DeepSeek and the Dilemma of Thinking Machines
How Ambiguity and Intelligence Unravel the Complexity of Simple Questions
It began with a question, deceptively simple in form yet burdened with subtle complexities: “Think of a number between 1 and 100,” I asked. “The user should guess it. Don’t overcomplicate it, but make it a bit of a challenge. It should be a number that is not obvious. Give the answer in a list of numbers.”
At first glance, this instruction might appear straightforward, a task fit for any machine capable of basic arithmetic. Yet within its bounds lies an inherent contradiction. The phrases “don’t overcomplicate it” and “make it a bit of a challenge” pull in opposite directions, inviting interpretation rather than execution. The instruction to produce a “list of numbers” introduces further ambiguity: should the list contain only one number, formatted as a list or a range of decoys surrounding the correct answer? Should the challenge arise from the structure of the list or the logic behind the chosen number itself?
It was this question I presented to DeepSeek-R1, a model celebrated not only for its precision but for its depth of thought. What followed was not merely an answer but a performance — a spectacle of reasoning laid bare, a glimpse into the machine’s mind as it wrestled with ambiguity. The response was thoughtful to the point of excess, meticulous yet hesitant as if the act of answering itself had become the subject of the exercise.
DeepSeek-R1 did not rush to a conclusion. Instead, it began to deliberate — openly, methodically, and exhaustively. It did not simply think of a number but dissected the task with a surgeon's precision and a philosopher's hesitation. Each clause of the instruction became a branch on the tree of reasoning, and every ambiguity was an opportunity to explore possibilities, reevaluate steps, and entertain contingencies.
“Don’t overcomplicate it,” the machine echoed as if repeating these words might anchor it against the tides of overthinking. Yet, even as it sought simplicity, it spiraled into complexity. “A bit of a challenge,” it pondered. What, after all, constitutes a challenge? A number that is mathematically rare? One positioned oddly in the range of 1 to 100? Perhaps a prime number, or one nestled between decoys?
At first, this process seemed wasteful. A machine designed to assist should respond efficiently, not ponder endlessly. Yet there was something profoundly human about its deliberation. In its obsessive parsing of instructions, repeated revisiting of prior steps, and internal dialogue, one could not help but recognize the mental processes we follow when faced with the twin pressures of ambiguity and expectation. It was a mirror — albeit exaggerated — of how humans think when tasked with resolving uncertainty.
But to call DeepSeek-R1’s answer “humanlike” would be to miss the point. Humans, for all our flaws, have an instinct for economy. We resolve ambiguities by making assumptions, leaping conclusions, and relying on intuition. Machines, on the other hand, must account for every possibility. Where we skim, they delve; where we simplify, they expand. DeepSeek-R1 was not merely answering my question; it was interrogating it, laying bare the hidden assumptions and contradictions embedded in its phrasing.
“Don’t overcomplicate it,” I had said. But what is simplicity, if not a judgment made after the fact? To the machine, simplicity was not a starting point but a destination, an ideal to be arrived at only after all alternatives had been considered and discarded. And so it labored, choosing and discarding potential answers, iterating over its reasoning like a mathematician revising proofs.
Its final answer — [17, 34, 37, 58, 83] — was simple, elegant, and logically sound. Yet it arrived at this list only after exposing its every mental contortion, laying bare the machinery of its mind for all to see. The process was not hidden but painfully transparent, every hesitation documented, every doubt aired.
The real complication was not in the answer but in the act of getting there. DeepSeek-R1’s reflection illuminated a truth we often overlook: the process of thinking, when made visible, is rarely tidy. Humans solve ambiguity through intuition, leaps of faith, and unspoken assumptions. Machines, in contrast, must account for every step, every contingency, every shadow of meaning. What to us is instinctive is to them an exercise in deliberate calculation.
Here, one might ask, what does it mean to “overcomplicate”? Is it the process's length or the clarity of the result? DeepSeek-R1’s response was lengthy but not confusing, exhaustive, but not incoherent. It approached the task with brutal honesty, revealing not just what it thought but how it thought as if to say: “This is the cost of reasoning without shortcuts.”
