There’s an imagination that goes like this:

If we can analyze all technical patterns, systems can automatically generate buy and sell signals. If we can analyze users’ age and assets, systems can automatically recommend the most suitable insurance products. If we can collect enough behavioral data, algorithms can predict what you’ll do next.

“Systematizing decision-making”—this has been the fundamental belief of countless engineering-background startup teams over the past decade or more.

But this is less logic than religion. They are believers in scientism.

The Unbridgeable Chasm

From data organization to making decisions is not a continuous spectrum, but a belt-like process with a cliff in the middle.

Let me break down this process. Data comes in three forms: structured, semi-structured, and unstructured. Structured data is numbers in spreadsheets—revenue, user counts, conversion rates. Semi-structured data has certain formats but requires interpretation—customer feedback emails, meeting notes, market reports. Unstructured data is the stuff floating in the air—a glance, an unspoken word, an intuitive feeling that “this direction doesn’t seem quite right.”

What platforms and algorithms can handle perfectly is basically limited to structured data. Give it a table, and it can help you sort, filter, find outliers, and plot trend charts. This is useful, but it’s still far from “decision-making.”

Because decision-making is a subjective leap of thought—data cannot provide that final step.

You’ve read ten market reports, all pointing toward direction A. But at yesterday’s dinner, you heard an industry veteran casually mention something that makes you vaguely feel direction B is right. You can’t quantify the weight of that remark, can’t even explain to your team why you “feel” B is better. But in that moment, you make the decision.

What happens in that moment, algorithms cannot see or simulate.

The Faith Structure of Scientism

I call the excessive belief that algorithms can replace human decision-making “scientism”—not to insult rational thinking, but because its structure bears striking resemblance to religious faith.

The core of religious faith is: there exists a transcendent being (God, fate, karmic law) that can eliminate uncertainty for you. You just need to believe, and you’ll have answers.

The core of scientism is: there exists a transcendent system (big data, AI, algorithms) that can eliminate uncertainty for you. You just need to feed it enough data, and you’ll have the optimal solution.

The common psychological drive is the same: escaping the pain of decision-making.

Making decisions is painful. That immediate ambiguity, duality, uncertainty—it’s a weight each person must face alone. Career choices, investment judgments, relationship decisions—these are all unstructured subjective judgments, carrying human warmth, bias, and arbitrariness.

Scientism believers think algorithms can bridge the enormous gap between semi-structured and unstructured. But the gap hasn’t shrunk. It’s just that because they “believe,” they feel the problem has disappeared.

A 2017 Warning More Relevant in 2026

I first wrote these thoughts in 2017. Back then, AI didn’t have ChatGPT’s aura, big data was the trendiest term, and every entrepreneur was talking about being “data-driven.”

Nine years later, AI capabilities have indeed made qualitative leaps. GPT can write articles, Claude can analyze contracts, various AI agents can automatically execute complex tasks. But the core problem hasn’t disappeared—it’s become sharper.

Because as AI increasingly resembles “something that can make decisions,” people become more likely to push decision-making responsibility onto it.

In running my company, I’ve seen too many such situations. Teams use AI for market analysis, AI says a certain market opportunity scores highest, so they decide to go in that direction. No one asks: “Who designed AI’s scoring model? Are the scoring weights reasonable? Are there factors the model can’t see?” This isn’t AI’s fault—it faithfully produces results based on the framework you give it. The problem is that people abandon the responsibility of thinking whether the framework itself is correct.

This is the same issue as the taste problem I discussed in “Post-Code Era Thinking: When Taste Becomes Humanity’s Key Competitive Advantage.” AI can find optimal solutions within your defined coordinate system, but defining the coordinate system itself—what is “good,” what’s worth pursuing, what risks are acceptable—these remain human responsibilities.

The True Role of Information Platforms

Let me be clear: I’m not anti-technology. Quite the opposite—I use AI every day.

But I’m very clear about AI’s role in my decision-making process: it’s a “decision support tool,” not a “decision dependency system.”

This distinction is crucial.

Decision support means: AI helps me organize structured data, initially categorize semi-structured information, and provide options and analysis within frameworks I set. Then, I take responsibility for that final “leap.”

Decision dependency means: AI tells me what to do, and I follow. If the results are bad, it’s AI’s fault.

If platforms claim they can help you make better decisions and judgments—use this feature to buy stocks that will rise more, use that model to select the most suitable employees—such platforms, nine years ago I said they’re either naive or deceptive, and my judgment hasn’t changed today.

Not because the technology isn’t good enough. It’s because the essence of decision-making isn’t an optimization problem. It’s a process of making choices under incomplete information while carrying value judgments. You can have better information, but you cannot eliminate the weight of “choosing” itself.

Understanding Decision-Making Pain Through Theology

I learned a concept in theological training that I later found very helpful for understanding decision-making: finitude.

Christian theology has a core premise: humans are finite beings. Your knowledge is limited, your perspective is limited, your understanding is limited. This isn’t a flaw—it’s a fundamental condition of existence. Accepting finitude isn’t giving up on pursuing better, but acknowledging the fact that “you can’t know everything,” then making the most responsible judgment you can based on that fact.

Scientism’s problem lies precisely in its refusal to accept finitude. It assumes that with enough data and good enough models, you can approach “omniscience.” But omniscience is an attribute of God, not humans. Projecting this attribute onto algorithms is essentially a form of idolatry—wrapping the craving for certainty in technological garb.

I further explore this issue in “Algorithms as Judges”: when we let algorithms judge human worth and allocate human opportunities, what exactly are we trusting?

The pain of decision-making won’t disappear just because we have better tools. Tools can help you see more, calculate faster, simulate more scenarios. But that final moment of “I’ve decided”—that’s still your business alone.

Prerequisites for Using AI Well

Back to practical matters. If you’re an executive using AI to assist decision-making, I suggest posting this sentence next to your screen:

AI’s output quality will never exceed the quality of the questions you input.

AI won’t tell you you’re asking the wrong question. It will only faithfully answer the question you ask, no matter how ridiculous that question might be. So your responsibility isn’t learning to use AI, but learning to ask the right questions. And asking the right questions requires deep domain understanding, sensitivity to human nature, and humility to admit you might be wrong.

I’m grateful there are so many people willing to believe algorithms can solve everything. Their existence gives those who truly understand the nature of decision-making an irreproducible competitive advantage.

Because in a world where everyone has the same AI tools, there’s only one source of differentiation left: how you use them, and whether you dare choose B based on your judgment when AI says A.

There’s no well-tempered clavier. No unified happiness equation. The weight of decision-making is the weight of being alive.