Cognitive Tax
Who’s paying the hidden price of AI?
I plopped into bed. I closed my eyes. Within fifteen minutes, I nodded off to sleep. Suddenly, I jolted awake.
“I forgot to remind Leo to take his meds,” I blurted aloud. My feet hit the floor as the words still tumbled out of my mouth, my eyes half-closed.
Beside me in bed, my husband marveled, “Wow. Your mind just doesn’t stop.”
Let’s back up a bit.
It had been a typical week. On Monday, I dropped Leo off for Freshman Orientation Week, flew to Detroit for client meetings, and coached a friend on an upcoming job interview. Tuesday, I wrapped up an expansive client MarTech strategy project, pitched multiple new projects, kicked off a new project, and finalized the loan on my new home purchase. Wednesday, I demoed the latest in AI innovations to 100 clients, flew home, and planned my husband’s birthday. Thursday, I went to the office, orchestrated three RFPs, promoted my newly launched wine brand, and coordinated Leo’s logistics across his dad, his grandparents, his aunt, his stepdad and myself.
Just a typical week.
When my feet hit the floor in the dark that Thursday night, remembering something left undone, it wasn’t all that unusual. And it’s not unusual for most women and moms. The mental load is real.
Recently, researchers uncovered a new nuance: gendered cognitive stickiness. Findings among gender theorists show that for women in heteronormative households, regardless of their jobs in or out of the home, cognitive tasks tend to “stick” and persist more than for men. Their findings weren’t about biology. Women aren’t biologically wired toward this trait. Their findings were about structural gender norms. “Inequality,” they concluded, “ is reproduced even without visible performance.”
As anyone conducting an AI demo in Detroit will tell you, AI will help solve for this cognitive burden. By offloading tasks and thought processes, AI stands to fundamentally change the way we work, operate, and live. In theory, AI will give us all more space to shift our focus towards what’s valuable and human.
In part, I believe this. I experience the benefits of AI daily. But the calculations behind AI’s value proposition leave certain groups (women and people of color included) more vulnerable than others to its hidden costs.
The Value Equation
Across the technology industry, articles abound on how AI has triggered the great SaaSpocalypse. As organizations enable more capabilities through data and AI, organizations challenge the necessity and value of the elaborate marketing, sales, and service platforms they once relied on. Historically priced on users or “seats” within their platforms, SaaS companies now field regular client questions on how many seats the organization truly needs. If AI can offload some of the work, it can reduce the number of seats required to complete the work. Furthermore, as platforms infuse AI into their core technology stack, consumption-based pricing has risen. Data volumes and tokens used become the measuring stick for the cost of data, technology and AI.
Pricing shifts at the technology layer reverberate into professional services as well. Increasingly, clients purchase consulting not based on the time required to complete the work or the promised deliverables. They purchase based on guaranteed outcomes. The number of people in the seats and the number of deliverables those people produce grow less valuable than achieving the business outcome itself.
In theory, this is a good thing. Who doesn’t want to focus on outcomes?
But the value equation obscures a few invisible calculations—some of which only are visible in the dark, on a Thursday night, when you jolt out of bed to remember a task left undone.
Hidden Figures
Most organizations start by defining desired outcomes at the executive level. Executive objectives are then translated into success measures by senior managers. Success measures are then distilled further into smaller key performance indicators by another layer of managers. And so the process continues.
But, the executive suite remains primarily occupied by white men. As does the senior manager level. And the middle manager level. Only within the context of entry-level roles do women represent 49% of the workforce—still not even half.
So, as organizations define the outcomes they seek to achieve through technology, data, AI, and professional services, authorship of those outcomes remains seated with white men. As a result, certain outcomes risk getting lost—particularly those informed by the unique perspectives, challenges, and experiences of women and people of color.
If women continue to bear a greater cognitive load than men, irrespective of title or role, and if outcomes are continually defined by men, how do organizations ensure their desired outcomes are realistic and inclusive of the true effort associated with achieving them?
The View From The Seats
As AI promises to accelerate work and increase efficiency, organizations are curtailing the number of seats required to do the work—within their platforms and within their organizations. Often, the promise of AI bears fruit. The time required to get the work done decreases.
But, sometimes the promise extends too far. A focus on outcomes, paired with an “AI can do it” assumption, hacks away at a project’s estimated level of effort and resources with a machete. The team left to execute on the outcomes is left with less time and less resources to complete the job. The outcomes still get achieved. But the actual price of achieving those outcomes never is fully quantified.
The seats are where the work gets done— within the platforms and within the company. Typically, those seats are occupied by entry-level and mid-level employees. When you look across those seats, you’ll find more women. More people of color. The view from the seats is different from the view from the executive suite.
When outcomes are all we’re counting, and when outcomes are defined from the skybox, how do we account for the unique tax upon those occupying the seats where the work gets done?
Cognitive Tax
Even when we account for the shared tax upon men and women who are fulfilling the outcomes, the cognitive burden on women remains unseen. Regardless of employment status. Regardless of organizational tenure or title. Gendered cognitive stickiness points to the invisible load women carry in heteronormative households—the remembering; the reminding; the checking; the coordinating. Even when women and mothers are surrounded by physical household support, as I am, the gendered performance of who thinks through the household logistics remains.
In the age of AI, when we are all, regardless of gender, asked to do more and operate with greater efficiency, there is more to remember. More to check. More to coordinate. The current calculus on how we measure value based on outcomes fails to quantify these efforts.
But the cost is real. It shows in the data that illustrates that women are just as career-motivated as men, but less likely to seek out a promotion. It shows up in the data that highlights women exiting the workforce under the pressures of balancing their careers with their caregiving responsibilities. And it shows up in the moments right after you’ve fallen asleep, when you remember something unchecked from your to-remember list.
The costs have moved. But the total price hasn’t changed. We just aren’t always quantifying the hidden figures.