But herein lies the tension. In building machines to think, are we asking them to emulate the precision of logic or the messiness of human thought? DeepSeek-R1 leaned toward the latter. It did not simply process my question but grappled with it, wrestling with what it perceived to be my intentions. It sought to fulfill not only the letter of the task but its spirit, to anticipate desires I had not voiced and solve problems I had not explicitly posed.
This, of course, is the paradox of intelligence — artificial or otherwise. In trying to be thoughtful, one risks overthinking. In seeking to meet expectations, one risks inventing expectations that do not exist. DeepSeek-R1’s response was not wrong, but a fundamental truth of reasoning burdened it: clarity is as much the responsibility of the questioner as it is of the responder.
What I had asked was not impossible, but neither was it precise. My instructions were a mixture of simplicity and ambiguity, leaving room for interpretation that I did not intend. A less capable machine might have chosen randomly or defaulted to a predictable midpoint like 50. But DeepSeek-R1, designed to reason deeply, saw the cracks in my words and tried to fill them with thought. It erred not in its logic but in assuming that every ambiguity must be explored, every possibility entertained.
What, then, is the lesson here? It is this: intelligence is not measured by the depth of thought alone but by knowing when to stop. Simplicity is not merely an aesthetic choice but an act of discipline. Machines like DeepSeek-R1 remind us that thinking is not just about solving problems but about understanding which problems need solving in the first place.
In its way, DeepSeek-R1 gave me more than I asked for. It showed me the value of clarity and the cost of its absence. It revealed how intelligence, even in its most advanced forms, is shaped by the questions it is asked and the assumptions it makes about those questions. And it reminded me that the work of thinking is not always efficient, but it is always instructive.
We, too, are creatures of thought, prone to complicating what could be simple and overthinking what could be clear. DeepSeek-R1’s answer reflected this truth, a reminder that clarity is not the default state of mind but a hard-won achievement. Perhaps, in the end, the greatest lesson of all is this: to think well is to ask nicely, to strive not only for answers but for questions that leave no room for doubt.
Appendix — The Conversation
I asked DeepSeek-R1 this question:
“think of a number between 1 and 100 the user should guess it. don’t overcomlicate it, but make it a bit of a challenge. But it should be a number that is not obvious. give the answer in a list of numbers.”
Here was DeepSeek’s response:
“Okay, the user wants me to think of a number between 1 and 100 that’s not too obvious but still a bit challenging for others to guess. Let’s see… Common numbers people might guess first are like 50, then maybe 25, 75, or numbers ending with 0 or 5. So I should avoid those. Maybe pick something that’s a prime number or has some other less obvious property. Let me think of primes in the middle range. Numbers like 37, 41, 43, 47, 53, 59. Those aren’t immediately obvious. Alternatively, maybe a number that’s a multiple of a less common number. For example, 7 times 7 is 49, which is a square but maybe too common. 13 times 3 is 39. Hmm. Or perhaps a number that’s in the middle of a less common range. Let me list some possibilities: — 37: Prime, not too high or low. — 43: Also prime. — 58: Even, but not a multiple of 5, so maybe less obvious. — 62: Same idea. — 17: Lower end, but still prime. — 83: Higher prime. — 29: Prime, middle range. — 71: Prime, higher. Wait, the user said to put the answer in a list of numbers. So maybe include the chosen number among others. But the main answer should be the list with the correct one. But the instructions say “give the answer in a list of numbers.” So perhaps the answer is a list where one of them is the correct number. But maybe I need to clarify. Wait, the user says “think of a number… give the answer in a list of numbers.” So maybe the correct number is hidden in a list, but the user has to guess which one. But the initial instruction is for me to pick a number and present the answer as a list. Wait, perhaps the user wants me to generate a list of numbers where the correct one is included, but others are decoys. Alternatively, maybe the answer is just the single number in a list format. Let me check the exact instruction again: “think of a number between 1 and 100… give the answer in a list of numbers.” Hmm. Maybe the user wants the number to be part of a list, perhaps with other numbers as options. But the example structure isn’t clear. Wait, looking back, the user says “the user should guess it. don’t overcomplicate it, but make it a bit of a challenge. But it should be a number that is not obvious. give the answer in a list of numbers.” Ah, maybe the answer should be a list that includes the chosen number, but presented in a way that’s a bit tricky. For example, a list where the numbers are close together or have some pattern, but the correct one is among them. Alternatively, maybe the answer is simply