What Leena Nair’s Leadership Made Me Rethink About Scale in Tech

AI systems are increasingly capable of working with non-linear data. From a modeling perspective, this is a gradual progress. Attention mechanisms allow models to focus selectively rather than sequentially or in a structured manner.

I would not say that the problem is that non-linear data is impossible to model. The problem lies in the fact that scale still rewards what is easiest to optimize.

Even as models become more flexible, they continue to perform best when patterns are legible, comparable, and stable. So while non-linearity can be handled, it is often treated as something to manage rather than something to center and cater to the audiences.

Technically, we choose to make this an an optimization decision. But when these systems are deployed in high-stakes platforms, those optimization choices start to shape who becomes legible to machines and who does not.

As a recent software engineering graduate, this tension feels familiar. I see it in AI systems, but I’ve also started to see it in how organizations think about growth in their respective domains. Scale is often discussed as a technical achievement. Less often is it discussed as a human one.

That is what led me to think about Leena Nair.

Why Leena Nair Caught My Attention

Leena Nair stood out because she leads a large, global organization while explicitly centering human experience.

Before becoming CEO of Chanel, Nair spent decades working on organizational and people systems, including as Chief Human Resources Officer at Unilever. In public interviews, she consistently frames dignity, listening, and wellbeing not as cultural side effects, but as structural inputs to how an organization functions at scale (Financial Times, 2022).

As someone early in my career, I’m still learning what leadership looks like beyond job titles and metrics. Reading about Nair’s background made me realize how rarely we talk about leadership as a design problem: one that involves trade-offs, information loss, and feedback loops.

What stood out to me about Leena Nair’s leadership is that it seems to resist resolving complexity too early. Chanel under her leadership has publicly emphasized long-term employee investment, craftsmanship, and cultural continuity. These are areas that are difficult to compress into short-term performance indicators (The Guardian, 2023).

Empathy as a Technical Mechanism

One idea that kept surfacing for me while reading about Nair’s leadership was empathy. In AI systems, feedback quality matters as much as model architecture. When feedback loops are narrow or delayed, systems drift. When important signals are filtered out early, errors compound silently. Rich feedback is harder to process, but it improves long-term alignment.

Empathy functions similarly in human systems.

It increases bandwidth. It captures context that doesn’t fit neatly into metrics. It surfaces weak signals before they become structural failures. From this perspective, empathy is not inefficiency. It is a way of maintaining signal fidelity as scale increases.

Leena Nair has spoken about listening at scale and staying connected to employees even as the organization grows globally (Financial Times, 2023). Framed this way, empathy looks less like softness and more like a deliberate design decision about information flow.

What This Made Me Rethink

This isn’t a statement about how companies should operate, and it isn’t a comparison between industries. I’m still learning, and I don’t think there is a single correct model for leadership or scale.

What this changed for me is how I think about optimization.

In AI, we’ve learned that non-linearity doesn’t have to be eliminated, but it still requires intentional handling. If uncertainty is always treated as risk, systems will quietly favor what looks clean and familiar. The same seems true for organizations.

Scale does not automatically erase human complexity. But the way we design systems determines whether that complexity is preserved, deferred, or discarded.

Leena Nair’s leadership stood out to me because it appears to treat human variation as expected input rather than noise. Not by rejecting structure, but by acknowledging that some information is too valuable to flatten without consequence.

Carrying This Forward

As a new engineer, I’m still figuring out what kinds of systems I want to help build. Reading about Leena Nair didn’t give me answers, but it definitely gave me a clearer lens.

When systems encounter irregularity, do we smooth it away or design for it? When uncertainty appears, do we route it toward learning or rejection? And how do those choices compound as systems grow?

AI models are getting better at handling non-linear data. But technical capability alone doesn’t determine outcomes. Design decisions do.

Leadership, like system design, is ultimately about deciding what remains legible at scale and what quietly disappears in the process.

References

1. Financial Times interviews with Leena Nair on leadership, culture, and scale (2022–2023)

2. The Guardian reporting on Chanel’s employee- and culture-focused initiatives under Leena Nair (2023)

3. Stanford article about Leena Nair leading with empathy and compassion (2025) https://www.gsb.stanford.edu/insights/leena-nair-leading-empathy-compassion