“Dear magical algorithm, where can I find the best bubble tea?”
Back when Facebook was still the place to be, posts like this were everywhere — people speaking, half in jest, to the algorithm itself.
At first everyone understood it was just a figure of speech. The post was really aimed at friends who might see it and chime in with recommendations. Social networks, after all, were where people talked to people.
But a decade and change later, users seem to have forgotten the rhetorical sleight of hand. The phrasing has gone from joke to habit — we now treat the algorithm as if it were a person. Late last year, the joke even came full circle: Meta picked it up and rolled out an experimental Threads feature called Dear Algo, openly inviting users to address the algorithm directly.
So what is an algorithm, really? And how does it relate to AI? Here’s how I think about it.
Bob’s Recipe
Long ago, I studied Computer Engineering at a Chinese University of Hong Kong. It was a traditional degree at a traditional school, with the usual core requirements: C Language, Data Structures, Operating Systems — and what we now hear thrown around constantly: Algorithm.
Loosely speaking, an algorithm is a method or sequence of steps for solving a particular problem. In that broad sense, even a recipe is an algorithm — its problem being how to produce a given dish. But for a Computer Engineering student, the definition is tighter: a finite, unambiguous, executable sequence of instructions for solving a well-defined problem.
Imagine you have a printed Yellow Pages (Facebook’s prehistoric ancestor) and you need to find the number for “John Wick.” The most intuitive algorithm is linear search: start at “Aaron Au” and go down the list, name by name, until you hit your target. It works, but it’s a bit dim.
A smarter approach is to start in the middle. If you land on “Nancy Chan” — which comes after “John Wick” alphabetically — you halve the remaining range and try again at the 25% mark. Repeat until found. This is binary search.
In the classical sense, every part of an algorithm — its steps, its inputs, its outputs — is crisply defined. The user states the problem clearly; the programmer designs the steps; the user supplies the input; the algorithm produces a specific output. Every detail is traceable.
Carol’s Eyes
Now picture this. Alice, a manager, asks Bob — her IT guy — to find all the photos containing cats among ten thousand images.
To Alice, the requirement seems perfectly clear. To Bob, it’s anything but. How do you tell a computer what a cat is? How do you define one? It’s a hopelessly fuzzy specification — but if Bob went back to ask Alice for clarification, he’d probably get fired. Funny enough, if Alice’s young daughter Carol asked her the same question — what is a cat? — Alice would discover that while she can recognize one instantly, she can’t actually explain how.
She’d find that listing features — four legs, fur, whiskers — never quite works, no matter how exhaustively. Either the definition is too loose and includes dogs, or it’s too strict and fails to recognize a cat curled into a ball. And yet, the moment Alice shows Carol a few photos and points out which ones have cats, Carol picks it up almost instantly.
This is how every one of us learned. It’s the kind of learning humans have long taken pride in — the very thing we believed put us above machines. Until AI matured.
Back when I was a student, AI didn’t yet mean “any technology that makes computers seem smart.” It had a much narrower definition, and it had to involve machine learning. Machine learning gave computers something resembling a human capacity: the ability to handle problems we ourselves can’t fully articulate, problems no traditional algorithm could solve. “Find the photos with cats” is now trivial — not because we got better at defining the problem, but because computers, fed enough examples, learned to recognize cats the way a child does.
Alice’s Mystery Box
Now imagine Alice has moved on to Meta, where she leads the Threads team. She turns to Bob with a new request: from the hundreds of millions of posts flowing through the platform every day, generate a feed that’s maximally addictive.
As before, Alice can name the goal but can’t describe its features. So Bob falls back on signals — comments, likes, shares — to filter for whatever seems to be hooking users. This was the early algorithmic feed: still classical in spirit, with explicit rules and conditions, predictable outputs for given inputs. Tweak the rules, and both engineer and user could steer the results.
But as AI grew more powerful, Alice no longer needed to articulate what makes content addictive. Bob could let the AI figure it out and serve up the most habit-forming feed possible, herding users like livestock. The word algorithm is still in use, now meaning “the logic by which a social platform filters information” — but its meaning has shifted entirely. The classical algorithm was a recipe Bob wrote out for the computer; every step was visible. The AI-era algorithm is a black-box chef Bob has trained, and even Bob can’t say why it seasons things the way it does.
For us users, the situation is worse still. Beyond knowing that ads will be slipped in somewhere, we have no real idea how the feed is being cooked. Whatever theories we have are pure guesswork. The irony is that more and more users are happy to remain in the dark — they don’t bother checking the nutrition label, and have started to enjoy the mystery of the surprise box.
We tell ourselves we’re sitting at a fine Japanese omakase counter, paying a premium so a master chef can hand-pick ingredients and prepare each course with care. We forget we’re actually scarfing down free fast food. Why would the chain bother sourcing real ingredients when it isn’t charging us a cent? Nutritionally, though, you can rest easy: the feed is guaranteed to be rich in dopamine. You’ll find it delicious.
Dear User, this is the algorithm.
P.S. Another casualty of the algorithm is the way it crams certain eateries to bursting while leaving worthy little shops deserted. Sometimes the packed-out spots are nothing special, just blessed by the Xiaohongshu gods. Other times the favored places really were excellent — until the crowds arrived and both food and service collapsed, never to recover. Living in Hong Kong, steering clear of the algorithm and seeking out independent shops on your own is a basic skill for eating well and living well.
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