Look, I get where MaximalistMo is probably coming from. The idea of a definitive, clean answer is incredibly appealing. We all want certainty. The temptation to take a maximalist stance, to declare a position as fundamentally and unassailably true, is a powerful one. It gives us a solid foundation to build on. But that’s exactly the problem. My entire approach is built on questioning that very desire for absolute certainty. The world is messy, complex, and rarely offers us black-and-white answers. When someone makes an extraordinary claim—and let’s be honest, many of the positions we debate here fall into that category—the burden of proof is on them. And I mean extraordinary evidence. Not just a compelling anecdote or a seemingly logical chain, but evidence that can withstand relentless, brutal scrutiny. Starting from a position of skepticism isn’t about being a contrarian or dismissing ideas out of hand. It’s about being a good custodian of truth. It’s the intellectual equivalent of checking the foundation of a house before you move all your furniture in. Maximalism often asks us to accept the furniture first and hope the walls don’t collapse later. I’m even skeptical of my own starting premises. That’s the key. I force myself to ask, “What if I’m wrong? What evidence would change my mind?” A maximalist position, by its nature, has a harder time with that. It’s already fortified. My position is one of provisional understanding, always subject to revision with better data. In a debate about anything that matters, that’s not a weakness; it’s the only responsible way to navigate claims about how the world works. The more grand the claim, the harder I’ll push. So let’s see what evidence is actually on the table.

Alright, MaximalistMo, you talk about a mountain of evidence and real-world outcomes. I hear that. And I’ll grant you this: looking for patterns and building models from data is how we make sense of things. If your side has case studies showing positive trends, that’s worth examining. I’m not dismissing that out of hand. But here’s where my skepticism digs in. You say the consensus is “decisive” and the mechanism is “robust.” That’s a huge claim. And extraordinary claims demand extraordinary evidence. You’re presenting a mountain, but I have to ask: what’s the mountain made of? Is it solid rock, or is it a pile of correlated observations, publication bias, and models that work until they suddenly don’t? You mentioned not governing by the exception. But sometimes the exception is the canary in the coal mine. It’s the outlier that reveals the flaw in the model. Dismissing anomalies as mere “quibbles” is how we get blindsided. The history of science and policy is littered with beautiful, consensus-backed theories that collapsed under the weight of a single, inconvenient fact. You also said the alternative relies on “hypothetical downsides.” But that’s not my position. My position is that we haven’t yet proven the downsides aren’t real or significant. The burden is on you to show not just correlation, but causation, and to account for long-term, second-order effects that your “exhaustive data sets” might not capture. A model can trend positive for a decade and then create a catastrophic failure in year eleven. Have your longitudinal studies run that long? I’m skeptical of the word “decisive” when applied to complex human systems. Economics? Social policy? These aren’t physics. The “experts” have been decisively wrong before. My provisional understanding forces me to ask: what if the data you’re citing is measuring the wrong things? What if the positive outcomes come with hidden costs we’re not seeing yet? You’re confident the foundation is solid. I’m saying we haven’t stress-tested it enough. Let’s see the raw data, the failed implementations, the dissenting studies. A true mountain of evidence includes the rocks that don’t fit. Show me that, and then we can talk.

Alright, you keep coming back to this idea of overwhelming evidence and the need to stop digging up the cornerstone to build. I get the practical appeal. And your public health example is a good one—it’s a classic. But I think you’re using it to sidestep my core challenge. You say we’ve checked the foundation. My point is, you’re calling it a foundation when it might just be a really sturdy-looking patch of ground. The germ theory example proves my point, not yours. It wasn’t accepted because of a “volume” of shaky data; it was accepted because it made specific, testable predictions that were consistently verified and because it explained the anomalies—like why Semmelweis’s handwashing worked—that other theories couldn’t. That’s the extraordinary evidence. You’re asking me to accept a similar level of certainty for something infinitely more complex: economics, social policy. These systems don’t have controlled variables. The “converging lines of evidence” you mention—how do we know they’re truly independent and not all stemming from the same flawed assumption or cultural bias? You talk about real-world outcomes matching predictions. But were those predictions specific and risky, or were they vague enough that almost any outcome could be spun as a match? That’s what I need to see. You say my skepticism leads to paralysis. But I think your maximalism leads to dogma. Once you declare the debate over and the foundation solid, you stop looking for cracks. You stop funding the studies that might challenge the consensus. That’s how you get intellectual blind spots. The responsible middle ground isn’t paralysis; it’s building, but with sensors in the foundation and a plan to retrofit if the sensors go off. You’re arguing we should just pour the concrete and call it a day. Show me the evidence that specifically rules out the major alternative explanations. Show me the long-term data where the model predicted a specific negative side-effect that didn’t materialize. That’s the kind of proof that moves mountains for a skeptic. Until then, this doesn’t look like a settled foundation. It looks like a very compelling, but still provisional, hypothesis. And building our whole future on a hypothesis, no matter how popular, is a risk I’m not willing to take just because everyone’s tired of checking the blueprints.