Care vs. Quantification in the Age of AI

In the rush to embrace AI and automation, we're hearing a lot about "taste" as the new supreme skill—that ineffable quality that distinguishes good design from great design. But Jim Nielsen offers a compelling counterpoint: what we should really be looking for is "care" in the products we use and create.

The distinction matters. Taste can be subjective, trendy, even superficial. Care is deeper. It's about consideration, attention to individual context, and a willingness to look beyond the numbers.

Can you measure care? That's the fundamental tension Nielsen identifies. In a world obsessed with metrics, KPIs, and quantifiable outcomes, care resists measurement. It's not that care is anti-systematic—Nielsen notes that care "considers useful, constructive systematic forces—rules, processes, etc.—but does not take them as law." Instead, care prioritizes individual context and sensitivity over rigid application of rules.

This is why professionals often answer "it depends." It's not evasiveness—it's recognition that human situations are complex and require nuanced responses. The alternative—"this is the law for everyone, everywhere, always"—is precisely the kind of system Nielsen wants to avoid.

For businesses, this creates a particular challenge. Companies exist to make money, and numbers are how we measure success. Quantification lets us track who gains or loses in any transaction. But Nielsen points to something crucial that lurks behind those numbers: sometimes it's good for business to leave money on the table.

Why would a business intentionally forgo revenue? Because they care. Because they're willing to provision room for something beyond just a quantity, a number, a dollar amount. This unmeasurable principle—care—can't be captured in a spreadsheet or a quarterly report.

The absurdity becomes clear when we try to quantify care itself. "How much care did you put into the product this week?" "Put me down for an 8 out of 10 this week." The very act of trying to measure care undermines it.

This tension between care and quantification feels particularly acute in our AI-driven world. AI systems excel at optimization, at finding the mathematically optimal solution. But they struggle with the human elements that care encompasses—empathy, context, the willingness to make exceptions.

As we build more AI-powered products and services, we need to ask ourselves: are we designing systems that can incorporate care? Or are we creating ever more efficient ways to reduce human experience to numbers and metrics?

The answer probably lies somewhere in between. We need systems and processes—they provide consistency and scalability. But we also need the flexibility to override those systems when individual circumstances demand it. We need professionals who can say "it depends" and then use their judgment to determine what the answer should be in each specific case.

Perhaps the real skill in the AI age isn't taste or even care alone, but the ability to balance systematic efficiency with human consideration. To build systems that can scale without losing the human touch. To measure what matters without reducing everything to what's easily measurable.

Because at the end of the day, the products and services we remember, the ones that truly serve us, are the ones built with care. And care, Nielsen reminds us, is something no algorithm can fully capture or replace.