How AI is Transforming the Way We Read, Learn, and Think

AI has the potential to elevate human intelligence to a whole new level. It’s already reshaping how I read, learn, and think.

Last week, I read Work: A Deep History, from the Stone Age to the Age of Robots by James Suzman. As usual, I underlined passages that caught my attention and transcribed notes for future reference.

In the past, this would’ve been it. I’d read the book, taken the notes, and moved on. Then, six months later, I’d remember very little from it. A conversation about the book might go something like this:

You: Have you read Work by James Suzman?

Me: Yes, I remember enjoying it. It was incredibly detailed, covering thousands of years of history.

You: What stood out to you the most?

Me: I can’t recall much now, but one phrase has stayed with me: “To live is to work.” The author described work as the transfer of energy, which means that when we stop working (i.e., no longer transfer energy), we die. I know, it's a bit morbid.

You: Would you recommend the book?

Me: Absolutely. It was very insightful.

And that would be it. Despite the 464 pages and years of research the author put into writing the book, that would be all I could say. It feels wasteful, doesn’t it?

The bigger problem is that I wouldn’t have an opportunity to fully process what I read or connect it to what I already knew. Even with my interest in the evolution of work, I’d move on with just a few scattered memories.

But with AI, it doesn’t have to end there. This one book, with the assistance of AI, has allowed me to take what I read to an entirely new level. After finishing the book, I shared my notes with Claude AI, and here’s what followed…

Me:

I just finished reading James Suzman's "Work: A Deep History, from the Stone Age to the Age of Robots." It was a fascinating read, and I took a ton of notes.

The book was very detailed, but here are a few ideas that stood out to me:

1. Each individual learns the same lessons from scratch and repeats the same energy-costly mistakes of their ancestors. The way we learn is extremely inefficient. Most of us never get a chance to create new knowledge because we're too busy learning what others already know. But now, since AI "knows" everything humans have ever written, we can skip years of relearning the same information and start building on it to create the next level of knowledge.

2. For many of us, the only distinction between work and leisure is whether we are paid to do an activity or whether we are doing it by choice. The author mentioned several activities that people used to do to earn a living (ex., farming/gardening, fishing, sewing, knitting), which we now do for relaxation.

3. We have genuinely useful jobs, like teaching, healthcare, and farming, and other jobs that serve no obvious purpose other than giving people something to do. So many of the jobs we do today fall into the latter category. No wonder we struggle to find meaning and fulfillment at work.

4. "Since the Great Decoupling, asset ownership has proved a far more lucrative way of generating additional wealth than hard work." We were always told the harder we work, the more we will make. However, in reality, many people who earn a lot of money don't work very hard at all. On the other hand, a single mom might work three jobs to provide for her children and still barely make ends meet.

What does this all tell us about the future of work?

Claude:

Your analysis raises fascinating points about work's evolution and future. Let me explore each insight and its implications:

1. Regarding knowledge acquisition, your observation about AI potentially helping us skip redundant learning is intriguing. However, we should consider Suzman's point about plasticity: "our species' extraordinary plasticity when young and the extent to which it declines as we age..." suggests that the process of learning itself might be valuable beyond just acquiring information. The challenge may be finding the right balance between leveraging AI to accelerate learning while maintaining the cognitive benefits of working through problems ourselves.

2. The work-leisure paradox you highlighted is particularly relevant today. The book notes that "many hobbies and leisure pursuits... involve the development, refinement, and use of the kinds of manual and intellectual skills that we depended upon through our evolutionary history, and that are increasingly absent in the modern workplace." This suggests we might be seeing a disconnect between what humans find inherently satisfying and what modern work requires.

3. Your point about meaningless jobs connects to a broader trend Suzman identifies: "we clearly aren't that clever when it comes to creating (or rewarding) jobs people are likely to find meaningful or fulfilling." Ironically, as he notes, "the people who we depend on to educate our children or nurse us when we are ill... are now paid considerably less than those who make a living advising the wealthy how to avoid taxation."

4. Regarding the relationship between work and wealth, Suzman provides stark statistics: "The richest 10 percent of people on earth now own an estimated 85 percent of all global assets." This supports your point about the disconnection between effort and reward.

Looking to the future, Suzman suggests change might come not from technology alone but from external pressures: "Far more likely catalysts take the form of a rapidly changing climate... anger ignited by systematic inequalities... or perhaps even a viral pandemic that exposes the obsolescence of our economic institutions and working culture."

What particularly strikes me is how these issues interconnect: the misalignment between meaningful work and compensation, the tension between efficiency and human satisfaction, and the growing gap between work and wealth creation. Does this suggest we need to fundamentally rethink not just how we work, but how we value different types of work?

Me:

That's an interesting point, but I’d like to take a more pragmatic approach. Where do we go from here? How do we use what we've learned from formal education and work experience to move forward in a world where AI takes over tasks people have done for decades? How can we create meaningful work that also drives economic growth?

Claude:

This is fundamentally about reinventing how we create and distribute value in a world where traditional notions of work are being upended.

