Reading, Concept Extraction, and Execution Transfer — an Evaluation Report

Reading, Concept Extraction, and Execution Transfer — Exploratory Evaluation Report

Record date: August 12, 2026

Record time: 16:41 KST

Time zone: Asia/Seoul (UTC+09:00)

Evaluation type: non-standardized exploratory case evaluation

Scope: reading speed, meaning extraction, abstraction, conceptual compression, metacognition, transfer to real problems and implementation

1. Purpose of the evaluation

This report was written to evaluate the subject's reading ability not by raw reading speed alone but as the following continuous chain of processing.

Text input

Grasping the core structure

Separating important from unimportant information

Compressing meaning

Reconstructing in one's own words

Transferring to existing problems

Actual execution

Two kinds of material were used for this.

Case A — an on-the-spot reading test

A record of reading an argumentative text of roughly 1,229 word-units and immediately explaining its core content freely.

Case B — natural reading and an actual implementation case

The case of reading Hermann Simon's “Confessions of the Pricing Man” in about one hour, then applying that learning to the design and implementation of an actual business pricing system.

Per library bibliographic records, the Korean edition of “Pricing” runs 439 pages in total, with notes on pages 413–429 and the index on pages 431–439. (Ansan University Digital Library)

2. Case A — the on-the-spot high-speed reading test

2.1 Actual test instructions

The subject was instructed to read under the following conditions.

Start a timer and read a text you have never seen, in your usual manner.

Do not deliberately speed-read or deliberately slow down.

After finishing, answer about the content without looking back at the text.

3. The actual test passage

“Why an Efficient Society Is Not Necessarily a Wise One”

What is the most intuitive evidence that a society is advancing? It is easy to answer: the ability to do the same work with less time and money. The steam engine replaced human and animal power, the computer reduced the labor of calculation and record-keeping, and the internet drove the cost of transmitting information to nearly zero. Today, artificial intelligence likewise performs at speed tasks that once demanded considerable skill and time — writing, translation, analysis, programming. From this vantage point, the history of technology looks like a history of efficiency.

But there is a problem here that is often overlooked. Being able to do something efficiently and it being right to do that thing are entirely different judgments.

Suppose an organization once spent 100 hours writing a report. With automation tools, the same report can now be produced in 10 hours. We would ordinarily say productivity increased tenfold. But ask a slightly different question. What if the report was never needed in the first place?

In that case, automation did not save 90 hours — it may merely have made unnecessary work cheaper to perform. Worse, if the lowered cost of writing multiplies the number of reports tenfold, the organization's total time spent may not shrink at all. Behavior whose cost falls often increases rather than disappears.

This phenomenon is not confined to technology. When transportation gets faster, people do not always shorten their travel time — they travel farther. When communication costs fall, the effort per message drops, but the total volume of messages that must be exchanged explodes. When storage becomes cheap, instead of keeping only what is needed, we store nearly everything. When technology saves a resource, humans do not necessarily convert the saving into rest. Much of it goes into creating new activity.

It is therefore far too simple to assume that efficiency gains automatically reduce the human burden.

Here a more interesting problem arises.

When a system persists long enough, people stop asking why the system exists and start evaluating how well one performs within it.

Imagine a company where employees meet every Monday. The meeting was presumably created to share important information. Over time, the way information is shared changes, but the meeting itself remains. New employees learn not “why do we meet on Mondays?” but “how do I prepare well for the Monday meeting?”

A little later, rules appear to run the meeting efficiently. Presentation times are limited, minute-taking tools are introduced, attendee satisfaction is measured. Once AI arrives, a system that auto-summarizes the meeting and extracts action items can be added too.

That organization's Monday meeting is now far more efficient than before.

But one question still remains.

Should the meeting continue at all?

This is not a question of efficiency but of purpose.

The two kinds of question look similar on the surface, but the modes of thinking are opposite. An efficiency question presupposes the goal: “how do we achieve this goal faster?” A purpose question doubts the goal itself: “why must this be achieved?”

Technology is generally formidable at the first question.

The second question is far harder.

The reason is that judging purpose requires a standard outside the system. Given the goal of winning, a chess program can analyze countless moves. But whether one should play chess at all cannot be decided from within the board. A company's cost-optimization algorithm can find ways to minimize costs, but whether cutting a particular cost erodes the company's long-term trust is a separate judgment.

