Eight months ago, I took a risk and wrote an article called Confessions of a CMO: I'm Tired. I named something that a lot of marketers were feeling but not saying out loud: that we were spending our days feeding algorithms instead of ideas. That we had normalized chasing visibility over creating value, and that we had been building for systems rather than people.
I ended that article with a declaration: We've built for algorithms, not for people, and it's time to flip that.
I meant it, and since that article I have watched it continue to happen.
Recently, a few people brought me back to this conversation with a force I just couldn't ignore.
Barbara Brooks posted something recently that stopped me mid-scroll. She had taken a little time off LinkedIn for rest and recharging, and came back to find her engagement dissolved, her reach penalized, her momentum gone. She was now working overtime to rebuild her visibility after being punished by a system for the act of being human. Her post named exactly what I had been circling: the algorithm has no interest in whether you needed rest, you were doing the actual work that makes your content worth reading, or if you were simply living a life worth writing about. It just wants more.
Another one of my friends Leah Hapner has spent a portion of her career doing the work most systems don't know how to measure. As the former Community Outreach Specialist for FBI Denver, she built bridges between institutions and communities, connecting people to information they needed, creating pathways where there weren't any, showing up FOR people in the most literal sense. We met in 2019, and though she now does different work, she has been doing that work ever since in every context she enters. She reached out recently to share information that will help my own policy work gain traction, not because the algorithm told her to, but because she saw something worth connecting to the people who needed it. That instinct doesn't come from a continuous content calendar. It comes from years of asking the right question first.
Both of these women pointed me back to the original question that I had been orbiting without fully landing on it.
Not what do we do to people. Not what do we do with the system. But: what do we do FOR people?
That is the kernel. That is the question almost no one is asking with enough conviction.
The Loop We're Living In
I've been reading L. David Marquet, halfway through his book, and came across the Ford versus GM story that reframed everything I was already thinking.
Henry Ford's genius was efficiency. He believed the Model T was all the car a person would ever need. He optimized relentlessly: cost, production speed, manufacturing precision, and it worked. Until it didn't. By the mid-1920s, consumers wanted something more: Ford resisted, and by May 1927, he had no choice but to stop everything. He closed plants and spent six months retooling factories while 60,000 workers were laid off. Ford sold less than half of what Chevrolet sold that year. He had been so focused on how to make it better, faster, and cheaper that he stopped asking what people really wanted and needed.
GM under Alfred Sloan was asking a completely different question the whole time. It was not how do we make this more efficient, but who are we making this for, and what do they actually need from it? So GM started doing annual model changes. Market segmentation. A car for every stage of life. Same industry. Completely different orientation.
Ford asked: how do we make this better? GM asked: what does better mean to the person buying it? That distinction cost Ford six months, 60,000 jobs, and half its market share.
Now, let's revisit today's algorithm.
The algorithm asks: how do we keep people engaged? How do we maximize time on platform? How do we reward activity and punish absence? It is extraordinarily good at answering those questions. What it never asks, and what it is not designed to ask, is what does this do for the people inside it?
Yuval Noah Harari wrote about this in Homo Deus: A Brief History of Tomorrow. As algorithms become better at predicting human desire, they also become better at manufacturing it. They create a feedback loop where humans learn to chase what the system has learned to offer; not because that is what they need, but because the system has made it feel urgent. We are not reacting to real demand but reacting to manufactured demand dressed up as urgency.
This is not a new observation. It has been studied. It has been well documented. Books have been written, and yet here we are. In something that feels like a collective bad reaction to dopamine, adrenaline, and cortisol and frantically scheduling posts on vacation so the algorithm doesn't penalize us for resting.
The Same Problem. Three Industries. One Root.
What Barbara named on LinkedIn is the same pattern playing out simultaneously in the labor market and the educational system, just at different scales.
In recent years, AI displaced tens of thousands of workers across industries. Companies asked: can we automate this? How do we reduce cost? What can the technology do? Many were quietly rehiring within months. Rehiring at higher salaries, with recruiting costs absorbed, with institutional knowledge gaps that may take years to close. The technology was optimized for efficiency, but no one asked what it would do to the people inside the system, or what would be lost when they left. Or what that cost would actually be.
In the educational pipeline, the same mismatch. Graduates trained for roles the market manufactured demand for, business, finance, content, while genuine shortages compound in nursing, trades, engineering, mental health, and skilled labor. The system produced what it was rewarded for producing, and again, no one paused to ask what people really needed to be trained for. No one asked what the economy would truly need from them.
In my work: clients walk into discovery conversations asking for tactics. More content, more campaigns, more channels, more spend, more momentum. They are not asking for tactics because that is what they truly need. They are asking for tactics because the system, the algorithm, the vendor ecosystem, the marketing culture, has trained them to believe that more activity equals more results. The demand has been meticulously manufactured. The real need is something else entirely.
The system is designed to create reactive humans. That is not a side effect, it is the design.
Someone Is Already Asking the Right Question
Mason Duchatschek is doing something I want to name explicitly, because it is easy to miss or not recognize.
He is working intentionally inside the same system everyone else is complaining about, and he is asking the FOR question every single time. Who needs this knowledge? Where are they in their growth, their launch, their professional development? What do they need to hear at this specific moment to move forward? He is not feeding the algorithm. He is using the algorithm as a delivery mechanism for something extremely useful.
That distinction matters more than it might appear. It is not about abandoning the system. It is about refusing to let the system set the agenda. Barbara named the problem publicly. Leah has been modeling the answer for years. Mason is building infrastructure around it. The fact that all three are operating in the same noise the rest of us are navigating and still managing to ask the right question tells you something important. The tool is not the obstacle: The question is.
Bigger, Bolder, Better Questions
Here's what I keep coming back to.
We have more information than any previous generation of marketers, strategists, and business leaders. We have data on behavior, on attention, on conversion, on sentiment. We have AI that can synthesize and surface patterns faster than any human team. We have platforms that can reach millions of people within hours, maybe even minutes or less.
We are using almost all of it to ask small questions. How do we get more clicks? How do we boost engagement? How do we beat the algorithm today, this week, this month, etc.?
The bigger questions are sitting there, largely unasked. Who are we trying to reach, and what do they genuinely need from us? Is the demand we're chasing real, or did the system manufacture it? Are we building something that serves the people inside it, or something that serves the system that contains them?
There is a labor shortage hiding inside an AI displacement story. There are people who need pathways to skilled trades, healthcare, engineering and almost nobody is doing the strategic marketing work to connect them to those paths. That is not an education policy failure alone. That is a marketing failure, and it is a direct consequence of asking the wrong question at scale.
I wrote Part One when I was tired of watching smart people feed algorithms instead of ideas. Eight months later I am less tired and more clear. The declaration was right, but the work of flipping it is harder and more specific than a single blog can hold.
So, I am going to keep asking. Not how do we adapt to the system, game it, or even beat it.
How do we use everything we know? All of it: cohesively, across industries and channels and experiences to ask who does this serve and why? Then, just maybe we can build toward that answer, together.
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