A New Google: Combining Verticals and Building Habitual Recall

A New Google: Combining Verticals and Building Habitual Recall

In the ever-evolving world of technology, competition is fierce. Companies are constantly vying for the top spot, trying to offer something new and innovative that will capture the attention of users. One such company that has been at the forefront of this race is Google. Known primarily for its search engine, Google has become a household name, with millions of people relying on it for their everyday searches. However, there is a new wave of startups emerging that are looking to challenge Google's dominance in the search market. These startups believe that they can offer something better, something that will not only provide accurate search results but also keep users engaged and coming back for more.

One approach that these startups are taking is to focus on specific verticals. Rather than trying to compete with Google across all industries, they are honing in on a particular niche, such as electric product search, travel, or code. By doing so, they can specialize in providing the best search experience for that specific industry. This targeted approach allows them to tailor their algorithms and search results to meet the unique needs of their users. For example, a startup specializing in electric product search can provide detailed specifications, reviews, and comparisons for various electric products, making it the go-to destination for anyone in need of such information.

But how do these startups build habitual recall, ensuring that users choose their product over Google's? This is where the concept of "Come for search, stay for something else" comes into play. Early adopters of these new startups are finding that they no longer need to rely on Google for their search needs. Instead, they are drawn to these startups for the additional value they offer. For instance, a startup focused on travel search can provide personalized recommendations, insider tips, and exclusive deals, making it a one-stop-shop for all things travel-related. By providing this added value, these startups are able to establish themselves as more than just a search engine, but rather as a destination for a specific vertical.

To further enhance the user experience and foster a sense of community, these startups are incorporating community features into their search apps. This allows users to connect with like-minded individuals, share their experiences and knowledge, and collaborate on various projects. By creating a community around their product, these startups are not only fostering a sense of loyalty among their users but also creating a network effect, where the value of the product increases as more users join. This community aspect sets these startups apart from Google, as it provides users with a sense of belonging and a place to interact with others who share their interests.

In the realm of Artificial General Intelligence (AGI), another interesting concept comes into play. Jeff Hawkins, in his book "A Thousand Brains," argues that a simple scaled-up learning algorithm could reach AGI. Hawkins suggests that the neocortex, the organ of intelligence, is responsible for almost all the capabilities we associate with intelligence. These capabilities, such as vision, language, and math, are created by the neocortex. Hawkins further argues that the complexity of the neocortex lies in the learned content, not the learning algorithm itself. This implies that building brain-like AGI is feasible without having to replicate the intricate circuitry of the neocortex. Instead, the focus should be on finding the right learning algorithm and letting it do its work.

This idea has profound implications for the future of AGI development. If the neocortex is indeed a relatively simple, human-legible learning algorithm, then it is not far-fetched to believe that brain-like AGI is not centuries away but already on the horizon. This challenges the traditional view that AGI is an insurmountable task requiring years, if not centuries, of research and development. It suggests that by understanding the neocortex and its learning algorithm, we can pave the way for the creation of AGI in a much shorter timeframe.

So, what can we learn from these insights and ideas? Firstly, for startups looking to challenge Google's dominance, a focused approach on specific verticals can be highly effective. By tailoring their search algorithms to meet the unique needs of a particular industry, they can provide a superior search experience and build habitual recall among their users. Secondly, incorporating community features into their search apps can foster a sense of loyalty and create a network effect, further strengthening their position in the market. Lastly, in the realm of AGI, understanding the neocortex and its learning algorithm may be the key to unlocking the potential of brain-like AGI.

In conclusion, the landscape of search engines is changing. Startups are emerging that are looking to challenge Google's dominance by offering specialized search experiences and additional value beyond search results. By focusing on specific verticals, building communities, and understanding the learning algorithm of the neocortex, these startups are positioning themselves as viable alternatives to Google. As the search market continues to evolve, it will be interesting to see how these startups fare and whether they can truly become the "New Google."

Actionable Advice:

• 1. Identify a specific vertical or niche that you are passionate about and consider building a search engine or search app tailored to that industry. By providing specialized search results and additional value, you can attract users who are looking for a more targeted search experience.

• 2. Incorporate community features into your search app to foster a sense of belonging and encourage user engagement. By creating a space where users can connect, share their experiences, and collaborate, you can create a loyal user base and increase the value of your product.

• 3. Stay informed about advancements in AGI research, particularly in understanding the neocortex and its learning algorithm. This knowledge may provide valuable insights and opportunities for innovation in the field of AGI development.