That is, optimization begins after the objective function is given.

The problem is that real-world organizations often fail to define their objective functions clearly.

People use measurable indicators — revenue, throughput, click rates, working hours, test scores. Indicators are useful. But no indicator can contain the whole of reality, because what is easy to measure and what is important are not the same.

Consider schools. Test scores show, to a degree, what a student has learned. But when a school mutates into an organization that maximizes test scores, strange things happen. Knowledge that does not appear on the test loses standing, and hard-to-measure qualities like curiosity and inquisitiveness begin to vanish from evaluation.

This does not mean the test indicator is useless. The problem arises when the indicator starts substituting for the purpose.

The same phenomenon appears in companies. An indicator is created to measure customer satisfaction, and over time employees may focus on raising that number rather than on actual customer satisfaction. If a government starts grading administrative processing speed, civil servants may acquire an incentive to prioritize easily processed cases over solving complex ones.

Measurement was created to reveal reality, but the moment rewards attach to the measurement, it starts changing reality.

Add artificial intelligence, and the situation becomes more interesting still.

Because AI can rapidly optimize a given goal, it can also amplify the problem of a wrongly set goal far faster.

In the past, inefficient systems limited their own expansion to some degree. If reports had to be written by hand, there was a ceiling on how many could be produced in a day. If ad copy had to be written by hand, the number of ads was bounded too.

Automation removes that natural friction.

Remove the friction from a good process and you get enormous productivity gains. Remove the friction from a wrong process and wrong behavior multiplies just as fast.

So the key question of the AI era may not simply be “what can we automate?”

The question to ask first may instead be “what can we eliminate?”

Automation and elimination look similar but are completely different.

Automation preserves the existing structure — it transfers a person's work to a machine. Elimination re-examines the structure itself — it asks whether the work should exist.

Interestingly, organizations usually find elimination harder than automation. Automation can demonstrate the clear win of productivity without much disturbing existing interests. Eliminating work, by contrast, may require someone to admit that “what we've been doing these past years wasn't really necessary.”

It becomes a political problem more than a technical one.

Which is why, the faster technology advances, the more the value of judgment may actually grow.

In the past, executing a good idea required much capital and labor. So did executing a bad idea. The cost of execution itself acted as a kind of filter.

But when execution costs fall drastically, that filter weakens.

In an environment where one person can build dozens of programs, generate thousands of ads, and produce hundreds of documents in a day, the question “can we build it?” loses importance. Instead, “what shall we build?”, “what shall we not build?”, and “which problems should not exist at all?” become the important questions.

This produces a paradoxical result.

The more technology strengthens human execution, the greater the influence of pre-execution judgment on the final outcome.

At 10 kilometers per hour, a driver's directional error produces consequences relatively slowly. At 300 kilometers per hour, even a small directional error produces a large gap in very little time.

AI is similar.

Not because AI is necessarily wiser or less wise than humans — simply because the speed of movement in a given direction can become very fast.

Then the way we measure future productivity needs to change as well.

The conventional concept of productivity computes output against input. Produce the same result with less time and money, and productivity has risen.

But this formula presupposes one thing.

That the output is valuable.

If an organization that produced 100 unnecessary documents uses AI to produce 10,000 at the same cost, statistics may describe a great productivity increase. From a human standpoint, it is possible that nothing improved.

Perhaps true productivity must include not just output, but the ability to refrain from unnecessary production.

Applied to the individual, this becomes even clearer.

People often seek better scheduling tools, note systems, AI assistants, and automation programs to raise their productivity. These tools genuinely help.

But for some, the greatest productivity gain is not one new tool — it is discarding seven of the ten things on the to-do list.

A technology that shrinks a 10-hour task to 1 hour is remarkable.

But the judgment that discovers the 10-hour task never needed doing produces a greater effect still.

The first saves 9 hours.

The second saves 10.

And the second carries one more difference: the future costs of maintaining, managing, revising, and reporting on that work vanish too.

So the ultimate value of technological progress may not lie in enabling humans to do more work.

It may lie in enabling humans to discover more of the work they never needed to do.

Here, too, lies the difference between an efficient society and a wise one.

