Almost four and a half years ago in early 2021, I went to work at Shopify as Technical Advisor to the CEO. And, I have to say, it was one of the best jobs I’ve had. It’s like it was custom made, just for me. Working alongside Tobi Lütke as a right hand advisor meant that I was part of almost every major technical discussion and had a very close up view of how a hundred-billion dollar company works while retaining its founder-mode ambitions and flexibility.

From helping to define and prototype Hydrogen and Oxygen, Shopify’s headless e-commerce stack, to changing Shopify’s compensation strategy with Flex Comp, there weren’t too many things that I didn’t have visibility and input into. And, as Vice President of Developer Productivity, I helped with Shopify’s push to use AI and LLMs, including helping to rollout Claude Code internally when it was first launched.

Of course, not every day was great. There were two major layoffs and the divestiture of Shopify Logistics, none of which were easy. But easy isn’t part of the job description. Every challenge required applying my skillset in a new and interesting way. And grow new skills quickly as needed.

I said more than once that working at Shopify was the best MBA I could have gotten, and it came with direct experience (and a paycheck!) attached. But, like studying for a degree or serving a tour of duty, there’s a time when you know it’s done and you need to take the next step. As companies evolve, so do the roles within them.

Last week, I served my last day at Shopify. Today, I’m a free agent.

Why? There are several reasons, big and small. Not many really matter in the long view. The one I’ll share here — probably the most important one when all is said and done — is that the world is changing thanks to AI. It's like 1997 and the early web all over again. But different, and more extreme. Of course, like then, many will be looking for a free lunch or to create party tricks. And, like the web, it’ll take us a while to really figure out how it’s going to change the world. For those that invest the time and effort, however, entirely new horizons are opening up even as entire industries are about to be rewritten.

So, I’m taking my virtual degree from the School of Shopify and heading into the world. Instead of lining up my next role before leaving, I’m going to take a few months this summer to really reflect on what I’ve learned and what I want to take forward with me. And, I want to step back and do that reflection outside the scope of any single company’s perspective.

It’s time to Observe and Orient. Decision and Action will come soon enough.

When I first started working with Large Language Models, I was immediately struck with how reflexive they are. You probably were too. You give one an input, and through the magic of next token prediction, you get a response. Change the input, even a bit, and you‘ll get a different output. LLMs are all about pattern matching, associative thinking, and as we know, can produce errors confidently without hesitation.

If you’ve read the book Thinking Fast and Slow by Daniel Kahneman, you’ll immediately recognize these traits map well to the characteristics of System 1 thinking. It might be a bit of a stretch to compare a pile of floating point numbers to a mind hosted by a biological system. After all, LLM responses are shaped by statistical patterns, not the survival instincts or emotions that drive System 1 thinking in humans. Nevertheless, I’ve found that leaning into this metaphor is extremely useful.

If you didn’t read the book, here’s a TL;DR: System 1 thinking is the instinctive way we perceive the world and make decisions based on those perceptions. It’s fast and doesn’t take much effort. It’s mostly subconscious, automatic, and can be prone to error. But, it helps keep us alive thanks to its ability to quickly identify and react to threats. System 2 thinking is more deliberate and logical. It requires conscious attention and a lot more effort, but is often more reliable. If System 1 thinking makes you suspicious of somebody who reminds you of somebody else, System 2 helps you realize that they are different people and may not share the same flaws.

When I’m sitting down to work with an LLM, I take a quick moment to try to put myself as far into a System 2 mindset as I can and prepare myself to treat what the model generates as System 1 output. I say to myself, “Ok Duncan, this thing thinks amazingly fast. It’s your job to think slow. You’re the System 2 thinker in this partnership.” With that little reminder out of the way, I get to work.

The benefit of doing this is that it sets up a good mental model for how to work with the LLM. I can give it various prompts, see the System 1 thoughts fly, and then deliberate over what I’m seeing and decide whether to accept the model’s output, ask for changes, or just try a different approach altogether.

This also allows me to better accept the foibles of a model’s output without getting frustrated with it. If a model gives me a silly or incorrect answer, I don’t need to get upset with it for getting it wrong. After all, it’s operating in System 1, right? If it tells me that there’s 5 columns in a CSV file, but I know there’s 7, I can just nudge it and move on instead of getting upset.

In other words, it’s my job to be the slow thinker, provide input, watch what’s going on, and keep going. And, it’s not just about checking the AI’s work, but monitoring my own temptation to over-rely on it. At the same time, I have to keep in mind that humans aren’t perfect either and often fail at System 2 thinking. And, my own System 2 thinking can be fooled by things that a model is confidently wrong about. Thinking is hard, and it’s always useful to exercise all the mental models we have at our disposal to help get things right.

Of course, none of this is static. Models will continue to evolve and incorporate more System 2-like capabilities through explicit reasoning chains, evaluating multiple reasoning paths, reflection, verification chains, and self-critique. Even so, I think that this framework will continue to have value and help me stay grounded in maintaining thoughtful oversight instead of defaulting to automatic acceptance.

