Artificial Intelligence (AI) is undoubtedly a powerful tool in our modern world. Like a skilled player who changes the direction of a game, AI is reshaping numerous industries, making processes faster, smarter, and more efficient. From diagnosing diseases in healthcare to predicting market trends in finance, to streamlining supply chains in logistics, and personalizing customer experiences in marketing – AI’s influence is broad and transformative. However, such advancements don’t just magically appear; they come with a price.
Incorporating AI into your business operations is like embarking on an exciting but costly journey. It requires careful planning, resources, and investment. It’s not as simple as buying a software package and installing it. You need to understand what you’re getting into, how it works, and most importantly, what it’s going to cost you.
In this article, we’re going to break down these costs for you. As we step into the year 2023, we’ll explore the different components that add up to the total cost of AI. We’ll look at hardware and software costs, labor costs, training and maintenance costs, and some often overlooked expenses like data collection and legal fees. We’ll also consider the various factors that can influence these costs, such as the complexity of the problem you’re solving, the data you have at your disposal, and the time frame in which you want results.
Our goal is to provide you with a clear and comprehensive understanding of the financial commitment involved in AI implementation. So, whether you’re a burgeoning startup or an established company in any industry, this guide will help you navigate the economic landscape of AI in 2023. Let’s delve into the world of AI costs and see how you can incorporate this game-changing technology without burning a hole in your pocket.
One of the major cost factors in AI implementation is the hardware required to run AI algorithms. This hardware is generally more expensive than standard computer hardware, making the initial setup and running of an AI system quite significant in cost. To run AI algorithms efficiently, specialized hardware that can handle a high volume of data and computations is needed. The most common types of hardware used for AI include:
- GPUs, which provide the high level of computational power required for training neural networks. However, they can be expensive and might not fit all budgets.
- FPGAs, a less expensive alternative to GPUs, still offer high computational power but can be challenging to program.
- ASICs, purpose-built chips for specific tasks that offer high performance but can be expensive.
Different provisioning methods can also affect the hardware costs. On-premises hardware can be expensive to set up and maintain but offers complete control over the environment. Cloud-based hardware can be more cost-effective, but you may have less control over the underlying infrastructure. A hybrid approach uses a combination of on-premises and cloud-based resources.
A single “middle-of-the-road” server can cost north of $10,000, with an additional $2,000-plus for a backup system.
When we talk about the costs of using AI, we usually think about hardware expenses, like servers and special equipment. But the software, the programs and data that the AI uses, can also add up.
First, there’s the cost of collecting data. AI needs a lot of information to learn and make decisions, so you might need to spend money on getting that data. This could involve buying data from another company, or investing in tools and resources to collect it yourself.
Next, there’s the cost of preparing the data. The data you collect might be messy or confusing, so you need to clean it up and organize it in a way that the AI can understand. This is called data preprocessing and it’s a very important step. But it can also be time-consuming and expensive.
Then there’s the cost of labeling data. AI learns from examples, so for many AI systems, you need to provide data where you’ve already marked or ‘labeled’ the right answer. For example, if you’re training an AI to recognize dogs in photos, you need to give it a bunch of photos where dogs are already marked. This process, known as data labeling, can be costly and time-consuming because it often needs to be done manually by humans.
Another major cost is for the actual AI software itself. This software is what allows you to use AI algorithms and usually requires a license to use. These licenses can be expensive, and they’re often charged per server, which means the cost can quickly add up if you’re using lots of servers.
In short, the software costs for AI can be a big part of the total expense. It’s not just about buying the right hardware – you also need to budget for collecting, preparing, and labeling data, as well as the cost of the AI software itself.
AI Labor Costs
When a business wants to develop and use artificial intelligence (AI), it needs a team of specialists. These specialists usually include data scientists, machine learning engineers, and software developers. Each of these roles is critical to make AI work.
A data scientist is like a detective. They look at the data the business has and try to find patterns or trends. They use this information to make predictions or solve problems. On average, a data scientist in the United States makes over $102,000 per year.
A machine learning engineer is like a builder. They take the patterns and trends that the data scientist found and use them to build AI models. These models can do things like recognize speech, recommend products, or predict sales. The average yearly salary for a machine learning engineer is $112,421.
A software developer is like an architect. They design and write the software that lets the AI models work with the business’s systems. They make sure that the AI can do its job effectively and reliably. The typical yearly salary for a software developer is $110,140.
So if a business wants to have its own small AI team, it needs at least one of each of these specialists. If you add up the salaries, it costs the business more than $320,000 every year just for these people’s salaries. This doesn’t even include other costs like benefits, office space, and tools they need to do their jobs.
Training and Maintenance Costs
“Training AI models require computational resources, which come at a cost.”
This means that to teach an AI (Artificial Intelligence) system, we need a lot of computing power. Think of it as a really smart brain that needs a lot of energy to learn. This “energy” in the case of AI is provided by powerful computer devices, known as GPUs (Graphics Processing Units). These devices do the heavy calculations required for the AI to learn. Just like a high-quality school or college might have high fees, these high-powered GPUs can be expensive. For example, a top-of-the-line GPU like the Tesla V100 can cost around $10,000.
“Maintaining an AI system requires both hardware and software resources, which also come with costs.”