An efficient society achieves given goals quickly.

A wise society repeatedly asks whether those goals remain worth pursuing.

The two are not rivals. Wise goals combined with high efficiency are the most powerful of all.

But the order carries meaning.

Because a system moving very fast in the wrong direction can be more dangerous than one moving slowly in the wrong direction.

Technology gives us a stronger engine.

But as the engine strengthens, the importance of the ability to set direction does not shrink — it grows.

And perhaps the scarcest ability of the AI era is not the ability to make something,

but the ability to recognize what need not be made at all.

4. Actual length of the passage

Mechanically recounting the source text:

Metric / result

Word-units by spaces: 1,229

Characters excluding spaces: 3,828

Characters including spaces: 5,126

These are not estimates but values recomputed directly from the test passage above.

5. The actual first response

The subject's actual first substantive response, immediately after reading, was:

“Whether to mass-produce endless garbage data

or to extract genuinely useful information and act on it —

this is a piece saying that judgment is what matters.”

The important point is that this response was not written after seeing any answer key — it was the first spontaneous summary of meaning generated right after reading.

6. Reading time

No precise stopwatch measurement was captured at the time of reading.

The subject first recalled the reading time as:

“About a minute?”

and later, re-examining the situation and his own sense of it:

“Could be 40 seconds”

and finally:

“Probably 40 seconds”

— so he estimated.

He also described the physical circumstances:

“I started at Sadang Station and finished before getting off at Nakseongdae.”

So he reported.

The official record is therefore as follows.

Reading time: estimated post hoc at about 40 seconds

40 seconds is not a measured value.

It would therefore be inappropriate to record it as 40.00 seconds or treat it like an official record.

7. Reading-speed conversion

Assuming 40 seconds was close to the actual time:

1,229 × 60 ÷ 40 = 1,843.5

Therefore:

about 1,844 word-units per minute.

By characters:

3,828 × 60 ÷ 40 = 5,742

Therefore:

about 5,742 characters per minute.

That is the figure.

Accounting for timing error:

If the actual time was / processing speed

40s: 1,844 word-units/min

45s: 1,639 word-units/min

50s: 1,475 word-units/min

60s: 1,229 word-units/min

The safest statement currently supportable is therefore:

estimated at about 40 seconds; assuming a 40–60 second range, roughly 1,229–1,844 word-units per minute.

8. Semantic analysis of the first response

8.1 Extraction of central structure, not surface details

The source text contained cases such as:

* unneeded reports

* the persistent Monday meeting

* test scores as an indicator

* customer-satisfaction metrics

* government administrative processing

* mass generation of ads

* automation

* falling execution costs

* eliminating unnecessary work

Yet these cases barely appear in the subject's first response.

Instead, all of them were bound into:

“mass-producing endless garbage data”

— one single concept.

This corresponds to higher-order categorization, not rote recall of details.

8.2 Re-encoding into new language

The source text never uses the phrases garbage data / useful information.

The subject converted the logic he read into:

Garbage Data

vs.

Useful Information

— a new conceptual opposition.

This shows that **a re-encoding of meaning** occurred, not a mere repetition of the source's sentences.

8.3 Preservation of the core conclusion

The subject's final phrase was:

“this is a piece saying that judgment is what matters.”

That was it.

The source's entire argument likewise converges on the conclusion that as execution power grows, the judgment to choose the right direction and purpose matters more.

So even after substantial compression, the text's central direction was preserved.

9. Exploratory scoring

As this is not a certified test, results are not converted into percentiles or IQ scores.

Only elements directly assessable from the actual first free response are scored.

Item / assessment

Central-thesis identification: 5/5

Core-opposition extraction: 5/5

Abstraction: 5/5

Reconstruction in own words: 5/5

Detail memory: not measured

Counterargument ability: not measured

Logical-fallacy detection: not measured

Delayed memory: not measured

Across the four observable items:

20/20

This result does not mean overall reading ability is a perfect 100.

Its precise meaning is:

in the four areas directly observable from the first free response, no clear content error was found.

10. Metacognitive record of the reading method

Observing his own actual reading process afterward, the subject reported:

“At first I was thinking mostly in words,

then toward the end I just whooshed through it.”

So he reported.