While reviewing this post, Claude came up with this related thought:

This reminds me of how chess players work with engines. The computer can calculate millions of positions instantly (System 1-like), but the human grandmaster still provides strategic understanding and knows when to question the engine's suggestions. Even as engines have become superhuman, this collaborative dynamic persists.

That’s exactly it!

It’s never been so easy to create prototypes and churn out interesting approaches. When Claude or ChatGPT can create 50 different API designs in minutes — all you have to do is ask for it! — the essential skill is no longer generating work. It’s about managing the vast number of options available, recognizing what aligns with your goals, and saying no to the ones that don’t meet the bar.

Creatives have long known that trying to solve a problem without constraints can be paralyzing. Too many options can lead to analysis paralysis and decision fatigue. Sometimes the most important thing to do is define some guardrails just so that you can limit the possibility space and move forward. Otherwise, you can find yourself drowning in possibilities, shipping kitchen sink features, or constantly pivoting between half-explored ideas.

As I learn (and re-learn) how to use LLMs in my work, I keep having a flashback to 2013 and Apple’s WWDC opening video about product design that I return to time and time again. Its message seems more relevant than ever:

The first thing we ask is what do we want people to feel? Delight. Surprise. Love. Connection. Then we begin to craft around our intention. It takes time. There are a thousand no’s for every yes. We simplify. We perfect.

Now that AI can turbocharge your creativity and generate nearly infinite variations of solutions in seconds, thousands of no’s probably aren’t enough. Every decision, even the smallest ones, can shape increasingly large outputs. As generation cost drops exponentially, the ability to curate becomes even more essential to creating things now.

When I had Claude review this post, it came up with an interesting related thought:

Your piece reminds me of the paradox of choice research - how people are actually less satisfied when choosing jam from 30 options versus 6. But now we're not choosing between 30 jams, we're choosing between infinite jams that can be instantly reformulated. The cognitive load is unprecedented, which makes your point about guardrails and saying no even more critical.

Thanks Claude! You got that exactly right.

I had a conversation last week with a friend about people in positions of power, and at some point I quoted the “power corrupts” adage. You know the one. It’s from a critique of the moral accountability of those in power by Lord Acton, written in 1887:

Power tends to corrupt and absolute power corrupts absolutely. Great men are almost always bad men, even when they exercise influence and not authority: still more when you superadd the tendency or the certainty of corruption by authority.

After half a laugh and then a brief silence, my friend looked at me seriously and said, “If you listen to the ancient Greeks, they had a bit of a different take: Power reveals the person.”

After that conversation, I went on a bit of a research spree. While there isn’t a direct quote to be found, the saying is often attributed to Plato’s writings, especially in the story of the Ring of Gyges in Republic. Aristotle also explored ideas about how circumstances, including power, test and reveal a person’s moral character. Power doesn’t create flaws or virtues. It magnifies what is already there, testing a person’s resilience, integrity, and moral compass.

This reflection feels especially relevant amid January 2025’s political transitions in Washington DC and how people are reacting to the shifting landscape, especially our titans of industry. But, the idea isn’t just a lens for observing the powerful. It can be a useful mirror for our own lives.

We reveal ourselves in how we use the power we hold, however large or small. Actions reflect character. We should make sure it’s the character that we want it to be, and that we are using our power in the best way possible.

Even then, we should constantly ask ourselves: for whom do we use our power?

Inexorably, inevitably, 2025 has arrived. Of course, another rotation of our planet around our sun is an arbitrary milestone, but there’s a certain usefulness in marking it. Whatever you’ve done (or haven’t), whatever you’ve been through (or avoided), it’s brought you here to this point and prepared you (or not) for whatever comes next. And, oh boy, is there a lot that will come next.

Making predictions about where we’re going is dangerous business. The Pandora’s box of human advancement continues to open. We’re simultaneously on the edge of everything falling apart and everything being better than it’s ever been. It’s scary, and tempting to pine for a nostalgic past that maybe never really existed, or at least not in the way it’s remembered. Every age has had its existential challenges, after all. What makes our challenges so scary right now is that they’re unresolved. We don’t yet know how it’ll all turn out.

Despite that, I think that this is the best time in human history to be alive. I’m not being naïve (I hope). Our challenges are real. Our geopolitical world is more volatile than it’s been in decades, with not much to suggest it’ll get better soon. Our environment remains in peril, and it doesn’t feel like we’re doing enough. And, artificial intelligence will inevitably disrupt livelihoods even as it brings us amazing new tools. The list goes on. But, those are givens. We can’t change them. We can only control how we respond to them and what we do next.

In that vein, I find myself reflecting a lot on three books I read in 2024: Areté, Clear Thinking and Extreme Ownership. All three underscore a crucial truth that Epictetus first taught two thousand years ago: the only thing you can control is yourself. Everything else — weather, traffic, politics, the current state of the environment, the angry person yelling on the corner, the frustration of coworkers — is input. It's up to us to decide how to react to that input (or not). To decide what to do next, what decision to make, and what action to take.

This is the mindset I want to continue to improve through 2025. And, if I could make a wish for 2025, it would be that we can all find ways to be more intentional in how we respond to, and shape the world we find ourselves in.

Happy New Year! Let’s make it a good one.