If you are reading this article, you are on the way to inheriting a substantial fortune in the coming decade for sure. I’m not telling you this just for the sake of improving our user retention.
Being in the field of Artificial Intelligence (AI) as a content creator, I’ve predicted the growth of generative AI when it starts to sprout in the month of September 2022.
In the month of September 2022, I created content on Midjourney, DALL.E 2, and Stable Diffusion for our blog. And, we are the ones who predicted these generative AI models very early and produced content for users around the world to cope with these models.
At the time of writing these articles and exploring these sophisticated AI models, I knew something revolutionary was going to happen.
What I didn’t predict was that AI advancement will be in the light of speed.
Table of Contents
AI Race
From the month of September 2022 to April 2023, there were a lot of breathtaking releases of generative models, large language models (LLMs), and a slew of AI tools released and being released.
To name a few,
- Midjourney from Midjourney Lab
- DALL.E 2 from OpenAI
- Stable Diffusion from Stability AI
- GPT models and ChatGPT from OpenAI
- PaLM and Bard from Google
- Microsoft incorporated AI in Bing and Office Suite (called Copilot)
- Google incorporated AI into Google Workspace
Most people are not aware of these AI advancements. On the other side, people who are aware of these life-changing AI advancements are in great fear as these AI could take many of the jobs.
Yes, AI could do architectural designs, interior designs, UI/UX design, videogame assets, cinematic portraits, coding and debugging, and more.
AI could impact 80% of jobs. Check out the research paper published by OpenAI, University of Pennsylvania, and OpenReasearch.
In case you are one of the people among the people who don’t know about all these, let me brief about these AI models.
Generative AI models: Generative AI models are highly sophisticated models that generate art, music, videos from text descriptions known as “Prompts”. Just describe about your requirements in the natural human language, and the AI will create art, music, or videos in less than a minute. Examples: Midjourney, DALL.E 2, Stable Diffusion, and Runway ML.
LLMs: Large Language Models (LLMs) are machine learning models that use deep learning and neural networks to generate text and source code. Examples of LLMs are OpenAI’s GPT-3 and GPT-4, Google’s PaLM, and Meta (formerly Facebook)’s LLaMA.
Conversational AI: As the name suggests, these are all AI models that were trained to have human-like conversations. These models remember what the user said earlier in the conversation and provide responses according to it. Examples of conversational AI include ChatGPT by OpenAI and Bard by Google.
These AI models are highly sophisticated, and they are growing at the pace of light. They are powerful enough to question the future of Google search.
Now, you must have a good grasp of what AI advancements are and their potential. Let’s dive into the hardware that powers the AI revolution.
Hardware that Powers the Whole AI Race
We all know Central Processing Units (CPUs). CPUs are the processors that perform basic computer tasks, such as arithmetic, logical functions, and I/O operations. CPUs are the units that power every computer.
CPUs used to have a single core in the beginning, but now they have multiple cores. It performs tasks in a sequential manner.
When it comes to AI and machine learning, there will be huge datasets that need to be processed to make predictions. To process millions and billions of parameters, there is a need for high computational power.
This is where GPUs come in. Graphics processing units (GPUs) are also computer processor chips that are designed to handle the complex calculations needed for rendering graphics, images, videos, and animations on electronic devices such as computers, game consoles, and mobile phones.
Today’s GPUs are powerful enough to be used for a variety of tasks other than just graphics processing, such as big analytics and machine learning. The term “GPGPU,” or “General Purpose GPU,” is frequently used to describe this type of computing.
Unlike CPUs, GPUs process tasks in parallel manner. GPUs divide one big task into several small subtasks and distribute them among the vast majority of their processor cores. Hence, faster processing is achieved for difficult tasks.
NVIDIA, the World Leader in AI: The Stock That Can Make Many Millionaires?
NVIDIA is a technology company that specializes in designing and manufacturing advanced graphics processing units (GPUs) and systems on a chip (SoCs) for various computing applications. The company is widely known for its GPUs, which are used in high-performance gaming, data centers, artificial intelligence, and autonomous vehicles.
In addition to GPUs, NVIDIA also produces other technologies such as system software, development tools, and hardware for deep learning and computer vision applications.
