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There have been a lot of talks about A.I. I think now the question is how to move A.I. beyond the traditional and conventional way of deployment.

Edge Computing is not new, it has been around for quite some time, but since 2018 it has been used to take in A.I. software.

Now question is how do companies take advantage of this edge computing to further move Generative A.I. models to be run offline on local devices, or rather local remote places?

Cloud Computing vs Edge Computing

As we can see from the recent Big Tech Cloud providers earnings report, Amazon has decided to cut staffs from their profitable AWS (Cloud Computing Unit). What trend have the market been showing or evolving?

Are companies going to shift their focus from Cloud Computing to Edge Computing?

Cloud computing stocks have been top performers over the last 10 years. But if you have been following the news, a new trend have been emerging for the new decade: Edge Computing.

Cloud Computing allows data and software services in a centralized data center and delivers them to users via the internet.

Edge computing moves data and software into regional data centers located closer to the user.

Edge computing helps to reduces the time taken for users to receive information (the latency) and decreases the amount of traffic traveling across the internet's primary infrastructure.

For A.I. information to be delivered is critical in terms of speed and time (latency). So companies who want to increase the performance of their A.I. services for employees, customers, and smart devices would want to take advantage of edge computing to bring their apps out of the cloud and host them at smaller localized data centers.

It would be a different game plan when cloud computing would be more for enterprise A.I. providers, and once the services is available.

Deployment for customer would be via Edge Computing.

Benefit of Edge A.I.

Intelligence:

AI applications are getting more powerful and flexible than conventional applications that can respond only to inputs that the programmer had anticipated.

With the new GPT model, an AI neural network is not trained only on how to answer a specific question, but rather how to answer a particular type of question, even if the question itself is new.

With more advanced GPT, applications would process infinitely diverse inputs like texts, spoken words or video.

Real-time insights:

Since Edge Technology analyzes data locally rather than in a faraway cloud delayed by long-distance communications, the time taken to respond to a user’s queries would be in real-time.

Reduced cost:

By bringing processing power closer to the edge, applications need less internet bandwidth, greatly reducing networking costs.

Increased privacy:

A.I. can now process and analyze real-world information without ever exposing it to a human being, this has help to improve the privacy for anyone whose appearance, voice, medical image or any other personal information needs to be analyzed.

Edge A.I. further enhances privacy by containing that data locally, uploading only the analysis and insights to the cloud.

Even if there is a real need to use the data for training purposes, it can be made anonymized to protect user identities. By preserving privacy, Edge A.I. simplifies the challenges associated with data regulatory compliance.

High availability:

Decentralization and offline capabilities make Edge A.I. more robust as internet access is no longer required for processing data. This will really help in gaining higher availability and reliability for mission-critical, production-grade AI applications.

Persistent improvement:

A.I. models has grown increasingly to be more and more accurate as they train on more data.

When an Edge A.I. application has data that it cannot accurately or confidently process, it simply just need to uploads it so that the AI can retrain and learn from it.

So the longer a model is in production at the edge, the more accurate the model will be.

Which Stocks Would Benefit from Edge A.I.

When we look at stocks which are going to benefit from Edge A.I., I would look at these 3 areas.

1. Large Public Cloud Computing Provider

2. Edge Computing Hardware

3. Edge Computing Network Provider

Large Public Cloud Computing Provider - Microsoft (MSFT)

We have to remember that edge computing is not new, it is basically an extension of cloud computing. So when we look at who would be in a better position to play the roles to help in Edge Computing.

I would look at $Microsoft(MSFT)$ as they already have a very matured ecosystem to support edge computing in both hardware and software.

Gartner has projected that edge computing will grow 75% over the next few years, and 15 billion devices will be connected to enterprises via the Edge network by the end of this decade.

Companies would start to pull data out of the cloud and start to adopt and deploy edge computing.

This is something that I believe Microsoft would have an advantage over the others.

https://azure.microsoft.com/en-us/solutions/private-multi-access-edge-compute-mec/

Azure Private MEC

If you look at how the ecosystem have been established, it is really something that could save organization time to deploy their A.I. via the Edge Computing.

Edge Computing Hardware - Nvidia (NVDA)

Semiconductor companies are designing new chips and equipment to enable the faster computing power needed for many edge network applications such as AI, connected and autonomous machines and robotics, and augmented and virtual reality.

$NVIDIA Corp(NVDA)$ is one of the few companies who are investing more in research and development.

Their graphics processing units (GPUs) are already being deployed in centralized, cloud-based data centers as computing accelerators.

But Nvidia have the vision to deploy and apply its GPU technology beyond centralized locations. They are already expanding to deploy the hardware designs to address AI, robotics, self-driving cars, and other tech that will benefit greatly from edge computing infrastructure.

On top of the hardware, Nvidia has also a comprehensive library of software that users can license to create new services built at the network edge.

So if I look into nuts-and-bolts investment in edge computing networks, I would seriously consider Nvidia.

Edge Computing Network Provider - Akamai

With the hosting and hardware considered, what is important would be the medium to carry the data and application to users. The Content delivery networks (CDNs) are responsible for moving and securing data between parties.

These companies manage the flow of information online and play an important role in migrating data from the cloud to the edge.

$Akamai(AKAM)$ is the largest CDN in the world, handling a sizable piece of all daily data traveling on the internet.

They have been around since the dot-com era in 2000s and has been growing at a steady pace as the level of traffic on the internet increases every year.

As a leader in managing web infrastructure, Akamai is in a good position to bet on edge computing.

Moving internet infrastructure and operations closer to users would become the norm, Akamai’s network performance and security platforms are evolving.

They may not be the fastest growing stock, but I would pick them as a solid pick for their sloid standing in evolution of the internet.

Summary

I would think that we should look beyond ChatGPT for A.I. development, because A.I. is a long term commitment and companies who are looking to invest properly in establishing the correct infrastructure, would stand to win in the long term.

Bringing A.I. closer to end-users would be the game changer in future as they would really mean users can access and use A.I. services much faster and more convenient.

Speed and accessibility is the key.

Appreciate if you could share your thoughts in the comment section on what are your thoughts of edge computing in the development of A.I.? And these A.I. stocks, which one will benefit the most?

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Disclaimer: The analysis and result presented does not recommend or suggest any investing in the said stock. This is purely for Analysis.