The hidden cost of LLM learning: The efficiency trap

is a tool to accelerate learning, and deep thinking is eroded

LLM makes learning faster, but it implicitly weakens in-depth thinking. This is the price of efficiency.
My colleague Xiao Liu wanted to master the concurrency model of Go last year, so he directly threw the "go concurrency tutorial" into ChatGPT and got a code and explanation in 15 minutes. In contrast, I read through the chapters in "Advanced Programming in Go" two years ago. It took me two nights and ten lines of handwritten notes before I really imprinted the blocking behavior of select in my mind. On the surface, the difference in production time between the two is nearly seven times, but the latter's memory depth and migration ability are more than three times that of the former.

A 2023 survey of 2,200 technology practitioners showed that 68% of respondents admitted that using LLM could reduce the time spent learning new technologies to about 40%; however, 45% of the same group reported that their recall of key concepts after a week could only reach 60% raw levels. What's even more outrageous is that only 22% of the questionnaire for "deep reproduction"-which requires respondents to fully implement a distributed lock prototype without looking at any data-were completed in 30 minutes. Compared with similar surveys last year (30% reduction in study time and 80% recall rate), we can see that LLM is really turning "fast" into "superficial fast" while consuming the underlying structure of "remembering".

From a cognitive science perspective, retrieval exercises are a key part of long-term memory consolidation. Bjork's 1994 experiment showed that active recall increased memory retention by 2.5 times compared to passive reading. LLM feeds the answers directly to users, eliminating the process of "thinking" and thus eliminating the search exercise. To make matters worse, many people use LLM as a tool for "externalizing thinking" and hand over the burden of "thinking" to the model. As a result, the brain's "working memory" capacity is forced to shrink. Last year, at an internal technology sharing meeting, I saw a senior architect deploying Terraform scripts generated by ChatGPT directly to production, but later on, he was stuck when asked "Why did you choose this module" on the spot-obviously he had never experienced "why" self-examination.

Zhang in ### Data Center
Lao Zhang is an engineer in charge of data governance at a financial company. Recently, the company asked him to complete a characteristic engineering report on "customer portraits" within two days. Lao Zhang first glued the business requirements to the LLM, and the model immediately output more than a dozen lines of Python code, even with comments saying "explain every step." Lao Zhang copied and ran it directly, and the report was delivered within three hours. But the next day, when the business party asked for an explanation of "Why use Kmeans instead of DBSCAN", Lao Zhang could only throw the same hint to the model again, and the answer he got was a self-consistent explanation of the model-he himself did not form a judgment on the selection of clustering algorithms. After the meeting, Lao Zhang returned to his work station and found that it had been a long time since he had written the code for the feature project, and the path of thinking had been completely cut off by the shortcut of "model is the answer."

Opponents will say that LLM is actually "intelligent tutoring" rather than "thinking substitution". The instant feedback it provides can help learners quickly correct misunderstandings, similar to the guidance of experienced instructors. Indeed, in some areas that require a lot of background knowledge (such as medical imaging), LLM can condense the latest literature into a few lines of key points, saving retrieval time. However, this kind of "instant pointing" also deprives learners of the opportunity to "independent verification". Once I asked my team members to use LLM to explain Rust's ownership model, and everyone said "Got it"-but then during code review, there were 12 memory leaks caused by misunderstandings. The root cause is that they took the interpretation given by the model as truth, and did not personally experience the conflict of "ownership" in the compiler error. If models are regarded as the only "source of truth", the training opportunity for deep thinking will be completely deprived.

The cost of efficiency has long been precedent in other industries. The fast food industry replaced handmade grilles with prefabricated patties, doubling customer flow, but reducing nutritional value by 40%. In the same way, LLM turned "Thinking", a dish that requires slow stewing, into a fast food heated by microwaves. A 2022 report by the Harvard Business Review in the United States pointed out that employees who use automated tools have experienced an innovation indicator when solving complex problems by about 28%, while the speed of completing tasks has increased by 55%. This is not to say that fast food is necessarily bad, but to realize: When we change time for speed, we often also change depth and quality for surface.

The following code shows the time difference between traditional manual implementation of binary search and direct code generation by LLM.

# 手工实现二分查找
def binary_search(arr, target):
    lo, hi = 0, len(arr) - 1
    while lo <= hi:
        mid = (lo + hi) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid - 1
    return -1

# 用 LLM 直接生成（伪代码）
# prompt: "Write a binary search in Python"
# → 返回代码即上面函数，耗时约 2 秒

The process of handwriting allowed me to experience the subtleties of "boundary conditions" when debugging mid calculation errors, while directly copying the model code eliminated this step. The time saved seems to be an improvement in "learning efficiency", but it hides the understanding of the nature of the algorithm behind the code.

As can be seen from the above figure, LLM has reduced the learning time to 40%, but the memory retention rate under the same conditions is only 80%. If "depth" is regarded as the underlying structure of learning, this 20% gap is the hidden cost of efficiency.

Golden sentence : Behind efficiency is the dilution of thinking.

Therefore, LLM is not the "ultimate answer" to learning. It is more like a sharp knife-it can cut materials quickly, but it is also easy to cut off details. What we need to do is not blindly pursue speed, but use LLM as an "accelerator" rather than a "substitute", actively turn it off at nodes that require deep thinking, and let the brain complete a "retrieval exercise" on its own. Otherwise, the aura of efficiency will push us into the abyss of "only answering quickly but forgetting to ask questions."