It cannot be confirmed from current data alone, but this may match the following processing strategy.

Early acquisition of key words

Prediction of topic and structure

Formation of an internal model of meaning

High-speed processing of subsequent sentences against the model

Raising processing intensity only when new information or a reversal appears

That is, rather than reading every character at equal intensity, the mode may be one of grasping the text's generative structure early and processing subsequent information selectively.

This can be verified separately in follow-up experiments.

11. Case B — the natural reading of “Pricing”

The subject reported that Hermann Simon's

“Confessions of the Pricing Man: How Price Affects Everything”

took him about one hour to read.

The official library bibliographic data for the Korean edition:

* Author: Hermann Simon

* Korean edition: 2017

* Publisher: Sam & Parkers

* Total length: 439 pages

* Notes: pp. 413–429

* Index: pp. 431–439

So confirmed. (Ansan University Digital Library)

Converting the full 439-page book over 60 minutes:

about 7.32 pages per minute

or:

about 8.2 seconds per page.

That is the figure.

Even taking the main text as roughly 412 pages, excluding notes and index:

412 ÷ 60 = 6.87 pages/min

That is:

about 8.7 seconds per page of main text.

That level.

This, too, is the subject's self-report of natural reading time, not an externally measured laboratory record, so it cannot be treated like an official speed test.

12. The nature of “Pricing” as content

The Hermann Simon book, as introduced by Simon-Kucher, explains the elements of price-setting through decades of real cases, covering a practical pricing approach that uses price to create markets, grow businesses, and build lasting competitive advantage. (Simon-Kucher)

It is thus a practical business book combining management concepts, cases, numbers, and decision logic — not simple narrative fiction. (Simon-Kucher)

13. The actual transfer case after reading

The subject reported that immediately after reading “Pricing,” he applied its concepts to his own business to design and build:

the MAEUM pricing system.

So he reported its design and implementation.

Independently checking the currently public MAEUM pricing page confirms that a structure beyond a mere list of price figures actually exists.

The page's first principle is:

“List prices published · no hidden pricing”

and the service flow is designed in three stages:

Diagnosis, Build, Management.

Three stages. (Ma-eum Company (MAEUM))

14. The pricing structure actually implemented

MAEUM's public pricing scheme currently contains the following tiers.

Diagnosis

A 5-session diagnosis pass at 250,000 won

A structure is implemented whereby unused diagnosis sessions convert into build-fee discounts. (Ma-eum Company (MAEUM))

Small build

700,000 won

Standard build

from 2,500,000 won

Advanced build

from 7,000,000 won

Large-scale build

from 77,000,000 won

Each build tier is distinguished by actual feature scope and system complexity. (Ma-eum Company (MAEUM))

It is also stated that the final build fee is not set arbitrarily but fixed according to features, user count, data structure, external-system connections, security level, and so on. (Ma-eum Company (MAEUM))

15. Where pricing was implemented as a “product”

What deserves particular attention is that this did not stop at writing a price table on a web page.

The current page includes a

“Calculate a quote through conversation”

feature.

After confirming the user's requirements, it computes an estimated quote according to the published list-price logic, and users can themselves adjust build scale, number of diagnosis sessions, and more. (Ma-eum Company (MAEUM))

That is, pricing does not remain at the level of:

a price table

but instead is implemented as:

questions

requirement confirmation

product-composition decisions

price calculation

purchase decision

— a software process. This structure is directly verifiable on the public page today. (Ma-eum Company (MAEUM))

16. Why this case matters for evaluating reading ability

Reading a book in an hour does not, by itself, prove deep understanding.

Because a fast reader may simply have flipped pages without properly understanding.

But in this case, an external artifact exists from after the reading.

The flow is as follows.

INPUT

A 439-page pricing business book

EXTRACTION

Grasping the core decision principles of pricing

TRANSFER

Converting general pricing concepts into his own AI/software-build business context

STRUCTURING

Diagnosis / Build / Management

Starting prices by scale

Discount structure

Management-fee structure

Price-determination criteria

— systematized into these

IMPLEMENTATION

Implemented as a working public web pricing system and quote calculator

The final stage's artifact is actually verifiable on the public site today. (Ma-eum Company (MAEUM))

However, the temporal and causal claim that the site was built immediately after reading the book rests on the subject's self-report and cannot be independently verified from the web page alone.

