Simplifying the 4 forces of AI

April 9, 2024
Simplifying the 4 forces of AI

A version of this article was originally published in Forbes Technology Council.

Niklaus Wirth—a pioneer in the computer science industry—passed away on January 1, 2024 at the age of 89. His legacy stems from the way he simplified complicated computer science problems.

In the 1960s, computer languages were often complicated and prone to errors. Wirth responded by creating streamlined languages—such as Pascal. In his mind, simplicity was better than complexity, as it minimized the chance of being wrong.

Today, Artificial Intelligence (AI) is becoming more prevalent, but less understood.

There are four key forces shaping AI that are crucial for navigating this rapidly evolving landscape:

  • Hardware

  • Software

  • Computers

  • Humans

Just as Niklaus Wirth simplified complex computer science problems with his streamlined languages, we must simplify our understanding of AI by dissecting its key components.

Through examples of organizations leading the charge in each of these areas, I hope to empower you to make better decisions for your enterprise and career.

1. Hardware

In the world of Formula One, even the best driver needs a top-notch vehicle to win. Similarly, in AI, powerful hardware is essential to meet the demands of new tools.

Currently, the hardware available is comparable to VCR technology—where advancements like DVD players, BluRay, and streaming services are on the horizon.

When it comes to hardware, Nvidia is leading the charge. Known for its graphics processing units (GPUs) and semiconductor products, the company holds more than 70% of the market share for hardware solutions. Its products are so transformative that many companies are willing to wait over a year for them, even though alternatives are available.

Despite Nvidia’s large competitive advantage, other players like AMD, Intel, and startups such as Cerebras Systems are pushing the boundaries of AI-optimized hardware. These processors and systems are designed to handle the specific demands of AI workloads, such as parallel processing and neural network computations.

This hardware landscape is shaping AI much like a well-tuned car shapes a Formula One race—with continuous improvements making systems faster and more powerful.

2. Software

Software serves as the interface for humans to interact with hardware tools—much like a steering wheel allows a driver to guide a car. It's essential for navigating the security, ethical and moral implications of new AI tools.

Software must be user-friendly if it’s going to simplify tasks and reduce cognitive strain. At the end of the day, AI is just one way to make information and data more useful, leading us toward better decisions.

Platforms like Google’s TensorFlow and Meta’s PyTorch demonstrate a shift toward comprehensive ecosystems supporting various AI applications—from basic chatbots to advanced neural networks.

In AI development, user-centric design will determine which tools succeed and which are forgotten. TensorFlow and PyTorch are open-source platforms that enable machine learning, computer vision, and natural language processing.

3. Computers

AI is the latest technological trend to revolutionize society, building off of previous transformations such as steam-powered energy, electricity, and the rise of information technology.

Unlike the previous reliance on central processing units (CPUs), we now have accelerated computing.

According to Nvidia, there is a staggering $1 trillion invested in data centers worldwide, which is expected to double in the next five years. AI has made leaps and bounds over the last decade, prompting us to continue accelerating the competency of computers over the next 10 years.

Traditionally, computing has operated on a retrieval-based model, where touching your phone or clicking your computer would prompt the device to fetch information from a distant data center. However, the future of computing will pivot towards both retrieval and generation—requiring changes in how we interact with computers.

Apple’s strategic focus underscores this paradigm shift. Canceling previous plans for an electric car, it’s now prioritizing robust, generative AI for the next phone model.

In the Formula One analogy, computers can be compared to the engines powering the vehicles. Just as an engine propels a car forward, computers drive the processing and decision-making capabilities of AI systems.

4. Humans

In the Formula One analogy, humans are—of course—the drivers of the car. Just like a skilled driver impacts the direction and speed of a vehicle, humans play the crucial role in shaping AI’s trajectory.

The AI revolution focuses on the production of intelligence, with humans holding the power to shape AI by providing the right data and building the necessary infrastructure.

If you want energy, you build a power plant or wind turbine park; if you need more food, you build more farms. Similarly, organizations and governments are constructing the infrastructure necessary to support AI.

If you have personally used AI to create a story or an email, you may not be impressed with it yet. That’s in part because it’s only as good as the data it has available—but advancements are already underway. OpenAI’s Sora, for instance, can generate videos and 3D interactive environments.

In this AI era, everyone can be a technologist. Unlike the past where programming skills were essential, now anyone can contribute to technology development without coding knowledge. While expert technologists remain valuable, their role shifts towards strategic guidance. Just as only a few can be world-class drivers, the humans who are experts in AI will be sought after in navigating this transformative landscape.

My hope is that five years from now you will remember this article somehow and recall the car analogy. We will be living in much different times in terms of how we interact with technology and how it shapes us. Get in the driver’s seat.

Mark Johnson
Mark Johnson
Co-founder & Managing Partner

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