The Hard Way: What the rise of AI and GLP-1s tells us about designing through cultural shifts
Two technologies, one cultural arc
Just a couple of years ago, people everywhere were quietly deleting em-dashes. Using AI tools to write had a tell: an unusual fondness for that particular punctuation mark. So before hitting send, people hunted for them. The deletion was an act of concealment, a small ritual of shame.
GLP-1 medications had their own version of the same story. People without an underlying medical condition used them privately to quickly shed a few pounds, maybe disclosed it reluctantly, and braced for judgment. Those around them speculated in hushed tones. “Did they? Didn’t they?”
Then, almost overnight, something shifted. AI went from something you scrubbed your work for evidence of to tablestakes in nearly every professional tool. Using GLP-1s for minor weight loss went from a closely-held secret to an open conversation. Rather than the smooth upward slope of the adoption curve we’re used to, it looked more like a hockey stick. Two behaviors previously shrouded in shame became ubiquitous and it’s worth taking note. We’ve built entire value systems around life’s hardest frictions: being the Biggest Loser, writing the great American novel. What happens to those values when the friction goes away and what does it mean for the people designing what replaces it?
The Effort Paradox
To understand why the resistance was initially so charged, you have to understand what these technologies ran into: a deeply held belief that struggle is the proof of legitimacy.
“Nothing good ever comes easy” isn’t just a proverb. For many people, it’s an identity framework. We’ve conflated effort with value so completely that outcomes achieved without visible struggle often feel suspect to us. The strategist who synthesized an insight by combing through 50 hours worth of interview transcripts demonstrates expertise and rigor. The one who got there in twenty minutes with AI? That’s harder to defend. Or at least it was.
Some have called this the Effort Paradox: the belief that difficulty isn’t just the price of achievement, it’s the source of its value. The struggle isn’t incidental. It’s the whole point.
GLP-1s and generative AI didn’t just offer shortcuts. They made people ask an uncomfortable question: if the outcome appears to be the same either way, what was all that effort actually for? While the benefits are front-and-center, the trade-offs remain in the shadows. By bypassing the struggle, we exchange the “tax” of effort for a different kind of debt: a creeping dependency, a thinning of mastery, a mounting toll on our environmental or social systems.
How stigma collapses
As with those instances, the stigma doesn’t always fade gradually. Under the right conditions, it breaks. Here’s what we observe:
Step 1
Early adopters pull visibly ahead
When the perceived advantages of a taboo behavior becomes difficult to ignore, the social calculation shifts. With Generative AI, this took the form of a perceived boost in speed and volume. With GLP-1s, it showed up as rapid, visible physical changes.
Step 2
The cost of abstaining flips
There’s a tipping point when holding out stops feeling principled and starts feeling like falling behind. When your coworker starts producing twice the output in half the time. When your co-star shows up twenty pounds lighter and nobody on set pretends not to notice. Once that tips, it tips fast.
Step 3
The moral logic inverts
In the end, the stigma reverses. The behavior that was once judged becomes the new expectation. Proficiency in AI becomes a job requirement. The speculation shifts from “did she take something?” to “what is she taking?” The holdout becomes the one who needs to rationalize why they’ve intentionally chosen the more difficult way.
Where this happens next
Once you see this pattern, it becomes easier to identify categories it might happen to next. It needs two conditions: a domain where effort is still moralized, and an augmentation technology arriving to offer an easier path. Some examples that are mid-arc right now are:
Wellness
Used initially by biohackers, looksmaxxers, and wellness influencers, peptide therapies tout a slew of promises for enhanced vitality: better hair, better skin, faster muscle growth, a fertility boost. These treatments have been defended with the careful language of “optimization” and “self-experimentation.” Today, we see the conversation starting to shift as peptides become more mainstream.
Dating
We’ve long viewed the search for a partner as a character-building odyssey. A gauntlet of awkward first dates, vulnerability and rejection that supposedly prepares us for the work of a real relationship. Just as we got over the embarrassment of saying we met our partner swiping on an app, a new crop of hyper-niche matchmaking tools, “GaaS” (Girlfriend-as-a-service) AI companions, and AI agents that go on the first date for you are beginning to bypass all social friction entirely, no more dating around required.
Parenting
Parenting might be the final frontier of moralized struggle, built on the belief that being there for the repetitive, mundane frictions of care is the only way to forge a good relationship with your kid. We’re now starting to see AI that writes bedtime stories, sensors that manage a child’s emotional regulation, and parenting agents that outsource the mental load of guidance. What will happen if these tools move from conveniences to a baseline expectation of a well-adjusted child?
Three ways to respond
Identifying where a category sits on this arc is the first move. The bigger question is what to do about it. We see three responses and the right one depends on where you are in the curve.
Re-evaluate the Job to be Done
When a shortcut emerges, it forces a reckoning: were your users finding value in the process or the results?
This question came to light in work with a global technology leader as we explored the evolving role of AI in education. In a world where a finished book report or a solved equation can be generated in seconds, the real value of education lies in the process of developing critical thinking skills. Our solution focused on reframing AI from an answer engine into a socratic partner to ask clarifying questions, challenge logic, and encourage students to defend reasoning.
Design the bridge
Resistance to a new technology is rarely about the technology. It’s about what people feel they’d be giving up in the transition.
Most creatives don’t find their soul in the drudgery of resizing assets or cleaning up mask edges, they find it in the conceptual spark. We worked with a global technology company to define exactly where AI should and shouldn’t touch a creative’s workflow. The goal was to identify and enhance the “flow state” moments where the creator feels the most agency, while eliminating the frictions that slow them down.
Anticipate the counter-signal
Every wave of effortless outcomes eventually creates a premium for what it’s displaced. As augmentation becomes the new baseline, the things that signal un-reproduceable human effort become more valuable.
We see this in our work with Poursteady. In a category where speedy, push-button coffee has become a frictionless commodity, the perfect pour-over emerged as a counter-signal. We didn’t design a machine to hide the process for the sake of speed; we designed a system that mimics the meticulous, rhythmic ritual of a master barista. By automating the precision but preserving the “theater” of the craft, we helped Poursteady win by doubling down on the very friction the rest of the industry was trying to delete.
Understanding the hidden drivers of adoption
The organizations that navigate this era well won’t just be the ones who adopt fast. They’ll be the ones who understand what’s underneath adoption, which identities are at stake, where the threshold is, and how to bring people across in a way that builds trust rather than erodes it. Getting there means going beyond what people do and into why: the unarticulated fears, the identity investments, the quiet resistance that doesn’t show up in a survey. That kind of understanding comes from spending time with real people, in the right conditions, asking questions that create enough safety for honest answers.