NVIDIA, the leader of cloud GPUs:
Recently, the CEO of NVIDIA Mr. Jensen Huang made a huge announcement about NVIDIA’s AI preparation at the NVIDIA GTC global AI conference. These include:
- NVIDIA AI Foundations: The NVIDIA AI Foundations is a GPU as a cloud service approach. Currently, individuals and organizations can use this service to build, refine, and operate custom LLMs and generative AI models.
The foundation comprises language, visual, and biology model making services.
Language: NEMO. NVIDIA NEMO is an enterprise framework for developers to build, customize, and deploy generative AI models with billions of parameters.
Visual: PICASSO. NVIDIA Picasso is a cloud-based service for building and deploying generative AI-powered image, video, and 3D applications.
Biology: BIONEMO. NVIDIA BIONEMO is a cloud service for generative AI in drug discovery.
- NVIDIA Canvas: NVIDIA Canvas is a free AI application that turns your digital brush strokes and doodles into real-world art.
NVIDIA AI Foundations is a huge leap forward to power the whole AI revolution. And, NVIDIA is the only leader in it.
NVIDIA’s Dominance in the Field of Artificial Intelligence
All of the giant companies, such as Google, Microsoft, OpenAI, and Midjourney Lab are relying on NVIDIA for GPU services.
NVIDIA’s GPUs have played a pivotal role in the development of cutting-edge AI technologies, including OpenAI’s ChatGPT and Stable Diffusion models.
OpenAI relied on over 10,000 NVIDIA H100 and A100 GPUs to train ChatGPT, while Stable Diffusion required approximately 200,000 GPU hours on the NVIDIA A100 GPU.
Moreover, leading cloud service providers such as AWS and Azure have collaborated with NVIDIA to create massive clusters of GPUs for enterprise training workloads.
With all these facts, NVIDIA must definitely be the stock of a few decades.
Don’t Put All your Eggs in One Basket
All investors are aware of the importance of diversification when it comes to stock investments. But, in this case of the AI race, where to diversify? Well, I’m here to help you with this.
Let’s think in terms of competitors of NVIDIA.
NVIDIA’s main competitors include:
- Google: While not a direct competitor in the manufacturing of hardware, Google is a significant competitor for NVIDIA in the artificial intelligence space, offering its Tensor Processing Units (TPUs) for machine learning applications.
- TSMC (Taiwan Semiconductor Manufacturing Company): TSMC is not a direct competitor of NVIDIA, as it does not design or manufacture GPUs or other end-user computing products. However, TSMC is a major supplier of semiconductor manufacturing services to many companies, including NVIDIA, AMD, Apple, Qualcomm, and Huawei which means that it indirectly competes with other foundries and companies that provide similar services.
- AMD (Advanced Micro Devices): AMD is a semiconductor company that produces CPUs and GPUs for gaming and professional applications.
- Intel: Intel is a multinational technology company that designs and manufactures computer processors and other hardware.
- Qualcomm: Qualcomm is a semiconductor company that designs and manufactures processors and modems for smartphones and other mobile devices.
- ARM: ARM is a British semiconductor company that designs and licenses processor architectures for a variety of computing applications, including smartphones, tablets, and embedded systems.
- Imagination Technologies: Imagination Technologies is a British semiconductor and software design company that produces graphics and multimedia processors for consumer electronics, mobile devices, and automotive applications.
- Xilinx: Xilinx is a semiconductor company that specializes in programmable logic devices and software tools for accelerating computing applications in data centers and other high-performance computing environments.
Though not all of the competitors of NVIDIA that I mentioned are manufacturers of GPUs, most of the competitors on the list produce some form of semiconductor-based technology, and some of them specialize in different areas of computing and technology.
So, my suggestion would be NVIDIA, TSMC, and Google.
Disclaimer: Please bear in mind that I’m not a registered stock adviser, and this article is purely for informational purposes only. I/ Decentralizedcreator.com shall not be responsible for your profit or loss. Please confirm with your investment advisor.
TL;DR
NVIDIA is poised to dominate the rapidly growing AI industry with its strategic focus on cloud GPUs and partnerships with major tech companies. Its leadership position, coupled with the pace of AI adoption, suggests that it could be a strong investment for the next few decades. However, it may be prudent to diversify with competitors such as TSMC and Google for added stability in the portfolio.
Conclusion
Based on the analysis presented, it is recommended to invest in the suggested stock for the long term, considering its strong market position, technological advancements, and potential for growth.
This article is for informational purposes only. Consider your investment advisor or do your own research before investing in the stocks suggested here.
