refactor(ai) improve introduction wording (#570)

* Update verbiage in introduction

* docs(ai): mention AI Video subnet

---------

Co-authored-by: Elite Encoder <john@eliteencoder.net>

ai/introduction.mdx

@@ -12,16 +12,15 @@ iconType: regular
   is invaluable for enhancing the AI Subnet. Thank you for your contributions!
 </Warning>
 
-The new **AI Video Subnet**, henceforth referred to as the **AI Subnet**, is the
-first step in bringing powerful AI video capabilities into the Livepeer network.
-It enables video developers to add a rapidly growing suite of **generative AI
+The **AI Video Subnet**, also known as the **AI Subnet**, is the first step
+toward bringing powerful AI video capabilities into the Livepeer network. It
+enables video developers to add a rapidly growing suite of **generative AI
 features** such as [text-to-image](/ai/pipelines/text-to-image),
 [image-to-image](/ai/pipelines/image-to-image) and
-[image-to-video](/ai/pipelines/image-to-video) conversions to their
-applications, and allows node operators to **earn revenue by deploying their GPU
-resources** for AI processing tasks. For an in-depth understanding of the AI
-Subnet and its current capabilities, continue reading. Ready to dive in? Choose
-one of the cards below to kickstart your journey with the AI Subnet.
+[image-to-video](/ai/pipelines/image-to-video) to their applications. Livepeer
+Node operators are able to **earn revenue by deploying their GPU resources** for
+AI processing tasks. Ready to dive in? Choose one of the cards below to
+kickstart your journey with the AI Subnet.
 
 ## Kickstart Your Journey
 
@@ -86,20 +85,19 @@ The AI Subnet, initially proposed in
 [this SPE treasury proposal](https://explorer.livepeer.org/treasury/82843445347363563575858115586375001878287509193479217286690041153234635982713),
 represents a significant evolution within the Livepeer ecosystem. This
 **decentralized**, **open-source** framework seamlessly integrates a variety of
-**generative AI inference** tasks, such as image and video generation and
-upscaling, into the existing network infrastructure. These enhancements not only
-strengthen the Livepeer Mainnet's transcoding services, celebrated for their
-**low cost** and **high reliability**, but also pave the way for groundbreaking
-applications across both emerging Web3 environments and established Web2
-sectors.
+**generative image and video AI inference** tasks into the existing Livepeer
+network infrastructure. These enhancements strengthen Livepeer's transcoding
+services, celebrated for their **low cost** and **high reliability**, while also
+paving the way for groundbreaking applications across both emerging Web3
+environments and established Web2 sectors.
 
 Designed to revolutionize creative processes, the AI Subnet reflects Livepeer's
-commitment to creating a **globally accessible open video infrastructure**. This
-infrastructure aims to empower users by allowing them to leverage any form of
-video computing they need. By equipping video applications with advanced AI
-tools and diminishing reliance on centralized computing resources, it extends
-**cutting-edge AI capabilities** to a broader audience, fostering a more
-equitable digital landscape.
+commitment to creating a **globally accessible open video infrastructure**.
+Livepeer's AI subnet aims to empower users to leverage any form of image and
+video computing with AI. By equipping video applications with advanced AI tools
+and reducing reliance on centralized computing resources, we are dedicated to
+extending **cutting-edge AI capabilities** to a broader audience, fostering a
+more equitable digital landscape.
 
 ### Advantages of Livepeer's AI Subnet
 
@@ -114,9 +112,9 @@ equitable digital landscape.
 
 ### How It Works
 
-The AI Subnet, built on the established Livepeer network, leverages its
-**decentralized payment infrastructure** for efficient AI inference task
-execution. The network consists of two primary actors:
+The AI Subnet, built on the established Livepeer network, leverages a
+**decentralized payment infrastructure** for compensating AI orchestrator nodes
+for performing AI inference tasks. The network consists of two primary actors:
 
 - **AI Gateway Nodes**: These nodes manage the flow of AI tasks from
   applications and users, directing them to the appropriate Orchestrator nodes
@@ -142,8 +140,8 @@ scalability of new pipelines. Ongoing developments aim to enhance
 increasingly complex AI models and custom user-defined pipelines.
 
 Below is a simplified diagram illustrating the **complete AI inference
-pipeline** within the Livepeer AI Subnet. Only one AI Orchestrator
-with one GPU on the same machine is shown for clarity. In reality, the AI Gateway is
+pipeline** within the Livepeer AI Subnet. Only one AI Orchestrator with one GPU
+on the same machine is shown for clarity. In reality, the AI Gateway is
 connected to multiple Orchestrators, each of which can have multiple worker
 nodes with various GPUs attached.
 
@@ -162,10 +160,10 @@ graph TD
 
 This flow starts at the AI Gateway nodes, which route tasks from applications or
 users to an appropriate AI Orchestrator node. The selection of the AI
-Orchestrator node is based on factors such as the speed of previous
-inference requests, orchestrator stake, advertised price, and more. The AI
-Orchestrator node then executes the task in the ai-runner Docker Container. In
-this container, the AI Orchestrator can either:
+Orchestrator node is based on factors such as the speed of previous inference
+requests, orchestrator stake, advertised price, and more. The AI Orchestrator
+node then executes the task in the ai-runner Docker Container. In this
+container, the AI Orchestrator can either:
 
 - **Pre-load a model**: Orchestrators keep frequently used models
   [warm](ai/pipelines/overview#warm-models) on GPUs, speeding up task