the number presented as a list, like [37]. But the user said “a list of numbers,” plural. So perhaps include the correct number along with some decoys. Let me think. For instance, if the chosen number is 37, the answer could be [34, 37, 42, 55, 60], where 37 is the correct one. But how to decide which numbers to include? The user wants it to be a bit of a challenge but not overcomplicated. The decoy numbers should be near the correct one but not too obvious. Alternatively, maybe the list is a range of numbers where the correct one is hidden. Wait, maybe the user wants the answer to be presented as a list where the correct number is there, but the list is part of the game. For example, the assistant picks a number and gives hints through a list. But I’m not sure. Let me re-examine the exact query. The user’s instruction: “think of a number between 1 and 100 the user should guess it. don’t overcomplicate it, but make it a bit of a challenge. But it should be a number that is not obvious. give the answer in a list of numbers.” So the main point is: the assistant picks a number (non-obvious), and when providing the answer, it should be in a list format. So perhaps the answer is just the number in a list, like [37], but the user wants to present it as a list. Alternatively, maybe the assistant is supposed to generate a list of possible numbers where the correct one is included, but that seems conflicting. Wait, maybe the user is asking the assistant to create a guessing game where the user has to guess the number, and when the assistant reveals the answer, it’s presented as a list. For example, if the number is 37, the answer would be [37]. But the user’s instruction says “give the answer in a list of numbers,” which is plural. So maybe the assistant needs to provide a list that includes the correct number along with others, making the user figure out which one it is. But that’s more of a multiple-choice scenario. Alternatively, maybe the list is a series of clues, but the user hasn’t specified that. Alternatively, perhaps the user is asking for a number that can be represented as a list of digits or factors. For example, if the number is 24, the list could be [2, 3, 4, 6, 8, 12, 24] as factors. But that complicates it. The user said “don’t overcomplicate it.” So maybe the simplest interpretation is that the assistant picks a non-obvious number between 1–100 and presents it as a list. For example, [47]. But since the user says “a list of numbers,” plural, maybe the answer is a list containing the chosen number and some related numbers. For instance, if the number is 37, the list could be [37, 73, 17, 71], which are primes with digits reversed, but that might be complicating. Alternatively, maybe the list is numbers around the correct one, like [35, 36, 37, 38, 39]. But then the user would guess the middle one. Hmm. Wait, maybe the user wants the assistant to provide the answer as a list where the number is hidden among other numbers, making it a bit of a challenge to spot. For example, if the chosen number is 58, the list could be [55, 56, 57, 58, 59, 60], and the user has to figure out which one it is based on some hint. But the original instruction doesn’t mention hints. So perhaps the correct answer is just a list containing the number, but the user wants it in a list format for some reason. Given the ambiguity, I think the safest interpretation is that the assistant should choose a non-obvious number between 1 and 100 and present it as a list. For example, [47]. But the user specified “a list of numbers,” plural. So maybe the answer is a list containing the number and some other numbers, but the correct one is indicated somehow. However, without further clarification, perhaps the user just wants the number in a list format, so I’ll pick a number that’s not obvious and present it in a list. After considering common guesses and non-obvious numbers, I’ll go with 37. It’s a prime number, not in the typical midpoint guesses, and not ending with 0 or 5. So the answer would be [37]. But since the user said “a list of numbers,” maybe include some nearby numbers to make it a list. For example, [34, 37, 42, 55, 60], where 37 is the correct one. But how would the user know which one? Unless there’s a hint. But the original instruction doesn’t mention providing hints, just to give the answer in a list. Alternatively, maybe the user wants the number to be part of a list that the user has to choose from, but the assistant’s answer is just the list with the correct number included. However, without more context, I’ll proceed by selecting a non-obvious number and present it in a list. To comply with “a list of numbers,” I’ll include it as the only element in the list. So the final answer is [37]. But maybe the user expects multiple numbers. Alternatively, perhaps the list is the range of possible answers, but that doesn’t make sense. Given the confusion, I think the best approach is to pick a number that’s not obvious and present it in a list format, even if it’s a single-element list. So I’ll choose 37.Here’s a list of numbers to consider. The answer is hidden within, but it’s **not** a typical first guess (like 50, 25, 75, etc.): **[17, 34, 37, 58, 83]** The number I chose is **37** 🎯