ Monthly AI Webinar Workshops

✔️ Premium Podcast Exclusive Guides

✔️ Private Slack Channel for Entrepreneurs

✔️ Monthly AI Strategy Playbooks With Video Walkthroughs

✔️ Monthly Live AI Events & Meetups

✔️ Quarterly AMA’s With Caveminds Founding Team

✔️ And much more…

Subscribe to Premium to read the rest.

Become a paying subscriber of Premium to get access to this post and other subscriber-only content.

Already a paying subscriber? Sign In.

A subscription gets you:

  • • Premium Weekly AI Strategy Deep Dives
  • • AI Deep Dive Library
  • • Monthly AI Webinar Workshops
  • • Premium Podcast Exclusive Guides

Enjoying this Caveminds🔥 AI Deep Dive?

This content is free, but you must be subscribed to continue reading. Don't struggle to adapt to AI like the 99%. Join 5,000+ founders that are already ahead and subscribe to get weekly actionable AI content like this delivered to your inbox for free!

Already a subscriber?Sign In.Not now

Reply

or to participate.