Capacity

The Impact of AI on Capacity is Exponential

Two years ago, I wrote about the idea of capacity.  It was a reflection on a career coach that I hired to reorient how I thought about my business.  It led to an exercise in thinking about my capacity and how I apply value to my time as a solopreneur.  

I found myself doing that same exercise this week, but now with the idea of how I am using and will be using AI to turn my capacity into an exponential output that allows me to be differentiated in my craft. 

This leads me to you, dear reader.  I am curious to know how you have leveraged or envision leveraging AI into your workflows.  Would you say you are achieving a sense of competency in using AI or would you say you are bewildered by all of the hype and still trying to make sense of things?  

I am fascinated to revisit the concept of capacity and how you see that playing out in your world.  I believe the term “capacity”, prior to 2023, was bound by time and productivity parameters.  As in, how much can you do in one day?

The dictionary defined Capacity as:

Ca·pac·i·ty (/kəˈpasədē/)

noun

1. the maximum amount that something can contain.

2. the amount that something can produce.

3. a specified role or position.

I asked ChatGPT 4o to define “Capacity” in the AI era. Citing the differences between pre-AI, today’s AI, and tomorrow’s AI.

The ChatGPT 4o response is below (in Italics):

The term “capacity” in the AI era has evolved significantly and can refer to various aspects depending on the context. Below is a breakdown of how “capacity” can be interpreted, and how its meaning has shifted from the pre-AI era to today’s AI and potential future developments.

1. Capacity in the AI Era

In the AI context, “capacity” often refers to the computational power, resources, and capabilities of AI systems to process, learn, and make decisions. It encompasses aspects like:

• Computational capacity: The amount of data that AI systems can process and analyze at any given time.

• Learning capacity: The ability of AI models to absorb data, recognize patterns, and improve their performance over time (i.e., through training).

• Inference capacity: How well AI systems can generalize learned knowledge to make predictions or decisions in novel situations.

• Memory and storage capacity: The storage requirements needed to hold large datasets and model parameters.

2. Pre-AI Era

Before AI became prevalent, “capacity” was generally understood in more traditional contexts, like:

• Human capacity: Referring to a person’s physical or cognitive abilities (e.g., learning, memory, or problem-solving skills).

• Technological capacity: The processing power of computers (in terms of storage, speed, etc.) and their ability to solve problems or run software.

• Organizational capacity: An organization’s ability to achieve its objectives through resources like personnel, finances, and infrastructure.

3. Today’s AI (2020s)

With the rise of AI, “capacity” has expanded to include the performance of AI models in terms of their efficiency, accuracy, and scalability. Some key shifts include:

• Computational power: AI today relies on massive computational resources, such as GPUs and cloud-based servers, to train large models (e.g., GPT, DALL·E). AI systems can now process and analyze vast amounts of data much faster than pre-AI systems.

• Model capacity: Today’s AI models, especially deep learning models, have enormous learning capacities. They can handle more data, learn complex representations, and outperform human abilities in specific tasks (e.g., image recognition, language processing).

• Scalability: The ability of AI to scale across various industries (healthcare, finance, transportation) is a key feature of AI capacity today, driven by advances in machine learning algorithms and cloud computing.

• Efficiency: AI models are becoming more efficient, allowing them to perform complex tasks (like natural language understanding or image generation) with fewer resources.

4. Tomorrow’s AI

In the future, AI capacity will likely continue to evolve along several dimensions:

• Increased autonomy: AI systems will become more autonomous, with greater capacity to make decisions independently, requiring less human intervention. This could result in AI systems having more decision-making power in industries like healthcare and finance.

• General AI capacity: Moving towards Artificial General Intelligence (AGI) — AI systems that can perform any intellectual task that a human can do — will redefine the idea of capacity. AGI systems would have nearly unlimited learning and inference capacity across diverse fields.

• Energy efficiency and sustainability: A future focus will be on reducing the computational and environmental costs of AI systems by improving energy efficiency and model optimization.

