Technology

It feels good to be writing in public again.

My time spent in the world of capital allocation, whether in venture or at a hedge fund, taught me that writing is often more about institutional messaging than personal inquiry. The layers of compliance and strategic consideration are necessary, of course, but I found they can temper the spirit of open exploration. The liberty to now write for myself, to simply think in public, is a freedom I’m excited to reclaim.

So, I’m back. And I thought I’d start by sharing a bit about the evolution of my process: how I think, how I write, and how I’m learning to partner with Artificial Intelligence.

Why I Write: The Search for Clarity

Since 2011, I’ve kept a running notebook (physical and digital) that has become a sprawling archive of my brain. It’s filled with notes, reflections, and half-baked essays on everything from statistical models and product strategy to being a better father and husband.

This started as a simple habit, but over time, I’ve found it’s become essential for my own mental well-being. Writing is the most reliable tool I have for untangling complexity. When I’m stuck on a problem, when a decision feels fuzzy, or when I just feel a general sense of unease about something, I write. For me, wrestling with an unformed idea is like navigating a thick fog. There’s a disquieting sense of knowing something is there without being able to see it, and the act of writing is what finally parts the mist, revealing the path ahead. It reveals the flaws in my own logic and the gaps in my thinking. I find that I can’t bluff myself on a blank page.

For years, this practice was mostly private, a tool for my own clarity. Now, I’m trying to add that final step of publishing, and I’m finding it adds a new, welcome layer of rigor to my process.

My New Sparring Partner

My writing workflow has changed a lot in the last few years. While I use AI every day, I’ve found my process looks a bit different from how some people seem to use these tools. For me, asking it to “write a blog post about X” would feel like outsourcing the part of the work I value most.

Instead, I’ve settled into treating it like an endlessly patient and brutally honest sparring partner. It’s become the bridge that helps me get from the initial chaos of an idea to a coherent, pressure-tested structure. My job is still to provide clear, nuanced direction; its job is to organize, challenge, and reflect my thinking back to me at the speed of light.

To share what this looks like in practice, let me walk you through a recent example. Awhile back, I was building the business case for a major investment in a new data analytics platform. My head was a mess of disconnected thoughts: total cost of ownership, complex integration challenges, potential ROI, vendor comparisons, competing stakeholder needs, optimistic vs. realistic implementation timelines, and the significant risks of doing nothing while deeply understanding the firm’s aversion to additional costs..

Step 1: The Brain Dump and Thematic Sort I dumped all of it into a prompt, hundreds of words of pure stream-of-consciousness. My request was simple: “I’m building a business case for a new analytics platform. Here are my raw thoughts. Please organize them into a logical structure for a strategy document.”

Instantly, the AI took my jumbled list and returned a clean structure with five sections: The Problem, The Proposed Solution, Financial Impact (ROI & TCO), Implementation Plan & Risks, and Expected Outcomes. It was a solid, standard starting point that immediately gave a skeleton to my chaotic thoughts.

Step 2: The Adversarial Pressure Test This, for me, is where the process gets really interesting. I then gave the AI (often a different LLM) a new persona: “Now, act as a skeptical CFO who is unconvinced of the ROI and deeply concerned about the budget and headcount required. Rip this business case apart. Tell me where the financial assumptions are weak, where the plan is naive, and what crucial questions I’m failing to answer.”

The response was informative and humbling. It pointed out that my ROI calculations were based on overly optimistic adoption rates and that I hadn’t adequately budgeted for the “hidden costs” of training. It flagged that my timeline didn’t account for likely delays from the engineering team. It was the kind of direct, ego-free feedback that is incredibly difficult to get from a human colleague on a busy afternoon, and it was exactly what I needed to see the weaknesses in my own case. It forced me to confront the base rate of failures, even though my contextual perspective suggested higher probabilities of success. It wasn’t clear who was right, but it sharped my understanding of the considerations.

Step 3: The Iterative Loop I spent the next hour in a back-and-forth dialogue with the model, refining my points while it challenged the revisions. By the end of this process, I didn’t have a finished document, but I had something much more valuable: a robust, coherent, and battle-tested argument that I felt much more confident in. Every once in awhile, if the step-by-step incremental approach isn’t yielding what I want, I’d paste a chunk of the conversation into a fresh chat window, with my feedback on how I want to “holistically re-imagine” the write-up with specific feedback, and I’ll often get a complete re-orientation that’s a marked improvement.

Only then did I open a blank page and begin to write the prose myself, with a lot less help from an LLM.

Protecting the Muscle

This partnership is working for me so far, but I’m still figuring out where to draw the line. For me, relying on AI to do the final writing would feel like taking an escalator instead of the stairs. It’s faster, but I worry that if I did it all the time, the mental muscles I need for the climb would begin to fade.

The act of choosing the right word, structuring a sentence, and weaving a narrative is where I find the deepest thinking about persuasion and effectiveness happens. That work relies on a set of skills I’m trying to cultivate: taste, intuition, and strategic empathy.

More importantly, it forces me to draw on context that AI simply does not have. So much of what informs my perspective comes from the data that isn’t in a dataset like the hesitation in a project lead’s voice, the unspoken dynamic between departments in a strategy meeting, or a pattern of resistance I might recognize from a dozen similar initiatives. I feel my writing needs to be infused with that full, messy, human picture to be valuable.

For me, figuring out what matters and how to say it remains the core work. My working theory is that in a world increasingly saturated with machine-generated content, this human act of judgment will become more important, not less.

What’s Next

Going forward, I’m excited to use this space to explore the questions I’m wrestling with. I’ll be writing about the evolution of data science and product development, the messy reality of decision-making in business, and the economic frameworks I’m using to try to make sense of a rapidly changing world.

This is just the process I’ve landed on for now, and I’m sure it will continue to evolve. I’m genuinely curious to hear how others are navigating this. If you have a workflow of your own, or see things differently, I’d love to hear about it.

Thanks for reading.