Here's a more forward-looking perspective:

1. The New Division of Labor

Rather than viewing AI as a replacement, we should think about new human-AI collaboration models where:

• AI handles the repetitive, data-heavy tasks

• Humans focus on judgment, innovation, and relationship-building

• The combination creates outcomes neither could achieve alone

2. Economic Value Creation

We need to solve two problems simultaneously:

• Creating meaningful work that leverages human capabilities

• Ensuring the economic benefits are broadly distributed

Some potential approaches:

• Developing new business models where value comes from human insight and creativity enhanced by AI

• Creating systems where technological efficiency gains translate to broader economic benefits

• Building platforms that enable more people to participate in the knowledge economy

3. Skills Evolution

Instead of trying to compete with AI, focus on developing:

• Meta-learning abilities (learning how to learn quickly)

• Complex problem-solving across domains

• Emotional intelligence and social skills

• The ability to frame problems and ask the right questions

4. Practical Next Steps 

For individuals:

• Identify which aspects of your expertise are uniquely human

• Develop skills in working with AI tools

• Build networks and relationships that create value beyond traditional job roles

For organizations:

• Reimagine work processes to combine human and AI capabilities

• Create new roles that focus on innovation and human interaction

• Develop metrics for measuring value beyond traditional productivity

This is where I believe the conversation needs to go - beyond adapting to change and toward actively shaping a future that maximizes human potential while ensuring broader economic benefits. What specific aspects of this would you like to explore further?

Me:

These are all very obvious conclusions. I want to go much deeper than that.

Claude:

You're right - I fell into providing surface-level observations. Let me attempt a more provocative and nuanced analysis:

The Core Tension Suzman's work reveals a fascinating paradox: our biological imperative to "work" (as organisms constantly harvesting and using energy) versus our socially constructed notion of "work" (as paid labor). This isn't just about AI replacing jobs - it's about a fundamental misalignment between our evolutionary wiring and our modern economic systems.

Consider:

1. We've created an economic system that increasingly rewards passive capital over active labor ("asset ownership has proved far more lucrative than hard work"), yet we're biologically driven to find meaning through active engagement with our environment.

2. The activities we find most fulfilling as "leisure" (gardening, crafting, creating) are precisely the types of energy-transforming work our species evolved to do. Yet we've built an economy that pushes us toward what Graeber calls "bullshit jobs" that feel meaningless because they're disconnected from tangible energy transformation.

3. The "Great Decoupling" of productivity from wages isn't just an economic phenomenon - it's a symptom of how far we've strayed from connecting work to actual value creation. When public relations executives earn more than teachers, we're not measuring value - we're measuring something else entirely.

The Deeper Questions

1. Instead of asking "How do we create meaningful work in an AI age?", perhaps we should ask: "Why have we built an economic system that fundamentally misaligns with our biological drive for meaningful energy transformation?"

2. Rather than focusing on skills development, maybe we need to examine: "How do we restructure our economy to reward actual value creation rather than capital accumulation?"

3. Instead of worrying about AI taking jobs, perhaps the real question is: "How do we return to a system where work is directly connected to tangible value creation, while using AI to handle the artificial constructs we've built around 'jobs'?"

This suggests a much more radical rethinking than just adapting to AI or creating new job categories. It suggests we need to:

• Reconnect economic reward to actual value creation

• Redesign our institutions around human biological imperatives rather than industrial-era efficiency

• Potentially separate basic survival needs from work entirely

Would you be interested in exploring any of these deeper structural questions? Which aspect do you think is most crucial to address first?

Me:

Now, we're on to something. I appreciate your provocative analysis.

Since we won't be able to create a new economic system in one conversation, I want to get back to more practical suggestions.

What three things can business leaders and educators do today to address the structural challenges you raised in your last response?

Claude:

Thank you. Let's translate those structural insights into actionable steps, while keeping the deeper context in mind:

1. Reconnect Work to Tangible Value Creation

For Business Leaders:

• Audit your organization's activities and ruthlessly eliminate what Graeber calls "bullshit" - tasks that exist only to perpetuate themselves

• Restructure compensation to reward actual value creation rather than hierarchical position

• Make the connection between work and its impact visible and measurable (e.g., having software developers regularly meet users whose lives are improved by their code, or having marketers track genuine customer benefit rather than just engagement metrics)

For Educators:

• Replace abstract assignments with projects that create real value for the community

• Teach students to critically examine the relationship between effort, value, and reward in different economic activities

• Design curricula around solving actual problems rather than passing standardized tests

2. Realign Incentives with Biological Imperatives

For Business Leaders:

• Recognize that humans are driven to transform energy in tangible ways - create opportunities for physical and visible impact even in knowledge work

• Build in time for what Suzman calls "fertile boredom" - the space where genuine innovation happens

• Allow for natural working rhythms rather than enforcing industrial-era time structures

For Educators:

• Incorporate physical making and tangible creation into all subjects, not just "shop class"

• Teach students to understand their own biological drives and how to align them with productive work

• Design learning environments that support natural curiosity and energy expenditure patterns

3. Bridge the Capital-Labor Divide

For Business Leaders:

• Implement broad-based ownership programs that give workers a genuine stake in capital appreciation

• Create transparent systems showing how value flows from creation to reward

• Design hybrid roles that combine routine tasks (which will increasingly be automated) with genuine value creation

For Educators:

• Teach financial literacy that goes beyond personal budgeting to understand systemic wealth creation

• Help students develop both labor skills (creating value) and capital skills (capturing value)

• Create opportunities for students to experience both sides of the value equation through real-world projects

The key is that these aren't just "nice to have" initiatives - they're essential responses to the fundamental misalignment between our biological nature and our economic systems. They're about creating sustainable ways to work that don't fight against human nature but rather harness it productively.

What interests me most is how these changes might cascade through organizations. Have you seen any examples of organizations successfully implementing similar changes?

The conversation with Claude could have gone on indefinitely. AI doesn’t get tired or annoyed when you keep pushing it for more.

In just a few minutes, I went from capturing a handful of interesting nuggets from a book to completely reshaping my thinking. For example, I hadn’t considered how so many of our favorite hobbies involve physical labor. 

With AI, reading isn't just passive consumption anymore. It's a conversation that pushes your thinking further than you could go alone. 

How many insights have you lost from all the books you've read? How many connections have gone unnoticed? Have you ever finished a book and wished you could discuss it with someone right away? Now, you can.