The premise. Andy Weir’s Project Hail Mary drops you into a mystery: a man wakes up alone on a spaceship with amnesia, and the story rebuilds itself (and him) one discovery at a time. It’s propulsive, funny, and relentlessly plotted — every chapter earns the next. Allen Levi’s Theo of Golden does almost the opposite. An elderly Portuguese stranger arrives in a small Southern town and starts buying pencil portraits from a coffee shop wall, quietly returning each one to the person it depicts. There’s a mystery here too — who is Theo, really? — but it unspools at porch-swing pace, not thriller pace.

The engine. Weir’s book runs on competence and problem-solving: science as a language of hope, ingenuity as the answer to isolation. Levi’s runs on attention and generosity: the act of seeing people fully as the thing that actually changes them. One is about surviving alone through cleverness; the other is about a stranger who refuses to let anyone stay unseen.

The tone. Project Hail Mary is buoyant even in crisis — there’s real stakes-of-the-species tension, but it’s balanced by humor and an almost buddy-comedy warmth (Grace and Rocky’s friendship is great). Theo of Golden is gentler and more melancholic underneath its warmth — it’s carrying grief the whole way through, and you feel it build even while the town around Theo is being quietly transformed.

What they share. Both books, for all their differences, land on the same note: connection is the point (as Rocky would say, “Life is Reason”). Weir gets there through a rescue mission and an unlikely friendship across species; Levi gets there through 92 portraits and a man determined to give anonymously before he’s known. Different vehicles, same destination — and both stuck with me well past the last page.

Up next: A Gentleman in Moscow. Trading spaceships and small Southern towns for a count under house arrest in the Metropol Hotel — looking forward to seeing where confinement and attention take me this time1.

It’s a fascinating framework. And the more I sat with it, the more one question kept surfacing:

What does it actually take to get there?

The Structural Argument

Nieto-Rodriguez’s central premise is hard to argue with. In a business environment defined by constant volatility, the organizations that win aren’t necessarily the ones with the best strategies — they’re the ones with the best capacity to execute on them. And execution, increasingly, happens through projects and initiatives rather than through steady-state operations.

The project-driven model is his answer to that reality. Rather than treating transformation as something that happens alongside the business, it proposes making project delivery a core organizational competency — built into structure, governance, and culture.

That’s a significant shift from how most organizations currently operate.

The Gap Between the Model and the Reality

Here’s where I find myself wanting to push back — or at least add some texture.

The project-driven organization makes tremendous sense as a destination. What the framework doesn’t fully reckon with is how difficult the journey is for organizations that are simultaneously trying to run their current business.

The operators responsible for driving transformation are typically the same people responsible for serving customers today. Strategic initiatives compete directly with day-to-day delivery for the same finite pool of time, attention, and resources. And when those two things collide — as they inevitably do — it’s rarely the customer commitment that gets deprioritized.

This isn’t a failure of will or vision. It’s a structural reality that any honest conversation about project-driven organizations has to address.

Where AI Changes the Calculus

This is where I find myself genuinely optimistic about something Nieto-Rodriguez doesn’t explore deeply in this piece: the role of generative AI in making the transition more achievable.

The dual burden of running the business while transforming it is fundamentally a capacity problem. And AI has real potential to address capacity in ways that weren’t available even a few years ago — compressing routine work, accelerating information synthesis, reducing the administrative overhead that consumes so much of an operator’s bandwidth.

If the project-driven organization requires carving out dedicated capacity for transformation work, AI may be one of the most practical tools available for creating that space — not by replacing people, but by multiplying what they can accomplish in the time they have.

Worth Your Time

Nieto-Rodriguez’s article is a worthwhile read for anyone thinking seriously about organizational design, program management, or how strategy actually gets executed in practice. It raises important questions even where it leaves some of them open.

I’d be curious whether others see the capacity tension the same way — or whether you’ve found models that address it effectively.

I just finished an episode of the HBR IdeaCast featuring Tsedal Neeley. She hit on a point that really resonated with me: we often focus so much on the "ROI of efficiency"—the minutes and pennies saved—that we lose sight of whether those efficiencies are actually leading to better results.

There was a specific moment toward the end of the episode that stayed with me:

We have to stop obsessing on the efficiency gains and start obsessing on the outcomes. If you’re saving time but the quality of the work or the impact on the customer hasn't changed, you haven’t actually realized the true ROI of AI.

Listening in on this conversation has prompted me to dive deeper. I’m planning to listen to her book, The Digital Mindset, on Audible next to see how these principles apply to long-term strategy.

Have you found yourself falling into the "efficiency" trap? I’d love to hear how you’re measuring the actual impact of AI in your own work.