Google is taking measures to enhance the search experience through the integration of conversational AI techniques. Specifically, the tech giant is incorporating large language models (LLMs) to augment its search engine’s capacity to understand and respond to user queries in a more natural and conversational manner.
Sundar Pichai, Google’s CEO, stated in an interview with The Wall Street Journal, “Will people be able to ask questions to Google and interact with LLMs in the context of search? Absolutely.”
Large language models are complex computations that enable machines to grasp the subtleties and complexities of natural language in order to generate appropriate responses. While the exact release date for the conversational AI feature in Google’s search engine remains uncertain, Pichai confirmed their plans to implement this upgrade soon.
So, why this significant shift? And what implications does it hold for SEO? Let’s delve into it.
The Bi(n)g Challenge…
Here’s the issue: Google isn’t sitting idly by as Bing gains traction. With the integration of ChatGPT into Bing Search, users can now engage with Bing in a conversational manner, akin to how one would converse with a friend.
As a result, Bing has recently seen a surge in its search engine market share, with page visits to Bing climbing to 15.8%. In contrast, Google experienced a 1% decline as of March 20, 2023.
This trend raises concerns for Google, as its primary source of revenue is search ads, generating $162 billion in 2022. For every percentage point Bing gains, Microsoft anticipates an additional $2 billion in revenue. There are indications that traffic from Bing has increased for specific brands and publishers. This is significant, as it implies we may not have to rely on Google traffic as heavily as before.
The public release of ChatGPT by OpenAI, a startup backed by Microsoft, has set a new standard for conversational AI technology. While Google’s own AI chatbot, Bard, aims to compete head-to-head with ChatGPT, you still need to join Google’s waitlist to access it. (However, in our experience, the wait isn’t too long before you can start using Bard after joining their waitlist.)
What Is Conversational AI?
That pretty much covers Google’s current plans. We don’t know the exact integration process, but Sundar assures us it’s on the horizon. However, this isn’t Google’s first foray into AI. In fact, they’ve been at the forefront of its development, even before Bard.
Google introduced LaMDA, which stands for Language Model for Dialogue Applications. It’s a large neural network-based language model that comprehends natural language and generates intelligent responses in context.
LaMDA facilitates more sophisticated interactions between users and machines, allowing the AI to grasp the meaning behind queries and offer relevant answers.
This AI has a range of applications, including:
- Artificial intelligence chatbot
- Speech recognition
- Virtual assistants
Unlike generative AI, Google’s current conversational AI for search and other software has primarily focused on response generation and analysis. In other words, the AI examines an input query, processes it, and then provides a response that best aligns with the context of the inquiry.
The Escalating Competition
AI is a central topic in tech discussions because it’s revolutionizing how we interact with technology. Companies like Microsoft, Amazon, and Google are racing to develop AI systems that can understand natural language better than any previous system.
Some of the most notable AI tools we have include:
- ChatGPT: A generative AI model with a wide range of applications, including writing local SEO content.
- Midjourney: An AI content generator specifically for image creation.
- Dall E: Another generative AI application for images and artwork.
- Krisp: An AI tool for isolating background noise to enhance audio quality.
With the exception of Krisp, most of today’s cutting-edge AI technology operates as AI-powered chatbots, likely because they’re more intuitive and “human” than other forms of AI.
However, while previous versions of GPT powering ChatGPT 3.5 are open-source, GPT 4 isn’t. OpenAI explains their decision in their technical report:
“Given both the competitive landscape and the safety implications of large-scale models like GPT-4, this report contains no further details about the architecture (including model size), hardware, training compute, dataset construction, training method, or similar.”
Essentially, OpenAI is ensuring that GPT 4 remains inaccessible to prevent potential misuse.
Open Source Large Language Models Released by Cerebras
Fortunately, Microsoft-backed OpenAI isn’t the sole contender in the generative AI realm. And Google isn’t poised to dominate this space anytime soon.
Enter Cerebras, an AI startup in Silicon Valley, which has unveiled a family of seven open-source GPT models, ranging from 111 million to 13 billion parameters, all trained using the Chinchilla formula.
We won’t delve into the technical specifics, but this essentially means that any organization utilizing Cerebras’ GPT models for generative AI development benefits from rapid training times, cost-effective training, and energy efficiency.
All models, weights, and checkpoints from Cerebras’ GPT release are available on Hugging Face and GitHub under the Apache 2.0 license.
Open Source vs. Closed Source
It’s imperative to discuss the distinction between open source and closed source models. Open source models are more accessible and user-friendly for developers. Conversely, closed source models provide enhanced security and privacy, as the developer retains control over who has access to the data.
