What’s in a Name – Why AI Terms Matter
Artificial Intelligence (AI) is everywhere. It's at the center of conversations about technology, business, and even our daily lives. But when you see "AI" in the news, advertisements, or policies, do you really understand what’s being discussed?
Here’s why it matters. The way we define and use AI terms isn’t just about tech jargon. It influences how products are marketed, how policy is drafted, and how funding is allocated. If AI is shaping our world, we need to grasp what it means—and what it doesn’t.
This blog dives into why clarity around AI terms is essential, from its economic and political impact to how it’s reshaping technology and culture.
How AI Is Driving Economics and Politics
Every day, people search for “AI” and related terms over 6.5 million times. The AI market is booming—between 2017 and 2024, the number of AI companies more than doubled to 70,000. According to a 2024 Databricks survey, companies are projected to spend between $1 million to $10 million this year on generative AI alone.
The total addressable market (TAM) for artificial intelligence is staggering. Back in 2018, McKinsey pegged the TAM for AI at $13 trillion by 2030. By 2023, generative AI—just one subset of AI—was predicted to add $4.4 trillion annually to the economy.
AI isn’t just an economic game; it's a geopolitical one as well. Countries are racing to lead the AI revolution. For instance, the U.S. Congress has been urged to create a Manhattan Project-level initiative to reach artificial general intelligence (AGI) before nations, such as China, do. Why? Because AI leadership isn’t just about innovation; it’s about economic dominance and global influence.
When AI has this level of impact, understanding what we mean when we say “AI” becomes not just important—but essential.
AI is Changing How Computing Works
At CES this year, NVIDIA CEO Jensen Huang discussed AI's recent history and NVIDIA's significant role in it:
Artificial intelligence has changed how computing works. AI isn’t an ephemeral term, or an application, but an important evolution in computer science.
Artificial intelligence depends on lots and lots of data. Once the data is digitized and stored, you need lots of high-performance computing available to process it. That’s where NVIDIA comes in. Their products can run compute for machine learning and deep learning, the techniques used to train machines to do a task.
We Have a Cultural Bias for the Term AI
“Artificial intelligence” is a very loaded term. It was coined in 1955 by Stanford professor John McCarthy. He defined it “the science and engineering of making intelligent machines”.
Let’s be honest. AI is just a marketing term.
However, our collective consciousness is already filled with imaginations of what AI is. The oldest is HAL 9000 from 2001: A Space Odyssey.
Or maybe your mind takes you to Robocop, the story of crime-eradicating cyborgs.
If you’re a comic lover like I am, maybe you think of Ultron, an intelligent AI that was created by Dr. Henry Pym to keep global peace. But it got weird when Ultron decided there could never be peace among humans.
The list goes on and on. The Jetsons, Star Wars, Wall-E, the Matrix, Blade Runner, and The Terminator all feature some sort of futuristic AI.
These shows are part of our culture. It’s what we’re bound to think AI will be. Combine that with the gold rush to cash in on the AI craze, it’s important to know what people mean when they say “AI”.
Types of AI
If AI is a marketing term, it only makes sense that the types of AI can be separated into different types:
- Narrow AI: We are here. This includes things like voice assistants, facial recognition, recommendation algorithms, language translation, and even generative AI.
- Artificial General Intelligence (AGI): This is the future. Sam Altman claims the latest version of OpenAI is at this level (although that’s up for debate). This type of AI will have the ability to understand, learn, and apply knowledge in a generalized manner.
In an interesting sidenote, most of the media examples given above would classify as AGI. - Artificial Super Intelligence (ASI): This is the future. It is theorized that this form of AI will surpass human levels of intelligence, capable of solving problems and creating solutions beyond what we can even imagine.
Ultron is the only example above that meets the criteria for ASI. I mean, he was smart enough to hypnotize Pym to forget about him!
Instead of AI types, I like the way Huang marked out the stages of AI:
- Perception AI: Speech recognition, medical imaging, recommendation engines
- Generative AI: Digital marketing, content creation
- Agentic AI: Coding assistant, customer service, patient care
- Physical AI: Self-driving cars, general robotics
From a technology perspective, we have a handle on perception and generative AI. Those were building blocks to get us agentic AI, software that can interact with data and other tools. They can create lists of steps and then perform them with minimal human intervention (via RedHat).
The lessons learned from agentic AI are what will get us to Physical AI.
The More You Know…
Let’s practice seeing all types of AI for what it is – the newest evolution of computer science. Instead of types of AI, let’s see where things are on the evolution of AI as a science scale.
Don’t be afraid to question what you read, that is how you can defeat AI FUD. If the claims sound like they came out of a movie, dig deeper. What is meant by AI? Does that make sense based on where AI technology is today? If not, maybe you’re being sold the dreams of our childhood.
Let’s not be afraid of what the future holds, because AI definitely will open doors to amazing things. But first we’ll have to build the technologies needed to make it real.
