I Want A New Drug

Is it helpful to think of improvement as optimization?

The meme of the decade is maxximization: looksmaxxing, proteinmaxxing, moneymaxxing, longevitymaxxing. There’s nothing in the world that can’t be maxxed. All the kids are optimizing these days.

Now, it’s easy to tut-tut obsessive maxxers and see them as cautionary tales of culture gone wrong. But why is maxxing culture bad? We all respect people on humble searches for personal “improvement,” “development,” or “betterment” for themselves and their families. But isn’t “making something better” the same as maxxing? I’m coming around to thinking that the answer is no, and I don’t think it’s a matter of degree. The technical language of optimization—embraced by engineers and economists and influencing the rest of society—makes every quest for self-improvement into something grotesque.

In her phenomenal essay “The Ozempicization of the Economy,” Kyla Scanlon juxtaposes two different dietary interventions, the elimination diet vs. GLP-1 agonists, to draw out the limits of the language and mindset of optimization.

I had a bunch of friends who developed stomach issues in their twenties. Many of them went on the elimination diet for relief. For a month, they cut out everything from their diet except a few basic foods, like chicken breast, carrots, and rice. Then, they slowly added things back to their plate, maybe some basic vegetables, maybe some new meats. They journaled how they felt, seeing if they could find clear triggers for their symptoms. Inevitably, they would all find some culprits to avoid, and would establish a good baseline of what they could and couldn’t eat.

Elimination diets are excruciating. It takes months to get to a nearly normal diet that identifies a few problematic foods. And there’s no clear goal other than finding a broad set of accessible foods that you are happy eating.

Is the elimination diet a form of numerical optimization? What is the objective function? Is it “finding the largest array of foods I can eat while not feeling like shit?” How do you quantify “not feeling like shit?” When is it acceptable to stop? I don’t think it’s beneficial to view this process as maximizing a portfolio of food. Most people are far more concerned with the constraints on living a happy, normal life with a healthy relationship to food.

By contrast, GLP-1 agonists do feel like a miraculous optimization tool. If you take them, and you can tolerate the side effects, you’re going to lose weight. The number goes down for everyone.

Scanlon’s essay describes the madness of hoping everything will work like Ozempic. The maxxing community couches its searches for quick fixes in the language of rational science, but they appeal to the postmodern science I discussed last week. It’s a science that conflates “discovery” with a functional demonstration of utility. Interventions are judged by improvement over a baseline. It abandons explanations of how an intervention translates into an effect and replaces them with crude proofs of an impoverished notion of causality. An intervention causes an outcome if (and only if?) some randomized trial demonstrates a statistical difference when the intervention is compared to an appropriate control.

This is precisely the management view of epistemology Lyotard was calling out 50 years ago. A discovery is now a quantitative improvement of an outcome by a discrete move in a complex game. This not only rules out many other forms of discovery and knowledge-making, but it also restricts our attention to interventions that quantitatively improve outcomes for populations.

And it means the only equivocal interventions are the ones that work for everyone. For drugs like GLP-1 agonists, the effect is stark and undeniable. Ozempic is a drug that didn’t need a trial to demonstrate efficacy. It so unambiguously works for almost everyone who takes it. I’ve written about it here before. The p-values are zero. Everyone loses weight. It optimizes for everyone.

So perhaps the grotesque part, the unavoidable part of optimization culture, is the idea that there must be clear rules that work for everyone and guarantee large, measurable, quantified improvements and that every intervention must work like a miracle drug. The rare case of an impressive discovery like Ozempic convinces people that every problem can be solved by one simple trick.

This warps the questions we ask. It forces us into metrics and quantified scores. It warps the process itself. As Scanlon puts it, “The reason we can’t solve our problems is not lack of tools or information - it’s that the dominant method (add, optimize, measure) is the wrong method for the problem (figure out what’s poisoning you).”

Moreover, there is a grotesque endpoint in the shift from trying to be better to trying to be the best. It’s a shift from “as good as I can be” to “better than everyone else.” That doubled x-ed maxx implies there is a maxximum. A perfect destination that is as good as possible. It implies we’re all a peptide stack away from being better than everyone else.

But rejecting the extremes of maxxing culture doesn’t mean abandoning personal improvement. At the population level, bureaucratic pragmatism feels grotesquely crude. At the individual level, we celebrate people who seek to better themselves. Getting better often doesn’t need quantification. A person can get better at writing blogs or playing the guitar without validation from a numerical score. Moreover, we embrace difference at the individual level and acknowledge what works for one individual’s journey might not work for another. We even embrace relativism. Most of us accept that people can find what’s true for them personally by finding what works for them personally.

So what should we call trying to be better? What’s a word that juxtaposes against optimization? Tell me in the comments.