If you’ve utilized generative AI like ChatGPT, you’re using an open-source conversational AI model created by OpenAI. Closed-source applications like Stable Diffusion, Midjourney, and even GPT 4 can only be accessed via API with authorization and access control measures in place.
Advantages of Open Source Models
There’s a lot to appreciate about open source generative AI. Firstly, you can utilize AI writing tools with open source applications for free. Developers don’t need to worry about data access or licensing fees.
This is particularly beneficial as free AI content writing tools are built on top of these models. In fact, most AI content writing tools in the current market, such as Jasper and Copy.ai, are constructed on open source models.
Additionally, developers can customize their AI models to meet specific needs. This might involve modifying the system architecture, training algorithms, datasets, and more. Dall E, an AI content generator for image creation, serves as an example of a customized AI model.
Open source models also offer higher accuracy, as they leverage extensive datasets with high-quality training data. Moreover, these models are typically well-tested and refined by a broader community of developers, leading to improved performance over time.
SEO in the Age of Conversational AI
Google’s shift towards conversational AI chatbots through Bard and, soon, Search indicates a transformation in the SEO landscape. It’s not just about concerns regarding publishers’ ad revenue; most companies are seeking ways to adapt to the surge of Large Language Models in SEO.
Apart from appraising the merits of open source models, the latest AI technology is projected to usher in a new era of SEO optimization.
Here are some insights from Leadshouse’s SEO team to help you get ready.
It’s All About Conversations
Certain techniques and strategies for SEO optimization will no longer hold sway in the era of
conversational AI-powered chatbots. The key now is to foster conversations – with your readers, customers, and potential leads.
The rise of conversational AI in search engines such as Bing and Google may alter how websites are ranked. We can focus on producing more conversational content. This gives your customer the impression that they are having a direct interaction with your brand.
This necessitates a different approach to content creation. Instead of fixating on keyword stuffing, the focus should shift towards generating content that engages readers through interactive dialogue.
However, much like ChatGPT and other AI-powered chatbots, Google’s own conversational AI will still rely on search volume and keyword-based searches to understand user intent. Therefore, while emphasis should shift towards fostering conversations, keywords remain pertinent.
Long-tail keywords cast a wider net and capture more conversational queries that may be challenging to predict in advance.
It’s important not to overlook traditional SEO strategies, as a balance between both approaches will yield the best results. By optimizing content for conversational queries, businesses can provide a more personalized user experience that matches the intent of the user.
Incorporating long-tail keywords in your content is crucial, as it provides context and allows your content to appear on more SERPs than with traditional keyword research methods. The significance of long-tail keywords in SEO is already evident today, but we expect it to play an even more pivotal role in the era of conversational AI.
Race for the Best AI Tools
From free AI writing tools to more technical software, there’s a wealth of content creation and optimization tools available today. As the competition heats up, it’s essential to conduct thorough research and select the right AI tools for your requirements.
For instance, AI content writing tools are tailored for generating text-based content. Other AI chatbots like Botanalytics excel in customer service and interactive conversations.
Yet, it’s not just about the flashy features. It is recommended to adhere to Google’s existing standards to prepare for this shift:
- Utilizing natural language for content creation, optimized for long-tail keywords and voice search.
- Leveraging structured data markup to enhance Google’s understanding of your website, resulting in improved SERP ranking.
- Developing and optimizing Google Business Profiles to feature in local search results.
- Proactively managing your brand to address negative reviews and handle customer concerns and complaints.
- Staying abreast of the latest SEO trends, including conversational AI and algorithm updates.
Optimizing for AI Chatbots
While there are numerous advantages to open source models, perhaps the most valuable is understanding how to optimize for them. For publishers, this entails comprehending the most effective methods for crafting conversational yet SEO-driven content.
With conversational AI, simply optimizing for search engines and human readers won’t suffice. Now, you must consider how to structure your content conversationally so that it aligns with Google’s AI algorithms.
Ken summed it up, stating, “Ultimately, businesses that embrace conversational SEO today will be better positioned to stay ahead of the curve and succeed in the future of search.”
Craft High-Ranking Content With Leadshouse
We stand on the brink of a new era in SEO optimization and content production. As conversational AI gains traction, so does the significance of conversation-driven content.
Leadshouse possesses the expertise needed to help you create high-ranking content optimized for both human readers and search engines. Our human-in-the-loop approach combines the best of AI tools with human insight to ensure your content is tailored for Google’s and Bing’s conversational AI chatbots and search engines.
We also believe in the power of data-driven insights, guaranteeing that your content strategy aligns with the new standards in search engine optimization.
Contact us today to discover how we can assist you in refining your SEO strategy!