From Here On, I Am Focusing on What Matters

Two years ago I was helping organizations navigate digital transformation, the kind of work I’d done for 25 years across public and private sectors. Today, I’m publishing a framework that connects evolutionary biology, artificial intelligence, and human consciousness.

The journey between those two points wasn’t linear, but it revealed something important: the same principles that make biological intelligence so efficient and adaptive can help us build better AI systems, better government services, and frankly, live more meaningful lives.

The Unplanned Pivot

In September 2023, a reduction-in-force stalled my consulting career unexpectedly. For the first time in decades, I had space to ask: “What actually excites me? What problems feel worth the next 15 years of my professional life?”

The answer surprised even me. It wasn’t about finding another consulting role or chasing the latest tech trend. It was about understanding intelligence itself, and not just artificial intelligence, but the kind that evolution spent 4 billion years perfecting.

What started as career uncertainty became the most intellectually rich period of my life. I read/listened to books on neuroscience, cognitive science, and AI architecture. I started seeing patterns everywhere: in how toddlers learn, in how government services could be designed, in how we all navigate endless modern choices.

The Framework That Emerged

This exploration crystallized into an integrated framework spanning four working papers:

1. Beyond FLOPS: The Evolutionary Processing Unit

Evolution isn’t just about survival, it’s the most sophisticated computational process we know. The “Evolutionary Processing Unit” represents 4 billion years of optimization that created modular, plastic, incredibly efficient biological intelligence. Rather than replicating this through brute-force computation, we should understand the architectural principles evolution discovered.

2. Attention Is All We Have

The 2017 Transformer paper revealed attention as AI’s breakthrough mechanism. But attention isn’t just technical, it’s the fundamental resource allocation strategy that evolution optimized across biological systems. Understanding how attention works reveals insights for both building better AI and living more deliberately as humans.

3. Beyond Scale: A Modular Architecture for Adaptive AI

Instead of endlessly scaling large language models, we can build AI systems inspired by evolutionary neuroscience: modular components, continuous adaptation, and built-in value alignment. This approach is more efficient, interpretable, and better suited for human collaboration.

4. The Mastery of Life Framework

Finally, I asked: how do we apply these principles to human happiness and fulfillment? The Mastery of Life framework treats deliberate living as an attention allocation problem and thereby helping individuals and organizations focus on what actually matters in a world of endless options.

Why This Matters Beyond Academia

Here’s what surprised me: these principles apply far beyond AI research.

The same challenges appear everywhere, whether building adaptive AI systems, designing government digital services, or creating enterprise technology platforms:

• How do we allocate limited resources effectively?
• How do we design systems that adapt to changing needs?
• How do we ensure technology serves people rather than complicating their lives?

Whether we’re talking about AI safety, modular architectures for machine learning, government service delivery, or personal decision-making, the core challenge remains the same: intelligent resource allocation in attention-scarce environments.

The Path Forward

I’m sharing this work publicly because these conversations matter, and they shouldn’t happen only in Silicon Valley boardrooms or academic conferences. We need diverse perspectives from AI researchers, technologists building these systems, public sector leaders implementing them, and citizens who actually interact with them. I am humble and curious. I told my sons that the most important thing I could teach them is that nobody knows everything, especially their dad, and if someone suggests they do, you probably want to get away from them in short order.

This work continues to evolve (appropriately enough), and I’m particularly interested in applications across multiple domains: AI architecture and safety, human-AI collaboration systems, government digital services, and creating more equitable technological platforms.

I’d love your thoughts, critiques, and conversations. The papers are available at jamesmaconochie.com/papers, and I’m actively seeking collaborations with organizations and individuals who find these ideas resonant.

The most intelligent systems, whether biological or artificial, are those that learn, adapt, and collaborate. I’m excited to see what we can build together.