Cleve Gibbon

Pablo Picasso once said:

“The meaning of life is to find your gift. The purpose of life is to give it away.”

It can be difficult to give our gifts away in capitalist societies where we struggle to achieve economic security for our families. Regardless, we need to discover our gifts in order to decide how to provide for ourselves and others, whether that means selling our talents or giving them away.

When I was young, I thought my gift was long-distance running. This evolved into a passion for athletics, and then football. But then I discovered technology. As I learned more about myself, I found that I’m an extrovert in public settings and an introvert in private ones. In other words, I love to research independently and share my findings broadly in person. My ideal platform for sharing is on a stage or in a meeting. I dislike using other people’s material; I need to develop the story and tell it my way. My gift is storytelling—specifically, how technology evolves and impacts people and their environments. And those people could be employees, consumers, friends, family, or you!

Technology Storytelling

Technology storytelling is crucial. Our technological canvas is expanding, with more and more ways to color in between blurred lines. Storytelling gives technology purpose, establishes boundaries, and guides how we begin our journey with it. It clearly articulates where you are today, why things matter to you, where you could be, and your best next steps to get there.

I didn’t know it at the time, but my first career as a university lecturer revolved around storytelling. I taught computer science students about the application of technology within industry. My subsequent careers in the private sector allowed me to gain experience and expertise in doing just that. And guess what? 30 years later, technology storytelling remains essential—a gift that keeps on giving. There are so many ways to share it, even beyond the realm of technology. So I’m super excited about what’s to come.

We all know the oft-cited analogy of a glass half full versus half empty. In his book Atomic Habits, James Clear expands on this topic and explains how to put it into action.

When building a new habit, Clear discusses the subtle but important mindset shift of moving from obligation to opportunity. Here are a few everyday habits I’m building in 2024:

• Do 100 push-ups before my morning coffee
• Listen to a podcast
• Help my wife with a chore
• Read for 30 minutes
• Write for 30 minutes
• Practice Spanish for 5 minutes

But here’s the twist. Instead of feeling like I have to do these things, I tell myself I get to do them. I consider myself lucky to have the opportunity. And that’s the shift from obligation to opportunity—or seeing the glass as half full.

Oh, and by the way, I’m working up towards 100 push-ups…that’s a process.

We are using AI to enhance performance and improve decision-making.  The allocation economy suggests that knowledge workers will shift to managing AI tools that do more of the work rather than doing it themselves.  These AI tools are agents that have large language model (LLM) front ends to facilitate scaled interactions using plain language.  What does this mean for our knowledge and allocation economies?

Many hands, light work

A recent study by Cornell researchers found that increasing the number of AI agents collaborating can significantly improve performance. Imagine a world where you’re compensated more for how well you can allocate intelligence, rather than having it.  People who can effectively allocate intelligent agents to improve outcomes will arguably perform better than those who can’t. 

In Knowledge lies power

Knowledge workers are rewarded for what they know. Thanks to internet-powered tools like Google, access to world knowledge is fully democratized.  Now AI is making sense of all this accessible knowledge to put accessible intelligence into the hands of the many.   

Think of AI as having your own personal research assistants to make sense of all the world’s accessible knowledge.  So if everyone has access to intelligence, we are all smarter. However, delivering real value is the result of good execution on a good idea.  

Allocation is key for good execution

The allocation economy is largely about execution.  How well can you assemble an execution pipeline with the intelligence and means to deliver on a good idea?  Imagine a world where you don’t need to find smart people.  Instead, you are manager of accessible intelligent resources, where successful outcomes rely upon your ability to right-size allocations. What kind of skills do you need to thrive in a world like this? 

Summary

An agentic workforce is a digital workforce infused with AI agents. Research highlights that organizations are getting smarter through sheer strength in numbers, and those numbers are through the addition of intelligence, or AI agents, to the digital workforce.  As compute power and abilities continue to scale, automating unlimited amounts of agents to complete tasks could lead to mind-blowing increases in capabilities. 

In a recent conversation, Lex Fridman asked Sam Altman about OpenAI’s transformative product releases. Altman offered an intriguing perspective, noting that from the inside, OpenAI’s progress feels like a steady climb up an exponential AI curve. For those within the company, the roadmap is clear and smooth. However, from the outside, these releases appear as sudden, transformative leaps—fast and furious.