Artificial Intelligence (AI) is everywhere. It's at the center of conversations about technology, business, and even our daily lives. But when you see "AI" in the news, advertisements, or policies, do you really understand what’s being discussed?
Here’s why it matters. The way we define and use AI terms isn’t just about tech jargon. It influences how products are marketed, how policy is drafted, and how funding is allocated. If AI is shaping our world, we need to grasp what it means—and what it doesn’t.
This blog dives into why clarity around AI terms is essential, from its economic and political impact to how it’s reshaping technology and culture.
How AI Is Driving Economics and Politics
Every day, people search for “AI” and related terms over 6.5 million times. The AI market is booming—between 2017 and 2024, the number of AI companies more than doubled to 70,000. According to a 2024 Databricks survey, companies are projected to spend between $1 million to $10 million this year on generative AI alone.
The total addressable market (TAM) for artificial intelligence is staggering. Back in 2018, McKinsey pegged the TAM for AI at $13 trillion by 2030. By 2023, generative AI—just one subset of AI—was predicted to add $4.4 trillion annually to the economy.
AI isn’t just an economic game; it's a geopolitical one as well. Countries are racing to lead the AI revolution. For instance, the U.S. Congress has been urged to create a Manhattan Project-level initiative to reach artificial general intelligence (AGI) before nations, such as China, do. Why? Because AI leadership isn’t just about innovation; it’s about economic dominance and global influence.
When AI has this level of impact, understanding what we mean when we say “AI” becomes not just important—but essential.
AI is Changing How Computing Works
At CES this year, NVIDIA CEO Jensen Huang discussed AI's recent history and NVIDIA's significant role in it:
Artificial intelligence has changed how computing works. AI isn’t an ephemeral term, or an application, but an important evolution in computer science.
Artificial intelligence depends on lots and lots of data. Once the data is digitized and stored, you need lots of high-performance computing available to process it. That’s where NVIDIA comes in. Their products can run compute for machine learning and deep learning, the techniques used to train machines to do a task.
We Have a Cultural Bias for the Term AI
“Artificial intelligence” is a very loaded term. It was coined in 1955 by Stanford professor John McCarthy. He defined it “the science and engineering of making intelligent machines”.
Let’s be honest. AI is just a marketing term.
However, our collective consciousness is already filled with imaginations of what AI is. The oldest is HAL 9000 from 2001: A Space Odyssey.
Or maybe your mind takes you to Robocop, the story of crime-eradicating cyborgs.
If you’re a comic lover like I am, maybe you think of Ultron, an intelligent AI that was created by Dr. Henry Pym to keep global peace. But it got weird when Ultron decided there could never be peace among humans.
The list goes on and on. The Jetsons, Star Wars, Wall-E, the Matrix, Blade Runner, and The Terminator all feature some sort of futuristic AI.
These shows are part of our culture. It’s what we’re bound to think AI will be. Combine that with the gold rush to cash in on the AI craze, it’s important to know what people mean when they say “AI”.
Types of AI
If AI is a marketing term, it only makes sense that the types of AI can be separated into different types:
- Narrow AI: We are here. This includes things like voice assistants, facial recognition, recommendation algorithms, language translation, and even generative AI.
- Artificial General Intelligence (AGI): This is the future. Sam Altman claims the latest version of OpenAI is at this level (although that’s up for debate). This type of AI will have the ability to understand, learn, and apply knowledge in a generalized manner.
In an interesting sidenote, most of the media examples given above would classify as AGI. - Artificial Super Intelligence (ASI): This is the future. It is theorized that this form of AI will surpass human levels of intelligence, capable of solving problems and creating solutions beyond what we can even imagine.
Ultron is the only example above that meets the criteria for ASI. I mean, he was smart enough to hypnotize Pym to forget about him!
Instead of AI types, I like the way Huang marked out the stages of AI:
- Perception AI: Speech recognition, medical imaging, recommendation engines
- Generative AI: Digital marketing, content creation
- Agentic AI: Coding assistant, customer service, patient care
- Physical AI: Self-driving cars, general robotics
From a technology perspective, we have a handle on perception and generative AI. Those were building blocks to get us agentic AI, software that can interact with data and other tools. They can create lists of steps and then perform them with minimal human intervention (via RedHat).
The lessons learned from agentic AI are what will get us to Physical AI.
The More You Know…
Let’s practice seeing all types of AI for what it is – the newest evolution of computer science. Instead of types of AI, let’s see where things are on the evolution of AI as a science scale.
Don’t be afraid to question what you read, that is how you can defeat AI FUD. If the claims sound like they came out of a movie, dig deeper. What is meant by AI? Does that make sense based on where AI technology is today? If not, maybe you’re being sold the dreams of our childhood.
Let’s not be afraid of what the future holds, because AI definitely will open doors to amazing things. But first we’ll have to build the technologies needed to make it real.
