Hey everyone, it’s Ari.
Today I want to build on Tuesday’s article, “The Analyst as Architect,” and take you a bit deeper—beyond the words on the page—into what I really mean when I say that most financial models today are just not built to hold sustainability.
Let’s start with something simple: your Excel file or Python notebook is not neutral.
It doesn’t just calculate things—it frames them. It tells a story about how the world works.
And if that story is too simple—too linear, too static, too siloed—it risks misleading you in precisely the moments when nuance matters most.
I’ve seen this over and over. A sustainability score gets slapped onto a valuation model. A climate scenario becomes a multiplier. A social risk becomes a one-time penalty. These are band-aids, not blueprints.
So let’s talk blueprints.
Architecture Starts With Assumptions
When I say “be an architect,” I mean: Own the assumptions you make about how the world works.
In traditional models, many assumptions are implicit:
That relationships between variables are linear
That past performance is a good proxy for future outcomes
That externalities can be modeled as static adjustments
But in a sustainability-shaped world, these assumptions start to fail. Take something like biodiversity loss. It doesn’t hit margins directly—but it might wipe out a supplier base, spark regulatory backlash, or increase insurance costs in a way your model never anticipated.
If you're not modeling those pathways—or even recognizing they exist—you’re not modeling risk. You’re modeling wishful thinking.
Complexity Doesn’t Mean Confusion
Now here’s the catch: when people hear “model complexity,” they often panic.
They imagine impossible webs of variables and too many unknowns to make anything tractable.
But modeling sustainability doesn’t have to mean building a digital twin of the Earth.
It can mean choosing to model the right mechanisms.
Start with basic causal structure:
What influences what?
Where are the feedback loops?
What might shift the system out of its current regime?
Even sketching this out on paper before coding anything can help you move from reaction to reflection.
Because once you have a causal hypothesis, you can test it, simulate it, and ask: what happens under pressure?
That’s where real insight lives.
The Analyst as Architect = Analyst as Storyteller
This brings me to something I think is often overlooked: being an architect means becoming a better storyteller.
Not in the sense of marketing or persuasion, but in the sense of asking:
What’s the story your model is telling about how the world works?
Is it a story in which sustainability risks sit quietly in a side cell?
Or is it one where they shape the narrative arc—inflecting cash flows, shifting time horizons, accelerating change?
I don’t think we talk enough about this.
But when your model shapes decisions—and those decisions shape markets—you are shaping the world.
So what kind of story are you telling?
A Practice You Can Try
Here’s something I’d invite you to try this week:
Take one model you use often—something you trust, something that feels clean and reliable—and look at it with fresh eyes.
Ask yourself:
Where are the assumptions that could break under transition risk?
What sustainability variables am I treating as static or external?
What mechanisms might I be ignoring because they’re hard to quantify?
Then: sketch an alternative. It doesn’t have to be perfect. Just make the system visible to yourself.
You may find that your model needs a new wing. Or a new foundation.
And that’s the beginning of becoming an architect.
Final Thought
We are at a moment in finance where analysts have a choice:
To keep polishing the same familiar tools, or to start designing ones fit for a future in flux.
If you’re listening to this, I suspect you’re already leaning toward the second path.
Let’s keep walking it together.
Talk soon.
— Ari
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