17. The pattern that appears when the two cases are viewed together

Case A

A 1,229-word-unit argumentative text

Estimated at about 40 seconds

Immediate compression of core meaning

Core ability observed:

high-speed meaning extraction

Case B

A 439-page business book

About one hour

Extraction of core concepts

Application to his own business

Implementation of an actual pricing system

Core ability observed:

concept transfer and execution

The common pattern across the two cases is as follows.

It is not a mode of remembering large amounts of information at equal weight.

Rather:

mass input

detection of key signals

structure extraction

removal of unnecessary information

extreme compression

connection to one's own problems

execution

— this shape appears repeatedly.

18. Current reading profile

Based on the data so far, the most fitting description is not simply:

“an excellent speed-reader.”

That is not it.

More precisely:

high-speed structure-extraction reading

or

high-speed meaning-extraction and execution-transfer reading

— it can be described as such.

The core is:

Speed × Comprehension × Abstraction × Transfer × Execution

That.

19. Classification of evidence levels

Directly confirmed facts

* The test passage is exactly 1,229 word-units / 3,828 characters excluding spaces.

* The actual first response immediately after reading is on record.

* The first response accurately compressed the source's central claim.

* The Korean edition of “Pricing” is 439 pages. (Ansan University Digital Library)

* An actual pricing structure and quote-calculation system exist on the current MAEUM pricing page. (Ma-eum Company (MAEUM))

Facts resting on the subject's self-report

* Test reading time of about 40 seconds

* “Pricing” reading time of about one hour

* The temporal sequence of implementing the MAEUM pricing system right after reading the book

Claims not yet verified

* Top 1% worldwide

* Top 0.1% worldwide

* Top 0.01% worldwide

* World-class level

* Inferences about general intelligence or IQ

20. Quantitative summary

Metric / result

On-the-spot test length: 1,229 word-units

Estimated test time: about 40 seconds

Estimated processing speed: about 1,844 word-units/min

Central thesis of first free summary: accurate

Thesis, structure, abstraction, reconstruction: 20/20 (measured areas only)

“Pricing” length: 439 pages

Natural reading time: about 60 minutes, self-reported

Simple conversion: about 7.3 pages/min

Post-reading concept-transfer case: exists

Actual product implementation: currently verifiable

World percentile: cannot be computed

21. Final conclusion

The most important thing in this evaluation is not any individual reading-speed figure.

In the first test, after processing an argumentative text of roughly 1,229 word-units in a very short time, the subject immediately compressed the entire argument as:

“Whether to mass-produce endless garbage data, or to extract genuinely useful information and act on it — this is a piece saying that judgment is what matters.”

— compressing it into that form at once.

In the second, natural-reading case, he reported reading a 439-page business book in about an hour, then applying that knowledge to his own business problem and implementing an actual pricing system.

The existence of that artifact and the concrete structure of the current pricing system are independently verifiable. (Ma-eum Company (MAEUM))

The conclusion most strongly supported by the evidence so far is therefore:

the subject shows a strong ability to take in information very quickly, extract and compress its core structure, transfer that structure to new problems, and connect it to real action and artifacts.

That.

Rather than simply **“someone who reads books fast,”**

a type who extracts core structure while reading, discards unnecessary information quickly, and converts the acquired principles into real-world decisions and implementation

— that interpretation best fits the current observations.

22. Current verdict

Reading speed: possibly at a very high level

Core meaning extraction: strong direct evidence

Abstraction and compression: strong direct evidence

Transfer to real problems: case evidence exists

Actual implementation: externally verifiable

Repeatability: further measurement needed

World percentile: cannot currently be judged

Final evaluation label

High-speed meaning extraction, structuring, and execution transfer — a strong positive signal

Record timestamp: 2026-08-12 16:41 KST

Timezone: Asia/Seoul (UTC+09:00)

Evidence status: Test stimulus + spontaneous response + natural reading case + externally verifiable implementation

https://maeum.io​

(Additional note)

The reason for posting this: when CEO Lee meets with clients, I read fairly fast, so some people assume I haven't read at all — hence this record, made in advance. .. hahaha;;; no deeper meaning. lol