• Adaptation and personalization: Tomorrow’s AI may have the capacity to adapt in real-time to changing environments and personalize outputs based on user preferences without retraining or reprogramming.

In the AI era, “capacity” has expanded beyond human and technological limitations to include the capabilities of AI systems in handling massive amounts of data, learning efficiently, and making decisions autonomously. The future promises further breakthroughs in scalability, energy efficiency, and the rise of AGI, which will likely redefine the boundaries of capacity.

The Big Shift

John Hagel was talking about the Big Shift as far back as 2009, but published a two-part piece in his blog on the topic nearly a decade ago.  Part 1.  Part 2.

His opening quote in Part 1 resonates more today than when I read that back in 2014.

In an exponential world, it stands to reason that our traditional, linear approaches to strategy will need to be rethought from the ground up. One way to characterize the big shift in strategy is that we are moving from strategies shaped by terrain to strategies shaped by trajectory.

And, a follow on quote in Part 2 takes me back to a prior post on the need to Unlearn everything you thought you knew before.

In a world of accelerating change, one of our greatest imperatives is to "unlearn" - to challenge and ultimately abandon some of our most basic beliefs about how the world works and what is required for success.

John goes on to say - Here’s the paradox: strategies of trajectory become more and more essential in times of rapid change and uncertainty, while at the same time becoming more and more difficult.  But that’s exactly what makes strategies of trajectory so valuable. 

He points out that there are five elements can help to make these strategies successful (emphasis is mine):

• Challenging

  • In a world of accelerating change, one of our greatest imperatives is to "unlearn" - to challenge and ultimately abandon some of our most basic beliefs about how the world works and what is required for success.

• Shaping

  • In times of rapid change and growing uncertainty, we actually have far more degrees of freedom to restructure entire markets and industries than in more stable times.

• Motivating

  • There’s an opportunity to craft a powerful narrative, one that highlights a compelling opportunity out in the future and that provides a call to action for others to help achieve that opportunity. 

• Measuring

  • Forget about performance snapshots that focus on your performance at any specific point in time.  Strategies of trajectory focus on acceleration – they’re about performance over time. Is your performance stable, increasing linearly or accelerating? If it’s not accelerating in an exponential world, something is wrong.  So, strategies of trajectory are relentlessly focused on patterns of movement over time.

• Learning

  • No matter how fast things are moving, there’s a paradox: the more time we take to reflect on our experiences, the faster we’ll be able to move. But this only works if we have a destination in mind. 

Working On the Business vs. In the Business

This to me is the great paradox of our times and something I certainly struggle with is -  are you working on the business or in the business?  

In our pressure to deliver the numbers, we are living in the business.  In our need to realize our capacity, we must find time and energy to work on the business.  I believe that is where hiring someone like me comes into play. 

I can focus “on your business” objectively, while you live in your business meeting the demands of your customers and their customers.  That said, I face the same challenge of working on my business, while I am devoted to working on your business.  

How are you delivering on your capacity as it relates to your business while dealing with the pressure to hit the numbers today? 

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NOW (How you are realizing this today)

1. What grade would you give yourself on your AI prowess today? 

2. Are you applying AI to your business? 

3. Have you thought about what your capacity is for yourself as part of the business?    

NEW (How you will realize this tomorrow)

1. I will set aside one day or the equivalent of one day to work on the business this quarter.

2. I will automate or leverage AI in at least three areas of my business to grow my capacity by the end of the year.

3. I will seek out an AI change agent or thought partner to help me see beyond my current capacity.   

NEXT (I see a world in which) 

I see a world in which understanding and redefining your capacity will be essential to not only survive, but to thrive in the AI era.     

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THE PAYOFF

"As AI takes over routine tasks, we must focus on higher-order thinking and problem-solving. These uniquely human capacities will become increasingly valuable."  

~ Susan Wojcicki, Former CEO of YouTube

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If you are seeking a way to optimize your Now New Next, book a free consultation with me, and let's consider the possibilities. My work has raised a collective $1B+ for the companies I have worked with over the last 10 years.  And, I have helped dozens of companies and executives to find their narrative voice.