This discrepancy highlights a gap in storytelling. Better communication about the incremental steps towards a clear destination is essential. Storytelling can map out the kinks in the road, making the journey more comprehensible. OpenAI strategically times their product communications, likely for competitive reasons, and they are making numerous small to medium technological bets. These incremental advancements, when combined, aim to yield significant gains.

As we move into a future where compute power is the currency of AI, it’s crucial to chart your roadmap along the exponential AI curve based on the myriad of discrete vendor releases, aligned with your desired outcomes. And before you leave, also consider that the balance between biological intelligence (BI) and artificial intelligence (AI) continues to shift, with AI rapidly gaining ground. The exact endpoint of this shift remains unknown, but one certainty is that AI will take a bigger role. Whether that’s a leading or support role remains an open question.

Currently, there is significant emphasis on using Generative AI (GenAI) to create more content. Beyond the well-documented legal, ethical, and governance risks associated with GenAI, prioritizing value preparation over generation serves as an overarching mitigation strategy for delivering future-ready outputs.

Being pragmatic and practical can be frustrating, and it may seem to constrain innovation during a period of massive, rapid technological disruption. However, having the right data to train your models to achieve optimal outputs is strategically your best next move. This isn’t merely a choice between low risk or high innovation, but rather a balance where considered risk can unlock innovation. You need a combination of both risk and innovation to make progress.

Value Preparation over Generation

In the rush to generate, many are shortcutting preparation. This approach may yield short-term gains but fuels long-term pain. When decorating a room, for example, it’s about 95% preparation and 5% painting. Stripping back to the plaster, smoothing out cracks, sanding down the woodwork, and cleaning debris are time-consuming but necessary preparation tasks. The quality of the final coat depends heavily on the quality of these preparatory steps.

GenAI is no different. The various hoops you must jump through to prepare data for training are critical to ensuring the accuracy and quality of downstream generative outputs. It’s the small things that count. For instance, we discovered that when preparing assets to train a brand-specific custom model, centering the images within the digital asset was crucial. The more training images required, the more manual effort needed during the preparation phase. This quickly becomes a hurdle that automation can help overcome—and we are making strides in this area. However, this is just one of many tasks being added to a growing pipeline of preparation tasks.

In Summary

The importance of data preparation in training AI models is a burgeoning area of research. Invest time to take the long way around. Only then can you creatively, iteratively, and incrementally shorten the distance between your inputs and desired outputs.

I’ve always had a keen interest in productivity. Starting out with getting things done (GTD) and also plans, progress, and problems (PPP) reports. And then I just happened across Slow Productivity by Cal Newport.

Cal talks about pseudo-productivity that is basically a focus on busyness. I learnt from an early age that busy is failure to prioritze. Slow productivity is about how to focus and execute on the right things by following three simple principles:

1. Do fewer things
2. Work at your own pace
3. Obsess over quality

The books gives many examples of key people that succeeded using slow productivity. From Isaac Newton to Alanis Morrisette (which I’m looking forward to seeing this August 2024).

If you’re an overworked knowledge worker it’s worth a read.

Enjoy!

Google recently unveiled Vertex AI Agent Builder. This new tool allows for the creation of AI assistants. Despite hopes for a zero-code approach, technology proficiency remains essential. And just like Amazon, Microsoft, and IBM who are targeting enterprise users, Google’s Vertex needs experienced technical users to micromanage AI from design through to deployment.

At the other extreme, something like launch lemonade is an AI agent builder for the everyday user.  Good enough to get something up and running, monetizing AI agents from the get-go. However, not powerful enough yet to develop truly differentiated, enterprise grade, products. 

Then you have those platforms that live somewhere between these two extremes. Rasa specializes in creative conversational AI agents with a strong emphasis on natural language understanding. While DataRobot is a platform embraced by data super heavyweights to enable users to create deployable autonomous predicative models.  And then there is the platform of experts, or POE.  This platform creates AI agents capable of decision making and task execution. 

In short, it’s a mixed bag but the message is clear.  AI Agents are on the rise.  I’m waiting to see just how human friendly GPT5 AI Agents are.