Once we’ve taught our AI system and it’s working, we need to keep it running smoothly. This is what we mean by “maintenance”. Just like a car needs regular servicing, an AI system needs ongoing attention too. This maintenance involves both hardware (the physical devices like CPUs and GPUs) and software (the programs and data that the AI uses).
“Google’s DeepMind Alphago system required up to 1,920 CPUs and 280 GPUs to operate.”
This is an example of how much computing power an advanced AI system can need. Google’s DeepMind developed an AI system called AlphaGo, which was so advanced it could beat human champions at the game of Go. To run AlphaGo, they needed up to 1,920 CPUs (Central Processing Units, which are like the brains of a computer) and 280 GPUs. That’s a huge amount of computing power, and it shows just how much resource-intensive it can be to run a sophisticated AI system.
Other AI Costs
When setting up artificial intelligence technologies, there are other costs beyond the hardware, software, and labor expenses that you need to be aware of. These can quickly increase your overall expenditure.
Firstly, you have to think about data collection. AI models need data to learn and improve. Just like a human needs textbooks to study, an AI model needs data to train. Collecting this data can sometimes be costly. The cost can vary depending on the type and amount of data you need. For example, if you are developing an AI model to recognize images, you will need to gather many different pictures for the model to study. This might involve purchasing datasets, or spending time and resources to gather and organize the data yourself.
Next, once you have the data, it often needs to be annotated or labeled. This is like marking answers in a textbook for the AI to learn from. For instance, if you’re training an AI to recognize dogs in images, each image in your dataset needs to be labeled as ‘dog’ or ‘not dog’. This process can take a lot of time and, if you’re hiring people to do it, can cost quite a bit of money.
Lastly, there are legal costs. As AI technology advances, it can run into complex legal and ethical issues. For example, if your AI is dealing with people’s personal data, you need to ensure that it complies with privacy laws. Or, if your AI makes a mistake, you need to know who is legally responsible. Addressing these issues often requires expert legal advice, which can be expensive.
So, while the initial thought might be that AI involves costs mainly related to technology and technical expertise, it’s important to remember these other costs as well. They can greatly affect the overall expense of implementing AI technologies.
Factors Affecting the Cost of AI
Let’s break down each of these factors in simple terms:
- Type of Data Available: The kind of data you have can affect the cost of AI. For instance, if you have complex data or data that requires a lot of processing, it will be more expensive to train your AI model. This is because complex data often requires more advanced models and more computational resources to understand and learn from.
- Complexity of the Problem: The more complex the problem you’re trying to solve with AI, the more it will cost. This is because more complex problems require more training data and more processing power to solve. Think of it like a puzzle: the more pieces you have, the longer and harder it is to complete.
- Number of People Involved in the Project: The more people working on the project, the more it will cost. This is because each person needs to be paid for their work. For instance, you might need data scientists to prepare and analyze the data, machine learning engineers to build and train the models, and software developers to integrate the models into your systems.
- Time Frame for Results: If you want results quickly, it will cost more. This is because you’ll need to use more computational resources to speed up the training process. It’s similar to express shipping: if you want your package delivered faster, you’ll have to pay extra.
- Number of Applications: The more ways you want to use AI, the more it will cost. This is because each application may require a different AI model. For instance, an AI model for predicting stock prices will be different from an AI model for recognizing speech.
- Number of Devices: If you want to use AI on multiple devices, it can increase the costs. This is because each device might need a different version of the AI model or additional resources to run the model. For example, an AI model might run on a powerful server, but need to be simplified or adjusted to run on a smartphone.
Remember, these factors all interact. For example, a complex problem might need more people and more advanced data, which can increase the costs even more. It’s important to consider all these factors when planning your AI project and budget.
The cost of implementing AI in a business can be quite significant, but it varies greatly depending on various factors. These costs can be broadly classified into hardware costs, software costs, labor costs, training and maintenance costs, and other costs.
Hardware costs are typically high as AI requires specialized hardware to efficiently run the algorithms. The specific hardware used for AI includes GPUs, FPGAs, and ASICs, each with different price points and performance levels. Additionally, whether you host your hardware on-premises or in the cloud can affect the cost.
Software costs involve the expenses related to data collection, analysis, and processing. Labeling data for training AI models can be time-consuming and costly. Additionally, there are also licensing costs for different software tools.
Labor costs are associated with hiring skilled professionals like data scientists, machine learning engineers, and software developers. These roles command high salaries due to the high demand and limited supply of talent in these areas.
Training and maintenance costs are also a big part of AI implementation. Training AI models require expensive computational resources, and the maintenance of these systems also incurs cost due to the need for continuous updates and potential hardware failures.
There are also other costs related to data collection, legal fees, and the complexity of the problem being solved by AI. The type of data, the quality and quantity of the data, and the complexity of the problem can all influence the cost.
In conclusion, the cost of implementing AI can be quite high, but it can also provide significant benefits if done correctly. Therefore, understanding these costs and planning accordingly is crucial for businesses considering adopting AI.
James is a writer who specializes in writing about AI and education for our blog. He believes in the power of lifelong learning and hopes to inspire his readers to take control of their education.
James is passionate about self-education as a means of personal growth and fulfillment, and aims to empower others to pursue their own paths of learning.